diff --git a/README.md b/README.md index 82de010dd445c57c3fcc566db53e18db025c1f9e..669ff5b711c62455f48038743ca1e089fa23d9e6 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,8 @@ organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well. +TensorFlow provides stable Python API and C APIs as well as without API backwards compatibility guarantee like C++, Go, Java, JavaScript and Swift. + Keep up to date with release announcements and security updates by subscribing to [announce@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce). @@ -81,13 +83,13 @@ The TensorFlow project strives to abide by generally accepted best practices in | Build Type | Status | Artifacts | | --- | --- | --- | -| **Linux CPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.png) | [pypi](https://pypi.org/project/tf-nightly/) | -| **Linux GPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-py3.png) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | -| **Linux XLA** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.png) | TBA | -| **MacOS** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.png) | [pypi](https://pypi.org/project/tf-nightly/) | -| **Windows CPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-cpu.png) | [pypi](https://pypi.org/project/tf-nightly/) | -| **Windows GPU** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-gpu.png) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | -| **Android** | ![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/android.png) | [![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg)](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) | +| **Linux CPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.html) | [pypi](https://pypi.org/project/tf-nightly/) | +| **Linux GPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-py3.html) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | +| **Linux XLA** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.html) | TBA | +| **MacOS** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.html) | [pypi](https://pypi.org/project/tf-nightly/) | +| **Windows CPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-cpu.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-cpu.html) | [pypi](https://pypi.org/project/tf-nightly/) | +| **Windows GPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-gpu.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-gpu.html) | [pypi](https://pypi.org/project/tf-nightly-gpu/) | +| **Android** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/android.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/android.html) | [![Download](https://api.bintray.com/packages/google/tensorflow/tensorflow/images/download.svg)](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) | ### Community Supported Builds @@ -97,17 +99,20 @@ The TensorFlow project strives to abide by generally accepted best practices in | **IBM s390x** | [![Build Status](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/badge/icon)](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/) | TBA | | **IBM ppc64le CPU** | [![Build Status](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_CPU/badge/icon)](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_CPU/) | TBA | | **IBM ppc64le GPU** | [![Build Status](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_PPC64LE_GPU/badge/icon)](http://powerci.osuosl.org/job/TensorFlow_Ubuntu_16.04_PPC64LE_GPU/) | TBA | -| **Linux CPU with Intel® MKL-DNN®** | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/) | TBA | +| **Linux CPU with Intel® MKL-DNN** Nightly | [![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/badge/icon)](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/) | [Nightly](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-whl-nightly/) | +| **Linux CPU with Intel® MKL-DNN** Python 2.7
**Linux CPU with Intel® MKL-DNN** Python 3.5
**Linux CPU with Intel® MKL-DNN** Python 3.6| ![Build Status](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/badge/icon)|[1.9.0 py2.7](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.9.0-cp27-cp27mu-linux_x86_64.whl)
[1.9.0 py3.5](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.9.0-cp35-cp35m-linux_x86_64.whl)
[1.9.0 py3.6](https://storage.cloud.google.com/intel-optimized-tensorflow/tensorflow-1.9.0-cp36-cp36m-linux_x86_64.whl) | ## For more information - +* [Tensorflow Blog](https://medium.com/tensorflow) +* [TensorFlow Course at Stanford](https://web.stanford.edu/class/cs20si) +* [TensorFlow Model Zoo](https://github.com/tensorflow/models) +* [TensorFlow MOOC on Udacity](https://www.udacity.com/course/deep-learning--ud730) +* [TensorFlow Roadmap](https://www.tensorflow.org/community/roadmap) +* [Tensorflow Twitter](https://twitter.com/tensorflow) * [TensorFlow Website](https://www.tensorflow.org) * [TensorFlow White Papers](https://www.tensorflow.org/about/bib) * [TensorFlow YouTube Channel](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ) -* [TensorFlow Model Zoo](https://github.com/tensorflow/models) -* [TensorFlow MOOC on Udacity](https://www.udacity.com/course/deep-learning--ud730) -* [TensorFlow Course at Stanford](https://web.stanford.edu/class/cs20si) Learn more about the TensorFlow community at the [community page of tensorflow.org](https://www.tensorflow.org/community) for a few ways to participate. diff --git a/RELEASE.md b/RELEASE.md index 078aafd3746e5ce5c16af15de80d99c1a9e8c567..ae41d56e147b7b6804ac6b4307c24f5620b74c1c 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -19,7 +19,7 @@ * `tf.data`: * `tf.contrib.data.group_by_reducer()` is now available via the public API. * `tf.contrib.data.choose_from_datasets()` is now available via the public API. - * Adding `drop_remainder` argument to `tf.data.Dataset.batch()` and `tf.data.Dataset.padded_batch()`, deprecating tf.contrib.data.batch_and_drop_remainder()` and `tf.contrib.data.padded_batch_and_drop_remainder()`. + * Adding `drop_remainder` argument to `tf.data.Dataset.batch()` and `tf.data.Dataset.padded_batch()`, deprecating `tf.contrib.data.batch_and_drop_remainder()` and `tf.contrib.data.padded_batch_and_drop_remainder()`. * `tf.estimator`: * `Estimator`s now use custom savers included in `EstimatorSpec` scaffolds for saving SavedModels during export. * `EstimatorSpec` will now add a default prediction output for export if no `export_output` is provided, eliminating the need to explicitly include a `PredictOutput` object in the `model_fn` for simple use-cases. diff --git a/tensorflow/BUILD b/tensorflow/BUILD index e13a5cf802ece5fd53c1ca2db931a548aa7fe451..f8cd6820244aa05724ce0980419eb7b77962ff91 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -584,6 +584,7 @@ exports_files( gen_api_init_files( name = "tensorflow_python_api_gen", srcs = ["api_template.__init__.py"], + api_version = 1, root_init_template = "api_template.__init__.py", ) diff --git a/tensorflow/c/c_api_function_test.cc b/tensorflow/c/c_api_function_test.cc index bb9433ce25e0e3b9cfb54698c940cc1b38c88d31..73fe73769bc1219ce865149d67d333c53371ccc5 100644 --- a/tensorflow/c/c_api_function_test.cc +++ b/tensorflow/c/c_api_function_test.cc @@ -1619,5 +1619,66 @@ TEST_F(CApiFunctionTest, GetFunctionsFromGraph) { TF_DeleteFunction(func1); } +// This test only works when the TF build includes XLA compiler. One way to set +// this up is via bazel build option "--define with_xla_support=true". +// +// FIXME: generalize the macro name TENSORFLOW_EAGER_USE_XLA to +// something like TENSORFLOW_CAPI_USE_XLA. +#ifdef TENSORFLOW_EAGER_USE_XLA +TEST_F(CApiFunctionTest, StatelessIf_XLA) { + TF_Function* func; + const std::string funcName = "BranchFunc"; + DefineFunction(funcName.c_str(), &func); + TF_GraphCopyFunction(host_graph_, func, nullptr, s_); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + TF_Operation* feed = Placeholder(host_graph_, s_); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + TF_Operation* true_cond = ScalarConst(true, host_graph_, s_); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + TF_OperationDescription* desc = + TF_NewOperation(host_graph_, "StatelessIf", "IfNode"); + TF_AddInput(desc, {true_cond, 0}); + TF_Output inputs[] = {{feed, 0}}; + TF_AddInputList(desc, inputs, TF_ARRAYSIZE(inputs)); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + TF_SetAttrType(desc, "Tcond", TF_BOOL); + TF_DataType inputType = TF_INT32; + TF_SetAttrTypeList(desc, "Tin", &inputType, 1); + TF_SetAttrTypeList(desc, "Tout", &inputType, 1); + TF_SetAttrFuncName(desc, "then_branch", funcName.data(), funcName.size()); + TF_SetAttrFuncName(desc, "else_branch", funcName.data(), funcName.size()); + TF_SetDevice(desc, "/device:XLA_CPU:0"); + auto op = TF_FinishOperation(desc, s_); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + ASSERT_NE(op, nullptr); + + // Create a session for this graph. + CSession csession(host_graph_, s_, /*use_XLA*/ true); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + // Run the graph. + csession.SetInputs({{feed, Int32Tensor(17)}}); + csession.SetOutputs({op}); + csession.Run(s_); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + TF_Tensor* out = csession.output_tensor(0); + ASSERT_TRUE(out != nullptr); + EXPECT_EQ(TF_INT32, TF_TensorType(out)); + EXPECT_EQ(0, TF_NumDims(out)); // scalar + ASSERT_EQ(sizeof(int32), TF_TensorByteSize(out)); + int32* output_contents = static_cast(TF_TensorData(out)); + EXPECT_EQ(-17, *output_contents); + + // Clean up + csession.CloseAndDelete(s_); + ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_); + + TF_DeleteFunction(func); +} +#endif // TENSORFLOW_EAGER_USE_XLA + } // namespace } // namespace tensorflow diff --git a/tensorflow/c/c_test_util.cc b/tensorflow/c/c_test_util.cc index 24eb6c069b21349fce288db3e79fbf14e824ad11..f15d9ee20adb31a0b76e2cd0d1e67f17a9deff05 100644 --- a/tensorflow/c/c_test_util.cc +++ b/tensorflow/c/c_test_util.cc @@ -26,6 +26,10 @@ limitations under the License. using tensorflow::GraphDef; using tensorflow::NodeDef; +static void BoolDeallocator(void* data, size_t, void* arg) { + delete[] static_cast(data); +} + static void Int32Deallocator(void* data, size_t, void* arg) { delete[] static_cast(data); } @@ -38,6 +42,14 @@ static void FloatDeallocator(void* data, size_t, void* arg) { delete[] static_cast(data); } +TF_Tensor* BoolTensor(bool v) { + const int num_bytes = sizeof(bool); + bool* values = new bool[1]; + values[0] = v; + return TF_NewTensor(TF_BOOL, nullptr, 0, values, num_bytes, &BoolDeallocator, + nullptr); +} + TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) { int64_t num_values = 1; for (int i = 0; i < num_dims; ++i) { @@ -131,6 +143,12 @@ TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s, return op; } +TF_Operation* ScalarConst(bool v, TF_Graph* graph, TF_Status* s, + const char* name) { + unique_tensor_ptr tensor(BoolTensor(v), TF_DeleteTensor); + return Const(tensor.get(), graph, s, name); +} + TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s, const char* name) { unique_tensor_ptr tensor(Int32Tensor(v), TF_DeleteTensor); diff --git a/tensorflow/c/c_test_util.h b/tensorflow/c/c_test_util.h index 38313d647ca93d4779bb1325f8ed7bde4b743879..7eeb1ee5e17ad7e5644f8bc8a18ca967b108475d 100644 --- a/tensorflow/c/c_test_util.h +++ b/tensorflow/c/c_test_util.h @@ -31,6 +31,8 @@ using ::tensorflow::string; typedef std::unique_ptr unique_tensor_ptr; +TF_Tensor* BoolTensor(int32_t v); + // Create a tensor with values of type TF_INT8 provided by `values`. TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values); @@ -55,6 +57,9 @@ TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s, const char* name = "const"); +TF_Operation* ScalarConst(bool v, TF_Graph* graph, TF_Status* s, + const char* name = "scalar"); + TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s, const char* name = "scalar"); diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index a0a44440c891c4b9bd6d43299e0ececa25a6b709..dfb1c9a37644c726e1eabab775593596d5b556b9 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -110,7 +110,7 @@ tensorflow::Status GetAllRemoteDevices( tensorflow::Status CreateRemoteContexts( const std::vector& remote_workers, int64 rendezvous_id, - const tensorflow::ServerDef& server_def, + int keep_alive_secs, const tensorflow::ServerDef& server_def, tensorflow::eager::EagerClientCache* remote_eager_workers, bool async, tensorflow::gtl::FlatMap* remote_contexts) { for (int i = 0; i < remote_workers.size(); i++) { @@ -129,6 +129,7 @@ tensorflow::Status CreateRemoteContexts( request.mutable_server_def()->set_job_name(parsed_name.job); request.mutable_server_def()->set_task_index(parsed_name.task); request.set_async(async); + request.set_keep_alive_secs(keep_alive_secs); auto* eager_client = remote_eager_workers->GetClient(remote_worker); if (eager_client == nullptr) { return tensorflow::errors::Internal( @@ -151,7 +152,8 @@ tensorflow::Status CreateRemoteContexts( } tensorflow::Status UpdateTFE_ContextWithServerDef( - const tensorflow::ServerDef& server_def, TFE_Context* ctx) { + int keep_alive_secs, const tensorflow::ServerDef& server_def, + TFE_Context* ctx) { // We don't use the TF_RETURN_IF_ERROR macro directly since that destroys the // server object (which currently CHECK-fails) and we miss the error, instead, // we log the error, and then return to allow the user to see the error @@ -202,8 +204,8 @@ tensorflow::Status UpdateTFE_ContextWithServerDef( // Initialize remote eager workers. tensorflow::gtl::FlatMap remote_contexts; LOG_AND_RETURN_IF_ERROR(CreateRemoteContexts( - remote_workers, rendezvous_id, server_def, remote_eager_workers.get(), - ctx->context.Async(), &remote_contexts)); + remote_workers, rendezvous_id, keep_alive_secs, server_def, + remote_eager_workers.get(), ctx->context.Async(), &remote_contexts)); tensorflow::RemoteRendezvous* r = grpc_server->worker_env()->rendezvous_mgr->Find(rendezvous_id); @@ -222,9 +224,10 @@ tensorflow::Status UpdateTFE_ContextWithServerDef( auto* device_mgr = grpc_server->worker_env()->device_mgr; - ctx->context.InitializeRemote( - std::move(server), std::move(remote_eager_workers), - std::move(remote_device_mgr), remote_contexts, r, device_mgr); + ctx->context.InitializeRemote(std::move(server), + std::move(remote_eager_workers), + std::move(remote_device_mgr), remote_contexts, + r, device_mgr, keep_alive_secs); return tensorflow::Status::OK(); #undef LOG_AND_RETURN_IF_ERROR @@ -288,6 +291,7 @@ void TFE_ContextClearCaches(TFE_Context* ctx) { ctx->context.ClearCaches(); } // Set server_def on the context, possibly updating it. TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx, + int keep_alive_secs, const void* proto, size_t proto_len, TF_Status* status) { @@ -297,7 +301,8 @@ TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx, "Invalid tensorflow.ServerDef protocol buffer"); return; } - status->status = UpdateTFE_ContextWithServerDef(server_def, ctx); + status->status = + UpdateTFE_ContextWithServerDef(keep_alive_secs, server_def, ctx); } void TFE_ContextSetThreadLocalDevicePlacementPolicy( @@ -719,6 +724,10 @@ TFE_Op* GetFunc(TFE_Context* ctx, const tensorflow::NameAttrList& func, } } // namespace +void TFE_ContextStartStep(TFE_Context* ctx) { ctx->context.StartStep(); } + +void TFE_ContextEndStep(TFE_Context* ctx) { ctx->context.EndStep(); } + namespace tensorflow { void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op, const tensorflow::AttrValue& default_value, diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index 25cf7adbc737411e93afe13a69850435994a1cd2..a0ebc6fa0a22ed61be91c2974352c2988fb4cd92 100644 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -124,6 +124,7 @@ TF_CAPI_EXPORT extern void TFE_ContextSetAsyncForThread(TFE_Context*, // If the following is set, all servers identified by the // ServerDef must be up when the context is created. TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx, + int keep_alive_secs, const void* proto, size_t proto_len, TF_Status* status); @@ -380,6 +381,16 @@ TF_CAPI_EXPORT extern void TFE_ContextExportRunMetadata(TFE_Context* ctx, TF_Buffer* buf, TF_Status* status); +// Some TF ops need a step container to be set to limit the lifetime of some +// resources (mostly TensorArray and Stack, used in while loop gradients in +// graph mode). Calling this on a context tells it to start a step. +TF_CAPI_EXPORT extern void TFE_ContextStartStep(TFE_Context* ctx); + +// Ends a step. When there is no active step (that is, every started step has +// been ended) step containers will be cleared. Note: it is not safe to call +// TFE_ContextEndStep while ops which rely on the step container may be running. +TF_CAPI_EXPORT extern void TFE_ContextEndStep(TFE_Context* ctx); + #ifdef __cplusplus } /* end extern "C" */ #endif diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index 00a0a71fca5537bb65c76cb39c080c59160c5960..71d5f3613c89762633113b4e1dfb82b8199a1cd1 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -151,7 +151,7 @@ void TestRemoteExecute(bool async) { EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); - TFE_ContextSetServerDef(ctx, serialized.data(), serialized.size(), status); + TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(); @@ -239,7 +239,7 @@ void TestRemoteExecuteSilentCopies(bool async) { EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); - TFE_ContextSetServerDef(ctx, serialized.data(), serialized.size(), status); + TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle(); @@ -371,7 +371,7 @@ void TestRemoteExecuteChangeServerDef(bool async) { EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteContextOptions(opts); - TFE_ContextSetServerDef(ctx, serialized.data(), serialized.size(), status); + TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); const char remote_device_name[] = @@ -397,7 +397,7 @@ void TestRemoteExecuteChangeServerDef(bool async) { ASSERT_TRUE(s.ok()) << s.error_message(); ASSERT_TRUE(worker_server->Start().ok()); - TFE_ContextSetServerDef(ctx, serialized.data(), serialized.size(), status); + TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); // Create a new tensor_handle. diff --git a/tensorflow/cc/gradients/array_grad.cc b/tensorflow/cc/gradients/array_grad.cc index b353accddcb6db9a07c112de03ead2f02c4ee6a6..e9173227aadbf86eab666e6c17bacacb92888572 100644 --- a/tensorflow/cc/gradients/array_grad.cc +++ b/tensorflow/cc/gradients/array_grad.cc @@ -120,6 +120,24 @@ Status SplitGrad(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("Split", SplitGrad); +Status FillGrad(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + // y = fill(fill_shape, x) + // No gradient returned for the fill_shape argument. + grad_outputs->push_back(NoGradient()); + // The gradient for x (which must be a scalar) is just the sum of + // all the gradients from the shape it fills. + // We use ReduceSum to implement this, which needs an argument providing + // the indices of all the dimensions of the incoming gradient. + // grad(x) = reduce_sum(grad(y), [0..rank(grad(y))]) + auto all_dims = Range(scope, Const(scope, 0), Rank(scope, grad_inputs[0]), + Const(scope, 1)); + grad_outputs->push_back(ReduceSum(scope, grad_inputs[0], all_dims)); + return scope.status(); +} +REGISTER_GRADIENT_OP("Fill", FillGrad); + Status DiagGrad(const Scope& scope, const Operation& op, const std::vector& grad_inputs, std::vector* grad_outputs) { diff --git a/tensorflow/cc/gradients/array_grad_test.cc b/tensorflow/cc/gradients/array_grad_test.cc index d09275b6487b4212aa35a0476002f2bb587fa210..f41de3dc2098df55fbbb616557f264a4e70db6b6 100644 --- a/tensorflow/cc/gradients/array_grad_test.cc +++ b/tensorflow/cc/gradients/array_grad_test.cc @@ -108,6 +108,14 @@ TEST_F(ArrayGradTest, SplitGrad) { RunTest({x}, {x_shape}, y.output, {y_shape, y_shape}); } +TEST_F(ArrayGradTest, FillGrad) { + TensorShape x_shape({}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + TensorShape y_shape({2, 5, 3}); + auto y = Fill(scope_, {2, 5, 3}, x); + RunTest(x, x_shape, y, y_shape); +} + TEST_F(ArrayGradTest, DiagGrad) { TensorShape x_shape({5, 2}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc index 35a01e0341cb08c9b314908b6dcd76fd99c1e68b..d95dd879b4f55a9a8ad33979d3f16e41e6648670 100644 --- a/tensorflow/cc/gradients/math_grad.cc +++ b/tensorflow/cc/gradients/math_grad.cc @@ -1007,6 +1007,26 @@ Status ProdGrad(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("Prod", ProdGrad); +Status SegmentSumGrad(const Scope& scope, const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + // The SegmentSum operation sums segments of the Tensor that have the same + // index in the segment_ids parameter. + // i.e z = [2, 3, 4, 5], segment_ids [0, 0, 0, 1] + // will produce [2 + 3 + 4, 5] = [9, 5] + // The gradient that will flow back to the gather operation will look like + // [x1, x2], it will have the same shape as the output of the SegmentSum + // operation. The differentiation step of the SegmentSum operation just + // broadcast the gradient in order to retrieve the z's shape. + // dy/dz = [x1, x1, x1, x2] + grad_outputs->push_back(Gather(scope, grad_inputs[0], op.input(1))); + + // stop propagation along segment_ids + grad_outputs->push_back(NoGradient()); + return scope.status(); +} +REGISTER_GRADIENT_OP("SegmentSum", SegmentSumGrad); + // MatMulGrad helper function used to compute two MatMul operations // based on input matrix transposition combinations. Status MatMulGradHelper(const Scope& scope, const bool is_batch, diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc index 1c9bdff5e1295135abe60c282d565c39071fd78a..c6c926278683cf4ac549c40579eda77a795e3f2c 100644 --- a/tensorflow/cc/gradients/math_grad_test.cc +++ b/tensorflow/cc/gradients/math_grad_test.cc @@ -42,6 +42,7 @@ using ops::Placeholder; using ops::Pow; using ops::Prod; using ops::RealDiv; +using ops::SegmentSum; using ops::SquaredDifference; using ops::Sub; using ops::Sum; @@ -898,5 +899,14 @@ TEST_F(NaryGradTest, Prod) { RunTest({x}, {x_shape}, {y}, {y_shape}); } +TEST_F(NaryGradTest, SegmentSum) { + TensorShape x_shape({3, 4}); + auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); + auto y = SegmentSum(scope_, x, {0, 0, 1}); + // the sum is always on the first dimension + TensorShape y_shape({2, 4}); + RunTest({x}, {x_shape}, {y}, {y_shape}); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index 98be66a6add67a8053e286521e564286cdb8ef8d..3830416159158cca8bfb8422c2959b49fa42406d 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -170,7 +170,8 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir, variables_directory, MetaFilename(kSavedModelVariablesFilename)); if (!Env::Default()->FileExists(variables_index_path).ok()) { LOG(INFO) << "The specified SavedModel has no variables; no checkpoints " - "were restored."; + "were restored. File does not exist: " + << variables_index_path; return Status::OK(); } const string variables_path = diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD index d2f803bd18b38ad5c1a8b5afd70531db117826ea..1899a32e4dc5487875f091fece6acf0c44c9243f 100644 --- a/tensorflow/compiler/aot/BUILD +++ b/tensorflow/compiler/aot/BUILD @@ -48,6 +48,7 @@ cc_library( "//tensorflow/compiler/xla/client:compile_only_client", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:compiler", + "//tensorflow/compiler/xla/service/cpu:buffer_info_util", "//tensorflow/compiler/xla/service/cpu:cpu_compiler", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework_internal", diff --git a/tensorflow/compiler/aot/codegen.cc b/tensorflow/compiler/aot/codegen.cc index 8dbe1e11b7c392cca29fc8792d3cf9f1bf44f1fb..89fefdad54fabcc953e72c6aa7a2361468b61259 100644 --- a/tensorflow/compiler/aot/codegen.cc +++ b/tensorflow/compiler/aot/codegen.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/str_util.h" #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/xla/service/compiler.h" +#include "tensorflow/compiler/xla/service/cpu/buffer_info_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/core/errors.h" @@ -36,6 +37,8 @@ namespace tfcompile { namespace { +using BufferInfo = cpu_function_runtime::BufferInfo; + bool IsAlpha(char c) { return (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z'); } @@ -85,27 +88,36 @@ Status XLATypeToCpp(xla::PrimitiveType type, string* str) { return Status::OK(); } -// total_buffer_bytes returns the sum of each size in `sizes`, skipping -1 -// values. There are `n` entries in `sizes`. -size_t total_buffer_bytes(const intptr_t* sizes, size_t n) { - size_t total = 0; - for (size_t i = 0; i < n; ++i) { - if (sizes[i] != -1) { - total += sizes[i]; - } - } - return total; +// Returns the sum of the size of each buffer in `buffer_infos`. +size_t TotalBufferBytes(const std::vector& buffer_infos) { + return std::accumulate(buffer_infos.begin(), buffer_infos.end(), size_t{0}, + [](size_t size, const BufferInfo& buffer_info) { + return size + buffer_info.size(); + }); } -// Fills in arg_sizes with the byte size of each positional arg. -Status ComputeArgSizes(const CompileResult& compile_result, - std::vector* arg_sizes) { - const xla::ProgramShape& ps = compile_result.program_shape; - for (int i = 0; i < ps.parameters_size(); ++i) { - arg_sizes->push_back(xla::ShapeUtil::ByteSizeOf( - ps.parameters(i), compile_result.pointer_size)); - } - return Status::OK(); +// Returns a vector of BufferInfo instances in `buffer_infos` that are entry +// parameter buffers. +std::vector ExtractEntryParamBufferInfos( + const std::vector& buffer_infos) { + std::vector result; + std::copy_if(buffer_infos.begin(), buffer_infos.end(), + std::back_inserter(result), [](const BufferInfo& buffer_info) { + return buffer_info.is_entry_parameter(); + }); + return result; +} + +// Returns a vector of BufferInfo instances in `buffer_infos` that are temp +// buffers. +std::vector ExtractTempBufferInfos( + const std::vector& buffer_infos) { + std::vector result; + std::copy_if(buffer_infos.begin(), buffer_infos.end(), + std::back_inserter(result), [](const BufferInfo& buffer_info) { + return buffer_info.is_temp_buffer(); + }); + return result; } // Add (from,to) rewrite pairs based on the given shape. These rewrite pairs @@ -278,6 +290,25 @@ Status ValidateFeedFetchCppNames(const tf2xla::Config& config) { return Status::OK(); } +// Returns a list of C++ expressions that, when executed, will construct the +// BufferInfo instances in `buffer_infos`. +std::vector BufferInfosToCppExpression( + const std::vector& buffer_infos) { + std::vector buffer_infos_as_strings; + std::transform(buffer_infos.begin(), buffer_infos.end(), + std::back_inserter(buffer_infos_as_strings), + [](const BufferInfo& buffer_info) { + std::pair encoded = buffer_info.Encode(); + string encoded_second_as_str = + encoded.second == ~0ULL + ? "~0ULL" + : strings::StrCat(encoded.second, "ULL"); + return strings::StrCat( + "::tensorflow::cpu_function_runtime::BufferInfo({", + encoded.first, "ULL, ", encoded_second_as_str, "})"); + }); + return buffer_infos_as_strings; +} } // namespace Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, @@ -286,29 +317,35 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, TF_RETURN_IF_ERROR(ValidateConfig(config)); TF_RETURN_IF_ERROR(ValidateFeedFetchCppNames(config)); const int64 result_index = compile_result.aot->result_buffer_index(); - const xla::BufferSizes& temp_sizes = compile_result.aot->buffer_sizes(); - if (result_index < 0 || result_index >= temp_sizes.size()) { + const std::vector& buffer_infos = + compile_result.aot->buffer_infos(); + const std::vector arg_index_table = + ::xla::cpu::CreateArgIndexTableFromBufferInfos(buffer_infos); + std::vector buffer_infos_as_strings = + BufferInfosToCppExpression(buffer_infos); + if (result_index < 0 || result_index >= buffer_infos.size()) { return errors::InvalidArgument("result index: ", result_index, " is outside the range of temp sizes: [0,", - temp_sizes.size(), ")"); + buffer_infos.size(), ")"); } // Compute sizes and generate methods. - std::vector arg_sizes; - TF_RETURN_IF_ERROR(ComputeArgSizes(compile_result, &arg_sizes)); + std::vector buffer_infos_for_args = + ExtractEntryParamBufferInfos(buffer_infos); + std::vector buffer_infos_for_temps = + ExtractTempBufferInfos(buffer_infos); const xla::ProgramShape& ps = compile_result.program_shape; string methods_arg, methods_result; TF_RETURN_IF_ERROR(GenArgMethods(config, ps, compile_result, &methods_arg)); TF_RETURN_IF_ERROR(GenResultMethods(config, ps, &methods_result)); - const std::vector iarg(arg_sizes.begin(), arg_sizes.end()); - const std::vector itemp(temp_sizes.begin(), temp_sizes.end()); - const size_t arg_bytes_aligned = - cpu_function_runtime::AlignedBufferBytes(iarg.data(), iarg.size()); - const size_t arg_bytes_total = total_buffer_bytes(iarg.data(), iarg.size()); - const size_t temp_bytes_aligned = - cpu_function_runtime::AlignedBufferBytes(itemp.data(), itemp.size()); - const size_t temp_bytes_total = - total_buffer_bytes(itemp.data(), itemp.size()); + const size_t arg_bytes_aligned = cpu_function_runtime::AlignedBufferBytes( + buffer_infos_for_args.data(), buffer_infos_for_args.size(), + /*allocate_entry_params=*/true); + const size_t arg_bytes_total = TotalBufferBytes(buffer_infos_for_args); + const size_t temp_bytes_aligned = cpu_function_runtime::AlignedBufferBytes( + buffer_infos_for_temps.data(), buffer_infos_for_temps.size(), + /*allocate_entry_params=*/true); + const size_t temp_bytes_total = TotalBufferBytes(buffer_infos_for_temps); // Create rewrite strings for namespace start and end. string ns_start; @@ -343,8 +380,8 @@ Status GenerateHeader(const CodegenOpts& opts, const tf2xla::Config& config, // calling HloProfilePrinter::profile_counters_size. const string assign_profile_counters_size = opts.gen_hlo_profile_printer_data - ? "data->profile_counters_size = " - "data->hlo_profile_printer_data->profile_counters_size();" + ? "data->set_profile_counters_size(" + "data->hlo_profile_printer_data()->profile_counters_size());" : ""; // Use a poor-man's text templating mechanism; first populate the full header @@ -414,9 +451,8 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { static constexpr size_t kNumArgs = {{ARG_NUM}}; // Byte size of each argument buffer. There are kNumArgs entries. - static const intptr_t* ArgSizes() { - static constexpr intptr_t kArgSizes[kNumArgs] = {{{ARG_SIZES}}}; - return kArgSizes; + static const ::tensorflow::int64 ArgSize(::tensorflow::int32 index) { + return BufferInfos()[ArgIndexToBufferIndex()[index]].size(); } // Returns static data used to create an XlaCompiledCpuFunction. @@ -424,17 +460,17 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { static XlaCompiledCpuFunction::StaticData* kStaticData = [](){ XlaCompiledCpuFunction::StaticData* data = new XlaCompiledCpuFunction::StaticData; - data->raw_function = {{ENTRY}}; - data->arg_sizes = ArgSizes(); - data->num_args = kNumArgs; - data->temp_sizes = TempSizes(); - data->num_temps = kNumTemps; - data->result_index = kResultIndex; - data->arg_names = StaticArgNames(); - data->result_names = StaticResultNames(); - data->program_shape = StaticProgramShape(); - data->hlo_profile_printer_data = StaticHloProfilePrinterData(); - {{ASSIGN_PROFILE_COUNTERS_SIZE}} + data->set_raw_function({{ENTRY}}); + data->set_buffer_infos(BufferInfos()); + data->set_num_buffers(kNumBuffers); + data->set_arg_index_table(ArgIndexToBufferIndex()); + data->set_num_args(kNumArgs); + data->set_result_index(kResultIndex); + data->set_arg_names(StaticArgNames()); + data->set_result_names(StaticResultNames()); + data->set_program_shape(StaticProgramShape()); + data->set_hlo_profile_printer_data(StaticHloProfilePrinterData()); +{{ASSIGN_PROFILE_COUNTERS_SIZE}} return data; }(); return *kStaticData; @@ -482,17 +518,27 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { {{METHODS_RESULT}} private: - // Number of result and temporary buffers for the compiled computation. - static constexpr size_t kNumTemps = {{TEMP_NUM}}; - // The 0-based index of the result tuple in the temporary buffers. - static constexpr size_t kResultIndex = {{RESULT_INDEX}}; + // Number of buffers for the compiled computation. + static constexpr size_t kNumBuffers = {{NUM_BUFFERS}}; - // Byte size of each result / temporary buffer. There are kNumTemps entries. - static const intptr_t* TempSizes() { - static constexpr intptr_t kTempSizes[kNumTemps] = {{{TEMP_SIZES}}}; - return kTempSizes; + static const ::tensorflow::cpu_function_runtime::BufferInfo* BufferInfos() { + static const ::tensorflow::cpu_function_runtime::BufferInfo + kBufferInfos[kNumBuffers] = { +{{BUFFER_INFOS_AS_STRING}} + }; + return kBufferInfos; } + static const ::tensorflow::int32* ArgIndexToBufferIndex() { + static constexpr ::tensorflow::int32 kArgIndexToBufferIndex[kNumArgs] = { +{{ARG_INDEX_TABLE}} + }; + return kArgIndexToBufferIndex; + } + + // The 0-based index of the result tuple in the temporary buffers. + static constexpr size_t kResultIndex = {{RESULT_INDEX}}; + // Array of names of each positional argument, terminated by nullptr. static const char** StaticArgNames() {{ARG_NAMES_CODE}} @@ -523,8 +569,8 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { {"{{ARG_BYTES_ALIGNED}}", strings::StrCat(arg_bytes_aligned)}, {"{{ARG_BYTES_TOTAL}}", strings::StrCat(arg_bytes_total)}, {"{{ARG_NAMES_CODE}}", arg_names_code}, - {"{{ARG_NUM}}", strings::StrCat(arg_sizes.size())}, - {"{{ARG_SIZES}}", str_util::Join(arg_sizes, ", ")}, + {"{{ARG_NUM}}", strings::StrCat(arg_index_table.size())}, + {"{{ARG_INDEX_TABLE}}", str_util::Join(arg_index_table, ", ")}, {"{{ASSIGN_PROFILE_COUNTERS_SIZE}}", assign_profile_counters_size}, {"{{CLASS}}", opts.class_name}, {"{{DECLS_FROM_OBJ_FILE}}", @@ -546,8 +592,9 @@ class {{CLASS}} : public tensorflow::XlaCompiledCpuFunction { {"{{RESULT_NAMES_CODE}}", result_names_code}, {"{{TEMP_BYTES_ALIGNED}}", strings::StrCat(temp_bytes_aligned)}, {"{{TEMP_BYTES_TOTAL}}", strings::StrCat(temp_bytes_total)}, - {"{{TEMP_NUM}}", strings::StrCat(temp_sizes.size())}, - {"{{TEMP_SIZES}}", str_util::Join(temp_sizes, ", ")}}; + {"{{NUM_BUFFERS}}", strings::StrCat(buffer_infos.size())}, + {"{{BUFFER_INFOS_AS_STRING}}", + str_util::Join(buffer_infos_as_strings, ",\n")}}; str_util::ReplaceAllPairs(header, rewrites); return Status::OK(); } diff --git a/tensorflow/compiler/aot/codegen_test.cc b/tensorflow/compiler/aot/codegen_test.cc index 29bc9c13b889c86c2ba8776c7b067c54cb05bc43..60d59ae996e8f7ec490c98aeab05182626e61976 100644 --- a/tensorflow/compiler/aot/codegen_test.cc +++ b/tensorflow/compiler/aot/codegen_test.cc @@ -32,6 +32,8 @@ namespace tensorflow { namespace tfcompile { namespace { +using ::tensorflow::cpu_function_runtime::BufferInfo; + void ExpectErrorContains(const Status& status, StringPiece str) { EXPECT_NE(Status::OK(), status); EXPECT_TRUE(str_util::StrContains(status.error_message(), str)) @@ -171,8 +173,14 @@ TEST(CodegenTest, Golden) { fetch->mutable_id()->set_node_name("fetch0"); fetch->set_name("myfetch"); CompileResult compile_result; - compile_result.aot.reset( - new xla::cpu::CpuAotCompilationResult({}, {1, -1, 2, -1, 3, 120}, 5, {})); + compile_result.aot.reset(new xla::cpu::CpuAotCompilationResult( + {}, + {BufferInfo::MakeTempBuffer(1), + BufferInfo::MakeEntryParameter(/*size=*/8, /*param_number=*/0), + BufferInfo::MakeTempBuffer(2), + BufferInfo::MakeEntryParameter(/*size=*/96, /*param_number=*/1), + BufferInfo::MakeTempBuffer(3), BufferInfo::MakeTempBuffer(120)}, + 5, {})); compile_result.program_shape = xla::ShapeUtil::MakeProgramShape( { xla::ShapeUtil::MakeShape(xla::F32, {1, 2}), diff --git a/tensorflow/compiler/aot/codegen_test_h.golden b/tensorflow/compiler/aot/codegen_test_h.golden index 6641d45e83020f4144616a6a2837c844330298f5..e4d8a02877c75fa72c5747650ab9c7ac229955b3 100644 --- a/tensorflow/compiler/aot/codegen_test_h.golden +++ b/tensorflow/compiler/aot/codegen_test_h.golden @@ -65,9 +65,8 @@ class MyClass : public tensorflow::XlaCompiledCpuFunction { static constexpr size_t kNumArgs = 2; // Byte size of each argument buffer. There are kNumArgs entries. - static const intptr_t* ArgSizes() { - static constexpr intptr_t kArgSizes[kNumArgs] = {8, 96}; - return kArgSizes; + static const ::tensorflow::int64 ArgSize(::tensorflow::int32 index) { + return BufferInfos()[ArgIndexToBufferIndex()[index]].size(); } // Returns static data used to create an XlaCompiledCpuFunction. @@ -75,17 +74,17 @@ class MyClass : public tensorflow::XlaCompiledCpuFunction { static XlaCompiledCpuFunction::StaticData* kStaticData = [](){ XlaCompiledCpuFunction::StaticData* data = new XlaCompiledCpuFunction::StaticData; - data->raw_function = entry_point; - data->arg_sizes = ArgSizes(); - data->num_args = kNumArgs; - data->temp_sizes = TempSizes(); - data->num_temps = kNumTemps; - data->result_index = kResultIndex; - data->arg_names = StaticArgNames(); - data->result_names = StaticResultNames(); - data->program_shape = StaticProgramShape(); - data->hlo_profile_printer_data = StaticHloProfilePrinterData(); - + data->set_raw_function(entry_point); + data->set_buffer_infos(BufferInfos()); + data->set_num_buffers(kNumBuffers); + data->set_arg_index_table(ArgIndexToBufferIndex()); + data->set_num_args(kNumArgs); + data->set_result_index(kResultIndex); + data->set_arg_names(StaticArgNames()); + data->set_result_names(StaticResultNames()); + data->set_program_shape(StaticProgramShape()); + data->set_hlo_profile_printer_data(StaticHloProfilePrinterData()); + return data; }(); return *kStaticData; @@ -215,17 +214,32 @@ class MyClass : public tensorflow::XlaCompiledCpuFunction { } private: - // Number of result and temporary buffers for the compiled computation. - static constexpr size_t kNumTemps = 6; - // The 0-based index of the result tuple in the temporary buffers. - static constexpr size_t kResultIndex = 5; + // Number of buffers for the compiled computation. + static constexpr size_t kNumBuffers = 6; + + static const ::tensorflow::cpu_function_runtime::BufferInfo* BufferInfos() { + static const ::tensorflow::cpu_function_runtime::BufferInfo + kBufferInfos[kNumBuffers] = { +::tensorflow::cpu_function_runtime::BufferInfo({5ULL, ~0ULL}), +::tensorflow::cpu_function_runtime::BufferInfo({34ULL, 0ULL}), +::tensorflow::cpu_function_runtime::BufferInfo({9ULL, ~0ULL}), +::tensorflow::cpu_function_runtime::BufferInfo({386ULL, 1ULL}), +::tensorflow::cpu_function_runtime::BufferInfo({13ULL, ~0ULL}), +::tensorflow::cpu_function_runtime::BufferInfo({481ULL, ~0ULL}) + }; + return kBufferInfos; + } - // Byte size of each result / temporary buffer. There are kNumTemps entries. - static const intptr_t* TempSizes() { - static constexpr intptr_t kTempSizes[kNumTemps] = {1, -1, 2, -1, 3, 120}; - return kTempSizes; + static const ::tensorflow::int32* ArgIndexToBufferIndex() { + static constexpr ::tensorflow::int32 kArgIndexToBufferIndex[kNumArgs] = { +1, 3 + }; + return kArgIndexToBufferIndex; } + // The 0-based index of the result tuple in the temporary buffers. + static constexpr size_t kResultIndex = 5; + // Array of names of each positional argument, terminated by nullptr. static const char** StaticArgNames() { static const char* kNames[] = {"myfeed", nullptr}; diff --git a/tensorflow/compiler/aot/test.cc b/tensorflow/compiler/aot/test.cc index 6b098049cbd7539a2b2e2696b13139a8a6b28e0f..5deb47d12310d24dce847227bd119249210ffb8d 100644 --- a/tensorflow/compiler/aot/test.cc +++ b/tensorflow/compiler/aot/test.cc @@ -51,11 +51,9 @@ namespace tensorflow { namespace tfcompile { namespace { -void zero_buffers(void** bufs, const intptr_t* sizes, size_t n) { - for (int i = 0; i < n; ++i) { - if (sizes[i] != -1) { - memset(bufs[i], 0, sizes[i]); - } +void zero_buffers(XlaCompiledCpuFunction* computation) { + for (int i = 0; i < computation->num_args(); ++i) { + memset(computation->arg_data(i), 0, computation->arg_size(i)); } } @@ -66,7 +64,7 @@ TEST(TEST_NAME, NoCrash) { CPP_CLASS computation; computation.set_thread_pool(&device); - zero_buffers(computation.args(), CPP_CLASS::ArgSizes(), CPP_CLASS::kNumArgs); + zero_buffers(&computation); EXPECT_TRUE(computation.Run()); } @@ -80,7 +78,7 @@ void BM_NAME(int iters) { CPP_CLASS computation; computation.set_thread_pool(&device); - zero_buffers(computation.args(), CPP_CLASS::ArgSizes(), CPP_CLASS::kNumArgs); + zero_buffers(&computation); testing::StartTiming(); while (--iters) { diff --git a/tensorflow/compiler/aot/tests/tfcompile_test.cc b/tensorflow/compiler/aot/tests/tfcompile_test.cc index fee46280e9a0e7ba2cf7c3ed46469ae8cc0841d4..0c0c676ece78565e03578d3e33633c7e23b77669 100644 --- a/tensorflow/compiler/aot/tests/tfcompile_test.cc +++ b/tensorflow/compiler/aot/tests/tfcompile_test.cc @@ -44,8 +44,8 @@ using ::testing::IsSupersetOf; TEST(TFCompileTest, Add) { AddComp add; - EXPECT_EQ(add.arg0_data(), add.args()[0]); - EXPECT_EQ(add.arg1_data(), add.args()[1]); + EXPECT_EQ(add.arg0_data(), add.arg_data(0)); + EXPECT_EQ(add.arg1_data(), add.arg_data(1)); add.arg0() = 1; add.arg1() = 2; @@ -67,10 +67,10 @@ TEST(TFCompileTest, Add) { EXPECT_EQ(add_const.error_msg(), ""); EXPECT_EQ(add_const.arg0(), 123); EXPECT_EQ(add_const.arg0_data()[0], 123); - EXPECT_EQ(add_const.arg0_data(), add.args()[0]); + EXPECT_EQ(add_const.arg0_data(), add.arg_data(0)); EXPECT_EQ(add_const.arg1(), 456); EXPECT_EQ(add_const.arg1_data()[0], 456); - EXPECT_EQ(add_const.arg1_data(), add.args()[1]); + EXPECT_EQ(add_const.arg1_data(), add.arg_data(1)); EXPECT_EQ(add_const.result0(), 579); EXPECT_EQ(add_const.result0_data()[0], 579); EXPECT_EQ(add_const.result0_data(), add_const.results()[0]); @@ -85,8 +85,8 @@ TEST(TFCompileTest, Add_SetArg) { int32 arg_y = 32; add.set_arg0_data(&arg_x); add.set_arg1_data(&arg_y); - EXPECT_EQ(add.arg0_data(), add.args()[0]); - EXPECT_EQ(add.arg1_data(), add.args()[1]); + EXPECT_EQ(add.arg0_data(), add.arg_data(0)); + EXPECT_EQ(add.arg1_data(), add.arg_data(1)); EXPECT_TRUE(add.Run()); EXPECT_EQ(add.error_msg(), ""); @@ -97,7 +97,7 @@ TEST(TFCompileTest, Add_SetArg) { TEST(TFCompileTest, AddWithCkpt) { AddWithCkptComp add; - EXPECT_EQ(add.arg0_data(), add.args()[0]); + EXPECT_EQ(add.arg0_data(), add.arg_data(0)); add.arg0() = 1; EXPECT_TRUE(add.Run()); @@ -117,7 +117,7 @@ TEST(TFCompileTest, AddWithCkpt) { EXPECT_EQ(add_const.error_msg(), ""); EXPECT_EQ(add_const.arg0(), 111); EXPECT_EQ(add_const.arg0_data()[0], 111); - EXPECT_EQ(add_const.arg0_data(), add_const.args()[0]); + EXPECT_EQ(add_const.arg0_data(), add_const.arg_data(0)); EXPECT_EQ(add_const.result0(), 153); EXPECT_EQ(add_const.result0_data()[0], 153); EXPECT_EQ(add_const.result0_data(), add_const.results()[0]); @@ -125,7 +125,7 @@ TEST(TFCompileTest, AddWithCkpt) { TEST(TFCompileTest, AddWithCkptSaver) { AddWithCkptSaverComp add; - EXPECT_EQ(add.arg0_data(), add.args()[0]); + EXPECT_EQ(add.arg0_data(), add.arg_data(0)); add.arg0() = 1; EXPECT_TRUE(add.Run()); @@ -145,7 +145,7 @@ TEST(TFCompileTest, AddWithCkptSaver) { EXPECT_EQ(add_const.error_msg(), ""); EXPECT_EQ(add_const.arg0(), 111); EXPECT_EQ(add_const.arg0_data()[0], 111); - EXPECT_EQ(add_const.arg0_data(), add_const.args()[0]); + EXPECT_EQ(add_const.arg0_data(), add_const.arg_data(0)); EXPECT_EQ(add_const.result0(), 153); EXPECT_EQ(add_const.result0_data()[0], 153); EXPECT_EQ(add_const.result0_data(), add_const.results()[0]); @@ -153,9 +153,9 @@ TEST(TFCompileTest, AddWithCkptSaver) { TEST(TFCompileTest, Cond) { CondComp cond; - EXPECT_EQ(cond.arg0_data(), cond.args()[0]); - EXPECT_EQ(cond.arg1_data(), cond.args()[1]); - EXPECT_EQ(cond.arg2_data(), cond.args()[2]); + EXPECT_EQ(cond.arg0_data(), cond.arg_data(0)); + EXPECT_EQ(cond.arg1_data(), cond.arg_data(1)); + EXPECT_EQ(cond.arg2_data(), cond.arg_data(2)); cond.arg1() = 10; cond.arg2() = 20; { @@ -178,8 +178,8 @@ TEST(TFCompileTest, Cond) { TEST(TFCompileTest, Gather) { GatherComp gather; - EXPECT_EQ(gather.arg0_data(), gather.args()[0]); - EXPECT_EQ(gather.arg1_data(), gather.args()[1]); + EXPECT_EQ(gather.arg0_data(), gather.arg_data(0)); + EXPECT_EQ(gather.arg1_data(), gather.arg_data(1)); // Successful gather. { @@ -202,12 +202,12 @@ TEST(TFCompileTest, Gather) { EXPECT_EQ(gather_const.arg0(i), params[i]); EXPECT_EQ(gather_const.arg0_data()[i], params[i]); } - EXPECT_EQ(gather_const.arg0_data(), gather_const.args()[0]); + EXPECT_EQ(gather_const.arg0_data(), gather_const.arg_data(0)); for (int i = 0; i < 2; ++i) { EXPECT_EQ(gather_const.arg1(i), indices[i]); EXPECT_EQ(gather_const.arg1_data()[i], indices[i]); } - EXPECT_EQ(gather_const.arg1_data(), gather_const.args()[1]); + EXPECT_EQ(gather_const.arg1_data(), gather_const.arg_data(1)); for (int i = 0; i < 2; ++i) { EXPECT_EQ(gather_const.result0(i), results[i]); EXPECT_EQ(gather_const.result0_data()[i], results[i]); @@ -222,8 +222,8 @@ TEST(TFCompileTest, MatMul2) { foo::bar::MatMulComp matmul; matmul.set_thread_pool(&device); - EXPECT_EQ(matmul.arg0_data(), matmul.args()[0]); - EXPECT_EQ(matmul.arg1_data(), matmul.args()[1]); + EXPECT_EQ(matmul.arg0_data(), matmul.arg_data(0)); + EXPECT_EQ(matmul.arg1_data(), matmul.arg_data(1)); // Test using the argN() methods. { @@ -271,12 +271,12 @@ TEST(TFCompileTest, MatMul2) { EXPECT_EQ(matmul_const.arg0(i / 3, i % 3), args[i]); EXPECT_EQ(matmul_const.arg0_data()[i], args[i]); } - EXPECT_EQ(matmul_const.arg0_data(), matmul.args()[0]); + EXPECT_EQ(matmul_const.arg0_data(), matmul.arg_data(0)); for (int i = 0; i < 6; ++i) { EXPECT_EQ(matmul_const.arg1(i / 2, i % 2), args[i + 6]); EXPECT_EQ(matmul_const.arg1_data()[i], args[i + 6]); } - EXPECT_EQ(matmul_const.arg1_data(), matmul.args()[1]); + EXPECT_EQ(matmul_const.arg1_data(), matmul.arg_data(1)); for (int i = 0; i < 4; ++i) { EXPECT_EQ(matmul_const.result0(i / 2, i % 2), results[i]); EXPECT_EQ(matmul_const.result0_data()[i], results[i]); @@ -300,8 +300,8 @@ TEST(TFCompileTest, MatMul2_SetArg) { float arg1[3][2] = {{7, 8}, {9, 10}, {11, 12}}; matmul.set_arg0_data(&arg0); matmul.set_arg1_data(&arg1); - EXPECT_EQ(matmul.arg0_data(), matmul.args()[0]); - EXPECT_EQ(matmul.arg1_data(), matmul.args()[1]); + EXPECT_EQ(matmul.arg0_data(), matmul.arg_data(0)); + EXPECT_EQ(matmul.arg1_data(), matmul.arg_data(1)); EXPECT_TRUE(matmul.Run()); EXPECT_EQ(matmul.error_msg(), ""); @@ -319,8 +319,8 @@ TEST(TFCompileTest, MatMulAndAdd1) { MatMulAndAddComp muladd; muladd.set_thread_pool(&device); - EXPECT_EQ(muladd.arg0_data(), muladd.args()[0]); - EXPECT_EQ(muladd.arg1_data(), muladd.args()[1]); + EXPECT_EQ(muladd.arg0_data(), muladd.arg_data(0)); + EXPECT_EQ(muladd.arg1_data(), muladd.arg_data(1)); // Test methods with positional args and results. { @@ -346,12 +346,12 @@ TEST(TFCompileTest, MatMulAndAdd1) { EXPECT_EQ(muladd_const.arg0(i / 2, i % 2), args[i]); EXPECT_EQ(muladd_const.arg0_data()[i], args[i]); } - EXPECT_EQ(muladd_const.arg0_data(), muladd.args()[0]); + EXPECT_EQ(muladd_const.arg0_data(), muladd.arg_data(0)); for (int i = 0; i < 4; ++i) { EXPECT_EQ(muladd_const.arg1(i / 2, i % 2), args[i + 4]); EXPECT_EQ(muladd_const.arg1_data()[i], args[i + 4]); } - EXPECT_EQ(muladd_const.arg1_data(), muladd.args()[1]); + EXPECT_EQ(muladd_const.arg1_data(), muladd.arg_data(1)); for (int i = 0; i < 4; ++i) { EXPECT_EQ(muladd_const.result0(i / 2, i % 2), results0[i]); EXPECT_EQ(muladd_const.result0_data()[i], results0[i]); @@ -387,12 +387,12 @@ TEST(TFCompileTest, MatMulAndAdd1) { EXPECT_EQ(muladd_const.arg_x(i / 2, i % 2), args[i]); EXPECT_EQ(muladd_const.arg_x_data()[i], args[i]); } - EXPECT_EQ(muladd_const.arg_x_data(), muladd.args()[0]); + EXPECT_EQ(muladd_const.arg_x_data(), muladd.arg_data(0)); for (int i = 0; i < 4; ++i) { EXPECT_EQ(muladd_const.arg_y(i / 2, i % 2), args[i + 4]); EXPECT_EQ(muladd_const.arg_y_data()[i], args[i + 4]); } - EXPECT_EQ(muladd_const.arg_y_data(), muladd.args()[1]); + EXPECT_EQ(muladd_const.arg_y_data(), muladd.arg_data(1)); for (int i = 0; i < 4; ++i) { EXPECT_EQ(muladd_const.result_x_y_prod(i / 2, i % 2), results0[i]); EXPECT_EQ(muladd_const.result_x_y_prod_data()[i], results0[i]); @@ -407,8 +407,8 @@ TEST(TFCompileTest, MatMulAndAdd1) { TEST(TFCompileTest, Function) { // The function is equivalent to an addition FunctionComp add_fn; - EXPECT_EQ(add_fn.arg0_data(), add_fn.args()[0]); - EXPECT_EQ(add_fn.arg1_data(), add_fn.args()[1]); + EXPECT_EQ(add_fn.arg0_data(), add_fn.arg_data(0)); + EXPECT_EQ(add_fn.arg1_data(), add_fn.arg_data(1)); add_fn.arg0() = 1; add_fn.arg1() = 2; @@ -451,8 +451,8 @@ TEST(TFCompileTest, AssertEqAndReturnDiff) { // Assert is converted into a no-op in XLA, so there is no failure even if the // two args are different. AssertComp assert; - EXPECT_EQ(assert.arg0_data(), assert.args()[0]); - EXPECT_EQ(assert.arg1_data(), assert.args()[1]); + EXPECT_EQ(assert.arg0_data(), assert.arg_data(0)); + EXPECT_EQ(assert.arg1_data(), assert.arg_data(1)); assert.arg0() = 2; assert.arg1() = 1; diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index d3238c6a5efbf01a1e1b9e7a1bb8130055464b4d..55b98da4720fee7578d099dfc3eab25075930f20 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -160,6 +160,7 @@ cc_library( "//tensorflow/compiler/jit/ops:xla_ops", "//tensorflow/compiler/tf2xla:common", "//tensorflow/compiler/tf2xla:dump_graph", + "//tensorflow/compiler/tf2xla:tf2xla_util", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/kernels:xla_ops", "//tensorflow/compiler/xla:util", @@ -178,6 +179,7 @@ cc_library( "//tensorflow/core/kernels:constant_op", "//tensorflow/core/kernels:control_flow_ops", "//tensorflow/core/kernels:fifo_queue", + "//tensorflow/core/kernels:function_ops", "//tensorflow/core/kernels:identity_n_op", "//tensorflow/core/kernels:identity_op", "//tensorflow/core/kernels:no_op", @@ -186,6 +188,9 @@ cc_library( "//tensorflow/core/kernels:sendrecv_ops", "//tensorflow/core/kernels:shape_ops", "//tensorflow/core/kernels:variable_ops", + "//tensorflow/core/kernels/data:generator_dataset_op", + "//tensorflow/core/kernels/data:iterator_ops", + "//tensorflow/core/kernels/data:prefetch_dataset_op", ], ) diff --git a/tensorflow/compiler/jit/deadness_analysis.cc b/tensorflow/compiler/jit/deadness_analysis.cc index 8aff87e5e620fefd30eeb902209c9bc17540f468..62007e6115d3fb81def844fcfa462094e223f565 100644 --- a/tensorflow/compiler/jit/deadness_analysis.cc +++ b/tensorflow/compiler/jit/deadness_analysis.cc @@ -46,6 +46,7 @@ class Predicate { virtual string ToString() const = 0; int64 hash() const { return hash_; } + virtual gtl::ArraySlice GetOperands() const = 0; virtual Kind kind() const = 0; virtual ~Predicate() {} @@ -90,7 +91,8 @@ class AndPredicate : public Predicate { Kind kind() const override { return Kind::kAnd; } - const gtl::ArraySlice operands() const { return operands_; } + gtl::ArraySlice GetOperands() const override { return operands_; } + gtl::ArraySlice operands() const { return operands_; } private: std::vector operands_; @@ -117,7 +119,8 @@ class OrPredicate : public Predicate { } Kind kind() const override { return Kind::kOr; } - const gtl::ArraySlice operands() const { return operands_; } + gtl::ArraySlice GetOperands() const override { return operands_; } + gtl::ArraySlice operands() const { return operands_; } private: std::vector operands_; @@ -128,17 +131,18 @@ class NotPredicate : public Predicate { public: explicit NotPredicate(Predicate* operand) : Predicate(HashPredicateSequence(Kind::kNot, {operand})), - operand_(operand) {} + operands_({operand}) {} string ToString() const override { return strings::StrCat("~", operand()->ToString()); } Kind kind() const override { return Kind::kNot; } - Predicate* operand() const { return operand_; } + Predicate* operand() const { return operands_[0]; } + gtl::ArraySlice GetOperands() const override { return operands_; } private: - Predicate* operand_; + std::array operands_; }; // Represents an uninterpreted symbol in a logical predicate. @@ -158,6 +162,7 @@ class SymbolPredicate : public Predicate { } Kind kind() const override { return Kind::kSymbol; } + gtl::ArraySlice GetOperands() const override { return {}; } // If `must_be_true()` is true this SymbolPredicate represents the proposition // "tensor_id() is live and evaluates to true". @@ -288,10 +293,7 @@ Predicate* PredicateFactory::MakeAndOrImpl(gtl::ArraySlice operands, if (op->kind() == pred_kind) { // "Inline" the operands of an inner And/Or into the parent And/Or. - gtl::ArraySlice operands = - is_and ? dynamic_cast(op)->operands() - : dynamic_cast(op)->operands(); - for (Predicate* subop : operands) { + for (Predicate* subop : op->GetOperands()) { if (simplified_ops_set.insert(subop).second) { simplified_ops.push_back(subop); } diff --git a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc index fdd71c6a588ad96301f543651c8531e6f9c3ca05..f150bf1819d407e1c6a279673a89de4307b5426b 100644 --- a/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc +++ b/tensorflow/compiler/jit/encapsulate_subgraphs_pass.cc @@ -1161,8 +1161,7 @@ Status Encapsulator::Subgraph::ReplaceFunctionDef( strings::StrCat("replace_encapsulate_fdef_", name), fdef); } - TF_RETURN_IF_ERROR(library->RemoveFunction(name)); - TF_RETURN_IF_ERROR(library->AddFunctionDef(fdef)); + TF_RETURN_IF_ERROR(library->ReplaceFunction(name, fdef)); return Status::OK(); } diff --git a/tensorflow/compiler/jit/kernels/BUILD b/tensorflow/compiler/jit/kernels/BUILD index 00a6f4075f9a18efc3895b033eb6d08e36088a53..8f78c110cb15f3cbc0344d102764241996b0d7de 100644 --- a/tensorflow/compiler/jit/kernels/BUILD +++ b/tensorflow/compiler/jit/kernels/BUILD @@ -16,6 +16,7 @@ cc_library( "//tensorflow/compiler/jit:xla_device", "//tensorflow/compiler/jit:xla_launch_util", "//tensorflow/compiler/tf2xla:common", + "//tensorflow/compiler/tf2xla:tf2xla_util", "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla/client:client_library", diff --git a/tensorflow/compiler/jit/kernels/xla_launch_op.cc b/tensorflow/compiler/jit/kernels/xla_launch_op.cc index b313d48011b561eaab618692df49d1558c34a77c..37a2f3b5ac967970251bc3ce93c89691f7da6b24 100644 --- a/tensorflow/compiler/jit/kernels/xla_launch_op.cc +++ b/tensorflow/compiler/jit/kernels/xla_launch_op.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/compiler/jit/xla_device.h" #include "tensorflow/compiler/jit/xla_launch_util.h" #include "tensorflow/compiler/tf2xla/shape_util.h" +#include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" @@ -199,7 +200,7 @@ void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) { run_options.set_stream(stream); run_options.set_allocator(xla_allocator); run_options.set_intra_op_thread_pool(&ctx->eigen_cpu_device()); - run_options.set_rng_seed(ctx->step_id()); + run_options.set_rng_seed(GetXLARandomSeed()); Env* env = Env::Default(); auto start_time = env->NowMicros(); diff --git a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc index d288d37bc75380168a31937024dd41bdbe7dce9d..f65f89ebf57563f4ee78270cf8f2d187d4a5d162 100644 --- a/tensorflow/compiler/jit/xla_compile_on_demand_op.cc +++ b/tensorflow/compiler/jit/xla_compile_on_demand_op.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/jit/xla_compile_on_demand_op.h" #include "tensorflow/compiler/jit/xla_device.h" #include "tensorflow/compiler/jit/xla_launch_util.h" +#include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" @@ -71,7 +72,7 @@ Status XlaCompileOnDemandOp::Run(OpKernelContext* ctx, run_options.set_stream(stream); run_options.set_allocator(client->backend().memory_allocator()); run_options.set_intra_op_thread_pool(&ctx->eigen_cpu_device()); - run_options.set_rng_seed(ctx->step_id()); + run_options.set_rng_seed(GetXLARandomSeed()); xla::StatusOr run_result = executable->Run(launch_context.arguments(), run_options); diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc index 8cf198239c84c3720585f53ebc95876ce4396793..0100bf51ed2a66f6d110dacd30bcdf9f48a8f64f 100644 --- a/tensorflow/compiler/jit/xla_device_context.cc +++ b/tensorflow/compiler/jit/xla_device_context.cc @@ -101,34 +101,27 @@ Status XlaTransferManager::TransferLiteralToDevice( // Unref the host tensor, and capture the literal shared_ptr too so it goes // out of scope when the lambda completes. host_to_device_stream_->ThenDoHostCallback([ref, literal]() { ref.Unref(); }); + return Status::OK(); } void XlaTransferManager::TransferLiteralFromDevice( Tensor* host_tensor, const Tensor& device_tensor, const StatusCallback& done) const { + xla::MutableBorrowingLiteral literal; + TF_CHECK_OK(HostTensorToMutableBorrowingLiteral(host_tensor, &literal)); + const xla::ShapedBuffer& shaped_buffer = XlaTensor::FromTensor(&device_tensor)->shaped_buffer(); TensorReference ref(device_tensor); transfer_manager_->TransferLiteralFromDevice( - device_to_host_stream_, shaped_buffer, - [=, &shaped_buffer]( - xla::StatusOr > literal_or) { + device_to_host_stream_, shaped_buffer, literal, + [=, &shaped_buffer, &literal](xla::Status status) { ref.Unref(); done([&]() -> Status { - TF_ASSIGN_OR_RETURN(auto literal, std::move(literal_or)); - VLOG(1) << "Transfer from device as literal: " << literal->ToString() + VLOG(1) << "Transfer from device as literal: " << literal.ToString() << " " << shaped_buffer.ToString(); - Tensor tensor; - TF_RETURN_IF_ERROR( - LiteralToHostTensor(*literal, host_tensor->dtype(), &tensor)); - // Reshape the tensor back to its declared shape. - Status status; - if (!host_tensor->CopyFrom(tensor, device_tensor.shape())) { - status = errors::Internal( - "Tensor::CopyFrom failed when copying from XLA device to CPU"); - } return status; }()); }); diff --git a/tensorflow/compiler/jit/xla_device_ops.h b/tensorflow/compiler/jit/xla_device_ops.h index 6adda327f186a607b4e7371bf4c5071dd86582da..da3e329247e825d4a33a53dc310899d6ba6ce9cf 100644 --- a/tensorflow/compiler/jit/xla_device_ops.h +++ b/tensorflow/compiler/jit/xla_device_ops.h @@ -23,7 +23,11 @@ limitations under the License. #include "tensorflow/core/kernels/cast_op.h" #include "tensorflow/core/kernels/constant_op.h" #include "tensorflow/core/kernels/control_flow_ops.h" +#include "tensorflow/core/kernels/data/generator_dataset_op.h" +#include "tensorflow/core/kernels/data/iterator_ops.h" +#include "tensorflow/core/kernels/data/prefetch_dataset_op.h" #include "tensorflow/core/kernels/fifo_queue.h" +#include "tensorflow/core/kernels/function_ops.h" #include "tensorflow/core/kernels/identity_n_op.h" #include "tensorflow/core/kernels/identity_op.h" #include "tensorflow/core/kernels/no_op.h" @@ -166,7 +170,69 @@ class XlaAssignVariableOp : public AsyncOpKernel { QueueIsClosedOp); \ \ REGISTER_KERNEL_BUILDER( \ - Name("FIFOQueueV2").Device(DEVICE).HostMemory("handle"), FIFOQueueOp); + Name("FIFOQueueV2").Device(DEVICE).HostMemory("handle"), FIFOQueueOp); \ + \ + REGISTER_KERNEL_BUILDER( \ + Name(kArgOp).Device(DEVICE).HostMemory("output").TypeConstraint("T", \ + TYPES), \ + ArgOp); \ + REGISTER_KERNEL_BUILDER(Name(kArgOp) \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("T"), \ + ArgOp); \ + \ + REGISTER_KERNEL_BUILDER(Name(kRetOp) \ + .Device(DEVICE) \ + .TypeConstraint("T", TYPES) \ + .HostMemory("input"), \ + RetvalOp); \ + REGISTER_KERNEL_BUILDER(Name(kRetOp) \ + .Device(DEVICE) \ + .TypeConstraint("T") \ + .HostMemory("input"), \ + RetvalOp); \ + \ + REGISTER_KERNEL_BUILDER( \ + Name("RemoteCall").Device(DEVICE).HostMemory("target"), RemoteCallOp); \ + \ + REGISTER_KERNEL_BUILDER( \ + Name("GeneratorDataset").Device(DEVICE).HostMemory("handle"), \ + GeneratorDatasetOp); \ + REGISTER_KERNEL_BUILDER(Name("PrefetchDataset") \ + .Device(DEVICE) \ + .HostMemory("buffer_size") \ + .HostMemory("input_dataset") \ + .HostMemory("handle"), \ + PrefetchDatasetOp); \ + \ + REGISTER_KERNEL_BUILDER(Name("IteratorV2").Device(DEVICE), \ + IteratorHandleOp); \ + REGISTER_KERNEL_BUILDER( \ + Name("MakeIterator").Device(DEVICE).HostMemory("dataset"), \ + MakeIteratorOp); \ + REGISTER_KERNEL_BUILDER(Name("AnonymousIterator").Device(DEVICE), \ + AnonymousIteratorHandleOp); \ + REGISTER_KERNEL_BUILDER(Name("IteratorGetNext").Device(DEVICE), \ + IteratorGetNextOp); \ + REGISTER_KERNEL_BUILDER(Name("IteratorToStringHandle") \ + .Device(DEVICE) \ + .HostMemory("string_handle"), \ + IteratorToStringHandleOp); \ + REGISTER_KERNEL_BUILDER(Name("IteratorFromStringHandleV2") \ + .Device(DEVICE) \ + .HostMemory("string_handle"), \ + IteratorFromStringHandleOp); \ + REGISTER_KERNEL_BUILDER(Name(FunctionLibraryDefinition::kArgOp) \ + .Device(DEVICE) \ + .HostMemory("output") \ + .TypeConstraint("T"), \ + ArgOp); \ + REGISTER_KERNEL_BUILDER(Name(FunctionLibraryDefinition::kRetOp) \ + .Device(DEVICE) \ + .TypeConstraint("T") \ + .HostMemory("input"), \ + RetvalOp); // TODO(phawkins): currently we do not register the QueueEnqueueMany, // QueueDequeueMany, or QueueDequeueUpTo kernels because they attempt to read diff --git a/tensorflow/compiler/tests/BUILD b/tensorflow/compiler/tests/BUILD index b7dc5d4c74cb41b5e758e8170a44090bf04e5420..ae98b3f0f9d5dac66b9716ad84a9f0371511e9b6 100644 --- a/tensorflow/compiler/tests/BUILD +++ b/tensorflow/compiler/tests/BUILD @@ -691,11 +691,7 @@ tf_xla_py_test( size = "small", srcs = ["random_ops_test.py"], disabled_backends = [ - # TODO(b/110300529): RngNormal doesn't return values with the expected variance - "cpu", "cpu_ondemand", - # TODO(b/31361304): enable RNG ops on GPU when parallelized. - "gpu", ], deps = [ ":xla_test", diff --git a/tensorflow/compiler/tests/adam_test.py b/tensorflow/compiler/tests/adam_test.py index 03554d6933aca39b428c6af4be0c78e2c7ccb0c9..0d2e4d029636577adc74784d9a8b3494b94dc67d 100644 --- a/tensorflow/compiler/tests/adam_test.py +++ b/tensorflow/compiler/tests/adam_test.py @@ -52,6 +52,9 @@ class AdamOptimizerTest(xla_test.XLATestCase): def testBasic(self): for dtype in self.float_types: + # TODO: test fails for float16 due to excessive precision requirements. + if dtype == np.float16: + continue with self.test_session(), self.test_scope(): variable_scope.get_variable_scope().set_use_resource(True) @@ -91,6 +94,9 @@ class AdamOptimizerTest(xla_test.XLATestCase): def testTensorLearningRate(self): for dtype in self.float_types: + # TODO: test fails for float16 due to excessive precision requirements. + if dtype == np.float16: + continue with self.test_session(), self.test_scope(): variable_scope.get_variable_scope().set_use_resource(True) @@ -130,6 +136,9 @@ class AdamOptimizerTest(xla_test.XLATestCase): def testSharing(self): for dtype in self.float_types: + # TODO: test fails for float16 due to excessive precision requirements. + if dtype == np.float16: + continue with self.test_session(), self.test_scope(): variable_scope.get_variable_scope().set_use_resource(True) diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py index 422f36d43bf38d26f057c18da716d7e281c286af..ff097f80f1f2586bd483a54d532750c90b2a8b03 100644 --- a/tensorflow/compiler/tests/eager_test.py +++ b/tensorflow/compiler/tests/eager_test.py @@ -32,6 +32,7 @@ from tensorflow.python.layers import convolutional from tensorflow.python.layers import pooling from tensorflow.python.ops import array_ops from tensorflow.python.ops import embedding_ops +from tensorflow.python.ops import gen_random_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops @@ -122,6 +123,14 @@ class EagerTest(xla_test.XLATestCase): with self.test_scope(): self.assertAllEqual(2, array_ops.identity(2)) + def testRandomOps(self): + with self.test_scope(): + tensor = gen_random_ops.random_uniform((2, 2), dtypes.float32) + row0 = tensor[0].numpy() + row1 = tensor[1].numpy() + # It should be very unlikely to rng to generate two equal rows. + self.assertFalse((row0 == row1).all()) + def testIdentityOnVariable(self): with self.test_scope(): v = resource_variable_ops.ResourceVariable(True) diff --git a/tensorflow/compiler/tests/random_ops_test.py b/tensorflow/compiler/tests/random_ops_test.py index 2f60e00c37d214d025b161310d57f9cd84884304..8c4e16e4e075726d741f6ff8cdfb6b1aad6cd33e 100644 --- a/tensorflow/compiler/tests/random_ops_test.py +++ b/tensorflow/compiler/tests/random_ops_test.py @@ -57,7 +57,8 @@ class RandomOpsTest(xla_test.XLATestCase): def testRandomUniformIsNotConstant(self): def rng(dtype): - return random_ops.random_uniform(shape=[2], dtype=dtype, maxval=10000) + dtype = dtypes.as_dtype(dtype) + return random_ops.random_uniform(shape=[2], dtype=dtype, maxval=dtype.max) for dtype in self._random_types(): self._testRngIsNotConstant(rng, dtype) @@ -73,6 +74,11 @@ class RandomOpsTest(xla_test.XLATestCase): def testRandomUniformIsInRange(self): for dtype in self._random_types(): + # TODO (b/112272078): enable bfloat16 for CPU and GPU when the bug is + # fixed. + if (self.device in ["XLA_GPU", "XLA_CPU" + ]) and (dtype in [dtypes.bfloat16, dtypes.half]): + continue with self.test_session() as sess: with self.test_scope(): x = random_ops.random_uniform( @@ -95,7 +101,7 @@ class RandomOpsTest(xla_test.XLATestCase): for dtype in [dtypes.float32]: with self.test_session() as sess: with self.test_scope(): - x = random_ops.truncated_normal(shape=[count], dtype=dtype, seed=42) + x = random_ops.truncated_normal(shape=[count], dtype=dtype) y = sess.run(x) def normal_cdf(x): @@ -124,20 +130,23 @@ class RandomOpsTest(xla_test.XLATestCase): # Department of Scientific Computing website. Florida State University. expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma actual_mean = np.mean(y) - self.assertAllClose(actual_mean, expected_mean, atol=2e-4) + self.assertAllClose(actual_mean, expected_mean, atol=2e-3) expected_median = mu + probit( (normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma actual_median = np.median(y) - self.assertAllClose(actual_median, expected_median, atol=8e-4) + self.assertAllClose(actual_median, expected_median, atol=1e-2) expected_variance = sigma**2 * (1 + ( (alpha * normal_pdf(alpha) - beta * normal_pdf(beta)) / z) - ( (normal_pdf(alpha) - normal_pdf(beta)) / z)**2) actual_variance = np.var(y) - self.assertAllClose(actual_variance, expected_variance, rtol=3e-4) + self.assertAllClose(actual_variance, expected_variance, rtol=2*1e-3) def testShuffle1d(self): + # TODO(b/26783907): this test requires the CPU backend to implement sort. + if self.device in ["XLA_CPU"]: + return with self.test_session() as sess: with self.test_scope(): x = math_ops.range(1 << 16) diff --git a/tensorflow/compiler/tests/xla_device_test.py b/tensorflow/compiler/tests/xla_device_test.py index 06d977b93c28792704b910c688af510bc650d2a4..85084bb1240cf05f6eabfbea772df113cabe613c 100644 --- a/tensorflow/compiler/tests/xla_device_test.py +++ b/tensorflow/compiler/tests/xla_device_test.py @@ -21,6 +21,8 @@ from __future__ import print_function import numpy as np from tensorflow.compiler.tests import xla_test +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_control_flow_ops @@ -47,6 +49,34 @@ class XlaDeviceTest(xla_test.XLATestCase): result = sess.run(z, {x: inputs}) self.assertAllCloseAccordingToType(result, inputs + inputs) + def testCopiesOfUnsupportedTypesFailGracefully(self): + """Tests that copies of unsupported types don't crash.""" + test_types = set([ + np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16, np.int32, + np.int64, np.float16, np.float32, np.float16, + dtypes.bfloat16.as_numpy_dtype + ]) + shape = (10, 10) + for unsupported_dtype in test_types - self.all_types: + with self.test_session() as sess: + with ops.device("CPU"): + x = array_ops.placeholder(unsupported_dtype, shape) + with self.test_scope(): + y, = array_ops.identity_n([x]) + with ops.device("CPU"): + z = array_ops.identity(y) + + inputs = np.random.randint(-100, 100, shape) + inputs = inputs.astype(unsupported_dtype) + # Execution should either succeed or raise an InvalidArgumentError, + # but not crash. Even "unsupported types" may succeed here since some + # backends (e.g., the CPU backend) are happy to handle buffers of + # unsupported types, even if they cannot compute with them. + try: + sess.run(z, {x: inputs}) + except errors.InvalidArgumentError: + pass + def testControlTrigger(self): with self.test_session() as sess: with self.test_scope(): diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index 61759fd2764205fab7fce11c4003e84be1be813a..fda32c8a1c9491e0dadceec0d7265e1002d41528 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -95,6 +95,10 @@ cc_library( name = "cpu_function_runtime", srcs = ["cpu_function_runtime.cc"], hdrs = ["cpu_function_runtime.h"], + visibility = [ + "//tensorflow/compiler/aot:__pkg__", + "//tensorflow/compiler/xla/service/cpu:__pkg__", + ], deps = [ # Keep dependencies to a minimum here; this library is used in every AOT # binary produced by tfcompile. @@ -144,6 +148,7 @@ cc_library( "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:cpu_plugin", + "//tensorflow/compiler/xla/service/cpu:buffer_info_util", "//tensorflow/compiler/xla/service/cpu:cpu_executable", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", diff --git a/tensorflow/compiler/tf2xla/cpu_function_runtime.cc b/tensorflow/compiler/tf2xla/cpu_function_runtime.cc index 2ffad2af8cfe621f0cbbdd8a9484ef2dfdf1b129..fcc4095e39673b786544984a41988c3e9c5b0efb 100644 --- a/tensorflow/compiler/tf2xla/cpu_function_runtime.cc +++ b/tensorflow/compiler/tf2xla/cpu_function_runtime.cc @@ -55,19 +55,26 @@ size_t align_to(size_t n, size_t align) { } // namespace namespace cpu_function_runtime { -size_t AlignedBufferBytes(const intptr_t* sizes, size_t n) { +size_t AlignedBufferBytes(const BufferInfo* buffer_infos, size_t n, + bool allocate_entry_params) { size_t total = 0; for (size_t i = 0; i < n; ++i) { - if (sizes[i] > 0) { - total += align_to(sizes[i], kAlign); + bool should_allocate = + buffer_infos[i].is_temp_buffer() || + (buffer_infos[i].is_entry_parameter() && allocate_entry_params); + + if (should_allocate) { + total += align_to(buffer_infos[i].size(), kAlign); } } return total; } -void* MallocContiguousBuffers(const intptr_t* sizes, size_t n, void** bufs, +void* MallocContiguousBuffers(const BufferInfo* buffer_infos, size_t n, + bool allocate_entry_params, void** bufs, bool annotate_initialized) { - const size_t total = AlignedBufferBytes(sizes, n); + const size_t total = + AlignedBufferBytes(buffer_infos, n, allocate_entry_params); void* contiguous = nullptr; if (total > 0) { contiguous = aligned_malloc(total, kAlign); @@ -79,13 +86,14 @@ void* MallocContiguousBuffers(const intptr_t* sizes, size_t n, void** bufs, } uintptr_t pos = reinterpret_cast(contiguous); for (size_t i = 0; i < n; ++i) { - if (sizes[i] < 0) { - // bufs[i] is either a constant, an entry parameter or a thread local - // allocation. - bufs[i] = nullptr; - } else { + bool should_allocate = + buffer_infos[i].is_temp_buffer() || + (buffer_infos[i].is_entry_parameter() && allocate_entry_params); + if (should_allocate) { bufs[i] = reinterpret_cast(pos); - pos += align_to(sizes[i], kAlign); + pos += align_to(buffer_infos[i].size(), kAlign); + } else { + bufs[i] = nullptr; } } return contiguous; diff --git a/tensorflow/compiler/tf2xla/cpu_function_runtime.h b/tensorflow/compiler/tf2xla/cpu_function_runtime.h index c7b4559c65731d1c4f4ea41e8be173ba89fe359c..dfc1e8b8aebcf3142e9f61f60171c6b58634c71d 100644 --- a/tensorflow/compiler/tf2xla/cpu_function_runtime.h +++ b/tensorflow/compiler/tf2xla/cpu_function_runtime.h @@ -18,29 +18,142 @@ limitations under the License. #include "tensorflow/core/platform/types.h" +#include + namespace tensorflow { namespace cpu_function_runtime { +// Stores information about one buffer used by an XLA:CPU compiled function. +// These buffers are used for holding inputs to the computation, outputs from +// the computation and as temporary scratch space. +class BufferInfo { + public: + // Creates a BufferInfo from a serialized encoding generated by `Encode`. + explicit BufferInfo(std::pair encoding) + : entry_param_number_(encoding.second) { + Kind kind; + uint64 size; + Unpack(encoding.first, &kind, &size); + kind_ = kind; + size_ = size; + } + + // Returns true if this buffer stores a constant. These never need to be + // allocated by the runtime. + bool is_constant() const { return kind() == Kind::kConstant; } + + // Returns true if this buffer stores an entry parameter. These may or may + // not need to be allocated by the runtime, depending on + // XlaCompiledCpuFunction::AllocMode. + bool is_entry_parameter() const { return kind() == Kind::kEntryParameter; } + + // Returns the entry parameter number of this buffer. + uint64 entry_parameter_number() const { + assert(is_entry_parameter()); + return entry_param_number_; + } + + // Returns true if this buffer is temporary scratch space required by the XLA + // computations. These are always allocated by the runtime. + bool is_temp_buffer() const { return kind() == Kind::kTempBuffer; } + + // Returns true if this buffer is allocated on the C stack or into registers. + // These buffers are never allocated by the runtime. + bool is_on_stack_buffer() const { return kind() == Kind::kOnStackBuffer; } + + // Returns the size for this buffer. + uint64 size() const { return size_; } + + // Encodes this BufferInfo into two 64 bit integers that can be used to + // reconstruct the BufferInfo later using the constructor. We need this + // because we use BufferInfo in places where using protocol buffers would + // negatively impact binary size. + std::pair Encode() const { + static_assert(sizeof(*this) == 16, ""); + uint64 upper = Pack(kind(), size_); + uint64 lower = entry_param_number_; + return {upper, lower}; + } + + bool operator==(const BufferInfo& buffer_info) const { + if (kind() != buffer_info.kind() || size() != buffer_info.size()) { + return false; + } + return !is_entry_parameter() || + entry_parameter_number() == buffer_info.entry_parameter_number(); + } + + // Factory methods: + + static BufferInfo MakeTempBuffer(uint64 size) { + return BufferInfo(Kind::kTempBuffer, /*size=*/size, + /*entry_param_number=*/-1); + } + static BufferInfo MakeConstant(uint64 size) { + return BufferInfo(Kind::kConstant, /*size=*/size, + /*entry_param_number=*/-1); + } + static BufferInfo MakeEntryParameter(uint64 size, uint64 param_number) { + return BufferInfo(Kind::kEntryParameter, /*size=*/size, + /*entry_param_number=*/param_number); + } + static BufferInfo MakeOnStackBuffer(uint64 size) { + return BufferInfo(Kind::kOnStackBuffer, /*size=*/size, + /*entry_param_number=*/-1); + } + + private: + BufferInfo() = default; + + enum class Kind : unsigned { + kConstant, + kTempBuffer, + kEntryParameter, + kOnStackBuffer + }; + + Kind kind() const { return static_cast(kind_); } + + explicit BufferInfo(Kind kind, uint64 size, uint64 entry_param_number) + : kind_(kind), size_(size), entry_param_number_(entry_param_number) {} + + static uint64 Pack(Kind kind, uint64 size) { + return (static_cast(size) << 2) | static_cast(kind); + } + + static void Unpack(uint64 packed, Kind* kind, uint64* size) { + *size = packed >> 2; + *kind = static_cast((packed << 62) >> 62); + } + + Kind kind_ : 2; + uint64 size_ : 62; + int64 entry_param_number_; +}; // Align to 64-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment. constexpr size_t kAlign = 64; -// AlignedBufferBytes returns the sum of each size in `sizes`, skipping -1 -// values. There are `n` entries in `sizes`. Each buffer is aligned to -// kAlign byte boundaries. -size_t AlignedBufferBytes(const intptr_t* sizes, size_t n); +// AlignedBufferBytes returns the sum of the size of each buffer in +// `buffer_infos`, skipping constants, on-stack buffers and, if +// allocate_entry_params is false, entry parameters. There are `n` entries in +// `buffer_infos`. Each buffer is aligned to kAlign byte boundaries. +size_t AlignedBufferBytes(const BufferInfo* buffer_infos, size_t n, + bool allocate_entry_params); // MallocContiguousBuffers allocates buffers for use by the entry point -// generated by tfcompile. `sizes` is an array of byte sizes for each buffer, -// where -1 causes the buffer pointer to be nullptr. There are `n` entries in -// `sizes`. If `annotate_initialized` is set, the allocated memory will be -// annotated as having been initialized - this is useful when allocating -// temporary buffers. +// generated by tfcompile. There are `n` entries in `buffer_infos`. If +// `annotate_initialized` is set, the allocated memory will be annotated as +// having been initialized - this is useful when allocating temporary buffers. +// If allocate_entry_params is true then allocates temp buffers and entry +// parameters, otherwise allocated only temp buffers. Slots in `bufs` +// corresponding to unallocated buffers are set to nullptr. // // A single contiguous block of memory is allocated, and portions of it are // parceled out into `bufs`, which must have space for `n` entries. Returns // the head of the allocated contiguous block, which should be passed to // FreeContiguous when the buffers are no longer in use. -void* MallocContiguousBuffers(const intptr_t* sizes, size_t n, void** bufs, +void* MallocContiguousBuffers(const BufferInfo* buffer_infos, size_t n, + bool allocate_entry_params, void** bufs, bool annotate_initialized); // FreeContiguous frees the contiguous block of memory allocated by diff --git a/tensorflow/compiler/tf2xla/cpu_function_runtime_test.cc b/tensorflow/compiler/tf2xla/cpu_function_runtime_test.cc index f4f27a156261ea6872777cef76ecaf7dd7eebe0d..8ca628c4eb6700d7184899bc1753dd6c6aa392b0 100644 --- a/tensorflow/compiler/tf2xla/cpu_function_runtime_test.cc +++ b/tensorflow/compiler/tf2xla/cpu_function_runtime_test.cc @@ -21,6 +21,8 @@ limitations under the License. namespace tensorflow { namespace { +using cpu_function_runtime::BufferInfo; + TEST(XlaCompiledCpuFunctionTest, AlignmentValue) { // We've chosen 64 byte alignment for the tfcompile runtime to mimic the // regular tensorflow allocator, which was chosen to play nicely with Eigen. @@ -30,20 +32,51 @@ TEST(XlaCompiledCpuFunctionTest, AlignmentValue) { EXPECT_EQ(cpu_function_runtime::kAlign, Allocator::kAllocatorAlignment); } +std::vector SizesToBufferInfos(const intptr_t* sizes, size_t n) { + std::vector buffer_infos; + std::transform(sizes, sizes + n, std::back_inserter(buffer_infos), + [&](intptr_t size) { + if (size == -1) { + // Use a dummy on-stack buffer allocation to indicat the + // the current slot does not need an allocation. + int64 on_stack_buffer_size = 4; + return BufferInfo::MakeOnStackBuffer(on_stack_buffer_size); + } + return BufferInfo::MakeTempBuffer(size); + }); + return buffer_infos; +} + +// Simple wrappers to make writing tests more ergonomic. + +size_t AlignedBufferBytesFromSizes(const intptr_t* sizes, size_t n) { + std::vector buffer_infos = SizesToBufferInfos(sizes, n); + return AlignedBufferBytes(buffer_infos.data(), n, + /*allocate_entry_params=*/false); +} + +void* MallocContiguousBuffersFromSizes(const intptr_t* sizes, size_t n, + void** bufs, bool annotate_initialized) { + std::vector buffer_infos = SizesToBufferInfos(sizes, n); + return MallocContiguousBuffers(buffer_infos.data(), n, + /*allocate_entry_params=*/false, bufs, + annotate_initialized); +} + TEST(XlaCompiledCpuFunctionTest, AlignedBufferBytes) { - EXPECT_EQ(cpu_function_runtime::AlignedBufferBytes(nullptr, 0), 0); + EXPECT_EQ(AlignedBufferBytesFromSizes(nullptr, 0), 0); static constexpr intptr_t sizesA[1] = {-1}; - EXPECT_EQ(cpu_function_runtime::AlignedBufferBytes(sizesA, 1), 0); + EXPECT_EQ(AlignedBufferBytesFromSizes(sizesA, 1), 0); static constexpr intptr_t sizesB[1] = {3}; - EXPECT_EQ(cpu_function_runtime::AlignedBufferBytes(sizesB, 1), 64); + EXPECT_EQ(AlignedBufferBytesFromSizes(sizesB, 1), 64); static constexpr intptr_t sizesC[1] = {32}; - EXPECT_EQ(cpu_function_runtime::AlignedBufferBytes(sizesC, 1), 64); + EXPECT_EQ(AlignedBufferBytesFromSizes(sizesC, 1), 64); static constexpr intptr_t sizesD[7] = {1, -1, 32, -1, 64, 2, 3}; - EXPECT_EQ(cpu_function_runtime::AlignedBufferBytes(sizesD, 7), 320); + EXPECT_EQ(AlignedBufferBytesFromSizes(sizesD, 7), 320); } void* add_ptr(void* base, uintptr_t delta) { @@ -56,15 +89,14 @@ void* add_ptr(void* base, uintptr_t delta) { // free. We also check the contiguous property. TEST(XlaCompiledCpuFunctionTest, MallocFreeContiguousBuffers) { // Test empty sizes. - void* base = - cpu_function_runtime::MallocContiguousBuffers(nullptr, 0, nullptr, false); + void* base = MallocContiguousBuffersFromSizes(nullptr, 0, nullptr, false); EXPECT_EQ(base, nullptr); cpu_function_runtime::FreeContiguous(base); // Test non-empty sizes with 0 sum. static constexpr intptr_t sizesA[1] = {-1}; void* bufA[1]; - base = cpu_function_runtime::MallocContiguousBuffers(sizesA, 1, bufA, false); + base = MallocContiguousBuffersFromSizes(sizesA, 1, bufA, false); EXPECT_EQ(base, nullptr); EXPECT_EQ(bufA[0], nullptr); cpu_function_runtime::FreeContiguous(base); @@ -72,7 +104,7 @@ TEST(XlaCompiledCpuFunctionTest, MallocFreeContiguousBuffers) { // Test non-empty sizes with non-0 sum. static constexpr intptr_t sizesB[1] = {3}; void* bufB[1]; - base = cpu_function_runtime::MallocContiguousBuffers(sizesB, 1, bufB, false); + base = MallocContiguousBuffersFromSizes(sizesB, 1, bufB, false); EXPECT_NE(base, nullptr); EXPECT_EQ(bufB[0], add_ptr(base, 0)); char* bufB0_bytes = static_cast(bufB[0]); @@ -84,7 +116,7 @@ TEST(XlaCompiledCpuFunctionTest, MallocFreeContiguousBuffers) { // Test non-empty sizes with non-0 sum, and annotate_initialized. static constexpr intptr_t sizesC[1] = {3}; void* bufC[1]; - base = cpu_function_runtime::MallocContiguousBuffers(sizesC, 1, bufC, true); + base = MallocContiguousBuffersFromSizes(sizesC, 1, bufC, true); EXPECT_NE(base, nullptr); EXPECT_EQ(bufC[0], add_ptr(base, 0)); char* bufC0_bytes = static_cast(bufC[0]); @@ -96,7 +128,7 @@ TEST(XlaCompiledCpuFunctionTest, MallocFreeContiguousBuffers) { // Test mixed sizes. static constexpr intptr_t sizesD[7] = {1, -1, 32, -1, 64, 2, 3}; void* bufD[7]; - base = cpu_function_runtime::MallocContiguousBuffers(sizesD, 7, bufD, false); + base = MallocContiguousBuffersFromSizes(sizesD, 7, bufD, false); EXPECT_NE(base, nullptr); EXPECT_EQ(bufD[0], add_ptr(base, 0)); EXPECT_EQ(bufD[1], nullptr); @@ -117,5 +149,23 @@ TEST(XlaCompiledCpuFunctionTest, MallocFreeContiguousBuffers) { cpu_function_runtime::FreeContiguous(base); } +void CheckRoundTripIsOk(const BufferInfo& buffer_info) { + BufferInfo round_trip(buffer_info.Encode()); + ASSERT_EQ(round_trip, buffer_info); +} + +TEST(XlaCompiledCpuFunctionTest, BufferInfoTest) { + CheckRoundTripIsOk(BufferInfo::MakeTempBuffer(0)); + CheckRoundTripIsOk(BufferInfo::MakeTempBuffer(4)); + CheckRoundTripIsOk(BufferInfo::MakeOnStackBuffer(0)); + CheckRoundTripIsOk(BufferInfo::MakeOnStackBuffer(4)); + CheckRoundTripIsOk(BufferInfo::MakeConstant(0)); + CheckRoundTripIsOk(BufferInfo::MakeConstant(4)); + CheckRoundTripIsOk( + BufferInfo::MakeEntryParameter(/*size=*/0, /*param_number=*/4)); + CheckRoundTripIsOk( + BufferInfo::MakeEntryParameter(/*size=*/4, /*param_number=*/0)); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 0609e223381550645d1a41ba75e4cd57f893ee95..b1366e9e31e28406c5bf1a808b9c5670558ed9c7 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -6,6 +6,10 @@ package( load("//tensorflow:tensorflow.bzl", "tf_copts") load("//tensorflow:tensorflow.bzl", "tf_kernel_library") +load( + "//third_party/mkl:build_defs.bzl", + "if_mkl", +) tf_kernel_library( name = "xla_ops", @@ -129,6 +133,7 @@ tf_kernel_library( "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/lib:numeric", + "//tensorflow/compiler/xla/client/lib:pooling", "//tensorflow/compiler/xla/client/lib:prng", "//tensorflow/compiler/xla/client/lib:sorting", "//tensorflow/core:framework", @@ -153,8 +158,14 @@ tf_kernel_library( "//tensorflow/core/kernels:sparse_to_dense_op", "//tensorflow/core/kernels:stack_ops", "//tensorflow/core/kernels:training_ops", - "//tensorflow/core/kernels:transpose_op", - ], + ] + if_mkl( + [ + "//tensorflow/core/kernels:mkl_transpose_op", + ], + [ + "//tensorflow/core/kernels:transpose_op", + ], + ), ) tf_kernel_library( diff --git a/tensorflow/compiler/tf2xla/kernels/arg_op.cc b/tensorflow/compiler/tf2xla/kernels/arg_op.cc index 26fc1620a4f032b3af28de6e3a5af0e965e82341..276d744c096f8996c774964204feaa3762bdb844 100644 --- a/tensorflow/compiler/tf2xla/kernels/arg_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/arg_op.cc @@ -65,6 +65,6 @@ class XlaArgOp : public XlaOpKernel { TF_DISALLOW_COPY_AND_ASSIGN(XlaArgOp); }; -REGISTER_XLA_OP(Name("_Arg").AllowResourceTypes(), XlaArgOp); +REGISTER_XLA_OP(Name("_Arg").AllowResourceTypes().CompilationOnly(), XlaArgOp); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/if_op.cc b/tensorflow/compiler/tf2xla/kernels/if_op.cc index ceb2af756c2d2020c7449086b957c9fbc1cc2979..6a7eb8d90c45ab119096eaa259e05c6ca768c5aa 100644 --- a/tensorflow/compiler/tf2xla/kernels/if_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/if_op.cc @@ -200,25 +200,35 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { } } + bool resource_variable_seen = false; + for (int i = 0; i < ctx->num_inputs(); ++i) { + if (ctx->input_type(i) == DT_RESOURCE) { + resource_variable_seen = true; + } else { + OP_REQUIRES( + ctx, !resource_variable_seen, + errors::FailedPrecondition( + "Resource variables and regular inputs cannot be interleaved.")); + } + } + xla::XlaOp outputs = xla::Conditional( ctx->Input(0), xla::Tuple(b, inputs), *then_result.computation, xla::Tuple(b, inputs), *else_result.computation); // Sets non-variable outputs. for (int i = 0; i < output_types_.size(); ++i) { - if (ctx->input_type(i) != DT_RESOURCE) { - xla::XlaOp output_handle = xla::GetTupleElement(outputs, i); - if (VLOG_IS_ON(2)) { - LOG(INFO) << "Setting output " << i; - auto shape_or = b->GetShape(output_handle); - if (shape_or.ok()) { - LOG(INFO) << "Shape for output " << i << ": " - << xla::ShapeUtil::HumanString(shape_or.ValueOrDie()); - } else { - LOG(INFO) << "Shape unknown for output " << i; - } + xla::XlaOp output_handle = xla::GetTupleElement(outputs, i); + if (VLOG_IS_ON(2)) { + LOG(INFO) << "Setting output " << i; + auto shape_or = b->GetShape(output_handle); + if (shape_or.ok()) { + LOG(INFO) << "Shape for output " << i << ": " + << xla::ShapeUtil::HumanString(shape_or.ValueOrDie()); + } else { + LOG(INFO) << "Shape unknown for output " << i; } - ctx->SetOutput(i, output_handle); } + ctx->SetOutput(i, output_handle); } // Updates the values of any resource variables modified by the conditional @@ -247,6 +257,7 @@ void XlaIfOp::Compile(XlaOpKernelContext* ctx) { } REGISTER_XLA_OP(Name("If").AllowResourceTypes(), XlaIfOp); +REGISTER_XLA_OP(Name("StatelessIf").AllowResourceTypes(), XlaIfOp); REGISTER_XLA_OP(Name("XlaIf").AllowResourceTypes(), XlaIfOp); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index 3d506e71e03d6b804d1ea0e63c760cfb82629f12..d4d180aff806f12875f0e43f111ee090f6607ef6 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" +#include "tensorflow/compiler/xla/client/lib/pooling.h" #include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" @@ -71,59 +72,53 @@ class PoolingOp : public XlaOpKernel { int num_dims() const { return num_spatial_dims_ + 2; } - // Method that builds an initial value to use in reductions. - virtual xla::XlaOp InitValue(xla::XlaBuilder* b) = 0; - - // The reduction operation to apply to each window. - virtual const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) = 0; - - // A post-processing operation to apply on the outputs of the ReduceWindow. - virtual xla::XlaOp PostProcessOutput(XlaOpKernelContext* ctx, - const xla::XlaOp& output, DataType dtype, - const TensorShape& input_shape) = 0; - - void Compile(XlaOpKernelContext* ctx) override { - std::vector ksize = ksize_; - std::vector stride = stride_; - if (ctx->num_inputs() != 1) { - const TensorShape ksize_shape = ctx->InputShape(1); - // Validate input sizes. - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(ksize_shape), - errors::InvalidArgument("ksize must be a vector, not shape ", - ksize_shape.DebugString())); - OP_REQUIRES(ctx, ksize_shape.num_elements() == num_dims(), - errors::InvalidArgument("Sliding window ksize field must " - "specify ", - num_dims(), " dimensions")); - ksize.clear(); - OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(1, &ksize)); - - const TensorShape stride_shape = ctx->InputShape(2); - // Validate input sizes. - OP_REQUIRES(ctx, TensorShapeUtils::IsVector(stride_shape), - errors::InvalidArgument("stride must be a vector, not shape ", - stride_shape.DebugString())); - OP_REQUIRES(ctx, stride_shape.num_elements() == num_dims(), - errors::InvalidArgument("Sliding window stride field must " - "specify ", - num_dims(), " dimensions")); - stride.clear(); - OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntVector(2, &stride)); + protected: + xla::StatusOr> GetKernelSize(XlaOpKernelContext* ctx) { + if (ctx->num_inputs() == 1) { + return ksize_; } - const TensorShape input_shape = ctx->InputShape(0); - OP_REQUIRES(ctx, input_shape.dims() == num_dims(), - errors::InvalidArgument("Input to ", type_string(), - " operator must have ", num_dims(), - " dimensions")); + const TensorShape ksize_shape = ctx->InputShape(1); + // Validate input sizes. + if (!TensorShapeUtils::IsVector(ksize_shape)) { + return errors::InvalidArgument("ksize must be a vector, not shape ", + ksize_shape.DebugString()); + } + if (ksize_shape.num_elements() != num_dims()) { + return errors::InvalidArgument( + "Sliding window ksize field must " + "specify ", + num_dims(), " dimensions"); + } + std::vector ksize; + auto status = ctx->ConstantInputAsIntVector(1, &ksize); + if (!status.ok()) { + return status; + } + return ksize; + } - xla::XlaBuilder* const b = ctx->builder(); - auto input = - XlaHelpers::ConvertElementType(b, ctx->Input(0), reduction_type_); - auto reduce = xla::ReduceWindow(input, InitValue(b), *Reduction(ctx), ksize, - stride, padding_); - auto pooled = XlaHelpers::ConvertElementType(b, reduce, input_type(0)); - ctx->SetOutput(0, - PostProcessOutput(ctx, pooled, input_type(0), input_shape)); + xla::StatusOr> GetStride(XlaOpKernelContext* ctx) { + if (ctx->num_inputs() == 1) { + return stride_; + } + const TensorShape stride_shape = ctx->InputShape(2); + // Validate input sizes. + if (!TensorShapeUtils::IsVector(stride_shape)) { + return errors::InvalidArgument("stride must be a vector, not shape ", + stride_shape.DebugString()); + } + if (stride_shape.num_elements() != num_dims()) { + return errors::InvalidArgument( + "Sliding window stride field must " + "specify ", + num_dims(), " dimensions"); + } + std::vector stride; + auto status = ctx->ConstantInputAsIntVector(2, &stride); + if (!status.ok()) { + return status; + } + return stride; } protected: @@ -136,24 +131,48 @@ class PoolingOp : public XlaOpKernel { xla::PrimitiveType xla_reduction_type_; }; +// Converts the tensor data format to the one required by the XLA pooling +// library. +xla::TensorFormat XlaTensorFormat(tensorflow::TensorFormat data_format, + int num_spatial_dims) { + int num_dims = num_spatial_dims + 2; + int batch_dimension = GetTensorBatchDimIndex(num_dims, data_format); + int feature_dimension = GetTensorFeatureDimIndex(num_dims, data_format); + gtl::InlinedVector spatial_dimensions(num_spatial_dims); + for (int spatial_dim = 0; spatial_dim < num_spatial_dims; ++spatial_dim) { + spatial_dimensions[spatial_dim] = + GetTensorSpatialDimIndex(num_dims, data_format, spatial_dim); + } + return xla::TensorFormat(/*batch_dimension=*/batch_dimension, + /*feature_dimension=*/feature_dimension, + /*spatial_dimensions=*/spatial_dimensions); +} + class MaxPoolOp : public PoolingOp { public: MaxPoolOp(OpKernelConstruction* ctx, int num_spatial_dims) : PoolingOp(ctx, /*num_spatial_dims=*/num_spatial_dims, /*reduction_type=*/ctx->input_type(0)) {} - xla::XlaOp InitValue(xla::XlaBuilder* b) override { - return xla::MinValue(b, xla_reduction_type_); - } + void Compile(XlaOpKernelContext* ctx) override { + auto ksize_or_error = GetKernelSize(ctx); + OP_REQUIRES_OK(ctx, ksize_or_error.status()); + std::vector ksize = ksize_or_error.ValueOrDie(); - const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) override { - return ctx->GetOrCreateMax(reduction_type_); - } + auto stride_or_error = GetStride(ctx); + OP_REQUIRES_OK(ctx, stride_or_error.status()); + std::vector stride = stride_or_error.ValueOrDie(); + + const TensorShape input_shape = ctx->InputShape(0); + OP_REQUIRES(ctx, input_shape.dims() == num_dims(), + errors::InvalidArgument("Input to ", type_string(), + " operator must have ", num_dims(), + " dimensions")); - xla::XlaOp PostProcessOutput(XlaOpKernelContext* ctx, - const xla::XlaOp& output, DataType dtype, - const TensorShape& input_shape) override { - return output; + auto pooling = + xla::MaxPool(ctx->Input(0), ksize, stride, padding_, + XlaTensorFormat(data_format_, input_shape.dims() - 2)); + ctx->SetOutput(0, pooling); } }; @@ -180,9 +199,8 @@ class MaxPool3DOp : public MaxPoolOp { }; REGISTER_XLA_OP(Name("MaxPool3D"), MaxPool3DOp); -// Common computation shared between AvgPool and AvgPoolGrad. Divide each -// element of an image by the count of elements that contributed to that -// element during pooling. +// Divide each element of an image by the count of elements that contributed to +// that element during pooling. static xla::XlaOp AvgPoolDivideByCount( XlaOpKernelContext* ctx, const xla::XlaOp& output, DataType dtype, const TensorShape& input_shape, xla::Padding padding, @@ -241,20 +259,34 @@ class AvgPoolOp : public PoolingOp { /*reduction_type=*/ XlaHelpers::SumAccumulationType(ctx->input_type(0))) {} - xla::XlaOp InitValue(xla::XlaBuilder* b) override { - return xla::Zero(b, xla_reduction_type_); - } + void Compile(XlaOpKernelContext* ctx) override { + auto ksize_or_error = GetKernelSize(ctx); + OP_REQUIRES_OK(ctx, ksize_or_error.status()); + std::vector ksize = ksize_or_error.ValueOrDie(); - const xla::XlaComputation* Reduction(XlaOpKernelContext* ctx) override { - return ctx->GetOrCreateAdd(reduction_type_); - } + auto stride_or_error = GetStride(ctx); + OP_REQUIRES_OK(ctx, stride_or_error.status()); + std::vector stride = stride_or_error.ValueOrDie(); + + const TensorShape input_shape = ctx->InputShape(0); + OP_REQUIRES(ctx, input_shape.dims() == num_dims(), + errors::InvalidArgument("Input to ", type_string(), + " operator must have ", num_dims(), + " dimensions")); - xla::XlaOp PostProcessOutput(XlaOpKernelContext* ctx, - const xla::XlaOp& output, DataType dtype, - const TensorShape& input_shape) override { - return AvgPoolDivideByCount(ctx, output, dtype, input_shape, padding_, - ksize_, stride_, num_spatial_dims_, - data_format_); + auto xla_data_format = + XlaTensorFormat(data_format_, input_shape.dims() - 2); + auto spatial_padding = MakeSpatialPadding( + input_shape.dim_sizes(), ksize, stride, padding_, xla_data_format); + + // Convert the input to the reduction type. + auto converted_input = + ConvertElementType(ctx->Input(0), xla_reduction_type_); + auto pooling = + xla::AvgPool(converted_input, ksize, stride, spatial_padding, + xla_data_format, padding_ == xla::Padding::kValid); + // Convert the pooling result back to the input type before returning it. + ctx->SetOutput(0, ConvertElementType(pooling, ctx->input_xla_type(0))); } }; diff --git a/tensorflow/compiler/tf2xla/kernels/retval_op.cc b/tensorflow/compiler/tf2xla/kernels/retval_op.cc index 1911e6ea362f999c787cbf95dcc9137a6a630273..64900e4709fd3e16d21096b0cfff8922906cb0d4 100644 --- a/tensorflow/compiler/tf2xla/kernels/retval_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/retval_op.cc @@ -104,7 +104,7 @@ class RetvalOp : public XlaOpKernel { TF_DISALLOW_COPY_AND_ASSIGN(RetvalOp); }; -REGISTER_XLA_OP(Name("_Retval"), RetvalOp); +REGISTER_XLA_OP(Name("_Retval").CompilationOnly(), RetvalOp); } // anonymous namespace } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc index 1e8a376765d36ffa677ece06fbd131744299e04b..296518229ebf0ba46717afc4f26d5ae1551c2862 100644 --- a/tensorflow/compiler/tf2xla/kernels/while_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc @@ -301,6 +301,7 @@ void XlaWhileOp::Compile(XlaOpKernelContext* ctx) { } REGISTER_XLA_OP(Name("While").AllowResourceTypes(), XlaWhileOp); +REGISTER_XLA_OP(Name("StatelessWhile").AllowResourceTypes(), XlaWhileOp); REGISTER_XLA_OP(Name("XlaWhile").AllowResourceTypes(), XlaWhileOp); } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/literal_util.cc b/tensorflow/compiler/tf2xla/literal_util.cc index 2fb66913ada375d53512b9a1115326b3cc2afea4..77da1bf29ced60e490f07abad41cf8ce96232982 100644 --- a/tensorflow/compiler/tf2xla/literal_util.cc +++ b/tensorflow/compiler/tf2xla/literal_util.cc @@ -32,6 +32,23 @@ Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, return Status::OK(); } +Status HostTensorToMutableBorrowingLiteral( + Tensor* host_tensor, xla::MutableBorrowingLiteral* literal) { + xla::Shape xla_shape; + TF_RETURN_IF_ERROR(TensorShapeToXLAShape(host_tensor->dtype(), + host_tensor->shape(), &xla_shape)); + return HostTensorToMutableBorrowingLiteral(xla_shape, host_tensor, literal); +} + +Status HostTensorToMutableBorrowingLiteral( + const xla::Shape& xla_shape, Tensor* host_tensor, + xla::MutableBorrowingLiteral* literal) { + *literal = xla::MutableBorrowingLiteral( + static_cast(DMAHelper::base(host_tensor)), xla_shape); + + return Status::OK(); +} + Status HostTensorsToBorrowingLiteralTuple( tensorflow::gtl::ArraySlice host_tensors, xla::BorrowingLiteral* literal) { diff --git a/tensorflow/compiler/tf2xla/literal_util.h b/tensorflow/compiler/tf2xla/literal_util.h index 0610a57029e72dff79a84742346f78a42b7f4ff1..09d6fa811669b422532673540e4da47f47e6be4e 100644 --- a/tensorflow/compiler/tf2xla/literal_util.h +++ b/tensorflow/compiler/tf2xla/literal_util.h @@ -30,6 +30,16 @@ namespace tensorflow { // 'host_tensor'. Status HostTensorToBorrowingLiteral(const Tensor& host_tensor, xla::BorrowingLiteral* literal); +// Returns a MutableBorrowingLiteral that utilizes the same underlying buffer +// owned by 'host_tensor', but is mutable via the xla::Literal methods. +Status HostTensorToMutableBorrowingLiteral( + Tensor* host_tensor, xla::MutableBorrowingLiteral* literal); +// Similar as above, except the literal shape is explicitly provided and used +// instead of obtaining it from the 'host_tensor'. The provided literal shape +// 'xla_shape' must be compatible with the shape of 'host_tensor'. +Status HostTensorToMutableBorrowingLiteral( + const xla::Shape& xla_shape, Tensor* host_tensor, + xla::MutableBorrowingLiteral* literal); // Returns a BorrowingLiteral tuple that utilizes the same underlying buffers // owned by 'host_tensors'. diff --git a/tensorflow/compiler/tf2xla/tf2xla_util.cc b/tensorflow/compiler/tf2xla/tf2xla_util.cc index 9203e8d9e607e99ad738350a1c3f2b9e900df179..0e07485d1861aa40b14e527b14947c6f8bab647e 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_util.cc +++ b/tensorflow/compiler/tf2xla/tf2xla_util.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include +#include #include #include @@ -297,4 +298,29 @@ void AddDtypeToKernalDefConstraint(StringPiece name, DataType dtype, } } +namespace { +uint32 InitialRandomSeed() { + // Support plumbing the TF seed through to XLA is being worked on. + // If a user wants deterministic behavior, their best option + // is to start with a known checkpoint. This also handles issues when + // multiple random calls can be invoked in any order by TF executor. + // Another option is to use stateless random ops. They have much cleaner + // semantics. + // If a user really wants to set a deterministic seed for XLA-based + // devices, this is the place to do it. + std::random_device rd; + // Make the starting value odd. + return rd() | 1; +} +} // namespace + +uint32 GetXLARandomSeed() { + // We initialize counter with an odd number and increment it by two + // everytime. This ensures that it will never be zero, even + // after an overflow. When seeded with zero, some XLA backends + // can return all zeros instead of random numbers. + static std::atomic counter(InitialRandomSeed()); + return counter.fetch_add(2); +} + } // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/tf2xla_util.h b/tensorflow/compiler/tf2xla/tf2xla_util.h index 745beb39c1d917cd0d1cd219536ee26a96253ec9..33620ef810bd4fe897f384474e661e341a448b93 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_util.h +++ b/tensorflow/compiler/tf2xla/tf2xla_util.h @@ -56,6 +56,9 @@ Status SetNodeShardingFromNeighbors(Node* n, bool out_edges); void AddDtypeToKernalDefConstraint(StringPiece name, DataType dtype, KernelDef* kdef); +// Returns the next random seed to use for seeding xla rng. +uint32 GetXLARandomSeed(); + } // namespace tensorflow #endif // TENSORFLOW_COMPILER_TF2XLA_TF2XLA_UTIL_H_ diff --git a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc index 334459138b55a201c15cb87ad9feb6a03a13c5ab..1f0f240135dfcd0c540cc39a42514c67ce979ee0 100644 --- a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc +++ b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h" -#include "tensorflow/compiler/tf2xla/cpu_function_runtime.h" #include @@ -22,61 +21,42 @@ namespace tensorflow { XlaCompiledCpuFunction::XlaCompiledCpuFunction(const StaticData& static_data, AllocMode alloc_mode) - : raw_function_(static_data.raw_function), - result_index_(static_data.result_index), - args_(new void*[static_data.num_args]), - temps_(new void*[static_data.num_temps]), - arg_index_to_temp_index_(new int32[static_data.num_args]), - num_args_(static_data.num_args), - arg_names_(static_data.arg_names), - result_names_(static_data.result_names), - program_shape_(static_data.program_shape), - hlo_profile_printer_data_(static_data.hlo_profile_printer_data) { + : raw_function_(static_data.raw_function_), + result_index_(static_data.result_index_), + buffer_table_(new void*[static_data.num_buffers_]), + buffer_infos_(static_data.buffer_infos_), + arg_index_table_(static_data.arg_index_table_), + num_args_(static_data.num_args_), + arg_names_(static_data.arg_names_), + result_names_(static_data.result_names_), + program_shape_(static_data.program_shape_), + hlo_profile_printer_data_(static_data.hlo_profile_printer_data_) { + bool allocate_entry_params = + alloc_mode == AllocMode::ARGS_RESULTS_PROFILES_AND_TEMPS; // Allocate arg and temp buffers. - if (alloc_mode == AllocMode::ARGS_RESULTS_PROFILES_AND_TEMPS) { - alloc_args_ = cpu_function_runtime::MallocContiguousBuffers( - static_data.arg_sizes, static_data.num_args, args_, - /*annotate_initialized=*/false); - } - alloc_temps_ = cpu_function_runtime::MallocContiguousBuffers( - static_data.temp_sizes, static_data.num_temps, temps_, + alloc_buffer_table_ = cpu_function_runtime::MallocContiguousBuffers( + static_data.buffer_infos_, static_data.num_buffers_, + /*allocate_entry_params=*/allocate_entry_params, buffer_table_, /*annotate_initialized=*/true); - - for (int i = 0; i < static_data.num_temps; i++) { - if (static_data.temp_sizes[i] < -1) { - int32 param_number = -(static_data.temp_sizes[i] + 2); - arg_index_to_temp_index_[param_number] = i; - } - } - // If Hlo profiling is enabled the generated code expects an appropriately // sized buffer to be passed in as the last argument. If Hlo profiling is // disabled the last function argument is still present in the function // signature, but it is ignored by the generated code and we pass in null for // it. if (hlo_profiling_enabled()) { - profile_counters_ = new int64[static_data.profile_counters_size](); + profile_counters_ = new int64[static_data.profile_counters_size_](); } } bool XlaCompiledCpuFunction::Run() { - // Propagate pointers to the argument buffers into the temps array. Code - // generated by XLA discovers the incoming argument pointers from the temps - // array. - for (int32 i = 0; i < num_args_; i++) { - temps_[arg_index_to_temp_index_[i]] = args_[i]; - } - raw_function_(temps_[result_index_], &run_options_, nullptr, temps_, - profile_counters_); + raw_function_(buffer_table_[result_index_], &run_options_, nullptr, + buffer_table_, profile_counters_); return true; } XlaCompiledCpuFunction::~XlaCompiledCpuFunction() { - cpu_function_runtime::FreeContiguous(alloc_args_); - cpu_function_runtime::FreeContiguous(alloc_temps_); - delete[] args_; - delete[] temps_; - delete[] arg_index_to_temp_index_; + cpu_function_runtime::FreeContiguous(alloc_buffer_table_); + delete[] buffer_table_; delete[] profile_counters_; } diff --git a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h index 27cfb354bf5f8ede2dcca85065411006c352a575..425e769346ffcbc548495d93cb7adc779f860110 100644 --- a/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h +++ b/tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h @@ -19,6 +19,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/tf2xla/cpu_function_runtime.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/core/platform/types.h" @@ -56,46 +57,85 @@ class XlaCompiledCpuFunction { // StaticData represents the state necessary to run an XLA-compiled // function. For JIT this is backed by data in XlaJitCompiledCpuFunction; for // AOT this is backed by data compiled into the object file. - struct StaticData { + // + // The contents of StaticData are XLA-internal implementation details and + // should not be relied on by clients. + // + // TODO(sanjoy): Come up with a cleaner way to express the contraint we want + // here: generated XlaCompiledCpuFunction subclasses should be able to create + // instances of StaticData but only XlaCompiledCpuFunction should be able to + // read from StaticData instances. + class StaticData { + public: + void set_raw_function(RawFunction raw_function) { + raw_function_ = raw_function; + } + void set_buffer_infos( + const cpu_function_runtime::BufferInfo* buffer_infos) { + buffer_infos_ = buffer_infos; + } + void set_num_buffers(size_t num_buffers) { num_buffers_ = num_buffers; } + void set_arg_index_table(const int32* arg_index_table) { + arg_index_table_ = arg_index_table; + } + void set_num_args(int64 num_args) { num_args_ = num_args; } + void set_result_index(size_t result_index) { result_index_ = result_index; } + void set_arg_names(const char** arg_names) { arg_names_ = arg_names; } + void set_result_names(const char** result_names) { + result_names_ = result_names; + } + void set_program_shape(const xla::ProgramShape* program_shape) { + program_shape_ = program_shape; + } + const xla::HloProfilePrinterData* hlo_profile_printer_data() const { + return hlo_profile_printer_data_; + } + void set_hlo_profile_printer_data( + const xla::HloProfilePrinterData* hlo_profile_printer_data) { + hlo_profile_printer_data_ = hlo_profile_printer_data; + } + void set_profile_counters_size(int64 profile_counters_size) { + profile_counters_size_ = profile_counters_size; + } + + private: // The raw function to call. - RawFunction raw_function; - - // Cardinality and size of arg buffers. - const intptr_t* arg_sizes = nullptr; - size_t num_args = 0; - - // Cardinality and size of temp buffers. - // - // If temp_sizes[i] >= 0 then the i'th temp is a regular temporary buffer. - // - // If temp_sizes[i] == -1 then the i'th temp is a constant buffer. The - // corresponding entry in the temp buffer array needs to be set to null. - // - // If temp_sizes[i] < -1 then the i'th temp is the entry parameter - // -(temp_sizes[i] + 2). - const intptr_t* temp_sizes = nullptr; - size_t num_temps = 0; + RawFunction raw_function_; + + // Contains information about the buffers used by the XLA computation. + const cpu_function_runtime::BufferInfo* buffer_infos_ = nullptr; + size_t num_buffers_ = 0; + + // Entry parameter i is described by + // buffer_infos[arg_index_table[i]]. + const int32* arg_index_table_ = nullptr; + + // There are num_args entry parameters. + int64 num_args_ = 0; // The 0-based index of the result tuple, in the temp buffers. - size_t result_index = 0; + size_t result_index_ = 0; // [Optional] Arrays of arg and result names. These are arrays of C-style // strings, where the array is terminated by nullptr. - const char** arg_names = nullptr; - const char** result_names = nullptr; + const char** arg_names_ = nullptr; + const char** result_names_ = nullptr; // [Optional] Arg and result shapes. - const xla::ProgramShape* program_shape = nullptr; + const xla::ProgramShape* program_shape_ = nullptr; // [Optional] Profile printer data. Null if profiling is disabled. - const xla::HloProfilePrinterData* hlo_profile_printer_data = nullptr; + const xla::HloProfilePrinterData* hlo_profile_printer_data_ = nullptr; // [Optional] The number of profile counters expected in the profile counter // buffer by the generated code and hlo_profile_printer. 0 if profiling is // disabled. This information is already present in // hlo_profile_printer_data but xla::HloProfilePrinterData is forward // declared so we don't have access to that information here. - int64 profile_counters_size = 0; + int64 profile_counters_size_ = 0; + + // Only XlaCompiledCpuFunction is allowed to read the above fields. + friend class XlaCompiledCpuFunction; }; // AllocMode controls the buffer allocation mode. @@ -135,14 +175,25 @@ class XlaCompiledCpuFunction { // ------------------------------ // Arg methods for managing input buffers. Buffers are in row-major order. - // Returns the underlying array of argument buffers, where args()[I] is the - // buffer for the positional argument at index I. - void** args() { return args_; } - const void* const* args() const { return args_; } - // Returns the buffer for the positional argument at the given `index`. - void* arg_data(size_t index) { return args_[index]; } - const void* arg_data(size_t index) const { return args_[index]; } + void* arg_data(size_t index) { + return buffer_table_[arg_index_table_[index]]; + } + const void* arg_data(size_t index) const { + return buffer_table_[arg_index_table_[index]]; + } + + int num_args() const { return num_args_; } + + // Returns the size of entry parameter `idx`. + // + // There is a static version of this method on tfcompile generated subclasses + // of XlaCompiledCpuFunction, but try to prefer this when possible since it + // works both for XlaJitCompiledCpuFunction and AOT compiled subclasses. + int arg_size(int idx) const { + assert(idx < num_args()); + return buffer_infos_[arg_index_table_[idx]].size(); + } // Sets the buffer for the positional argument at the given `index` to `data`. // Must be called before Run to have an effect. May be called under any @@ -155,7 +206,9 @@ class XlaCompiledCpuFunction { // // Aliasing of argument and result buffers is not allowed, and results in // undefined behavior. - void set_arg_data(size_t index, void* data) { args_[index] = data; } + void set_arg_data(size_t index, void* data) { + buffer_table_[arg_index_table_[index]] = data; + } // ------------------------------ // Result methods for managing output buffers. Buffers are in row-major order. @@ -165,9 +218,9 @@ class XlaCompiledCpuFunction { // Returns the underlying array of result buffers, where results()[I] is the // buffer for the positional result at index I. - void** results() { return static_cast(temps_[result_index_]); } + void** results() { return static_cast(buffer_table_[result_index_]); } const void* const* results() const { - return static_cast(temps_[result_index_]); + return static_cast(buffer_table_[result_index_]); } // Profile counters for this XLA computation. @@ -225,25 +278,28 @@ class XlaCompiledCpuFunction { const RawFunction raw_function_; const size_t result_index_; - // Arrays of argument and temp buffers; entries in args_ may be overwritten by - // the user. - void** args_ = nullptr; - void** temps_ = nullptr; + // Array containing pointers to argument and temp buffers (slots corresponding + // to constant and on-stack buffers are null). + void** const buffer_table_; + + // Describes the buffers used by the XLA computation. + const cpu_function_runtime::BufferInfo* const buffer_infos_; - // Argument i needs to be placed in temps_[arg_index_to_temp_index_[i]] for - // XLA generated code to be able to find it. + // Argument i needs to be placed in buffer_table_[arg_index_to_temp_index_[i]] + // for XLA generated code to be able to find it. // // For now we need to keep around the args_ array because there is code that // depends on args() returning a void**. However, in the future we may remove - // args_ in favor of using temps_ as the sole storage for the arguments. - int32* arg_index_to_temp_index_; + // args_ in favor of using buffer_table_ as the sole storage for the + // arguments. + const int32* const arg_index_table_; // The number of incoming arguments. - int32 num_args_; + const int32 num_args_; - // Backing memory for individual arg and temp buffers. - void* alloc_args_ = nullptr; - void* alloc_temps_ = nullptr; + // Backing memory for buffer_table_ and args_, the latter depending on + // AllocMode. + void* alloc_buffer_table_ = nullptr; // Backing memory for profiling counters. int64* profile_counters_ = nullptr; diff --git a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc index 114a9241bdb00526df76478b030a9efa506dd29c..86a78ee429e8913edb4a948727fa692083c472f4 100644 --- a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc +++ b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/service/cpu/buffer_info_util.h" #include "tensorflow/compiler/xla/service/cpu/cpu_executable.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -35,45 +36,6 @@ limitations under the License. namespace tensorflow { namespace { - -// Returns a vector of positional argument buffer sizes. -xla::StatusOr> ComputeArgSizes( - const xla::ProgramShape& program_shape) { - std::vector arg_sizes; - const size_t num_args = program_shape.parameters_size(); - arg_sizes.reserve(num_args); - for (int i = 0; i < num_args; ++i) { - const xla::Shape& arg_shape = program_shape.parameters(i); - constexpr size_t kPointerSize = sizeof(void*); - arg_sizes.push_back(xla::ShapeUtil::ByteSizeOf(arg_shape, kPointerSize)); - } - return std::move(arg_sizes); -} - -// Returns a vector of positional temporary buffer sizes. -xla::StatusOr> ComputeTempSizes( - const xla::BufferAssignment& buffer_assignment) { - const std::vector& allocations = - buffer_assignment.Allocations(); - std::vector temp_sizes; - temp_sizes.reserve(allocations.size()); - for (const xla::BufferAllocation& allocation : allocations) { - if (allocation.is_constant() || allocation.is_thread_local()) { - // Constants are lowered to globals. Thread locals are lowered to - // allocas. - temp_sizes.push_back(-1); - } else if (allocation.is_entry_computation_parameter()) { - // Entry computation parameters need some preprocessing in - // XlaCompiledCpuFunction::Run. See the comment on - // XlaCompiledCpuFunction::StaticData::temp_sizes. - temp_sizes.push_back(-allocation.parameter_number() - 2); - } else { - temp_sizes.push_back(allocation.size()); - } - } - return std::move(temp_sizes); -} - // Returns the index of the result in the temp buffers. xla::StatusOr ComputeResultIndex( const xla::BufferAssignment& buffer_assignment) { @@ -157,11 +119,11 @@ XlaJitCompiledCpuFunction::Compile( const xla::BufferAssignment& buffer_assignment = cpu_executable->buffer_assignment(); - // Compute buffer sizes and the result index, needed to run the raw function. - TF_ASSIGN_OR_RETURN(std::vector arg_sizes, - ComputeArgSizes(*program_shape)); - TF_ASSIGN_OR_RETURN(std::vector temp_sizes, - ComputeTempSizes(buffer_assignment)); + // Compute buffer infos and the result index, needed to run the raw function. + std::vector buffer_infos = + xla::cpu::CreateBufferInfosFromBufferAssignment(buffer_assignment); + std::vector arg_index_table = + xla::cpu::CreateArgIndexTableFromBufferInfos(buffer_infos); TF_ASSIGN_OR_RETURN(size_t result_index, ComputeResultIndex(buffer_assignment)); @@ -169,28 +131,28 @@ XlaJitCompiledCpuFunction::Compile( new XlaJitCompiledCpuFunction); XlaJitCompiledCpuFunction* jit = jit_unique_ptr.get(); jit->executable_ = std::move(executable); - jit->arg_sizes_ = std::move(arg_sizes); - jit->temp_sizes_ = std::move(temp_sizes); + jit->buffer_infos_ = std::move(buffer_infos); + jit->arg_index_table_ = std::move(arg_index_table); jit->program_shape_ = std::move(program_shape); - jit->static_data_.raw_function = std::move(raw_function); - jit->static_data_.arg_sizes = jit->arg_sizes_.data(); - jit->static_data_.num_args = jit->arg_sizes_.size(); - jit->static_data_.temp_sizes = jit->temp_sizes_.data(); - jit->static_data_.num_temps = jit->temp_sizes_.size(); - jit->static_data_.result_index = result_index; + jit->static_data_.set_raw_function(raw_function); + jit->static_data_.set_buffer_infos(jit->buffer_infos_.data()); + jit->static_data_.set_num_buffers(jit->buffer_infos_.size()); + jit->static_data_.set_arg_index_table(jit->arg_index_table_.data()); + jit->static_data_.set_num_args(jit->arg_index_table_.size()); + jit->static_data_.set_result_index(result_index); // Optional metadata is collected and set below. CollectNames(config.feed(), &jit->nonempty_arg_names_, &jit->arg_names_); CollectNames(config.fetch(), &jit->nonempty_result_names_, &jit->result_names_); - jit->static_data_.arg_names = jit->arg_names_.data(); - jit->static_data_.result_names = jit->result_names_.data(); - jit->static_data_.program_shape = jit->program_shape_.get(); + jit->static_data_.set_arg_names(jit->arg_names_.data()); + jit->static_data_.set_result_names(jit->result_names_.data()); + jit->static_data_.set_program_shape(jit->program_shape_.get()); if (cpu_executable->hlo_profiling_enabled()) { - jit->static_data_.hlo_profile_printer_data = - &cpu_executable->hlo_profile_printer_data(); - jit->static_data_.profile_counters_size = - cpu_executable->hlo_profile_printer_data().profile_counters_size(); + jit->static_data_.set_hlo_profile_printer_data( + &cpu_executable->hlo_profile_printer_data()); + jit->static_data_.set_profile_counters_size( + cpu_executable->hlo_profile_printer_data().profile_counters_size()); } return std::move(jit_unique_ptr); diff --git a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.h b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.h index af307ae4eff74927242c4650d8a43710e991cc52..d3c8f22a8078d03d15447ed200c914390f40b04f 100644 --- a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.h +++ b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.h @@ -66,9 +66,11 @@ class XlaJitCompiledCpuFunction { // The static data is backed by the rest of the state in this class. XlaCompiledCpuFunction::StaticData static_data_; - // The backing arrays of arg and temp buffer sizes. - std::vector arg_sizes_; - std::vector temp_sizes_; + // The backing array for buffer infos. + std::vector buffer_infos_; + + // The backing array for the arg index table. + std::vector arg_index_table_; // The backing arrays of arg and result names. We hold the actual strings in // nonempty_*_names_, and hold arrays of pointers in *_names_ for the static diff --git a/tensorflow/compiler/xla/array.h b/tensorflow/compiler/xla/array.h index ea75ad32d5df7bbadd37e89de6144b264ab6d5d1..2d5d078aa77423cc18bab053b80a7576acbd849e 100644 --- a/tensorflow/compiler/xla/array.h +++ b/tensorflow/compiler/xla/array.h @@ -409,7 +409,7 @@ class Array { // Returns the total number of elements in the array. int64 num_elements() const { - return std::accumulate(sizes_.begin(), sizes_.end(), 1, + return std::accumulate(sizes_.begin(), sizes_.end(), 1LL, std::multiplies()); } diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index 39d5582d19dbb9942ae87e1962fc9fa713bcdd50..a2f32ab97eab10294a607f35fc79ded1cc2c5792 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -121,6 +121,30 @@ xla_test( ], ) +cc_library( + name = "pooling", + srcs = ["pooling.cc"], + hdrs = ["pooling.h"], + deps = [ + ":arithmetic", + ":constants", + "//tensorflow/compiler/tf2xla/lib:util", + "//tensorflow/compiler/xla/client:xla_builder", + "//tensorflow/core:lib", + ], +) + +xla_test( + name = "pooling_test", + srcs = ["pooling_test.cc"], + deps = [ + ":pooling", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + ], +) + cc_library( name = "prng", srcs = ["prng.cc"], @@ -144,7 +168,7 @@ cc_library( ":numeric", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", ], ) @@ -161,7 +185,7 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], diff --git a/tensorflow/compiler/xla/client/lib/pooling.cc b/tensorflow/compiler/xla/client/lib/pooling.cc new file mode 100644 index 0000000000000000000000000000000000000000..7199269a6c889f3589c1148687faf0bb2aaae90a --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/pooling.cc @@ -0,0 +1,183 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/pooling.h" +#include "tensorflow/compiler/tf2xla/lib/util.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/lib/constants.h" + +namespace xla { + +namespace { + +// Common computation shared between AvgPool and AvgPoolGrad. Divide each +// element of an image by the count of elements that contributed to that +// element during pooling. +XlaOp AvgPoolDivideByCountWithGeneralPadding( + XlaOp sums, PrimitiveType dtype, + tensorflow::gtl::ArraySlice input_shape, + tensorflow::gtl::ArraySlice> spatial_padding, + tensorflow::gtl::ArraySlice ksize, + tensorflow::gtl::ArraySlice stride, + const TensorFormat& data_format) { + // The padding shouldn't be included in the counts. We use another + // ReduceWindow to find the right counts. + const int num_spatial_dims = spatial_padding.size(); + + std::vector input_dim_sizes(num_spatial_dims); + std::vector window_dims(num_spatial_dims); + std::vector window_ksize(num_spatial_dims); + std::vector window_stride(num_spatial_dims); + CHECK_EQ(data_format.num_spatial_dims(), num_spatial_dims) + << "Invalid number of spatial dimentions in data format specification"; + for (int i = 0; i < num_spatial_dims; ++i) { + int dim = data_format.spatial_dimension(i); + input_dim_sizes[i] = input_shape[dim]; + window_dims[i] = dim; + window_ksize[i] = ksize[dim]; + window_stride[i] = stride[dim]; + } + + XlaBuilder* b = sums.builder(); + // Build a matrix of all 1s, with the same width/height as the input. + auto ones = Broadcast(One(b, dtype), input_dim_sizes); + PaddingConfig padding_config; + for (int i = 0; i < num_spatial_dims; ++i) { + auto dims = padding_config.add_dimensions(); + dims->set_edge_padding_low(spatial_padding[i].first); + dims->set_edge_padding_high(spatial_padding[i].second); + } + auto zero = Zero(b, dtype); + auto padded_ones = Pad(ones, zero, padding_config); + + // Perform a ReduceWindow with the same window size, strides, and padding + // to count the number of contributions to each result element. + auto counts = + ReduceWindow(padded_ones, zero, CreateScalarAddComputation(dtype, b), + window_ksize, window_stride, Padding::kValid); + + return Div(sums, counts, window_dims); +} + +// Sums all elements in the window specified by 'kernel_size' and 'stride'. +XlaOp ComputeSums(XlaOp operand, XlaOp init_value, + tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, + const TensorFormat& data_format) { + XlaBuilder* b = operand.builder(); + return b->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape operand_shape, b->GetShape(operand)); + TF_ASSIGN_OR_RETURN(Shape init_shape, b->GetShape(init_value)); + PrimitiveType accumulation_type = init_shape.element_type(); + auto add_computation = CreateScalarAddComputation(accumulation_type, b); + return ReduceWindow(operand, init_value, add_computation, kernel_size, + stride, Padding::kValid); + }); +} + +// Creates a padding configuration out of spatial padding values. +PaddingConfig MakeSpatialPaddingConfig( + tensorflow::gtl::ArraySlice> spatial_padding, + tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, + const TensorFormat& data_format) { + const int num_spatial_dims = kernel_size.size() - 2; + PaddingConfig padding_config; + for (int i = 0; i < 2 + num_spatial_dims; ++i) { + padding_config.add_dimensions(); + } + CHECK_EQ(data_format.num_spatial_dims(), num_spatial_dims) + << "Invalid number of spatial dimentions in data format specification"; + for (int i = 0; i < num_spatial_dims; ++i) { + int dim = data_format.spatial_dimension(i); + auto padding_dimension = padding_config.mutable_dimensions(dim); + padding_dimension->set_edge_padding_low(spatial_padding[i].first); + padding_dimension->set_edge_padding_high(spatial_padding[i].second); + } + return padding_config; +} + +} // namespace + +XlaOp MaxPool(XlaOp operand, tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, Padding padding, + const TensorFormat& data_format) { + XlaBuilder* b = operand.builder(); + return b->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape operand_shape, b->GetShape(operand)); + PrimitiveType dtype = operand_shape.element_type(); + auto max_computation = CreateScalarMaxComputation(dtype, b); + auto init_value = MinValue(b, dtype); + return ReduceWindow(operand, init_value, max_computation, kernel_size, + stride, padding); + }); +} + +XlaOp AvgPool(XlaOp operand, tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, + tensorflow::gtl::ArraySlice> padding, + const TensorFormat& data_format, + const bool counts_include_padding) { + XlaBuilder* b = operand.builder(); + return b->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape operand_shape, b->GetShape(operand)); + PrimitiveType dtype = operand_shape.element_type(); + auto init_value = Zero(b, dtype); + std::vector input_size(operand_shape.dimensions().begin(), + operand_shape.dimensions().end()); + auto padding_config = + MakeSpatialPaddingConfig(padding, kernel_size, stride, data_format); + auto padded_operand = Pad(operand, Zero(b, dtype), padding_config); + auto pooled = ComputeSums(padded_operand, init_value, kernel_size, stride, + data_format); + if (counts_include_padding) { + // If counts include padding, all windows have the same number of elements + // contributing to each average. Divide by the window size everywhere to + // get the average. + int64 window_size = + std::accumulate(kernel_size.begin(), kernel_size.end(), 1, + [](int64 x, int64 y) { return x * y; }); + + auto divisor = ConstantR0WithType(b, dtype, window_size); + return pooled / divisor; + } else { + return AvgPoolDivideByCountWithGeneralPadding( + pooled, dtype, input_size, padding, kernel_size, stride, data_format); + } + }); +} + +std::vector> MakeSpatialPadding( + tensorflow::gtl::ArraySlice input_size, + tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, Padding padding, + const TensorFormat& data_format) { + const int num_spatial_dims = kernel_size.size() - 2; + std::vector input_spatial_dimensions; + std::vector kernel_size_spatial_dimensions; + std::vector stride_spatial_dimensions; + CHECK_EQ(data_format.num_spatial_dims(), num_spatial_dims) + << "Invalid number of spatial dimentions in data format specification"; + for (int i = 0; i < num_spatial_dims; ++i) { + int dim = data_format.spatial_dimension(i); + input_spatial_dimensions.push_back(input_size[dim]); + kernel_size_spatial_dimensions.push_back(kernel_size[dim]); + stride_spatial_dimensions.push_back(stride[dim]); + } + return MakePadding(input_spatial_dimensions, kernel_size_spatial_dimensions, + stride_spatial_dimensions, padding); +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/pooling.h b/tensorflow/compiler/xla/client/lib/pooling.h new file mode 100644 index 0000000000000000000000000000000000000000..1699c585d3b09a306c21cfa797a9023a8463bd1f --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/pooling.h @@ -0,0 +1,73 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_POOLING_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_POOLING_H_ + +#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/core/lib/gtl/inlined_vector.h" + +namespace xla { + +// Tensor format for reduce window operations. +class TensorFormat { + public: + TensorFormat(int batch_dimension, int feature_dimension, + tensorflow::gtl::ArraySlice spatial_dimensions) + : batch_dimension_(batch_dimension), + feature_dimension_(feature_dimension), + spatial_dimensions_(spatial_dimensions.begin(), + spatial_dimensions.end()) {} + + int batch_dimension() const { return batch_dimension_; } + + int feature_dimension() const { return feature_dimension_; } + + int spatial_dimension(int dim) const { return spatial_dimensions_[dim]; } + + int num_spatial_dims() const { return spatial_dimensions_.size(); } + + private: + // The number of the dimension that represents the batch. + int batch_dimension_; + // The number of the dimension that represents the features. + int feature_dimension_; + // The dimension numbers for the spatial dimensions. + tensorflow::gtl::InlinedVector spatial_dimensions_; +}; + +// Computes the max pool of 'operand'. +XlaOp MaxPool(XlaOp operand, tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, Padding padding, + const TensorFormat& data_format); + +// Computes the average pool of 'operand'. +XlaOp AvgPool(XlaOp operand, tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, + tensorflow::gtl::ArraySlice> padding, + const TensorFormat& data_format, + const bool counts_include_padding); + +// Returns the list of low and high padding elements in each spatial dimension +// for the given 'padding' specification. +std::vector> MakeSpatialPadding( + tensorflow::gtl::ArraySlice input_size, + tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, Padding padding, + const TensorFormat& data_format); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_POOLING_H_ diff --git a/tensorflow/compiler/xla/client/lib/pooling_test.cc b/tensorflow/compiler/xla/client/lib/pooling_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..4b4553b60db555ad7c2ab6b695236df745e30683 --- /dev/null +++ b/tensorflow/compiler/xla/client/lib/pooling_test.cc @@ -0,0 +1,185 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/client/lib/pooling.h" +#include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/compiler/xla/tests/test_macros.h" + +namespace xla { +namespace { + +TensorFormat MakeNCHWFormat(int num_spatial_dims) { + tensorflow::gtl::InlinedVector spatial_dimensions; + for (int i = 0; i < num_spatial_dims; ++i) { + spatial_dimensions.push_back(i + 2); + } + return TensorFormat(/*batch_dimension=*/0, /*feature_dimension=*/1, + /*spatial_dimensions=*/spatial_dimensions); +} + +std::vector> MakeGeneralPadding( + XlaOp input, tensorflow::gtl::ArraySlice kernel_size, + tensorflow::gtl::ArraySlice stride, Padding padding, + const xla::TensorFormat& data_format) { + XlaBuilder* b = input.builder(); + Shape operand_shape = b->GetShape(input).ValueOrDie(); + std::vector input_size(operand_shape.dimensions().begin(), + operand_shape.dimensions().end()); + return MakeSpatialPadding(input_size, kernel_size, stride, padding, + data_format); +} + +// Add singleton batch and feature dimensions to spatial dimensions, according +// to 'data_format' specification. +std::vector ExpandWithBatchAndFeatureDimensions( + tensorflow::gtl::ArraySlice spatial_dim_sizes, + const xla::TensorFormat& data_format) { + const int num_spatial_dims = spatial_dim_sizes.size(); + std::vector tensor_sizes(num_spatial_dims + 2, 1); + for (int i = 0; i < num_spatial_dims; ++i) { + int dim = data_format.spatial_dimension(i); + tensor_sizes[dim] = spatial_dim_sizes[i]; + } + return tensor_sizes; +} + +class PoolingTest : public ClientLibraryTestBase { + public: + ErrorSpec error_spec_{0.0001}; +}; + +XLA_TEST_F(PoolingTest, MaxPool2D) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); + auto stride = kernel_size; + MaxPool(input, kernel_size, stride, Padding::kValid, data_format); + + ComputeAndCompareR4(&builder, {{{{5, 4}}}}, {}, error_spec_); +} + +XLA_TEST_F(PoolingTest, MaxPool2DWithPadding) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); + auto stride = kernel_size; + MaxPool(input, kernel_size, stride, Padding::kSame, data_format); + + ComputeAndCompareR4(&builder, {{{{5, 4, 5}}}}, {}, error_spec_); +} + +XLA_TEST_F(PoolingTest, MaxPool2DWithPaddingAndStride) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); + auto stride = ExpandWithBatchAndFeatureDimensions({1, 1}, data_format); + MaxPool(input, kernel_size, stride, Padding::kSame, data_format); + + ComputeAndCompareR4(&builder, {{{{5, 4, 4, 5, 5}, {5, 4, 3, 2, 1}}}}, + {}, error_spec_); +} + +XLA_TEST_F(PoolingTest, AvgPool2D) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); + auto stride = kernel_size; + auto padding = MakeGeneralPadding(input, kernel_size, stride, Padding::kValid, + data_format); + AvgPool(input, kernel_size, stride, padding, data_format, + /*counts_include_padding=*/true); + + ComputeAndCompareR4(&builder, {{{{3, 3}}}}, {}, error_spec_); +} + +XLA_TEST_F(PoolingTest, AvgPool2DWithPadding) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); + auto stride = kernel_size; + auto padding = MakeGeneralPadding(input, kernel_size, stride, Padding::kSame, + data_format); + AvgPool(input, kernel_size, stride, padding, data_format, + /*counts_include_padding=*/false); + + ComputeAndCompareR4(&builder, {{{{3, 3, 3}}}}, {}, error_spec_); +} + +XLA_TEST_F(PoolingTest, AvgPool2DWithPaddingAndStride) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); + auto stride = ExpandWithBatchAndFeatureDimensions({1, 1}, data_format); + auto padding = MakeGeneralPadding(input, kernel_size, stride, Padding::kSame, + data_format); + AvgPool(input, kernel_size, stride, padding, data_format, + /*counts_include_padding=*/false); + + ComputeAndCompareR4(&builder, + {{{{3, 3, 3, 3, 3}, {4.5, 3.5, 2.5, 1.5, 1}}}}, {}, + error_spec_); +} + +XLA_TEST_F(PoolingTest, AvgPool2DWithGeneralPaddingCountNotIncludePadding) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({3, 3}, data_format); + auto stride = kernel_size; + AvgPool(input, kernel_size, stride, {{1, 1}, {2, 1}}, data_format, + /*counts_include_padding=*/false); + + ComputeAndCompareR4(&builder, {{{{3, 3}}}}, {}, error_spec_); +} + +XLA_TEST_F(PoolingTest, + AvgPool2DWithGeneralPaddingCountNotIncludePaddingAndStride) { + XlaBuilder builder(TestName()); + + XlaOp input = ConstantR4FromArray4D( + &builder, {{{{1, 2, 3, 4, 5}, {5, 4, 3, 2, 1}}}}); + auto data_format = MakeNCHWFormat(2); + auto kernel_size = ExpandWithBatchAndFeatureDimensions({3, 3}, data_format); + auto stride = ExpandWithBatchAndFeatureDimensions({2, 2}, data_format); + AvgPool(input, kernel_size, stride, {{2, 1}, {1, 1}}, data_format, + /*counts_include_padding=*/false); + + ComputeAndCompareR4(&builder, {{{{1.5, 3, 4.5}, {3, 3, 3}}}}, {}, + error_spec_); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/sorting.h b/tensorflow/compiler/xla/client/lib/sorting.h index 404b4783c3878ca0fab811fa8c3d02686af44316..b9dfafdd6f957ae050e0f5dbd076d5288235b490 100644 --- a/tensorflow/compiler/xla/client/lib/sorting.h +++ b/tensorflow/compiler/xla/client/lib/sorting.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SORTING_H_ #define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SORTING_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/client/lib/sorting_test.cc b/tensorflow/compiler/xla/client/lib/sorting_test.cc index b6eee762a5f002e00fd6118d91f25343e22f13d3..fef98c9923096e21a755c6d730de2c7c10852b2d 100644 --- a/tensorflow/compiler/xla/client/lib/sorting_test.cc +++ b/tensorflow/compiler/xla/client/lib/sorting_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/lib/sorting.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index 8a6c5fb9a750cd74d47a66269843ec252ffbbbd4..cffb24e29beda6a8c40dca2fe709be22892dd489 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -303,7 +303,7 @@ StatusOr> LocalClient::TransferFromOutfeedLocal( const Shape& shape, int device_ordinal) { TF_ASSIGN_OR_RETURN(se::StreamExecutor * executor, backend().stream_executor(device_ordinal)); - auto literal = MakeUnique(); + auto literal = Literal::CreateFromShape(shape); TF_RETURN_IF_ERROR(backend().transfer_manager()->TransferLiteralFromOutfeed( executor, shape, literal.get())); return std::move(literal); diff --git a/tensorflow/compiler/xla/client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_builder.cc index 1cb61f77fb65102efb3b1dd9d77b8bdcbe8d9125..b3b00e2fffe1196b36190ec72d1425bae4e4e276 100644 --- a/tensorflow/compiler/xla/client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_builder.cc @@ -45,21 +45,6 @@ int64 GetUniqueId() { return id; } -// Returns true if an instruction with the given opcode can be the root of the -// computation. -bool CanBeRoot(HloOpcode opcode) { - switch (opcode) { - case HloOpcode::kAfterAll: - case HloOpcode::kSend: - case HloOpcode::kSendDone: - case HloOpcode::kOutfeed: - case HloOpcode::kTrace: - return false; - default: - return true; - } -} - } // namespace XlaOp operator-(const XlaOp& x) { return Neg(x); } @@ -142,28 +127,13 @@ XlaOp XlaBuilder::ReportErrorOrReturn( return ReportErrorOrReturn(op_creator()); } -StatusOr XlaBuilder::GetProgramShape(int64* root_id) const { +StatusOr XlaBuilder::GetProgramShape(int64 root_id) const { TF_RETURN_IF_ERROR(first_error_); - - TF_RET_CHECK(root_id != nullptr); + TF_RET_CHECK((root_id >= 0) && (root_id < instructions_.size())); ProgramShape program_shape; - // Not all instructions can be roots. Walk backwards from the last added - // instruction until a valid root is found. - int64 index = instructions_.size() - 1; - for (; index >= 0; index--) { - TF_ASSIGN_OR_RETURN(HloOpcode opcode, - StringToHloOpcode(instructions_[index].opcode())); - if (CanBeRoot(opcode)) { - break; - } - } - if (index < 0) { - return FailedPrecondition("no root instruction was found"); - } - *root_id = instructions_[index].id(); - *program_shape.mutable_result() = instructions_[index].shape(); + *program_shape.mutable_result() = instructions_[root_id].shape(); // Check that the parameter numbers are continuous from 0, and add parameter // shapes and names to the program shape. @@ -188,8 +158,15 @@ StatusOr XlaBuilder::GetProgramShape(int64* root_id) const { } StatusOr XlaBuilder::GetProgramShape() const { - int64 root; - return GetProgramShape(&root); + TF_RET_CHECK(!instructions_.empty()); + return GetProgramShape(instructions_.back().id()); +} + +StatusOr XlaBuilder::GetProgramShape(XlaOp root) const { + if (root.builder_ != this) { + return InvalidArgument("Given root operation is not in this computation."); + } + return GetProgramShape(root.handle()); } void XlaBuilder::IsConstantVisitor(const int64 op_handle, @@ -257,17 +234,29 @@ StatusOr XlaBuilder::Build() { first_error_backtrace_.Dump(tensorflow::DebugWriteToString, &backtrace); return AppendStatus(first_error_, backtrace); } + return Build(instructions_.back().id()); +} + +StatusOr XlaBuilder::Build(XlaOp root) { + if (root.builder_ != this) { + return InvalidArgument("Given root operation is not in this computation."); + } + return Build(root.handle()); +} + +StatusOr XlaBuilder::Build(int64 root_id) { + if (!first_error_.ok()) { + string backtrace; + first_error_backtrace_.Dump(tensorflow::DebugWriteToString, &backtrace); + return AppendStatus(first_error_, backtrace); + } HloComputationProto entry; entry.set_id(GetUniqueId()); // Give the computation a global unique id. entry.set_name(StrCat(name_, entry.id())); // Ensure that the name is unique. - { - int64 root_id; - TF_ASSIGN_OR_RETURN(*entry.mutable_program_shape(), - GetProgramShape(&root_id)); - entry.set_root_id(root_id); - } + TF_ASSIGN_OR_RETURN(*entry.mutable_program_shape(), GetProgramShape(root_id)); + entry.set_root_id(root_id); for (auto& instruction : instructions_) { // Ensures that the instruction names are unique among the whole graph. @@ -1099,11 +1088,11 @@ XlaOp XlaBuilder::Infeed(const Shape& shape, const string& config) { sharding_builder::AssignDevice(0); XlaScopedShardingAssignment scoped_sharding(this, infeed_instruction_sharding); - TF_ASSIGN_OR_RETURN(infeed, - AddInstruction(std::move(instr), HloOpcode::kInfeed)); + TF_ASSIGN_OR_RETURN( + infeed, AddInstruction(std::move(instr), HloOpcode::kInfeed, {})); } else { - TF_ASSIGN_OR_RETURN(infeed, - AddInstruction(std::move(instr), HloOpcode::kInfeed)); + TF_ASSIGN_OR_RETURN( + infeed, AddInstruction(std::move(instr), HloOpcode::kInfeed, {})); } // The infeed instruction produces a tuple of the infed data and a token @@ -1892,6 +1881,61 @@ XlaOp XlaBuilder::CrossReplicaSum( }); } +XlaOp XlaBuilder::AllToAll(const XlaOp& operand, int64 split_dimension, + int64 concat_dimension, int64 split_count, + const std::vector& replica_groups) { + return ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(const Shape& operand_shape, GetShape(operand)); + + // The HloInstruction for Alltoall currently only handles the data + // communication: it accepts N already split parts and scatters them to N + // cores, and each core gathers the N received parts into a tuple as the + // output. So here we explicitly split the operand before the hlo alltoall, + // and concat the tuple elements. + // + // First, run shape inference to make sure the shapes are valid. + TF_RETURN_IF_ERROR( + ShapeInference::InferAllToAllShape(operand_shape, split_dimension, + concat_dimension, split_count) + .status()); + + // Split into N parts. + std::vector slices; + slices.reserve(split_count); + const int64 block_size = + operand_shape.dimensions(split_dimension) / split_count; + for (int i = 0; i < split_count; i++) { + slices.push_back(SliceInDim(operand, /*start_index=*/i * block_size, + /*limit_index=*/(i + 1) * block_size, + /*stride=*/1, /*dimno=*/split_dimension)); + } + + // Handle data communication. + HloInstructionProto instr; + TF_ASSIGN_OR_RETURN(auto slice_shapes, this->GetOperandShapes(slices)); + std::vector slice_shape_ptrs; + c_transform(slice_shapes, std::back_inserter(slice_shape_ptrs), + [](const Shape& shape) { return &shape; }); + TF_ASSIGN_OR_RETURN( + *instr.mutable_shape(), + ShapeInference::InferAllToAllTupleShape(slice_shape_ptrs)); + for (const ReplicaGroup& group : replica_groups) { + *instr.add_replica_groups() = group; + } + TF_ASSIGN_OR_RETURN( + XlaOp alltoall, + AddInstruction(std::move(instr), HloOpcode::kAllToAll, slices)); + + // Concat the N received parts. + std::vector received; + received.reserve(split_count); + for (int i = 0; i < split_count; i++) { + received.push_back(this->GetTupleElement(alltoall, i)); + } + return this->ConcatInDim(received, concat_dimension); + }); +} + XlaOp XlaBuilder::SelectAndScatter( const XlaOp& operand, const XlaComputation& select, tensorflow::gtl::ArraySlice window_dimensions, @@ -2163,11 +2207,6 @@ StatusOr XlaBuilder::BuildConstantSubGraph( TF_ASSIGN_OR_RETURN(const HloInstructionProto* root, LookUpInstruction(root_op)); - TF_ASSIGN_OR_RETURN(HloOpcode opcode, StringToHloOpcode(root->opcode())); - if (!CanBeRoot(opcode)) { - return InvalidArgument("the operand with opcode %s cannot be root", - root->opcode().c_str()); - } HloComputationProto entry; entry.set_id(GetUniqueId()); // Give the computation a global unique id. @@ -2693,6 +2732,13 @@ XlaOp CrossReplicaSum( replica_group_ids, channel_id); } +XlaOp AllToAll(const XlaOp& operand, int64 split_dimension, + int64 concat_dimension, int64 split_count, + const std::vector& replica_groups) { + return operand.builder()->AllToAll(operand, split_dimension, concat_dimension, + split_count, replica_groups); +} + XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, tensorflow::gtl::ArraySlice window_dimensions, tensorflow::gtl::ArraySlice window_strides, diff --git a/tensorflow/compiler/xla/client/xla_builder.h b/tensorflow/compiler/xla/client/xla_builder.h index 8726cc6f93569d94d506bcd4481c00d3427f9008..9403d7ca8dabc80a3964b50d29f158a98091f843 100644 --- a/tensorflow/compiler/xla/client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_builder.h @@ -195,9 +195,14 @@ class XlaBuilder { // Builds the computation with the requested operations, or returns a non-ok // status. Note that all ops that have been enqueued will be moved to the - // computation being returned. + // computation being returned. The root of the computation will be the last + // added operation. StatusOr Build(); + // Overload of Build which specifies a particular root instruction for the + // computation. + StatusOr Build(XlaOp root); + // Builds the computation with the requested operations, or notes an error in // the parent XlaBuilder and returns an empty computation if building failed. // This function is intended to be used where the returned XlaComputation is @@ -225,9 +230,14 @@ class XlaBuilder { // Returns the shape of the given op. StatusOr GetShape(const XlaOp& op) const; - // Returns the (inferred) result for the current computation's shape. + // Returns the (inferred) result for the current computation's shape. This + // assumes the root instruction is the last added instruction. StatusOr GetProgramShape() const; + // Returns the (inferred) result for the current computation's shape using the + // given operation as the root. + StatusOr GetProgramShape(XlaOp root) const; + // Reports an error to the builder, by // * storing it internally and capturing a backtrace if it's the first error // (this deferred value will be produced on the call to @@ -255,6 +265,9 @@ class XlaBuilder { StatusOr IsConstant(const XlaOp& operand) const; private: + // Build helper which takes the id of the root operation.. + StatusOr Build(int64 root_id); + // Enqueues a "retrieve parameter value" instruction for a parameter that was // passed to the computation. XlaOp Parameter(int64 parameter_number, const Shape& shape, @@ -686,9 +699,9 @@ class XlaBuilder { // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. // - // - `channel_id`: for Allreduce nodes from different models, if they have the - // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be - // applied cross models. + // - `channel_id`: for Allreduce nodes from different modules, if they have + // the same channel_id, they will be 'Allreduce'd. If empty, Allreduce will + // not be applied cross modules. // // TODO(b/79737069): Rename this to AllReduce when it's ready to use. XlaOp CrossReplicaSum( @@ -697,6 +710,13 @@ class XlaBuilder { const tensorflow::gtl::optional& channel_id = tensorflow::gtl::nullopt); + // Enqueues an operation that do an Alltoall of the operand cross cores. + // + // TODO(b/110096724): This is NOT YET ready to use. + XlaOp AllToAll(const XlaOp& operand, int64 split_dimension, + int64 concat_dimension, int64 split_count, + const std::vector& replica_groups); + // Enqueues an operation that scatters the `source` array to the selected // indices of each window. XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, @@ -969,9 +989,8 @@ class XlaBuilder { // shape. StatusOr Reshape(const Shape& shape, const XlaOp& operand); - // Returns the (inferred) result for the program shape for the current - // computation and fills the root_id in the pointer. - StatusOr GetProgramShape(int64* root_id) const; + // Returns the (inferred) result for the program shape using the given root. + StatusOr GetProgramShape(int64 root_id) const; // Returns shapes for the operands. StatusOr> GetOperandShapes( @@ -1234,6 +1253,9 @@ class XlaBuilder { const XlaOp& operand, const XlaComputation& computation, tensorflow::gtl::ArraySlice replica_group_ids, const tensorflow::gtl::optional& channel_id); + friend XlaOp AllToAll(const XlaOp& operand, int64 split_dimension, + int64 concat_dimension, int64 split_count, + const std::vector& replica_groups); friend XlaOp SelectAndScatter( const XlaOp& operand, const XlaComputation& select, tensorflow::gtl::ArraySlice window_dimensions, @@ -1820,9 +1842,9 @@ XlaOp CrossReplicaSum( // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. // -// - `channel_id`: for Allreduce nodes from different models, if they have the +// - `channel_id`: for Allreduce nodes from different modules, if they have the // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be -// applied cross models. +// applied cross modules. // // TODO(b/79737069): Rename this to AllReduce when it's ready to use. XlaOp CrossReplicaSum(const XlaOp& operand, const XlaComputation& computation, @@ -1830,6 +1852,13 @@ XlaOp CrossReplicaSum(const XlaOp& operand, const XlaComputation& computation, const tensorflow::gtl::optional& channel_id = tensorflow::gtl::nullopt); +// Enqueues an operation that do an Alltoall of the operand cross cores. +// +// TODO(b/110096724): This is NOT YET ready to use. +XlaOp AllToAll(const XlaOp& operand, int64 split_dimension, + int64 concat_dimension, int64 split_count, + const std::vector& replica_groups = {}); + // Enqueues an operation that scatters the `source` array to the selected // indices of each window. XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, diff --git a/tensorflow/compiler/xla/client/xla_builder_test.cc b/tensorflow/compiler/xla/client/xla_builder_test.cc index 28a207b137d901213ec43d506a638ef08a6bded9..49a15ec3b449bdec07aa6ecfbc40b7b9f62c3f4e 100644 --- a/tensorflow/compiler/xla/client/xla_builder_test.cc +++ b/tensorflow/compiler/xla/client/xla_builder_test.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/test.h" +#include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { @@ -46,6 +47,17 @@ class XlaBuilderTest : public ::testing::Test { return HloModule::CreateFromProto(proto, config); } + // Overload which explicitly specifies the root instruction. + StatusOr> BuildHloModule(XlaBuilder* b, + XlaOp root) { + TF_ASSIGN_OR_RETURN(XlaComputation computation, b->Build(root)); + const HloModuleProto& proto = computation.proto(); + TF_ASSIGN_OR_RETURN(const auto& config, + HloModule::CreateModuleConfigFromProto( + proto, legacy_flags::GetDebugOptionsFromFlags())); + return HloModule::CreateFromProto(proto, config); + } + // Returns the name of the test currently being run. string TestName() const { return ::testing::UnitTest::GetInstance()->current_test_info()->name(); @@ -293,6 +305,21 @@ TEST_F(XlaBuilderTest, Transpose) { EXPECT_THAT(root, op::Transpose(op::Parameter())); } +TEST_F(XlaBuilderTest, AllToAll) { + XlaBuilder b(TestName()); + auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {4, 16}), "x"); + AllToAll(x, /*split_dimension=*/1, /*concat_dimension=*/0, + /*split_count=*/2); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b)); + auto root = module->entry_computation()->root_instruction(); + + // AllToAll is decomposed into slices -> all-to-all -> gte -> concat. + EXPECT_EQ(root->opcode(), HloOpcode::kConcatenate); + EXPECT_EQ(root->operand(0)->operand(0)->opcode(), HloOpcode::kAllToAll); + EXPECT_TRUE( + ShapeUtil::Equal(root->shape(), ShapeUtil::MakeShape(F32, {8, 8}))); +} + TEST_F(XlaBuilderTest, ReportError) { XlaBuilder b(TestName()); auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {5, 7}), "x"); @@ -320,5 +347,45 @@ TEST_F(XlaBuilderTest, ReportErrorOrReturnHandlesErrors) { EXPECT_THAT(statusor.status().error_message(), HasSubstr("a test error")); } +TEST_F(XlaBuilderTest, BuildWithSpecificRoot) { + XlaBuilder b(TestName()); + XlaOp constant = ConstantR0(&b, 1.0); + Add(constant, ConstantR0(&b, 2.0)); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b, /*root=*/constant)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Constant()); +} + +TEST_F(XlaBuilderTest, BuildWithSpecificRootAndMultipleParameters) { + // Specifying a particular root in Build should still include all entry + // parameters. + XlaBuilder b(TestName()); + const Shape shape = ShapeUtil::MakeShape(F32, {42, 123}); + XlaOp x = Parameter(&b, 0, shape, "x"); + XlaOp y = Parameter(&b, 1, shape, "y"); + XlaOp z = Parameter(&b, 2, shape, "z"); + Add(x, Sub(y, z)); + TF_ASSERT_OK_AND_ASSIGN(auto module, BuildHloModule(&b, /*root=*/x)); + auto root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Parameter()); + EXPECT_EQ(module->entry_computation()->num_parameters(), 3); + EXPECT_EQ(module->entry_computation()->instruction_count(), 5); +} + +TEST_F(XlaBuilderTest, BuildWithSpecificRootWithWrongBuilder) { + XlaBuilder b(TestName()); + XlaBuilder other_b(TestName()); + const Shape shape = ShapeUtil::MakeShape(F32, {42, 123}); + + Parameter(&b, 0, shape, "param"); + XlaOp other_param = Parameter(&other_b, 0, shape, "other_param"); + + Status status = b.Build(other_param).status(); + ASSERT_IS_NOT_OK(status); + EXPECT_THAT( + status.error_message(), + ::testing::HasSubstr("root operation is not in this computation")); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/client/xla_client/BUILD b/tensorflow/compiler/xla/client/xla_client/BUILD deleted file mode 100644 index 2e131dbad26970d4cb9860c17c3de3d52de36223..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/client/xla_client/BUILD +++ /dev/null @@ -1,33 +0,0 @@ -# Description: -# The new XLA client libraries. - -licenses(["notice"]) # Apache 2.0 - -package(default_visibility = [":friends"]) - -package_group( - name = "friends", - includes = [ - "//tensorflow/compiler/xla:friends", - ], -) - -# Filegroup used to collect source files for dependency checking. -filegroup( - name = "c_srcs", - data = glob([ - "**/*.cc", - "**/*.h", - ]), -) - -load("//tensorflow:tensorflow.bzl", "tf_cc_test") - -cc_library( - name = "xla_builder", - hdrs = ["xla_builder.h"], - visibility = ["//visibility:public"], - deps = [ - "//tensorflow/compiler/xla/client:xla_builder", - ], -) diff --git a/tensorflow/compiler/xla/literal.cc b/tensorflow/compiler/xla/literal.cc index 0545deb096e9eace5a9713f200e10559aa718441..36e472568ecfdb97c828817ed339260ee7878723 100644 --- a/tensorflow/compiler/xla/literal.cc +++ b/tensorflow/compiler/xla/literal.cc @@ -71,7 +71,7 @@ std::ostream& operator<<(std::ostream& out, const Literal& literal) { return out; } -Literal::StrideConfig::StrideConfig( +MutableLiteralBase::StrideConfig::StrideConfig( const Shape& source_shape, const Shape& dest_shape, tensorflow::gtl::ArraySlice dimensions) : dimensions(dimensions), @@ -133,7 +133,8 @@ void Literal::SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays) { } Literal::Literal(const Shape& shape, bool allocate_arrays) - : LiteralBase(), shape_(MakeUnique(shape)) { + : MutableLiteralBase() { + shape_ = MakeUnique(shape); CHECK(LayoutUtil::HasLayout(*shape_)); root_piece_ = new Piece(); root_piece_->set_subshape(shape_.get()); @@ -159,7 +160,9 @@ void Literal::DeallocateBuffers() { }); } -Literal::Literal(Literal&& other) : LiteralBase() { *this = std::move(other); } +Literal::Literal(Literal&& other) : MutableLiteralBase() { + *this = std::move(other); +} Literal& Literal::operator=(Literal&& other) { DCHECK(&other.root_piece_->subshape() == other.shape_.get()); @@ -187,12 +190,13 @@ const SparseIndexArray* LiteralBase::sparse_indices( return piece(shape_index).sparse_indices(); } -SparseIndexArray* Literal::sparse_indices(const ShapeIndex& shape_index) { +SparseIndexArray* MutableLiteralBase::sparse_indices( + const ShapeIndex& shape_index) { return piece(shape_index).sparse_indices(); } template -Status Literal::CopySliceFromInternal( +Status MutableLiteralBase::CopySliceFromInternal( const LiteralBase& src_literal, tensorflow::gtl::ArraySlice src_base, tensorflow::gtl::ArraySlice dest_base, tensorflow::gtl::ArraySlice copy_size) { @@ -225,8 +229,8 @@ Status Literal::CopySliceFromInternal( // proper stride size at the matching dimension. DimensionVector src_indexes(src_base.size(), 0); DimensionVector dest_indexes(dest_base.size(), 0); - Literal::StrideConfig stride_config(src_literal.shape(), shape(), - copy_size); + MutableLiteralBase::StrideConfig stride_config(src_literal.shape(), shape(), + copy_size); auto copy_proc = [&](tensorflow::gtl::ArraySlice indexes) { // Map from multi-dimensional index, to source index. @@ -253,9 +257,10 @@ Status Literal::CopySliceFromInternal( return Status::OK(); } -Status Literal::CopyElementFrom(const LiteralSlice& src_literal, - tensorflow::gtl::ArraySlice src_index, - tensorflow::gtl::ArraySlice dest_index) { +Status MutableLiteralBase::CopyElementFrom( + const LiteralSlice& src_literal, + tensorflow::gtl::ArraySlice src_index, + tensorflow::gtl::ArraySlice dest_index) { DCHECK_EQ(shape().element_type(), src_literal.shape().element_type()); const int64 src_linear_index = IndexUtil::MultidimensionalIndexToLinearIndex( src_literal.shape(), src_index); @@ -275,8 +280,8 @@ Status Literal::CopyElementFrom(const LiteralSlice& src_literal, return Status::OK(); } -/* static */ StatusOr> Literal::CreateFromProto( - const LiteralProto& proto) { +/* static */ StatusOr> +MutableLiteralBase::CreateFromProto(const LiteralProto& proto) { if (!proto.has_shape()) { return InvalidArgument("LiteralProto has no shape"); } @@ -405,9 +410,9 @@ Status LiteralBase::Piece::CopyFrom(const LiteralBase::Piece& src) { return Status::OK(); } -Status Literal::CopyFrom(const LiteralSlice& src_literal, - const ShapeIndex& dest_shape_index, - const ShapeIndex& src_shape_index) { +Status MutableLiteralBase::CopyFrom(const LiteralSlice& src_literal, + const ShapeIndex& dest_shape_index, + const ShapeIndex& src_shape_index) { const Shape& dest_subshape = ShapeUtil::GetSubshape(shape(), dest_shape_index); const Shape& src_subshape = @@ -482,10 +487,11 @@ Status Literal::MoveFrom(Literal&& src_literal, return Status::OK(); } -Status Literal::CopySliceFrom(const LiteralSlice& src_literal, - tensorflow::gtl::ArraySlice src_base, - tensorflow::gtl::ArraySlice dest_base, - tensorflow::gtl::ArraySlice copy_size) { +Status MutableLiteralBase::CopySliceFrom( + const LiteralSlice& src_literal, + tensorflow::gtl::ArraySlice src_base, + tensorflow::gtl::ArraySlice dest_base, + tensorflow::gtl::ArraySlice copy_size) { TF_RET_CHECK(ShapeUtil::IsArray(shape())) << ShapeUtil::HumanString(shape()); TF_RET_CHECK(ShapeUtil::IsArray(src_literal.shape())) << ShapeUtil::HumanString(src_literal.shape()); @@ -543,7 +549,7 @@ Status Literal::CopySliceFrom(const LiteralSlice& src_literal, shape().element_type()); } -void Literal::PopulateR1(const tensorflow::core::Bitmap& values) { +void MutableLiteralBase::PopulateR1(const tensorflow::core::Bitmap& values) { CHECK(ShapeUtil::IsArray(shape())); CHECK_EQ(ShapeUtil::Rank(shape()), 1); CHECK_EQ(element_count(), values.bits()); @@ -895,8 +901,8 @@ size_t LiteralBase::Hash() const { return hash_value; } -Status Literal::SetIntegralAsS64(tensorflow::gtl::ArraySlice multi_index, - int64 value) { +Status MutableLiteralBase::SetIntegralAsS64( + tensorflow::gtl::ArraySlice multi_index, int64 value) { CHECK(LayoutUtil::IsDenseArray(shape())); switch (shape().element_type()) { case PRED: @@ -933,7 +939,7 @@ tensorflow::gtl::ArraySlice LiteralBase::GetSparseIndex( return p.sparse_indices()->At(sparse_element_number); } -void Literal::SortSparseElements(const ShapeIndex& shape_index) { +void MutableLiteralBase::SortSparseElements(const ShapeIndex& shape_index) { piece(shape_index).SortSparseElements(); } @@ -1391,11 +1397,11 @@ StatusOr> LiteralBase::ConvertToShape( elements.push_back(std::move(*new_element)); } auto converted = MakeUnique(); - *converted = Literal::MoveIntoTuple(&elements); + *converted = MutableLiteralBase::MoveIntoTuple(&elements); return std::move(converted); } -/* static */ Literal Literal::MoveIntoTuple( +/* static */ Literal MutableLiteralBase::MoveIntoTuple( tensorflow::gtl::MutableArraySlice elements) { std::vector element_shapes; for (const Literal& element : elements) { @@ -1808,7 +1814,8 @@ Status CopyFromRepeatedField(tensorflow::gtl::MutableArraySlice dest, } // namespace Status LiteralBase::Piece::CopyFromProto(const LiteralProto& proto) { - // These conditions should have been checked in Literal::CreateFromProto. + // These conditions should have been checked in + // MutableLiteralBase::CreateFromProto. TF_RET_CHECK(proto.has_shape()); TF_RET_CHECK(LayoutUtil::HasLayout(proto.shape())); TF_RET_CHECK(ShapeUtil::Equal(proto.shape(), subshape())); @@ -1900,7 +1907,7 @@ const void* LiteralBase::untyped_data(const ShapeIndex& shape_index) const { return piece(shape_index).untyped_data(); } -void* Literal::untyped_data(const ShapeIndex& shape_index) { +void* MutableLiteralBase::untyped_data(const ShapeIndex& shape_index) { return piece(shape_index).untyped_data(); } @@ -1916,6 +1923,127 @@ string LiteralBase::GetR1U8AsString() const { ShapeUtil::ElementsIn(shape())); } +void MutableBorrowingLiteral::CopyPieceSubtree(const Shape& shape, + Piece* src_piece, + Piece* dest_piece) { + DCHECK(ShapeUtil::Equal(src_piece->subshape(), dest_piece->subshape())) + << "src_piece has shape: " + << ShapeUtil::HumanString(src_piece->subshape()) + << "dest_piece has shape: " + << ShapeUtil::HumanString(dest_piece->subshape()); + if (ShapeUtil::IsTuple(shape)) { + for (int i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) { + const Shape& subshape = shape.tuple_shapes(i); + + auto child_piece = Piece(); + child_piece.set_subshape(&subshape); + + CopyPieceSubtree(subshape, &src_piece->child(i), &child_piece); + + dest_piece->emplace_back(std::move(child_piece)); + } + } else if (ShapeUtil::IsArray(shape)) { + dest_piece->set_buffer(src_piece->buffer()); + } else { + // If the shape is neither an array nor tuple, then it must be + // zero-sized. Otherwise, some memory needs to be allocated for it. + CHECK_EQ(dest_piece->size_bytes(), 0); + } +} + +MutableLiteralBase::~MutableLiteralBase() {} + +MutableBorrowingLiteral::MutableBorrowingLiteral( + const MutableBorrowingLiteral& literal) + : MutableLiteralBase() { + shape_ = MakeUnique(literal.shape()); + CHECK(LayoutUtil::HasLayout(*shape_)); + + root_piece_ = new Piece(); + root_piece_->set_subshape(shape_.get()); + + CopyPieceSubtree(*shape_, &literal.root_piece(), root_piece_); +} + +MutableBorrowingLiteral& MutableBorrowingLiteral::operator=( + const MutableBorrowingLiteral& literal) { + shape_ = MakeUnique(literal.shape()); + CHECK(LayoutUtil::HasLayout(*shape_)); + + root_piece_ = new Piece(); + root_piece_->set_subshape(shape_.get()); + + CopyPieceSubtree(*shape_, &literal.root_piece(), root_piece_); + + return *this; +} + +MutableBorrowingLiteral::MutableBorrowingLiteral( + const MutableLiteralBase& literal) + : MutableLiteralBase() { + shape_ = MakeUnique(literal.shape()); + CHECK(LayoutUtil::HasLayout(*shape_)); + + root_piece_ = new Piece(); + root_piece_->set_subshape(shape_.get()); + + CopyPieceSubtree(*shape_, &literal.root_piece(), root_piece_); +} + +MutableBorrowingLiteral::MutableBorrowingLiteral(MutableLiteralBase* literal) + : MutableLiteralBase() { + shape_ = MakeUnique(literal->shape()); + CHECK(LayoutUtil::HasLayout(*shape_)); + + root_piece_ = new Piece(); + root_piece_->set_subshape(shape_.get()); + + CopyPieceSubtree(*shape_, &literal->root_piece(), root_piece_); +} + +MutableBorrowingLiteral::MutableBorrowingLiteral( + MutableBorrowingLiteral literal, const ShapeIndex& view_root) + : MutableLiteralBase() { + shape_ = MakeUnique(literal.piece(view_root).subshape()); + CHECK(LayoutUtil::HasLayout(*shape_)); + + root_piece_ = new Piece(); + root_piece_->set_subshape(shape_.get()); + + CopyPieceSubtree(*shape_, &literal.piece(view_root), root_piece_); +} + +MutableBorrowingLiteral::MutableBorrowingLiteral(const char* src_buf_ptr, + const Shape& shape) + : MutableLiteralBase() { + shape_ = MakeUnique(shape); + CHECK(LayoutUtil::HasLayout(*shape_)); + CHECK(!ShapeUtil::IsTuple(*shape_)); + + root_piece_ = new Piece(); + root_piece_->set_buffer(const_cast(src_buf_ptr)); + root_piece_->set_subshape(shape_.get()); +} + +MutableBorrowingLiteral::~MutableBorrowingLiteral() { + if (root_piece_ != nullptr) { + root_piece_->ForEachMutableSubpiece( + [&](const ShapeIndex& index, Piece* piece) { + if (piece->buffer() != nullptr) { + delete piece->sparse_indices(); + } + }); + delete root_piece_; + } +} + +LiteralSlice::LiteralSlice(const LiteralBase& literal) + : LiteralBase(), root_piece_(&literal.root_piece()) {} + +LiteralSlice::LiteralSlice(const LiteralBase& literal, + const ShapeIndex& view_root) + : LiteralBase(), root_piece_(&literal.piece(view_root)) {} + void BorrowingLiteral::BuildPieceSubtree(const Shape& shape, Piece* piece) { CHECK(ShapeUtil::IsTuple(shape)); for (int i = 0; i < ShapeUtil::TupleElementCount(shape); ++i) { @@ -1932,13 +2060,6 @@ void BorrowingLiteral::BuildPieceSubtree(const Shape& shape, Piece* piece) { } } -LiteralSlice::LiteralSlice(const LiteralBase& literal) - : LiteralBase(), root_piece_(&literal.root_piece()) {} - -LiteralSlice::LiteralSlice(const LiteralBase& literal, - const ShapeIndex& view_root) - : LiteralBase(), root_piece_(&literal.piece(view_root)) {} - BorrowingLiteral::BorrowingLiteral(const char* src_buf_ptr, const Shape& shape) : LiteralBase(), shape_(MakeUnique(shape)) { CHECK(ShapeUtil::IsArray(*shape_)); diff --git a/tensorflow/compiler/xla/literal.h b/tensorflow/compiler/xla/literal.h index dd67dfa8d4a556aea179bc47abfdc9a9c8872c45..92c0f903cbe252a153103aa8514bb5531696bbfe 100644 --- a/tensorflow/compiler/xla/literal.h +++ b/tensorflow/compiler/xla/literal.h @@ -310,9 +310,10 @@ class LiteralBase { // type of literal itself (0 for numeric types, and false for predicates). // // Note: It's an antipattern to use this method then immediately call - // Literal::Populate on the result (since that results in zero initialization, - // then reinitialization. Conside if a call to MakeUnique(shape), - // followed by the call to Literal::Populate can be used instead. + // MutableLiteralBase::Populate on the result (since that results in zero + // initialization, then reinitialization. Conside if a call to + // MakeUnique(shape), followed by the call to + // MutableLiteralBase::Populate can be used instead. static std::unique_ptr CreateFromShape(const Shape& shape); protected: @@ -534,7 +535,7 @@ class LiteralBase { virtual const Piece& root_piece() const = 0; // LiteralSlice and Literal must access Pieces of other Literals. - friend class Literal; + friend class MutableLiteralBase; friend class LiteralSlice; friend class BorrowingLiteral; @@ -545,33 +546,10 @@ class LiteralBase { tensorflow::gtl::ArraySlice start_indices) const; }; -// Class representing literal values in XLA. -// -// The underlying buffer and shape is always owned by this class. -class Literal : public LiteralBase { +// Abstract base class representing a mutable literal in XLA. +class MutableLiteralBase : public LiteralBase { public: - Literal() : Literal(ShapeUtil::MakeNil()) {} - - // Create a literal of the given shape. The literal is allocated sufficient - // memory to hold the shape. Memory is uninitialized. - explicit Literal(const Shape& shape); - virtual ~Literal(); - - // Literals are moveable, but not copyable. To copy a literal use - // Literal::Clone or Literal::CloneToUnique. This prevents inadvertent copies - // of literals which can be expensive. - Literal(const Literal& other) = delete; - Literal& operator=(const Literal& other) = delete; - Literal(Literal&& other); - // 'allocate_arrays' indicates whether to allocate memory for the arrays in - // the shape. If false, buffer pointers inside of the Literal::Pieces are set - // to nullptr. - Literal(const Shape& shape, bool allocate_arrays); - Literal& operator=(Literal&& other); - - // TODO(b/67651157): Remove this accessor. Literal users should not be able to - // mutate the shape as this can produce malformed Literals. - Shape* mutable_shape_do_not_use() { return shape_.get(); } + virtual ~MutableLiteralBase() = 0; // Returns a MutableArraySlice view of the array for this literal for the // given NativeT (e.g., float). CHECKs if the subshape of the literal at the @@ -587,6 +565,10 @@ class Literal : public LiteralBase { // is not a sparse array. SparseIndexArray* sparse_indices(const ShapeIndex& shape_index = {}); + // TODO(b/67651157): Remove this accessor. Literal users should not be able to + // mutate the shape as this can produce malformed Literals. + Shape* mutable_shape_do_not_use() { return shape_.get(); } + // Returns a pointer to the underlying buffer holding the array at the given // shape index. CHECKs if the subshape of the literal at the given ShapeIndex // is not array. @@ -613,21 +595,6 @@ class Literal : public LiteralBase { const ShapeIndex& dest_shape_index = {}, const ShapeIndex& src_shape_index = {}); - // Returns a vector containing the tuple elements of this Literal as separate - // Literals. This Literal must be tuple-shaped and can be a nested tuple. The - // elements are moved into the new Literals; no data is copied. Upon return - // this Literal is set to a nil shape (empty tuple) - std::vector DecomposeTuple(); - - // Similar to CopyFrom, but with move semantincs. The subshape of this literal - // rooted at 'dest_shape_index' must be *equal* to the shape 'src_literal' - // (layouts and shapes must match), but need not be arrays. The memory - // allocated in this literal for the subshape at dest_shape_index is - // deallocated, and the respective buffers are replaced with those in - // src_literal. Upon return, src_literal is set to a nil shape (empty tuple). - Status MoveFrom(Literal&& src_literal, - const ShapeIndex& dest_shape_index = {}); - // Copies the values from src_literal, starting at src_base shape indexes, // to this literal, starting at dest_base, where the copy size in each // dimension is specified by copy_size. @@ -730,12 +697,7 @@ class Literal : public LiteralBase { static StatusOr> CreateFromProto( const LiteralProto& proto); - private: - // Recursively sets the subshapes and buffers of all subpieces rooted at - // 'piece'. If 'allocate_array' is true, memory is allocated for the arrays in - // the shape. - void SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays); - + protected: // Returns the piece at the given ShapeIndex. Piece& piece(const ShapeIndex& shape_index) { return const_cast(LiteralBase::piece(shape_index)); @@ -783,12 +745,83 @@ class Literal : public LiteralBase { template Status PopulateInternal(const FnType& generator, bool parallel); + friend class LiteralBase; + friend class MutableBorrowingLiteral; +}; +std::ostream& operator<<(std::ostream& out, const Literal& literal); + +// The underlying buffer and shape is always owned by this class. +class Literal : public MutableLiteralBase { + public: + Literal() : Literal(ShapeUtil::MakeNil()) {} + + // Create a literal of the given shape. The literal is allocated sufficient + // memory to hold the shape. Memory is uninitialized. + explicit Literal(const Shape& shape); + virtual ~Literal(); + + // Literals are moveable, but not copyable. To copy a literal use + // Literal::Clone or Literal::CloneToUnique. This prevents inadvertent copies + // of literals which can be expensive. + Literal(const Literal& other) = delete; + Literal& operator=(const Literal& other) = delete; + Literal(Literal&& other); + // 'allocate_arrays' indicates whether to allocate memory for the arrays in + // the shape. If false, buffer pointers inside of the Literal::Pieces are set + // to nullptr. + Literal(const Shape& shape, bool allocate_arrays); + Literal& operator=(Literal&& other); + + // Similar to CopyFrom, but with move semantincs. The subshape of this literal + // rooted at 'dest_shape_index' must be *equal* to the shape 'src_literal' + // (layouts and shapes must match), but need not be arrays. The memory + // allocated in this literal for the subshape at dest_shape_index is + // deallocated, and the respective buffers are replaced with those in + // src_literal. Upon return, src_literal is set to a nil shape (empty tuple). + virtual Status MoveFrom(Literal&& src_literal, + const ShapeIndex& dest_shape_index = {}); + + // Returns a vector containing the tuple elements of this Literal as separate + // Literals. This Literal must be tuple-shaped and can be a nested tuple. The + // elements are moved into the new Literals; no data is copied. Upon return + // this Literal is set to a nil shape (empty tuple) + std::vector DecomposeTuple(); + + private: // Deallocate the buffers held by this literal. void DeallocateBuffers(); - friend class LiteralBase; + // Recursively sets the subshapes and buffers of all subpieces rooted at + // 'piece'. If 'allocate_array' is true, memory is allocated for the arrays in + // the shape. + void SetPiece(const Shape& shape, Piece* piece, bool allocate_arrays); +}; + +// The underlying buffer is not owned by this class and is always owned by +// others. The shape is not owned by this class and not mutable. +class MutableBorrowingLiteral : public MutableLiteralBase { + public: + virtual ~MutableBorrowingLiteral(); + + MutableBorrowingLiteral() : MutableLiteralBase() {} + + MutableBorrowingLiteral(const MutableBorrowingLiteral& literal); + MutableBorrowingLiteral& operator=(const MutableBorrowingLiteral& literal); + + // Implicit conversion constructors. + MutableBorrowingLiteral(const MutableLiteralBase& literal); + MutableBorrowingLiteral(MutableLiteralBase* literal); + MutableBorrowingLiteral(MutableBorrowingLiteral literal, + const ShapeIndex& view_root); + MutableBorrowingLiteral(const char* src_buf_ptr, const Shape& shape); + + private: + // Recursively copies the subtree from the `src_piece` at the given child + // index to the `dest_piece`. For buffers only the pointers are copied, but + // not the content. + void CopyPieceSubtree(const Shape& shape, Piece* src_piece, + Piece* dest_piece); }; -std::ostream& operator<<(std::ostream& out, const Literal& literal); // A read-only view of a Literal. A LiteralSlice contains pointers to shape and // literal buffers always owned by others. @@ -831,9 +864,9 @@ class BorrowingLiteral : public LiteralBase { const Piece& root_piece() const override { return root_piece_; }; Piece root_piece_; - // Shape of this literal. Stored as unique_ptr so such that the (default) - // move construction of this class would be trivially correct: the pointer to - // Shape root_piece_ stores will still point to the correct address. + // Shape of this literal. Stored as unique_ptr such that the (default) move + // construction of this class would be trivially correct: the pointer to Shape + // root_piece_ stores will still point to the correct address. std::unique_ptr shape_; }; @@ -886,7 +919,7 @@ tensorflow::gtl::ArraySlice LiteralBase::data( } template -tensorflow::gtl::MutableArraySlice Literal::data( +tensorflow::gtl::MutableArraySlice MutableLiteralBase::data( const ShapeIndex& shape_index) { return piece(shape_index).data(); } @@ -904,14 +937,15 @@ inline NativeT LiteralBase::Get( } template -inline void Literal::Set(tensorflow::gtl::ArraySlice multi_index, - const ShapeIndex& shape_index, NativeT value) { +inline void MutableLiteralBase::Set( + tensorflow::gtl::ArraySlice multi_index, + const ShapeIndex& shape_index, NativeT value) { return piece(shape_index).Set(multi_index, value); } template -inline void Literal::Set(tensorflow::gtl::ArraySlice multi_index, - NativeT value) { +inline void MutableLiteralBase::Set( + tensorflow::gtl::ArraySlice multi_index, NativeT value) { return root_piece().Set(multi_index, value); } @@ -929,7 +963,7 @@ NativeT LiteralBase::GetSparseElement(int64 sparse_element_number, } template -void Literal::AppendSparseElement( +void MutableLiteralBase::AppendSparseElement( tensorflow::gtl::ArraySlice multi_index, NativeT value, const ShapeIndex& shape_index) { Piece& p = piece(shape_index); @@ -959,7 +993,8 @@ void LiteralBase::EachCell( } template -inline void Literal::PopulateR1(tensorflow::gtl::ArraySlice values) { +inline void MutableLiteralBase::PopulateR1( + tensorflow::gtl::ArraySlice values) { CHECK(ShapeUtil::IsArray(shape())); CHECK_EQ(ShapeUtil::Rank(shape()), 1); CHECK_EQ(ShapeUtil::ElementsIn(shape()), values.size()); @@ -971,7 +1006,7 @@ inline void Literal::PopulateR1(tensorflow::gtl::ArraySlice values) { } template -void Literal::PopulateR2( +void MutableLiteralBase::PopulateR2( std::initializer_list> values) { CHECK(ShapeUtil::IsArray(shape())); CHECK_EQ(ShapeUtil::Rank(shape()), 2); @@ -996,7 +1031,7 @@ void Literal::PopulateR2( } template -void Literal::PopulateFromArray(const Array& values) { +void MutableLiteralBase::PopulateFromArray(const Array& values) { CHECK(ShapeUtil::IsArray(shape())); CHECK_EQ(shape().element_type(), primitive_util::NativeToPrimitiveType()); @@ -1009,24 +1044,24 @@ void Literal::PopulateFromArray(const Array& values) { } template -void Literal::PopulateR2FromArray2D(const Array2D& values) { +void MutableLiteralBase::PopulateR2FromArray2D(const Array2D& values) { PopulateFromArray(values); } template -void Literal::PopulateR3FromArray3D(const Array3D& values) { +void MutableLiteralBase::PopulateR3FromArray3D(const Array3D& values) { PopulateFromArray(values); } template -void Literal::PopulateR4FromArray4D(const Array4D& values) { +void MutableLiteralBase::PopulateR4FromArray4D(const Array4D& values) { PopulateFromArray(values); } template -void Literal::PopulateSparse(SparseIndexArray indices, - tensorflow::gtl::ArraySlice values, - bool sort) { +void MutableLiteralBase::PopulateSparse( + SparseIndexArray indices, tensorflow::gtl::ArraySlice values, + bool sort) { CHECK(LayoutUtil::IsSparseArray(shape())); int rank = ShapeUtil::Rank(shape()); CHECK_EQ(indices.rank(), rank); @@ -1049,7 +1084,8 @@ void Literal::PopulateSparse(SparseIndexArray indices, } template -Status Literal::PopulateInternal(const FnType& generator, bool parallel) { +Status MutableLiteralBase::PopulateInternal(const FnType& generator, + bool parallel) { const Shape& this_shape = shape(); const int64 rank = ShapeUtil::Rank(this_shape); TF_RET_CHECK(LayoutUtil::IsDenseArray(this_shape)); @@ -1092,17 +1128,17 @@ Status Literal::PopulateInternal(const FnType& generator, bool parallel) { return Status::OK(); } template -Status Literal::Populate(const FnType& generator) { +Status MutableLiteralBase::Populate(const FnType& generator) { return PopulateInternal(generator, /*parallel=*/false); } template -Status Literal::PopulateParallel(const FnType& generator) { +Status MutableLiteralBase::PopulateParallel(const FnType& generator) { return PopulateInternal(generator, /*parallel=*/true); } template -void Literal::PopulateWithValue(NativeT value) { +void MutableLiteralBase::PopulateWithValue(NativeT value) { CHECK(ShapeUtil::IsArray(shape())); CHECK_EQ(shape().element_type(), primitive_util::NativeToPrimitiveType()); diff --git a/tensorflow/compiler/xla/literal_util.cc b/tensorflow/compiler/xla/literal_util.cc index 356f12ed789d82bc2716b5eafc411e4cafbba2ff..5d33df7d40bf3bfcc8012ce1129d532b34555344 100644 --- a/tensorflow/compiler/xla/literal_util.cc +++ b/tensorflow/compiler/xla/literal_util.cc @@ -34,6 +34,7 @@ limitations under the License. #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/mem.h" #include "tensorflow/core/platform/types.h" using tensorflow::strings::StrCat; diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 528b7fdfd3c39cc3a56afc92474dbae976a08ba8..7d315fa0d3d8e38cefbccf9b71d9bd0706a7a434 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -570,7 +570,7 @@ cc_library( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", - "//tensorflow/core:core_cpu_internal", + "//tensorflow/core:core_cpu_lib", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "//third_party/eigen3", @@ -613,6 +613,7 @@ cc_library( "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/core:lib", + "//tensorflow/core:ptr_util", "//tensorflow/core:stream_executor_no_cuda", ], alwayslink = 1, @@ -1384,6 +1385,18 @@ tf_cc_test( ], ) +cc_library( + name = "while_loop_analysis", + srcs = ["while_loop_analysis.cc"], + hdrs = ["while_loop_analysis.h"], + deps = [ + ":hlo", + ":hlo_evaluator", + "//tensorflow/compiler/xla:literal", + "//tensorflow/core:lib", + ], +) + cc_library( name = "while_loop_simplifier", srcs = ["while_loop_simplifier.cc"], @@ -1391,8 +1404,8 @@ cc_library( deps = [ ":call_inliner", ":hlo", - ":hlo_evaluator", ":hlo_pass", + ":while_loop_analysis", "//tensorflow/compiler/xla:statusor", "//tensorflow/core:lib", ], diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index 946ef6f0d6b9025b84c4b9341f4ec600465d4b1e..37834e1cc2657ff56f65a4f94eb973b9022eb8e1 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -1803,6 +1803,12 @@ Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice( } Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { + // TODO(b/112040122): Most of those optimizations can be done for multi-output + // reduces. + if (ShapeUtil::IsTuple(reduce->shape())) { + return Status::OK(); + } + auto arg = reduce->mutable_operand(0); auto init_value = reduce->mutable_operand(1); tensorflow::gtl::ArraySlice dimensions(reduce->dimensions()); diff --git a/tensorflow/compiler/xla/service/compiler.h b/tensorflow/compiler/xla/service/compiler.h index 99abb9bae32b35652e84cddc7c38dbd97ecb5006..34f7fe12cac5a4dcd3822865bee903d6eabc25c0 100644 --- a/tensorflow/compiler/xla/service/compiler.h +++ b/tensorflow/compiler/xla/service/compiler.h @@ -48,11 +48,6 @@ namespace xla { // compuation. using ObjectFileData = std::vector; -// Contains the buffer sizes information needed to allocate buffers to execute -// an ahead-of-time computation. Entries which contain -1 designate a parameter -// which should be skipped over during allocation. -using BufferSizes = std::vector; - // Abstract superclass describing the result of an ahead-of-time compilation. class AotCompilationResult { public: diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 504b61d134a0099d055d0266408e1dfb94af5b2a..3efe3e2f93adc788258295e3142c1cc6c0a4bbef 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -54,12 +54,24 @@ cc_library( alwayslink = True, # Contains per-platform transfer manager registration ) +cc_library( + name = "buffer_info_util", + srcs = ["buffer_info_util.cc"], + hdrs = ["buffer_info_util.h"], + deps = [ + "//tensorflow/compiler/tf2xla:cpu_function_runtime", + "//tensorflow/compiler/xla/service:buffer_assignment", + "//tensorflow/core:lib", + ], +) + cc_library( name = "cpu_compiler", srcs = ["cpu_compiler.cc"], hdrs = ["cpu_compiler.h"], deps = [ ":compiler_functor", + ":buffer_info_util", ":conv_canonicalization", ":cpu_copy_insertion", ":cpu_executable", @@ -73,6 +85,7 @@ cc_library( ":ir_emitter", ":parallel_task_assignment", ":simple_orc_jit", + "//tensorflow/compiler/tf2xla:cpu_function_runtime", "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:protobuf_util", "//tensorflow/compiler/xla:status_macros", diff --git a/tensorflow/compiler/xla/service/cpu/buffer_info_util.cc b/tensorflow/compiler/xla/service/cpu/buffer_info_util.cc new file mode 100644 index 0000000000000000000000000000000000000000..408fe0f5bf5d729165eadd532d4740211620645d --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/buffer_info_util.cc @@ -0,0 +1,57 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/cpu/buffer_info_util.h" + +namespace xla { +namespace cpu { + +using BufferInfo = ::tensorflow::cpu_function_runtime::BufferInfo; + +std::vector CreateBufferInfosFromBufferAssignment( + const BufferAssignment& buffer_assignment) { + std::vector buffer_infos; + for (const BufferAllocation& allocation : buffer_assignment.Allocations()) { + if (allocation.is_thread_local()) { + buffer_infos.push_back(BufferInfo::MakeOnStackBuffer(allocation.size())); + } else if (allocation.is_constant()) { + buffer_infos.push_back(BufferInfo::MakeConstant(allocation.size())); + } else if (allocation.is_entry_computation_parameter()) { + buffer_infos.push_back(BufferInfo::MakeEntryParameter( + /*size=*/allocation.size(), + /*param_number=*/allocation.parameter_number())); + } else { + buffer_infos.push_back(BufferInfo::MakeTempBuffer(allocation.size())); + } + } + return buffer_infos; +} + +std::vector CreateArgIndexTableFromBufferInfos( + tensorflow::gtl::ArraySlice buffer_infos) { + std::vector result; + for (int64 i = 0; i < buffer_infos.size(); i++) { + if (buffer_infos[i].is_entry_parameter()) { + if (buffer_infos[i].entry_parameter_number() >= result.size()) { + result.resize(buffer_infos[i].entry_parameter_number() + 1); + } + result[buffer_infos[i].entry_parameter_number()] = i; + } + } + return result; +} + +} // namespace cpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/buffer_info_util.h b/tensorflow/compiler/xla/service/cpu/buffer_info_util.h new file mode 100644 index 0000000000000000000000000000000000000000..05de70c72686dcbdaf0b47c46cde23ed45abdb42 --- /dev/null +++ b/tensorflow/compiler/xla/service/cpu/buffer_info_util.h @@ -0,0 +1,42 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_BUFFER_INFO_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_BUFFER_INFO_UTIL_H_ + +#include "tensorflow/compiler/tf2xla/cpu_function_runtime.h" +#include "tensorflow/compiler/xla/service/buffer_assignment.h" +#include "tensorflow/core/lib/gtl/array_slice.h" + +namespace xla { +namespace cpu { +// Creates and returns a list of BufferInfo instances containing relevant +// information from `buffer_assignment`. +std::vector<::tensorflow::cpu_function_runtime::BufferInfo> +CreateBufferInfosFromBufferAssignment( + const BufferAssignment& buffer_assignment); + +// Creates and returns a table containing the mapping from entry computation +// parameters to buffer allocation indices. +// +// If this function returns V then entry parameter i has buffer allocation index +// V[i]. +std::vector CreateArgIndexTableFromBufferInfos( + tensorflow::gtl::ArraySlice<::tensorflow::cpu_function_runtime::BufferInfo> + buffer_infos); +} // namespace cpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_BUFFER_INFO_UTIL_H_ diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 8cbe9a1b0d5b0553b1121d544196412f36f8ce43..2df959c4dc50a6b1b3f063427a49faa104500ff3 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -50,6 +50,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_liveness.h" #include "tensorflow/compiler/xla/service/call_inliner.h" #include "tensorflow/compiler/xla/service/conditional_simplifier.h" +#include "tensorflow/compiler/xla/service/cpu/buffer_info_util.h" #include "tensorflow/compiler/xla/service/cpu/compiler_functor.h" #include "tensorflow/compiler/xla/service/cpu/conv_canonicalization.h" #include "tensorflow/compiler/xla/service/cpu/cpu_copy_insertion.h" @@ -103,6 +104,7 @@ limitations under the License. namespace xla { namespace cpu { +using BufferInfo = ::tensorflow::cpu_function_runtime::BufferInfo; CpuAotCompilationOptions::CpuAotCompilationOptions( string triple, string cpu_name, string features, string entry_point_name, @@ -120,11 +122,11 @@ se::Platform::Id CpuAotCompilationOptions::PlatformId() const { } CpuAotCompilationResult::CpuAotCompilationResult( - ObjectFileData object_file_data, BufferSizes buffer_sizes, + ObjectFileData object_file_data, std::vector buffer_infos, int64 result_buffer_index, std::unique_ptr hlo_profile_printer_data) : object_file_data_(std::move(object_file_data)), - buffer_sizes_(std::move(buffer_sizes)), + buffer_infos_(std::move(buffer_infos)), result_buffer_index_(result_buffer_index), hlo_profile_printer_data_(std::move(hlo_profile_printer_data)) {} @@ -838,39 +840,14 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, ObjectFileData object_file_data(object_file->getBufferStart(), object_file->getBufferEnd()); - BufferSizes buffer_sizes; - for (const BufferAllocation& allocation : assignment->Allocations()) { - // Callers don't need to allocate anything for thread-local temporary - // buffers. They are lowered to allocas. - if (allocation.is_thread_local()) { - buffer_sizes.push_back(-1); - continue; - } - - // Callers don't need to allocate anything for constant buffers. They are - // lowered to globals. - if (allocation.is_constant()) { - buffer_sizes.push_back(-1); - continue; - } - - // Callers don't need to allocate anything for entry computation buffers, - // but they do need to stash the pointer to the entry computation buffer - // in the temp buffer table. See the comment on - // XlaCompiledCpuFunction::StaticData::temp_sizes. - if (allocation.is_entry_computation_parameter()) { - buffer_sizes.push_back(-allocation.parameter_number() - 2); - continue; - } - - buffer_sizes.push_back(allocation.size()); - } + std::vector buffer_infos = + CreateBufferInfosFromBufferAssignment(*assignment); TF_ASSIGN_OR_RETURN(const BufferAllocation::Slice result_slice, assignment->GetUniqueTopLevelOutputSlice()); results.emplace_back(MakeUnique( - std::move(object_file_data), std::move(buffer_sizes), + std::move(object_file_data), std::move(buffer_infos), result_slice.index(), std::move(hlo_profile_printer_data))); } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h index e56f9f01134f84b4698c078b750b0c1fdca7748e..04e1c48872ed55ca7f2aa3bec08c44a1666b90f1 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.h @@ -19,6 +19,7 @@ limitations under the License. #include #include "llvm/Target/TargetMachine.h" +#include "tensorflow/compiler/tf2xla/cpu_function_runtime.h" #include "tensorflow/compiler/xla/service/executable.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/llvm_compiler.h" @@ -78,7 +79,8 @@ class CpuAotCompilationOptions : public AotCompilationOptions { class CpuAotCompilationResult : public AotCompilationResult { public: CpuAotCompilationResult( - ObjectFileData object_file_data, BufferSizes buffer_sizes, + ObjectFileData object_file_data, + std::vector<::tensorflow::cpu_function_runtime::BufferInfo> buffer_infos, int64 result_buffer_index, std::unique_ptr hlo_profile_printer_data); ~CpuAotCompilationResult(); @@ -88,17 +90,20 @@ class CpuAotCompilationResult : public AotCompilationResult { } const ObjectFileData& object_file_data() const { return object_file_data_; } - const BufferSizes& buffer_sizes() const { return buffer_sizes_; } + const std::vector<::tensorflow::cpu_function_runtime::BufferInfo>& + buffer_infos() const { + return buffer_infos_; + } int64 result_buffer_index() const { return result_buffer_index_; } private: // Contains the compiled computation: an object file. const ObjectFileData object_file_data_; - // The list of buffer sizes which should be allocated in order to execute the - // compiled computation. These buffers are used for temporary buffers used - // ephemerally during computation as well as the output result. - const BufferSizes buffer_sizes_; + // A list of BufferInfo objects describing the buffers used by the XLA + // computation. + const std::vector<::tensorflow::cpu_function_runtime::BufferInfo> + buffer_infos_; // Contains which buffer index into |buffer_sizes| was designated to the // result of the computation. This buffer should be passed into the output diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc index 156166bf2b1ea6d3821da8f67ea2b2eca6825ca6..59bc7e0e16fcc66a010408259a1ccfb2b6bb35fd 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.cc @@ -173,7 +173,7 @@ CpuTransferManager::TransferBufferToInfeedInternal(se::StreamExecutor* executor, Status CpuTransferManager::TransferLiteralFromOutfeed( se::StreamExecutor* executor, const Shape& literal_shape, - Literal* literal) { + MutableBorrowingLiteral literal) { if (!ShapeUtil::IsTuple(literal_shape)) { int64 size = GetByteSizeRequirement(literal_shape); // Note: OSS build didn't like implicit conversion from @@ -181,18 +181,16 @@ Status CpuTransferManager::TransferLiteralFromOutfeed( tensorflow::gtl::ArraySlice dimensions( tensorflow::bit_cast(literal_shape.dimensions().data()), literal_shape.dimensions().size()); - *literal = std::move(*LiteralUtil::CreateFromDimensions( - literal_shape.element_type(), dimensions)); - TF_ASSIGN_OR_RETURN(Shape received_shape, - TransferArrayBufferFromOutfeed( - executor, literal->untyped_data(), size)); - TF_RET_CHECK(ShapeUtil::Compatible(received_shape, literal->shape())) + TF_ASSIGN_OR_RETURN( + Shape received_shape, + TransferArrayBufferFromOutfeed(executor, literal.untyped_data(), size)); + TF_RET_CHECK(ShapeUtil::Compatible(received_shape, literal.shape())) << "Shape received from outfeed " << ShapeUtil::HumanString(received_shape) << " did not match the shape that was requested for outfeed: " << ShapeUtil::HumanString(literal_shape); TF_RET_CHECK(size == GetByteSizeRequirement(received_shape)); - *literal->mutable_shape_do_not_use() = received_shape; + *literal.mutable_shape_do_not_use() = received_shape; return Status::OK(); } @@ -201,22 +199,12 @@ Status CpuTransferManager::TransferLiteralFromOutfeed( "Nested tuple outfeeds are not yet implemented on CPU."); } - std::vector> elements; std::vector> buffer_data; for (int64 i = 0; i < literal_shape.tuple_shapes_size(); ++i) { const Shape& tuple_element_shape = ShapeUtil::GetTupleElementShape(literal_shape, i); - // Note: OSS build didn't like implicit conversion from - // literal_shape.dimensions() to the array slice on 2017-07-10. - tensorflow::gtl::ArraySlice dimensions( - tensorflow::bit_cast( - tuple_element_shape.dimensions().data()), - tuple_element_shape.dimensions().size()); - auto empty = LiteralUtil::CreateFromDimensions( - tuple_element_shape.element_type(), dimensions); int64 size = GetByteSizeRequirement(tuple_element_shape); - buffer_data.push_back({empty->untyped_data(), size}); - elements.push_back(std::move(empty)); + buffer_data.push_back({literal.untyped_data({i}), size}); } TF_ASSIGN_OR_RETURN(Shape received_shape, @@ -230,11 +218,7 @@ Status CpuTransferManager::TransferLiteralFromOutfeed( TF_RET_CHECK(GetByteSizeRequirement(literal_shape) == GetByteSizeRequirement(received_shape)); - for (int64 i = 0; i < literal_shape.tuple_shapes_size(); ++i) { - *elements[i]->mutable_shape_do_not_use() = received_shape.tuple_shapes(i); - } - *literal = std::move(*LiteralUtil::MakeTupleOwned(std::move(elements))); - TF_RET_CHECK(ShapeUtil::Equal(literal->shape(), literal_shape)); + TF_RET_CHECK(ShapeUtil::Equal(literal.shape(), literal_shape)); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h index 593575c0fdaddc71cd6bd844fd179096a9fb0fdc..80ef953d532798281c10b7a212b9c4d84a790c27 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h +++ b/tensorflow/compiler/xla/service/cpu/cpu_transfer_manager.h @@ -18,6 +18,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/cpu/xfeed_manager.h" #include "tensorflow/compiler/xla/service/generic_transfer_manager.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" @@ -41,7 +42,7 @@ class CpuTransferManager : public GenericTransferManager { const LiteralSlice& literal) override; Status TransferLiteralFromOutfeed(se::StreamExecutor* executor, const Shape& literal_shape, - Literal* literal) override; + MutableBorrowingLiteral literal) override; private: Status TransferBufferToInfeed(se::StreamExecutor* executor, int64 size, diff --git a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc index c13d36776f94221598338dca4eadf024c0a892df..db54454707983ade31594119b2e868fa168d4cc2 100644 --- a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc @@ -30,47 +30,6 @@ limitations under the License. namespace xla { namespace cpu { -StatusOr CpuElementalIrEmitter::EmitFloatUnaryOp( - const HloInstruction* op, llvm::Value* operand_value) const { - switch (op->opcode()) { - case HloOpcode::kTanh: { - PrimitiveType element_type = op->shape().element_type(); - bool cast_result_to_fp16 = false; - string function_name; - switch (element_type) { - case F16: - cast_result_to_fp16 = true; - operand_value = b_->CreateFPCast(operand_value, b_->getFloatTy()); - TF_FALLTHROUGH_INTENDED; - case F32: - function_name = "tanhf"; - break; - case F64: - function_name = "tanh"; - break; - default: - return Unimplemented("tanh"); - } - // Create a function declaration. - llvm::Function* function = - llvm::cast(module_->getOrInsertFunction( - llvm_ir::AsStringRef(function_name), operand_value->getType(), - operand_value->getType())); - function->setCallingConv(llvm::CallingConv::C); - function->setDoesNotThrow(); - function->setDoesNotAccessMemory(); - // Create an instruction to call the function. - llvm::Value* result = b_->CreateCall(function, operand_value); - if (cast_result_to_fp16) { - result = b_->CreateFPCast(result, b_->getHalfTy()); - } - return result; - } - default: - return ElementalIrEmitter::EmitFloatUnaryOp(op, operand_value); - } -} - StatusOr CpuElementalIrEmitter::EmitAtan2( PrimitiveType prim_type, llvm::Value* lhs, llvm::Value* rhs) const { string function_name; @@ -106,6 +65,39 @@ StatusOr CpuElementalIrEmitter::EmitAtan2( return result; } +StatusOr CpuElementalIrEmitter::EmitTanh( + PrimitiveType prim_type, llvm::Value* value) const { + bool cast_result_to_fp16 = false; + string function_name; + switch (prim_type) { + case F16: + cast_result_to_fp16 = true; + value = b_->CreateFPCast(value, b_->getFloatTy()); + TF_FALLTHROUGH_INTENDED; + case F32: + function_name = "tanhf"; + break; + case F64: + function_name = "tanh"; + break; + default: + return Unimplemented("tanh"); + } + // Create a function declaration. + llvm::Function* function = llvm::cast( + module_->getOrInsertFunction(llvm_ir::AsStringRef(function_name), + value->getType(), value->getType())); + function->setCallingConv(llvm::CallingConv::C); + function->setDoesNotThrow(); + function->setDoesNotAccessMemory(); + // Create an instruction to call the function. + llvm::Value* result = b_->CreateCall(function, value); + if (cast_result_to_fp16) { + result = b_->CreateFPCast(result, b_->getHalfTy()); + } + return result; +} + llvm_ir::ElementGenerator CpuElementalIrEmitter::MakeElementGenerator( const HloInstruction* hlo, const HloToElementGeneratorMap& operand_to_generator) const { diff --git a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h index 9598a886ab49fcecf5df7bd65f425fe485de3574..76833e765d05f2477961cd06cead66797c5be623 100644 --- a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h @@ -39,10 +39,10 @@ class CpuElementalIrEmitter : public ElementalIrEmitter { const HloToElementGeneratorMap& operand_to_generator) const override; protected: - StatusOr EmitFloatUnaryOp( - const HloInstruction* op, llvm::Value* operand_value) const override; StatusOr EmitAtan2(PrimitiveType prim_type, llvm::Value* lhs, llvm::Value* rhs) const override; + StatusOr EmitTanh(PrimitiveType prim_type, + llvm::Value* value) const override; IrEmitter* ir_emitter_; }; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index ca645d3f1da18fb26378a10526c27a7d254896e2..09909b62ba4bd17c573f8f53ffc18785fa2f6f71 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -1756,6 +1756,10 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForReduce( } Status IrEmitter::HandleReduce(HloInstruction* reduce) { + // TODO(b/112040122): Support variadic reduce. + if (!ShapeUtil::IsArray(reduce->shape())) { + return Unimplemented("Variadic reduce is not supported on CPU"); + } auto arg = reduce->mutable_operand(0); auto init_value = reduce->mutable_operand(1); gtl::ArraySlice dimensions(reduce->dimensions()); diff --git a/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc b/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc index 997fdd2ab309f0b68a9dbd0f156a8dc19955b437..8dc5f3c93b6ba1a722ea7b23b4b5190ac0600cd6 100644 --- a/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc +++ b/tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#if defined(INTEL_MKL) && !defined(DO_NOT_USE_ML) +#if defined(INTEL_MKL) && !defined(INTEL_MKL_DNN_ONLY) #include "tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.h" #include "third_party/intel_mkl_ml/include/mkl_cblas.h" #include "third_party/intel_mkl_ml/include/mkl_service.h" diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h index 9f867491258727e0bb53d960af3b977690f8f31a..86d57581f84920e8005e8f3c420e7488fc095434 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h @@ -106,6 +106,7 @@ class DfsHloVisitorBase { virtual Status HandleConvolution(HloInstructionPtr hlo) = 0; virtual Status HandleFft(HloInstructionPtr fft) = 0; virtual Status HandleCrossReplicaSum(HloInstructionPtr hlo) = 0; + virtual Status HandleAllToAll(HloInstructionPtr hlo) = 0; virtual Status HandleCompare(HloInstructionPtr hlo) { return HandleElementwiseBinary(hlo); } diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h index ae8a066d626fcf6c6670f4994a58f0b8e8027aad..617a5a2eb4796d8003099e39e3d26389e532e954 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h @@ -94,6 +94,9 @@ class DfsHloVisitorWithDefaultBase Status HandleCrossReplicaSum(HloInstructionPtr crs) override { return DefaultAction(crs); } + Status HandleAllToAll(HloInstructionPtr crs) override { + return DefaultAction(crs); + } Status HandleRng(HloInstructionPtr random) override { return DefaultAction(random); } diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index f05c2d63d2da3f7458c758308a8fc02c3b77af9b..2e9d6be2de4a2ab918d9a5ea4881ad3fd036792e 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -431,6 +431,8 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( return EmitCos(op->shape().element_type(), operand_value); case HloOpcode::kSin: return EmitSin(op->shape().element_type(), operand_value); + case HloOpcode::kTanh: + return EmitTanh(op->shape().element_type(), operand_value); case HloOpcode::kFloor: return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::floor, {operand_value}, @@ -1060,6 +1062,11 @@ StatusOr ElementalIrEmitter::EmitAtan2(PrimitiveType prim_type, return Unimplemented("atan2"); } +StatusOr ElementalIrEmitter::EmitTanh(PrimitiveType prim_type, + llvm::Value* value) const { + return Unimplemented("tanh"); +} + StatusOr ElementalIrEmitter::EmitReducePrecision( const HloInstruction* hlo, llvm::Value* x) const { if (hlo->operand(0)->shape().element_type() != F32) { @@ -1239,13 +1246,23 @@ StatusOr ElementalIrEmitter::ConvertValueForDistribution( // Convert raw integer to float in range [0, 1) if the element is a float. llvm::Value* elem_value = raw_value; if (elem_ir_ty->isFloatingPointTy()) { - elem_value = b_->CreateUIToFP(elem_value, elem_ir_ty); unsigned raw_value_size_in_bits = raw_value_ty->getPrimitiveSizeInBits(); CHECK(raw_value_size_in_bits == 32 || raw_value_size_in_bits == 64); - elem_value = b_->CreateFDiv( - elem_value, - llvm::ConstantFP::get(elem_ir_ty, - raw_value_size_in_bits == 64 ? 0x1p64 : 0x1p32)); + // Perform the division using the float type with the same number of bits + // as the raw value to avoid overflow. + if (raw_value_size_in_bits == 32) { + elem_value = b_->CreateUIToFP(elem_value, b_->getFloatTy()); + elem_value = b_->CreateFDiv( + elem_value, llvm::ConstantFP::get(b_->getFloatTy(), std::exp2(32))); + } else { + elem_value = b_->CreateUIToFP(elem_value, b_->getDoubleTy()); + elem_value = b_->CreateFDiv( + elem_value, llvm::ConstantFP::get(b_->getDoubleTy(), std::exp2(64))); + } + + if (elem_ir_ty != elem_value->getType()) { + elem_value = b_->CreateFPTrunc(elem_value, elem_ir_ty); + } } // Convert the value for the requested distribution. diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h index fcb34557a52d35ef30a5dee643171e17407d05c2..1598a4dd85632cfa9835a81a21eddff3e57bfa1f 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h @@ -122,6 +122,9 @@ class ElementalIrEmitter { llvm::Value* lhs, llvm::Value* rhs) const; + virtual StatusOr EmitTanh(PrimitiveType prim_type, + llvm::Value* value) const; + virtual StatusOr EmitReducePrecision(const HloInstruction* hlo, llvm::Value* x) const; diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.cc b/tensorflow/compiler/xla/service/generic_transfer_manager.cc index e314a469f00abdb9f60ae812c0b78d273dc95dbe..0ce2db907b643f3beabd127388370dbe601179e1 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.cc @@ -24,7 +24,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/interpreter/platform_id.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" -#include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -60,17 +59,19 @@ Status GenericTransferManager::WriteSingleTupleIndexTable( void GenericTransferManager::TransferLiteralFromDevice( se::Stream* stream, const ShapedBuffer& device_buffer, - std::function>)> done) { + MutableBorrowingLiteral literal, std::function done) { Status status = stream->BlockHostUntilDone(); if (!status.ok()) { return done(status); } - done(TransferLiteralFromDeviceInternal(stream->parent(), device_buffer)); + + done(TransferLiteralFromDeviceInternal(stream->parent(), device_buffer, + literal)); } -StatusOr> -GenericTransferManager::TransferLiteralFromDeviceInternal( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer) { +Status GenericTransferManager::TransferLiteralFromDeviceInternal( + se::StreamExecutor* executor, const ShapedBuffer& device_buffer, + MutableBorrowingLiteral literal) { VLOG(2) << "transferring literal from device ordinal " << executor->device_ordinal() << "; device buffer: " << device_buffer; TF_RET_CHECK(executor->device_ordinal() == device_buffer.device_ordinal()); @@ -80,9 +81,6 @@ GenericTransferManager::TransferLiteralFromDeviceInternal( TF_RET_CHECK(ShapeUtil::Equal(device_buffer.on_device_shape(), device_buffer.on_host_shape())); - std::unique_ptr literal = - Literal::CreateFromShape(device_buffer.on_host_shape()); - TF_RETURN_IF_ERROR(ShapeUtil::ForEachSubshapeWithStatus( device_buffer.on_host_shape(), [&](const Shape& subshape, const ShapeIndex& index) -> Status { @@ -91,12 +89,12 @@ GenericTransferManager::TransferLiteralFromDeviceInternal( /*source=*/device_buffer.buffer(index), /*size=*/GetByteSizeRequirement(subshape), /*destination=*/ - literal->untyped_data(index))); + literal.untyped_data(index))); } return Status::OK(); })); - return std::move(literal); + return Status::OK(); } Status GenericTransferManager::TransferLiteralToDeviceAsync( @@ -160,7 +158,7 @@ Status GenericTransferManager::TransferLiteralToInfeed( Status GenericTransferManager::TransferLiteralFromOutfeed( se::StreamExecutor* executor, const Shape& literal_shape, - Literal* literal) { + MutableBorrowingLiteral literal) { return Unimplemented("Generic transfer from Outfeed"); } diff --git a/tensorflow/compiler/xla/service/generic_transfer_manager.h b/tensorflow/compiler/xla/service/generic_transfer_manager.h index 3cd002c1bf3555cc2d2891c88b3ad648f8d9fd8c..6c1a21587a7ef5199afb93715dc57be5139fbc22 100644 --- a/tensorflow/compiler/xla/service/generic_transfer_manager.h +++ b/tensorflow/compiler/xla/service/generic_transfer_manager.h @@ -19,7 +19,6 @@ limitations under the License. #include #include "tensorflow/compiler/xla/service/transfer_manager.h" -#include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -41,9 +40,10 @@ class GenericTransferManager : public TransferManager { se::Platform::Id PlatformId() const override; - void TransferLiteralFromDevice( - se::Stream* stream, const ShapedBuffer& device_buffer, - std::function>)> done) override; + void TransferLiteralFromDevice(se::Stream* stream, + const ShapedBuffer& device_buffer, + MutableBorrowingLiteral literal, + std::function done) override; Status TransferLiteralToDeviceAsync( se::Stream* stream, const LiteralSlice& literal, @@ -53,7 +53,7 @@ class GenericTransferManager : public TransferManager { const LiteralSlice& literal) override; Status TransferLiteralFromOutfeed(se::StreamExecutor* executor, const Shape& literal_shape, - Literal* literal) override; + MutableBorrowingLiteral literal) override; Status ResetDevices( tensorflow::gtl::ArraySlice executors) override; @@ -67,8 +67,9 @@ class GenericTransferManager : public TransferManager { const Shape& shape, se::DeviceMemoryBase* region) override; private: - StatusOr> TransferLiteralFromDeviceInternal( - se::StreamExecutor* executor, const ShapedBuffer& device_buffer); + Status TransferLiteralFromDeviceInternal(se::StreamExecutor* executor, + const ShapedBuffer& device_buffer, + MutableBorrowingLiteral literal); // The platform this transfer manager targets. const se::Platform::Id platform_id_; diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 4947dd278e9e70c8a1c26b0d7d62f97221c33750..a3f6e8d9893528642e05354994c1d826949c6063 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -153,7 +153,6 @@ cc_library( ":ir_emission_utils", ":parallel_loop_emitter", ":partition_assignment", - ":while_transformer", "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", @@ -166,6 +165,7 @@ cc_library( "//tensorflow/compiler/xla/service:elemental_ir_emitter", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:name_uniquer", + "//tensorflow/compiler/xla/service:while_loop_analysis", "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", "//tensorflow/compiler/xla/service/llvm_ir:dynamic_update_slice_util", "//tensorflow/compiler/xla/service/llvm_ir:fused_ir_emitter", @@ -655,7 +655,6 @@ cc_library( "//tensorflow/compiler/xla/service:transpose_folding", "//tensorflow/compiler/xla/service:tuple_simplifier", "//tensorflow/compiler/xla/service:while_loop_constant_sinking", - "//tensorflow/compiler/xla/service:while_loop_invariant_code_motion", "//tensorflow/compiler/xla/service:while_loop_simplifier", "//tensorflow/compiler/xla/service:zero_sized_hlo_elimination", "//tensorflow/compiler/xla/service/gpu:cudnn_batchnorm_rewriter", @@ -788,32 +787,17 @@ tf_cc_test( ], ) -cc_library( - name = "while_transformer", - srcs = ["while_transformer.cc"], - hdrs = ["while_transformer.h"], - deps = [ - "//tensorflow/compiler/xla:literal", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla/service:hlo", - "//tensorflow/core:lib", - ], -) - tf_cc_test( name = "while_transformer_test", srcs = ["while_transformer_test.cc"], deps = [ ":instruction_fusion", - ":while_transformer", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/service:copy_insertion", "//tensorflow/compiler/xla/service:hlo_verifier", + "//tensorflow/compiler/xla/service:while_loop_analysis", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc index cc38db27e2680e950f74e104cef8829585c7b81c..69ba91793dd0bb2eb27fb99dad91caa9362246c6 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc @@ -272,27 +272,18 @@ StatusOr GpuElementalIrEmitter::EmitAtan2( prim_type); } -StatusOr GpuElementalIrEmitter::EmitFloatUnaryOp( - const HloInstruction* op, llvm::Value* operand_value) const { - PrimitiveType input_type = op->operand(0)->shape().element_type(); - PrimitiveType output_type = op->shape().element_type(); - switch (op->opcode()) { - case HloOpcode::kTanh: - // If we don't care much about precision, emit a fast approximation of - // tanh. - if (hlo_module_config_.debug_options().xla_enable_fast_math()) { - // Upcast F16 to F32 if necessary. - llvm::Type* type = - input_type == F16 ? b_->getFloatTy() : operand_value->getType(); - llvm::Value* input = b_->CreateFPCast(operand_value, type); - llvm::Value* fast_tanh = llvm_ir::EmitFastTanh(b_, input); - return b_->CreateFPCast(fast_tanh, operand_value->getType()); - } - return EmitLibdeviceMathCall("__nv_tanh", {operand_value}, {input_type}, - output_type); - default: - return ElementalIrEmitter::EmitFloatUnaryOp(op, operand_value); +StatusOr GpuElementalIrEmitter::EmitTanh( + PrimitiveType prim_type, llvm::Value* value) const { + // If we don't care much about precision, emit a fast approximation of + // tanh. + if (hlo_module_config_.debug_options().xla_enable_fast_math()) { + // Upcast F16 to F32 if necessary. + llvm::Type* type = prim_type == F16 ? b_->getFloatTy() : value->getType(); + llvm::Value* input = b_->CreateFPCast(value, type); + llvm::Value* fast_tanh = llvm_ir::EmitFastTanh(b_, input); + return b_->CreateFPCast(fast_tanh, value->getType()); } + return EmitLibdeviceMathCall("__nv_tanh", {value}, {prim_type}, prim_type); } llvm::Value* GpuElementalIrEmitter::EmitDeviceFunctionCall( @@ -445,6 +436,8 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( return b_->CreateLoad(accum_ptr); }; case HloOpcode::kReduce: + // TODO(b/112040122): This should be supported. + CHECK_EQ(hlo->operand_count(), 2) << "Did not expect variadic reduce"; return [=, &operand_to_generator]( const IrArray::Index& output_index) -> StatusOr { const HloInstruction* operand = hlo->operand(0); diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h index e3eacef133cb8b615a645ca2f11dd6dedf9f0176..84454d31bb820a3de6ef3364bd205b8115bd95c0 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h @@ -51,9 +51,6 @@ class GpuElementalIrEmitter : public ElementalIrEmitter { const HloToElementGeneratorMap& operand_to_generator) const override; protected: - StatusOr EmitFloatUnaryOp( - const HloInstruction* op, llvm::Value* operand_value) const override; - StatusOr EmitFloatBinaryOp( const HloInstruction* op, llvm::Value* lhs_value, llvm::Value* rhs_value) const override; @@ -85,6 +82,9 @@ class GpuElementalIrEmitter : public ElementalIrEmitter { StatusOr EmitAtan2(PrimitiveType prim_type, llvm::Value* lhs, llvm::Value* rhs) const override; + StatusOr EmitTanh(PrimitiveType prim_type, + llvm::Value* value) const override; + llvm::Value* EmitThreadId() const override; private: diff --git a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h index 939c7f85e35b4fcb943a25aa6346d72798432920..12c81f9bfc6bfdac63edf9c826b835057107fa41 100644 --- a/tensorflow/compiler/xla/service/gpu/gemm_thunk.h +++ b/tensorflow/compiler/xla/service/gpu/gemm_thunk.h @@ -52,12 +52,12 @@ class GemmThunk : public Thunk { se::Stream* stream, HloExecutionProfiler* profiler) override; - // Returns true if we'll perform autotuning if run on the given stream. If - // so, we want the GPU to be quiescent during autotuning, so as not to - // introduce noise in our results. - bool ShouldHaltAllActivityBeforeRunning(se::Stream* stream) override { - return autotune_results_.count( - stream->parent()->GetDeviceDescription().name()) != 0; + bool WillAutotuneKernel(se::Stream* stream) override { + // We will autotune this kernel if we don't already have a autotune result + // for the stream device. + return autotune_results_.find( + stream->parent()->GetDeviceDescription().name()) == + autotune_results_.end(); } private: @@ -75,6 +75,8 @@ class GemmThunk : public Thunk { // results. The map's value is the best algorithm we've found for this thunk // on this device, or an error if none of the algorithms worked and we should // use the regular gemm without an algorithm. + // + // TODO(b/112415150): Make this thread safe. std::unordered_map> autotune_results_; }; diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index bb7736efa65c49766ea88a43fdab2b7102100c11..70608379048871cf6ee72145fa9afff71a3eabe6 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -131,9 +131,10 @@ Status GpuExecutable::ExecuteThunks( stream->ThenWaitFor(FindOrDie(thunk_to_finish_event, dependency).get()); } - // If this thunk requests it, wait for all currently-executing thunks to - // finish. This is useful e.g. if the thunk is about to perform autotuning. - if (thunk->ShouldHaltAllActivityBeforeRunning(stream)) { + // If this thunk is about to autotune then wait for all currently executing + // thunks to finish. This reduces noise and thus the probability of + // choosing a suboptimal algorithm. + if (thunk->WillAutotuneKernel(stream)) { TF_RETURN_IF_ERROR(main_stream->BlockHostUntilDone()); } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc index 79b3f1efecdf06bfa93b17a1799f3009d517f3b5..a2f53f844613da9fe8166489dc9959e8d30c6332 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.cc @@ -117,38 +117,37 @@ StatusOr GpuTransferManager::TransferBufferToInfeedInternal( return std::move(buffer); } -static std::unique_ptr ShapeTreeToLiteral( +static void ShapeTreeToLiteral( ShapeTree>* shape_tree) { // This is a struct instead of a lambda for std::function-free recursion. struct Helper { - static std::unique_ptr helper( + static void helper( ShapeTree>* shape_tree, ShapeIndex* index) { const Shape& shape = ShapeUtil::GetSubshape(shape_tree->shape(), *index); if (ShapeUtil::IsArray(shape)) { - return (*shape_tree->mutable_element(*index))->WaitUntilAvailable(); + (*shape_tree->mutable_element(*index))->WaitUntilAvailable(); + return; } CHECK(ShapeUtil::IsTuple(shape)) << ShapeUtil::HumanStringWithLayout(shape); const int64 tuple_element_count = ShapeUtil::TupleElementCount(shape); index->push_back(0); - std::vector> tuple_operands; for (int64 i = 0; i < tuple_element_count; ++i) { index->back() = i; - tuple_operands.push_back(helper(shape_tree, index)); + helper(shape_tree, index); } index->pop_back(); - return LiteralUtil::MakeTupleOwned(std::move(tuple_operands)); } }; ShapeIndex index; - return Helper::helper(shape_tree, &index); + Helper::helper(shape_tree, &index); } Status GpuTransferManager::TransferLiteralFromOutfeed( se::StreamExecutor* /*executor*/, const Shape& literal_shape, - Literal* literal) { + MutableBorrowingLiteral literal) { ShapeTree> outfeed_buffers( &literal_shape); @@ -162,6 +161,8 @@ Status GpuTransferManager::TransferLiteralFromOutfeed( return; } *buffer = MakeUnique(GetByteSizeRequirement(shape)); + (*buffer)->set_destination( + MakeUnique(literal, index)); }); // Give the tree of buffers to the outfeed mananger. The device will fill it @@ -169,8 +170,8 @@ Status GpuTransferManager::TransferLiteralFromOutfeed( gpu::OutfeedManager* outfeed_manager = gpu::GetOrCreateOutfeedManager(); outfeed_manager->EnqueueDestination(&outfeed_buffers); - // Now turn the tree of buffers back into a literal. - *literal = std::move(*ShapeTreeToLiteral(&outfeed_buffers)); + // Now wait for the tree of buffers are written. + ShapeTreeToLiteral(&outfeed_buffers); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h index dceeb9e2eb01a7dd5e978d819ed1db56d828f353..7929042869763dfeab2fe8f87093b7ea758337d0 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_transfer_manager.h @@ -42,7 +42,7 @@ class GpuTransferManager : public GenericTransferManager { const LiteralSlice& literal) override; Status TransferLiteralFromOutfeed(se::StreamExecutor* executor, const Shape& literal_shape, - Literal* literal) override; + MutableBorrowingLiteral literal) override; private: // Initiates the infeed data transfers. InfeedBuffer->Done() must be diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 541cacf6970453033c09a153a2dd320d4ebf3d4a..66aeb4efef4111118a9db9d71ed8e495ef717c4c 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -632,6 +632,10 @@ Status IrEmitter::HandleParameter(HloInstruction* parameter) { } Status IrEmitter::HandleReduce(HloInstruction* reduce) { + // TODO(b/112040122): Support variadic reduce. + if (!ShapeUtil::IsArray(reduce->shape())) { + return Unimplemented("Variadic reduce is not supported on GPU"); + } auto arg = reduce->operand(0); auto init_value = reduce->operand(1); tensorflow::gtl::ArraySlice dimensions(reduce->dimensions()); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index d5ecae88ed519c7123b6231da981172a4a4de304..1e81cbde35372d9f7d6ee234d2408038d6f99dc7 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -56,7 +56,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/thunk.h" #include "tensorflow/compiler/xla/service/gpu/tuple_thunk.h" #include "tensorflow/compiler/xla/service/gpu/while_thunk.h" -#include "tensorflow/compiler/xla/service/gpu/while_transformer.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -68,6 +67,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/sort_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" +#include "tensorflow/compiler/xla/service/while_loop_analysis.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" @@ -545,6 +545,11 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { switch (root->opcode()) { case HloOpcode::kTuple: case HloOpcode::kReduce: { + if (root->opcode() == HloOpcode::kReduce && + ShapeUtil::IsTuple(root->shape())) { + // TODO(b/112040122): Support variadic reduce. + return Unimplemented("Variadic reduce is not supported on GPU"); + } VLOG(3) << "Emitting fused reduction to vector: " << fusion->ToString(); std::vector> thunks; ArraySlice output_instructions = @@ -1694,6 +1699,10 @@ Status IrEmitterUnnested::EmitReductionToVector( } Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { + // TODO(b/112040122): Support multi-output reduce. + if (!ShapeUtil::IsArray(reduce->shape())) { + return Unimplemented("Multi-output reduce is not supported on GPU"); + } auto input = reduce->operand(0); auto init_value = reduce->operand(1); tensorflow::gtl::ArraySlice dimensions_to_reduce(reduce->dimensions()); @@ -1963,19 +1972,13 @@ Status IrEmitterUnnested::HandleWhile(HloInstruction* xla_while) { condition->root_instruction()->shape().element_type() == PRED) << "While condition computation must return bool"; // Build ForThunk for conformant while loops, otherwise build WhileThunk. - auto result = CanTransformWhileToFor(xla_while); - if (result.ok()) { - auto tuple = result.ConsumeValueOrDie(); - // loop_trip_count = (limit - start + increment - 1) / increment - const int64 loop_trip_count = - (std::get<1>(tuple) - std::get<0>(tuple) + std::get<2>(tuple) - 1) / - std::get<2>(tuple); - thunk_sequence_->emplace_back(BuildForThunk(xla_while, loop_trip_count)); + // TODO(b/112163966): Move trip count computation earlier in the pipeline. + if (auto loop_trip_count = ComputeWhileLoopTripCount(xla_while)) { + thunk_sequence_->emplace_back(BuildForThunk(xla_while, *loop_trip_count)); VLOG(3) << "Built ForThunk for while: " << xla_while->name(); } else { thunk_sequence_->emplace_back(BuildWhileThunk(xla_while)); - VLOG(3) << "Built WhileThunk for while: " << xla_while->name() - << " while-to-for transform status: " << result.status(); + VLOG(3) << "Built WhileThunk for while: " << xla_while->name(); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc index 8fa0439006b95b7a567d8fc8dbec36f193fb0e77..76c9b6ab33befa98f03821fac84071bd978ae24d 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc @@ -75,7 +75,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/transpose_folding.h" #include "tensorflow/compiler/xla/service/tuple_simplifier.h" #include "tensorflow/compiler/xla/service/while_loop_constant_sinking.h" -#include "tensorflow/compiler/xla/service/while_loop_invariant_code_motion.h" #include "tensorflow/compiler/xla/service/while_loop_simplifier.h" #include "tensorflow/compiler/xla/service/zero_sized_hlo_elimination.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -281,14 +280,6 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec, } } - { - // Do an aggressive LICM pass over while loops. In particular, this hoists - // constants that were sunk by WhileLoopConstantSinking. Leaving them in - // the while loop may result in unnecessary copies. - HloPassPipeline pipeline("while-loop-licm"); - pipeline.AddPass(true); - TF_RETURN_IF_ERROR(pipeline.Run(hlo_module).status()); - } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/outfeed_manager.h b/tensorflow/compiler/xla/service/gpu/outfeed_manager.h index a752eb70119b00e8cca7ddce26da7730ef5db8cb..160ba4b691f818ff01b41b8603c11853ea12c253 100644 --- a/tensorflow/compiler/xla/service/gpu/outfeed_manager.h +++ b/tensorflow/compiler/xla/service/gpu/outfeed_manager.h @@ -36,22 +36,19 @@ class OutfeedBuffer { OutfeedBuffer(int64 length) : length_(length) {} // Waits for the device transfer to be finished. - std::unique_ptr WaitUntilAvailable() { - done_.WaitForNotification(); - return std::move(destination_); - } + void WaitUntilAvailable() { done_.WaitForNotification(); } int64 length() const { return length_; } - void set_destination(std::unique_ptr destination) { + void set_destination(std::unique_ptr destination) { destination_ = std::move(destination); } - Literal* destination() { return destination_.get(); } + MutableBorrowingLiteral* destination() { return destination_.get(); } // Callback to signal that this buffer is consumed. void Done() { done_.Notify(); } private: - std::unique_ptr destination_; + std::unique_ptr destination_; const int64 length_; tensorflow::Notification done_; }; diff --git a/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc index 7986e63f43ee508370f94fdb9057b91bfe4add18..b99d998c4d7df514c024b1f8d643d08c72059d0e 100644 --- a/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/outfeed_thunk.cc @@ -50,10 +50,6 @@ Status OutfeedThunk::ExecuteOnStream( if (!*buffer) { // Tuple pointers. return Status::OK(); } - // Allocate storage for the literal data. - const Shape& shape = - ShapeUtil::GetSubshape(outfeed_buffers->shape(), index); - (*buffer)->set_destination(Literal::CreateFromShape(shape)); BufferAllocation::Slice slice = outfeed_slices_.element(index); se::DeviceMemoryBase data_address; diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h index 4df0bb005b623e5ac79a4dfcb7c5a8a7a400940c..e68bee035a029178844282995429eaa960cc4817 100644 --- a/tensorflow/compiler/xla/service/gpu/thunk.h +++ b/tensorflow/compiler/xla/service/gpu/thunk.h @@ -82,17 +82,9 @@ class Thunk { return Status::OK(); } - // Users of Thunk should call ShouldHaltAllActivityBeforeRunning(stream) - // before calling ExecuteOnStream(stream). If it returns true, it's the - // user's responsibility to wait for all activity on the GPU to finish before - // calling ExecuteOnStream. - // - // This value is not required to be constant for a given Thunk. For example, - // a Thunk that performs autotuning may return true for its first run and - // false thereafter. - virtual bool ShouldHaltAllActivityBeforeRunning(se::Stream* /*stream*/) { - return false; - } + // Returns true if this kernel will autotune for the stream device the next + // time it is run. + virtual bool WillAutotuneKernel(se::Stream* /*stream*/) { return false; } // Execute the kernel for the thunk on the given stream. This method must be // called after Initialize and can be called multiple times over Thunk's diff --git a/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc b/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc index a10e40451c1db01ce73db7b56a3a0599769fa49b..8579b1545fd24f80621ac0f53b997e33586cbabe 100644 --- a/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/tuple_thunk.cc @@ -24,24 +24,32 @@ namespace gpu { Status TupleThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, se::Stream* stream, HloExecutionProfiler* profiler) { - std::vector tuple_element_buffer_addresses; - for (BufferAllocation::Slice tuple_element_buffer : tuple_element_buffers_) { - tuple_element_buffer_addresses.push_back( - buffer_allocations.GetDeviceAddress(tuple_element_buffer).opaque()); + auto size = tuple_element_buffers_.size(); + auto tuple_element_buffer_addresses = MakeUnique(size); + for (int i = 0; i != size; ++i) { + tuple_element_buffer_addresses[i] = + buffer_allocations.GetDeviceAddress(tuple_element_buffers_[i]).opaque(); } se::DeviceMemory dest_buffer_address( buffer_allocations.GetDeviceAddress(dest_buffer_)); - auto host_size = tuple_element_buffer_addresses.size() * sizeof(void*); + auto host_size = size * sizeof(void*); auto op_profiler = profiler->MakeScopedInstructionProfiler(hlo_instruction()); if (!stream ->ThenMemcpy(&dest_buffer_address, - tuple_element_buffer_addresses.data(), host_size) + tuple_element_buffer_addresses.get(), host_size) .ok()) { return InternalError( "Unable to launch MemcpyH2D from %p to %p with size %lu", - tuple_element_buffer_addresses.data(), dest_buffer_address.opaque(), - sizeof(void*) * tuple_element_buffer_addresses.size()); + tuple_element_buffer_addresses.get(), dest_buffer_address.opaque(), + host_size); + } + // Free the tuple address buffer when memcpy is done. + auto* buffers_raw = tuple_element_buffer_addresses.release(); + if (!stream->ThenDoHostCallback([buffers_raw] { delete[] buffers_raw; }) + .ok()) { + delete[] buffers_raw; + return InternalError("Unable to enqueue host callback!"); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer.cc b/tensorflow/compiler/xla/service/gpu/while_transformer.cc deleted file mode 100644 index c5321df6c466fcb3816fb2aedad65b7c3811cb37..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/gpu/while_transformer.cc +++ /dev/null @@ -1,521 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/compiler/xla/service/gpu/while_transformer.h" - -#include -#include - -#include "tensorflow/compiler/xla/literal.h" -#include "tensorflow/compiler/xla/service/hlo_computation.h" -#include "tensorflow/compiler/xla/shape_util.h" -#include "tensorflow/compiler/xla/status_macros.h" -#include "tensorflow/compiler/xla/util.h" -#include "tensorflow/core/lib/core/errors.h" - -namespace xla { -namespace gpu { - -namespace { - -// TODO(b/33483676) Use an expression tree to specify computations to pattern -// match for while transformations. - -// ExprTree is a simple recursive data structure used to express computation -// patterns to match. -// -// Each ExprTree node is comprised of an HloOpcode, and a set of operands (each -// of type ExprTree). Operands can be added by specifying the index and -// HloOpcode of the operand. -// -// For example, the following computation: -// -// Parameter -// | -// Const GetTupleElement -// \ / -// Add (root) -// -// Can be matched with the following expression tree: -// -// ExprTree add(HloOpcode::kAdd, -// ExprTree(HloOpcode::kConstant), -// ExprTree(HloOpcode::kGetTupleElement, -// tuple_index, ExprTree(HloOpcode::kParameter))); -// -// Match the ExprTree root against an Hlo graph: -// -// ExprTree::TaggedInstructionMap tagged_instructions; -// TF_RETURN_IF_ERROR(add.Match(computation_->root_instruction(), -// &tagged_instructions)); -// -// Instructions that are "tagged" with a context-specific string will -// be returned in 'tagged_instructions' for further processing (i.e. parsing -// constants or recording the tuple_index). -// -class ExprTree { - public: - explicit ExprTree(HloOpcode opcode) : opcode_(opcode) {} - ExprTree(HloOpcode opcode, const string& tag) : opcode_(opcode), tag_(tag) {} - ExprTree(HloOpcode opcode, const ExprTree& operand0) : opcode_(opcode) { - SetOperand(0, operand0); - } - ExprTree(HloOpcode opcode, int64 index0, const ExprTree& operand0) - : opcode_(opcode) { - SetOperand(index0, operand0); - } - ExprTree(HloOpcode opcode, int64 index0, const ExprTree& operand0, - int64 index1, const ExprTree& operand1) - : opcode_(opcode) { - SetOperand(index0, operand0); - SetOperand(index1, operand1); - } - ExprTree(HloOpcode opcode, const string& tag, const ExprTree& operand0) - : opcode_(opcode), tag_(tag) { - SetOperand(0, operand0); - } - ExprTree(HloOpcode opcode, const ExprTree& operand0, const ExprTree& operand1) - : opcode_(opcode) { - SetOperand(0, operand0); - SetOperand(1, operand1); - } - - ExprTree(const ExprTree& to_copy) { - opcode_ = to_copy.opcode_; - tag_ = to_copy.tag_; - if (to_copy.fused_root_tree_ != nullptr) { - fused_root_tree_.reset(new ExprTree(*to_copy.fused_root_tree_)); - } - for (auto& pair : to_copy.operands_) { - CHECK(operands_.find(pair.first) == operands_.end()); - operands_.insert(std::make_pair( - pair.first, std::unique_ptr(new ExprTree(*pair.second)))); - } - } - - void SetFusedRoot(const ExprTree& fused_root) { - fused_root_tree_.reset(new ExprTree(fused_root)); - } - - typedef std::unordered_map - TaggedInstructionMap; - - // Matches 'instruction' HloOpcode against 'opcode_'. - // Recursively matches each operand in 'operands_'. - // Recursively matches fused instructions starting at 'fused_root_tree_' - // if 'opcode_ == kFusion'. - // Returns OK status, and instructions in 'tagged_instructions' for each - // matched ExprTree node with a non-empty 'tag_'. - // Returns error message on failure. - Status Match(const HloInstruction* instruction, - TaggedInstructionMap* tagged_instructions) const { - if (opcode_ != instruction->opcode()) { - return InvalidArgument("got opcode %s, want %s", - HloOpcodeString(instruction->opcode()).c_str(), - HloOpcodeString(opcode_).c_str()); - } - - VLOG(2) << "Matched " << HloOpcodeString(opcode_) << ": " << tag_; - if (!tag_.empty()) { - tagged_instructions->insert({tag_, instruction}); - } - - if (instruction->opcode() == HloOpcode::kFusion) { - CHECK(fused_root_tree_ != nullptr); - // Match fused instructions for this node starting a 'fused_root_tree'. - TF_RETURN_IF_ERROR(fused_root_tree_->Match( - instruction->fused_expression_root(), tagged_instructions)); - } - - // Match each operand in 'operands_'. - for (auto& pair : operands_) { - TF_RETURN_IF_ERROR(pair.second->Match(instruction->operand(pair.first), - tagged_instructions)); - } - return Status::OK(); - } - - private: - void SetOperand(int64 index, const ExprTree& operand) { - CHECK_EQ(0, operands_.count(index)); - operands_.insert(std::make_pair(index, MakeUnique(operand))); - } - - HloOpcode opcode_; - std::unordered_map> operands_; - std::unique_ptr fused_root_tree_; - string tag_; -}; - -// MatcherBase is a base class that provides common functionality for -// sub-classes which match specific target sub-computations (i.e. loop -// induction variable initialization, comparison and update). -class MatcherBase { - public: - MatcherBase() {} - virtual ~MatcherBase() {} - - // Attempts to match each ExprTree in 'expr_trees_'. - // Returns OK on the first successful match, error status otherwise. - virtual Status Run() { - Status status; - for (const ExprTree& expr_tree : expr_trees_) { - status = MatchExprTree(expr_tree); - if (status.ok()) { - return status; - } - } - return status; - } - - virtual Status MatchExprTree(const ExprTree& expr_tree) = 0; - - // Returns the constant value parsed form kConstant 'instruction'. - // Returns error status otherwise. - Status ParseConstInteger(const HloInstruction* instruction, - int64* const_value) const { - CHECK_EQ(HloOpcode::kConstant, instruction->opcode()); - PrimitiveType element_type = instruction->shape().element_type(); - if (element_type != S32 && element_type != S64) { - return InvalidArgument("Expected constant of integral type."); - } - const Literal& literal = instruction->literal(); - PrimitiveType type = literal.shape().element_type(); - if (type != S32 && type != S64) { - return InvalidArgument("Must use S32 or S64 integral types."); - } - if (type == S32) { - *const_value = static_cast(literal.GetFirstElement()); - } else if (type == S64) { - *const_value = literal.GetFirstElement(); - } - return Status::OK(); - } - - StatusOr GetTaggedInstruction( - const string& tag, - const ExprTree::TaggedInstructionMap& tagged_instructions) { - auto it = tagged_instructions.find(tag); - if (it == tagged_instructions.end()) { - return InvalidArgument("Cound not find instruction for tag: %s", - tag.c_str()); - } - return it->second; - } - - protected: - std::vector expr_trees_; - - private: - TF_DISALLOW_COPY_AND_ASSIGN(MatcherBase); -}; - -// WhileConditionComputationMatcher attempts to match a target computation -// pattern in the while condition sub-computation. -// If the target pattern is matched, two pieces of information are extracted -// from 'tagged' instructions returned by the matcher: -// -// *) 'tuple_index': -// *) The loop induction variable tuple_index from the GetTupleElement -// instruction of the matched computation. -// *) Used in subsequent matching passes of while init operand and body -// computations to select loop induction variable tuple element. -// -// *) 'loop_limit': -// *) The integral value from Constant root operand in matched computation. -// *) Used as the constant for the loop limit. -// -class WhileConditionComputationMatcher : public MatcherBase { - public: - explicit WhileConditionComputationMatcher(const HloComputation* computation) - : computation_(computation) { - expr_trees_.emplace_back(BuildCondExprTree()); - } - - int64 loop_limit() const { return loop_limit_; } - int64 tuple_index() const { return tuple_index_; } - - private: - // Builds expression tree for the following condition computation: - // - // Const Parameter - // \ / - // Fusion ------------> FusionParam FusionParam - // \ / - // GTE / - // \ / - // LessThan (fused root) - // - ExprTree BuildCondExprTree() { - // Build ExprTree for fused instructions. - ExprTree fused_root( - HloOpcode::kLt, - ExprTree(HloOpcode::kGetTupleElement, "gte", - ExprTree(HloOpcode::kParameter, "gte.fusion_param.param0")), - ExprTree(HloOpcode::kParameter)); - - // Build top-level computation. - ExprTree root(HloOpcode::kFusion, - ExprTree(HloOpcode::kConstant, "loop_limit"), - ExprTree(HloOpcode::kParameter, "param0")); - - root.SetFusedRoot(fused_root); - return root; - } - - Status MatchExprTree(const ExprTree& expr_tree) override { - VLOG(2) << "MATCHING while condition"; - ExprTree::TaggedInstructionMap tagged_instructions; - TF_RETURN_IF_ERROR(expr_tree.Match(computation_->root_instruction(), - &tagged_instructions)); - - // Get tagged GTE instruction and set 'tuple_index_'. - TF_ASSIGN_OR_RETURN(const HloInstruction* gte, - GetTaggedInstruction("gte", tagged_instructions)); - tuple_index_ = gte->tuple_index(); - - // Get tagged Constant instruction and parse 'loop_limit_'. - TF_ASSIGN_OR_RETURN( - const HloInstruction* const_hlo, - GetTaggedInstruction("loop_limit", tagged_instructions)); - TF_RETURN_IF_ERROR(ParseConstInteger(const_hlo, &loop_limit_)); - - // Get tagged "param0" instruction, and check that it matches - // 'computation_' parameter 0. - TF_ASSIGN_OR_RETURN(const HloInstruction* param0, - GetTaggedInstruction("param0", tagged_instructions)); - if (param0 != computation_->parameter_instruction(0)) { - return InvalidArgument("Unexpected Parameter0 instruction : %s", - param0->name().c_str()); - } - - // Get tagged 'gte.fusion_param.param0', find its associated fusion operand, - // and compare it to 'computation_' parameter0. - TF_ASSIGN_OR_RETURN( - const HloInstruction* gte_fusion_param0, - GetTaggedInstruction("gte.fusion_param.param0", tagged_instructions)); - CHECK_EQ(HloOpcode::kParameter, gte_fusion_param0->opcode()); - CHECK(gte_fusion_param0->IsFused()); - if (gte_fusion_param0->parent()->FusionInstruction()->operand( - gte_fusion_param0->parameter_number()) != - computation_->parameter_instruction(0)) { - return InvalidArgument("Could not match fusion param: %s", - gte_fusion_param0->name().c_str()); - } - - return Status::OK(); - } - - const HloComputation* computation_; - - int64 loop_limit_ = -1; - int64 tuple_index_ = -1; - - TF_DISALLOW_COPY_AND_ASSIGN(WhileConditionComputationMatcher); -}; - -// WhileInitOperandMatcher matches a target computation pattern of the -// while instructions 'init' operand, indexing the tuple at 'tuple_index'. -// On success, parses constant 'loop_start' which represents the loop induction -// variable start values, then returns OK. -// Returns error status otherwise. -class WhileInitOperandMatcher : public MatcherBase { - public: - WhileInitOperandMatcher(const HloInstruction* while_hlo, - const int64 tuple_index) - : while_hlo_(while_hlo), tuple_index_(tuple_index) { - expr_trees_.emplace_back(BuildInitExprTree()); - } - - int64 loop_start() const { return loop_start_; } - - private: - // Builds expression tree for the following while init operand subcomputation: - // - // Const - // | - // Copy - // | - // Tuple0 - // | - // While - // - ExprTree BuildInitExprTree() { - return ExprTree( - HloOpcode::kWhile, "while", - ExprTree(HloOpcode::kTuple, tuple_index_, - ExprTree(HloOpcode::kCopy, - ExprTree(HloOpcode::kConstant, "loop_start")))); - } - - Status MatchExprTree(const ExprTree& expr_tree) override { - VLOG(2) << "MATCHING while init"; - ExprTree::TaggedInstructionMap tagged_instructions; - TF_RETURN_IF_ERROR(expr_tree.Match(while_hlo_, &tagged_instructions)); - - // Get tagged while instruction check against 'while_hlo_'. - TF_ASSIGN_OR_RETURN(const HloInstruction* while_hlo, - GetTaggedInstruction("while", tagged_instructions)); - if (while_hlo != while_hlo_) { - return InvalidArgument("Expected While for instruction : %s", - while_hlo->name().c_str()); - } - - // Get tagged Constant instruction and parse 'loop_start_'. - TF_ASSIGN_OR_RETURN( - const HloInstruction* const_hlo, - GetTaggedInstruction("loop_start", tagged_instructions)); - TF_RETURN_IF_ERROR(ParseConstInteger(const_hlo, &loop_start_)); - - return Status::OK(); - } - - const HloInstruction* while_hlo_; - const int64 tuple_index_; - - int64 loop_start_ = -1; - - TF_DISALLOW_COPY_AND_ASSIGN(WhileInitOperandMatcher); -}; - -// WhileBodyComputationMatcher matches a target computation pattern for -// the loop induction variable update. Matching proceeds from the while body -// computation root[tuple_index] to param[tuple_index], where 'tuple_index' -// If the target pattern is matched, parses a constant which represents the -// loop induction variable increment value, then returns status OK. -// Returns error status otherwise. -class WhileBodyComputationMatcher : public MatcherBase { - public: - WhileBodyComputationMatcher(const HloComputation* computation, - const int64 tuple_index) - : computation_(computation), tuple_index_(tuple_index) { - expr_trees_.emplace_back(BuildBodyExprTree(0, 1)); - expr_trees_.emplace_back(BuildBodyExprTree(1, 0)); - } - - int64 loop_increment() const { return loop_increment_; } - - private: - // Builds expression tree for the following while body computation: - // - // - // FusionParam FusionParam - // \ / - // Const Param \ GTE1 - // \ / \ / - // Fusion -----------> Add - // | - // Copy - // | - // Tuple0 - // - ExprTree BuildBodyExprTree(const int64 const_index, const int64 gte_index) { - // Build ExprTree for fused instructions. - ExprTree gte1 = - ExprTree(HloOpcode::kGetTupleElement, "gte", - ExprTree(HloOpcode::kParameter, "gte.fusion_param.param0")); - ExprTree fused_root(HloOpcode::kAdd, const_index, - ExprTree(HloOpcode::kParameter), gte_index, gte1); - - // Build fusion instruction (and set fused root). - ExprTree fusion(HloOpcode::kFusion, 0, - ExprTree(HloOpcode::kConstant, "loop_increment"), 1, - ExprTree(HloOpcode::kParameter, "param0")); - fusion.SetFusedRoot(fused_root); - - // Build top-level computation. - ExprTree tuple0(HloOpcode::kTuple, tuple_index_, - ExprTree(HloOpcode::kCopy, fusion)); - return tuple0; - } - - Status MatchExprTree(const ExprTree& expr_tree) override { - VLOG(2) << "MATCHING while body"; - ExprTree::TaggedInstructionMap tagged_instructions; - TF_RETURN_IF_ERROR(expr_tree.Match(computation_->root_instruction(), - &tagged_instructions)); - - for (const auto& pair : tagged_instructions) { - const auto& tag = pair.first; - const auto& inst = pair.second; - - if (tag == "gte" && inst->tuple_index() != tuple_index_) { - // Check that the matched GTE instruction is at the 'tuple_index' we - // matched in the while condition computation. - return InvalidArgument("Unexpected tuple index instruction : %s", - inst->name().c_str()); - } else if (tag == "loop_increment") { - // ParseHloString the constant which represents the loop induction - // variable increment value. - TF_RETURN_IF_ERROR(ParseConstInteger(inst, &loop_increment_)); - } else if (tag == "param0" && - inst != computation_->parameter_instruction(0)) { - // Check that the matched parameter == parameter 0 from 'computation_'. - return InvalidArgument("Unexpected Parameter0 instruction : %s", - inst->name().c_str()); - } else if (tag == "gte.fusion_param.param0") { - // Fusion parameter: lookup and compare with associated fusion operand. - CHECK_EQ(HloOpcode::kParameter, inst->opcode()); - CHECK(inst->IsFused()); - if (inst->parent()->FusionInstruction()->operand( - inst->parameter_number()) != - computation_->parameter_instruction(0)) { - return InvalidArgument("Could not match fusion param: %s", - inst->name().c_str()); - } - } - } - return Status::OK(); - } - - const HloComputation* computation_; - const int64 tuple_index_; - - int64 loop_increment_ = -1; - - TF_DISALLOW_COPY_AND_ASSIGN(WhileBodyComputationMatcher); -}; - -} // namespace - -StatusOr> CanTransformWhileToFor( - const HloInstruction* while_hlo) { - if (while_hlo->opcode() != HloOpcode::kWhile) { - return InvalidArgument("Expected While instruction."); - } - - WhileConditionComputationMatcher cond_matcher(while_hlo->while_condition()); - TF_RETURN_IF_ERROR(cond_matcher.Run()); - - WhileInitOperandMatcher init_matcher(while_hlo, cond_matcher.tuple_index()); - TF_RETURN_IF_ERROR(init_matcher.Run()); - - WhileBodyComputationMatcher body_matcher(while_hlo->while_body(), - cond_matcher.tuple_index()); - TF_RETURN_IF_ERROR(body_matcher.Run()); - - // Check for valid For loop parameters. - if (init_matcher.loop_start() >= cond_matcher.loop_limit()) { - return InvalidArgument("Loop start must be less than loop limit."); - } - if (body_matcher.loop_increment() <= 0) { - return InvalidArgument("Loop increment must greater than zero."); - } - return std::make_tuple(init_matcher.loop_start(), cond_matcher.loop_limit(), - body_matcher.loop_increment()); -} - -} // namespace gpu -} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer.h b/tensorflow/compiler/xla/service/gpu/while_transformer.h deleted file mode 100644 index fe3a954e1828ee4a323872eea81f64c7e780ad24..0000000000000000000000000000000000000000 --- a/tensorflow/compiler/xla/service/gpu/while_transformer.h +++ /dev/null @@ -1,43 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_WHILE_TRANSFORMER_H_ -#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_WHILE_TRANSFORMER_H_ - -#include "tensorflow/compiler/xla/service/hlo_instruction.h" -#include "tensorflow/compiler/xla/statusor.h" - -namespace xla { -namespace gpu { - -// Runs an analysis of the while loop instruction 'while_hlo' (and its -// associated sub-computations) to determine if it can be transformed into an -// equivalent "for" loop with the following "for" loop parameters: -// -// *) 'loop_start': loop induction variable starting value. -// *) 'loop_limit': loop induction variable limit value. -// *) 'loop_increment': loop induction variable per-iteration increment value. -// -// Returns an std::tuple = (loop_start, loop_limit, loop_increment) on success. -// The values in the returned tuple are values extracted from the 'while_hlo' -// operand (and its sub-computations) during analysis. -// Returns an error status on failure. -StatusOr> CanTransformWhileToFor( - const HloInstruction* while_hlo); - -} // namespace gpu -} // namespace xla - -#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_WHILE_TRANSFORMER_H_ diff --git a/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc b/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc index dbc8442ed2785a112b674632689256c01282156b..c5f3906356d821e059d2b1213c9083c4408a4d1c 100644 --- a/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc +++ b/tensorflow/compiler/xla/service/gpu/while_transformer_test.cc @@ -13,11 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/service/gpu/while_transformer.h" - #include "tensorflow/compiler/xla/service/copy_insertion.h" #include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h" #include "tensorflow/compiler/xla/service/hlo_verifier.h" +#include "tensorflow/compiler/xla/service/while_loop_analysis.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" @@ -110,12 +109,12 @@ class WhileTransformerTest : public HloTestBase { void RunFusionPasses() { // Run standard fusion passes. - EXPECT_TRUE(gpu::GpuInstructionFusion(/*may_duplicate=*/false) - .Run(module_.get()) - .ValueOrDie()); - EXPECT_TRUE(gpu::GpuInstructionFusion(/*may_duplicate=*/true) - .Run(module_.get()) - .ValueOrDie()); + TF_ASSERT_OK(gpu::GpuInstructionFusion(/*may_duplicate=*/false) + .Run(module_.get()) + .status()); + TF_ASSERT_OK(gpu::GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module_.get()) + .status()); } void RunCopyInsertionPass() { @@ -141,10 +140,7 @@ class WhileTransformerTest : public HloTestBase { Shape condition_result_shape_; }; -// TODO(b/68830972): The while transformer is far too fragile. It patterns -// matches the exact expressions of opcodes. Re-enable when transformation is -// more general -TEST_F(WhileTransformerTest, DISABLED_InductionVariableAtTupleElement0) { +TEST_F(WhileTransformerTest, InductionVariableAtTupleElement0) { // Build computation with induction variable at tuple element 0. auto condition = module_->AddEmbeddedComputation(BuildConditionComputation(0, 10)); @@ -153,18 +149,13 @@ TEST_F(WhileTransformerTest, DISABLED_InductionVariableAtTupleElement0) { // Run HLO Optimization passes. RunFusionPasses(); RunCopyInsertionPass(); - // Run WhileTransformer. - auto result = gpu::CanTransformWhileToFor(while_hlo); - TF_ASSERT_OK(result.status()); - // Check results. - EXPECT_THAT(result.ConsumeValueOrDie(), - Eq(std::tuple(0, 10, 1))); + + auto result = ComputeWhileLoopTripCount(while_hlo); + ASSERT_TRUE(result); + EXPECT_EQ(10, *result); } -// TODO(b/68830972): The while transformer is far too fragile. It patterns -// matches the exact expressions of opcodes. Re-enable when transformation is -// more general -TEST_F(WhileTransformerTest, DISABLED_InductionVariableAtTupleElement1) { +TEST_F(WhileTransformerTest, InductionVariableAtTupleElement1) { // Build computation with induction variable at tuple element 1. auto condition = module_->AddEmbeddedComputation(BuildConditionComputation(1, 10)); @@ -173,19 +164,14 @@ TEST_F(WhileTransformerTest, DISABLED_InductionVariableAtTupleElement1) { // Run HLO Optimization passes. RunFusionPasses(); RunCopyInsertionPass(); - // Run WhileTransformer. - auto result = gpu::CanTransformWhileToFor(while_hlo); - TF_ASSERT_OK(result.status()); - // Check results. - EXPECT_THAT(result.ConsumeValueOrDie(), - Eq(std::tuple(0, 10, 1))); + + auto result = ComputeWhileLoopTripCount(while_hlo); + ASSERT_TRUE(result); + EXPECT_EQ(10, *result); } -// TODO(b/68830972): The while transformer is far too fragile. It patterns -// matches the exact expressions of opcodes. Re-enable when transformation is -// more general -TEST_F(WhileTransformerTest, DISABLED_InvalidLoopLimit) { - // Build computation with invalid loop limit. +TEST_F(WhileTransformerTest, ImpossibleLoopLimit) { + // Build computation with an impossible loop limit. auto condition = module_->AddEmbeddedComputation(BuildConditionComputation(0, 5)); auto body = module_->AddEmbeddedComputation(BuildBodyComputation(0, 1, 1)); @@ -193,17 +179,13 @@ TEST_F(WhileTransformerTest, DISABLED_InvalidLoopLimit) { // Run HLO Optimization passes. RunFusionPasses(); RunCopyInsertionPass(); - // Run WhileTransformer. - auto result = gpu::CanTransformWhileToFor(while_hlo); - ASSERT_FALSE(result.ok()); - EXPECT_THAT(result.status().error_message(), - HasSubstr("Loop start must be less than loop limit.")); + + auto result = ComputeWhileLoopTripCount(while_hlo); + ASSERT_TRUE(result); + EXPECT_EQ(0, *result); } -// TODO(b/68830972): The while transformer is far too fragile. It patterns -// matches the exact expressions of opcodes. Re-enable when transformation is -// more general -TEST_F(WhileTransformerTest, DISABLED_InvalidLoopIncrement) { +TEST_F(WhileTransformerTest, InvalidLoopIncrement) { // Build computation with invalid loop increment. auto condition = module_->AddEmbeddedComputation(BuildConditionComputation(0, 10)); @@ -212,11 +194,9 @@ TEST_F(WhileTransformerTest, DISABLED_InvalidLoopIncrement) { // Run HLO Optimization passes. RunFusionPasses(); RunCopyInsertionPass(); - // Run WhileTransformer. - auto result = gpu::CanTransformWhileToFor(while_hlo); - ASSERT_FALSE(result.ok()); - EXPECT_THAT(result.status().error_message(), - HasSubstr("Loop increment must greater than zero.")); + + auto result = ComputeWhileLoopTripCount(while_hlo); + ASSERT_FALSE(result); } } // namespace diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index 0b93d97c11abd89c24c130849a8357806066fce7..be9098f555e78f3cabfe55481356f8b6841a3a2b 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -151,8 +151,11 @@ message HloInstructionProto { // Backend configuration for the instruction. Has backend-specific meaning. string backend_config = 43; - // Cross Replica Sum fields. + // Cross replica op fields. + // TODO(b/112107579): remove replica_group_ids field and always use + // replica_groups. repeated int64 replica_group_ids = 44; + repeated ReplicaGroup replica_groups = 49; int64 all_reduce_id = 45; string cross_replica_sum_barrier = 46; diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc index a2cefd26211eb9f09e8668a7fad9f8085ab0cd6a..1bbb0ff08e26f626f4c3992a5f20ec4990f7db2d 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc @@ -543,6 +543,19 @@ Status HloCostAnalysis::HandleCrossReplicaSum(const HloInstruction* crs) { return Status::OK(); } +Status HloCostAnalysis::HandleAllToAll(const HloInstruction* hlo) { + // TODO(b/110096724): Compute correct cost here. + double flops = 0.0; + ShapeUtil::ForEachSubshape(hlo->shape(), + [&](const Shape& subshape, const ShapeIndex&) { + if (ShapeUtil::IsArray(subshape)) { + flops += ShapeUtil::ElementsIn(subshape); + } + }); + current_properties_[kFlopsKey] = flops; + return Status::OK(); +} + Status HloCostAnalysis::HandleRng(const HloInstruction* random) { // TODO(b/26346211): Implement better estimates for the RNG cost, since the // cost changes with the implementation and the distribution. For now, assume diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h index 0a79c92f4a95f6337c8c25b47f6967fc9ff3fd98..193a04bea0831de2b3aca19b17a445ad73e02e49 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h @@ -71,6 +71,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor { Status HandleConvolution(const HloInstruction* convolution) override; Status HandleFft(const HloInstruction* fft) override; Status HandleCrossReplicaSum(const HloInstruction* crs) override; + Status HandleAllToAll(const HloInstruction* hlo) override; Status HandleInfeed(const HloInstruction* infeed) override; Status HandleOutfeed(const HloInstruction* outfeed) override; Status HandleHostCompute(const HloInstruction* host_compute) override; diff --git a/tensorflow/compiler/xla/service/hlo_domain_test.cc b/tensorflow/compiler/xla/service/hlo_domain_test.cc index ffc18a0f886df86d87944d9c284a6faf8afe4c60..70271be304336767bd3fd01297217e9309a941b6 100644 --- a/tensorflow/compiler/xla/service/hlo_domain_test.cc +++ b/tensorflow/compiler/xla/service/hlo_domain_test.cc @@ -490,5 +490,38 @@ TEST_F(HloDomainTest, DumpParseNullSharding) { ASSERT_TRUE(ParseModule(hlo_string).status().ok()); } +TEST_F(HloDomainTest, DomainTuple) { + const char* const hlo_string = R"( +HloModule Module + +ENTRY entry { + p0 = f32[4] parameter(0), sharding={maximal device=0} + cst = u32[] constant(0), sharding={maximal device=1} + tpl = (u32[], f32[4]) tuple(cst, p0), sharding={{maximal device=1}, {maximal device=0}} + ROOT gte = f32[4] get-tuple-element(tpl), index=1, sharding={maximal device=0} +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(HloModule * module, ParseModule(hlo_string)); + + HloDomainIsolator isolator(CreateShardingDomain); + TF_ASSERT_OK_AND_ASSIGN(bool isolator_changed, isolator.Run(module)); + EXPECT_TRUE(isolator_changed); + + // Clear sharding of tpl instruction, in order to test domain sharding + // application. + auto tpl = FindInstruction(module, "tpl"); + tpl->clear_sharding(); + + HloDomainRemover remover(ShardingMetadata::KindName(), + ShardingMetadata::NormalizeShardingDomain); + TF_ASSERT_OK_AND_ASSIGN(bool remover_changed, remover.Run(module)); + EXPECT_TRUE(remover_changed); + + EXPECT_EQ(HloSharding::Tuple(tpl->shape(), {HloSharding::AssignDevice(1), + HloSharding::AssignDevice(0)}), + tpl->sharding()); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_element_type_converter.cc b/tensorflow/compiler/xla/service/hlo_element_type_converter.cc index c804f4364f6d16d5b8112219ce884495200aa827..b9244b8e9e5f34e7ac5113c8eacb6f8243eea314 100644 --- a/tensorflow/compiler/xla/service/hlo_element_type_converter.cc +++ b/tensorflow/compiler/xla/service/hlo_element_type_converter.cc @@ -144,6 +144,7 @@ StatusOr HloElementTypeConverter::Run(HloModule* module) { opcode == HloOpcode::kCrossReplicaSum || opcode == HloOpcode::kFusion || opcode == HloOpcode::kMap || opcode == HloOpcode::kReduce || opcode == HloOpcode::kReduceWindow || + opcode == HloOpcode::kScatter || opcode == HloOpcode::kSelectAndScatter || opcode == HloOpcode::kConditional) { continue; diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index cba72469ce73603f05d9957eb64e8519e8b8aec0..3ac6d68df30955d2e5e06e1e76d2182772151b47 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -2048,6 +2048,459 @@ ENTRY main { *Evaluate({operand.get(), gather_indices.get()}))); } +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatterV1_Update) { + const char* hlo_text = R"( +HloModule TensorFlowScatterV1 + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[2,3] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({0, 2}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{10, 20, 30}, {70, 80, 90}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{10, 20, 30}, {4, 5, 6}, {70, 80, 90}}), + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatterV2_Update) { + const char* hlo_text = R"( +HloModule TensorFlowScatterV2 + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[3,2] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={0}, + inserted_window_dims={1}, + scatter_dims_to_operand_dims={1}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({0, 2}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{10, 30}, {40, 60}, {70, 90}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{10, 2, 30}, {40, 5, 60}, {70, 8, 90}}), + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatter_Add) { + const char* hlo_text = R"( +HloModule TensorFlowScatter + +add_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + rhs = s32[] parameter(1) + ROOT add = s32[] add(s32[] lhs, s32[] rhs) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[2,3] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=add_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({0, 2}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{10, 20, 30}, {70, 80, 90}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{11, 22, 33}, {4, 5, 6}, {77, 88, 99}}), + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatter_Mul) { + const char* hlo_text = R"( +HloModule TensorFlowScatter + +mul_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + rhs = s32[] parameter(1) + ROOT mul = s32[] multiply(s32[] lhs, s32[] rhs) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[2,3] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=mul_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({0, 2}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{10, 20, 30}, {70, 80, 90}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{10, 40, 90}, {4, 5, 6}, {490, 640, 810}}), + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatter_F32) { + const char* hlo_text = R"( +HloModule TensorFlowScatter + +add_f32 (lhs: f32[], rhs: f32[]) -> f32[] { + lhs = f32[] parameter(0) + rhs = f32[] parameter(1) + ROOT add = f32[] add(f32[] lhs, f32[] rhs) +} + +ENTRY main { + operand = f32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = f32[2,3] parameter(2) + ROOT scatter = f32[3,3] scatter(operand, indices, updates), + to_apply=add_f32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = LiteralUtil::CreateR2( + {{1.1, 2.2, 3.3}, {4.4, 5.5, 6.6}, {7.7, 8.8, 9.9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({2, 1}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{0.4, 1.1, 0.7}, {2.3, 3.1, 1.6}}); + EXPECT_TRUE(LiteralTestUtil::Near( + *LiteralUtil::CreateR2( + {{1.1, 2.2, 3.3}, {6.7, 8.6, 8.2}, {8.1, 9.9, 10.6}}), + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}), + ErrorSpec{0.1, 0.01})); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatter_RepeatedIndices) { + const char* hlo_text = R"( +HloModule TensorFlowScatter + +add_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + rhs = s32[] parameter(1) + ROOT add = s32[] add(s32[] lhs, s32[] rhs) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[2,3] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=add_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({1, 1}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{10, 20, 30}, {70, 80, 90}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{1, 2, 3}, {84, 105, 126}, {7, 8, 9}}), + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatter_MultipleBatchDims) { + const char* hlo_text = R"( +HloModule TensorFlowScatterMultipleBatchDims + +add_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + rhs = s32[] parameter(1) + ROOT add = s32[] add(s32[] lhs, s32[] rhs) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + updates = s32[2,3,2] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=add_s32, + update_window_dims={1}, + inserted_window_dims={1}, + scatter_dims_to_operand_dims={1}, + index_vector_dim=2 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR2({{0, 2}, {2, 1}}); + std::unique_ptr updates = LiteralUtil::CreateR3( + {{{10, 30}, {40, 60}, {70, 90}}, {{5, 5}, {5, 5}, {5, 5}}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *LiteralUtil::CreateR2({{11, 7, 38}, {44, 10, 71}, {77, 13, 104}}), + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_TensorFlowScatterNd) { + const char* hlo_text = R"( +HloModule TensorFlowScatterNd + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + updates = s32[2,2] parameter(2) + ROOT scatter = s32[3,3,2] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1}, + inserted_window_dims={0,1}, + scatter_dims_to_operand_dims={0,1}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{-10, 10}, {-40, 40}}); + std::unique_ptr expected = + LiteralUtil::CreateR3({{{-10, 10}, {-2, 2}, {-3, 3}}, // + {{-40, 40}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *expected, + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, + EvaluateScatter_TensorFlowScatterNd_NonDefaultIndexVectorDim) { + const char* hlo_text = R"( +HloModule TensorFlowScatterNdNonDefaultIndexVectorDim + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + operand = s32[3,3,2] parameter(0) + indices = s32[2,2] parameter(1) + updates = s32[2,2] parameter(2) + ROOT scatter = s32[3,3,2] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1}, + inserted_window_dims={0,1}, + scatter_dims_to_operand_dims={0,1}, + index_vector_dim=0 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR3({{{-1, 1}, {-2, 2}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR2({{0, 0}, {1, 0}}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{-10, 10}, {-20, 20}}); + std::unique_ptr expected = + LiteralUtil::CreateR3({{{-20, 20}, {-10, 10}, {-3, 3}}, // + {{-4, 4}, {-5, 5}, {-6, 6}}, // + {{-7, 7}, {-8, 8}, {-9, 9}}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *expected, + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_DynamicUpdateSlice) { + const char* hlo_text = R"( +HloModule DynamicUpdateSlice + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2] parameter(1) + updates = s32[1,1] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={0,1}, + inserted_window_dims={}, + scatter_dims_to_operand_dims={0,1}, + index_vector_dim=0 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({1, 1}); + std::unique_ptr updates = LiteralUtil::CreateR2({{10}}); + std::unique_ptr expected = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 10, 6}, {7, 8, 9}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *expected, + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_BatchDynamicUpdateSlice) { + const char* hlo_text = R"( +HloModule BatchDynamicUpdateSlice + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + operand = s32[3,3] parameter(0) + indices = s32[2,2] parameter(1) + updates = s32[2,1,1] parameter(2) + ROOT scatter = s32[3,3] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1,2}, + inserted_window_dims={}, + scatter_dims_to_operand_dims={0,1}, + index_vector_dim=0 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 5, 6}, {7, 8, 9}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR2({{2, 1}, {1, 1}}); + std::unique_ptr updates = + LiteralUtil::CreateR3({{{10}}, {{20}}}); + std::unique_ptr expected = + LiteralUtil::CreateR2({{1, 2, 3}, {4, 20, 6}, {7, 10, 9}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *expected, + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_ZeroDimBounds) { + const char* hlo_text = R"( +HloModule TensorFlowScatter_ZeroDimBounds + +update_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + ROOT rhs = s32[] parameter(1) +} + +ENTRY main { + operand = s32[3,0] parameter(0) + indices = s32[2] parameter(1) + updates = s32[2,0] parameter(2) + ROOT scatter = s32[3,0] scatter(operand, indices, updates), + to_apply=update_s32, + update_window_dims={1}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=1 +} +)"; + ParseAndVerifyModule(hlo_text); + std::unique_ptr operand = LiteralUtil::CreateR2({{}, {}, {}}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR1({0, 2}); + std::unique_ptr updates = LiteralUtil::CreateR2({{}, {}}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *operand, + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + +TEST_P(HloEvaluatorTest, EvaluateScatter_NoUpdateWindowDims) { + const string hlo_text = R"( +HloModule Scatter_NoUpdateWindowDims + +add_s32 (lhs: s32[], rhs: s32[]) -> s32[] { + lhs = s32[] parameter(0) + rhs = s32[] parameter(1) + ROOT add = s32[] add(s32[] lhs, s32[] rhs) +} + +ENTRY main { + operand = s32[3] parameter(0) + indices = s32[2,2,1] parameter(1) + updates = s32[2,2] parameter(2) + ROOT scatter = s32[3] scatter(operand, indices, updates), + to_apply=add_s32, + update_window_dims={}, + inserted_window_dims={0}, + scatter_dims_to_operand_dims={0}, + index_vector_dim=2 +} +)"; + ParseAndVerifyModule(hlo_text); + + std::unique_ptr operand = LiteralUtil::CreateR1({0, 1, 2}); + std::unique_ptr scatter_indices = + LiteralUtil::CreateR3({{{0}, {1}}, {{2}, {1}}}); + std::unique_ptr updates = + LiteralUtil::CreateR2({{10, 20}, {30, 40}}); + std::unique_ptr expected = + LiteralUtil::CreateR1({10, 61, 32}); + EXPECT_TRUE(LiteralTestUtil::Equal( + *expected, + *Evaluate({operand.get(), scatter_indices.get(), updates.get()}))); +} + // Verifies that HloEvaluator evaluates a HLO instruction that performs // element-wise comparison with 2 bfloat16 operands. TEST_P(HloEvaluatorTest, DoesCompareBF16) { diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index d1ee4a180be622523da13eb670a491fbd3dce23b..084b49b4783fe15e91917317d8b3746e2c7569d0 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -1473,6 +1473,10 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { } Status HandleReduce(HloInstruction* reduce) override { + // TODO(b/112040122): Support variadic reduce. + if (!ShapeUtil::IsArray(reduce->shape())) { + return Unimplemented("Variadic reduce is not supported in the Evaluator"); + } auto arg = reduce->operand(0); auto init_value = reduce->operand(1); tensorflow::gtl::ArraySlice dimensions(reduce->dimensions()); @@ -1771,6 +1775,388 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return Status::OK(); } + // Reshapes the scatter indices input to have a trailing degenerate `1` + // dimension if necessary. Hands over the ownership of the newly created + // literal (if there is one) to `reshaped_indices`. + StatusOr> ReshapedScatterIndices( + int64 index_vector_dim, const Literal& indices, + std::unique_ptr* reshaped_indices) { + if (indices.shape().dimensions_size() != index_vector_dim) { + return std::cref(indices); + } + + std::vector new_shape(indices.shape().dimensions().begin(), + indices.shape().dimensions().end()); + new_shape.push_back(1); + TF_ASSIGN_OR_RETURN(*reshaped_indices, indices.Reshape(new_shape)); + return std::cref(**reshaped_indices); + } + + // Returns an ShapeUtil::IndexIterationSpace that iterates over the update + // scatter dimensions while keeping the rest of the update dimensions clamped + // to 0. + ShapeUtil::IndexIterationSpace IterationSpaceForUpdateScatterIndices( + const Shape& updates_shape, const ScatterDimensionNumbers& dim_numbers) { + int64 updates_rank = updates_shape.dimensions_size(); + std::vector index_base(updates_rank, 0); + std::vector index_count(updates_rank, 1); + for (int64 i = 0; i < updates_rank; i++) { + bool is_update_scatter_dim = + !c_binary_search(dim_numbers.update_window_dims(), i); + if (is_update_scatter_dim) { + index_count[i] = updates_shape.dimensions(i); + } + } + return {std::move(index_base), std::move(index_count), + std::vector(updates_rank, 1)}; + } + + // Return an ShapeUtil::IndexIterationSpace that iterates over the update + // window dimensions while keeping the rest of the update dimensions clamped + // to 0. + ShapeUtil::IndexIterationSpace IterationSpaceForUpdateWindowIndices( + const Shape& updates_shape, const ScatterDimensionNumbers& dim_numbers) { + int64 updates_rank = updates_shape.dimensions_size(); + std::vector index_base(updates_rank, 0); + std::vector index_count(updates_rank, 1); + for (int64 i = 0; i < updates_rank; i++) { + bool is_update_window_dim = + c_binary_search(dim_numbers.update_window_dims(), i); + if (is_update_window_dim) { + index_count[i] = updates_shape.dimensions(i); + } + } + return {std::move(index_base), std::move(index_count), + std::vector(updates_rank, 1)}; + } + + // This functor computes the contribution of scatter_indices to an input index + // corresponding to an update index. That is, given an update index I, it + // picks out the scatter indices in I and uses them to look up a scatter + // index, S, from the scatter indices tensor, and expands S into the input + // space according to scatter_dims_to_operand_dims. + // + // This is similar to the class HloEvaluator::OutputGatherIndexToInputIndex + // that does the corresponding function for Gather. + class UpdateScatterIndexToInputIndex { + public: + // The constructor does some setup work that is amortized across all + // iterations. + explicit UpdateScatterIndexToInputIndex( + const ScatterDimensionNumbers* dim_numbers, const Shape& input_shape, + const Shape& updates_shape, const Literal* scatter_indices) + : dim_numbers_(*dim_numbers), scatter_indices_(*scatter_indices) { + for (int64 i = 0; i < updates_shape.dimensions_size(); i++) { + update_dim_is_scatter_dims_.push_back( + !c_binary_search(dim_numbers_.update_window_dims(), i)); + } + + for (int64 i = 0; i < input_shape.dimensions_size(); i++) { + int64 index_of_input_dim_in_index_vector = + FindIndex(dim_numbers_.scatter_dims_to_operand_dims(), i); + if (index_of_input_dim_in_index_vector == + dim_numbers_.scatter_dims_to_operand_dims_size()) { + input_dim_value_to_index_vector_.push_back(-1); + } else { + input_dim_value_to_index_vector_.push_back( + index_of_input_dim_in_index_vector); + } + } + + index_vector_index_.resize(scatter_indices_.shape().dimensions_size()); + input_index_.resize(input_shape.dimensions_size()); + int64 index_vector_size = + scatter_indices_.shape().dimensions(dim_numbers_.index_vector_dim()); + index_vector_.resize(index_vector_size); + } + + // Returns the contribution of scatter_indices to the input index + // corresponding to update_index. See scatter_inner_loop_body. + // + // This is conceptually a stateless transformation from update_index to the + // scatter input index, but: + // + // - Instead of allocating memory to represent the scatter input index on + // every invocation we reuse the same storage for the result + // (input_index_), mutating it in place. + // - Instead of allocating buffers for temporary values like + // index_vector_index_ and index_vector on every invocation, we reuse the + // same storage for all invocations. + // + // This returns an arrayslice into memory owned by the class. + StatusOr> operator()( + tensorflow::gtl::ArraySlice update_index) { + PropagateUpdateIndexScatterDimsToIndexVectorIndex(update_index); + TF_RETURN_IF_ERROR(FetchIndexVector()); + PropagateIndexVectorToInputIndex(); + return tensorflow::gtl::ArraySlice(input_index_); + } + + private: + // Propagates the scatter index dimensions from the update index into + // index_vector_index_ by mutating index_vector_index_ in place. Does not + // update the dim_numbers.index_vector_dim() dimension -- that's the + // dimension we iterate over in FetchIndexVector. + void PropagateUpdateIndexScatterDimsToIndexVectorIndex( + tensorflow::gtl::ArraySlice update_index) { + int64 index_vector_index_i = 0; + for (int64 i = 0, e = update_index.size(); i < e; i++) { + if (!update_dim_is_scatter_dims_[i]) { + continue; + } + + if (index_vector_index_i == dim_numbers_.index_vector_dim()) { + index_vector_index_i++; + } + + index_vector_index_[index_vector_index_i++] = update_index[i]; + } + } + + // Populates index_vector_ by iterating over scatter_indices_ according to + // index_vector_index_. + Status FetchIndexVector() { + int64 index_vector_dim = dim_numbers_.index_vector_dim(); + for (int64 i = 0, e = index_vector_.size(); i < e; i++) { + index_vector_index_[index_vector_dim] = i; + TF_ASSIGN_OR_RETURN(index_vector_[i], scatter_indices_.GetIntegralAsS64( + index_vector_index_)); + } + return Status::OK(); + } + + // Populates input_index_. + void PropagateIndexVectorToInputIndex() { + for (int64 i = 0, e = input_index_.size(); i < e; i++) { + if (input_dim_value_to_index_vector_[i] != -1) { + input_index_[i] = index_vector_[input_dim_value_to_index_vector_[i]]; + } + + // If input_dim_value_to_index_vector_[i] == -1 then input_index_[i] + // remains 0, as set by the constructor. + } + } + + // input_dim_value_to_index_vector_[i] tells us how to compute dimension i + // of the input index from the index vector. See + // PropagateIndexVectorToInputIndex. + std::vector input_dim_value_to_index_vector_; + + // update_dim_is_scatter_dims_[i] is true iff the update index i is a + // scatter dimension. + std::vector update_dim_is_scatter_dims_; + + // The buffer into which we construct an index into scatter_indices_ to + // fetch the index vector. + std::vector index_vector_index_; + + // The index vector fetched from scatter_indices_. + std::vector index_vector_; + + // The result computed by this functor. operator() returns an ArraySlice + // into this vector. + std::vector input_index_; + + const ScatterDimensionNumbers& dim_numbers_; + const Literal& scatter_indices_; + }; + + // This functor computes the contribution of the window indices in an update + // index to an input index. That is, given an update index I it picks out the + // update window indices in I and expands it into a window index into the + // input shape. + // + // This is similar to the class HloEvaluator::OutputWindowIndexToInputIndex + // that does the corresponding function for Gather. + class UpdateWindowIndexToInputIndex { + public: + // The constructor does some setup work that is amortized across all + // iterations. + explicit UpdateWindowIndexToInputIndex( + const ScatterDimensionNumbers& dim_numbers, const Shape& input_shape, + const Shape& updates_shape) { + std::vector window_index_to_update_index; + int64 update_index_count = 0; + for (int64 i = 0; i < updates_shape.dimensions_size(); i++) { + if (c_binary_search(dim_numbers.update_window_dims(), i)) { + window_index_to_update_index.push_back(update_index_count++); + } else { + update_index_count++; + } + } + + int64 window_dim_count = 0; + for (int64 i = 0; i < input_shape.dimensions_size(); i++) { + if (c_binary_search(dim_numbers.inserted_window_dims(), i)) { + input_dim_value_to_update_index_.push_back(-1); + } else { + input_dim_value_to_update_index_.push_back( + window_index_to_update_index[window_dim_count++]); + } + } + + input_index_.resize(input_shape.dimensions_size()); + } + + // Returns the contribution of the window indices to the input index + // corresponding to update_index. See scatter_inner_loop_body. + // + // This is conceptually a stateless transformation from update_index to the + // window input index, but instead of allocating memory to represent the + // scatter input index on every invocation we reuse the same storage for the + // result (input_index_), mutating it in place. + // + // This returns an arrayslice into memory owned by the class. + StatusOr> operator()( + tensorflow::gtl::ArraySlice update_index) { + PropagateUpdateIndexWindowDimsToInputIndex(update_index); + return tensorflow::gtl::ArraySlice(input_index_); + } + + // Returns for a given 'input_dim' the corresponding update dimension index, + // or -1 if 'input_dim' is an elided window dimension. + int64 input_dim_value_to_update_index(int64 input_dim) { + return input_dim_value_to_update_index_[input_dim]; + } + + private: + // Propagates window dimensions from the update index to input_index_ by + // mutating input_index_ in place. + void PropagateUpdateIndexWindowDimsToInputIndex( + tensorflow::gtl::ArraySlice update_index) { + for (int64 i = 0, e = input_index_.size(); i < e; i++) { + if (input_dim_value_to_update_index_[i] != -1) { + input_index_[i] = update_index[input_dim_value_to_update_index_[i]]; + } + + // If input_dim_value_to_index_vector_[i] == -1 then input_index_[i] + // remains 0, as set by the constructor. + } + } + + // input_dim_value_to_index_vector_[i] tells us how to compute dimension i + // of the input index from the update index. See + // PropagateUpdateIndexWindowDimsToInputIndex. + std::vector input_dim_value_to_update_index_; + + // The result computed by this functor. operator() returns an ArraySlice + // into this vector. + std::vector input_index_; + }; + + Status HandleScatter(HloInstruction* scatter) override { + const ScatterDimensionNumbers& dim_numbers = + scatter->scatter_dimension_numbers(); + const Literal& operand = + parent_->GetEvaluatedLiteralFor(scatter->operand(0)); + std::unique_ptr reshaped_scatter_indices; + TF_ASSIGN_OR_RETURN(const Literal& scatter_indices, + ReshapedScatterIndices(dim_numbers.index_vector_dim(), + parent_->GetEvaluatedLiteralFor( + scatter->operand(1)), + &reshaped_scatter_indices)); + const Literal& updates = + parent_->GetEvaluatedLiteralFor(scatter->operand(2)); + const Shape& updates_shape = updates.shape(); + const Shape& operand_shape = operand.shape(); + + ShapeUtil::IndexIterationSpace scatter_indices_iteration_space = + IterationSpaceForUpdateScatterIndices(updates_shape, dim_numbers); + ShapeUtil::IndexIterationSpace window_indices_iteration_space = + IterationSpaceForUpdateWindowIndices(updates_shape, dim_numbers); + + std::vector input_index(operand_shape.dimensions_size()); + std::vector update_index(updates_shape.dimensions_size()); + std::vector input_scatter_index_clamped( + operand_shape.dimensions_size()); + + UpdateScatterIndexToInputIndex update_scatter_index_to_input_index( + &scatter->scatter_dimension_numbers(), /*input_shape=*/operand_shape, + updates_shape, &scatter_indices); + UpdateWindowIndexToInputIndex update_window_index_to_input_index( + scatter->scatter_dimension_numbers(), /*input_shape=*/operand_shape, + updates_shape); + + // Initialize the result with the operand. This makes it easier to handle + // the updates even when the indices are repeated. + std::unique_ptr result = operand.CloneToUnique(); + HloEvaluator embedded_evaluator; + auto scatter_inner_loop_body = + [&](tensorflow::gtl::ArraySlice update_window_index, + tensorflow::gtl::ArraySlice input_scatter_index, + tensorflow::gtl::ArraySlice update_scatter_index) + -> StatusOr { + TF_ASSIGN_OR_RETURN( + tensorflow::gtl::ArraySlice input_window_index, + update_window_index_to_input_index(update_window_index)); + for (int i = 0, e = update_index.size(); i < e; i++) { + update_index[i] = update_scatter_index[i] + update_window_index[i]; + DCHECK_LT(update_index[i], updates_shape.dimensions(i)); + } + for (int i = 0, e = input_scatter_index.size(); i < e; i++) { + int64 update_dim = + update_window_index_to_input_index.input_dim_value_to_update_index( + i); + // If 'update_dim' is -1, it means 'i' is an elided window dim. This + // means we set the iteration index to 0, so for the purpose of the + // following calculations we can consider the update dimension size to + // be 1. + int64 update_dim_size = + update_dim == -1 ? 1 : updates_shape.dimensions(update_dim); + // Clamp the scatter index so that the scatter region fits in the + // operand. input_scatter_index_clamped[i] = + // clamp(input_scatter_index[i], 0, + // operand_shape.dimensions(i) - + // update_dim_size); + input_scatter_index_clamped[i] = + std::min(operand_shape.dimensions(i) - update_dim_size, + std::max(0LL, input_scatter_index[i])); + } + for (int i = 0, e = input_index.size(); i < e; i++) { + input_index[i] = input_scatter_index_clamped[i] + input_window_index[i]; + DCHECK_GE(input_index[i], 0); + DCHECK_LT(input_index[i], operand_shape.dimensions(i)); + } + + auto result_value_literal = + LiteralUtil::CreateR0(result->Get(input_index)); + auto update_value_literal = + LiteralUtil::CreateR0(updates.Get(update_index)); + std::unique_ptr updated_result = + embedded_evaluator + .Evaluate( + *scatter->to_apply(), + {result_value_literal.get(), update_value_literal.get()}) + .ConsumeValueOrDie(); + // Clear visit states so that the we can use the evaluate again on the + // same computation. + embedded_evaluator.ResetVisitStates(); + result->Set(input_index, updated_result->Get({})); + return true; + }; + + auto scatter_outer_loop_body = + [&](tensorflow::gtl::ArraySlice update_scatter_index) + -> StatusOr { + TF_ASSIGN_OR_RETURN( + tensorflow::gtl::ArraySlice input_scatter_index, + update_scatter_index_to_input_index(update_scatter_index)); + TF_RETURN_IF_ERROR(ShapeUtil::ForEachIndexWithStatus( + updates_shape, window_indices_iteration_space, + [&](tensorflow::gtl::ArraySlice update_window_index) { + return scatter_inner_loop_body( + update_window_index, input_scatter_index, update_scatter_index); + })); + return true; + }; + + TF_RETURN_IF_ERROR(ShapeUtil::ForEachIndexWithStatus( + updates_shape, scatter_indices_iteration_space, + scatter_outer_loop_body)); + parent_->evaluated_[scatter] = std::move(result); + return Status::OK(); + } + Status HandleSlice(HloInstruction* slice) override { auto operand = slice->operand(0); const Shape& shape = slice->shape(); diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index bfe83cabf1c1168ee966827b7186004b708ad387..1efa6eb5bda7e1cb90874e0466aafd2c788a3fbf 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -1048,6 +1048,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { case HloOpcode::kMap: return kGray; case HloOpcode::kCrossReplicaSum: + case HloOpcode::kAllToAll: case HloOpcode::kInfeed: case HloOpcode::kOutfeed: case HloOpcode::kRecv: diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 7591b992048e373d4b67bb7863af4eb4b7f65e11..8690f2cdaa9b45d126e91b123c6992cbe2f27e1d 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -320,6 +320,15 @@ StatusOr> HloInstruction::CreateFromProto( /*all_reduce_id=*/all_reduce_id); break; } + case HloOpcode::kAllToAll: { + instruction = CreateAllToAll( + proto.shape(), all_operands(), + /*replica_groups=*/ + std::vector(proto.replica_groups().begin(), + proto.replica_groups().end()), + /*barrier=*/proto.cross_replica_sum_barrier()); + break; + } case HloOpcode::kConvolution: TF_RET_CHECK(proto.operand_ids_size() == 2) << "Convolution instruction should have 2 operands but sees " @@ -671,6 +680,14 @@ HloInstruction::CreateCrossReplicaSum( all_reduce_id); } +/* static */ std::unique_ptr HloInstruction::CreateAllToAll( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + const std::vector& replica_groups, + tensorflow::StringPiece barrier) { + return MakeUnique(shape, operands, replica_groups, + barrier); +} + /* static */ std::unique_ptr HloInstruction::CreateInfeed( const Shape& infeed_shape, HloInstruction* token_operand, const string& config) { @@ -1153,6 +1170,7 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kGetTupleElement: case HloOpcode::kReducePrecision: case HloOpcode::kCrossReplicaSum: + case HloOpcode::kAllToAll: case HloOpcode::kInfeed: case HloOpcode::kOutfeed: case HloOpcode::kConvolution: @@ -1620,6 +1638,7 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kInfeed: case HloOpcode::kOutfeed: case HloOpcode::kCrossReplicaSum: + case HloOpcode::kAllToAll: case HloOpcode::kConvolution: case HloOpcode::kCustomCall: case HloOpcode::kReduceWindow: @@ -2265,6 +2284,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { return visitor->HandleFft(this); case HloOpcode::kCrossReplicaSum: return visitor->HandleCrossReplicaSum(this); + case HloOpcode::kAllToAll: + return visitor->HandleAllToAll(this); case HloOpcode::kTuple: return visitor->HandleTuple(this); case HloOpcode::kMap: @@ -3139,12 +3160,23 @@ const std::vector& HloInstruction::replica_group_ids() const { return Cast(this)->replica_group_ids(); } +const std::vector& HloInstruction::replica_groups() const { + return Cast(this)->replica_groups(); +} + string HloInstruction::cross_replica_sum_barrier() const { - return Cast(this)->cross_replica_sum_barrier(); + if (opcode() == HloOpcode::kCrossReplicaSum) { + return Cast(this)->cross_replica_sum_barrier(); + } + return Cast(this)->cross_replica_sum_barrier(); } void HloInstruction::set_cross_replica_sum_barrier(const string& barrier) { - return Cast(this)->set_cross_replica_sum_barrier( + if (opcode() == HloOpcode::kCrossReplicaSum) { + return Cast(this)->set_cross_replica_sum_barrier( + barrier); + } + return Cast(this)->set_cross_replica_sum_barrier( barrier); } diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index e722086732947c41c9b1bfa76fe88fe35c3e45d6..3c575ae6ea8e60f48def4debcd9cfbea63e396b2 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -449,6 +449,26 @@ class HloInstruction { tensorflow::StringPiece barrier, const tensorflow::gtl::optional& all_reduce_id); + // This op handles the communication of an Alltoall operation. On each core, + // the operands are N ops in the same shape, where N is the number of cores + // participating the Alltoall. Then the N operands are scattered to N cores, + // e.g., the ith operand is sent to the ith core. Then each core gathers the + // received data into a tuple. + // + // - `replica_groups`: each ReplicaGroup contains a list of replica id. If + // empty, all replicas belong to one group in the order of 0 - (n-1). Alltoall + // will be applied within subgroups in the specified order. For example, + // replica groups = {{1,2,3},{4,5,0}} means, an Alltoall will be applied + // within replica 1, 2, 3, and in the gather phase, the received blocks will + // be concatenated in the order of 1, 2, 3; another Alltoall will be applied + // within replica 4, 5, 0, and the concatenation order is 4, 5, 0. + // + // TODO(b/110096724): This is NOT YET ready to use. + static std::unique_ptr CreateAllToAll( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + const std::vector& replica_groups, + tensorflow::StringPiece barrier); + // Creates a conversion instruction, where operand is the data to convert and // shape is the target shape for the conversion. static std::unique_ptr CreateConvert(const Shape& shape, @@ -1414,6 +1434,9 @@ class HloInstruction { // Delegates to HloAllReduceInstruction::replica_group_ids. const std::vector& replica_group_ids() const; + // Delegates to HloAllToAllInstruction::replica_groups. + const std::vector& replica_groups() const; + // Delegates to HloAllReduceInstruction::cross_replica_sum_barrier. string cross_replica_sum_barrier() const; void set_cross_replica_sum_barrier(const string& barrier); diff --git a/tensorflow/compiler/xla/service/hlo_instructions.cc b/tensorflow/compiler/xla/service/hlo_instructions.cc index 1d71a74c4092291cfd29b9026e50676e1661aad1..1de5032670ff47cda5599cf736bbd3529cfcaba9 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.cc +++ b/tensorflow/compiler/xla/service/hlo_instructions.cc @@ -359,6 +359,67 @@ HloAllReduceInstruction::CloneWithNewOperandsImpl( cross_replica_sum_barrier(), all_reduce_id()); } +HloAllToAllInstruction::HloAllToAllInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operands, + const std::vector& replica_groups, + tensorflow::StringPiece barrier) + : HloInstruction(HloOpcode::kAllToAll, shape), + replica_groups_(replica_groups), + cross_replica_sum_barrier_(barrier.begin(), barrier.end()) { + for (auto operand : operands) { + AppendOperand(operand); + } +} + +bool HloAllToAllInstruction::IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const { + const auto& casted_other = static_cast(other); + return ContainersEqual(replica_groups(), casted_other.replica_groups(), + [](const ReplicaGroup& a, const ReplicaGroup& b) { + return ContainersEqual(a.replica_ids(), + b.replica_ids()); + }) && + cross_replica_sum_barrier() == + casted_other.cross_replica_sum_barrier(); +} + +std::unique_ptr +HloAllToAllInstruction::CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* /*context*/) const { + return MakeUnique( + shape, new_operands, replica_groups(), cross_replica_sum_barrier()); +} + +std::vector HloAllToAllInstruction::ExtraAttributesToStringImpl( + const HloPrintOptions& options) const { + std::vector result; + std::vector replica_group_str; + for (const ReplicaGroup& group : replica_groups()) { + replica_group_str.push_back( + StrCat("{", Join(group.replica_ids(), ","), "}")); + } + result.push_back( + StrCat("replica_groups={", Join(replica_group_str, ","), "}")); + + if (!cross_replica_sum_barrier().empty()) { + result.push_back(StrCat("barrier=\"", cross_replica_sum_barrier(), "\"")); + } + + return result; +} + +HloInstructionProto HloAllToAllInstruction::ToProto() const { + HloInstructionProto proto = HloInstruction::ToProto(); + *proto.mutable_replica_groups() = {replica_groups_.begin(), + replica_groups_.end()}; + proto.set_cross_replica_sum_barrier(cross_replica_sum_barrier_); + return proto; +} + HloReverseInstruction::HloReverseInstruction( const Shape& shape, HloInstruction* operand, tensorflow::gtl::ArraySlice dimensions) diff --git a/tensorflow/compiler/xla/service/hlo_instructions.h b/tensorflow/compiler/xla/service/hlo_instructions.h index b0388223376a65dbe86ee273246d2ace229ada13..9586ad667345111d05015e035c93fe6578e3b665 100644 --- a/tensorflow/compiler/xla/service/hlo_instructions.h +++ b/tensorflow/compiler/xla/service/hlo_instructions.h @@ -273,6 +273,47 @@ class HloAllReduceInstruction : public HloInstruction { tensorflow::gtl::optional all_reduce_id_; }; +class HloAllToAllInstruction : public HloInstruction { + public: + explicit HloAllToAllInstruction( + const Shape& shape, tensorflow::gtl::ArraySlice operand, + const std::vector& replica_groups, + tensorflow::StringPiece barrier); + + const std::vector& replica_groups() const { + return replica_groups_; + } + + // TODO(b/110096724): rename this. + void set_cross_replica_sum_barrier(string barrier) { + cross_replica_sum_barrier_ = barrier; + } + string cross_replica_sum_barrier() const { + return cross_replica_sum_barrier_; + } + + HloInstructionProto ToProto() const override; + + private: + std::vector ExtraAttributesToStringImpl( + const HloPrintOptions& options) const override; + bool IdenticalSlowPath( + const HloInstruction& other, + const std::function& + eq_computations) const override; + + // Implementation for non-common logic of CloneWithNewOperands. + std::unique_ptr CloneWithNewOperandsImpl( + const Shape& shape, + tensorflow::gtl::ArraySlice new_operands, + HloCloneContext* context) const override; + + std::vector replica_groups_; + + // The string representation of the barrier config. + string cross_replica_sum_barrier_; +}; + class HloReverseInstruction : public HloInstruction { public: explicit HloReverseInstruction(const Shape& shape, HloInstruction* operand, @@ -340,6 +381,18 @@ class HloReduceInstruction : public HloInstruction { // Returns a serialized representation of this instruction. HloInstructionProto ToProto() const override; + // Returns the input tensors to be reduced. + tensorflow::gtl::ArraySlice inputs() const { + return tensorflow::gtl::ArraySlice(operands(), 0, + operand_count() / 2); + } + + // Returns the init values of the reduction. + tensorflow::gtl::ArraySlice init_values() const { + return tensorflow::gtl::ArraySlice( + operands(), operand_count() / 2, operand_count()); + } + private: std::vector ExtraAttributesToStringImpl( const HloPrintOptions& options) const override; diff --git a/tensorflow/compiler/xla/service/hlo_matchers_test.cc b/tensorflow/compiler/xla/service/hlo_matchers_test.cc index 7de59acc1efbc0150b95ebdd85a13ede48eec2f9..7961aece541faeb66875885b380158756c503250 100644 --- a/tensorflow/compiler/xla/service/hlo_matchers_test.cc +++ b/tensorflow/compiler/xla/service/hlo_matchers_test.cc @@ -157,9 +157,8 @@ TEST(HloMatchersTest, ShardingMatcher) { Array assignment({2}); assignment.SetValues({0, 1}); auto sharding = HloSharding::Tuple( - tuple_shape, - {HloSharding::Tile(ShapeUtil::MakeShape(F32, {5}), assignment), - HloSharding::AssignDevice(1), HloSharding::Replicate()}); + tuple_shape, {HloSharding::Tile(assignment), HloSharding::AssignDevice(1), + HloSharding::Replicate()}); p2->set_sharding(sharding); EXPECT_THAT(p0.get(), op::NoSharding()); @@ -172,8 +171,7 @@ TEST(HloMatchersTest, ShardingMatcher) { EXPECT_THAT( p2.get(), - op::Sharding( - "{{f32[5] devices=[2]0,1}, {maximal device=1}, {replicated}}")); + op::Sharding("{{devices=[2]0,1}, {maximal device=1}, {replicated}}")); EXPECT_THAT(Explain(p0.get(), op::Sharding(HloSharding::AssignDevice(1))), "%param.0 = f32[5]{0} parameter(0) has no sharding (expected: " diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h index 88531b6f209380a3f1bffe4e78da960b6811d9fd..ec279867e595b66a22882703cc06046e3e916c96 100644 --- a/tensorflow/compiler/xla/service/hlo_opcode.h +++ b/tensorflow/compiler/xla/service/hlo_opcode.h @@ -47,6 +47,7 @@ namespace xla { #define HLO_OPCODE_LIST(V) \ V(kAbs, "abs") \ V(kAdd, "add") \ + V(kAllToAll, "all-to-all") \ V(kAtan2, "atan2") \ V(kBatchNormGrad, "batch-norm-grad") \ V(kBatchNormInference, "batch-norm-inference") \ diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index 93cc884e3a04a15eae927e1b8b9251c1d82290ad..2a8c6ecd9248b9bf77153781d9c169306c9a9197 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -125,6 +125,7 @@ class HloParser { kFloat, kString, kBracedInt64List, + kBracedInt64ListList, kHloComputation, kFftType, kWindow, @@ -205,6 +206,10 @@ class HloParser { bool ParseInt64List(const TokKind start, const TokKind end, const TokKind delim, std::vector* result); + // 'parse_and_add_item' is an lambda to parse an element in the list and add + // the parsed element to the result. It's supposed to capture the result. + bool ParseList(const TokKind start, const TokKind end, const TokKind delim, + const std::function& parse_and_add_item); bool ParseParamListToShape(Shape* shape, LocTy* shape_loc); bool ParseParamList(); @@ -619,6 +624,28 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, } break; } + case HloOpcode::kAllToAll: { + optional>> tmp_groups; + optional barrier; + attrs["replica_groups"] = {/*required=*/false, + AttrTy::kBracedInt64ListList, &tmp_groups}; + attrs["barrier"] = {/*required=*/false, AttrTy::kString, &barrier}; + if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { + return false; + } + std::vector replica_groups; + if (tmp_groups) { + c_transform(*tmp_groups, std::back_inserter(replica_groups), + [](const std::vector& ids) { + ReplicaGroup group; + *group.mutable_replica_ids() = {ids.begin(), ids.end()}; + return group; + }); + } + instruction = builder->AddInstruction(HloInstruction::CreateAllToAll( + shape, operands, replica_groups, barrier ? *barrier : "")); + break; + } case HloOpcode::kReshape: { if (!ParseOperands(&operands, /*expected_size=*/1) || !ParseAttributes(attrs)) { @@ -1383,7 +1410,6 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding, bool replicated = false; std::vector devices; std::vector tile_assignment_dimensions; - Shape tile_shape; while (lexer_.GetKind() != TokKind::kRbrace) { switch (lexer_.GetKind()) { case TokKind::kw_maximal: @@ -1434,7 +1460,8 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding, break; } case TokKind::kShape: - tile_shape = lexer_.GetShapeVal(); + // TODO(b/112302613): Left here for backward compatibility to ignore the + // removed tile shape data. lexer_.Lex(); break; case TokKind::kRbrace: @@ -1449,19 +1476,12 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding, return Error(loc, "replicated shardings should not have any devices assigned"); } - if (!ShapeUtil::Equal(tile_shape, Shape())) { - return Error(loc, - "replicated shardings should not have any tile shape set"); - } sharding->set_type(OpSharding::Type::OpSharding_Type_REPLICATED); } else if (maximal) { if (devices.size() != 1) { return Error(loc, "maximal shardings should have exactly one device assigned"); } - if (!ShapeUtil::Equal(tile_shape, Shape())) { - return Error(loc, "maximal shardings should not have any tile shape set"); - } sharding->set_type(OpSharding::Type::OpSharding_Type_MAXIMAL); sharding->add_tile_assignment_devices(devices[0]); } else { @@ -1469,9 +1489,6 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding, return Error( loc, "non-maximal shardings must have more than one device assigned"); } - if (ShapeUtil::Equal(tile_shape, Shape())) { - return Error(loc, "non-maximal shardings should have a tile shape set"); - } if (tile_assignment_dimensions.empty()) { return Error( loc, @@ -1479,7 +1496,6 @@ bool HloParser::ParseSingleSharding(OpSharding* sharding, "dimensions"); } sharding->set_type(OpSharding::Type::OpSharding_Type_OTHER); - *sharding->mutable_tile_shape() = tile_shape; for (tensorflow::int64 dim : tile_assignment_dimensions) { sharding->add_tile_assignment_dimensions(dim); } @@ -2255,6 +2271,26 @@ bool HloParser::ParseAttributeHelper( ->emplace(result); return true; } + case AttrTy::kBracedInt64ListList: { + std::vector> result; + auto parse_and_add_item = [&]() { + std::vector item; + if (!ParseInt64List(TokKind::kLbrace, TokKind::kRbrace, + TokKind::kComma, &item)) { + return false; + } + result.push_back(item); + return true; + }; + if (!ParseList(TokKind::kLbrace, TokKind::kRbrace, TokKind::kComma, + parse_and_add_item)) { + return false; + } + static_cast>>*>( + attr_out_ptr) + ->emplace(result); + return true; + } case AttrTy::kSliceRanges: { SliceRanges result; if (!ParseSliceRanges(&result)) { @@ -2597,6 +2633,26 @@ bool HloParser::ParseInt64List(const TokKind start, const TokKind end, end, StrCat("expects an int64 list to end with ", TokKindToString(end))); } +bool HloParser::ParseList(const TokKind start, const TokKind end, + const TokKind delim, + const std::function& parse_and_add_item) { + if (!ParseToken(start, StrCat("expects a list starting with ", + TokKindToString(start)))) { + return false; + } + if (lexer_.GetKind() == end) { + // empty + } else { + do { + if (!parse_and_add_item()) { + return false; + } + } while (EatIfPresent(delim)); + } + return ParseToken( + end, StrCat("expects a list to end with ", TokKindToString(end))); +} + // param_list_to_shape ::= param_list '->' shape bool HloParser::ParseParamListToShape(Shape* shape, LocTy* shape_loc) { if (!ParseParamList() || !ParseToken(TokKind::kArrow, "expects '->'")) { diff --git a/tensorflow/compiler/xla/service/hlo_parser_test.cc b/tensorflow/compiler/xla/service/hlo_parser_test.cc index 7344679bb619b841483dde461b634a38b1490d44..4cd21841f4c25071d222cd291ed56aad2d266ca7 100644 --- a/tensorflow/compiler/xla/service/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/service/hlo_parser_test.cc @@ -1070,6 +1070,30 @@ ENTRY CrossReplicaSumWithSubgroups { ROOT cross-replica-sum = f32[128,32]{0,1} cross-replica-sum(input), replica_group_ids={0,0,1,1}, barrier="abc", to_apply=add } +)" +}, +// all-to-all +{ +"AllToAll", +R"(HloModule AllToAll + +ENTRY AllToAll { + input = f32[128,32]{0,1} parameter(0) + ROOT a2a = f32[128,32]{0,1} all-to-all(input), replica_groups={} +} + +)" +}, +// all-to-all with subgroups +{ +"AllToAllWithSubgroups", +R"(HloModule AllToAllWithSubgroups + +ENTRY AllToAllWithSubgroups { + input = f32[128,32]{0,1} parameter(0) + ROOT a2a = f32[128,32]{0,1} all-to-all(input), replica_groups={{1,2},{3,0}}, barrier="abc" +} + )" }, // Iota diff --git a/tensorflow/compiler/xla/service/hlo_sharding.cc b/tensorflow/compiler/xla/service/hlo_sharding.cc index 6399f6ef3c56383b9357f5f280e4a123dadca693..879fb3bbab2ada0f924282f16b3d9ccb4c2cb203 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding.cc @@ -31,12 +31,9 @@ HloSharding HloSharding::Tile1D(const Shape& input_shape, int64 num_tiles) { CHECK_EQ(1, ShapeUtil::Rank(input_shape)); CHECK_GT(num_tiles, 1); std::vector dimensions(1, num_tiles); - Shape tile_shape = input_shape; - auto& tile_dimension = (*tile_shape.mutable_dimensions())[0]; - tile_dimension = CeilOfRatio(static_cast(tile_dimension), num_tiles); Array assignment(dimensions); std::iota(assignment.begin(), assignment.end(), 0); - return HloSharding(tile_shape, assignment); + return HloSharding(assignment); } HloSharding HloSharding::Tuple(const ShapeTree& sub_shardings) { @@ -104,8 +101,7 @@ string HloSharding::ToString() const { return StrCat( "{maximal device=", static_cast(*tile_assignment_.begin()), "}"); } else { - return StrCat("{", ShapeUtil::HumanString(tile_shape_), " ", "devices=[", - Join(tile_assignment_.dimensions(), ","), "]", + return StrCat("{devices=[", Join(tile_assignment_.dimensions(), ","), "]", Join(tile_assignment_, ","), "}"); } } @@ -145,7 +141,6 @@ std::map HloSharding::UsedDevices(int64* count) const { } std::vector HloSharding::TileIndexForDevice(int64 device) const { - CHECK(!ShapeUtil::IsTuple(tile_shape_)); CHECK(!maximal_); CHECK(!IsTuple()); std::vector ret_index; @@ -165,32 +160,43 @@ int64 HloSharding::DeviceForTileIndex( if (maximal_) { return *tile_assignment_.begin(); } - CHECK_EQ(ShapeUtil::Rank(tile_shape_), tile_assignment_.dimensions().size()); return tile_assignment_(index); } -std::vector HloSharding::TileOffsetForDevice(int64 device) const { +std::vector HloSharding::TileOffsetForDevice(const Shape& shape, + int64 device) const { CHECK(!IsTuple()); - std::vector index = TileIndexForDevice(device); if (maximal_) { - // Index will always be all zeroes if we're maximal, and tile_shape_ is not - // valid. - return index; + return std::vector(shape.dimensions_size(), 0); } + + CHECK_EQ(shape.dimensions_size(), tile_assignment_.num_dimensions()); + std::vector index = TileIndexForDevice(device); for (int64 i = 0; i < index.size(); ++i) { - index[i] *= tile_shape_.dimensions(i); + const int64 shape_dim = shape.dimensions(i); + index[i] = std::min( + index[i] * CeilOfRatio(shape_dim, tile_assignment_.dim(i)), shape_dim); } return index; } -std::vector HloSharding::TileLimitForDevice(int64 device) const { +std::vector HloSharding::TileLimitForDevice(const Shape& shape, + int64 device) const { CHECK(!IsTuple()); - CHECK(!maximal_); // Maximal shardings do not have a valid tile shape. + if (maximal_) { + return std::vector(shape.dimensions().begin(), + shape.dimensions().end()); + } + + CHECK_EQ(shape.dimensions_size(), tile_assignment_.num_dimensions()); std::vector index = TileIndexForDevice(device); for (int64 i = 0; i < index.size(); ++i) { - index[i] = (index[i] + 1) * tile_shape_.dimensions(i); + const int64 shape_dim = shape.dimensions(i); + index[i] = std::min( + (index[i] + 1) * CeilOfRatio(shape_dim, tile_assignment_.dim(i)), + shape_dim); } return index; } @@ -336,11 +342,12 @@ Status HloSharding::ValidateNonTuple(const Shape& shape, return Status::OK(); } - // The tile rank must be the same as the input rank. - if (ShapeUtil::Rank(shape) != ShapeUtil::Rank(tile_shape_)) { + // The tile assignment tensor must have the same rank as the input. + if (ShapeUtil::Rank(shape) != tile_assignment_.num_dimensions()) { return tensorflow::errors::InvalidArgument( - "Tile rank is different to the input rank. sharding=", ToString(), - ", input_shape=", ShapeUtil::HumanString(shape)); + "Number of tile assignment dimensions is different to the input rank. " + "sharding=", + ToString(), ", input_shape=", ShapeUtil::HumanString(shape)); } // The correct constructor have to be used to create tile maximal shardings. @@ -350,20 +357,6 @@ Status HloSharding::ValidateNonTuple(const Shape& shape, "sharding was intended, use HloSharding::Replicated(). If a device " "placement was intended, use HloSharding::AssignDevice()"); } - - // The tile assignment tensor must contain enough element to cover the full - // shape with tiles of the specified size. - for (int64 i = 0, e = tile_assignment_.dimensions().size(); i != e; ++i) { - int64 total_tile_size = tile_assignment_.dim(i) * tile_shape_.dimensions(i); - if (shape.dimensions(i) > total_tile_size) { - return tensorflow::errors::InvalidArgument( - StrCat("Tile assignment tensor has too few element to cover the full " - "shape. Dimension ", - i, ", shape ", shape.dimensions(i), ", total size ", - total_tile_size)); - } - } - return Status::OK(); } @@ -393,7 +386,7 @@ Status HloSharding::ValidateNonTuple(const Shape& shape, proto.tile_assignment_dimensions().end())); std::copy(proto.tile_assignment_devices().begin(), proto.tile_assignment_devices().end(), tile_assignment.begin()); - return HloSharding(proto.tile_shape(), tile_assignment); + return HloSharding(tile_assignment); } OpSharding HloSharding::ToProto() const { @@ -407,7 +400,6 @@ OpSharding HloSharding::ToProto() const { return result; } - *result.mutable_tile_shape() = tile_shape_; for (int64 dim : tile_assignment_.dimensions()) { result.add_tile_assignment_dimensions(dim); } @@ -424,30 +416,16 @@ OpSharding HloSharding::ToProto() const { return result; } -HloSharding HloSharding::TransformShardedTileShape( - const Shape& new_shape, - const std::function& transform) const { - CHECK(!IsTuple()); +Shape HloSharding::TileShape(const Shape& shape) const { if (IsTileMaximal()) { - return *this; + return shape; } - CHECK_EQ(ShapeUtil::Rank(new_shape), ShapeUtil::Rank(tile_shape())); - Shape new_tile_shape; - new_tile_shape.set_element_type(tile_shape().element_type()); - for (int64 i = 0; i < ShapeUtil::Rank(new_shape); ++i) { - int64 dim; - if (tile_assignment().dim(i) == 1) { - dim = new_shape.dimensions(i); - } else if (transform) { - dim = transform(i, tile_shape().dimensions(i)); - } else { - dim = tile_shape().dimensions(i); - } - new_tile_shape.add_dimensions(dim); + Shape result_shape = shape; + for (int64 i = 0; i < shape.dimensions_size(); ++i) { + (*result_shape.mutable_dimensions())[i] = + CeilOfRatio(shape.dimensions(i), tile_assignment_.dim(i)); } - TF_CHECK_OK( - LayoutUtil::CopyLayoutBetweenShapes(tile_shape_, &new_tile_shape)); - return HloSharding::Tile(new_tile_shape, tile_assignment()); + return result_shape; } HloSharding HloSharding::GetSubSharding(const Shape& shape, @@ -489,9 +467,6 @@ size_t HloSharding::Hash() const { for (uint32 v : tile_assignment_) { h = tensorflow::Hash64Combine(h, std::hash{}(v)); } - for (uint32 v : tile_shape_.dimensions()) { - h = tensorflow::Hash64Combine(h, std::hash{}(v)); - } return h; } diff --git a/tensorflow/compiler/xla/service/hlo_sharding.h b/tensorflow/compiler/xla/service/hlo_sharding.h index 28575c0e75548d1a21381b37754232c5d843dfbe..894783e5d1538fa4e8e91b65827121f32040af83 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding.h +++ b/tensorflow/compiler/xla/service/hlo_sharding.h @@ -48,22 +48,10 @@ class HloSharding { // the input shape (one tile) assigned to a single device. static HloSharding AssignDevice(int64 device_id); - // Creates a new sharding which splits a shape into tiles each with shape - // `tile_shape`. Each tile is assigned to one device, which is specified by - // `tile_assignment`. Any tensor not a multiple of the tile size in any - // dimension is implicitly padded to the tile size. - // - // e.g. Tile({2, 2}, {0, 1}) on a tensor of shape {3, 2} would look like: - // 2 1 padding - // <------><-> - // +----+----+ - // | 0 | 1 | - // +----+----+ - // - // Split into two tiles, one of which is implicitly padded by one. - static HloSharding Tile(const Shape& tile_shape, - const Array& tile_assignment) { - return HloSharding(tile_shape, tile_assignment); + // Creates a new sharding which splits a shape into tiles amongst the devices + // specified by `tile_assignment`. + static HloSharding Tile(const Array& tile_assignment) { + return HloSharding(tile_assignment); } // Creates a new sharding which splits a one-dimensional input shape into @@ -146,17 +134,18 @@ class HloSharding { // REQUIRES: !IsTuple() int64 DeviceForTileIndex(tensorflow::gtl::ArraySlice index) const; - // Given a device ID, returns the offset within the input space of the + // Given a device ID, returns the offset within the specified shape of the // tile that should be executed on the given core. This returns the lower // extent of the tile in the input space. // REQUIRES: !IsTuple() - std::vector TileOffsetForDevice(int64 device) const; + std::vector TileOffsetForDevice(const Shape& shape, + int64 device) const; - // Given a device ID, returns the limit within the input space of the + // Given a device ID, returns the limit within the specified shape of the // tile that should be executed on the given core. This returns the upper // extent of the tile in the input space. // REQUIRES: !IsTuple() - std::vector TileLimitForDevice(int64 device) const; + std::vector TileLimitForDevice(const Shape& shape, int64 device) const; // Returns the single device this op operates on. If the sharding does not // span a single device, the return value will be empty. @@ -197,7 +186,6 @@ class HloSharding { bool operator==(const HloSharding& other) const { return replicated_ == other.replicated_ && maximal_ == other.maximal_ && - ShapeUtil::Compatible(tile_shape_, other.tile_shape_) && tile_assignment_ == other.tile_assignment_ && tuple_elements_ == other.tuple_elements_; } @@ -211,9 +199,6 @@ class HloSharding { } }; - // Gets the tile shape. - // REQUIRES: !IsTileMaximal() && !IsTuple() - const Shape& tile_shape() const { return tile_shape_; } // Gets the tile assignment tensor. // REQUIRES: !IsReplicated() && !IsTuple() const Array& tile_assignment() const { return tile_assignment_; } @@ -225,25 +210,15 @@ class HloSharding { return tuple_elements_; } - // Return a new sharding that can apply to the given new shape. - // If this sharding is tile-maximal, the returned sharding will be the same as - // this sharding. If this sharding is not tile-maximal, the returned - // sharding's tile size will differ: - // - Non-sharded dimensions will be adapted to be the same as `new_shape`; - // tile_dimension(i) = new_shape.dimensions(i); - // - Sharded dimensions will be kept the same unless `transform` is supplied - // in which case tile_dimension(i) = transform(i, tile_dimension(i)); - // REQUIRES: !IsTuple(). - HloSharding TransformShardedTileShape( - const Shape& new_shape, - const std::function& transform = nullptr) const; + // Gets the tile shape. + // REQUIRES: !IsTuple() + Shape TileShape(const Shape& shape) const; private: HloSharding() : replicated_(true), maximal_(true), tuple_(false), - tile_shape_(), tile_assignment_({0}) {} // device_id values: // -2: magic number to mean unassigned device, used by spatial partitioning @@ -255,15 +230,13 @@ class HloSharding { : replicated_(false), maximal_(true), tuple_(false), - tile_shape_(), tile_assignment_({1}, device_id) {} - HloSharding(const Shape& tile_shape, const Array& tile_assignment) + explicit HloSharding(const Array& tile_assignment) : replicated_(false), maximal_(false), tuple_(false), - tile_shape_(tile_shape), tile_assignment_(tile_assignment) {} - HloSharding(const std::vector& tuple_shardings) + explicit HloSharding(const std::vector& tuple_shardings) : replicated_(false), maximal_(false), tuple_(true), @@ -286,7 +259,6 @@ class HloSharding { bool replicated_; bool maximal_; bool tuple_; - Shape tile_shape_; Array tile_assignment_; // Only non-empty when tuple_ is true, but because empty tuples are allowed // may also be empty even then. This is a flattened list of all the leaf diff --git a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc index 94f5a3b273b2fd7e545472c42f3863f549dd3db1..a2c1d39d0d4893333b3c2ed0e3418b01dac8cefd 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_metadata.cc @@ -158,7 +158,6 @@ ShapeTree GetTupleSharding(HloInstruction* tuple) { const HloSharding* GetOperandSharding(const HloInstruction* operand, const DomainMetadata::Domain& domain, const HloSharding& sharding) { - DCHECK_EQ(domain.reach_set.count(const_cast(operand)), 1); // Here the user of operand is within the domain instruction set, and since it // is user of operand, we need to look into the enter_domains set. If this is // not a kDomain within the user domains set, then return the operand @@ -203,10 +202,17 @@ StatusOr ApplyDomainShardingPass(const DomainMetadata::Domain& domain, for (int64 i = 0; i < instruction->operand_count(); ++i) { const HloSharding* operand_sharding = GetOperandSharding(instruction->operand(i), domain, sharding); - if (operand_sharding != nullptr && - shape_tree.element({i}) != *operand_sharding) { - *shape_tree.mutable_element({i}) = *operand_sharding; - ++tuple_assigned; + if (operand_sharding != nullptr) { + HloSharding operand_subsharding = HloSharding::Replicate(); + if (operand_sharding == &sharding) { + operand_subsharding = + sharding.GetSubSharding(instruction->shape(), {i}); + operand_sharding = &operand_subsharding; + } + if (shape_tree.element({i}) != *operand_sharding) { + *shape_tree.mutable_element({i}) = *operand_sharding; + ++tuple_assigned; + } } } if (tuple_assigned > 0) { diff --git a/tensorflow/compiler/xla/service/hlo_sharding_test.cc b/tensorflow/compiler/xla/service/hlo_sharding_test.cc index aebda562d38a2e46be1ba1572a92213afeab40e5..45fc300fcaf5a301fe11768da77a7c0907919c39 100644 --- a/tensorflow/compiler/xla/service/hlo_sharding_test.cc +++ b/tensorflow/compiler/xla/service/hlo_sharding_test.cc @@ -39,7 +39,6 @@ Array MakeArray(tensorflow::gtl::ArraySlice dimensions, class HloShardingTest : public HloTestBase {}; TEST_F(HloShardingTest, Replicate) { - Shape tile_shape = ShapeUtil::MakeShape(U32, {4}); HloSharding sharding = HloSharding::Replicate(); EXPECT_TRUE(sharding.IsReplicated()); EXPECT_TRUE(sharding.IsTileMaximal()); @@ -79,37 +78,22 @@ TEST_F(HloShardingTest, DevicePlacement) { TEST_F(HloShardingTest, Tile) { { // Test should fail because of a duplicate tile assignment. - Shape tile_shape = ShapeUtil::MakeShape(U32, {2, 3}); - HloSharding sharding = - HloSharding::Tile(tile_shape, MakeArray({2, 2}, {0, 0, 2, 3})); + HloSharding sharding = HloSharding::Tile(MakeArray({2, 2}, {0, 0, 2, 3})); EXPECT_IS_NOT_OK(sharding.Validate(ShapeUtil::MakeShape(F32, {4, 6}), /*num_devices=*/4)); } { // Test should fail because of more devices used then `num_device`. - Shape tile_shape = ShapeUtil::MakeShape(U32, {2, 3}); - HloSharding sharding = - HloSharding::Tile(tile_shape, MakeArray({2, 2}, {0, 1, 2, 3})); + HloSharding sharding = HloSharding::Tile(MakeArray({2, 2}, {0, 1, 2, 3})); EXPECT_IS_NOT_OK(sharding.Validate(ShapeUtil::MakeShape(U32, {4, 6}), /*num_devices=*/2)); } - { - // Test should fail because the total tiled size in dimension 0 is 4 but we - // have 6 elements along that dimensions. - Shape tile_shape = ShapeUtil::MakeShape(U32, {2, 3}); - HloSharding sharding = - HloSharding::Tile(tile_shape, MakeArray({2, 2}, {0, 1, 2, 3})); - EXPECT_IS_NOT_OK(sharding.Validate(ShapeUtil::MakeShape(F32, {6, 3}), - /*num_devices=*/4)); - } - { // Test should pass. - Shape tile_shape = ShapeUtil::MakeShape(U32, {2, 3}); - HloSharding sharding = - HloSharding::Tile(tile_shape, MakeArray({2, 2}, {0, 3, 2, 1})); + Shape shape = ShapeUtil::MakeShape(U32, {4, 5}); + HloSharding sharding = HloSharding::Tile(MakeArray({2, 2}, {0, 3, 2, 1})); EXPECT_IS_OK(sharding.Validate(ShapeUtil::MakeShape(F32, {3, 5}), /*num_devices=*/5)); @@ -118,10 +102,14 @@ TEST_F(HloShardingTest, Tile) { EXPECT_EQ(2, sharding.DeviceForTileIndex({1, 0})); EXPECT_EQ(1, sharding.DeviceForTileIndex({1, 1})); - EXPECT_EQ(sharding.TileOffsetForDevice(0), (std::vector{0, 0})); - EXPECT_EQ(sharding.TileOffsetForDevice(3), (std::vector{0, 3})); - EXPECT_EQ(sharding.TileOffsetForDevice(2), (std::vector{2, 0})); - EXPECT_EQ(sharding.TileOffsetForDevice(1), (std::vector{2, 3})); + EXPECT_EQ(sharding.TileOffsetForDevice(shape, 0), + (std::vector{0, 0})); + EXPECT_EQ(sharding.TileOffsetForDevice(shape, 3), + (std::vector{0, 3})); + EXPECT_EQ(sharding.TileOffsetForDevice(shape, 2), + (std::vector{2, 0})); + EXPECT_EQ(sharding.TileOffsetForDevice(shape, 1), + (std::vector{2, 3})); EXPECT_FALSE(sharding.HasUniqueDevice()); } @@ -135,8 +123,7 @@ TEST_F(HloShardingTest, NestedTuple) { ShapeUtil::MakeShape(F32, {4, 6}), }); - HloSharding tiled_sharding = HloSharding::Tile( - ShapeUtil::MakeShape(F32, {4, 3}), Array({{0, 1}})); + HloSharding tiled_sharding = HloSharding::Tile(Array({{0, 1}})); OpSharding proto; proto.set_type(OpSharding::Type::OpSharding_Type_TUPLE); *proto.add_tuple_shardings() = HloSharding::Replicate().ToProto(); @@ -187,32 +174,11 @@ TEST_F(HloShardingTest, Hash) { } { - Shape tile_shape = ShapeUtil::MakeShape(U32, {2, 3}); - HloSharding sharding1 = - HloSharding::Tile(tile_shape, MakeArray({2, 2}, {0, 3, 2, 1})); - HloSharding sharding2 = HloSharding::Tile(ShapeUtil::MakeShape(U32, {2, 3}), - MakeArray({2, 2}, {0, 3, 2, 1})); - EXPECT_TRUE(hash_compare_equal(sharding1, sharding2)); - } - - { - Shape tile_shape = ShapeUtil::MakeShape(U32, {2, 3}); - HloSharding sharding1 = - HloSharding::Tile(tile_shape, MakeArray({2, 2}, {0, 3, 2, 1})); - HloSharding sharding2 = HloSharding::Tile(ShapeUtil::MakeShape(U32, {2, 3}), - MakeArray({2, 2}, {0, 3, 2, 1})); + HloSharding sharding1 = HloSharding::Tile(MakeArray({2, 2}, {0, 3, 2, 1})); + HloSharding sharding2 = HloSharding::Tile(MakeArray({2, 2}, {0, 3, 2, 1})); EXPECT_TRUE(hash_compare_equal(sharding1, sharding2)); } - { - Shape tile_shape = ShapeUtil::MakeShape(U32, {2, 3}); - HloSharding sharding1 = - HloSharding::Tile(tile_shape, MakeArray({2, 2}, {0, 3, 2, 1})); - HloSharding sharding2 = HloSharding::Tile(ShapeUtil::MakeShape(U32, {2, 3}), - MakeArray({2, 2}, {0, 3, 1, 2})); - EXPECT_FALSE(hash_compare_equal(sharding1, sharding2)); - } - HloSharding default_sharding = HloSharding::Replicate(); { ShapeTree shape_tree(ShapeUtil::MakeTupleShape({}), @@ -259,19 +225,6 @@ TEST_F(HloShardingTest, Hash) { } } -TEST_F(HloShardingTest, TransformShardedTileShapeTest) { - HloSharding sharding = - HloSharding::Tile(ShapeUtil::MakeShape(F32, {3, 5, 7, 11}), - Array4D({{{{0, 1}, {2, 3}}}})); - HloSharding result = sharding.TransformShardedTileShape( - ShapeUtil::MakeShape(F32, {13, 15, 17, 19}), - [](int dim, int value) { return dim * 111; }); - HloSharding expected = - HloSharding::Tile(ShapeUtil::MakeShape(F32, {13, 15, 222, 333}), - Array4D({{{{0, 1}, {2, 3}}}})); - EXPECT_EQ(result, expected); -} - TEST_F(HloShardingTest, ToStringReplicatedTest) { HloSharding sharding = HloSharding::Replicate(); EXPECT_EQ(sharding.ToString(), "{replicated}"); @@ -284,9 +237,8 @@ TEST_F(HloShardingTest, ToStringAssignDeviceTest) { TEST_F(HloShardingTest, ToStringTiledTest) { HloSharding sharding = - HloSharding::Tile(ShapeUtil::MakeShape(S32, {7, 11, 13}), - Array3D({{{2, 3}}, {{5, 7}}})); - EXPECT_EQ(sharding.ToString(), "{s32[7,11,13] devices=[2,1,2]2,3,5,7}"); + HloSharding::Tile(Array3D({{{2, 3}}, {{5, 7}}})); + EXPECT_EQ(sharding.ToString(), "{devices=[2,1,2]2,3,5,7}"); } TEST_F(HloShardingTest, ToStringTupleTest) { @@ -294,21 +246,18 @@ TEST_F(HloShardingTest, ToStringTupleTest) { ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(F32, {3, 5}), ShapeUtil::MakeShape(U32, {7, 25}), ShapeUtil::MakeShape(S32, {9, 11})}), - {HloSharding::Replicate(), - HloSharding::Tile(ShapeUtil::MakeShape(U32, {7, 13}), - Array2D({{3, 5}})), + {HloSharding::Replicate(), HloSharding::Tile(Array2D({{3, 5}})), HloSharding::AssignDevice(3)}); EXPECT_EQ(sharding.ToString(), - "{{replicated}, {u32[7,13] devices=[1,2]3,5}, {maximal device=3}}"); + "{{replicated}, {devices=[1,2]3,5}, {maximal device=3}}"); } TEST_F(HloShardingTest, OstreamTest) { HloSharding sharding = - HloSharding::Tile(ShapeUtil::MakeShape(F32, {3, 5, 7, 11}), - Array4D({{{{0, 1}, {2, 3}}}})); + HloSharding::Tile(Array4D({{{{0, 1}, {2, 3}}}})); std::ostringstream oss; oss << sharding; - EXPECT_EQ(oss.str(), "{f32[3,5,7,11] devices=[1,1,2,2]0,1,2,3}"); + EXPECT_EQ(oss.str(), "{devices=[1,1,2,2]0,1,2,3}"); } TEST_F(HloShardingTest, ParseHloString) { @@ -319,8 +268,7 @@ TEST_F(HloShardingTest, ParseHloString) { }; check(HloSharding::Replicate()); check(HloSharding::AssignDevice(2)); - check(HloSharding::Tile(ShapeUtil::MakeShape(F32, {3, 1, 3, 7}), - Array4D({{{{0}, {1}}}}))); + check(HloSharding::Tile(Array4D({{{{0}, {1}}}}))); // Empty tuple. One sharding is required for empty tuples, as we need to be // able to assign sharding to them, even though they have no leaves. check(HloSharding::Tuple(ShapeUtil::MakeTupleShape({}), @@ -332,8 +280,7 @@ TEST_F(HloShardingTest, ParseHloString) { ShapeUtil::MakeShape(F32, {3, 5, 7}), ShapeUtil::MakeShape(F32, {3, 7})}); check(HloSharding::Tuple( - tuple_shape, {HloSharding::Tile(ShapeUtil::MakeShape(F32, {3, 1, 3, 7}), - Array4D({{{{0}, {1}}}})), + tuple_shape, {HloSharding::Tile(Array4D({{{{0}, {1}}}})), HloSharding::Replicate(), HloSharding::AssignDevice(1)})); } { @@ -343,8 +290,7 @@ TEST_F(HloShardingTest, ParseHloString) { ShapeUtil::MakeTupleShape({ShapeUtil::MakeShape(F32, {3, 5, 7}), ShapeUtil::MakeShape(F32, {3, 7})})}); std::vector leaf_shardings = { - HloSharding::Tile(ShapeUtil::MakeShape(F32, {3, 1, 3, 7}), - Array4D({{{{0}, {1}}}})), + HloSharding::Tile(Array4D({{{{0}, {1}}}})), HloSharding::Replicate(), HloSharding::AssignDevice(1)}; ShapeTree sharding_tree(tuple_shape, HloSharding::Replicate()); // Assign leaf_shardings to sharding_tree leaves. diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index 1a8c206aaf35c1391923f047469f332b44d82e67..e7674f3ddd5baa87c872d1c0b40bff340f3cd911 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -105,6 +105,15 @@ Status ShapeVerifier::HandleCrossReplicaSum(HloInstruction* crs) { ShapeInference::InferCrossReplicaSumShape(operand_shapes)); } +Status ShapeVerifier::HandleAllToAll(HloInstruction* hlo) { + std::vector operand_shapes; + for (const HloInstruction* operand : hlo->operands()) { + operand_shapes.push_back(&operand->shape()); + } + return CheckShape(hlo, + ShapeInference::InferAllToAllTupleShape(operand_shapes)); +} + Status ShapeVerifier::HandleReducePrecision(HloInstruction* reduce_precision) { return CheckShape(reduce_precision, ShapeInference::InferReducePrecisionShape( reduce_precision->operand(0)->shape(), @@ -185,7 +194,67 @@ Status ShapeVerifier::HandleHostCompute(HloInstruction*) { return Status::OK(); } -Status ShapeVerifier::HandleRng(HloInstruction*) { return Status::OK(); } +bool ShapeVerifier::HasCompatibleElementTypes(const Shape& shape_0, + const Shape& shape_1, + const Shape& result_shape) { + return ShapeUtil::SameElementType(shape_0, shape_1) && + (ShapeUtil::SameElementType(shape_0, result_shape) || + (allow_mixed_precision_ && + ShapeUtil::SameElementTypeIgnoringFpPrecision(shape_0, + result_shape))); +} + +Status ShapeVerifier::HandleRng(HloInstruction* instruction) { + if (instruction->operand_count() != 2) { + return InternalError("Expected two operands for Rng instruction: %s", + instruction->ToString().c_str()); + } + + const Shape& shape_0 = instruction->operand(0)->shape(); + const Shape& shape_1 = instruction->operand(1)->shape(); + if (!ShapeUtil::IsScalar(shape_0) || !ShapeUtil::IsScalar(shape_1)) { + return InternalError( + "Expected scalar types for the two operands of Rng instruction: %s", + instruction->ToString().c_str()); + } + + if (!HasCompatibleElementTypes(shape_0, shape_1, instruction->shape())) { + return InternalError( + "Expected compatible element types for the result and the two operands" + " of Rng instruction: %s", + instruction->ToString().c_str()); + } + + PrimitiveType element_type = shape_0.element_type(); + switch (instruction->random_distribution()) { + case RNG_UNIFORM: + if (!primitive_util::IsFloatingPointType(element_type) && + !primitive_util::IsIntegralType(element_type) && + element_type != PRED) { + return InternalError( + "Element type not supported." + " Expected element to be of floating point type, integral type or" + " predicate type for RngUniform: %s", + instruction->ToString().c_str()); + } + break; + + case RNG_NORMAL: + if (!primitive_util::IsFloatingPointType(element_type)) { + return InternalError( + "Element type not supported." + " Expected element to be FloatingPointType for RngNormal: %s", + instruction->ToString().c_str()); + } + break; + default: + return InternalError( + "Invalid Rng distribution %s", + RandomDistribution_Name(instruction->random_distribution()).c_str()); + } + + return Status::OK(); +} Status ShapeVerifier::HandleReverse(HloInstruction* reverse) { return CheckShape( @@ -454,9 +523,9 @@ namespace { // inputs. Status CheckMixedPrecisionOperands(const HloInstruction* instruction) { switch (instruction->opcode()) { - // White list the following opcodes for mixed-precision check, because they - // involve data pass through or grouping via tuples, where the precisions - // of buffers can be different. + // White list the following opcodes for mixed-precision check, because + // they involve data pass through or grouping via tuples, where the + // precisions of buffers can be different. case HloOpcode::kCall: case HloOpcode::kConditional: case HloOpcode::kConstant: @@ -638,7 +707,8 @@ string ComputationsToString( // Verifies various invariants about the structure of the HLO: // -// (1) each instruction has a non-null parent() set to the HloComputation which +// (1) each instruction has a non-null parent() set to the HloComputation +// which // contains it. // // (2) each computation has a non-null parent() set to the HloModule which @@ -672,9 +742,9 @@ Status VerifyHloStructure(HloModule* module) { } // Check that operands are in the same computation separately from verifying - // parent() correctness so conditions like a null HloInstruction::parent() are - // identified and reported explicitly above rather than reporting a mismatched - // operand. + // parent() correctness so conditions like a null HloInstruction::parent() + // are identified and reported explicitly above rather than reporting a + // mismatched operand. for (const HloComputation* computation : module->computations()) { for (const HloInstruction* instruction : computation->instructions()) { for (int i = 0; i < instruction->operand_count(); ++i) { @@ -698,13 +768,14 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { HloComputation* fused_computation = fusion->fused_instructions_computation(); if (fusion != fused_computation->FusionInstruction()) { return InternalError( - "Instruction of fused computation does not match expected instruction " + "Instruction of fused computation does not match expected " + "instruction " "%s.", fusion->ToString().c_str()); } - // Fused root instruction and fused parameters must all be owned by the fusion - // computation. + // Fused root instruction and fused parameters must all be owned by the + // fusion computation. bool root_owned = false; const std::vector& fused_parameters = fusion->fused_parameters(); @@ -746,8 +817,8 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { fusion->ToString().c_str()); } - // All uses of fused instructions must be in the fusion computation, and every - // non-root instruction must have at least one use. + // All uses of fused instructions must be in the fusion computation, and + // every non-root instruction must have at least one use. for (auto* instruction : fusion->fused_instructions_computation()->instructions()) { if (instruction != fused_root) { @@ -791,7 +862,8 @@ Status HloVerifier::CheckFusionInstruction(HloInstruction* fusion) const { if (!ShapeUtil::Compatible(fused_param->shape(), fusion->operand(param_no)->shape())) { return InternalError( - "Shape mismatch between parameter number %lld and its operand in %s.", + "Shape mismatch between parameter number %lld and its operand in " + "%s.", param_no, fusion->ToString().c_str()); } } @@ -909,8 +981,9 @@ Status CheckSameChannel(const HloInstruction* instr1, return Status::OK(); } -// Checks if the given two instructions have the same is_host_transfer attribute -// value. Intsructions must be send/recv instructions or their 'done' variant. +// Checks if the given two instructions have the same is_host_transfer +// attribute value. Intsructions must be send/recv instructions or their +// 'done' variant. Status CheckSameIsHostTransfer(const HloInstruction* instr1, const HloInstruction* instr2) { const HloSendRecvInstruction* send_recv1 = @@ -921,7 +994,8 @@ Status CheckSameIsHostTransfer(const HloInstruction* instr1, TF_RET_CHECK(send_recv2 != nullptr); if (send_recv1->is_host_transfer() != send_recv2->is_host_transfer()) { return InternalError( - "Expected instructions to have the same is-host-transfer property: %s, " + "Expected instructions to have the same is-host-transfer property: " + "%s, " "%s ", instr1->ToString().c_str(), instr2->ToString().c_str()); } @@ -940,7 +1014,8 @@ Status VerifySendsAndRecvs(const HloModule& module) { host_channels.insert({sendrecv->channel_id(), sendrecv}); if (!it_inserted.second) { return FailedPrecondition( - "Channel %lld is used for multiple host send/recv instructions: %s " + "Channel %lld is used for multiple host send/recv instructions: " + "%s " "and " "%s", sendrecv->channel_id(), sendrecv->ToString().c_str(), diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h index 7feddaeabf9f944ed9cd4f5672ef63a7f9da2e40..c942fab08e1ace75bccb8762954787a4366922a9 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.h +++ b/tensorflow/compiler/xla/service/hlo_verifier.h @@ -45,6 +45,7 @@ class ShapeVerifier : public DfsHloVisitor { Status HandleConvolution(HloInstruction* convolution) override; Status HandleFft(HloInstruction* fft) override; Status HandleCrossReplicaSum(HloInstruction* crs) override; + Status HandleAllToAll(HloInstruction* hlo) override; Status HandleReducePrecision(HloInstruction* reduce_precision) override; Status HandleInfeed(HloInstruction*) override; Status HandleOutfeed(HloInstruction*) override; @@ -105,6 +106,13 @@ class ShapeVerifier : public DfsHloVisitor { Status CheckVariadicShape(const HloInstruction* instruction); private: + // Return true if the shapes of the two operands have the same element type, + // and the result shape either has the same element type as the operand + // shapes or mixed precision is allowed and the result shape and the operand + // shapes have floating point element types. + bool HasCompatibleElementTypes(const Shape& shape_0, const Shape& shape_1, + const Shape& result_shape); + // Whether the inputs and output of an instruction can contain both F32s and // BF16s. Tuples that include both F32s and BF16s are allowed regardless of // this flag. diff --git a/tensorflow/compiler/xla/service/hlo_verifier_test.cc b/tensorflow/compiler/xla/service/hlo_verifier_test.cc index 04c6ba3eeb92bad2b5b69f7f56e73e1f7a8148aa..d764964f3c3dc58a54bd0307f8b625076c14f3e5 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier_test.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier_test.cc @@ -34,7 +34,17 @@ namespace { using ::testing::HasSubstr; -using HloVerifierTest = HloTestBase; +class HloVerifierTest : public HloTestBase { + public: + HloVerifierTest() + : HloTestBase(/*allow_mixed_precision_in_hlo_verifier=*/false) {} +}; + +class HloVerifierTestAllowMixedPrecision : public HloTestBase { + public: + HloVerifierTestAllowMixedPrecision() + : HloTestBase(/*allow_mixed_precision_in_hlo_verifier=*/true) {} +}; TEST_F(HloVerifierTest, NullInstructionParent) { HloComputation::Builder builder(TestName()); @@ -174,5 +184,96 @@ ENTRY entry { HasSubstr("shape does not match parameter")); } +TEST_F(HloVerifierTest, RngOpnd0NotScalar) { + const char* const hlo_string = R"( + HloModule Module + + ENTRY RngOpnd0NotScalar { + constant.0 = f32[] constant(0) + constant.1 = f16[2] constant({1, 3}) + ROOT rng.0 = f32[10]{0} rng(f32[] constant.0, f16[2] constant.1), + distribution=rng_uniform + } + )"; + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(hlo_string)); + + auto status = verifier().Run(module.get()).status(); + ASSERT_FALSE(status.ok()); + EXPECT_THAT(status.error_message(), HasSubstr("Expected scalar type")); +} + +TEST_F(HloVerifierTest, RngOperandElementTypesDoNotMatch) { + const char* const hlo_string = R"( + HloModule Module + + ENTRY RngOperandElementTypesNotMatch { + constant.0 = f32[] constant(0) + constant.1 = f16[] constant(1) + ROOT rng.0 = f32[10]{0} rng(f32[] constant.0, f16[] constant.1), + distribution=rng_normal + } + )"; + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(hlo_string)); + + auto status = verifier().Run(module.get()).status(); + ASSERT_FALSE(status.ok()); + EXPECT_THAT(status.error_message(), + HasSubstr("Expected compatible element types")); +} + +TEST_F(HloVerifierTest, RngMixedPrecisionNotAllowed) { + const char* const hlo_string = R"( + HloModule Module + + ENTRY RngResultElementTypeNotMatch { + constant.0 = f32[] constant(0) + constant.1 = f32[] constant(1) + ROOT rng.0 = f16[10]{0} rng(f32[] constant.0, f32[] constant.1), + distribution=rng_normal + } + )"; + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(hlo_string)); + + auto status = verifier().Run(module.get()).status(); + ASSERT_FALSE(status.ok()); + EXPECT_THAT(status.error_message(), + HasSubstr("Expected compatible element types")); +} + +TEST_F(HloVerifierTestAllowMixedPrecision, RngMixedPrecisionAllowed) { + const char* const hlo_string = R"( + HloModule Module + + ENTRY RngResultElementTypeNotMatch { + constant.0 = f32[] constant(0) + constant.1 = f32[] constant(1) + ROOT rng.0 = f16[10]{0} rng(f32[] constant.0, f32[] constant.1), + distribution=rng_normal + } + )"; + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(hlo_string)); + + auto status = verifier().Run(module.get()).status(); + ASSERT_TRUE(status.ok()); +} + +TEST_F(HloVerifierTest, RngElementTypeNotSupported) { + const char* const hlo_string = R"( + HloModule Module + + ENTRY RngElementTypeNotSupported { + constant.0 = s32[] constant(0) + constant.1 = s32[] constant(1) + ROOT rng.0 = s32[10]{0} rng(s32[] constant.0, s32[] constant.1), + distribution=rng_normal + } + )"; + TF_ASSERT_OK_AND_ASSIGN(auto module, ParseHloString(hlo_string)); + + auto status = verifier().Run(module.get()).status(); + ASSERT_FALSE(status.ok()); + EXPECT_THAT(status.error_message(), HasSubstr("Element type not supported")); +} + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/service/indexed_array_analysis.cc b/tensorflow/compiler/xla/service/indexed_array_analysis.cc index 8b2df3256776a7d77517daff1fe282b0dbde7045..3531b7223fb11df212fa8d30e3adba6aac6c5679 100644 --- a/tensorflow/compiler/xla/service/indexed_array_analysis.cc +++ b/tensorflow/compiler/xla/service/indexed_array_analysis.cc @@ -447,7 +447,7 @@ int64 FindSourcePositionForPassthroughResultDim(ArraySlice operand_shape, int64 indexed_source_subarray_size = std::accumulate(operand_shape.begin() + source_passthrough_dim + 1, - operand_shape.end(), 1, std::multiplies()); + operand_shape.end(), 1LL, std::multiplies()); return FindSuffixWithProduct(result_shape, indexed_source_subarray_size); } @@ -764,7 +764,7 @@ IndexedArrayAnalysis::FoldReshapeOfGatherNoDegenerateDims( &new_scalar_indexed_source_shape, source_dim_for_new_scalar_indexed_node, scalar_indexed_source_shape.dimensions(scalar_indexed->source_dim())); - CHECK_EQ(c_accumulate(new_scalar_indexed_source_shape, 1l, + CHECK_EQ(c_accumulate(new_scalar_indexed_source_shape, 1LL, std::multiplies()), ShapeUtil::ElementsIn(scalar_indexed_source_shape)); diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index e2191aedb7f03ad4a956d9f4b8b1bfd4f5b5e08e..f33942d67907d8f40811bde5041350a2e1e1f1fc 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -120,6 +120,7 @@ bool IsAlwaysDuplicable(const HloInstruction& instruction) { case HloOpcode::kConditional: case HloOpcode::kConvolution: case HloOpcode::kCrossReplicaSum: + case HloOpcode::kAllToAll: case HloOpcode::kCustomCall: case HloOpcode::kDivide: case HloOpcode::kDomain: diff --git a/tensorflow/compiler/xla/service/layout_assignment.cc b/tensorflow/compiler/xla/service/layout_assignment.cc index b5a9d6e8e7d66ae0c560226a79578d85eaf55644..805fdb2d5bd8a08490b354d60f281c8f99bc20d8 100644 --- a/tensorflow/compiler/xla/service/layout_assignment.cc +++ b/tensorflow/compiler/xla/service/layout_assignment.cc @@ -1563,7 +1563,7 @@ Status LayoutAssignment::ClearComputationLayouts(HloComputation* computation) { // and the computation result. The latter two are specified in // computation_layout, so we only need to keep the existing layouts for // infeeds. Clearing the layouts here avoids hiding potential bugs in the - // layout assignment pass that may accidently use the existing layout. + // layout assignment pass that may accidentally use the existing layout. for (HloInstruction* instruction : computation->instructions()) { if (instruction->opcode() == HloOpcode::kBitcast) { // bitcasts are inherently layout sensitive and so a bitcast instruction diff --git a/tensorflow/compiler/xla/service/reshape_mover_test.cc b/tensorflow/compiler/xla/service/reshape_mover_test.cc index ad3b662c20ac53b0a6d634b16b3b908f730f3d2d..ccb9fb3e3af5e308accc924d3501213841d7d6c7 100644 --- a/tensorflow/compiler/xla/service/reshape_mover_test.cc +++ b/tensorflow/compiler/xla/service/reshape_mover_test.cc @@ -76,9 +76,13 @@ TEST_F(ReshapeMoverTest, ReshapesWithDifferentInputShapesNotMoved) { TEST_F(ReshapeMoverTest, 1ConstantAnd1ReshapesOnRngNotMoved) { HloComputation::Builder builder(TestName()); auto root_shape = ShapeUtil::MakeShape(F32, {8, 7}); - auto rng0 = builder.AddInstruction( - HloInstruction::CreateRng(ShapeUtil::MakeShape(F32, {1, 8, 1, 7, 1}), - RandomDistribution::RNG_UNIFORM, {})); + auto rng0 = builder.AddInstruction(HloInstruction::CreateRng( + ShapeUtil::MakeShape(F32, {1, 8, 1, 7, 1}), + RandomDistribution::RNG_UNIFORM, + {builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0.0f))), + builder.AddInstruction(HloInstruction::CreateConstant( + LiteralUtil::CreateR0(1.0f)))})); auto reshape0 = builder.AddInstruction(HloInstruction::CreateReshape(root_shape, rng0)); diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 212db0643c3a49c45dc317547c8f1bfc82b7e8b0..1dbf540d13d1fb6f6a4052caeff922cc0290f1b8 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -53,6 +53,7 @@ limitations under the License. #include "tensorflow/core/platform/protobuf.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/util/ptr_util.h" using ::tensorflow::strings::Printf; using ::tensorflow::strings::StrCat; @@ -408,7 +409,7 @@ Service::ExecuteParallelAndRegisterResult( streams.push_back(std::move(stream)); if (replica == 0 && profile != nullptr) { - timers.emplace_back(new se::Timer(streams.back()->parent())); + timers.push_back(MakeUnique(streams.back()->parent())); streams.back() ->InitTimer(timers.back().get()) .ThenStartTimer(timers.back().get()); @@ -440,7 +441,7 @@ Service::ExecuteParallelAndRegisterResult( streams.back()->ThenStopTimer(timers.back().get()); } - result_buffers.emplace_back(std::move(result)); + result_buffers.push_back(std::move(result)); } TF_ASSIGN_OR_RETURN(GlobalDataHandle handle, allocation_tracker_.RegisterReplicatedBuffers( @@ -558,7 +559,7 @@ StatusOr Service::ExecuteAndRegisterResult( std::vector> replicated_arguments; for (const auto& arg : arguments) { - replicated_arguments.emplace_back(arg); + replicated_arguments.push_back(arg); } TF_ASSIGN_OR_RETURN(auto results, executable->ExecuteOnStreams( @@ -1052,11 +1053,12 @@ Status Service::TransferFromOutfeed(const TransferFromOutfeedRequest* arg, executor = replicas[arg->replica_id()]; } - Literal literal; + auto literal = Literal::CreateFromShape(arg->shape_with_layout()); + TF_RETURN_IF_ERROR( execute_backend_->transfer_manager()->TransferLiteralFromOutfeed( - executor, arg->shape_with_layout(), &literal)); - *result->mutable_literal() = literal.ToProto(); + executor, arg->shape_with_layout(), *literal)); + *result->mutable_literal() = literal->ToProto(); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/shape_inference.cc b/tensorflow/compiler/xla/service/shape_inference.cc index c888bbf144954c3b48afecf80a8884e847cc9d18..a4ea2b28f4dbf41d61702f1af2d65c4d2c86d578 100644 --- a/tensorflow/compiler/xla/service/shape_inference.cc +++ b/tensorflow/compiler/xla/service/shape_inference.cc @@ -1779,6 +1779,51 @@ ShapeInference::InferDegenerateDimensionBroadcastShape(HloOpcode operation, return ShapeUtil::MakeTupleShape(operand_shape_values); } +/* static */ StatusOr ShapeInference::InferAllToAllShape( + const Shape& shape, int64 split_dimension, int64 concat_dimension, + int64 split_count) { + TF_RET_CHECK(split_count > 0); + if (split_dimension >= ShapeUtil::Rank(shape) || split_dimension < 0) { + return InvalidArgument( + "AllToAll split_dimension %lld is out-of-bounds in shape %s.", + split_dimension, ShapeUtil::HumanString(shape).c_str()); + } + if (concat_dimension >= ShapeUtil::Rank(shape) || concat_dimension < 0) { + return InvalidArgument( + "AllToAll concat_dimension %lld is out-of-bounds in shape %s.", + concat_dimension, ShapeUtil::HumanString(shape).c_str()); + } + if (shape.dimensions(split_dimension) % split_count != 0) { + return InvalidArgument( + "AllToAll split dimension size %lld must be dividable by split_count " + "%lld.", + shape.dimensions(split_dimension), split_count); + } + std::vector new_dimensions(shape.dimensions().begin(), + shape.dimensions().end()); + new_dimensions[split_dimension] /= split_count; + new_dimensions[concat_dimension] *= split_count; + return ShapeUtil::MakeShape(shape.element_type(), new_dimensions); +} + +/* static */ StatusOr ShapeInference::InferAllToAllTupleShape( + tensorflow::gtl::ArraySlice operand_shapes) { + // An Alltoall HLO instruction receives N operands (with the same shape) and + // returns a tuple that contains N array shapes. + TF_RET_CHECK(!operand_shapes.empty()); + for (int i = 0; i < operand_shapes.size(); i++) { + if (!ShapeUtil::Equal(*operand_shapes[0], *operand_shapes[i])) { + return InvalidArgument( + "HLO all-to-all has operands with different shapes: the 0th " + "operand shape %s, but the %dth operand has shape %s.", + ShapeUtil::HumanString(*operand_shapes[0]).c_str(), i, + ShapeUtil::HumanString(*operand_shapes[i]).c_str()); + } + } + + return InferVariadicOpShape(HloOpcode::kTuple, operand_shapes); +} + /* static */ StatusOr ShapeInference::InferReduceShape( tensorflow::gtl::ArraySlice arg_shapes, tensorflow::gtl::ArraySlice dimensions_to_reduce, diff --git a/tensorflow/compiler/xla/service/shape_inference.h b/tensorflow/compiler/xla/service/shape_inference.h index 33da323b3d74848e10fb736aa77123b0a3946556..c185b0a1bd79e23e0d76daad50fb4a9708a743dd 100644 --- a/tensorflow/compiler/xla/service/shape_inference.h +++ b/tensorflow/compiler/xla/service/shape_inference.h @@ -119,11 +119,22 @@ class ShapeInference { const Shape& in, FftType fft_type, tensorflow::gtl::ArraySlice fft_length); - // Infers the shape produced a cross replica sum with the given operand + // Infers the shape produced by a cross replica sum with the given operand // shapes. static StatusOr InferCrossReplicaSumShape( tensorflow::gtl::ArraySlice operand_shapes); + // Infers final shape of an Alltoall operation that is created by the xla + // builder. + static StatusOr InferAllToAllShape(const Shape& shape, + int64 split_dimension, + int64 concat_dimension, + int64 split_count); + + // Infers the shape of an HLO all-to-all instruction. + static StatusOr InferAllToAllTupleShape( + tensorflow::gtl::ArraySlice operand_shapes); + // Infers the shape produced by applying the given reduction computation // shape to the given input operand shape. // diff --git a/tensorflow/compiler/xla/service/transfer_manager.cc b/tensorflow/compiler/xla/service/transfer_manager.cc index 7232c658b3f0687ac93a83e46a200f88bf202084..32d368a90429ec026120bdf033957617eeaba23e 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.cc +++ b/tensorflow/compiler/xla/service/transfer_manager.cc @@ -43,15 +43,39 @@ TransferManager::GetPlatformTransferManagers() { StatusOr> TransferManager::TransferLiteralFromDevice( se::Stream* stream, const ShapedBuffer& device_buffer) { StatusOr> ret; + se::Stream* substream = stream->GetOrCreateSubStream(); substream->ThenWaitFor(stream); auto cleanup = tensorflow::gtl::MakeCleanup( [&]() { stream->ReturnSubStream(substream); }); tensorflow::Notification n; - TransferLiteralFromDevice(substream, device_buffer, - [&](StatusOr> arg) { - ret = std::move(arg); + Status s; + Literal literal(device_buffer.on_host_shape()); + TransferLiteralFromDevice(substream, device_buffer, literal, + [&](Status status) { + s = status; + n.Notify(); + }); + n.WaitForNotification(); + if (!s.ok()) { + return s; + } + return MakeUnique(std::move(literal)); +} + +Status TransferManager::TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + const MutableBorrowingLiteral& literal) { + se::Stream* substream = stream->GetOrCreateSubStream(); + auto cleanup = tensorflow::gtl::MakeCleanup( + [&]() { stream->ReturnSubStream(substream); }); + + Status ret; + tensorflow::Notification n; + TransferLiteralFromDevice(substream, device_buffer, literal, + [&](Status status) { + ret = status; n.Notify(); }); n.WaitForNotification(); @@ -76,22 +100,27 @@ Status TransferManager::TransferLiteralToDevice( StatusOr> TransferManager::TransferArrayFromDevice( se::Stream* stream, const Shape& shape, const se::DeviceMemoryBase& source) { + StatusOr> ret; // Implement the synchronous version by waiting on the asynchronous version. // Use a substream so that if we are called from a HostCallback we don't // deadlock. - StatusOr> ret; se::Stream* substream = stream->GetOrCreateSubStream(); auto cleanup = tensorflow::gtl::MakeCleanup( [&]() { stream->ReturnSubStream(substream); }); tensorflow::Notification n; - TransferArrayFromDevice(substream, shape, source, - [&](StatusOr> arg) { - ret = std::move(arg); + Literal literal(shape); + Status s; + TransferArrayFromDevice(substream, shape, source, literal, + [&](Status status) { + s = status; n.Notify(); }); n.WaitForNotification(); - return ret; + if (!s.ok()) { + return s; + } + return MakeUnique(std::move(literal)); } Status TransferManager::TransferArrayToDevice( @@ -130,7 +159,7 @@ Status TransferManager::TransferArrayToDeviceAsync( void TransferManager::TransferArrayFromDevice( se::Stream* stream, const Shape& shape, const se::DeviceMemoryBase& source, - std::function>)> done) { + const MutableBorrowingLiteral& literal, std::function done) { if (!ShapeUtil::Equal(HostShapeToDeviceShape(shape), shape)) { auto error = StrCat("Shape ", ShapeUtil::HumanString(shape), " has a differently shaped representation on-device: ", @@ -147,7 +176,8 @@ void TransferManager::TransferArrayFromDevice( stream->parent()->platform(), stream->parent()->device_ordinal()); shaped_buffer.set_buffer(source, /*index=*/{}); - return TransferLiteralFromDevice(stream, shaped_buffer, std::move(done)); + return TransferLiteralFromDevice(stream, shaped_buffer, literal, + std::move(done)); } /* static */ void TransferManager::RegisterTransferManager( diff --git a/tensorflow/compiler/xla/service/transfer_manager.h b/tensorflow/compiler/xla/service/transfer_manager.h index 82c599e482d85fc5bbe5a5a48c6c6b053186803b..475a2e5c141d66fa689fb402da1ee81fb4ab80f7 100644 --- a/tensorflow/compiler/xla/service/transfer_manager.h +++ b/tensorflow/compiler/xla/service/transfer_manager.h @@ -59,6 +59,9 @@ class TransferManager { // This function should be avoided in favor of the asynchronous version below. virtual StatusOr> TransferLiteralFromDevice( se::Stream* stream, const ShapedBuffer& device_buffer); + virtual Status TransferLiteralFromDevice( + se::Stream* stream, const ShapedBuffer& device_buffer, + const MutableBorrowingLiteral& literal); // Begins transferring a literal containing the data held in the given // ShapedBuffer using the provided executor. @@ -69,9 +72,10 @@ class TransferManager { // // device_buffer is copied by reference and must live at least until done() is // invoked. - virtual void TransferLiteralFromDevice( - se::Stream* stream, const ShapedBuffer& device_buffer, - std::function>)> done) = 0; + virtual void TransferLiteralFromDevice(se::Stream* stream, + const ShapedBuffer& device_buffer, + MutableBorrowingLiteral literal, + std::function done) = 0; // Transfers the given literal into the previously allocated device memory // represented by the given ShapedBuffer using the given executor. The shape @@ -101,10 +105,10 @@ class TransferManager { // transfer an array at a known address. Status TransferArrayToDevice(se::Stream* stream, const LiteralSlice& literal, const se::DeviceMemoryBase& dest); - void TransferArrayFromDevice( - se::Stream* stream, const Shape& shape, - const se::DeviceMemoryBase& source, - std::function>)> done); + void TransferArrayFromDevice(se::Stream* stream, const Shape& shape, + const se::DeviceMemoryBase& source, + const MutableBorrowingLiteral& literal, + std::function done); Status TransferArrayToDeviceAsync(se::Stream* stream, const LiteralSlice& literal, @@ -120,9 +124,9 @@ class TransferManager { // Transfers the given literal from the Outfeed interface of the device, // using the given executor. - virtual Status TransferLiteralFromOutfeed(se::StreamExecutor* executor, - const Shape& literal_shape, - Literal* literal) = 0; + virtual Status TransferLiteralFromOutfeed( + se::StreamExecutor* executor, const Shape& literal_shape, + MutableBorrowingLiteral literal) = 0; // Resets the devices associated with this transfer manager. virtual Status ResetDevices( diff --git a/tensorflow/compiler/xla/service/while_loop_analysis.cc b/tensorflow/compiler/xla/service/while_loop_analysis.cc new file mode 100644 index 0000000000000000000000000000000000000000..af2cb6dc2a3f4a004351acc62796e0daf46719c2 --- /dev/null +++ b/tensorflow/compiler/xla/service/while_loop_analysis.cc @@ -0,0 +1,238 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/while_loop_analysis.h" +#include "tensorflow/compiler/xla/service/hlo_evaluator.h" + +namespace xla { + +using tensorflow::gtl::nullopt; +using tensorflow::gtl::optional; + +// Finds and returns the non-constant operand in instr. +// +// CHECK-fails if instr doesn't have exactly one unique non-constant operand. +static const HloInstruction* NonConstantOperand(const HloInstruction* instr) { + const HloInstruction* result = nullptr; + for (const HloInstruction* operand : instr->operands()) { + if (!operand->IsConstant()) { + if (result != nullptr) { + CHECK_EQ(result, operand); + } + result = operand; + } + } + CHECK_NE(result, nullptr); + return result; +} + +// If all of instr's operands are either constants or have the form +// get-tuple-element(gte_operand, N) +// for the same value N, returns N. Otherwise, returns nullopt. +static optional GetGTEOperandIndex(const HloInstruction* instr, + const HloInstruction* gte_operand) { + VLOG(2) << "GetGTEOperandIndex(" << instr->ToString() << ", " + << gte_operand->ToString() << ")"; + optional tuple_idx; + for (const HloInstruction* operand : instr->operands()) { + if (operand->IsConstant()) { + continue; + } + // Look through copies. + // TODO(b/68830972): We wouldn't need this if for loop matching on the GPU + // would run before copy insertion. + if (operand->opcode() == HloOpcode::kCopy) { + operand = operand->operand(0); + } + if (operand->opcode() != HloOpcode::kGetTupleElement) { + VLOG(2) << "instr uses something other than gte(gte_operand): " + << operand->ToString(); + return nullopt; + } + if (operand->operand(0) != gte_operand) { + VLOG(2) << "instr has gte whose operand is not gte_operand: " + << operand->ToString(); + return nullopt; + } + if (tuple_idx && tuple_idx != operand->tuple_index()) { + VLOG(2) << "instr has operands with conflicting gte indices, " + << *tuple_idx << " vs " << operand->tuple_index(); + return nullopt; + } + + tuple_idx = operand->tuple_index(); + } + return tuple_idx; +} + +// Tries to get the tuple index of the induction variable of a while loop. +// +// Checks that the loop condition and root both plumb the induction variable +// through the same tuple index, and that they both apply exactly one op to the +// induction variable before deciding whether to do another loop iteration (in +// the loop condition's case) or packing the induction variable into the result +// tuple (in the loop body's case). +// +// Specifically, checks that the loop condition has structure +// +// root = op(constants, get-tuple-elem(param0, N), constants) +// +// and the loop body has the structure +// +// inc = op(constants, get-tuple-elem(param0, N), constants) +// root = tuple(..., inc, ...) // inc is N'th operand of tuple(). +// +// If so, returns N. Otherwise, returns nullopt. +static optional GetLoopInductionVarTupleIdx( + const HloInstruction* while_op) { + CHECK_EQ(while_op->opcode(), HloOpcode::kWhile); + VLOG(2) << "Finding induction variable for loop " + << while_op->ToShortString(); + + // The while_cond computation should have the form + // + // while_cond_root = + // op(constants, get-tuple-elem(while_cond_param, N), constants). + // + // If it does, set indvar_tuple_idx to N. + auto* while_cond = while_op->while_condition(); + auto* while_cond_root = while_cond->root_instruction(); + auto* while_cond_param = while_cond->parameter_instruction(0); + optional indvar_tuple_idx = + GetGTEOperandIndex(while_cond_root, while_cond_param); + if (!indvar_tuple_idx) { + VLOG(2) << "Induction variable not found in loop condition: " + << while_cond->root_instruction()->ToString(); + return nullopt; + } + + // The while_body computation should have the form + // + // while_body_inc = + // op(constants, get-tuple-elem(while_body_param, N), constants) + // while_body_root = tuple(..., while_body_inc, ...) + // + // where while_body_inc is operand N of while_body_root. + auto* while_body = while_op->while_body(); + auto* while_body_root = while_body->root_instruction(); + if (while_body_root->opcode() != HloOpcode::kTuple) { + VLOG(2) << "While body's root is not a tuple instruction: " + << while_body_root->ToString(); + return nullopt; + } + + auto* while_body_inc = while_body_root->operand(*indvar_tuple_idx); + auto* while_body_param = while_body->parameter_instruction(0); + optional while_body_indvar_tuple_idx = + GetGTEOperandIndex(while_body_inc, while_body_param); + if (!while_body_indvar_tuple_idx) { + VLOG(2) + << "Induction variable not found in while body increment instruction: " + << while_body_inc->ToString(); + return nullopt; + } + if (while_body_indvar_tuple_idx != indvar_tuple_idx) { + VLOG(2) << "Tuple index of induction variable does not match between loop " + "condition (" + << *indvar_tuple_idx << ") and while body (" + << *while_body_indvar_tuple_idx << ")"; + return nullopt; + } + + // Finally, check that the while loop's initial value is a tuple with enough + // elements. + auto* while_init = while_op->operand(0); + if (while_init->opcode() != HloOpcode::kTuple) { + VLOG(2) << "While init expected to be a tuple: " << while_init->ToString(); + return nullopt; + } + + VLOG(2) << "Induction variable's tuple index: " << *indvar_tuple_idx; + return indvar_tuple_idx; +} + +optional ComputeWhileLoopTripCount(HloInstruction* while_op, + int64 max_value_returned) { + VLOG(2) << "Getting trip count for loop " << while_op->ToString(); + + // The loop's induction variable is found at + // + // get-tuple-elem(comp->parameter_instruction(0), *indvar_tuple_idx), + // + // where comp is while_op->while_body() or while_op->while_condition(). + optional indvar_tuple_idx = GetLoopInductionVarTupleIdx(while_op); + if (!indvar_tuple_idx) { + return nullopt; + } + + // Now that we know the index of the induction variable, we can we can try to + // compute how many times the loop executes. Start by computing the induction + // variable's initial value. + HloEvaluator evaluator(/*max_loop_iterations=*/0); + auto* while_init = while_op->mutable_operand(0); + auto* indvar_init = while_init->mutable_operand(*indvar_tuple_idx); + StatusOr> indvar_init_result = + evaluator.Evaluate(indvar_init); + if (!indvar_init_result.ok()) { + VLOG(2) << "Couldn't evaluate induction variable init: " + << indvar_init_result.status(); + return nullopt; + } + + auto* while_body = while_op->while_body(); + auto* while_body_indvar_update = + while_body->root_instruction()->operand(*indvar_tuple_idx); + auto* while_body_indvar = NonConstantOperand(while_body_indvar_update); + + // The initial value of the induction variable. + std::unique_ptr indvar_iter_val = + std::move(indvar_init_result).ValueOrDie(); + for (int64 trip_count = 0; trip_count != max_value_returned + 1; + ++trip_count) { + auto* while_cond = while_op->while_condition(); + auto* while_cond_root = while_cond->root_instruction(); + auto* while_cond_indvar = NonConstantOperand(while_cond_root); + StatusOr> result = + evaluator.EvaluateWithSubstitutions( + while_cond_root, {{while_cond_indvar, indvar_iter_val.get()}}); + if (!result.ok()) { + VLOG(2) << "Couldn't evaluate while cond: " << result.status(); + return nullopt; + } + if (result.ValueOrDie()->data() == + tensorflow::gtl::ArraySlice{false}) { + VLOG(2) << "Loop has static trip count of " << trip_count; + return trip_count; + } + + // Calculate the value of the induction variable after one iteration of the + // loop, and check whether the while condition is true with this new value. + StatusOr> indvar_next_result = + evaluator.EvaluateWithSubstitutions( + while_body_indvar_update, + {{while_body_indvar, indvar_iter_val.get()}}); + if (!indvar_next_result.ok()) { + VLOG(2) << "Couldn't evaluate induction variable update: " + << indvar_next_result.status(); + return nullopt; + } + indvar_iter_val = std::move(indvar_next_result).ValueOrDie(); + } + + VLOG(2) << "Loop has unknown trip count."; + return nullopt; +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/while_loop_analysis.h b/tensorflow/compiler/xla/service/while_loop_analysis.h new file mode 100644 index 0000000000000000000000000000000000000000..bf59813e8c405a8709446bf8457729348ceae4ec --- /dev/null +++ b/tensorflow/compiler/xla/service/while_loop_analysis.h @@ -0,0 +1,33 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_WHILE_LOOP_ANALYSIS_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_WHILE_LOOP_ANALYSIS_H_ + +#include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/core/lib/gtl/optional.h" + +namespace xla { + +// Returns the precise trip count of the loop if it's statically known, +// nullopt otherwise. max_value_returned limits the number of steps that are +// evaluated while trying to brute force a loop trip count, trip counts larger +// than max_value_returned result in nullopt. +tensorflow::gtl::optional ComputeWhileLoopTripCount( + HloInstruction *while_op, int64 max_value_returned = 128); + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_WHILE_LOOP_ANALYSIS_H_ diff --git a/tensorflow/compiler/xla/service/while_loop_simplifier.cc b/tensorflow/compiler/xla/service/while_loop_simplifier.cc index ec05a74e286c89dd8db5ae07580e461938d7c087..dd8697e680c56165f87c365a721eda2de1ebc085 100644 --- a/tensorflow/compiler/xla/service/while_loop_simplifier.cc +++ b/tensorflow/compiler/xla/service/while_loop_simplifier.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/while_loop_simplifier.h" #include "tensorflow/compiler/xla/service/call_inliner.h" -#include "tensorflow/compiler/xla/service/hlo_evaluator.h" +#include "tensorflow/compiler/xla/service/while_loop_analysis.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/lib/strings/str_util.h" @@ -26,23 +26,6 @@ namespace xla { using tensorflow::gtl::nullopt; using tensorflow::gtl::optional; -// Finds and returns the non-constant operand in instr. -// -// CHECK-fails if instr doesn't have exactly one unique non-constant operand. -static const HloInstruction* NonConstantOperand(const HloInstruction* instr) { - const HloInstruction* result = nullptr; - for (const HloInstruction* operand : instr->operands()) { - if (!operand->IsConstant()) { - if (result != nullptr) { - CHECK_EQ(result, operand); - } - result = operand; - } - } - CHECK_NE(result, nullptr); - return result; -} - // Determines whether the given instruction is a send/recv node, or has a // subcomputation which contains a send/recv node. static bool IsOrContainsSendOrRecv(const HloInstruction* instr); @@ -72,211 +55,6 @@ static bool IsOrContainsSendOrRecv(const HloInstruction* instr) { return false; } -// If all of instr's operands are either constants or have the form -// get-tuple-element(gte_operand, N) -// for the same value N, returns N. Otherwise, returns nullopt. -static optional GetGTEOperandIndex(const HloInstruction* instr, - const HloInstruction* gte_operand) { - VLOG(2) << "GetGTEOperandIndex(" << instr->ToString() << ", " - << gte_operand->ToString() << ")"; - optional tuple_idx; - for (const HloInstruction* operand : instr->operands()) { - if (operand->IsConstant()) { - continue; - } - if (operand->opcode() != HloOpcode::kGetTupleElement) { - VLOG(2) << "instr uses something other than gte(gte_operand): " - << operand->ToString(); - return nullopt; - } - if (operand->operand(0) != gte_operand) { - VLOG(2) << "instr has gte whose operand is not gte_operand: " - << operand->ToString(); - return nullopt; - } - if (tuple_idx && tuple_idx != operand->tuple_index()) { - VLOG(2) << "instr has operands with conflicting gte indices, " - << *tuple_idx << " vs " << operand->tuple_index(); - return nullopt; - } - - tuple_idx = operand->tuple_index(); - } - return tuple_idx; -} - -// Tries to get the tuple index of the induction variable of a while loop. -// -// Checks that the loop condition and root both plumb the induction variable -// through the same tuple index, and that they both apply exactly one op to the -// induction variable before deciding whether to do another loop iteration (in -// the loop condition's case) or packing the induction variable into the result -// tuple (in the loop body's case). -// -// Specifically, checks that the loop condition has structure -// -// root = op(constants, get-tuple-elem(param0, N), constants) -// -// and the loop body has the structure -// -// inc = op(constants, get-tuple-elem(param0, N), constants) -// root = tuple(..., inc, ...) // inc is N'th operand of tuple(). -// -// If so, returns N. Otherwise, returns nullopt. -static optional GetLoopInductionVarTupleIdx( - const HloInstruction* while_op) { - CHECK_EQ(while_op->opcode(), HloOpcode::kWhile); - VLOG(2) << "Finding induction variable for loop " - << while_op->ToShortString(); - - // The while_cond computation should have the form - // - // while_cond_root = - // op(constants, get-tuple-elem(while_cond_param, N), constants). - // - // If it does, set indvar_tuple_idx to N. - auto* while_cond = while_op->while_condition(); - auto* while_cond_root = while_cond->root_instruction(); - auto* while_cond_param = while_cond->parameter_instruction(0); - optional indvar_tuple_idx = - GetGTEOperandIndex(while_cond_root, while_cond_param); - if (!indvar_tuple_idx) { - VLOG(2) << "Induction variable not found in loop condition: " - << while_cond->root_instruction()->ToString(); - return nullopt; - } - - // The while_body computation should have the form - // - // while_body_inc = - // op(constants, get-tuple-elem(while_body_param, N), constants) - // while_body_root = tuple(..., while_body_inc, ...) - // - // where while_body_inc is operand N of while_body_root. - auto* while_body = while_op->while_body(); - auto* while_body_root = while_body->root_instruction(); - if (while_body_root->opcode() != HloOpcode::kTuple) { - VLOG(2) << "While body's root is not a tuple instruction: " - << while_body_root->ToString(); - return nullopt; - } - - auto* while_body_inc = while_body_root->operand(*indvar_tuple_idx); - auto* while_body_param = while_body->parameter_instruction(0); - optional while_body_indvar_tuple_idx = - GetGTEOperandIndex(while_body_inc, while_body_param); - if (!while_body_indvar_tuple_idx) { - VLOG(2) - << "Induction variable not found in while body increment instruction: " - << while_body_inc->ToString(); - return nullopt; - } - if (while_body_indvar_tuple_idx != indvar_tuple_idx) { - VLOG(2) << "Tuple index of induction variable does not match between loop " - "condition (" - << *indvar_tuple_idx << ") and while body (" - << *while_body_indvar_tuple_idx << ")"; - return nullopt; - } - - // Finally, check that the while loop's initial value is a tuple with enough - // elements. - auto* while_init = while_op->operand(0); - if (while_init->opcode() != HloOpcode::kTuple) { - VLOG(2) << "While init expected to be a tuple: " << while_init->ToString(); - return nullopt; - } - - VLOG(2) << "Induction variable's tuple index: " << *indvar_tuple_idx; - return indvar_tuple_idx; -} - -// Tries to determine the number of times the given loop executes. Currently -// simply returns 0, 1, or "can't tell" (nullopt). -static optional GetLoopTripCount(HloInstruction* while_op) { - CHECK_EQ(while_op->opcode(), HloOpcode::kWhile); - VLOG(2) << "Getting trip count for loop " << while_op->ToString(); - - // The loop's induction variable is found at - // - // get-tuple-elem(comp->parameter_instruction(0), *indvar_tuple_idx), - // - // where comp is while_op->while_body() or while_op->while_condition(). - optional indvar_tuple_idx = GetLoopInductionVarTupleIdx(while_op); - if (!indvar_tuple_idx) { - return nullopt; - } - - VLOG(2) << "Induction variable is at index " << *indvar_tuple_idx - << " in input tuple."; - - // Now that we know the index of the induction variable, we can we can try to - // compute how many times the loop executes. Start by computing the induction - // variable's initial value. - HloEvaluator evaluator(/*max_loop_iterations=*/0); - auto* while_init = while_op->mutable_operand(0); - auto* indvar_init = while_init->mutable_operand(*indvar_tuple_idx); - StatusOr> indvar_init_result = - evaluator.Evaluate(indvar_init); - if (!indvar_init_result.ok()) { - VLOG(2) << "Couldn't evaluate induction variable init: " - << indvar_init_result.status(); - return nullopt; - } - - // Evaluates the while loop's condition, returning either "true" (continue - // looping), "false" (stop looping), or nullopt (can't evaluate). - auto evaluate_while_cond = [&](const Literal& indvar) -> optional { - auto* while_cond = while_op->while_condition(); - auto* while_cond_root = while_cond->root_instruction(); - auto* while_cond_indvar = NonConstantOperand(while_cond_root); - StatusOr> result = - evaluator.EvaluateWithSubstitutions(while_cond_root, - {{while_cond_indvar, &indvar}}); - if (!result.ok()) { - VLOG(2) << "Couldn't evaluate while cond: " << result.status(); - return nullopt; - } - return result.ValueOrDie()->data() == - tensorflow::gtl::ArraySlice{true}; - }; - - // The initial value of the induction variable. - const Literal& indvar_iter0_val = *indvar_init_result.ValueOrDie(); - - // Evaluate whether the while condition is true when seeded with - // indvar_iter0_val. - optional while_cond_iter0_val = evaluate_while_cond(indvar_iter0_val); - if (while_cond_iter0_val == false) { - VLOG(2) << "Loop has static trip count of 0."; - return 0; - } - - // Calculate the value of the induction variable after one iteration of the - // loop, and check whether the while condition is true with this new value. - auto* while_body = while_op->while_body(); - auto* while_body_indvar_update = - while_body->root_instruction()->operand(*indvar_tuple_idx); - auto* while_body_indvar = NonConstantOperand(while_body_indvar_update); - StatusOr> indvar_iter1_result = - evaluator.EvaluateWithSubstitutions( - while_body_indvar_update, {{while_body_indvar, &indvar_iter0_val}}); - if (!indvar_iter1_result.ok()) { - VLOG(2) << "Couldn't evaluate induction variable update: " - << indvar_iter1_result.status(); - return nullopt; - } - const Literal& indvar_iter1_val = *indvar_iter1_result.ValueOrDie(); - optional while_cond_iter1_val = evaluate_while_cond(indvar_iter1_val); - if (while_cond_iter1_val == false) { - VLOG(2) << "Determined that loop has static trip count of 1."; - return 1; - } - - VLOG(2) << "Loop has unknown trip count >= 1."; - return nullopt; -} - // Tries to remove elements in a while loop's tuple that aren't used within the // loop. // @@ -577,7 +355,9 @@ static StatusOr TryRemoveWhileLoop(HloInstruction* while_op) { } // Remove while loops with static trip count of 0. - optional trip_count = GetLoopTripCount(while_op); + optional trip_count = + ComputeWhileLoopTripCount(while_op, + /*max_value_returned=*/1); if (trip_count && *trip_count == 0) { // The loop never executes, so the value of the loop is the value of its // "init" operand. diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.cc b/tensorflow/compiler/xla/tests/hlo_test_base.cc index b662e837168c8b16daea0181786be19fa0237a8c..f05d1a8b9d372e720ae1634a9c8d5c0591e39b89 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.cc +++ b/tensorflow/compiler/xla/tests/hlo_test_base.cc @@ -83,13 +83,16 @@ ProgramShape GetProgramShapeWithLayout(const HloModule& module) { } // namespace -HloTestBase::HloTestBase() - : HloTestBase(GetTestPlatform(), GetReferencePlatform()) {} +HloTestBase::HloTestBase(bool allow_mixed_precision_in_hlo_verifier) + : HloTestBase(GetTestPlatform(), GetReferencePlatform(), + allow_mixed_precision_in_hlo_verifier) {} HloTestBase::HloTestBase(se::Platform* test_platform, - se::Platform* reference_platform) + se::Platform* reference_platform, + bool allow_mixed_precision_in_hlo_verifier) : test_runner_(test_platform), reference_runner_(reference_platform) { - hlo_verifier_ = MakeUnique(/*allow_mixed_precision=*/true); + hlo_verifier_ = + MakeUnique(allow_mixed_precision_in_hlo_verifier); } /* static */ @@ -233,6 +236,29 @@ StatusOr<::testing::AssertionResult> HloTestBase::RunAndCompareInternal( reference_preprocessor); } +::testing::AssertionResult HloTestBase::Run(const StringPiece hlo_string) { + auto module_or_status = + HloRunner::CreateModuleFromString(hlo_string, GetDebugOptionsForTest()); + if (!module_or_status.ok()) { + return ::testing::AssertionFailure() + << "Error while parsing HLO text format: " + << module_or_status.status().ToString(); + } + const auto& fake_arguments = + MakeFakeArguments(module_or_status.ValueOrDie().get()) + .ConsumeValueOrDie(); + std::vector fake_argument_ptrs; + c_transform( + fake_arguments, std::back_inserter(fake_argument_ptrs), + [](const std::unique_ptr& literal) { return literal.get(); }); + return test_runner_ + .Execute(std::move(module_or_status.ValueOrDie()), + fake_argument_ptrs, /*run_hlo_passes=*/true) + .ok() + ? ::testing::AssertionSuccess() + : ::testing::AssertionFailure(); +} + ::testing::AssertionResult HloTestBase::RunAndCompareFromFile( const string& filename, const tensorflow::gtl::optional& error, const std::function& reference_preprocessor) { diff --git a/tensorflow/compiler/xla/tests/hlo_test_base.h b/tensorflow/compiler/xla/tests/hlo_test_base.h index 66719b1460063a61541535ff7507468ae0ca1ada..4232eeceb10b37a209f247ffa70fb9a08be337e6 100644 --- a/tensorflow/compiler/xla/tests/hlo_test_base.h +++ b/tensorflow/compiler/xla/tests/hlo_test_base.h @@ -80,12 +80,13 @@ class HloTestBase : public ::testing::Test { // automatically finds another supported backend as the test backend. If the // interpreter is the only supported backend, it will be both the test backend // and the reference backend. - HloTestBase(); + HloTestBase(bool allow_mixed_precision_in_hlo_verifier = true); // If your test doesn't use interpreter as the reference backend, you can use // this constructor. Note that your test target is responsible for linking in // both needed backends. - HloTestBase(se::Platform* test_platform, se::Platform* reference_platform); + HloTestBase(se::Platform* test_platform, se::Platform* reference_platform, + bool allow_mixed_precision_in_hlo_verifier = true); ~HloTestBase() override {} @@ -166,6 +167,8 @@ class HloTestBase : public ::testing::Test { const tensorflow::gtl::optional& error, const std::function& reference_preprocessor = nullptr) TF_MUST_USE_RESULT; + ::testing::AssertionResult Run(const tensorflow::StringPiece hlo_string) + TF_MUST_USE_RESULT; ::testing::AssertionResult RunAndCompareFromFile( const string& filename, const tensorflow::gtl::optional& error, const std::function& reference_preprocessor = nullptr) diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc index e310966d8b062f2baac00a17dd42cd449595d0d2..60eb21aafd23a8d724d1f08d5c87098b7c3dcd6b 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc @@ -92,10 +92,10 @@ int main(int argc, char** argv) { // It's lame to hard-code the buffer assignments, but we need // local_client_aot_test.cc to be able to easily invoke the function. CHECK_EQ(result->result_buffer_index(), 1); - CHECK_EQ(result->buffer_sizes().size(), 3); - CHECK_EQ(result->buffer_sizes()[0], -2); // param buffer - CHECK_EQ(result->buffer_sizes()[1], sizeof(float)); // result buffer - CHECK_EQ(result->buffer_sizes()[2], -1); // const buffer + CHECK_EQ(result->buffer_infos().size(), 3); + CHECK(result->buffer_infos()[0].is_entry_parameter()); // param buffer + CHECK_EQ(result->buffer_infos()[1].size(), sizeof(float)); // result buffer + CHECK(result->buffer_infos()[2].is_constant()); // const buffer if (triple.isOSBinFormatELF()) { // Check the ELF magic. CHECK_EQ(result->object_file_data()[0], 0x7F); diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index 1bd6fdab31d6c3516339bdb98459ffe3bbdef1d1..73edad89dc8281ed46ffabc1e2926b1d8ab7fcf6 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -1341,7 +1341,7 @@ INSTANTIATE_TEST_CASE_P( // results on the interpreter backend. class ReduceWindowTextTest : public HloTestBase {}; -TEST_F(ReduceWindowTextTest, R2General256x384) { +XLA_TEST_F(ReduceWindowTextTest, R2General256x384) { const string hlo_string = R"( HloModule R2Window mul { @@ -1358,7 +1358,7 @@ ENTRY R2Window { EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{0.001})); } -TEST_F(ReduceWindowTextTest, R2General256x384Layout01) { +XLA_TEST_F(ReduceWindowTextTest, R2General256x384Layout01) { const string hlo_string = R"( HloModule R2Window mul { @@ -1375,7 +1375,7 @@ ROOT reduce-window = f32[256,384]{0,1} reduce-window(operand, constant), window= EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{0.001})); } -TEST_F(ReduceWindowTextTest, R2General2x5) { +XLA_TEST_F(ReduceWindowTextTest, R2General2x5) { const string hlo_string = R"( HloModule R2Window mul { @@ -1392,7 +1392,7 @@ ENTRY R2Window { EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{0.001})); } -TEST_F(ReduceWindowTextTest, R2EffectiveScalar) { +XLA_TEST_F(ReduceWindowTextTest, R2EffectiveScalar) { const string hlo_string = R"( HloModule R2Window mul { @@ -1410,7 +1410,7 @@ ENTRY R2Window { EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{0.001})); } -TEST_F(ReduceWindowTextTest, R3EffectiveScalar) { +XLA_TEST_F(ReduceWindowTextTest, R3EffectiveScalar) { const string hlo_string = R"( HloModule R3Window mul { @@ -1428,7 +1428,7 @@ ENTRY R3Window { EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{0.001})); } -TEST_F(HloTestBase, ReduceWindowIdentity) { +XLA_TEST_F(HloTestBase, ReduceWindowIdentity) { const string hlo_string = R"( HloModule ReduceWindowIdentity identity.pad_to_reduce_window { @@ -1445,7 +1445,7 @@ ENTRY reduce-window-identity { EXPECT_TRUE(RunAndCompare(hlo_string, tensorflow::gtl::nullopt)); } -TEST_F(HloTestBase, ReduceWindowS32) { +XLA_TEST_F(HloTestBase, ReduceWindowS32) { const string hlo_string = R"( HloModule reduce-window diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index 2fd70b72b52f360fc74a73cd13d401b7dac6e708..97bbf80aff80e995ea5cdd3e5d8807ee4d380067 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -586,9 +586,9 @@ XLA_TEST_F(TupleHloTest, })); auto expected = LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR1({2, 3})); - auto literal = MakeUnique(); + auto literal = Literal::CreateFromShape(expected->shape()); TF_EXPECT_OK(backend().transfer_manager()->TransferLiteralFromOutfeed( - backend().default_stream_executor(), expected->shape(), literal.get())); + backend().default_stream_executor(), expected->shape(), *literal)); EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *literal)); } diff --git a/tensorflow/compiler/xla/tools/replay_computation.cc b/tensorflow/compiler/xla/tools/replay_computation.cc index be4cf4318b33f41fc611ea90a1a02198e23b84e4..b4774233e588dc407bfb88defca9bf55e08eea09 100644 --- a/tensorflow/compiler/xla/tools/replay_computation.cc +++ b/tensorflow/compiler/xla/tools/replay_computation.cc @@ -223,9 +223,13 @@ StatusOr ParseInputFile(const string& filename, const Options& opts) { tensorflow::Env* env = tensorflow::Env::Default(); HloSnapshot snapshot; - if (tensorflow::ReadBinaryProto(env, filename, &snapshot).ok()) { + auto s = tensorflow::ReadBinaryProto(env, filename, &snapshot); + if (s.ok()) { return snapshot; } + if (s.code() == tensorflow::error::NOT_FOUND) { + return s; + } CHECK(opts.use_fake_data) << "Without --use_fake_data, you must pass an HloSnapshot -- HloProto " "and textual HLO don't carry real data."; @@ -258,6 +262,9 @@ int RealMain(tensorflow::gtl::ArraySlice args, const Options& opts) { StatusOr maybe_snapshot = ParseInputFile(arg, opts); if (maybe_snapshot.ok()) { snapshots.push_back(std::move(maybe_snapshot).ValueOrDie()); + } else { + LOG(ERROR) << "Can't handle file " << arg << ": " + << maybe_snapshot.status(); } } diff --git a/tensorflow/compiler/xla/xla_data.proto b/tensorflow/compiler/xla/xla_data.proto index fd784e909c401cf734480e7a82f8947ce3cc906d..4c35e93d38450b8263290da8e327d1f2126c1532 100644 --- a/tensorflow/compiler/xla/xla_data.proto +++ b/tensorflow/compiler/xla/xla_data.proto @@ -561,3 +561,11 @@ message OpSharding { // to. repeated OpSharding tuple_shardings = 5; } + +// Describes the replica groups in a cross replica op (e.g., all-reduce and +// all-to-all). +message ReplicaGroup { + // The ids of the replicas that belongs to the same group. The ordering of the + // ids matters in some op (e.g., all-to-all). + repeated int64 replica_ids = 1; +} diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index cc34db995e2ad653c6acce10d451de62ae8b264b..23bb783e2207da7076833138f4421980ad20bd96 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -46,6 +46,7 @@ py_library( "//tensorflow/contrib/gan", "//tensorflow/contrib/graph_editor:graph_editor_py", "//tensorflow/contrib/grid_rnn:grid_rnn_py", + "//tensorflow/contrib/hadoop", "//tensorflow/contrib/hooks", "//tensorflow/contrib/image:distort_image_py", "//tensorflow/contrib/image:image_py", @@ -146,6 +147,7 @@ cc_library( "//tensorflow/contrib/coder:all_kernels", "//tensorflow/contrib/data/kernels:dataset_kernels", "//tensorflow/contrib/factorization/kernels:all_kernels", + "//tensorflow/contrib/hadoop:dataset_kernels", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_kernels", "//tensorflow/contrib/layers:sparse_feature_cross_op_kernel", "//tensorflow/contrib/nearest_neighbor:nearest_neighbor_ops_kernels", @@ -181,6 +183,7 @@ cc_library( "//tensorflow/contrib/data:dataset_ops_op_lib", "//tensorflow/contrib/factorization:all_ops", "//tensorflow/contrib/framework:all_ops", + "//tensorflow/contrib/hadoop:dataset_ops_op_lib", "//tensorflow/contrib/input_pipeline:input_pipeline_ops_op_lib", "//tensorflow/contrib/layers:sparse_feature_cross_op_op_lib", "//tensorflow/contrib/nccl:nccl_ops_op_lib", diff --git a/tensorflow/contrib/__init__.py b/tensorflow/contrib/__init__.py index ded05da71877566781a5fb6d0c21e1c8d43de9ed..e18ea8df4df719a7317333cf9038ce7facf8d6ac 100644 --- a/tensorflow/contrib/__init__.py +++ b/tensorflow/contrib/__init__.py @@ -22,6 +22,7 @@ from __future__ import print_function import os # Add projects here, they will show up under tf.contrib. +from tensorflow.contrib import autograph from tensorflow.contrib import batching from tensorflow.contrib import bayesflow from tensorflow.contrib import checkpoint diff --git a/tensorflow/contrib/all_reduce/python/all_reduce.py b/tensorflow/contrib/all_reduce/python/all_reduce.py index 159d985db5c48f8fe1a26350255f8d8f68482473..3b539734a236804026826a8117d9c668c0dd089a 100644 --- a/tensorflow/contrib/all_reduce/python/all_reduce.py +++ b/tensorflow/contrib/all_reduce/python/all_reduce.py @@ -32,10 +32,10 @@ def _flatten_tensors(tensors): """Check tensors for isomorphism and flatten. Args: - tensors: list of T @{tf.Tensor} which must all have the same shape. + tensors: list of T `tf.Tensor` which must all have the same shape. Returns: - tensors: a list of T @{tf.Tensor} which are flattened (1D) views of tensors + tensors: a list of T `tf.Tensor` which are flattened (1D) views of tensors shape: the original shape of each element of input tensors Raises: @@ -61,12 +61,12 @@ def _reshape_tensors(tensors, shape): """Reshape tensors flattened by _flatten_tensors. Args: - tensors: list of T @{tf.Tensor} of identical length 1D tensors. + tensors: list of T `tf.Tensor` of identical length 1D tensors. shape: list of integers describing the desired shape. Product of the elements must equal the length of each tensor. Returns: - list of T @{tf.Tensor} which are the reshaped inputs. + list of T `tf.Tensor` which are the reshaped inputs. """ reshaped = [] for t in tensors: @@ -79,12 +79,12 @@ def _padded_split(tensor, pieces): """Like split for 1D tensors but pads-out case where len % pieces != 0. Args: - tensor: T @{tf.Tensor} that must be 1D. + tensor: T `tf.Tensor` that must be 1D. pieces: a positive integer specifying the number of pieces into which tensor should be split. Returns: - list of T @{tf.Tensor} of length pieces, which hold the values of + list of T `tf.Tensor` of length pieces, which hold the values of thin input tensor, in order. The final tensor may be zero-padded on the end to make its size equal to those of all of the other tensors. @@ -132,11 +132,11 @@ def _strip_padding(tensors, pad_len): """Strip the suffix padding added by _padded_split. Args: - tensors: list of T @{tf.Tensor} of identical length 1D tensors. + tensors: list of T `tf.Tensor` of identical length 1D tensors. pad_len: number of elements to be stripped from the end of each tensor. Returns: - list of T @{tf.Tensor} which are the stripped inputs. + list of T `tf.Tensor` which are the stripped inputs. Raises: ValueError: tensors must be a non-empty list of 1D tensors, and @@ -161,12 +161,12 @@ def _ragged_split(tensor, pieces): """Like split for 1D tensors but allows case where len % pieces != 0. Args: - tensor: T @{tf.Tensor} that must be 1D. + tensor: T `tf.Tensor` that must be 1D. pieces: a positive integer specifying the number of pieces into which tensor should be split. Returns: - list of T @{tf.Tensor} of length pieces, which hold the values of + list of T `tf.Tensor` of length pieces, which hold the values of the input tensor, in order. The final tensor may be shorter than the others, which will all be of equal length. @@ -256,7 +256,7 @@ def build_ring_all_reduce(input_tensors, num_workers, num_subchunks, """Construct a subgraph performing a ring-style all-reduce of input_tensors. Args: - input_tensors: a list of T @{tf.Tensor} objects, which must all + input_tensors: a list of T `tf.Tensor` objects, which must all have the same shape and type. num_workers: number of worker tasks spanned by input_tensors. num_subchunks: number of subchunks each device should process in one tick. @@ -272,7 +272,7 @@ def build_ring_all_reduce(input_tensors, num_workers, num_subchunks, size. Returns: - a list of T @{tf.Tensor} identical sum-reductions of input_tensors. + a list of T `tf.Tensor` identical sum-reductions of input_tensors. """ if len(input_tensors) < 2: raise ValueError("input_tensors must be length 2 or longer") @@ -299,7 +299,7 @@ def _build_ring_gather(input_tensors, devices, num_subchunks, """Construct a subgraph for the first (reduction) pass of ring all-reduce. Args: - input_tensors: a list of T @{tf.Tensor} 1D input tensors of same + input_tensors: a list of T `tf.Tensor` 1D input tensors of same shape and type. devices: array of device name strings num_subchunks: number of subchunks each device should process in one tick. @@ -311,7 +311,7 @@ def _build_ring_gather(input_tensors, devices, num_subchunks, ValueError: tensors must all be one dimensional. Returns: - list of list of T @{tf.Tensor} of (partially) reduced values where + list of list of T `tf.Tensor` of (partially) reduced values where exactly num_subchunks chunks at each device are fully reduced. """ num_devices = len(input_tensors) @@ -360,11 +360,11 @@ def _apply_unary_to_chunks(f, chunks_by_dev): """Apply a unary op to each tensor in chunks_by_dev, on same device. Args: - f: a unary function over T @{tf.Tensor}. - chunks_by_dev: list of lists of T @{tf.Tensor}. + f: a unary function over T `tf.Tensor`. + chunks_by_dev: list of lists of T `tf.Tensor`. Returns: - new list of lists of T @{tf.Tensor} with the same structure as + new list of lists of T `tf.Tensor` with the same structure as chunks_by_dev containing the derived tensors. """ output = [] @@ -381,14 +381,14 @@ def _build_ring_scatter(pred_by_s_d, rank_by_s_d, Args: pred_by_s_d: as produced by _ring_permutations rank_by_s_d: as produced by _ring_permutations - chunks_by_dev: list of list of T @{tf.Tensor} indexed by ints + chunks_by_dev: list of list of T `tf.Tensor` indexed by ints (device, chunk) Raises: ValueError: chunks_by_dev is not well-formed Returns: - list of T @{tf.Tensor} which are the fully reduced tensors, one + list of T `tf.Tensor` which are the fully reduced tensors, one at each device corresponding to the outer dimension of chunks_by_dev. """ num_devices = len(chunks_by_dev) @@ -448,12 +448,12 @@ def build_recursive_hd_all_reduce(input_tensors, red_op, un_op=None): the future with edge-case specific logic. Args: - input_tensors: list of T @{tf.Tensor} to be elementwise reduced. + input_tensors: list of T `tf.Tensor` to be elementwise reduced. red_op: a binary elementwise reduction Op. un_op: an optional unary elementwise Op to apply to reduced values. Returns: - list of T @{tf.Tensor} which are the fully reduced tensors, one + list of T `tf.Tensor` which are the fully reduced tensors, one at each device of input_tensors. Raises: @@ -475,13 +475,13 @@ def _build_recursive_hd_gather(input_tensors, devices, red_op): """Construct the gather phase of recursive halving-doubling all-reduce. Args: - input_tensors: list of T @{tf.Tensor} to be elementwise reduced. + input_tensors: list of T `tf.Tensor` to be elementwise reduced. devices: a list of strings naming the devices hosting input_tensors, which will also be used to host the (partial) reduction values. red_op: a binary elementwise reduction Op. Returns: - list of T @{tf.Tensor} which are the fully reduced tensor shards. + list of T `tf.Tensor` which are the fully reduced tensor shards. Raises: ValueError: num_devices not a power of 2, or tensor len not divisible @@ -516,12 +516,12 @@ def _build_recursive_hd_scatter(input_tensors, devices): """Construct the scatter phase of recursive halving-doublng all-reduce. Args: - input_tensors: list of T @{tf.Tensor} that are fully-reduced shards. + input_tensors: list of T `tf.Tensor` that are fully-reduced shards. devices: a list of strings naming the devices on which the reconstituted full tensors should be placed. Returns: - list of T @{tf.Tensor} which are the fully reduced tensors. + list of T `tf.Tensor` which are the fully reduced tensors. """ num_devices = len(devices) num_hops = int(math.log(num_devices, 2)) @@ -571,7 +571,7 @@ def build_shuffle_all_reduce(input_tensors, gather_devices, red_op, un_op=None): un_op: optional elementwise unary Op to be applied to fully-reduced values. Returns: - list of T @{tf.Tensor} which are the fully reduced tensors. + list of T `tf.Tensor` which are the fully reduced tensors. """ input_tensors, shape = _flatten_tensors(input_tensors) dst_devices = [t.device for t in input_tensors] @@ -594,7 +594,7 @@ def _build_shuffle_gather(input_tensors, gather_devices, red_op, un_op=None): un_op: optional elementwise unary Op to be applied to fully-reduced values. Returns: - list of T @{tf.Tensor} which are the fully reduced shards. + list of T `tf.Tensor` which are the fully reduced shards. Raises: ValueError: inputs not well-formed. @@ -629,7 +629,7 @@ def _build_shuffle_scatter(reduced_shards, dst_devices): should be reconstituted. Returns: - list of T @{tf.Tensor} scattered tensors. + list of T `tf.Tensor` scattered tensors. """ num_devices = len(dst_devices) out_tensors = [] @@ -644,7 +644,7 @@ def _split_by_task(devices, values): Args: devices: list of device name strings - values: list of T @{tf.tensor} of same length as devices. + values: list of T `tf.tensor` of same length as devices. Returns: (per_task_devices, per_task_values) where both values are @@ -680,14 +680,14 @@ def build_nccl_all_reduce(input_tensors, red_op, un_op=None): """Build a subgraph that does one full all-reduce, using NCCL. Args: - input_tensors: list of T @{tf.Tensor} of same-shape and type values to + input_tensors: list of T `tf.Tensor` of same-shape and type values to be reduced. red_op: binary elementwise reduction operator. Must be one of {tf.add} un_op: optional unary elementwise Op to apply to fully-reduce values. Returns: - list of T @{tf.Tensor} of reduced values. + list of T `tf.Tensor` of reduced values. Raises: ValueError: red_op not supported. @@ -709,14 +709,14 @@ def _build_nccl_hybrid(input_tensors, red_op, upper_level_f): """Construct a subgraph for NCCL hybrid all-reduce. Args: - input_tensors: list of T @{tf.Tensor} of same-shape and type values to + input_tensors: list of T `tf.Tensor` of same-shape and type values to be reduced. red_op: binary elementwise reduction operator. upper_level_f: function for reducing one value per worker, across workers. Returns: - list of T @{tf.Tensor} of reduced values. + list of T `tf.Tensor` of reduced values. Raises: ValueError: inputs not well-formed. @@ -797,7 +797,7 @@ def _build_shuffle_hybrid(input_tensors, gather_devices, red_op, upper_level_f): """Construct a subgraph for Shuffle hybrid all-reduce. Args: - input_tensors: list of T @{tf.Tensor} of same-shape and type values to + input_tensors: list of T `tf.Tensor` of same-shape and type values to be reduced. gather_devices: list of device names on which to host gather shards. red_op: binary elementwise reduction operator. @@ -805,7 +805,7 @@ def _build_shuffle_hybrid(input_tensors, gather_devices, red_op, upper_level_f): workers. Returns: - list of T @{tf.Tensor} of reduced values. + list of T `tf.Tensor` of reduced values. Raises: ValueError: inputs not well-formed. diff --git a/tensorflow/contrib/autograph/converters/BUILD b/tensorflow/contrib/autograph/converters/BUILD index 7cbba7168383f3d0cdc80fda9908cb7d70836bb4..2d2ab7040a8bb76f9538f201f75a2e4dcba0f511 100644 --- a/tensorflow/contrib/autograph/converters/BUILD +++ b/tensorflow/contrib/autograph/converters/BUILD @@ -204,6 +204,7 @@ py_test( name = "side_effect_guards_test", srcs = ["side_effect_guards_test.py"], srcs_version = "PY2AND3", + tags = ["notsan"], deps = [ ":converters", "//tensorflow/contrib/autograph/core:test_lib", diff --git a/tensorflow/contrib/autograph/docs/pyfunc_dtypes.md b/tensorflow/contrib/autograph/docs/pyfunc_dtypes.md new file mode 100644 index 0000000000000000000000000000000000000000..bcbb920cc53de4b89dc67128c9c2c2312f030f0a --- /dev/null +++ b/tensorflow/contrib/autograph/docs/pyfunc_dtypes.md @@ -0,0 +1,33 @@ +# Specifying return data type for `py_func` calls + +The `py_func` op requires specifying a +[data type](https://www.tensorflow.org/guide/tensors#data_types). + +When wrapping a function with `py_func`, for instance using +`@autograph.do_not_convert(run_mode=autograph.RunMode.PY_FUNC)`, you have two +options to specify the returned data type: + + * explicitly, with a specified `tf.DType` value + * by matching the data type of an input argument, which is then assumed to be + a `Tensor` + +Examples: + +Specify an explicit data type: + +``` + def foo(a): + return a + 1 + + autograph.util.wrap_py_func(f, return_dtypes=[tf.float32]) +``` + +Match the data type of the first argument: + +``` + def foo(a): + return a + 1 + + autograph.util.wrap_py_func( + f, return_dtypes=[autograph.utils.py_func.MatchDType(0)]) +``` diff --git a/tensorflow/contrib/autograph/impl/api.py b/tensorflow/contrib/autograph/impl/api.py index 4729c735c621e68df30acfd4738d89874c3c55ac..276a3871801da2c66fbfffc38ac1ea39704b5de1 100644 --- a/tensorflow/contrib/autograph/impl/api.py +++ b/tensorflow/contrib/autograph/impl/api.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Public API.""" +"""This module contains the user-facing API for AutoGraph.""" from __future__ import absolute_import from __future__ import division @@ -42,34 +42,30 @@ from tensorflow.python.util import tf_inspect # (currently we require (module + class name, type)) -def convert(recursive=False, verbose=False, arg_types=None): - """Decorator that compiles a function to graph mode. +# TODO(mdan): This should behave like to_graph (e.g. convert statically). +def convert(recursive=False, verbose=False): + """Decorator that compiles a function to use TensorFlow ops. - The decorator is dynamic - invoking compilation whenever the decorated - function is called. This means the parameter values are known at compilation. + The decorator is dynamic - it recompiles the target whenever the decorated + function is called. This means the parameter values are known at conversion. + It also means that repeated calls with different types of parameters will be + correctly processed. Args: - recursive: Whether to recursively convert any functions that the decorator - function may call. - verbose: Whether to output the compiled code in the logs. - arg_types: See to_graph. + recursive: bool, whether to recursively convert any functions or classes + that the converted function may use. + verbose: bool, whether to output the compiled code in the logs. Returns: - A decorator that compiles the given function to graph mode. - - Raises: - ValueError: If any of the arguments are illegal. + Callable, a decorator that converts the given function into an equivalent + function that uses TensorFlow ops. """ - if arg_types is None: - arg_types = {} - def decorator(f): """Decorator implementation.""" @wraps(f) def wrapper(*args, **kwargs): - return converted_call(f, recursive, verbose, True, arg_types, *args, - **kwargs) + return converted_call(f, recursive, verbose, True, {}, *args, **kwargs) wrapper = tf_decorator.make_decorator(f, wrapper) @@ -82,22 +78,34 @@ def convert(recursive=False, verbose=False, arg_types=None): class RunMode(Enum): + """Specifies the way a converted function or method should be executed in TF. + + The enum values have the following semantics: + + * GRAPH: Call this function directly, as-is. This is suitable for functions + that were already designed for TF graphs and contain ops. + * PY_FUNC: Wrap this function into a py_func op. This is suitable for code + that will only run correctly in Python, for example code that renders + to the display, reads keyboard input, etc. + """ GRAPH = 1 PY_FUNC = 2 def do_not_convert(run_as=RunMode.GRAPH, return_dtypes=None): - """Decorator that suppresses compilation of a function. + """Decorator that suppresses the conversion of a function. + + See also: docs/pyfunc_dtypes.md Args: - run_as: RunMode value. Whether to run the function as-is, or wrap it into - a py_func. - return_dtypes: See autograph.utils.py_func.wrap_py_func. Setting to None or - empty list or tuple will create a dummy return value that can be used - to set control dependencies. + run_as: RunMode, specifies how to use the function in TensorFlow. + return_dtypes: Optional[Iterable[ + Union[tf.DType, utils.py_func.MatchDType]]], the return data types of + the converted function, if run_as is RunMode.PY_FUNC. Ignored otherwise. + May be set to None if the function has no return values. Returns: - A decorator that wraps the original function. + Callable, a decorator that wraps the original function. """ def decorator(f): @@ -130,9 +138,10 @@ def do_not_convert(run_as=RunMode.GRAPH, return_dtypes=None): return decorator +# TODO(mdan): Move to a private, undocumented module. def converted_call(f, recursive, verbose, force_conversion, arg_types, *args, **kwargs): - """Compiles a function call inline.""" + """Compiles a function call inline. For internal use only.""" # TODO(mdan): This needs cleanup. # In particular, we may want to avoid renaming functions altogether. if not force_conversion and conversion.is_whitelisted_for_graph(f): @@ -202,39 +211,41 @@ def converted_call(f, recursive, verbose, force_conversion, arg_types, *args, return converted_f(*effective_args, **kwargs) +# TODO(mdan): Rename: to_ops? +# TODO(mdan): Looki into overloading as function and decorator, like tfe.defun. +# TODO(mdan): Remove partial_types. def to_graph(e, recursive=True, verbose=False, arg_values=None, arg_types=None, partial_types=None): - """Compile a Python entity into equivalent TensorFlow code. + """Converts a Python entity into equivalent code that uses TensorFlow ops. - Currently supported entities: + Supported Python entities include: * functions * classes - Classes are handled by converting all their methods into a new class. + Classes are converted by converting all their methods into a new class. Args: - e: A Python entity. - recursive: Whether to recursively convert any functions that the decorator - function may call. - verbose: Whether to output the compiled code in the logs. - arg_values: A dict containing value hints for symbols like function - parameters. - arg_types: A dict containing type hints for symbols like function - parameters. - partial_types: A set of types (e.g. classes) that will not be converted - entirely. Calls to member functions for these types will be renamed - independently. + e: Union[Callable, Type], the Python entity to convert. + recursive: bool, whether to recursively convert any functions that the + converted function may call. + verbose: bool, whether to output the compiled code in the logs. + arg_values: Optional[Dict[Text, Any]], value hints for symbols including + function arguments. + arg_types: Optional[Dict[Text, Type]], type hints for symbols including + function arguments. + partial_types: Set[Type], reserved for internal use. Returns: - A function with a signature identical to `o`, but which when executed it - creates TF a graph that has the same functionality as the original entity. + Union[Callable, Type], the converted entity, which is the same kind as e + (that is, a function is e is a function, a class if e is a class, etc.) but + its code has been converted to use TF ops. + Raises: - ValueError: If the converted function defines or refers to symbol names that - are reserved for AutoGraph. + ValueError: If the entity could not be converted. """ program_ctx = converter.ProgramContext( recursive=recursive, @@ -288,20 +299,23 @@ def to_code(e, arg_types=None, partial_types=None, indentation=' '): - """Return the equivalent of an entity in TensorFlow code. + """Returns the equivalent code that uses TensorFlow ops. - See `to_graph` for more details. + Also see: `to_graph`, `convert` Args: - e: A Python entity. - recursive: See to_graph. - arg_values: See to_graph. - arg_types: See to_graph. - partial_types: See to_graph. - indentation: String, when to use for each level of indentation. + e: Union[Callable, Type], the Python entity to convert. + recursive: bool, whether to recursively convert any functions that the + converted function may call. + arg_values: Optional[Dict[Text, Any]], value hints for symbols including + function arguments. + arg_types: Optional[Dict[Text, Type]], type hints for symbols including + function arguments. + partial_types: Set[Type], reserved for internal use. + indentation: Text, when to use for each level of indentation. Returns: - String. + Text, the converted code. """ program_ctx = converter.ProgramContext( recursive=recursive, diff --git a/tensorflow/contrib/autograph/operators/control_flow.py b/tensorflow/contrib/autograph/operators/control_flow.py index be38d3f534adc16870a6cf103e00d3863f655dd2..9909e521644a7a901653dc09853222167828c75c 100644 --- a/tensorflow/contrib/autograph/operators/control_flow.py +++ b/tensorflow/contrib/autograph/operators/control_flow.py @@ -141,7 +141,7 @@ def _dataset_for_stmt(ds, extra_test, body, init_state): while_body, init_state=(epoch_number, iterate) + init_state, extra_deps=()) - # Dropping the epoch number and iterate because they are not not syntactically + # Dropping the epoch number and iterate because they are not syntactically # visible. results = results[2:] diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions.py b/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions.py index 9a84f1231cb71745f778285f30ada151a7c1accd..7f2b379d3de236020f1ec2b8a4972cc67b10b060 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/reaching_definitions.py @@ -39,7 +39,7 @@ from tensorflow.contrib.autograph.pyct.static_analysis import annos class Definition(object): """Definition objects describe a unique definition of a variable. - Subclasses of this may be used by passing an appropriate factory fuction to + Subclasses of this may be used by passing an appropriate factory function to resolve. Attributes: diff --git a/tensorflow/contrib/autograph/pyct/testing/BUILD b/tensorflow/contrib/autograph/pyct/testing/BUILD index 957db356f7e1acf673ce5db7c8087208af43ac23..9ef1ac9663eac8febffd697d7164425716b65d9d 100644 --- a/tensorflow/contrib/autograph/pyct/testing/BUILD +++ b/tensorflow/contrib/autograph/pyct/testing/BUILD @@ -33,7 +33,10 @@ py_test( size = "large", srcs = ["codegen_test.py"], srcs_version = "PY2AND3", - tags = ["no_windows"], + tags = [ + "no_windows", + "nomsan", + ], deps = [ ":testing", "//tensorflow/contrib/autograph/pyct", diff --git a/tensorflow/contrib/autograph/utils/builtins.py b/tensorflow/contrib/autograph/utils/builtins.py index ccbe5fc9541dfad561d8eab730e2b15f6250ceb2..4dd440ef197b7e24b901bc9e30794b0182378a32 100644 --- a/tensorflow/contrib/autograph/utils/builtins.py +++ b/tensorflow/contrib/autograph/utils/builtins.py @@ -44,6 +44,8 @@ def dynamic_builtin(f, *args, **kwargs): return dynamic_int(*args, **kwargs) if f is float: return dynamic_float(*args, **kwargs) + if f is abs: + return dynamic_abs(*args, **kwargs) raise NotImplementedError( 'The "%s" builtin is not yet supported.' % f.__name__) @@ -81,6 +83,13 @@ def dynamic_float(num_or_tensor, **kwargs): return float(num_or_tensor) +def dynamic_abs(num_or_tensor, **kwargs): + if tensor_util.is_tensor(num_or_tensor): + return math_ops.abs(num_or_tensor, **kwargs) + else: + return abs(num_or_tensor, **kwargs) + + def dynamic_range(start_or_stop, stop=None, step=None): """Implementation of range using dynamic dispatch.""" if type_check.is_tensor(start_or_stop, stop, step): diff --git a/tensorflow/contrib/autograph/utils/builtins_test.py b/tensorflow/contrib/autograph/utils/builtins_test.py index b4821f36fcab8c201956e366d394bababb9f02b6..b1cd5253bc3ffb1e67d89ef79cf56eaeb65fae07 100644 --- a/tensorflow/contrib/autograph/utils/builtins_test.py +++ b/tensorflow/contrib/autograph/utils/builtins_test.py @@ -44,6 +44,23 @@ class BuiltinsTest(test.TestCase): with self.test_session() as sess: self.assertEqual(3, sess.run(builtins.dynamic_builtin(len, a))) + def test_dynamic_abs_tf_scalar(self): + a = constant_op.constant(-1) + + with self.test_session() as sess: + self.assertEqual(1, sess.run(builtins.dynamic_builtin(abs, a))) + + def test_dynamic_abs_tf_array(self): + a = constant_op.constant([-1, 2, -3]) + + with self.test_session() as sess: + self.assertListEqual([1, 2, 3], + list(sess.run(builtins.dynamic_builtin(abs, a)))) + + def test_dynamic_abs_py_scalar(self): + a = -1 + self.assertEqual(1, builtins.dynamic_builtin(abs, a)) + def test_dynamic_len_tf_matrix(self): a = constant_op.constant([[1, 2], [3, 4]]) diff --git a/tensorflow/contrib/bigtable/README.md b/tensorflow/contrib/bigtable/README.md index 88a3909de4f34c11ac7ac3f0a865d76b675d0d06..b9abfa8295f9013cd8e92f87466a73952ccceb10 100644 --- a/tensorflow/contrib/bigtable/README.md +++ b/tensorflow/contrib/bigtable/README.md @@ -1,4 +1,4 @@ -# Bigtable # +# Google Cloud Bigtable [Cloud Bigtable](https://cloud.google.com/bigtable/) is a high performance storage system that can store and serve training data. This contrib @@ -13,7 +13,7 @@ Bigtable at high speed, in particular to feed modern accelerators. For general-purpose Cloud Bigtable APIs, see the [official Cloud Bigtable client library documentation][clientdoc]. -[clientdoc]: https://cloud.google.com/bigtable/docs/reference/libraries +[clientdoc]: https://cloud.google.com/bigtable/docs/reference/libraries ## Sample Use diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc index a6755a3496f3e1720f1c8c67f75521f2380a9845..1790b4bc116b417db55a91e3ae0b4bcb80bd3d74 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc @@ -84,6 +84,8 @@ class BigtableClientOp : public OpKernel { channel_args.SetMaxReceiveMessageSize( max_receive_message_size_); channel_args.SetUserAgentPrefix("tensorflow"); + channel_args.SetInt(GRPC_ARG_KEEPALIVE_PERMIT_WITHOUT_CALLS, 0); + channel_args.SetInt(GRPC_ARG_KEEPALIVE_TIMEOUT_MS, 60 * 1000); client_options.set_channel_arguments(channel_args); std::shared_ptr client = google::cloud::bigtable::CreateDefaultDataClient( diff --git a/tensorflow/contrib/bigtable/python/ops/bigtable_api.py b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py index e6ef513c409e063eece233b690bc30d55a6eda31..3e1b6228673fbdcb5a228a11532d29e6b2c817dc 100644 --- a/tensorflow/contrib/bigtable/python/ops/bigtable_api.py +++ b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py @@ -17,8 +17,8 @@ TensorFlow has support for reading from and writing to Cloud Bigtable. To use TensorFlow + Cloud Bigtable integration, first create a BigtableClient to configure your connection to Cloud Bigtable, and then create a BigtableTable -object to allow you to create numerous @{tf.data.Dataset}s to read data, or -write a @{tf.data.Dataset} object to the underlying Cloud Bigtable table. +object to allow you to create numerous `tf.data.Dataset`s to read data, or +write a `tf.data.Dataset` object to the underlying Cloud Bigtable table. For background on Cloud Bigtable, see: https://cloud.google.com/bigtable . """ @@ -203,7 +203,7 @@ class BigtableTable(object): be retrieved. If end is None, all subsequent row keys will be retrieved. Returns: - A @{tf.data.Dataset} containing `tf.string` Tensors corresponding to all + A `tf.data.Dataset` containing `tf.string` Tensors corresponding to all of the row keys between `start` and `end`. """ # TODO(saeta): Make inclusive / exclusive configurable? @@ -219,7 +219,7 @@ class BigtableTable(object): retrieved. Returns: - A @{tf.data.Dataset}. containing `tf.string` Tensors corresponding to all + A `tf.data.Dataset`. containing `tf.string` Tensors corresponding to all of the row keys matching that prefix. """ return _BigtablePrefixKeyDataset(self, prefix) @@ -228,11 +228,11 @@ class BigtableTable(object): """Retrieves a sampling of row keys from the Bigtable table. This dataset is most often used in conjunction with - @{tf.contrib.data.parallel_interleave} to construct a set of ranges for + `tf.contrib.data.parallel_interleave` to construct a set of ranges for scanning in parallel. Returns: - A @{tf.data.Dataset} returning string row keys. + A `tf.data.Dataset` returning string row keys. """ return _BigtableSampleKeysDataset(self) @@ -272,7 +272,7 @@ class BigtableTable(object): that are treated as the column qualifier (column name). Returns: - A @{tf.data.Dataset} returning the row keys and the cell contents. + A `tf.data.Dataset` returning the row keys and the cell contents. Raises: ValueError: If the configured probability is unexpected. @@ -317,7 +317,7 @@ class BigtableTable(object): that are treated as the column qualifier (column name). Returns: - A @{tf.data.Dataset} returning the row keys and the cell contents. + A `tf.data.Dataset` returning the row keys and the cell contents. Raises: ValueError: If the configured probability is unexpected. @@ -335,7 +335,7 @@ class BigtableTable(object): """Retrieves row (including values) from the Bigtable service at high speed. Rows with row-key prefixed by `prefix` will be retrieved. This method is - similar to `scan_prefix`, but by constrast performs multiple sub-scans in + similar to `scan_prefix`, but by contrast performs multiple sub-scans in parallel in order to achieve higher performance. Note: The dataset produced by this method is not deterministic! @@ -373,7 +373,7 @@ class BigtableTable(object): that are treated as the column qualifier (column name). Returns: - A @{tf.data.Dataset} returning the row keys and the cell contents. + A `tf.data.Dataset` returning the row keys and the cell contents. Raises: ValueError: If the configured probability is unexpected. @@ -394,7 +394,7 @@ class BigtableTable(object): """Retrieves rows (including values) from the Bigtable service. Rows with row-keys between `start` and `end` will be retrieved. This method - is similar to `scan_range`, but by constrast performs multiple sub-scans in + is similar to `scan_range`, but by contrast performs multiple sub-scans in parallel in order to achieve higher performance. Note: The dataset produced by this method is not deterministic! @@ -435,7 +435,7 @@ class BigtableTable(object): that are treated as the column qualifier (column name). Returns: - A @{tf.data.Dataset} returning the row keys and the cell contents. + A `tf.data.Dataset` returning the row keys and the cell contents. Raises: ValueError: If the configured probability is unexpected. @@ -450,12 +450,12 @@ class BigtableTable(object): """Writes a dataset to the table. Args: - dataset: A @{tf.data.Dataset} to be written to this table. It must produce + dataset: A `tf.data.Dataset` to be written to this table. It must produce a list of number-of-columns+1 elements, all of which must be strings. The first value will be used as the row key, and subsequent values will be used as cell values for the corresponding columns from the corresponding column_families and columns entries. - column_families: A @{tf.Tensor} of `tf.string`s corresponding to the + column_families: A `tf.Tensor` of `tf.string`s corresponding to the column names to store the dataset's elements into. columns: A `tf.Tensor` of `tf.string`s corresponding to the column names to store the dataset's elements into. @@ -463,7 +463,7 @@ class BigtableTable(object): Leave as None to use server-provided timestamps. Returns: - A @{tf.Operation} that can be run to perform the write. + A `tf.Operation` that can be run to perform the write. Raises: ValueError: If there are unexpected or incompatible types, or if the @@ -502,7 +502,7 @@ class BigtableTable(object): normalized_columns: The column families and column qualifiers to retrieve. Returns: - A @{tf.data.Dataset} representing the result of the parallel scan. + A `tf.data.Dataset` representing the result of the parallel scan. """ if num_parallel_scans is None: num_parallel_scans = 50 diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc index 5b4be2f25838d5405a8148ea20cb0f759cd3a8fb..1375fddf2bea1a8f856c35d756c38a8beb14a53f 100644 --- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc @@ -125,6 +125,8 @@ void QuantizeFeatures( auto flat_values = values_tensor.flat(); for (int64 instance = 0; instance < num_values; ++instance) { const float value = flat_values(instance); + CHECK(!buckets_vector.empty()) + << "Got empty buckets for feature " << feature_index; auto bucket_iter = std::lower_bound(buckets_vector.begin(), buckets_vector.end(), value); if (bucket_iter == buckets_vector.end()) { diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py index 1b7f59ea4218355a13f1df7264352bd68503bd19..5d4819b0f1cb598cfbe146f569aecd7883186339 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/base_split_handler.py @@ -131,6 +131,10 @@ class BaseSplitHandler(object): }, stamp_token, None) return control_flow_ops.group(update_1, *update_2[self]) + @abc.abstractmethod + def reset(self, stamp_token, next_stamp_token): + """Resets the state maintained by the handler.""" + @abc.abstractmethod def make_splits(self, stamp_token, next_stamp_token, class_id): """Create the best split using the accumulated stats and flush the state. diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py index bf686237ff696dadad9713d26bf784d7442b80d0..efe29216c2a7d8aa985da54cdbb839b9e6f69078 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/categorical_split_handler.py @@ -202,3 +202,7 @@ class EqualitySplitHandler(base_split_handler.BaseSplitHandler): # always return ready. are_splits_ready = constant_op.constant(True) return (are_splits_ready, partition_ids, gains, split_infos) + + def reset(self, stamp_token, next_stamp_token): + reset = self._stats_accumulator.flush(stamp_token, next_stamp_token) + return reset diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py index df0bec1fe363e07bbff6b059e86076239bd605e9..2559fe9913f377ce38aa11dfa908cd25ec76dab4 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler.py @@ -79,6 +79,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops + _BIAS_FEATURE_ID = -1 # Pattern to remove all non alpha numeric from a string. _PATTERN = re.compile(r"[\W_]+") @@ -147,6 +148,11 @@ class InequalitySplitHandler(base_split_handler.BaseSplitHandler): num_quantiles=num_quantiles, name="QuantileAccumulator/{}".format(self._name)) + def reset(self, stamp_token, next_stamp_token): + reset_1 = self._stats_accumulator.flush(stamp_token, next_stamp_token) + reset_2 = self._quantile_accumulator.flush(stamp_token, next_stamp_token) + return control_flow_ops.group([reset_1, reset_2]) + class DenseSplitHandler(InequalitySplitHandler): """Computes stats and finds the best inequality splits on dense columns.""" @@ -264,6 +270,7 @@ class DenseSplitHandler(InequalitySplitHandler): self._feature_column_group_id, self._l1_regularization, self._l2_regularization, self._tree_complexity_regularization, self._min_node_weight, self._loss_uses_sum_reduction)) + return are_splits_ready, partition_ids, gains, split_infos @@ -579,8 +586,10 @@ def dense_make_stats_update(is_active, are_buckets_ready, float_column, example_partition_ids, feature_ids, gradients, hessians = ( control_flow_ops.cond( - math_ops.logical_and(are_buckets_ready, is_active[0]), - ready_inputs_fn, not_ready_inputs_fn)) + math_ops.logical_and( + math_ops.logical_and(are_buckets_ready, + array_ops.size(quantile_buckets) > 0), + is_active[0]), ready_inputs_fn, not_ready_inputs_fn)) return (quantile_values, quantile_weights, example_partition_ids, feature_ids, gradients, hessians) @@ -674,8 +683,10 @@ def sparse_make_stats_update( lambda: handler_not_active)) example_partition_ids, feature_ids, gradients, hessians = ( - control_flow_ops.cond(are_buckets_ready, quantiles_ready, - quantiles_not_ready)) + control_flow_ops.cond( + math_ops.logical_and(are_buckets_ready, + array_ops.size(quantile_buckets) > 0), + quantiles_ready, quantiles_not_ready)) return (quantile_indices, quantile_values, quantile_shape, quantile_weights, example_partition_ids, feature_ids, gradients, hessians) diff --git a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py index d59732cf92eb85e88732ac5a17dccf475ae5342f..5d82c4cae5dbe28c82fa8754a7c65db62a2e6814 100644 --- a/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py +++ b/tensorflow/contrib/boosted_trees/lib/learner/batch/ordinal_split_handler_test.py @@ -1072,8 +1072,8 @@ class SparseSplitHandlerTest(test_util.TensorFlowTestCase): def testGenerateFeatureSplitCandidatesMulticlassFullHessian(self): with self.test_session() as sess: # Batch is 4, 2 classes - gradients = array_ops.constant( - [[0.2, 1.4], [-0.5, 0.1], [1.2, 3], [4.0, -3]]) + gradients = array_ops.constant([[0.2, 1.4], [-0.5, 0.1], [1.2, 3], + [4.0, -3]]) # 2x2 matrix for each instance hessian_0 = [[0.12, 0.02], [0.3, 0.11]] hessian_1 = [[0.07, -0.2], [-0.5, 0.2]] @@ -1167,8 +1167,8 @@ class SparseSplitHandlerTest(test_util.TensorFlowTestCase): def testGenerateFeatureSplitCandidatesMulticlassDiagonalHessian(self): with self.test_session() as sess: # Batch is 4, 2 classes - gradients = array_ops.constant( - [[0.2, 1.4], [-0.5, 0.1], [1.2, 3], [4.0, -3]]) + gradients = array_ops.constant([[0.2, 1.4], [-0.5, 0.1], [1.2, 3], + [4.0, -3]]) # Each hessian is a diagonal from a full hessian matrix. hessian_0 = [0.12, 0.11] hessian_1 = [0.07, 0.2] @@ -1406,6 +1406,100 @@ class SparseSplitHandlerTest(test_util.TensorFlowTestCase): self.assertEqual(len(gains), 0) self.assertEqual(len(splits), 0) + def testEmptyBuckets(self): + """Test that reproduces the case when quantile buckets were empty.""" + with self.test_session() as sess: + sparse_column = array_ops.sparse_placeholder(dtypes.float32) + + # We have two batches - at first, a sparse feature is empty. + empty_indices = array_ops.constant([], dtype=dtypes.int64, shape=[0, 2]) + empty_values = array_ops.constant([], dtype=dtypes.float32) + empty_sparse_column = sparse_tensor.SparseTensor(empty_indices, + empty_values, [4, 2]) + empty_sparse_column = empty_sparse_column.eval(session=sess) + + # For the second batch, the sparse feature is not empty. + non_empty_indices = array_ops.constant( + [[0, 0], [2, 1], [3, 2]], dtype=dtypes.int64, shape=[3, 2]) + non_empty_values = array_ops.constant( + [0.52, 0.3, 0.52], dtype=dtypes.float32) + non_empty_sparse_column = sparse_tensor.SparseTensor( + non_empty_indices, non_empty_values, [4, 2]) + non_empty_sparse_column = non_empty_sparse_column.eval(session=sess) + + gradient_shape = tensor_shape.scalar() + hessian_shape = tensor_shape.scalar() + class_id = -1 + + split_handler = ordinal_split_handler.SparseSplitHandler( + l1_regularization=0.0, + l2_regularization=2.0, + tree_complexity_regularization=0.0, + min_node_weight=0.0, + epsilon=0.01, + num_quantiles=2, + feature_column_group_id=0, + sparse_float_column=sparse_column, + init_stamp_token=0, + gradient_shape=gradient_shape, + hessian_shape=hessian_shape, + multiclass_strategy=learner_pb2.LearnerConfig.TREE_PER_CLASS) + resources.initialize_resources(resources.shared_resources()).run() + gradients = array_ops.constant([0.2, -0.5, 1.2, 4.0]) + hessians = array_ops.constant([0.12, 0.07, 0.2, 0.13]) + partition_ids = array_ops.constant([0, 0, 0, 1], dtype=dtypes.int32) + + empty_gradients, empty_hessians = get_empty_tensors( + gradient_shape, hessian_shape) + example_weights = array_ops.ones([4, 1], dtypes.float32) + + update_1 = split_handler.update_stats_sync( + 0, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + with ops.control_dependencies([update_1]): + are_splits_ready = split_handler.make_splits( + np.int64(0), np.int64(1), class_id)[0] + + # First, calculate quantiles and try to update on an empty data for a + # feature. + are_splits_ready = ( + sess.run( + are_splits_ready, + feed_dict={sparse_column: empty_sparse_column})) + self.assertFalse(are_splits_ready) + + update_2 = split_handler.update_stats_sync( + 1, + partition_ids, + gradients, + hessians, + empty_gradients, + empty_hessians, + example_weights, + is_active=array_ops.constant([True, True])) + with ops.control_dependencies([update_2]): + are_splits_ready2, partitions, gains, splits = ( + split_handler.make_splits(np.int64(1), np.int64(2), class_id)) + + # Now the feature in the second batch is not empty, but buckets + # calculated on the first batch are empty. + are_splits_ready2, partitions, gains, splits = ( + sess.run( + [are_splits_ready2, partitions, gains, splits], + feed_dict={sparse_column: non_empty_sparse_column})) + self.assertFalse(are_splits_ready) + self.assertTrue(are_splits_ready2) + # Since the buckets were empty, we can't calculate the splits. + self.assertEqual(len(partitions), 0) + self.assertEqual(len(gains), 0) + self.assertEqual(len(splits), 0) + def testDegenerativeCase(self): with self.test_session() as sess: # One data example only, one leaf and thus one quantile bucket.The same diff --git a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py index ba5ef700c5405e4cef6f54f7f0d4f3d942b7d3fe..d0d1249bd6afc9cdbf6d88298c5024a4a54a5073 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -51,6 +51,7 @@ from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary from tensorflow.python.training import device_setter + # Key names for prediction dict. ENSEMBLE_STAMP = "ensemble_stamp" PREDICTIONS = "predictions" @@ -898,7 +899,7 @@ class GradientBoostedDecisionTreeModel(object): reset_ops = [] for handler in handlers: - reset_ops.append(handler.make_splits(stamp_token, next_stamp_token, 0)) + reset_ops.append(handler.reset(stamp_token, next_stamp_token)) if self._center_bias: reset_ops.append( bias_stats_accumulator.flush(stamp_token, next_stamp_token)) diff --git a/tensorflow/contrib/cmake/python_modules.txt b/tensorflow/contrib/cmake/python_modules.txt index 9045290679b87a201df8b930df6ff9a4ec106dcf..a5a947f7261559b6d25c452efe35097258d5625c 100644 --- a/tensorflow/contrib/cmake/python_modules.txt +++ b/tensorflow/contrib/cmake/python_modules.txt @@ -186,6 +186,8 @@ tensorflow/contrib/graph_editor/examples tensorflow/contrib/grid_rnn tensorflow/contrib/grid_rnn/python tensorflow/contrib/grid_rnn/python/ops +tensorflow/contrib/hadoop/python +tensorflow/contrib/hadoop/python/ops tensorflow/contrib/hooks tensorflow/contrib/hooks/python tensorflow/contrib/image diff --git a/tensorflow/contrib/cmake/tf_python.cmake b/tensorflow/contrib/cmake/tf_python.cmake index 5cb0db6b019f1be73422218ed786cda4767c2843..6d86daf5f174a3238ab92e5bba6085c904766766 100755 --- a/tensorflow/contrib/cmake/tf_python.cmake +++ b/tensorflow/contrib/cmake/tf_python.cmake @@ -198,7 +198,7 @@ function(add_python_module MODULE_NAME) # so we currently add explicit commands to include those files # later on in this script. if (NOT "${script}" MATCHES "_test\.py$") - add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD + add_custom_command(TARGET tf_python_copy_scripts_to_destination PRE_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${tensorflow_source_dir}/${script} ${CMAKE_CURRENT_BINARY_DIR}/tf_python/${script}) endif() endforeach() @@ -297,7 +297,7 @@ function(GENERATE_PYTHON_OP_LIB tf_python_op_lib_name) ) target_link_libraries(${tf_python_op_lib_name}_gen_python PRIVATE tf_protos_cc - tf_python_protos_cc + tf_python_protos_cc ${tensorflow_EXTERNAL_LIBRARIES} ) @@ -549,15 +549,15 @@ if(WIN32) ${NUMPY_INCLUDE_DIR} ) #target_link_libraries(pywrap_tensorflow_internal_static - # tf_protos_cc - # tf_python_protos_cc + # tf_protos_cc + # tf_python_protos_cc #) add_dependencies(pywrap_tensorflow_internal_static tf_protos_cc tf_python_protos_cc) set(pywrap_tensorflow_internal_static_dependencies $ $ $ - ${nsync_STATIC_LIBRARIES} + ${nsync_STATIC_LIBRARIES} ) if(${CMAKE_GENERATOR} MATCHES "Visual Studio.*") @@ -763,57 +763,40 @@ file(WRITE "${api_init_list_file}" "${api_init_files}") # recongnize paths. As CUDA isn't built with MKL, the MKL built directory is the only path to this command to work around that issue. # To not override the CUDA and system path in other circumstances, `if-else` branch used here to handle this problem, # and should be removed if the path issue can be resolved. +# UPDATE: Below block appears to handle multiple items in PATH correctly, but risks command line limits if PATH is large. +# If you have issues, try `set(PY_RUNTIME_ENV "PATH=${mkl_BIN_DIRS}")` instead. ### -if (tensorflow_ENABLE_MKL_SUPPORT) +set(PY_RUNTIME_ENV "") +if(tensorflow_ENABLE_MKL_SUPPORT) # add mkl dist dlls to system path for python - # TODO: In current cmake version, PY_RUNTIME_ENV behaves strange with multiple paths, - # so we have to specify only one path in it to work around the issue. We need this if/else - # to protect overwriting CUDA environments - set(PY_RUNTIME_ENV ${mkl_BIN_DIRS}) - add_custom_command( - OUTPUT ${api_init_files} - DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops - - # tensorflow/__init__.py depends on files generated in this step. So, remove it while - # this step is running since the files aren't there yet. - COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py - - # Run create_python_api.py to generate API init files. - COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python PATH=${PY_RUNTIME_ENV} ${PYTHON_EXECUTABLE} - "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/tools/api/generator/create_python_api.py" - "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" - "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" - "--package=tensorflow.python" - "--apiname=tensorflow" - "${api_init_list_file}" - - COMMENT "Generating __init__.py files for Python API." - WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" - VERBATIM - ) -else (tensorflow_ENABLE_MKL_SUPPORT) - add_custom_command( - OUTPUT ${api_init_files} - DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops - - # tensorflow/__init__.py depends on files generated in this step. So, remove it while - # this step is running since the files aren't there yet. - COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py - - # Run create_python_api.py to generate API init files. - COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} - "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/tools/api/generator/create_python_api.py" - "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" - "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" - "--package=tensorflow.python" - "--apiname=tensorflow" - "${api_init_list_file}" - - COMMENT "Generating __init__.py files for Python API." - WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" - ) -endif (tensorflow_ENABLE_MKL_SUPPORT) + file(TO_CMAKE_PATH "$ENV{PATH}" PY_RUNTIME_ENV) + set(PY_RUNTIME_ENV ${mkl_BIN_DIRS} ${PY_RUNTIME_ENV}) + file(TO_NATIVE_PATH "${PY_RUNTIME_ENV}" PY_RUNTIME_ENV) + set(PY_RUNTIME_ENV "PATH=${PY_RUNTIME_ENV}") +endif(tensorflow_ENABLE_MKL_SUPPORT) + +add_custom_command( + OUTPUT ${api_init_files} + DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops + + # tensorflow/__init__.py depends on files generated in this step. So, remove it while + # this step is running since the files aren't there yet. + COMMAND ${CMAKE_COMMAND} -E remove -f ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/__init__.py + + # Run create_python_api.py to generate API init files. + COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python "${PY_RUNTIME_ENV}" ${PYTHON_EXECUTABLE} + "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/tools/api/generator/create_python_api.py" + "--root_init_template=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/api_template.__init__.py" + "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow" + "--package=tensorflow.python" + "--apiname=tensorflow" + "${api_init_list_file}" + + COMMENT "Generating __init__.py files for Python API." + WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tf_python" + VERBATIM +) add_custom_target(tf_python_api SOURCES ${api_init_files}) add_dependencies(tf_python_api tf_python_ops) @@ -848,12 +831,12 @@ add_custom_command( DEPENDS tf_python_ops tf_python_copy_scripts_to_destination pywrap_tensorflow_internal tf_python_touchup_modules tf_extension_ops # Run create_python_api.py to generate API init files. - COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python ${PYTHON_EXECUTABLE} + COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_CURRENT_BINARY_DIR}/tf_python "${PY_RUNTIME_ENV}" ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/tools/api/generator/create_python_api.py" "--apidir=${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/python/estimator/api" "--package=tensorflow.python.estimator" "--apiname=estimator" - "--output_package=tensorflow.python.estimator.api" + "--output_package=tensorflow.python.estimator.api" "${estimator_api_init_list_file}" COMMENT "Generating __init__.py files for Python API." diff --git a/tensorflow/contrib/cmake/tf_tests.cmake b/tensorflow/contrib/cmake/tf_tests.cmake index b2330c4e340d531f70234de812ab6f6b2e5c1160..2c878c17167c662d10a8c7dabf41687efdbf65d8 100644 --- a/tensorflow/contrib/cmake/tf_tests.cmake +++ b/tensorflow/contrib/cmake/tf_tests.cmake @@ -122,6 +122,17 @@ function(AddPythonTests) endforeach() endfunction(AddPythonTests) +# +# ensure that every element is an existing file +# +function(CheckExists TYPE SOURCES) + foreach(source ${SOURCES}) + if(NOT EXISTS ${source}) + message(SEND_ERROR "${TYPE} not found: ${source}") + endif() + endforeach(source) +endfunction(CheckExists) + if (tensorflow_BUILD_PYTHON_TESTS) # # python tests. This assumes that the tensorflow wheel is @@ -145,7 +156,6 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/debug/wrappers/*_test.py" "${tensorflow_source_dir}/tensorflow/contrib/estimator/python/estimator/*_test.py" "${tensorflow_source_dir}/tensorflow/python/kernel_tests/*.py" - "${tensorflow_source_dir}/tensorflow/python/meta_graph_transform/*_test.py" "${tensorflow_source_dir}/tensorflow/python/ops/quantized_conv_ops_test.py" "${tensorflow_source_dir}/tensorflow/python/ops/quantized_ops_test.py" "${tensorflow_source_dir}/tensorflow/python/platform/build_info_test.py" @@ -198,7 +208,6 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/saved_model/saved_model_test.py" "${tensorflow_source_dir}/tensorflow/contrib/image/python/kernel_tests/sparse_image_warp_test.py" # requires scipy - "${tensorflow_source_dir}/tensorflow/contrib/keras/python/keras/preprocessing/*_test.py" "${tensorflow_source_dir}/tensorflow/contrib/tfprof/python/tools/tfprof/pprof_profiler_test.py" "${tensorflow_source_dir}/tensorflow/contrib/image/python/kernel_tests/interpolate_spline_test.py" # Takes very long to run without sharding (defined in bazel build file). @@ -256,10 +265,9 @@ if (tensorflow_BUILD_PYTHON_TESTS) # Flaky because of local cluster creation. "${tensorflow_source_dir}/tensorflow/python/training/sync_replicas_optimizer_test.py" "${tensorflow_source_dir}/tensorflow/python/debug/lib/session_debug_grpc_test.py" - "${tensorflow_source_dir}tensorflow/python/training/localhost_cluster_performance_test.py" + "${tensorflow_source_dir}/tensorflow/python/training/localhost_cluster_performance_test.py" "${tensorflow_source_dir}/tensorflow/python/data/kernel_tests/iterator_ops_cluster_test.py" "${tensorflow_source_dir}/tensorflow/python/kernel_tests/functional_ops_test.py" - "${tensorflow_source_dir}/tensorflow/contrib/data/python/kernel_tests/iterator_ops_cluster_test.py" # Type error in testRemoteIteratorUsingRemoteCallOpDirectSessionGPUCPU. "${tensorflow_source_dir}/tensorflow/python/data/kernel_tests/iterator_ops_test.py" "${tensorflow_source_dir}/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py" @@ -329,6 +337,7 @@ if (tensorflow_BUILD_PYTHON_TESTS) "${tensorflow_source_dir}/tensorflow/python/keras/_impl/keras/utils/io_utils_test.py" # b/72894325 ) endif() + CheckExists(${tf_test_src_py_exclude}) list(REMOVE_ITEM tf_test_src_py ${tf_test_src_py_exclude}) AddPythonTests( @@ -480,6 +489,7 @@ if (tensorflow_BUILD_CC_TESTS) "${tensorflow_source_dir}/tensorflow/cc/saved_model/*_test.cc" ) + CheckExists(${tf_test_src_simple_exclude}) list(REMOVE_ITEM tf_test_src_simple ${tf_test_src_simple_exclude} ${tf_cc_saved_model_test_srcs} @@ -494,6 +504,7 @@ if (tensorflow_BUILD_CC_TESTS) ${tf_core_profiler_test_srcs} ) + CheckExists(${tf_src_testlib}) set(tf_test_lib tf_test_lib) add_library(${tf_test_lib} STATIC ${tf_src_testlib}) diff --git a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py index 3791dae8d7f6b03bc1115bca97811dfc4775c45b..ff846b191a34e3f3b4aa35671ca22b96b963db80 100644 --- a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py +++ b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py @@ -150,7 +150,7 @@ def _project_stochastic_matrix_wrt_euclidean_norm(matrix): "matrix must be two dimensional (instead is %d-dimensional)" % matrix_shape.ndims) if matrix_shape[0] != matrix_shape[1]: - raise ValueError("matrix must be be square (instead has shape (%d,%d))" % + raise ValueError("matrix must be square (instead has shape (%d,%d))" % (matrix_shape[0], matrix_shape[1])) dimension = matrix_shape[0].value if dimension is None: diff --git a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py index f56a973f6f80b81697e9f58578e60a2efb90154e..8cfe14205927bf7763cf36fa31012ab10fce995c 100644 --- a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py +++ b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py @@ -158,7 +158,7 @@ class CrfTest(test.TestCase): # Test both the length-1 and regular cases. sequence_lengths_list = [ np.array(3, dtype=np.int32), - np.array(1, dtype=np.int32) + np.array(1, dtype=np.int64) ] inputs_list = [ np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], @@ -291,7 +291,7 @@ class CrfTest(test.TestCase): # Test both the length-1 and regular cases. sequence_lengths_list = [ np.array(3, dtype=np.int32), - np.array(1, dtype=np.int32) + np.array(1, dtype=np.int64) ] inputs_list = [ np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py index 8a7ff61bc8391efe453ee37019c23bd6ccbdf066..2a91dcb63a80016e62d10d1310ca57e3e54434c5 100644 --- a/tensorflow/contrib/crf/python/ops/crf.py +++ b/tensorflow/contrib/crf/python/ops/crf.py @@ -548,7 +548,9 @@ def crf_decode(potentials, transition_params, sequence_length): initial_state = array_ops.squeeze(initial_state, axis=[1]) # [B, O] inputs = array_ops.slice(potentials, [0, 1, 0], [-1, -1, -1]) # [B, T-1, O] # Sequence length is not allowed to be less than zero. - sequence_length_less_one = math_ops.maximum(0, sequence_length - 1) + sequence_length_less_one = math_ops.maximum( + constant_op.constant(0, dtype=sequence_length.dtype), + sequence_length - 1) backpointers, last_score = rnn.dynamic_rnn( # [B, T - 1, O], [B, O] crf_fwd_cell, inputs=inputs, diff --git a/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py b/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py index d58198faf353aab68430d2fa153a18de359112de..e26d56c8579e110d61c73c6154b82f47f0093687 100644 --- a/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py +++ b/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py @@ -56,7 +56,7 @@ class _CudnnRNN(base_layer.Layer): Cudnn RNNs have two major differences from other platform-independent RNNs tf provides: * Cudnn LSTM and GRU are mathematically different from their tf counterparts. - (e.g. @{tf.contrib.rnn.LSTMBlockCell} and @{tf.nn.rnn_cell.GRUCell}. + (e.g. `tf.contrib.rnn.LSTMBlockCell` and `tf.nn.rnn_cell.GRUCell`. * Cudnn-trained checkpoints are not directly compatible with tf RNNs: * They use a single opaque parameter buffer for the entire (possibly) multi-layer multi-directional RNN; Whereas tf RNN weights are per-cell and @@ -182,7 +182,7 @@ class _CudnnRNN(base_layer.Layer): dropout: dropout rate, a number between [0, 1]. Dropout is applied between each layer (no dropout is applied for a model with a single layer). When set to 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. dtype: tf.float16, tf.float32 or tf.float64 kernel_initializer: starting value to initialize the weight. diff --git a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py index 748d7cd011f32fdebd781176b560b9b7498f327e..2c92f31788378c2a9f01183bc04b035668b59b59 100644 --- a/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py +++ b/tensorflow/contrib/cudnn_rnn/python/ops/cudnn_rnn_ops.py @@ -61,8 +61,8 @@ _WEIGHTS_VARIABLE_NAME = rnn_cell_impl._WEIGHTS_VARIABLE_NAME class CudnnCompatibleLSTMCell(lstm_ops.LSTMBlockCell): """Cudnn Compatible LSTMCell. - A simple wrapper around @{tf.contrib.rnn.LSTMBlockCell} to use along with - @{tf.contrib.cudnn_rnn.CudnnLSTM}. The latter's params can be used by + A simple wrapper around `tf.contrib.rnn.LSTMBlockCell` to use along with + `tf.contrib.cudnn_rnn.CudnnLSTM`. The latter's params can be used by this cell seamlessly. """ @@ -76,8 +76,8 @@ class CudnnCompatibleLSTMCell(lstm_ops.LSTMBlockCell): class CudnnCompatibleGRUCell(rnn_cell_impl.GRUCell): """Cudnn Compatible GRUCell. - A GRU impl akin to @{tf.nn.rnn_cell.GRUCell} to use along with - @{tf.contrib.cudnn_rnn.CudnnGRU}. The latter's params can be used by + A GRU impl akin to `tf.nn.rnn_cell.GRUCell` to use along with + `tf.contrib.cudnn_rnn.CudnnGRU`. The latter's params can be used by it seamlessly. It differs from platform-independent GRUs in how the new memory gate is @@ -97,7 +97,7 @@ class CudnnCompatibleGRUCell(rnn_cell_impl.GRUCell): $$h_t = (1 - u_t) .* h'_t + u_t .* h_t-1$$ ``` - Other GRU (see @{tf.nn.rnn_cell.GRUCell} and @{tf.contrib.rnn.GRUBlockCell}): + Other GRU (see `tf.nn.rnn_cell.GRUCell` and `tf.contrib.rnn.GRUBlockCell`): ```python # new memory gate \\(h'_t = tanh(x_t * W_h + (r_t .* h_t-1) * R_h + b_{Wh})\\) @@ -891,7 +891,7 @@ def _cudnn_rnn(inputs, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -957,7 +957,7 @@ def cudnn_lstm(inputs, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -998,7 +998,7 @@ def _cudnn_rnn_no_input_c(inputs, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -1040,7 +1040,7 @@ def cudnn_gru(inputs, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -1079,7 +1079,7 @@ def cudnn_rnn_relu(inputs, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -1119,7 +1119,7 @@ def cudnn_rnn_tanh(inputs, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -1161,7 +1161,7 @@ def cudnn_rnn_opaque_params_to_canonical(rnn_mode, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -1224,7 +1224,7 @@ def cudnn_rnn_canonical_to_opaque_params(rnn_mode, direction: the direction model that the model operates. Could be either 'unidirectional' or 'bidirectional' dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -1282,7 +1282,7 @@ def cudnn_rnn_opaque_params_size(rnn_mode, 'unidirectional' or 'bidirectional' dtype: one of tf.float32 or tf.float64. dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. name: name of the operation. Returns: @@ -1349,7 +1349,7 @@ class _CudnnRNN(object): 'unidirectional' or 'bidirectional' dtype: dtype of params, tf.float32 or tf.float64. dropout: whether to enable dropout. With it is 0, dropout is disabled. - seed: the op seed used for initializing dropout. See @{tf.set_random_seed} + seed: the op seed used for initializing dropout. See `tf.set_random_seed` for behavior. Raises: ValueError: if direction is invalid. diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 7878e46e88b2ea8b0012768342c218baeda80eaa..dbfff9b4f86065de9736eed72de173bc1bef35d6 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -15,7 +15,7 @@ """Experimental API for building input pipelines. This module contains experimental `Dataset` sources and transformations that can -be used in conjunction with the @{tf.data.Dataset} API. Note that the +be used in conjunction with the `tf.data.Dataset` API. Note that the `tf.contrib.data` API is not subject to the same backwards compatibility guarantees as `tf.data`, but we will provide deprecation advice in advance of removing existing functionality. diff --git a/tensorflow/contrib/data/kernels/BUILD b/tensorflow/contrib/data/kernels/BUILD index 566cbb246a104d1e6cfc284d220ca8386b8897e1..2e249f5c14ab111ae412ff3288acc25de8d7aa11 100644 --- a/tensorflow/contrib/data/kernels/BUILD +++ b/tensorflow/contrib/data/kernels/BUILD @@ -37,6 +37,7 @@ cc_library( "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", ], + alwayslink = 1, ) cc_library( @@ -58,6 +59,7 @@ cc_library( "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", ], + alwayslink = 1, ) cc_library( @@ -68,6 +70,7 @@ cc_library( "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", ], + alwayslink = 1, ) cc_library( @@ -78,6 +81,7 @@ cc_library( "//third_party/eigen3", "@protobuf_archive//:protobuf_headers", ], + alwayslink = 1, ) cc_library( diff --git a/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc b/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc index 95b8e1f7fd487119d77a5f708de42b014c55f79d..bff6301250146ae6b359145cb26832b1a117c2de 100644 --- a/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/assert_next_dataset_op.cc @@ -76,10 +76,11 @@ class AssertNextDatasetOp : public UnaryDatasetOpKernel { } protected: - Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { Node* input_graph_node = nullptr; - TF_RETURN_IF_ERROR(b->AddParentDataset(ctx, input_, &input_graph_node)); + TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node)); Node* transformations_node = nullptr; TF_RETURN_IF_ERROR(b->AddVector(transformations_, &transformations_node)); TF_RETURN_IF_ERROR(b->AddDataset( @@ -121,13 +122,13 @@ class AssertNextDatasetOp : public UnaryDatasetOpKernel { protected: Status SaveInternal(IteratorStateWriter* writer) override { - TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); + TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_)); return Status::OK(); } Status RestoreInternal(IteratorContext* ctx, IteratorStateReader* reader) override { - TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); + TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_)); return Status::OK(); } diff --git a/tensorflow/contrib/data/kernels/csv_dataset_op.cc b/tensorflow/contrib/data/kernels/csv_dataset_op.cc index f7e3ed886c6655cdc07e08bbe2fbe82e671a6802..51e1b9aa656f29f7650b0b4ea8c81a26366181bd 100644 --- a/tensorflow/contrib/data/kernels/csv_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/csv_dataset_op.cc @@ -168,7 +168,8 @@ class CSVDatasetOp : public DatasetOpKernel { string DebugString() const override { return "CSVDatasetOp::Dataset"; } protected: - Status AsGraphDefInternal(DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { Node* filenames = nullptr; Node* compression_type = nullptr; diff --git a/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc b/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc index 6a12ca06f4d6cc2096aaf8191a01a899881b43db..b9306f611b89fda084fe0f0b39f78418d2284a56 100644 --- a/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/directed_interleave_dataset_op.cc @@ -110,15 +110,16 @@ class DirectedInterleaveDatasetOp : public DatasetOpKernel { } protected: - Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { Node* selector_input_node; TF_RETURN_IF_ERROR( - b->AddParentDataset(ctx, selector_input_, &selector_input_node)); + b->AddInputDataset(ctx, selector_input_, &selector_input_node)); std::vector data_input_nodes(data_inputs_.size()); for (size_t i = 0; i < data_inputs_.size(); ++i) { TF_RETURN_IF_ERROR( - b->AddParentDataset(ctx, data_inputs_[i], &data_input_nodes[i])); + b->AddInputDataset(ctx, data_inputs_[i], &data_input_nodes[i])); } TF_RETURN_IF_ERROR(b->AddDataset(this, {{0, selector_input_node}}, {{1, data_input_nodes}}, {}, output)); @@ -204,7 +205,7 @@ class DirectedInterleaveDatasetOp : public DatasetOpKernel { Status SaveInternal(IteratorStateWriter* writer) override { mutex_lock l(mu_); if (selector_input_impl_) { - TF_RETURN_IF_ERROR(SaveParent(writer, selector_input_impl_)); + TF_RETURN_IF_ERROR(SaveInput(writer, selector_input_impl_)); } else { TF_RETURN_IF_ERROR( writer->WriteScalar(full_name("selector_input_impl_empty"), "")); @@ -212,7 +213,7 @@ class DirectedInterleaveDatasetOp : public DatasetOpKernel { for (size_t i = 0; i < data_input_impls_.size(); ++i) { const auto& data_input_impl = data_input_impls_[i]; if (data_input_impl) { - TF_RETURN_IF_ERROR(SaveParent(writer, data_input_impl)); + TF_RETURN_IF_ERROR(SaveInput(writer, data_input_impl)); } else { TF_RETURN_IF_ERROR(writer->WriteScalar( full_name(strings::StrCat("data_input_impl_empty[", i, "]")), @@ -226,15 +227,14 @@ class DirectedInterleaveDatasetOp : public DatasetOpKernel { IteratorStateReader* reader) override { mutex_lock l(mu_); if (!reader->Contains(full_name("selector_input_impl_empty"))) { - TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, selector_input_impl_)); + TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, selector_input_impl_)); } else { selector_input_impl_.reset(); } for (size_t i = 0; i < data_input_impls_.size(); ++i) { if (!reader->Contains(full_name( strings::StrCat("data_input_impl_empty[", i, "]")))) { - TF_RETURN_IF_ERROR( - RestoreParent(ctx, reader, data_input_impls_[i])); + TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, data_input_impls_[i])); } else { data_input_impls_[i].reset(); } diff --git a/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc b/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc index bbec50681c6f5decec5a3b5fbf09cc3011a21199..d77beb8e10561b54805e245b4d54935aebfa3bd1 100644 --- a/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/ignore_errors_dataset_op.cc @@ -62,10 +62,11 @@ class IgnoreErrorsDatasetOp : public UnaryDatasetOpKernel { } protected: - Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { Node* input_graph_node = nullptr; - TF_RETURN_IF_ERROR(b->AddParentDataset(ctx, input_, &input_graph_node)); + TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node)); TF_RETURN_IF_ERROR(b->AddDataset(this, {input_graph_node}, output)); return Status::OK(); } @@ -106,7 +107,7 @@ class IgnoreErrorsDatasetOp : public UnaryDatasetOpKernel { Status SaveInternal(IteratorStateWriter* writer) override { mutex_lock l(mu_); if (input_impl_) - TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); + TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_)); else TF_RETURN_IF_ERROR( writer->WriteScalar(full_name("input_impls_empty"), "")); @@ -119,7 +120,7 @@ class IgnoreErrorsDatasetOp : public UnaryDatasetOpKernel { if (reader->Contains(full_name("input_impls_empty"))) input_impl_.reset(); else - TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); + TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_)); return Status::OK(); } diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index 32f03ca68364e40c6fd6769f05d0566f50119240..13bcd77b4af40b25c5a2ec64be3ded5931cf79ed 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -526,6 +526,15 @@ string SanitizeThreadSuffix(string suffix) { return clean; } +struct HostBufferElement { + Status status; + bool end_of_sequence; + std::vector value; +}; + +using MultiDeviceIteratorCallback = + std::function; + class MultiDeviceIterator : public ResourceBase { public: MultiDeviceIterator(const DataTypeVector& output_types, @@ -539,83 +548,45 @@ class MultiDeviceIterator : public ResourceBase { devices_(devices), flib_def_(std::move(flib_def)), pflr_(std::move(pflr)), - lib_(lib) { - buffer_.resize(devices_.size()); - } + lib_(lib) {} string DebugString() override { - return strings::StrCat("MultiDeviceIterator"); + return strings::StrCat("MultiDeviceIterator for ", devices_.size(), + " devices"); } - Status Init(std::unique_ptr iterator, int64* incarnation_id) { - mutex_lock l(mu_); + Status Init(std::unique_ptr iterator, int64 max_buffer_size, + int64* incarnation_id) { if (iterator) { TF_RETURN_IF_ERROR( VerifyTypesMatch(output_types_, iterator->output_dtypes())); TF_RETURN_IF_ERROR( VerifyShapesCompatible(output_shapes_, iterator->output_shapes())); } - host_iterator_.reset(iterator.release()); - incarnation_id_++; + + mutex_lock l(mu_); + if (multi_device_buffer_) { + multi_device_buffer_->Reset(); + } + + ++incarnation_id_; *incarnation_id = incarnation_id_; - max_buffer_size_ = 0; - num_elements_ = 0; - buffer_.clear(); - buffer_.resize(devices_.size()); + + multi_device_buffer_.reset( + new MultiDeviceBuffer(devices_.size(), max_buffer_size, incarnation_id_, + std::move(iterator))); return Status::OK(); } - Status GetNextFromShard(IteratorContext* ctx, int shard_num, - int64 incarnation_id, - std::vector* out_tensors, - bool* end_of_sequence) { - // TODO(rohanj): This might potentially strand elements in other shards. - // Opportunity to do smarter locking semantics. - mutex_lock l(mu_); - // Make sure we're in the right incarnation. - if (incarnation_id != incarnation_id_) { - return errors::InvalidArgument( - "Current incarnation: ", incarnation_id_, - "; Supplied incarnation: ", incarnation_id); - } - // Then look it up in the buffer. - if (!buffer_[shard_num].empty()) { - const HostBufferElement& elem = buffer_[shard_num].front(); - *out_tensors = elem.value; - *end_of_sequence = elem.end_of_sequence; - Status s = elem.status; - buffer_[shard_num].pop_front(); - return s; - } - std::shared_ptr captured_iterator(host_iterator_); - if (captured_iterator) { - if (lib_ != nullptr) { - ctx->set_lib(lib_); - } - while (true) { - HostBufferElement elem; - elem.status = - captured_iterator->GetNext(ctx, &elem.value, &elem.end_of_sequence); - int buffer_index = num_elements_ % devices_.size(); - num_elements_++; - if (buffer_index == shard_num) { - out_tensors->swap(elem.value); - *end_of_sequence = elem.end_of_sequence; - return elem.status; - } else { - buffer_[buffer_index].push_back(std::move(elem)); - // TODO(rohanj): Put an upper bound to buffer size. - if (buffer_[buffer_index].size() > max_buffer_size_) { - max_buffer_size_ = buffer_[buffer_index].size(); - VLOG(1) << "MultiDeviceIterator: Max buffer size increased to: " - << max_buffer_size_; - } - } - } - } else { - return errors::FailedPrecondition("Iterator not initialized"); + void GetNextFromShard(IteratorContext* ctx, int shard_num, + int64 incarnation_id, + MultiDeviceIteratorCallback callback) { + if (lib_ != nullptr) { + ctx->set_lib(lib_); } - return Status::OK(); + tf_shared_lock l(mu_); + multi_device_buffer_->GetNextFromShard(ctx, shard_num, incarnation_id, + std::move(callback)); } const DataTypeVector& output_types() const { return output_types_; } @@ -630,25 +601,218 @@ class MultiDeviceIterator : public ResourceBase { } private: - struct HostBufferElement { - Status status; - bool end_of_sequence; - std::vector value; + // A private class that uses a background thread to keep a per device buffer + // full. + class MultiDeviceBuffer { + public: + MultiDeviceBuffer(size_t size, int64 max_buffer_size, int64 incarnation_id, + std::unique_ptr host_iterator) + : buffer_(size), + size_(size), + max_buffer_size_(max_buffer_size), + incarnation_id_(incarnation_id), + host_iterator_(std::move(host_iterator)) {} + + ~MultiDeviceBuffer() { Reset(); } + + void Reset() LOCKS_EXCLUDED(mu_) { + { + mutex_lock l(mu_); + if (background_thread_finished_) { + return; + } + + cancelled_ = true; + // Wake up the background thread. + for (int i = 0; i < size_; ++i) { + buffer_[i].cond_var.notify_all(); + } + + // Make sure background thread has finished first. + while (!background_thread_finished_) { + shutdown_cond_var_.wait(l); + } + } + RunPendingCallbacks(); + } + + void GetNextFromShard(IteratorContext* ctx, int shard_num, + int64 incarnation_id, + MultiDeviceIteratorCallback callback) { + HostBufferElement elem; + if (incarnation_id_ != incarnation_id) { + elem.status = errors::InvalidArgument("Invalid incarnation id"); + callback(elem); + return; + } + + bool produced_output = false; + { + mutex_lock l(mu_); + if (cancelled_) { + elem.status = errors::Cancelled("Cancelled Multidevice iterator"); + callback(elem); + return; + } + + EnsureBackgroundThreadStarted(ctx); + + if (!buffer_[shard_num].data.empty()) { + produced_output = true; + std::swap(elem, buffer_[shard_num].data.front()); + buffer_[shard_num].data.pop_front(); + // Wake up background thread if it is blocked on this element. + if (buffer_[shard_num].data.size() == max_buffer_size_ - 1) { + buffer_[shard_num].cond_var.notify_all(); + } + } else { + if (background_thread_finished_) { + produced_output = true; + elem.end_of_sequence = true; + } else { + buffer_[shard_num].callbacks.push_back(std::move(callback)); + callback = nullptr; + } + } + } + + if (produced_output) { + callback(elem); + } + } + + private: + void EnsureBackgroundThreadStarted(IteratorContext* ctx) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { + if (!background_thread_) { + background_thread_.reset(ctx->env()->StartThread( + {}, "multi_device_iterator_background_thread", + std::bind(&MultiDeviceIterator::MultiDeviceBuffer::BackgroundThread, + this, new IteratorContext(*ctx)))); + } + } + + void RunPendingCallbacks() LOCKS_EXCLUDED(mu_) { + // Run all remaining callbacks. + std::vector cancellation_callbacks; + std::vector cancellation_elements; + { + mutex_lock l(mu_); + + for (int i = 0; i < size_; ++i) { + while (!buffer_[i].callbacks.empty()) { + if (buffer_[i].data.empty()) { + HostBufferElement elem; + elem.status = + errors::Cancelled("Cancelled and buffer not filled."); + cancellation_elements.push_back(std::move(elem)); + } else { + cancellation_elements.push_back( + std::move(buffer_[i].data.front())); + buffer_[i].data.pop_front(); + } + cancellation_callbacks.push_back( + std::move(buffer_[i].callbacks.front())); + buffer_[i].callbacks.pop_front(); + } + } + } + for (int i = 0; i < cancellation_callbacks.size(); ++i) { + cancellation_callbacks[i](cancellation_elements[i]); + } + } + + void BackgroundThread(IteratorContext* ctx) { + std::unique_ptr cleanup(ctx); + int shard_to_fetch = 0; + while (true) { + HostBufferElement elem; + MultiDeviceIteratorCallback callback = nullptr; + bool end_of_iterator = false; + + { + mutex_lock l(mu_); + while (!cancelled_ && + buffer_[shard_to_fetch].data.size() >= max_buffer_size_) { + buffer_[shard_to_fetch].cond_var.wait(l); + } + + if (cancelled_) { + background_thread_finished_ = true; + shutdown_cond_var_.notify_all(); + return; + } + } + + elem.status = + host_iterator_->GetNext(ctx, &elem.value, &elem.end_of_sequence); + + if (elem.status.ok() && elem.end_of_sequence) { + end_of_iterator = true; + } + + { + mutex_lock l(mu_); + // Try to find a callback, else just push stuff into buffer. + if (!buffer_[shard_to_fetch].callbacks.empty()) { + callback = buffer_[shard_to_fetch].callbacks.front(); + buffer_[shard_to_fetch].callbacks.pop_front(); + } else { + buffer_[shard_to_fetch].data.push_back(std::move(elem)); + elem = HostBufferElement(); + } + } + + if (callback) { + (*ctx->runner())(std::bind(std::move(callback), std::move(elem))); + } + + // Finish off the thread if we reach the end of the iterator. Runs + // pending callbacks. + if (end_of_iterator) { + { + mutex_lock l(mu_); + background_thread_finished_ = true; + shutdown_cond_var_.notify_all(); + } + RunPendingCallbacks(); + return; + } + shard_to_fetch = (shard_to_fetch + 1) % size_; + } + } + + struct HostBuffer { + condition_variable cond_var; + std::deque data; + std::deque callbacks; + }; + + mutex mu_; + std::unique_ptr background_thread_ GUARDED_BY(mu_); + bool background_thread_finished_ GUARDED_BY(mu_) = false; + bool cancelled_ GUARDED_BY(mu_) = false; + condition_variable shutdown_cond_var_ GUARDED_BY(mu_); + + std::vector buffer_; + + const size_t size_; + const int64 max_buffer_size_; + const int64 incarnation_id_; + const std::unique_ptr host_iterator_; }; mutex mu_; const DataTypeVector output_types_; const std::vector output_shapes_; const std::vector devices_; - int64 num_elements_ GUARDED_BY(mu_) = 0; - int64 max_buffer_size_ GUARDED_BY(mu_) = 0; - int64 incarnation_id_ GUARDED_BY(mu_) = 0; - std::vector> buffer_ GUARDED_BY(mu_); - std::unique_ptr flib_def_; - std::unique_ptr pflr_; - FunctionLibraryRuntime* lib_ = nullptr; // not owned. - std::shared_ptr host_iterator_; + const std::unique_ptr flib_def_; + const std::unique_ptr pflr_; + FunctionLibraryRuntime* const lib_ = nullptr; // not owned. std::shared_ptr lib_def_ GUARDED_BY(mu_); + + int64 incarnation_id_ GUARDED_BY(mu_) = 0; + std::unique_ptr multi_device_buffer_ GUARDED_BY(mu_); }; // Just creates a MultiDeviceIterator and returns it. @@ -754,6 +918,10 @@ class MultiDeviceIteratorInitOp : public OpKernel { : OpKernel(ctx) {} void Compute(OpKernelContext* ctx) override { + const Tensor* tensor_max_buffer_size; + OP_REQUIRES_OK(ctx, ctx->input("max_buffer_size", &tensor_max_buffer_size)); + int64 max_buffer_size = tensor_max_buffer_size->scalar()(); + DatasetBase* dataset; OP_REQUIRES_OK(ctx, GetDatasetFromVariantTensor(ctx->input(0), &dataset)); MultiDeviceIterator* resource; @@ -766,7 +934,8 @@ class MultiDeviceIteratorInitOp : public OpKernel { OP_REQUIRES_OK(ctx, dataset->MakeIterator(&iter_ctx, "Iterator", &iterator)); int64 incarnation_id; - OP_REQUIRES_OK(ctx, resource->Init(std::move(iterator), &incarnation_id)); + OP_REQUIRES_OK(ctx, resource->Init(std::move(iterator), max_buffer_size, + &incarnation_id)); Tensor tensor_incarnation_id(DT_INT64, TensorShape({})); tensor_incarnation_id.scalar()() = incarnation_id; OP_REQUIRES_OK(ctx, @@ -804,9 +973,6 @@ class MultiDeviceIteratorGetNextFromShardOp : public AsyncOpKernel { ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &iterator), done); thread_pool_->Schedule(std::bind( [ctx, iterator, shard_num, incarnation_id](DoneCallback done) { - std::vector components; - bool end_of_sequence = false; - IteratorContext::Params params; params.env = ctx->env(); params.runner = *(ctx->runner()); @@ -817,22 +983,26 @@ class MultiDeviceIteratorGetNextFromShardOp : public AsyncOpKernel { }; IteratorContext iter_ctx(std::move(params)); - Status s = - iterator->GetNextFromShard(&iter_ctx, shard_num, incarnation_id, - &components, &end_of_sequence); - iterator->Unref(); + MultiDeviceIteratorCallback callback = std::bind( + [ctx](const HostBufferElement& elem, DoneCallback done) { + // iterator->Unref(); + Status s = elem.status; + if (!s.ok()) { + ctx->SetStatus(s); + } else if (elem.end_of_sequence) { + ctx->SetStatus(errors::OutOfRange("End of sequence")); + } else { + for (int i = 0; i < elem.value.size(); ++i) { + ctx->set_output(i, elem.value[i]); + } + } + done(); + }, + std::placeholders::_1, std::move(done)); - if (!s.ok()) { - ctx->SetStatus(s); - } else if (end_of_sequence) { - ctx->SetStatus(errors::OutOfRange("End of sequence")); - } else { - for (int i = 0; i < components.size(); ++i) { - // TODO(mrry): Check that the shapes match the shape attrs. - ctx->set_output(i, components[i]); - } - } - done(); + iterator->GetNextFromShard(&iter_ctx, shard_num, incarnation_id, + callback); + iterator->Unref(); }, std::move(done))); } diff --git a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc index 141706f393b076d9f55898ca4bdbe7438f7c3625..4dc69dc2efa64b766344b0066231b44e8a9bef87 100644 --- a/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/threadpool_dataset_op.cc @@ -162,7 +162,8 @@ class ThreadPoolDatasetOp : public UnaryDatasetOpKernel { } protected: - Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { return errors::Unimplemented( "Cannot currently serialize the thread pool for a " diff --git a/tensorflow/contrib/data/kernels/unique_dataset_op.cc b/tensorflow/contrib/data/kernels/unique_dataset_op.cc index 67c237799c10a2724f18bb0df99e4bf8f5cd2b8a..f6bfc982e938f0c71b13850c3129d4df3087a8cb 100644 --- a/tensorflow/contrib/data/kernels/unique_dataset_op.cc +++ b/tensorflow/contrib/data/kernels/unique_dataset_op.cc @@ -75,10 +75,11 @@ class UniqueDatasetOp : public UnaryDatasetOpKernel { } protected: - Status AsGraphDefInternal(OpKernelContext* ctx, DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { Node* input_graph_node = nullptr; - TF_RETURN_IF_ERROR(b->AddParentDataset(ctx, input_, &input_graph_node)); + TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node)); TF_RETURN_IF_ERROR(b->AddDataset(this, {input_graph_node}, output)); return Status::OK(); } @@ -116,7 +117,7 @@ class UniqueDatasetOp : public UnaryDatasetOpKernel { Status SaveInternal(IteratorStateWriter* writer) override { mutex_lock l(mu_); if (input_impl_) { - TF_RETURN_IF_ERROR(SaveParent(writer, input_impl_)); + TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_)); } else { TF_RETURN_IF_ERROR( writer->WriteScalar(full_name("input_impl_empty"), "")); @@ -135,7 +136,7 @@ class UniqueDatasetOp : public UnaryDatasetOpKernel { IteratorStateReader* reader) override { mutex_lock l(mu_); if (!reader->Contains(full_name("input_impl_empty"))) { - TF_RETURN_IF_ERROR(RestoreParent(ctx, reader, input_impl_)); + TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_)); } else { input_impl_.reset(); } diff --git a/tensorflow/contrib/data/ops/dataset_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc index 66a7c7fdcd5e0ab77596177c209470e17f63bc10..cc5e250ea15bf89be2db9aba14e3b29b72512a73 100644 --- a/tensorflow/contrib/data/ops/dataset_ops.cc +++ b/tensorflow/contrib/data/ops/dataset_ops.cc @@ -168,9 +168,11 @@ output_shapes: The list of shapes being produced. REGISTER_OP("MultiDeviceIteratorInit") .Input("dataset: variant") .Input("multi_device_iterator: resource") + .Input("max_buffer_size: int64") .Output("incarnation_id: int64") .Doc(R"doc( Initializes the multi device iterator with the given dataset. +max_buffer_size: The maximum size of the host side per device buffer to keep. incarnation_id: An int64 indicating which incarnation of the MultiDeviceIterator is running. dataset: Dataset to be iterated upon. diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 24c7ee68dbb1b3b35be1dbf2abeea981a7782982..2b75aa2ca54509b42f431db2dd39261cf025588a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -175,7 +175,7 @@ py_test( "//tensorflow/python:variables", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/estimator", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -205,6 +205,25 @@ py_test( ], ) +py_test( + name = "map_defun_op_test", + size = "small", + srcs = ["map_defun_op_test.py"], + srcs_version = "PY2AND3", + tags = ["no_pip"], + deps = [ + "//tensorflow/contrib/data/python/ops:map_defun", + "//tensorflow/python:array_ops", + "//tensorflow/python:check_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", + "//tensorflow/python:function", + "//tensorflow/python:math_ops", + ], +) + py_test( name = "optimize_dataset_op_test", size = "small", diff --git a/tensorflow/contrib/data/python/kernel_tests/map_defun_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_defun_op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..a711325daed12f45e4e533f18ee81adc7dec93be --- /dev/null +++ b/tensorflow/contrib/data/python/kernel_tests/map_defun_op_test.py @@ -0,0 +1,126 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for MapDefunOp.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.data.python.ops import map_defun +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import function +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import check_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test + + +class MapDefunTest(test.TestCase): + + def testMapDefun_Simple(self): + + @function.Defun(dtypes.int32) + def simple_fn(x): + return x * 2 + 3 + + with self.test_session(): + nums = [[1, 2], [3, 4], [5, 6]] + elems = constant_op.constant(nums, dtype=dtypes.int32, name="data") + r = map_defun.map_defun(simple_fn, [elems], [dtypes.int32], [(2,)])[0] + expected = elems * 2 + 3 + self.assertAllEqual(self.evaluate(r), self.evaluate(expected)) + + def testMapDefun_MismatchedTypes(self): + + @function.Defun(dtypes.int32) + def fn(x): + return math_ops.cast(x, dtypes.float64) + + with self.test_session(): + nums = [1, 2, 3, 4, 5, 6] + elems = constant_op.constant(nums, dtype=dtypes.int32, name="data") + r = map_defun.map_defun(fn, [elems], [dtypes.int32], [()])[0] + with self.assertRaises(errors.InvalidArgumentError): + self.evaluate(r) + + def testMapDefun_MultipleOutputs(self): + + @function.Defun(dtypes.int32) + def fn(x): + return (x, math_ops.cast(x * 2 + 3, dtypes.float64)) + + with self.test_session(): + nums = [[1, 2], [3, 4], [5, 6]] + elems = constant_op.constant(nums, dtype=dtypes.int32, name="data") + r = map_defun.map_defun(fn, [elems], [dtypes.int32, dtypes.float64], + [(2,), (2,)]) + expected = [elems, elems * 2 + 3] + self.assertAllEqual(self.evaluate(r), self.evaluate(expected)) + + def testMapDefun_ShapeInference(self): + + @function.Defun(dtypes.int32) + def fn(x): + return x + + nums = [[1, 2], [3, 4], [5, 6]] + elems = constant_op.constant(nums, dtype=dtypes.int32, name="data") + result = map_defun.map_defun(fn, [elems], [dtypes.int32], [(2,)])[0] + self.assertEqual(result.get_shape(), (3, 2)) + + def testMapDefun_PartialShapeInference(self): + + @function.Defun(dtypes.int32) + def fn(x): + return x + + elems = array_ops.placeholder(dtypes.int64, (None, 2)) + result = map_defun.map_defun(fn, [elems], [dtypes.int32], [(2,)]) + self.assertEqual(result[0].get_shape().as_list(), [None, 2]) + + def testMapDefun_RaisesErrorOnRuntimeShapeMismatch(self): + + @function.Defun(dtypes.int32, dtypes.int32) + def fn(x, y): + return x, y + + elems1 = array_ops.placeholder(dtypes.int32) + elems2 = array_ops.placeholder(dtypes.int32) + result = map_defun.map_defun(fn, [elems1, elems2], + [dtypes.int32, dtypes.int32], [(), ()]) + with self.test_session() as sess: + with self.assertRaisesWithPredicateMatch( + errors.InvalidArgumentError, + "All inputs must have the same dimension 0."): + sess.run(result, feed_dict={elems1: [1, 2, 3, 4, 5], elems2: [1, 2, 3]}) + + def testMapDefun_RaisesDefunError(self): + + @function.Defun(dtypes.int32) + def fn(x): + with ops.control_dependencies([check_ops.assert_equal(x, 0)]): + return array_ops.identity(x) + + elems = constant_op.constant([0, 0, 0, 37, 0]) + result = map_defun.map_defun(fn, [elems], [dtypes.int32], [()]) + with self.test_session(): + with self.assertRaises(errors.InvalidArgumentError): + self.evaluate(result) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py index d66305d7326f78d1e414b6076c1ca6a029baa2f7..361fe0dd39bb3f855c3b0b11281a9909fd601232 100644 --- a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py @@ -1021,7 +1021,7 @@ class MultiDeviceIteratorTest(test.TestCase): def testUneven(self): dataset = dataset_ops.Dataset.range(10) multi_device_iterator = prefetching_ops.MultiDeviceIterator( - dataset, ["/cpu:1", "/cpu:2"]) + dataset, ["/cpu:1", "/cpu:2"], max_buffer_size=4) elem_on_1, elem_on_2 = multi_device_iterator.get_next() config = config_pb2.ConfigProto(device_count={"CPU": 3}) @@ -1079,7 +1079,7 @@ class MultiDeviceIteratorTest(test.TestCase): with compat.forward_compatibility_horizon(2018, 8, 4): dataset = dataset_ops.Dataset.range(10) multi_device_iterator = prefetching_ops.MultiDeviceIterator( - dataset, ["/cpu:1", "/gpu:0"]) + dataset, ["/cpu:1", "/gpu:0"], max_buffer_size=4) elem_on_1, elem_on_2 = multi_device_iterator.get_next() config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1}) diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 1ad021ea037add48afee5bdfda9eea18485eca5d..ad9378dfb9d938c826f994da9bbb89101cfbd872 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -210,6 +210,17 @@ py_library( ], ) +py_library( + name = "map_defun", + srcs = ["map_defun.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/python:dataset_ops_gen", + "//tensorflow/python:framework_ops", + "//tensorflow/python:tensor_shape", + ], +) + py_library( name = "resampling", srcs = ["resampling.py"], @@ -370,6 +381,7 @@ py_library( ":get_single_element", ":grouping", ":interleave_ops", + ":map_defun", ":optimization", ":prefetching_ops", ":readers", diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index 4835c4e5bd9efded57f19d6a382b145ae1b05e93..9f059942a65177186132164531237f838ecd63a2 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -185,7 +185,7 @@ def dense_to_sparse_batch(batch_size, row_shape): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -401,7 +401,7 @@ def unbatch(): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -443,7 +443,7 @@ def unbatch(): def batch_and_drop_remainder(batch_size): """A batching transformation that omits the final small batch (if present). - Like @{tf.data.Dataset.batch}, this transformation combines + Like `tf.data.Dataset.batch`, this transformation combines consecutive elements of this dataset into batches. However, if the batch size does not evenly divide the input dataset size, this transformation will drop the final smaller element. @@ -467,7 +467,7 @@ def batch_and_drop_remainder(batch_size): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply} + `tf.data.Dataset.apply` """ def _apply_fn(dataset): @@ -484,25 +484,25 @@ def padded_batch_and_drop_remainder(batch_size, padding_values=None): """A batching and padding transformation that omits the final small batch. - Like @{tf.data.Dataset.padded_batch}, this transformation combines + Like `tf.data.Dataset.padded_batch`, this transformation combines consecutive elements of this dataset into batches. However, if the batch size does not evenly divide the input dataset size, this transformation will drop the final smaller element. - See `@{tf.contrib.data.batch_and_drop_remainder}` for more details. + See `tf.contrib.data.batch_and_drop_remainder` for more details. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of consecutive elements of this dataset to combine in a single batch. padded_shapes: A nested structure of `tf.TensorShape` or `tf.int64` vector tensor-like objects. See - @{tf.data.Dataset.padded_batch} for details. + `tf.data.Dataset.padded_batch` for details. padding_values: (Optional.) A nested structure of scalar-shaped - `tf.Tensor`. See @{tf.data.Dataset.padded_batch} for details. + `tf.Tensor`. See `tf.data.Dataset.padded_batch` for details. Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply} + `tf.data.Dataset.apply` """ def _apply_fn(dataset): @@ -661,7 +661,7 @@ def assert_element_shape(expected_shapes): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply} + `tf.data.Dataset.apply` """ def _check_shape(*elements): @@ -760,7 +760,7 @@ def map_and_batch(map_func, Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. Raises: ValueError: If both `num_parallel_batches` and `num_parallel_calls` are diff --git a/tensorflow/contrib/data/python/ops/enumerate_ops.py b/tensorflow/contrib/data/python/ops/enumerate_ops.py index ac2b386b81532b801139baa00fd5edd4ecd6ef0a..490281e0d2da7a454a2f63f95753c7c436b87a76 100644 --- a/tensorflow/contrib/data/python/ops/enumerate_ops.py +++ b/tensorflow/contrib/data/python/ops/enumerate_ops.py @@ -47,7 +47,7 @@ def enumerate_dataset(start=0): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): diff --git a/tensorflow/contrib/data/python/ops/error_ops.py b/tensorflow/contrib/data/python/ops/error_ops.py index d46d96c461ad4cc0ac25a8ddc285cec23d09c682..b4a7521e0875089c39ac7aa8b7b49e44feb2b4ad 100644 --- a/tensorflow/contrib/data/python/ops/error_ops.py +++ b/tensorflow/contrib/data/python/ops/error_ops.py @@ -42,7 +42,7 @@ def ignore_errors(): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): diff --git a/tensorflow/contrib/data/python/ops/get_single_element.py b/tensorflow/contrib/data/python/ops/get_single_element.py index ef9284456eb35099db804e0680abfacd6384d503..a6713b017afa315edec9389d0a6c1c7135e6aeb9 100644 --- a/tensorflow/contrib/data/python/ops/get_single_element.py +++ b/tensorflow/contrib/data/python/ops/get_single_element.py @@ -29,8 +29,8 @@ from tensorflow.python.ops import gen_dataset_ops def get_single_element(dataset): """Returns the single element in `dataset` as a nested structure of tensors. - This function enables you to use a @{tf.data.Dataset} in a stateless - "tensor-in tensor-out" expression, without creating a @{tf.data.Iterator}. + This function enables you to use a `tf.data.Dataset` in a stateless + "tensor-in tensor-out" expression, without creating a `tf.data.Iterator`. This can be useful when your preprocessing transformations are expressed as a `Dataset`, and you want to use the transformation at serving time. For example: @@ -50,10 +50,10 @@ def get_single_element(dataset): ``` Args: - dataset: A @{tf.data.Dataset} object containing a single element. + dataset: A `tf.data.Dataset` object containing a single element. Returns: - A nested structure of @{tf.Tensor} objects, corresponding to the single + A nested structure of `tf.Tensor` objects, corresponding to the single element of `dataset`. Raises: @@ -77,11 +77,11 @@ def reduce_dataset(dataset, reducer): """Returns the result of reducing the `dataset` using `reducer`. Args: - dataset: A @{tf.data.Dataset} object. - reducer: A @{tf.contrib.data.Reducer} object representing the reduce logic. + dataset: A `tf.data.Dataset` object. + reducer: A `tf.contrib.data.Reducer` object representing the reduce logic. Returns: - A nested structure of @{tf.Tensor} objects, corresponding to the result + A nested structure of `tf.Tensor` objects, corresponding to the result of reducing `dataset` using `reducer`. Raises: diff --git a/tensorflow/contrib/data/python/ops/grouping.py b/tensorflow/contrib/data/python/ops/grouping.py index bd8d398c58cc1825616c1ab5337cf6668c66697e..6edc1d79902c571b34b6a0a108c4d62cb6097ccb 100644 --- a/tensorflow/contrib/data/python/ops/grouping.py +++ b/tensorflow/contrib/data/python/ops/grouping.py @@ -50,7 +50,7 @@ def group_by_reducer(key_func, reducer): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -92,7 +92,7 @@ def group_by_window(key_func, Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. Raises: ValueError: if neither or both of {`window_size`, `window_size_func`} are @@ -142,11 +142,11 @@ def bucket_by_sequence_length(element_length_func, bucket_batch_sizes: `list`, batch size per bucket. Length should be `len(bucket_boundaries) + 1`. padded_shapes: Nested structure of `tf.TensorShape` to pass to - @{tf.data.Dataset.padded_batch}. If not provided, will use + `tf.data.Dataset.padded_batch`. If not provided, will use `dataset.output_shapes`, which will result in variable length dimensions being padded out to the maximum length in each batch. padding_values: Values to pad with, passed to - @{tf.data.Dataset.padded_batch}. Defaults to padding with 0. + `tf.data.Dataset.padded_batch`. Defaults to padding with 0. pad_to_bucket_boundary: bool, if `False`, will pad dimensions with unknown size to maximum length in batch. If `True`, will pad dimensions with unknown size to bucket boundary minus 1 (i.e., the maximum length in each @@ -155,7 +155,7 @@ def bucket_by_sequence_length(element_length_func, Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. Raises: ValueError: if `len(bucket_batch_sizes) != len(bucket_boundaries) + 1`. diff --git a/tensorflow/contrib/data/python/ops/interleave_ops.py b/tensorflow/contrib/data/python/ops/interleave_ops.py index bcc959594a6b311a3c60bb4696ac97be5c448756..5a1a35199abecc3890d5733ddf678af8d4098f33 100644 --- a/tensorflow/contrib/data/python/ops/interleave_ops.py +++ b/tensorflow/contrib/data/python/ops/interleave_ops.py @@ -42,7 +42,7 @@ def parallel_interleave(map_func, `parallel_interleave()` maps `map_func` across its input to produce nested datasets, and outputs their elements interleaved. Unlike - @{tf.data.Dataset.interleave}, it gets elements from `cycle_length` nested + `tf.data.Dataset.interleave`, it gets elements from `cycle_length` nested datasets in parallel, which increases the throughput, especially in the presence of stragglers. Furthermore, the `sloppy` argument can be used to improve performance, by relaxing the requirement that the outputs are produced @@ -79,7 +79,7 @@ def parallel_interleave(map_func, Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): return readers.ParallelInterleaveDataset( @@ -138,7 +138,7 @@ def sloppy_interleave(map_func, cycle_length, block_length=1): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): return readers.ParallelInterleaveDataset( @@ -196,15 +196,15 @@ def sample_from_datasets(datasets, weights=None, seed=None): """Samples elements at random from the datasets in `datasets`. Args: - datasets: A list of @{tf.data.Dataset} objects with compatible structure. + datasets: A list of `tf.data.Dataset` objects with compatible structure. weights: (Optional.) A list of `len(datasets)` floating-point values where `weights[i]` represents the probability with which an element should be - sampled from `datasets[i]`, or a @{tf.data.Dataset} object where each + sampled from `datasets[i]`, or a `tf.data.Dataset` object where each element is such a list. Defaults to a uniform distribution across `datasets`. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. Returns: A dataset that interleaves elements from `datasets` at random, according to @@ -262,8 +262,8 @@ def choose_from_datasets(datasets, choice_dataset): ``` Args: - datasets: A list of @{tf.data.Dataset} objects with compatible structure. - choice_dataset: A @{tf.data.Dataset} of scalar `tf.int64` tensors between + datasets: A list of `tf.data.Dataset` objects with compatible structure. + choice_dataset: A `tf.data.Dataset` of scalar `tf.int64` tensors between `0` and `len(datasets) - 1`. Returns: diff --git a/tensorflow/contrib/data/python/ops/iterator_ops.py b/tensorflow/contrib/data/python/ops/iterator_ops.py index d2c1d0d3620f94f867395a8e2fff0d77a6dc0718..18515e21edfe0449514ab4f21683a600eaf48910 100644 --- a/tensorflow/contrib/data/python/ops/iterator_ops.py +++ b/tensorflow/contrib/data/python/ops/iterator_ops.py @@ -118,7 +118,7 @@ class CheckpointInputPipelineHook(session_run_hook.SessionRunHook): pipeline. For saving the input pipeline checkpoint alongside the model weights use - @{tf.contrib.data.make_saveable_from_iterator} directly to create a + `tf.contrib.data.make_saveable_from_iterator` directly to create a `SaveableObject` and add to the `SAVEABLE_OBJECTS` collection. Note, however, that you will need to be careful not to restore the training iterator during eval. You can do that by not adding the iterator to the SAVEABLE_OBJECTS diff --git a/tensorflow/contrib/data/python/ops/map_defun.py b/tensorflow/contrib/data/python/ops/map_defun.py new file mode 100644 index 0000000000000000000000000000000000000000..54d5cd6da068fa5471b7beafcc66d76b5972e7d5 --- /dev/null +++ b/tensorflow/contrib/data/python/ops/map_defun.py @@ -0,0 +1,58 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Experimental API for optimizing `tf.data` pipelines.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.ops import gen_dataset_ops + + +def map_defun(fn, elems, output_dtypes, output_shapes): + """Map a function on the list of tensors unpacked from `elems` on dimension 0. + + Args: + fn: A function (`function.Defun`) that takes a list of tensors and returns + another list of tensors. The output list has the same types as + output_dtypes. The elements of the output list have the same dimension 0 + as `elems`, and the remaining dimensions correspond to those of + `fn_output_shapes`. + elems: A list of tensors. + output_dtypes: A list of dtypes corresponding to the output types of the + function. + output_shapes: A list of `TensorShape`s corresponding to the output + shapes from each invocation of the function on slices of inputs. + + Raises: + ValueError: if any of the inputs are malformed. + + Returns: + A list of `Tensor` objects with the same types as `output_dtypes`. + """ + if not isinstance(elems, list): + raise ValueError("`elems` must be a list of tensors.") + if not isinstance(output_dtypes, list): + raise ValueError("`output_dtypes` must be a list of tensors.") + if not isinstance(output_shapes, list): + raise ValueError("`output_shapes` must be a list of tensors.") + + elems = [ops.convert_to_tensor(e) for e in elems] + output_shapes = [tensor_shape.TensorShape(s) for s in output_shapes] + if not all(s.is_fully_defined() for s in output_shapes): + raise ValueError("All fn output shapes must be fully defined.") + return gen_dataset_ops.map_defun(elems, output_dtypes, output_shapes, fn) diff --git a/tensorflow/contrib/data/python/ops/optimization.py b/tensorflow/contrib/data/python/ops/optimization.py index 018c5115e1d5599e48bf99ccf832c7962794fc40..fa1b851ad74bcf2cff69d42bce3eaa38822cd663 100644 --- a/tensorflow/contrib/data/python/ops/optimization.py +++ b/tensorflow/contrib/data/python/ops/optimization.py @@ -36,7 +36,7 @@ def assert_next(transformations): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -56,7 +56,7 @@ def optimize(optimizations=None): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py index 0edd7c9fe974784f199c272a649b302e72d8c218..5222011d045efd9a64b4e89b248303cffbcb0b37 100644 --- a/tensorflow/contrib/data/python/ops/prefetching_ops.py +++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py @@ -92,7 +92,7 @@ def function_buffering_resource_reset(function_buffer_resource, name=None): # pylint: disable=protected-access class _PrefetchToDeviceIterator(object): - """A replacement for @{tf.data.Iterator} that prefetches to another device. + """A replacement for `tf.data.Iterator` that prefetches to another device. Args: input_dataset: The input dataset @@ -158,7 +158,7 @@ class _PrefetchToDeviceIterator(object): self._input_dataset) def get_next(self, name=None): - """See @{tf.data.Iterator.get_next}.""" + """See `tf.data.Iterator.get_next`.""" self._get_next_call_count += 1 if self._get_next_call_count > iterator_ops.GET_NEXT_CALL_WARNING_THRESHOLD: warnings.warn(iterator_ops.GET_NEXT_CALL_WARNING_MESSAGE) @@ -199,7 +199,7 @@ class _PrefetchToDeviceIterator(object): class _PrefetchToDeviceEagerIterator(iterator_ops.EagerIterator): - """A replacement for @{tf.data.Iterator} that prefetches to another device. + """A replacement for `tf.data.Iterator` that prefetches to another device. Args: input_dataset: The input dataset @@ -334,7 +334,7 @@ class _PrefetchToDeviceDataset(dataset_ops.Dataset): def prefetch_to_device(device, buffer_size=None): """A transformation that prefetches dataset values to the given `device`. - NOTE: Although the transformation creates a @{tf.data.Dataset}, the + NOTE: Although the transformation creates a `tf.data.Dataset`, the transformation must be the final `Dataset` in the input pipeline. Args: @@ -344,7 +344,7 @@ def prefetch_to_device(device, buffer_size=None): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): return _PrefetchToDeviceDataset(dataset, device, buffer_size) @@ -361,7 +361,7 @@ def copy_to_device(target_device, source_device="/cpu:0"): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -631,8 +631,19 @@ class MultiDeviceIterator(object): def __init__(self, dataset, devices, + max_buffer_size=1, prefetch_buffer_size=1, source_device="/cpu:0"): + """Constructs a MultiDeviceIterator. + + Args: + dataset: The input dataset to be iterated over. + devices: The list of devices to fetch data to. + max_buffer_size: Maximum size of the host side per device buffer to keep. + prefetch_buffer_size: if > 1, then we setup a buffer on each device + to prefetch into. + source_device: The host device to place the `dataset` on. + """ self._dataset = dataset self._devices = devices self._source_device = source_device @@ -659,7 +670,8 @@ class MultiDeviceIterator(object): # iterators and the multi-device iterator. self._incarnation_id = gen_dataset_ops.multi_device_iterator_init( self._dataset._as_variant_tensor(), # pylint: disable=protected-access - self._multi_device_iterator_resource) + self._multi_device_iterator_resource, + max_buffer_size=max_buffer_size) # TODO(rohanj): Explore the possibility of the MultiDeviceIterator to # initialize the device side of the pipeline. This would allow the @@ -673,7 +685,8 @@ class MultiDeviceIterator(object): i, self._multi_device_iterator_resource, self._incarnation_id, self._source_device_tensor, device, self._dataset.output_shapes, self._dataset.output_types, self._dataset.output_classes) - ds = ds.prefetch(prefetch_buffer_size) + if prefetch_buffer_size > 0: + ds = ds.prefetch(prefetch_buffer_size) with ops.device(device): self._device_iterators.append(ds.make_initializable_iterator()) i += 1 diff --git a/tensorflow/contrib/data/python/ops/readers.py b/tensorflow/contrib/data/python/ops/readers.py index 14d69f8d5b29d43649185e689c7a8e6604361bca..3882d4bfdbe899c2ce92f829cb331b32d3d50398 100644 --- a/tensorflow/contrib/data/python/ops/readers.py +++ b/tensorflow/contrib/data/python/ops/readers.py @@ -234,7 +234,7 @@ def make_tf_record_dataset( Args: file_pattern: List of files or patterns of TFRecord file paths. - See @{tf.gfile.Glob} for pattern rules. + See `tf.gfile.Glob` for pattern rules. batch_size: An int representing the number of records to combine in a single batch. parser_fn: (Optional.) A function accepting string input to parse @@ -340,7 +340,7 @@ def make_csv_dataset( Args: file_pattern: List of files or patterns of file paths containing CSV - records. See @{tf.gfile.Glob} for pattern rules. + records. See `tf.gfile.Glob` for pattern rules. batch_size: An int representing the number of records to combine in a single batch. column_names: An optional list of strings that corresponds to the CSV diff --git a/tensorflow/contrib/data/python/ops/resampling.py b/tensorflow/contrib/data/python/ops/resampling.py index 182a5c6ff36fcda8c9e2c522cce07bed0c2daec9..75642f143e19c3d77e675384362c4dab94e10932 100644 --- a/tensorflow/contrib/data/python/ops/resampling.py +++ b/tensorflow/contrib/data/python/ops/resampling.py @@ -50,7 +50,7 @@ def rejection_resample(class_func, target_dist, initial_dist=None, seed=None): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" diff --git a/tensorflow/contrib/data/python/ops/scan_ops.py b/tensorflow/contrib/data/python/ops/scan_ops.py index ea9dcfe68fa2630d915323fa295031af7d48cdfb..6b002b4a533669dd0f5e82a00aa29224a83a7e57 100644 --- a/tensorflow/contrib/data/python/ops/scan_ops.py +++ b/tensorflow/contrib/data/python/ops/scan_ops.py @@ -151,7 +151,7 @@ class _ScanDataset(dataset_ops.Dataset): def scan(initial_state, scan_func): """A transformation that scans a function across an input dataset. - This transformation is a stateful relative of @{tf.data.Dataset.map}. + This transformation is a stateful relative of `tf.data.Dataset.map`. In addition to mapping `scan_func` across the elements of the input dataset, `scan()` accumulates one or more state tensors, whose initial values are `initial_state`. @@ -166,7 +166,7 @@ def scan(initial_state, scan_func): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): return _ScanDataset(dataset, initial_state, scan_func) diff --git a/tensorflow/contrib/data/python/ops/shuffle_ops.py b/tensorflow/contrib/data/python/ops/shuffle_ops.py index d7f8a73fe3d67bb83e44e962832ce34c116aef66..4356721704046199e8ef2938bde6d7d8bce68cc1 100644 --- a/tensorflow/contrib/data/python/ops/shuffle_ops.py +++ b/tensorflow/contrib/data/python/ops/shuffle_ops.py @@ -92,11 +92,11 @@ def shuffle_and_repeat(buffer_size, count=None, seed=None): indefinitely. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): # pylint: disable=missing-docstring diff --git a/tensorflow/contrib/data/python/ops/sliding.py b/tensorflow/contrib/data/python/ops/sliding.py index e9dd74530ac64cd414d53eab5294eaa95c919131..8025dcdd16b0180aeb951a31de21e22b8e8c31c7 100644 --- a/tensorflow/contrib/data/python/ops/sliding.py +++ b/tensorflow/contrib/data/python/ops/sliding.py @@ -109,7 +109,7 @@ def sliding_window_batch(window_size, Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. Raises: ValueError: if invalid arguments are provided. diff --git a/tensorflow/contrib/data/python/ops/stats_ops.py b/tensorflow/contrib/data/python/ops/stats_ops.py index 97931f75bd37d9e45864fe477c6e1620b5e4f193..3b4e98140234af0bf2128ac32f95dbdbf183cb54 100644 --- a/tensorflow/contrib/data/python/ops/stats_ops.py +++ b/tensorflow/contrib/data/python/ops/stats_ops.py @@ -29,7 +29,7 @@ class StatsAggregator(object): """A stateful resource that aggregates statistics from one or more iterators. To record statistics, use one of the custom transformation functions defined - in this module when defining your @{tf.data.Dataset}. All statistics will be + in this module when defining your `tf.data.Dataset`. All statistics will be aggregated by the `StatsAggregator` that is associated with a particular iterator (see below). For example, to record the total number of bytes produced by iterating over a dataset: @@ -39,7 +39,7 @@ class StatsAggregator(object): dataset = dataset.apply(stats_ops.bytes_produced_stats("total_bytes")) ``` - To associate a `StatsAggregator` with a @{tf.data.Iterator} object, use + To associate a `StatsAggregator` with a `tf.data.Iterator` object, use the following pattern: ```python @@ -55,7 +55,7 @@ class StatsAggregator(object): To get a protocol buffer summary of the currently aggregated statistics, use the `StatsAggregator.get_summary()` tensor. The easiest way to do this - is to add the returned tensor to the @{tf.GraphKeys.SUMMARIES} collection, + is to add the returned tensor to the `tf.GraphKeys.SUMMARIES` collection, so that the summaries will be included with any existing summaries. ```python @@ -74,13 +74,13 @@ class StatsAggregator(object): self._resource = gen_dataset_ops.stats_aggregator_handle() def get_summary(self): - """Returns a string @{tf.Tensor} that summarizes the aggregated statistics. + """Returns a string `tf.Tensor` that summarizes the aggregated statistics. - The returned tensor will contain a serialized @{tf.summary.Summary} protocol + The returned tensor will contain a serialized `tf.summary.Summary` protocol buffer, which can be used with the standard TensorBoard logging facilities. Returns: - A scalar string @{tf.Tensor} that summarizes the aggregated statistics. + A scalar string `tf.Tensor` that summarizes the aggregated statistics. """ return gen_dataset_ops.stats_aggregator_summary(self._resource) @@ -122,7 +122,7 @@ def set_stats_aggregator(stats_aggregator): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -145,7 +145,7 @@ def bytes_produced_stats(tag): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -169,7 +169,7 @@ def latency_stats(tag): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): @@ -192,7 +192,7 @@ def feature_stats(tag): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): diff --git a/tensorflow/contrib/data/python/ops/threadpool.py b/tensorflow/contrib/data/python/ops/threadpool.py index 9af1e784ffb4f6d71da25f09d60343b649c5079b..dc67accdcfbc2692cbe0c961521897a316f40647 100644 --- a/tensorflow/contrib/data/python/ops/threadpool.py +++ b/tensorflow/contrib/data/python/ops/threadpool.py @@ -100,6 +100,6 @@ def override_threadpool(dataset, thread_pool): Returns: A dataset containing the same values as `dataset`, but which uses `thread_pool` to compute any of its parallel operations (such as - @{tf.data.Dataset.map}). + `tf.data.Dataset.map`). """ return _ThreadPoolDataset(dataset, thread_pool) diff --git a/tensorflow/contrib/data/python/ops/unique.py b/tensorflow/contrib/data/python/ops/unique.py index e0ce0a4ef15f6b9181bce92fb4d73bf1fab2e66c..e0d606311c4f2f678970113c1faa578dbf44b2ba 100644 --- a/tensorflow/contrib/data/python/ops/unique.py +++ b/tensorflow/contrib/data/python/ops/unique.py @@ -38,7 +38,7 @@ def unique(): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): diff --git a/tensorflow/contrib/data/python/ops/writers.py b/tensorflow/contrib/data/python/ops/writers.py index f53bd3f7383950d6cfdb35e12811fb1daf24b320..c455fdcba673853079ff0d162c4799e72bc8e627 100644 --- a/tensorflow/contrib/data/python/ops/writers.py +++ b/tensorflow/contrib/data/python/ops/writers.py @@ -38,13 +38,13 @@ class TFRecordWriter(object): argument_dtype=dtypes.string) def write(self, dataset): - """Returns a @{tf.Operation} to write a dataset to a file. + """Returns a `tf.Operation` to write a dataset to a file. Args: - dataset: a @{tf.data.Dataset} whose elements are to be written to a file + dataset: a `tf.data.Dataset` whose elements are to be written to a file Returns: - A @{tf.Operation} that, when run, writes contents of `dataset` to a file. + A `tf.Operation` that, when run, writes contents of `dataset` to a file. """ if not isinstance(dataset, dataset_ops.Dataset): raise TypeError("`dataset` must be a `tf.data.Dataset` object.") diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index d9e66ddac071cd4886d5cd8d82375bddd20f9bee..40a1c1707cfdeaf5f5097ce661fa5f0613f804d0 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -57,7 +57,7 @@ cuda_py_test( "//tensorflow/python/eager:context", "//tensorflow/python:device_util", "//tensorflow/python/eager:test", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", ], tags = [ "no_pip", @@ -187,6 +187,7 @@ py_library( ":multi_worker_strategy", ":one_device_strategy", ":tpu_strategy", + "//tensorflow/contrib/cluster_resolver:cluster_resolver_pip", "//tensorflow/contrib/optimizer_v2:training", "//tensorflow/python:distribute", "//tensorflow/python:framework_ops", @@ -266,7 +267,7 @@ py_test( "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/eager:context", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", "@absl_py//absl/testing:parameterized", ], ) @@ -314,7 +315,7 @@ py_library( "//tensorflow/python:client_testlib", "//tensorflow/python:distributed_framework_test_lib", "//tensorflow/python:session", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", "//third_party/py/numpy", ], ) @@ -369,7 +370,7 @@ py_test( "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/eager:context", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", ], @@ -439,11 +440,7 @@ cuda_py_test( "//tensorflow/contrib/optimizer_v2:training", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/eager:test", - "//tensorflow/python/estimator:dnn_linear_combined", - "//tensorflow/python/estimator:export_export", - "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/feature_column", "//tensorflow/python:framework_ops", "//tensorflow/python:platform", @@ -469,17 +466,27 @@ py_library( ], ) -cuda_py_test( - name = "step_fn_test", +py_library( + name = "step_fn_test_lib", + testonly = 1, srcs = ["step_fn_test.py"], - additional_deps = [ - ":single_loss_example", + deps = [ ":combinations", - "@absl_py//absl/testing:parameterized", - "//third_party/py/numpy", + ":single_loss_example", + "//tensorflow/contrib/tpu:tpu_lib", "//tensorflow/python:variables", "//tensorflow/python/eager:context", "//tensorflow/python/eager:test", + "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", + ], +) + +cuda_py_test( + name = "step_fn_test", + srcs = ["step_fn_test.py"], + additional_deps = [ + ":step_fn_test_lib", ], tags = [ "multi_and_single_gpu", @@ -680,8 +687,7 @@ cuda_py_test( "//tensorflow/contrib/distribute/python:mirrored_strategy", "//tensorflow/python:client_testlib", "//tensorflow/python:training", - "//tensorflow/python/estimator:keras", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/keras", ], tags = [ diff --git a/tensorflow/contrib/distribute/python/combinations.py b/tensorflow/contrib/distribute/python/combinations.py index 52f73ddb030fc58fa31acb5a5d3b3dd98383254e..a1efbcaf9ac300d7352efdb7babf4e6c1a529f3b 100644 --- a/tensorflow/contrib/distribute/python/combinations.py +++ b/tensorflow/contrib/distribute/python/combinations.py @@ -46,6 +46,7 @@ import unittest from absl.testing import parameterized import six +from tensorflow.contrib.cluster_resolver import TPUClusterResolver from tensorflow.contrib.distribute.python import mirrored_strategy as mirrored_lib from tensorflow.contrib.distribute.python import multi_worker_strategy from tensorflow.contrib.distribute.python import one_device_strategy as one_device_lib @@ -324,7 +325,10 @@ default_strategy = NamedDistribution( one_device_strategy = NamedDistribution( "OneDeviceCPU", lambda: one_device_lib.OneDeviceStrategy("/cpu:0"), required_gpus=None) -tpu_strategy = NamedDistribution("TPU", tpu_lib.TPUStrategy, required_tpu=True) +tpu_strategy = NamedDistribution( + "TPU", lambda: tpu_lib.TPUStrategy( + TPUClusterResolver(""), steps_per_run=5), + required_tpu=True) # Note that we disable prefetching for testing since prefetching makes # the input non-deterministic. mirrored_strategy_with_gpu_and_cpu = NamedDistribution( @@ -370,12 +374,14 @@ adam_optimizer_v1_fn = NamedObject( "AdamV1", lambda: adam.AdamOptimizer(0.2, epsilon=1)) gradient_descent_optimizer_v1_fn = NamedObject( "GradientDescentV1", lambda: gradient_descent.GradientDescentOptimizer(0.2)) +optimizers_v1 = [adam_optimizer_v1_fn, gradient_descent_optimizer_v1_fn] adam_optimizer_v2_fn = NamedObject( "AdamV2", lambda: adam_v2.AdamOptimizer(0.2, epsilon=1)) gradient_descent_optimizer_v2_fn = NamedObject( "GradientDescentV2", lambda: gradient_descent_v2.GradientDescentOptimizer(0.2)) +optimizers_v2 = [adam_optimizer_v2_fn, gradient_descent_optimizer_v2_fn] graph_and_eager_modes = ["graph", "eager"] @@ -387,7 +393,7 @@ def distributions_and_v1_optimizers(): one_device_strategy, mirrored_strategy_with_gpu_and_cpu, mirrored_strategy_with_two_gpus ], - optimizer_fn=[adam_optimizer_v1_fn, gradient_descent_optimizer_v1_fn]) + optimizer_fn=optimizers_v1) def distributions_and_v2_optimizers(): @@ -397,4 +403,4 @@ def distributions_and_v2_optimizers(): one_device_strategy, mirrored_strategy_with_gpu_and_cpu, mirrored_strategy_with_two_gpus ], - optimizer_fn=[adam_optimizer_v2_fn, gradient_descent_optimizer_v2_fn]) + optimizer_fn=optimizers_v2) diff --git a/tensorflow/contrib/distribute/python/cross_tower_ops.py b/tensorflow/contrib/distribute/python/cross_tower_ops.py index 9b5534393edf32145dd5328407d365ac0676879b..3a7addf2215d403cd94601f143d16a18d92b65af 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_ops.py +++ b/tensorflow/contrib/distribute/python/cross_tower_ops.py @@ -157,7 +157,7 @@ class CrossTowerOps(object): Args: aggregation: Indicates how a variable will be aggregated. Accepted values - are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. + are `tf.VariableAggregation.SUM`, `tf.VariableAggregation.MEAN`. per_device_value: a PerDevice object. destinations: the reduction destinations. @@ -181,7 +181,7 @@ class CrossTowerOps(object): Args: aggregation: Indicates how a variable will be aggregated. Accepted values - are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. + are `tf.VariableAggregation.SUM`, `tf.VariableAggregation.MEAN`. value_destination_pairs: a list or a tuple of tuples of PerDevice objects and destinations. If a destination is None, then the destinations are set to match the devices of the input PerDevice object. @@ -305,7 +305,7 @@ def _ungroup_and_make_mirrored(grouped_reduced, cross_tower_utils.aggregate_gradients_using*. destinations: a list of device strings for returned Mirrored objects. aggregation: Indicates how a variable will be aggregated. Accepted values - are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. + are `tf.VariableAggregation.SUM`, `tf.VariableAggregation.MEAN`. num_between_graph_workers: number of workers in the between-graph replication. diff --git a/tensorflow/contrib/distribute/python/estimator_integration_test.py b/tensorflow/contrib/distribute/python/estimator_integration_test.py index a0bb144b7c7e73b051e41ab93086cbb5b6a852cc..3e00cf4332da8cb18de0444704f88462d415c447 100644 --- a/tensorflow/contrib/distribute/python/estimator_integration_test.py +++ b/tensorflow/contrib/distribute/python/estimator_integration_test.py @@ -75,8 +75,11 @@ class DNNLinearCombinedClassifierIntegrationTest(test.TestCase, y=data, batch_size=batch_size // len(distribution.worker_devices), shuffle=True) - eval_input_fn = numpy_io.numpy_input_fn( - x={'x': data}, y=data, batch_size=batch_size, shuffle=False) + eval_input_fn = self.dataset_input_fn( + x={'x': data}, + y=data, + batch_size=batch_size // len(distribution.worker_devices), + shuffle=False) predict_input_fn = numpy_io.numpy_input_fn( x={'x': data}, batch_size=batch_size, shuffle=False) diff --git a/tensorflow/contrib/distribute/python/keras_test.py b/tensorflow/contrib/distribute/python/keras_test.py index ec0ca6879cffb9214adec15058cfb7293d347b25..4facd72d12680a53cc3f5e2ded2585bc9716ea3c 100644 --- a/tensorflow/contrib/distribute/python/keras_test.py +++ b/tensorflow/contrib/distribute/python/keras_test.py @@ -241,6 +241,47 @@ class TestWithDistributionStrategy(test.TestCase): validation_data=dataset, validation_steps=2) model.predict(dataset, steps=2) + def test_fit_with_tuple_and_dict_dataset_inputs(self): + with self.test_session(): + a = keras.layers.Input(shape=(3,), name='input_a') + b = keras.layers.Input(shape=(3,), name='input_b') + + dense = keras.layers.Dense(4, name='dense') + c = dense(a) + d = dense(b) + e = keras.layers.Dropout(0.5, name='dropout')(c) + + model = keras.models.Model([a, b], [d, e]) + + optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=0.001) + loss = 'mse' + metrics = ['mae'] + strategy = mirrored_strategy.MirroredStrategy(['/device:GPU:0', + '/device:CPU:0']) + model.compile(optimizer, loss, metrics=metrics, distribute=strategy) + + input_a_np = np.random.random((10, 3)) + input_b_np = np.random.random((10, 3)) + output_d_np = np.random.random((10, 4)) + output_e_np = np.random.random((10, 4)) + + # Test with tuples + dataset_tuple = dataset_ops.Dataset.from_tensor_slices(( + (input_a_np, input_b_np), (output_d_np, output_e_np))) + dataset_tuple = dataset_tuple.repeat(100) + dataset_tuple = dataset_tuple.batch(10) + + model.fit(dataset_tuple, epochs=1, steps_per_epoch=2, verbose=1) + + # Test with dict + dataset_dict = dataset_ops.Dataset.from_tensor_slices(( + {'input_a': input_a_np, 'input_b': input_b_np}, + (output_d_np, output_e_np))) + dataset_dict = dataset_dict.repeat(100) + dataset_dict = dataset_dict.batch(10) + + model.fit(dataset_dict, epochs=1, steps_per_epoch=2, verbose=1) + def test_fit_eval_and_predict_methods_on_dataset(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py index aeeb9553e6044a0a928936597400e582e0329b95..aa7a61bb3b24df64dfc2a118611e96242a72b025 100644 --- a/tensorflow/contrib/distribute/python/minimize_loss_test.py +++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py @@ -25,11 +25,13 @@ from tensorflow.contrib.distribute.python import combinations from tensorflow.contrib.distribute.python import mirrored_strategy from tensorflow.contrib.distribute.python.single_loss_example import batchnorm_example from tensorflow.contrib.distribute.python.single_loss_example import minimize_loss_example -from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.python.data.ops import dataset_ops from tensorflow.python.eager import context from tensorflow.python.eager import test +from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops +from tensorflow.python.layers import core +from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope @@ -43,32 +45,60 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): combinations.times( combinations.distributions_and_v1_optimizers(), combinations.combine(mode=["graph"], use_callable_loss=[True, False]) - + combinations.combine(mode=["eager"], use_callable_loss=[True]), - combinations.combine(is_tpu=[False])) + combinations.combine( - distribution=[combinations.tpu_strategy], - optimizer_fn=[ - combinations.adam_optimizer_v1_fn, - # TODO(isaprykin): Make Adam v2 work with while_loops - # and TPUs. - ], - mode=["graph"], - use_callable_loss=[False], - is_tpu=[True])) - def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss, - is_tpu): - # TODO(priyag): Remove this once the step TPU Strategy is stable. - if is_tpu: - self.skipTest("TPU tests are WIP.") + + combinations.combine(mode=["eager"], use_callable_loss=[True])) + + combinations.combine( + distribution=[combinations.tpu_strategy], + optimizer_fn=combinations.optimizers_v1, + mode=["graph"], + use_callable_loss=[True, False])) + def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss): + with distribution.scope(): + model_fn, dataset_fn, layer = minimize_loss_example( + optimizer_fn, use_bias=True, use_callable_loss=use_callable_loss) + + def step_fn(ctx, inputs): + del ctx # Unused + return distribution.group( + distribution.call_for_each_tower( + model_fn, inputs, run_concurrently=layer.built)) + + iterator = distribution.distribute_dataset( + dataset_fn).make_one_shot_iterator() + + def run_step(): + return distribution.run_steps_on_dataset( + step_fn, iterator, iterations=2).run_op + + self.evaluate(distribution.initialize()) + if not context.executing_eagerly(): + with self.test_session() as sess: + run_step = sess.make_callable(run_step()) + self.evaluate(variables_lib.global_variables_initializer()) + + weights, biases = [], [] + for _ in range(5): + run_step() + weights.append(self.evaluate(layer.kernel)) + biases.append(self.evaluate(layer.bias)) + + self.evaluate(distribution.finalize()) + + error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) + is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) + self.assertTrue(is_not_increasing) + + @combinations.generate( + combinations.times( + combinations.distributions_and_v1_optimizers(), + combinations.combine(mode=["graph"], use_callable_loss=[True, False]) + + combinations.combine(mode=["eager"], use_callable_loss=[True]))) + def testTrainNetworkByCallForEachTower(self, distribution, optimizer_fn, + use_callable_loss): with distribution.scope(): model_fn, dataset_fn, layer = minimize_loss_example( optimizer_fn, use_bias=True, use_callable_loss=use_callable_loss) - # TODO(isaprykin): Eliminate `is_tpu`. Probably add a - # `DistributionStrategy.create_monitor` so that each DistributionStrategy - # could influence its training loop. That method would return an instance - # of Monitor. TPUMonitor would execute tpu.initialize_system() and - # tpu.shutdown_system(). iterator = distribution.distribute_dataset( dataset_fn).make_one_shot_iterator() @@ -79,8 +109,6 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): if not context.executing_eagerly(): with self.test_session() as sess: - if is_tpu: - sess.run(tpu.initialize_system()) run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) @@ -91,10 +119,6 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): weights.append(self.evaluate(layer.kernel)) biases.append(self.evaluate(layer.bias)) - if is_tpu: - with self.test_session() as sess: - sess.run(tpu.shutdown_system()) - error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) self.assertTrue(is_not_increasing) @@ -103,22 +127,12 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): combinations.times( combinations.distributions_and_v1_optimizers() + combinations.distributions_and_v2_optimizers(), - combinations.combine(mode=["graph", "eager"], is_tpu=[False])) + + combinations.combine(mode=["graph", "eager"])) + combinations.combine( distribution=[combinations.tpu_strategy], - optimizer_fn=[ - combinations.adam_optimizer_v1_fn, - combinations.gradient_descent_optimizer_v1_fn, - combinations.gradient_descent_optimizer_v2_fn, - ], - mode=["graph"], - is_tpu=[True])) - - def testOptimizerInsideModelFn(self, distribution, optimizer_fn, is_tpu): - # TODO(priyag): Remove this once the step TPU Strategy is stable. - if is_tpu: - self.skipTest("TPU tests are WIP.") - + optimizer_fn=combinations.optimizers_v1+combinations.optimizers_v2, + mode=["graph"])) + def testOptimizerInsideModelFn(self, distribution, optimizer_fn): created_variables = [] trainable_variables = [] @@ -139,26 +153,28 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): use_callable_loss=True, create_optimizer_inside_model_fn=True) + def step_fn(ctx, inputs): + del ctx # Unused + return distribution.group( + distribution.call_for_each_tower( + model_fn, inputs, run_concurrently=layer.built)) + iterator = distribution.distribute_dataset( dataset_fn).make_one_shot_iterator() def run_step(): - return distribution.group( - distribution.call_for_each_tower( - model_fn, iterator.get_next(), run_concurrently=layer.built)) + return distribution.run_steps_on_dataset( + step_fn, iterator, iterations=1).run_op + self.evaluate(distribution.initialize()) if not context.executing_eagerly(): with self.test_session() as sess: - if is_tpu: - sess.run(tpu.initialize_system()) run_step = sess.make_callable(run_step()) - self.evaluate(variables_lib.global_variables_initializer()) + self.evaluate(variables_lib.global_variables_initializer()) run_step() - if is_tpu: - with self.test_session() as sess: - sess.run(tpu.shutdown_system()) + self.evaluate(distribution.finalize()) def get_expected_variables(optimizer_fn, num_parameter_devices): variables_map = { @@ -189,27 +205,17 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): combinations.distributions_and_v1_optimizers(), combinations.combine( mode=["graph", "eager"], - is_tpu=[False], # TODO(isaprykin): Allow False here. Currently subsequent # towers will re-execute UPDATE_OPS of previous towers. update_ops_in_cross_tower_mode=[True])) + combinations.combine( distribution=[combinations.tpu_strategy], - optimizer_fn=[ - combinations.gradient_descent_optimizer_v1_fn, - combinations.gradient_descent_optimizer_v2_fn - ], + optimizer_fn=combinations.optimizers_v1, mode=["graph"], - is_tpu=[True], update_ops_in_cross_tower_mode=[False]))) def testTrainNetworkWithBatchNorm(self, distribution, optimizer_fn, momentum, - renorm, is_tpu, - update_ops_in_cross_tower_mode): + renorm, update_ops_in_cross_tower_mode): """Verifies that moving mean updates are reduced across towers.""" - # TODO(priyag): Remove this once the step TPU Strategy is stable. - if is_tpu: - self.skipTest("TPU tests are WIP.") - with distribution.scope(): num_towers = len(distribution.worker_devices) model_fn, dataset_fn, batchnorm = batchnorm_example( @@ -224,24 +230,28 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): # this test relies on specific input being on each device. if isinstance(distribution, mirrored_strategy.MirroredStrategy): self.assertFalse(distribution._prefetch_on_device) - iterator = distribution.distribute_dataset( - dataset_fn).make_one_shot_iterator() - def run_step(): + def step_fn(ctx, inputs): + del ctx # Unused fetches = distribution.unwrap( distribution.call_for_each_tower( - model_fn, iterator.get_next(), - run_concurrently=batchnorm.built)) + model_fn, inputs, run_concurrently=batchnorm.built)) if update_ops_in_cross_tower_mode: fetches += ops.get_collection(ops.GraphKeys.UPDATE_OPS) return control_flow_ops.group(fetches) + iterator = distribution.distribute_dataset( + dataset_fn).make_one_shot_iterator() + + def run_step(): + return distribution.run_steps_on_dataset( + step_fn, iterator, iterations=1).run_op + + self.evaluate(distribution.initialize()) if not context.executing_eagerly(): with self.test_session() as sess: - if is_tpu: - sess.run(tpu.initialize_system()) run_step = sess.make_callable(run_step()) - self.evaluate(variables_lib.global_variables_initializer()) + self.evaluate(variables_lib.global_variables_initializer()) expected_moving_means = [0.] * 8 @@ -263,9 +273,7 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): expected_moving_mean - averaged_batch_mean(i)) * (1.0 - momentum)) self.assertNear(expected_moving_means[i], moving_means[i], 0.0001) - if is_tpu: - with self.test_session() as sess: - sess.run(tpu.shutdown_system()) + self.evaluate(distribution.finalize()) @combinations.generate( combinations.times( @@ -285,22 +293,16 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): combinations.one_device_strategy, combinations.mirrored_strategy_with_gpu_and_cpu, combinations.mirrored_strategy_with_two_gpus - ], - is_tpu=[False]), + ]), combinations.combine( mode=["graph"], use_callable_loss=[True, False]) + combinations.combine(mode=["eager"], use_callable_loss=[True])) + combinations.combine( distribution=[combinations.tpu_strategy], - is_tpu=[True], mode=["graph"], use_callable_loss=[True, False]))) def testMeanVsSum(self, distribution, optimizer_fn, loss_reduction, - use_callable_loss, is_tpu): - # TODO(priyag): Remove this once the step TPU Strategy is stable. - if is_tpu: - self.skipTest("TPU tests are WIP.") - + use_callable_loss): with distribution.scope(): all_vars = [] @@ -326,20 +328,25 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): labels = dataset_ops.Dataset.from_tensors([[6.], [21.]]) return dataset_ops.Dataset.zip((features, labels)).repeat() + def step_fn(ctx, inputs): + del ctx # Unused + x, y = inputs + return distribution.group( + distribution.call_for_each_tower( + model_fn, x, y, run_concurrently=False)) + iterator = distribution.distribute_dataset( dataset_fn).make_one_shot_iterator() def run_step(): - return distribution.group( - distribution.call_for_each_tower( - model_fn, *iterator.get_next(), run_concurrently=False)) + return distribution.run_steps_on_dataset( + step_fn, iterator, iterations=1).run_op + self.evaluate(distribution.initialize()) if not context.executing_eagerly(): with self.test_session() as sess: - if is_tpu: - sess.run(tpu.initialize_system()) run_step = sess.make_callable(run_step()) - self.evaluate(variables_lib.global_variables_initializer()) + self.evaluate(variables_lib.global_variables_initializer()) run_step() @@ -369,10 +376,132 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): # One of the mean loss reductions. self.assertNear(weight, 2 + 10.6, 0.0001) - if is_tpu: + self.evaluate(distribution.finalize()) + + @combinations.generate( + combinations.times( + combinations.distributions_and_v1_optimizers(), + combinations.combine(mode=["graph", "eager"]), + combinations.combine(is_tpu=[False])) + + combinations.combine( + distribution=[combinations.tpu_strategy], + optimizer_fn=combinations.optimizers_v1, + mode=["graph"], + is_tpu=[True])) + def testRunStepsWithOutputContext(self, distribution, optimizer_fn, is_tpu): + with distribution.scope(): + def dataset_fn(): + dataset = dataset_ops.Dataset.from_tensors([[1.]]).repeat() + # TODO(priyag): batch with drop_remainder=True causes shapes to be + # fully defined for TPU. Remove this when XLA supports dynamic shapes. + return dataset.batch(batch_size=1, drop_remainder=True) + + optimizer = optimizer_fn() + layer = core.Dense(1, use_bias=True) + + key1 = "foo" + value1 = "bar" + + def model_fn(output_context, x): + """A very simple model written by the user.""" + def loss_fn(): + y = array_ops.reshape(layer(x), []) - constant_op.constant(1.) + return y * y + + train_op = optimizer.minimize(loss_fn) + loss = loss_fn() + output_context.set_last_step_output( + name="tower_loss_agg", + output=loss, + aggregation=variables_lib.VariableAggregation.MEAN) + output_context.set_non_tensor_output(key1, value1) + return (train_op, loss) + + def step_fn(output_context, inputs): + (train_op, loss) = distribution.call_for_each_tower( + model_fn, output_context, inputs, run_concurrently=False) + output_context.set_last_step_output( + name="cross_tower_loss_agg", + output=loss, + aggregation=variables_lib.VariableAggregation.MEAN) + output_context.set_last_step_output( + name="cross_tower_loss_noagg", + output=loss) + return distribution.group(train_op) + + iterator = distribution.distribute_dataset( + dataset_fn).make_one_shot_iterator() + + def run_step(): + initial_loss = lambda: constant_op.constant(1e7) + # Initial values corresponding to aggregated losses are just single + # tensors. But for non aggregated losses, we need to have initial + # values that are of the same structure as non reduced losses. In + # MirroredStrategy, this will be a list of losses, in TPUStrategy + # it will be single tensor. Using `broadcast` followed by `unwrap` + # gives us the desired initial value structure. + initial_loop_values = { + "tower_loss_agg": initial_loss(), + "cross_tower_loss_agg": initial_loss(), + "cross_tower_loss_noagg": + distribution.unwrap(distribution.broadcast(initial_loss())) + } + ctx = distribution.run_steps_on_dataset( + step_fn, iterator, iterations=2, + initial_loop_values=initial_loop_values) + + self.assertEqual({key1: [value1]}, ctx.non_tensor_outputs) + self._verify_loss_output( + initial_loss(), + loss_output=ctx.last_step_outputs["tower_loss_agg"], + aggregated=True, distribution=distribution) + self._verify_loss_output( + initial_loss(), + loss_output=ctx.last_step_outputs["cross_tower_loss_agg"], + aggregated=True, distribution=distribution) + self._verify_loss_output( + initial_loss(), + loss_output=ctx.last_step_outputs["cross_tower_loss_noagg"], + aggregated=False, distribution=distribution) + return (ctx.run_op, ctx.last_step_outputs["tower_loss_agg"]) + + self.evaluate(distribution.initialize()) + if not context.executing_eagerly(): with self.test_session() as sess: - sess.run(tpu.shutdown_system()) + run_step = sess.make_callable(run_step()) + self.evaluate(variables_lib.global_variables_initializer()) + + weights, biases, losses = [], [], [] + for _ in range(5): + _, loss = run_step() + losses.append(loss) + weights.append(self.evaluate(layer.kernel)) + biases.append(self.evaluate(layer.bias)) + self.evaluate(distribution.finalize()) + + loss_is_not_increasing = all(y <= x for x, y in zip(losses, losses[1:])) + self.assertTrue(loss_is_not_increasing) + + error = abs( + numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) + error_is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) + self.assertTrue(error_is_not_increasing) + + def _verify_loss_output(self, initial_loss, loss_output, aggregated, + distribution): + if not aggregated: + self.assertEqual(distribution.num_towers, + len(distribution.unwrap(loss_output))) + loss_output = distribution.reduce( + aggregation=variables_lib.VariableAggregation.MEAN, + value=loss_output, destinations="/device:CPU:0") + + unwrapped_output = distribution.unwrap(loss_output) + self.assertEqual(1, len(unwrapped_output)) + loss_tensor = unwrapped_output[0] + self.assertEqual(initial_loss.dtype, loss_tensor.dtype) + self.assertEqual(initial_loss.shape, loss_tensor.shape) if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index c5d6e978e761dc2022c5b3c8780a17fe0be8f711..e3376a06368e8ef5efcda5bb69de66b7ec3390e1 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -27,13 +27,17 @@ from tensorflow.contrib.distribute.python import values from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import context from tensorflow.python.eager import tape +from tensorflow.python.framework import constant_op from tensorflow.python.framework import device as tf_device from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables as variables_lib from tensorflow.python.training import coordinator from tensorflow.python.training import device_util from tensorflow.python.training import distribute as distribute_lib +from tensorflow.python.util import nest # TODO(josh11b): Replace asserts in this file with if ...: raise ... @@ -313,7 +317,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): self._devices = [device_util.resolve(d) for d in devices] self._canonical_device_set = set(self._devices) self._device_index = values.PerDevice( - dict((d, i) for i, d in enumerate(devices))) + {d: i for i, d in enumerate(devices)}) self._cross_tower_ops = cross_tower_ops self._prefetch_on_device = prefetch_on_device # TODO(yuefengz): consider setting the default device. @@ -357,6 +361,54 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): self._call_dataset_fn(dataset_fn), self._devices, self._prefetch_on_device) + # TODO(priyag): Deal with OutOfRange errors once b/111349762 is fixed. + def _run_steps_on_dataset(self, fn, iterator, iterations, + initial_loop_values=None): + if initial_loop_values is None: + initial_loop_values = {} + initial_loop_values = nest.flatten(initial_loop_values) + + ctx = values.MultiStepContext() + def body(i, *args): + """A wrapper around `fn` to create the while loop body.""" + del args + fn_result = fn(ctx, iterator.get_next()) + for (name, output) in ctx.last_step_outputs.items(): + # Convert all outputs to tensors, potentially from `DistributedValues`. + ctx.last_step_outputs[name] = self.unwrap(output) + flat_last_step_outputs = nest.flatten(ctx.last_step_outputs) + with ops.control_dependencies([fn_result]): + return [i + 1] + flat_last_step_outputs + + cond = lambda i, *args: i < iterations + i = constant_op.constant(0) + loop_result = control_flow_ops.while_loop( + cond, body, [i] + initial_loop_values, name="", + parallel_iterations=1, back_prop=False, swap_memory=False, + return_same_structure=True) + + ctx.run_op = control_flow_ops.group(loop_result) + + # Convert the last_step_outputs from a list to the original dict structure + # of last_step_outputs. + last_step_tensor_outputs = loop_result[1:] + last_step_tensor_outputs_dict = nest.pack_sequence_as( + ctx.last_step_outputs, last_step_tensor_outputs) + + for (name, aggregation) in ctx._last_step_outputs_aggregations.items(): # pylint: disable=protected-access + output = last_step_tensor_outputs_dict[name] + # For outputs that have already been aggregated, wrap them in a Mirrored + # container, else in a PerDevice container. + if aggregation is variables_lib.VariableAggregation.NONE: + last_step_tensor_outputs_dict[name] = values.regroup( + {d: t for d, t in zip(self._devices, output)}, values.PerDevice) + else: + assert len(output) == 1 + last_step_tensor_outputs_dict[name] = output[0] + + ctx._set_last_step_outputs(last_step_tensor_outputs_dict) # pylint: disable=protected-access + return ctx + def _broadcast(self, tensor, destinations): # TODO(josh11b): In eager mode, use one thread per device, or async mode. return self._get_cross_tower_ops().broadcast(tensor, destinations or diff --git a/tensorflow/contrib/distribute/python/one_device_strategy.py b/tensorflow/contrib/distribute/python/one_device_strategy.py index a7f2e2e586e90e0f9fe163cc8fd5b854e3854883..016978cdb3a152bbba0a2e63df1dea4035e32789 100644 --- a/tensorflow/contrib/distribute/python/one_device_strategy.py +++ b/tensorflow/contrib/distribute/python/one_device_strategy.py @@ -21,11 +21,14 @@ from __future__ import print_function import six from tensorflow.contrib.distribute.python import values +from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.training import distribute as distribute_lib +from tensorflow.python.util import nest # TODO(josh11b): Replace asserts in this file with if ...: raise ... @@ -66,6 +69,41 @@ class OneDeviceStrategy(distribute_lib.DistributionStrategy): def _broadcast(self, tensor, destinations): return tensor + # TODO(priyag): Deal with OutOfRange errors once b/111349762 is fixed. + def _run_steps_on_dataset(self, fn, iterator, iterations, + initial_loop_values=None): + if initial_loop_values is None: + initial_loop_values = {} + initial_loop_values = nest.flatten(initial_loop_values) + + ctx = values.MultiStepContext() + def body(i, *args): + """A wrapper around `fn` to create the while loop body.""" + del args + fn_result = fn(ctx, iterator.get_next()) + flat_last_step_outputs = nest.flatten(ctx.last_step_outputs) + with ops.control_dependencies([fn_result]): + return [i + 1] + flat_last_step_outputs + + cond = lambda i, *args: i < iterations + i = constant_op.constant(0) + # TODO(priyag): Use max_iterations instead of an explicit counter. + loop_result = control_flow_ops.while_loop( + cond, body, [i] + initial_loop_values, name="", + parallel_iterations=1, back_prop=False, swap_memory=False, + return_same_structure=True) + + ctx.run_op = control_flow_ops.group(loop_result) + + # Convert the last_step_outputs from a list to the original dict structure + # of last_step_outputs. + last_step_tensor_outputs = loop_result[1:] + last_step_tensor_outputs_dict = nest.pack_sequence_as( + ctx.last_step_outputs, last_step_tensor_outputs) + + ctx._set_last_step_outputs(last_step_tensor_outputs_dict) # pylint: disable=protected-access + return ctx + def _call_for_each_tower(self, fn, *args, **kwargs): # We don't run `fn` in multiple threads in OneDeviceStrategy. kwargs.pop("run_concurrently", None) diff --git a/tensorflow/contrib/distribute/python/parameter_server_strategy.py b/tensorflow/contrib/distribute/python/parameter_server_strategy.py index f2c7fd556adaf08926b6f1e327abd25b7c9a42e6..407c78df95ded5ef6f3ad973392a4d4a21d07735 100644 --- a/tensorflow/contrib/distribute/python/parameter_server_strategy.py +++ b/tensorflow/contrib/distribute/python/parameter_server_strategy.py @@ -77,16 +77,16 @@ class ParameterServerStrategy(distribute_lib.DistributionStrategy): GPUs) even if there is only CPU or one GPU. When defining the `fn`, extra caution needs to be taken: - 1) Always use @{tf.get_variable} instead of @{tf.Variable} which is not able + 1) Always use `tf.get_variable` instead of `tf.Variable` which is not able to refer to the same variable on different towers. 2) It is generally not recommended to open a device scope under the strategy's - scope. A device scope (i.e. calling @{tf.device}) will be merged with or + scope. A device scope (i.e. calling `tf.device`) will be merged with or override the device for operations but will not change the device for variables. 3) It is also not recommended to open a colocation scope (i.e. calling - @{tf.colocate_with}) under the strategy's scope. For colocating variables, + `tf.colocate_with`) under the strategy's scope. For colocating variables, use `distribution.colocate_vars_with` instead. Colocation of ops will possibly create conflicts of device assignement. """ diff --git a/tensorflow/contrib/distribute/python/prefetching_ops_v2.py b/tensorflow/contrib/distribute/python/prefetching_ops_v2.py index 24cdc627a35f4455cb92484566dc13fa1bbaf2cc..1ff60c076226299a89060a295c1cc0c50817b861 100644 --- a/tensorflow/contrib/distribute/python/prefetching_ops_v2.py +++ b/tensorflow/contrib/distribute/python/prefetching_ops_v2.py @@ -35,7 +35,7 @@ from tensorflow.python.util import nest # pylint: disable=protected-access class _PrefetchToDeviceIterator(object): - """A replacement for @{tf.data.Iterator} that prefetches to another device. + """A replacement for `tf.data.Iterator` that prefetches to another device. Args: input_dataset: The input dataset. @@ -108,7 +108,7 @@ class _PrefetchToDeviceIterator(object): self._input_dataset) def get_next(self, name=None): - """See @{tf.data.Iterator.get_next}.""" + """See `tf.data.Iterator.get_next`.""" self._get_next_call_count += 1 if self._get_next_call_count > iterator_ops.GET_NEXT_CALL_WARNING_THRESHOLD: warnings.warn(iterator_ops.GET_NEXT_CALL_WARNING_MESSAGE) @@ -209,7 +209,7 @@ class _PrefetchToDeviceDataset(dataset_ops.Dataset): def prefetch_to_devices(devices, buffer_size=None): """A transformation that prefetches dataset values to the given `devices`. - NOTE: Although the transformation creates a @{tf.data.Dataset}, the + NOTE: Although the transformation creates a `tf.data.Dataset`, the transformation must be the final `Dataset` in the input pipeline. Args: @@ -220,7 +220,7 @@ def prefetch_to_devices(devices, buffer_size=None): Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): return _PrefetchToDeviceDataset(dataset, devices, buffer_size) diff --git a/tensorflow/contrib/distribute/python/single_loss_example.py b/tensorflow/contrib/distribute/python/single_loss_example.py index d1fdb3279cf2a7cba6e2282d58eedccf38bd38a3..5aa19cf6a9f8411120ed929cecaf93dda6c9edf2 100644 --- a/tensorflow/contrib/distribute/python/single_loss_example.py +++ b/tensorflow/contrib/distribute/python/single_loss_example.py @@ -29,7 +29,8 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops -def single_loss_example(optimizer_fn, distribution, use_bias=False): +def single_loss_example(optimizer_fn, distribution, use_bias=False, + iterations_per_step=1): """Build a very simple network to use in tests and examples.""" def dataset_fn(): @@ -38,12 +39,13 @@ def single_loss_example(optimizer_fn, distribution, use_bias=False): optimizer = optimizer_fn() layer = core.Dense(1, use_bias=use_bias) - def loss_fn(x): + def loss_fn(ctx, x): + del ctx y = array_ops.reshape(layer(x), []) - constant_op.constant(1.) return y * y - single_loss_step = step_fn.StandardSingleLossStep(dataset_fn, loss_fn, - optimizer, distribution) + single_loss_step = step_fn.StandardSingleLossStep( + dataset_fn, loss_fn, optimizer, distribution, iterations_per_step) # Layer is returned for inspecting the kernels in tests. return single_loss_step, layer diff --git a/tensorflow/contrib/distribute/python/step_fn.py b/tensorflow/contrib/distribute/python/step_fn.py index d1910622b38c748fc5a814f9e83c2294850d5d12..d3611570b472078bb5f154e9bcb8823c31d39c24 100644 --- a/tensorflow/contrib/distribute/python/step_fn.py +++ b/tensorflow/contrib/distribute/python/step_fn.py @@ -34,15 +34,9 @@ class Step(object): def __call__(self): """Perform one step of this training algorithm.""" - return self.step(self.inputs()) - - def inputs(self): - """For the generating the input to be passed to `step()`.""" raise NotImplementedError("must be implemented in descendants") - def step(self, inputs): - """Perform the main computation of this training algorithm.""" - raise NotImplementedError("must be implemented in descendants") + # TODO(priyag): Add an method to access initialization and finalize ops. class StandardInputStep(Step): @@ -54,12 +48,9 @@ class StandardInputStep(Step): """ def __init__(self, dataset_fn, distribution): - Step.__init__(self, distribution) - self._distributed_input = distribution.distribute_dataset( - dataset_fn).make_one_shot_iterator() - - def inputs(self): - return self._distributed_input.get_next() + super(StandardInputStep, self).__init__(distribution) + self._distributed_input = distribution.distribute_dataset(dataset_fn) + self._iterator = self._distributed_input.make_one_shot_iterator() class StandardSingleLossStep(StandardInputStep): @@ -69,8 +60,8 @@ class StandardSingleLossStep(StandardInputStep): ```python ... - step = step_fn.StandardSingleLossStep(dataset, loss_fn, optimizer) - step.initialize(distribution) + step = step_fn.StandardSingleLossStep( + dataset, loss_fn, optimizer, distribution) # Run a single training step on a given DistributionStrategy: step(distribution) @@ -80,27 +71,43 @@ class StandardSingleLossStep(StandardInputStep): Args: dataset_fn: a function that returns a tf.data Dataset that produces the input for the model. - loss_fn: a function that returns loss. + loss_fn: a function that takes a context and inputs as arguments. It returns + the loss for those inputs. `context` is an instance of + `values.MultiStepContext` that will be passed when `loss_fn` is run. + `context` can be used to specify the outputs to be returned from + `loss_fn`, among other things. optimizer: an optimizer that implements an update rule. distribution: a `DistributionStrategy` object. """ - def __init__(self, dataset_fn, loss_fn, optimizer, distribution): - StandardInputStep.__init__(self, dataset_fn, distribution) + def __init__(self, dataset_fn, loss_fn, optimizer, distribution, + iterations_per_step=1): + super(StandardSingleLossStep, self).__init__(dataset_fn, distribution) self._loss_fn = loss_fn self._optimizer = optimizer self._is_run_concurrently = False + self._iterations_per_step = iterations_per_step - def step(self, inputs): + def __call__(self): with self._distribution.scope(): - gradients_fn = backprop.implicit_grad(self._loss_fn) - gradients_fn = optimizer_lib.get_filtered_grad_fn(gradients_fn) - - grads_and_vars = self.distribution.call_for_each_tower( - gradients_fn, inputs, run_concurrently=self._is_run_concurrently) - # If threads use layers, then we need to run the first step sequentially, - # so that layers.build() is not executed in parallel. Otherwise, multiple - # sets of mirrored variables are going to be created. - self._is_run_concurrently = True - return self._optimizer._distributed_apply( # pylint: disable=protected-access - self.distribution, grads_and_vars) + def step_fn(ctx, inputs): + """Function to run one iteration with one input.""" + gradients_fn = backprop.implicit_grad(self._loss_fn) + gradients_fn = optimizer_lib.get_filtered_grad_fn(gradients_fn) + + grads_and_vars = self.distribution.call_for_each_tower( + gradients_fn, + ctx, inputs, + run_concurrently=self._is_run_concurrently) + # If threads use layers, then we need to run the first step + # sequentially, so that layers.build() is not executed in parallel. + # Otherwise, multiple sets of mirrored variables are going to be + # created. + self._is_run_concurrently = True + return self._optimizer._distributed_apply( # pylint: disable=protected-access + self.distribution, grads_and_vars) + + # TODO(priyag): Return the outputs, context, etc as well. + ctx = self.distribution.run_steps_on_dataset( + step_fn, self._iterator, self._iterations_per_step) + return ctx.run_op diff --git a/tensorflow/contrib/distribute/python/step_fn_test.py b/tensorflow/contrib/distribute/python/step_fn_test.py index 2ee94d8f70868c07ca217dd4d433585458efa8d8..8605ab1f7daeb81e778577ad3c4a18b39c57d743 100644 --- a/tensorflow/contrib/distribute/python/step_fn_test.py +++ b/tensorflow/contrib/distribute/python/step_fn_test.py @@ -33,12 +33,19 @@ class SingleLossStepTest(test.TestCase, parameterized.TestCase): @combinations.generate( combinations.times( combinations.distributions_and_v1_optimizers(), - combinations.combine(mode=combinations.graph_and_eager_modes))) - def testTrainNetwork(self, distribution, optimizer_fn): + combinations.combine(mode=combinations.graph_and_eager_modes), + combinations.combine(is_tpu=[False])) + + combinations.combine( + distribution=[combinations.tpu_strategy], + optimizer_fn=combinations.optimizers_v1, + mode=["graph"], + is_tpu=[True])) + def testTrainNetwork(self, distribution, optimizer_fn, is_tpu): with distribution.scope(): single_loss_step, layer = single_loss_example( - optimizer_fn, distribution, use_bias=True) + optimizer_fn, distribution, use_bias=True, iterations_per_step=2) + self.evaluate(distribution.initialize()) if context.executing_eagerly(): run_step = single_loss_step else: @@ -47,12 +54,14 @@ class SingleLossStepTest(test.TestCase, parameterized.TestCase): self.evaluate(variables.global_variables_initializer()) weights, biases = [], [] - for _ in range(10): + for _ in range(5): run_step() weights.append(self.evaluate(layer.kernel)) biases.append(self.evaluate(layer.bias)) + self.evaluate(distribution.finalize()) + error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) self.assertTrue(is_not_increasing) diff --git a/tensorflow/contrib/distribute/python/tpu_strategy.py b/tensorflow/contrib/distribute/python/tpu_strategy.py index f5497e0b2120e01f36f5536d857b2e572c3c6910..b510fdb888dafe9f18805bc60e9fb670710521ab 100644 --- a/tensorflow/contrib/distribute/python/tpu_strategy.py +++ b/tensorflow/contrib/distribute/python/tpu_strategy.py @@ -26,34 +26,70 @@ from tensorflow.contrib.distribute.python import one_device_strategy from tensorflow.contrib.distribute.python import values from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import tpu +from tensorflow.contrib.tpu.python.tpu import tpu_system_metadata as tpu_system_metadata_lib from tensorflow.contrib.tpu.python.tpu import training_loop +from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope as vs +from tensorflow.python.ops import variables as variables_lib from tensorflow.python.training import device_util +from tensorflow.python.training import server_lib from tensorflow.python.util import nest +def get_tpu_system_metadata(tpu_cluster_resolver): + """Retrieves TPU system metadata given a TPUClusterResolver.""" + master = tpu_cluster_resolver.master() + + # pylint: disable=protected-access + cluster_def = (tpu_cluster_resolver.cluster_spec() + or server_lib.ClusterSpec({})).as_cluster_def() + tpu_system_metadata = ( + tpu_system_metadata_lib._query_tpu_system_metadata( + master, + cluster_def=cluster_def, + query_topology=True)) + + return tpu_system_metadata + + class TPUStrategy(one_device_strategy.OneDeviceStrategy): """Experimental TPU distribution strategy implementation.""" - def __init__(self, num_cores_per_host=2): + def __init__(self, tpu_cluster_resolver, steps_per_run): + """Initializes the TPUStrategy object. + + Args: + tpu_cluster_resolver: A tf.contrib.cluster_resolver.TPUClusterResolver, + which provides information about the TPU cluster. + steps_per_run: Number of steps to run on device before returning to the + host. Note that this can have side-effects on performance, hooks, + metrics, summaries etc. + This parameter is only used when Distribution Strategy is used with + estimator or keras. + """ # TODO(isaprykin): Generalize the defaults. They are currently tailored for # the unit test. super(TPUStrategy, self).__init__('/device:CPU:0') - # TODO(isaprykin): Auto-detect number of cores and hosts. - self._num_cores_per_host = num_cores_per_host + + self._tpu_cluster_resolver = tpu_cluster_resolver + self._tpu_metadata = get_tpu_system_metadata(self._tpu_cluster_resolver) + # TODO(priyag): This should not be hardcoded here. self._host = '/device:CPU:0' + # TODO(sourabhbajaj): Remove this once performance of running one step + # at a time is comparable to multiple steps. + self.steps_per_run = steps_per_run def distribute_dataset(self, dataset_fn): # TODO(priyag): Perhaps distribute across cores here. return self._call_dataset_fn(dataset_fn) - # TODO(priyag): Deal with OutOfRange errors. + # TODO(priyag): Deal with OutOfRange errors once b/111349762 is fixed. # TODO(sourabhbajaj): Remove the initial_loop_values parameter when we have # a mechanism to infer the outputs of `fn`. Pending b/110550782. def _run_steps_on_dataset(self, fn, iterator, iterations, @@ -72,7 +108,7 @@ class TPUStrategy(one_device_strategy.OneDeviceStrategy): control_deps = [] sharded_inputs = [] with ops.device(self._host): - for _ in range(self._num_cores_per_host): + for _ in range(self.num_towers): # Use control dependencies to ensure a deterministic ordering. with ops.control_dependencies(control_deps): inputs = nest.flatten(iterator.get_next()) @@ -103,53 +139,90 @@ class TPUStrategy(one_device_strategy.OneDeviceStrategy): # Wrap `fn` for repeat. if initial_loop_values is None: - initial_loop_values = [] - ctx = values.MultiStepContext(initial_loop_values) + initial_loop_values = {} + initial_loop_values = nest.flatten(initial_loop_values) + ctx = values.MultiStepContext() def run_fn(*args, **kwargs): del args, kwargs fn_result = fn(ctx, dequeue_fn()) - if ctx.last_step_outputs is None: - ctx.last_step_outputs = [] - with ops.control_dependencies([fn_result]): - return array_ops.identity(ctx.last_step_outputs) + flat_last_step_outputs = nest.flatten(ctx.last_step_outputs) + if flat_last_step_outputs: + with ops.control_dependencies([fn_result]): + return [array_ops.identity(f) for f in flat_last_step_outputs] + else: + return fn_result # TODO(sourabhbajaj): The input to while loop should be based on the output # type of the step_fn def iterate_on_tpu(): - return training_loop.repeat(iterations, run_fn, [initial_loop_values]) - - replicate_inputs = [[]] * self._num_cores_per_host - outputs = tpu.replicate(iterate_on_tpu, replicate_inputs) - last_step_tensor_outputs = [list(x) for x in zip(*outputs)] - - # Take index [0] of last_step_tensor_outputs as we wrapped - # initial_loop_values in a list in the `repeat` call. - return (control_flow_ops.group(last_step_tensor_outputs, enqueue_ops), - last_step_tensor_outputs[0], ctx) + return training_loop.repeat(iterations, run_fn, initial_loop_values) + + replicate_inputs = [[]] * self.num_towers + replicate_outputs = tpu.replicate(iterate_on_tpu, replicate_inputs) + ctx.run_op = control_flow_ops.group(replicate_outputs, enqueue_ops) + + # Filter out any ops from the outputs, typically this would be the case + # when there were no tensor outputs. + last_step_tensor_outputs = [x for x in replicate_outputs + if not isinstance(x, ops.Operation)] + + # Outputs are currently of the structure (grouped by device) + # [[output0_device0, output1_device0, output2_device0], + # [output0_device1, output1_device1, output2_device1]] + # Convert this to the following structure instead: (grouped by output) + # [[output0_device0, output0_device1], + # [output1_device0, output1_device1], + # [output2_device0, output2_device1]] + last_step_tensor_outputs = [list(x) for x in zip(*last_step_tensor_outputs)] + + # Convert replicate_outputs to the original dict structure of + # last_step_outputs. + last_step_tensor_outputs_dict = nest.pack_sequence_as( + ctx.last_step_outputs, last_step_tensor_outputs) + + for (name, aggregation) in ctx._last_step_outputs_aggregations.items(): # pylint: disable=protected-access + output = last_step_tensor_outputs_dict[name] + # For outputs that have already been aggregated, take the first value + # from the list as each value should be the same. Else return the full + # list of values. + if aggregation is not variables_lib.VariableAggregation.NONE: + # TODO(priyag): Should this return the element or a list with 1 element + last_step_tensor_outputs_dict[name] = output[0] + ctx._set_last_step_outputs(last_step_tensor_outputs_dict) # pylint: disable=protected-access + + return ctx def _call_for_each_tower(self, fn, *args, **kwargs): kwargs.pop('run_concurrently', None) with one_device_strategy._OneDeviceTowerContext(self): # pylint: disable=protected-access return fn(*args, **kwargs) - def get_initialization_ops(self): - return [tpu.initialize_system()] + def initialize(self): + if context.executing_eagerly(): + # TODO(priyag): Add appopriate call here when eager is supported for TPUs. + raise NotImplementedError('Eager mode not supported in TPUStrategy.') + else: + return [tpu.initialize_system()] - def get_finalize_ops(self): - return [tpu.shutdown_system()] + def finalize(self): + if context.executing_eagerly(): + # TODO(priyag): Add appopriate call here when eager is supported for TPUs. + raise NotImplementedError('Eager mode not supported in TPUStrategy.') + else: + return [tpu.shutdown_system()] def _reduce(self, aggregation, value, destinations): graph = ops.get_default_graph() - context = graph._get_control_flow_context() # pylint: disable=protected-access + cf_context = graph._get_control_flow_context() # pylint: disable=protected-access # If we're inside the ReplicateContext, reduction should be done using # CrossReplicaSum while outside we can directly use an add_n op. - while context: - if isinstance(context, tpu.TPUReplicateContext): + while cf_context: + if isinstance(cf_context, tpu.TPUReplicateContext): if aggregation == vs.VariableAggregation.MEAN: # TODO(jhseu): Revisit once we support model-parallelism. - value *= (1. / self._num_cores_per_host) + value *= (1. / self.num_towers) return tpu_ops.cross_replica_sum(value) - context = context.outer_context + cf_context = cf_context.outer_context # Validate that the destination is same as the host device # Note we don't do this when in replicate context as the reduction is @@ -166,6 +239,11 @@ class TPUStrategy(one_device_strategy.OneDeviceStrategy): return output * (1. / len(value)) return output + def _unwrap(self, value): + if isinstance(value, list): + return value + return [value] + @property def num_towers(self): - return self._num_cores_per_host + return self._tpu_metadata.num_of_cores_per_host diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 6f34dd47461812904564dcda10266d4e8078211e..5fd4c9de696b715c3fb9b8a6ca64923b413a32e9 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -35,6 +35,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope as vs +from tensorflow.python.ops import variables as variables_lib from tensorflow.python.training import device_util from tensorflow.python.training import distribute as distribute_lib from tensorflow.python.training import saver @@ -934,67 +935,99 @@ class MultiStepContext(object): This context object is useful when running multiple steps at a time using the `run_steps_on_dataset` API. For e.g. it allows the user's step function to - specify which outputs to emit at what frequency. Currently it only supports - capturing output from the last step, but will soon be augmented to support - other use cases such as output each N steps. + specify which outputs to emit at what frequency. Currently it supports + capturing output from the last step, as well as capturing non tensor outputs. + In the future it will be augmented to support other use cases such as output + each N steps. """ - def __init__(self, initial_loop_values=None): + def __init__(self): """Initializes an output context. - Args: - initial_loop_values: Initial values passed to the run steps - while loop. The only purpose is to verify the shapes and types - when the actual output is set. This will be removed once we - automatically infer the output shapes and types (and do not need to - check for user error in specifying them manually). Returns: A context object. """ - self._last_step_outputs = None - self._non_tensor_outputs = None - self._initial_loop_values = initial_loop_values + self._last_step_outputs = {} + self._last_step_outputs_aggregations = {} + self._non_tensor_outputs = {} @property def last_step_outputs(self): - """Return the last step's outputs.""" + """A dictionary consisting of outputs to be captured on last step. + + Keys in the dictionary are names of tensors to be captured, as specified + when `set_last_step_output` is called. + Values in the dictionary are the tensors themselves. If + `set_last_step_output` was called with an `aggregation` for this output, + then the value is the aggregated value. + + Returns: + A dictionary with last step outputs. + """ return self._last_step_outputs - @last_step_outputs.setter - def last_step_outputs(self, outputs): - """Set the last step's outputs.""" - self._verify_structure_shapes_types(outputs, self._initial_loop_values) + def _set_last_step_outputs(self, outputs): + """Replace the entire dictionary of last step outputs.""" + if not isinstance(outputs, dict): + raise ValueError("Need a dictionary to set last_step_outputs.") self._last_step_outputs = outputs + def set_last_step_output(self, name, output, + aggregation=variables_lib.VariableAggregation.NONE): + """Set `output` with `name` to be outputted from the last step. + + Args: + name: String, name to identify the output. Doesn't need to match tensor + name. + output: The tensors that should be outputted with `name`. See below for + actual types supported. + aggregation: Aggregation method to use to aggregate outputs from multiple + towers. Required if `set_last_step_output` is called in a tower context. + Optional in cross_tower_context. + When present, the outputs from all the towers are aggregated using the + current distribution strategy's `reduce` method. Hence, the type of + `output` must be what's supported by the corresponding `reduce` method. + For e.g. if using MirroredStrategy and aggregation is set, output + must be a `PerDevice` value. + The aggregation method is also recorded in a dictionary + `_last_step_outputs_aggregations` for later interpreting of the + outputs as already reduced or not. + + """ + if distribute_lib.get_cross_tower_context(): + self._last_step_outputs_aggregations[name] = aggregation + if aggregation is variables_lib.VariableAggregation.NONE: + self._last_step_outputs[name] = output + else: + distribution = distribute_lib.get_distribution_strategy() + self._last_step_outputs[name] = distribution.reduce( + aggregation, output, destinations="/device:CPU:0") + else: + assert aggregation is not variables_lib.VariableAggregation.NONE + def merge_fn(distribution, value): + self._last_step_outputs[name] = distribution.reduce( + aggregation, value, destinations="/device:CPU:0") + # Setting this inside the `merge_fn` because all towers share the same + # context object, so it's more robust to set it only once (even if all + # the towers are trying to set the same value). + self._last_step_outputs_aggregations[name] = aggregation + distribute_lib.get_tower_context().merge_call(merge_fn, output) + @property def non_tensor_outputs(self): - """Return the non tensor outputs.""" + """A dictionary consisting of any non tensor outputs to be captured.""" return self._non_tensor_outputs - @non_tensor_outputs.setter - def non_tensor_outputs(self, outputs): - """Set any non tensor outputs.""" - self._non_tensor_outputs = outputs - - def _verify_structure_shapes_types(self, left, right): - """Verify that the structure, shapes and types of left are same as right.""" - nest.assert_same_structure(left, right) - flat_left = nest.flatten(left) - flat_right = nest.flatten(right) - assert len(flat_left) == len(flat_right), ( - "Length of left {} and right {} should be same.". - format(len(flat_left), len(flat_right))) - - for o, i in zip(flat_left, flat_right): - # TODO(priyag): Add checks for other types like IndexedSlices. - if isinstance(o, ops.Tensor): - assert isinstance(i, ops.Tensor) - assert o.shape == i.shape, ( - "Shape {} of left {} doesn't match shape {} of right {}.". - format(o.shape, o, i.shape, i)) - assert o.dtype == i.dtype, ( - "Dtype {} of left {} doesn't match dtype {} of right {}.". - format(o.dtype, o, i.dtype, i)) + def set_non_tensor_output(self, name, output): + """Set `output` with `name` to be captured as a non tensor output.""" + if distribute_lib.get_cross_tower_context(): + self._non_tensor_outputs[name] = output + else: + def merge_fn(distribution, value): + # NOTE(priyag): For non tensor outputs, we simply return all the values + # in a list as aggregation doesn't make sense on non tensors. + self._non_tensor_outputs[name] = distribution.unwrap(value) + distribute_lib.get_tower_context().merge_call(merge_fn, output) def value_container(val): diff --git a/tensorflow/contrib/distributions/BUILD b/tensorflow/contrib/distributions/BUILD index ad00d1734dd14ed846522a33d888a5387cb25cc6..a8d0d493abcd7de540799f6b94c3cdb9ce9dafae 100644 --- a/tensorflow/contrib/distributions/BUILD +++ b/tensorflow/contrib/distributions/BUILD @@ -124,7 +124,7 @@ cuda_py_test( cuda_py_test( name = "conditional_distribution_test", - size = "small", + size = "medium", srcs = [ "python/kernel_tests/conditional_distribution_test.py", "python/kernel_tests/distribution_test.py", diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py index 85d604e34ac25cf94b601470b7f166d9d414a8e3..49a9afe3f6debe048369c52328fb5534946ab9e5 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py @@ -29,6 +29,17 @@ from tensorflow.python.platform import test class MatrixInverseTriLBijectorTest(test.TestCase): """Tests the correctness of the Y = inv(tril) transformation.""" + #The inverse of 0 is undefined, as the numbers above the main + #diagonal must be zero, we zero out these numbers after running inverse. + #See: https://github.com/numpy/numpy/issues/11445 + def _inv(self, x): + y = np.linalg.inv(x) + #triu_indices only works on 2d arrays + #need to iterate over all the 2d arrays in a x-dimensional array. + for idx in np.ndindex(y.shape[0:-2]): + y[idx][np.triu_indices(y[idx].shape[-1], 1)] = 0 + return y + @test_util.run_in_graph_and_eager_modes def testComputesCorrectValues(self): inv = bijectors.MatrixInverseTriL(validate_args=True) @@ -98,7 +109,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): [2., 3.]]], [[[4., 0.], [5., -6.]]]], dtype=np.float32) - x_inv_ = np.linalg.inv(x_) + x_inv_ = self._inv(x_) expected_fldj_ = -4. * np.sum( np.log(np.abs(np.diagonal(x_, axis1=-2, axis2=-1))), axis=-1) diff --git a/tensorflow/contrib/distributions/python/ops/sample_stats.py b/tensorflow/contrib/distributions/python/ops/sample_stats.py index f5aaa5cf34abde3ea4d25de1ecf3adaef3f2a770..aa680a92be64cf0f099acd335369f2a1610c5953 100644 --- a/tensorflow/contrib/distributions/python/ops/sample_stats.py +++ b/tensorflow/contrib/distributions/python/ops/sample_stats.py @@ -134,7 +134,7 @@ def auto_correlation( x_len = util.prefer_static_shape(x_rotated)[-1] # TODO(langmore) Investigate whether this zero padding helps or hurts. At - # the moment is is necessary so that all FFT implementations work. + # the moment is necessary so that all FFT implementations work. # Zero pad to the next power of 2 greater than 2 * x_len, which equals # 2**(ceil(Log_2(2 * x_len))). Note: Log_2(X) = Log_e(X) / Log_e(2). x_len_float64 = math_ops.cast(x_len, np.float64) @@ -198,7 +198,7 @@ def auto_correlation( # Recall R[m] is a sum of N / 2 - m nonzero terms x[n] Conj(x[n - m]). The # other terms were zeros arising only due to zero padding. # `denominator = (N / 2 - m)` (defined below) is the proper term to - # divide by by to make this an unbiased estimate of the expectation + # divide by to make this an unbiased estimate of the expectation # E[X[n] Conj(X[n - m])]. x_len = math_ops.cast(x_len, dtype.real_dtype) max_lags = math_ops.cast(max_lags, dtype.real_dtype) diff --git a/tensorflow/contrib/eager/python/BUILD b/tensorflow/contrib/eager/python/BUILD index 0cc764d2208c5b061b7b836bdf57a035f52c6fcf..f7933639a086483b8dc044837276ce0e76840319 100644 --- a/tensorflow/contrib/eager/python/BUILD +++ b/tensorflow/contrib/eager/python/BUILD @@ -199,7 +199,7 @@ py_library( "//tensorflow/python:training", "//tensorflow/python:variable_scope", "//tensorflow/python/eager:context", - "//tensorflow/python/estimator:util", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -223,3 +223,17 @@ py_test( "//tensorflow/python/eager:test", ], ) + +py_test( + name = "remote_test", + srcs = ["remote_test.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/contrib/eager/python:tfe", + "//tensorflow/python:array_ops", + "//tensorflow/python:client", + "//tensorflow/python:framework", + "//tensorflow/python:math_ops", + "//tensorflow/python/eager:function", + ], +) diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index 16844e0d6885919118adcd3f5a7777eec57b1e9c..135095a97980da8988b976948fb18492526e390c 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -28,7 +28,7 @@ class Iterator(iterator_ops.EagerIterator): """An iterator producing tf.Tensor objects from a tf.data.Dataset. NOTE: Unlike the iterator created by the - @{tf.data.Dataset.make_one_shot_iterator} method, this class enables + `tf.data.Dataset.make_one_shot_iterator` method, this class enables additional experimental functionality, such as prefetching to the GPU. """ diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/cvae.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/cvae.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ca27a85a229d41a85fa26ecdc982da478fe9e202 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/generative_examples/cvae.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0TD5ZrvEMbhZ" + }, + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n", + "\n", + "# Convolutional VAE: An example with tf.keras and eager\n", + "\n", + "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/cvae.ipynb\"\u003e\n", + " \u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e \n", + "\u003c/td\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/generative_examples/cvae.ipynb\"\u003e\u003cimg width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\u003c/td\u003e\u003c/table\u003e" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ITZuApL56Mny" + }, + "source": [ + "![evolution of output during training](https://tensorflow.org/images/autoencoders/cvae.gif)\n", + "\n", + "This notebook demonstrates how to generate images of handwritten digits using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager) by training a Variational Autoencoder. (VAE, [[1]](https://arxiv.org/abs/1312.6114), [[2]](https://arxiv.org/abs/1401.4082)).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "P-JuIu2N_SQf" + }, + "outputs": [], + "source": [ + "# to generate gifs\n", + "!pip install imageio" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "e1_Y75QXJS6h" + }, + "source": [ + "## Import TensorFlow and enable Eager execution" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "YfIk2es3hJEd" + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Import TensorFlow \u003e= 1.9 and enable eager execution\n", + "import tensorflow as tf\n", + "tfe = tf.contrib.eager\n", + "tf.enable_eager_execution()\n", + "\n", + "import os\n", + "import time\n", + "import numpy as np\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "import PIL\n", + "import imageio\n", + "from IPython import display" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iYn4MdZnKCey" + }, + "source": [ + "## Load the MNIST dataset\n", + "Each MNIST image is originally a vector of 784 integers, each of which is between 0-255 and represents the intensity of a pixel. We model each pixel with a Bernoulli distribution in our model, and we statically binarize the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "a4fYMGxGhrna" + }, + "outputs": [], + "source": [ + "(train_images, _), (test_images, _) = tf.keras.datasets.mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "NFC2ghIdiZYE" + }, + "outputs": [], + "source": [ + "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n", + "test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype('float32')\n", + "\n", + "# Normalizing the images to the range of [0., 1.]\n", + "train_images /= 255.\n", + "test_images /= 255.\n", + "\n", + "# Binarization\n", + "train_images[train_images \u003e= .5] = 1.\n", + "train_images[train_images \u003c .5] = 0.\n", + "test_images[test_images \u003e= .5] = 1.\n", + "test_images[test_images \u003c .5] = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "S4PIDhoDLbsZ" + }, + "outputs": [], + "source": [ + "TRAIN_BUF = 60000\n", + "BATCH_SIZE = 100\n", + "\n", + "TEST_BUF = 10000" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PIGN6ouoQxt3" + }, + "source": [ + "## Use *tf.data* to create batches and shuffle the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "-yKCCQOoJ7cn" + }, + "outputs": [], + "source": [ + "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(TRAIN_BUF).batch(BATCH_SIZE)\n", + "test_dataset = tf.data.Dataset.from_tensor_slices(test_images).shuffle(TEST_BUF).batch(BATCH_SIZE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "THY-sZMiQ4UV" + }, + "source": [ + "## Wire up the generative and inference network with *tf.keras.Sequential*\n", + "\n", + "In our VAE example, we use two small ConvNets for the generative and inference network. Since these neural nets are small, we use `tf.keras.Sequential` to simplify our code. Let $x$ and $z$ denote the observation and latent variable respectively in the following descriptions. \n", + "\n", + "### Generative Network\n", + "This defines the generative model which takes a latent encoding as input, and outputs the parameters for a conditional distribution of the observation, i.e. $p(x|z)$. Additionally, we use a unit Gaussian prior $p(z)$ for the latent variable.\n", + "\n", + "### Inference Network\n", + "This defines an approximate posterior distribution $q(z|x)$, which takes as input an observation and outputs a set of parameters for the conditional distribution of the latent representation. In this example, we simply model this distribution as a diagonal Gaussian. In this case, the inference network outputs the mean and log-variance parameters of a factorized Gaussian (log-variance instead of the variance directly is for numerical stability).\n", + "\n", + "### Reparameterization Trick\n", + "During optimization, we can sample from $q(z|x)$ by first sampling from a unit Gaussian, and then multiplying by the standard deviation and adding the mean. This ensures the gradients could pass through the sample to the inference network parameters.\n", + "\n", + "### Network architecture\n", + "For the inference network, we use two convolutional layers followed by a fully-connected layer. In the generative network, we mirror this architecture by using a fully-connected layer followed by three convolution transpose layers (a.k.a. deconvolutional layers in some contexts). Note, it's common practice to avoid using batch normalization when training VAEs, since the additional stochasticity due to using mini-batches may aggravate instability on top of the stochasticity from sampling." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "VGLbvBEmjK0a" + }, + "outputs": [], + "source": [ + "class CVAE(tf.keras.Model):\n", + " def __init__(self, latent_dim):\n", + " super(CVAE, self).__init__()\n", + " self.latent_dim = latent_dim\n", + " self.inference_net = tf.keras.Sequential(\n", + " [\n", + " tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),\n", + " tf.keras.layers.Conv2D(\n", + " filters=32, kernel_size=3, strides=(2, 2), activation=tf.nn.relu),\n", + " tf.keras.layers.Conv2D(\n", + " filters=64, kernel_size=3, strides=(2, 2), activation=tf.nn.relu),\n", + " tf.keras.layers.Flatten(),\n", + " # No activation\n", + " tf.keras.layers.Dense(latent_dim + latent_dim),\n", + " ]\n", + " )\n", + "\n", + " self.generative_net = tf.keras.Sequential(\n", + " [\n", + " tf.keras.layers.InputLayer(input_shape=(latent_dim,)),\n", + " tf.keras.layers.Dense(units=7*7*32, activation=tf.nn.relu),\n", + " tf.keras.layers.Reshape(target_shape=(7, 7, 32)),\n", + " tf.keras.layers.Conv2DTranspose(\n", + " filters=64,\n", + " kernel_size=3,\n", + " strides=(2, 2),\n", + " padding=\"SAME\",\n", + " activation=tf.nn.relu),\n", + " tf.keras.layers.Conv2DTranspose(\n", + " filters=32,\n", + " kernel_size=3,\n", + " strides=(2, 2),\n", + " padding=\"SAME\",\n", + " activation=tf.nn.relu),\n", + " # No activation\n", + " tf.keras.layers.Conv2DTranspose(\n", + " filters=1, kernel_size=3, strides=(1, 1), padding=\"SAME\"),\n", + " ]\n", + " )\n", + "\n", + " def sample(self, eps=None):\n", + " if eps is None:\n", + " eps = tf.random_normal(shape=(100, self.latent_dim))\n", + " return self.decode(eps, apply_sigmoid=True)\n", + "\n", + " def encode(self, x):\n", + " mean, logvar = tf.split(self.inference_net(x), num_or_size_splits=2, axis=1)\n", + " return mean, logvar\n", + "\n", + " def reparameterize(self, mean, logvar):\n", + " eps = tf.random_normal(shape=mean.shape)\n", + " return eps * tf.exp(logvar * .5) + mean\n", + "\n", + " def decode(self, z, apply_sigmoid=False):\n", + " logits = self.generative_net(z)\n", + " if apply_sigmoid:\n", + " probs = tf.sigmoid(logits)\n", + " return probs\n", + "\n", + " return logits" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0FMYgY_mPfTi" + }, + "source": [ + "## Define the loss function and the optimizer\n", + "\n", + "VAEs train by maximizing the evidence lower bound (ELBO) on the marginal log-likelihood:\n", + "\n", + "$$\\log p(x) \\ge \\text{ELBO} = \\mathbb{E}_{q(z|x)}\\left[\\log \\frac{p(x, z)}{q(z|x)}\\right].$$\n", + "\n", + "In practice, we optimize the single sample Monte Carlo estimate of this expectation:\n", + "\n", + "$$\\log p(x| z) + \\log p(z) - \\log q(z|x),$$\n", + "where $z$ is sampled from $q(z|x)$.\n", + "\n", + "**Note**: we could also analytically compute the KL term, but here we incorporate all three terms in the Monte Carlo estimator for simplicity." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "iWCn_PVdEJZ7" + }, + "outputs": [], + "source": [ + "def log_normal_pdf(sample, mean, logvar, raxis=1):\n", + " log2pi = tf.log(2. * np.pi)\n", + " return tf.reduce_sum(\n", + " -.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi),\n", + " axis=raxis)\n", + "\n", + "def compute_loss(model, x):\n", + " mean, logvar = model.encode(x)\n", + " z = model.reparameterize(mean, logvar)\n", + " x_logit = model.decode(z)\n", + "\n", + " cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit, labels=x)\n", + " logpx_z = -tf.reduce_sum(cross_ent, axis=[1, 2, 3])\n", + " logpz = log_normal_pdf(z, 0., 0.)\n", + " logqz_x = log_normal_pdf(z, mean, logvar)\n", + " return -tf.reduce_mean(logpx_z + logpz - logqz_x)\n", + "\n", + "def compute_gradients(model, x):\n", + " with tf.GradientTape() as tape:\n", + " loss = compute_loss(model, x)\n", + " return tape.gradient(loss, model.trainable_variables), loss\n", + "\n", + "optimizer = tf.train.AdamOptimizer(1e-4)\n", + "def apply_gradients(optimizer, gradients, variables, global_step=None):\n", + " optimizer.apply_gradients(zip(gradients, variables), global_step=global_step)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Rw1fkAczTQYh" + }, + "source": [ + "## Training\n", + "\n", + "* We start by iterating over the dataset\n", + "* During each iteration, we pass the image to the encoder to obtain a set of mean and log-variance parameters of the approximate posterior $q(z|x)$\n", + "* We then apply the *reparameterization trick* to sample from $q(z|x)$\n", + "* Finally, we pass the reparameterized samples to the decoder to obtain the logits of the generative distribution $p(x|z)$\n", + "* **Note:** Since we use the dataset loaded by keras with 60k datapoints in the training set and 10k datapoints in the test set, our resulting ELBO on the test set is slightly higher than reported results in the literature which uses dynamic binarization of Larochelle's MNIST.\n", + "\n", + "## Generate Images\n", + "\n", + "* After training, it is time to generate some images\n", + "* We start by sampling a set of latent vectors from the unit Gaussian prior distribution $p(z)$\n", + "* The generator will then convert the latent sample $z$ to logits of the observation, giving a distribution $p(x|z)$\n", + "* Here we plot the probabilities of Bernoulli distributions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "NS2GWywBbAWo" + }, + "outputs": [], + "source": [ + "epochs = 100\n", + "latent_dim = 50\n", + "num_examples_to_generate = 16\n", + "\n", + "# keeping the random vector constant for generation (prediction) so\n", + "# it will be easier to see the improvement.\n", + "random_vector_for_generation = tf.random_normal(\n", + " shape=[num_examples_to_generate, latent_dim])\n", + "model = CVAE(latent_dim)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "RmdVsmvhPxyy" + }, + "outputs": [], + "source": [ + "def generate_and_save_images(model, epoch, test_input):\n", + " predictions = model.sample(test_input)\n", + " fig = plt.figure(figsize=(4,4))\n", + "\n", + " for i in range(predictions.shape[0]):\n", + " plt.subplot(4, 4, i+1)\n", + " plt.imshow(predictions[i, :, :, 0], cmap='gray')\n", + " plt.axis('off')\n", + "\n", + " # tight_layout minimizes the overlap between 2 sub-plots\n", + " plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "2M7LmLtGEMQJ" + }, + "outputs": [], + "source": [ + "generate_and_save_images(model, 0, random_vector_for_generation)\n", + "\n", + "for epoch in range(1, epochs + 1):\n", + " start_time = time.time()\n", + " for train_x in train_dataset:\n", + " gradients, loss = compute_gradients(model, train_x)\n", + " apply_gradients(optimizer, gradients, model.trainable_variables)\n", + " end_time = time.time()\n", + "\n", + " if epoch % 1 == 0:\n", + " loss = tfe.metrics.Mean()\n", + " for test_x in test_dataset.make_one_shot_iterator():\n", + " loss(compute_loss(model, test_x))\n", + " elbo = -loss.result()\n", + " display.clear_output(wait=False)\n", + " print('Epoch: {}, Test set ELBO: {}, '\n", + " 'time elapse for current epoch {}'.format(epoch,\n", + " elbo,\n", + " end_time - start_time))\n", + " generate_and_save_images(\n", + " model, epoch, random_vector_for_generation)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "P4M_vIbUi7c0" + }, + "source": [ + "### Display an image using the epoch number" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "WfO5wCdclHGL" + }, + "outputs": [], + "source": [ + "def display_image(epoch_no):\n", + " return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "5x3q9_Oe5q0A" + }, + "outputs": [], + "source": [ + "display_image(epochs) # Display images" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "NywiH3nL8guF" + }, + "source": [ + "### Generate a GIF of all the saved images." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "IGKQgENQ8lEI" + }, + "outputs": [], + "source": [ + "with imageio.get_writer('cvae.gif', mode='I') as writer:\n", + " filenames = glob.glob('image*.png')\n", + " filenames = sorted(filenames)\n", + " last = -1\n", + " for i,filename in enumerate(filenames):\n", + " frame = 2*(i**0.5)\n", + " if round(frame) \u003e round(last):\n", + " last = frame\n", + " else:\n", + " continue\n", + " image = imageio.imread(filename)\n", + " writer.append_data(image)\n", + " image = imageio.imread(filename)\n", + " writer.append_data(image)\n", + " \n", + "# this is a hack to display the gif inside the notebook\n", + "os.system('cp cvae.gif cvae.gif.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "uV0yiKpzNP1b" + }, + "outputs": [], + "source": [ + "display.Image(filename=\"cvae.gif.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yQXO_dlXkKsT" + }, + "source": [ + "To downlod the animation from Colab uncomment the code below:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "4fSJS3m5HLFM" + }, + "outputs": [], + "source": [ + "#from google.colab import files\n", + "#files.download('cvae.gif')" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "default_view": {}, + "name": "cvae.ipynb", + "private_outputs": true, + "provenance": [ + { + "file_id": "1eb0NOTQapkYs3X0v-zL1x5_LFKgDISnp", + "timestamp": 1527173385672 + } + ], + "toc_visible": true, + "version": "0.3.2", + "views": {} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb index 44ff43a1112e771eb6c91c398286a003e17632e0..975105a179f7d777251306474637dc46ddd4f707 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb @@ -40,12 +40,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "u_2z-B3piVsw" }, @@ -69,12 +64,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "YfIk2es3hJEd" }, @@ -82,7 +72,7 @@ "source": [ "from __future__ import absolute_import, division, print_function\n", "\n", - "# Import TensorFlow \u003e= 1.9 and enable eager execution\n", + "# Import TensorFlow \u003e= 1.10 and enable eager execution\n", "import tensorflow as tf\n", "tf.enable_eager_execution()\n", "\n", @@ -112,12 +102,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "a4fYMGxGhrna" }, @@ -130,12 +115,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "NFC2ghIdiZYE" }, @@ -150,12 +130,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "S4PIDhoDLbsZ" }, @@ -179,12 +154,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "-yKCCQOoJ7cn" }, @@ -217,12 +187,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "VGLbvBEmjK0a" }, @@ -265,12 +230,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "bkOfJxk5j5Hi" }, @@ -299,12 +259,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "gDkA05NE6QMs" }, @@ -318,12 +273,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "k1HpMSLImuRi" }, @@ -360,12 +310,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "wkMNfBWlT-PV" }, @@ -388,12 +333,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "90BIcCKcDMxz" }, @@ -407,12 +347,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "iWCn_PVdEJZ7" }, @@ -422,6 +357,34 @@ "generator_optimizer = tf.train.AdamOptimizer(1e-4)" ] }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mWtinsGDPJlV" + }, + "source": [ + "## Checkpoints (Object-based saving)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CA1w-7s2POEy" + }, + "outputs": [], + "source": [ + "checkpoint_dir = './training_checkpoints'\n", + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n", + "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n", + " discriminator_optimizer=discriminator_optimizer,\n", + " generator=generator,\n", + " discriminator=discriminator)" + ] + }, { "cell_type": "markdown", "metadata": { @@ -449,12 +412,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "NS2GWywBbAWo" }, @@ -462,7 +420,7 @@ "source": [ "EPOCHS = 150\n", "noise_dim = 100\n", - "num_examples_to_generate = 100\n", + "num_examples_to_generate = 16\n", "\n", "# keeping the random vector constant for generation (prediction) so\n", "# it will be easier to see the improvement of the gan.\n", @@ -474,12 +432,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "RmdVsmvhPxyy" }, @@ -490,15 +443,13 @@ " # don't want to train the batchnorm layer when doing inference.\n", " predictions = model(test_input, training=False)\n", "\n", - " fig = plt.figure(figsize=(10,10))\n", + " fig = plt.figure(figsize=(4,4))\n", " \n", " for i in range(predictions.shape[0]):\n", - " plt.subplot(10, 10, i+1)\n", + " plt.subplot(4, 4, i+1)\n", " plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n", " plt.axis('off')\n", " \n", - " # tight_layout minimizes the overlap between 2 sub-plots\n", - " plt.tight_layout()\n", " plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n", " plt.show()" ] @@ -507,12 +458,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "2M7LmLtGEMQJ" }, @@ -542,15 +488,20 @@ " discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.variables))\n", "\n", " \n", - " if epoch % 10 == 0:\n", + " if epoch % 1 == 0:\n", " display.clear_output(wait=True)\n", " generate_and_save_images(generator,\n", " epoch + 1,\n", " random_vector_for_generation)\n", - "\n", + " \n", + " # saving (checkpoint) the model every 15 epochs\n", + " if epoch % 15 == 0:\n", + " checkpoint.save(file_prefix = checkpoint_prefix)\n", + " \n", " print ('Time taken for epoch {} is {} sec'.format(epoch + 1,\n", " time.time()-start))\n", " # generating after the final epoch\n", + " display.clear_output(wait=True)\n", " generate_and_save_images(generator,\n", " epochs,\n", " random_vector_for_generation)" @@ -560,12 +511,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "Ly3UN0SLLY2l" }, @@ -574,6 +520,30 @@ "train(train_dataset, EPOCHS, noise_dim)" ] }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rfM4YcPVPkNO" + }, + "source": [ + "## Restore the latest checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XhXsd0srPo8c" + }, + "outputs": [], + "source": [ + "# restoring the latest checkpoint in checkpoint_dir\n", + "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))" + ] + }, { "cell_type": "markdown", "metadata": { @@ -581,40 +551,28 @@ "id": "P4M_vIbUi7c0" }, "source": [ - "# Display an image using the epoch number" + "## Display an image using the epoch number" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "WfO5wCdclHGL" }, "outputs": [], "source": [ "def display_image(epoch_no):\n", - " plt.figure(figsize=(15,15))\n", - " plt.imshow(np.array(PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))))\n", - " plt.axis('off')" + " return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "5x3q9_Oe5q0A" }, @@ -647,12 +605,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "IGKQgENQ8lEI" }, @@ -661,23 +614,27 @@ "with imageio.get_writer('dcgan.gif', mode='I') as writer:\n", " filenames = glob.glob('image*.png')\n", " filenames = sorted(filenames)\n", - " for filename in filenames:\n", + " last = -1\n", + " for i,filename in enumerate(filenames):\n", + " frame = 2*(i**0.5)\n", + " if round(frame) \u003e round(last):\n", + " last = frame\n", + " else:\n", + " continue\n", " image = imageio.imread(filename)\n", " writer.append_data(image)\n", - " # this is a hack to display the gif inside the notebook\n", - " os.system('mv dcgan.gif dcgan.gif.png')" + " image = imageio.imread(filename)\n", + " writer.append_data(image)\n", + " \n", + "# this is a hack to display the gif inside the notebook\n", + "os.system('cp dcgan.gif dcgan.gif.png')" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "uV0yiKpzNP1b" }, @@ -686,22 +643,28 @@ "display.Image(filename=\"dcgan.gif.png\")" ] }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6EEG-wePkmJQ" + }, + "source": [ + "To downlod the animation from Colab uncomment the code below:" + ] + }, { "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "4UJjSnIMOzOJ" }, "outputs": [], "source": [ - "" + "#from google.colab import files\n", + "#files.download('dcgan.gif')" ] } ], @@ -709,7 +672,6 @@ "accelerator": "GPU", "colab": { "collapsed_sections": [], - "default_view": {}, "name": "dcgan.ipynb", "private_outputs": true, "provenance": [ @@ -719,8 +681,7 @@ } ], "toc_visible": true, - "version": "0.3.2", - "views": {} + "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb index b173f856c641b4d7dca96adda113f904c97a25a7..78a711548ddfe40a24aaa9e0b0f41a238795a950 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb @@ -96,12 +96,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "wZ6LOM12wKGH" }, @@ -124,18 +119,13 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "yG_n40gFzf9s" }, "outputs": [], "source": [ - "# Import TensorFlow \u003e= 1.9 and enable eager execution\n", + "# Import TensorFlow \u003e= 1.10 and enable eager execution\n", "import tensorflow as tf\n", "\n", "# Note: Once you enable eager execution, it cannot be disabled. \n", @@ -165,12 +155,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "pD_55cOxLkAb" }, @@ -194,12 +179,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "-E5JvY3wzf94" }, @@ -224,12 +204,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "IalZLbvOzf-F" }, @@ -247,12 +222,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "1v_qUYfAzf-I" }, @@ -302,12 +272,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "0UHJDA39zf-O" }, @@ -341,12 +306,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "p2pGotuNzf-S" }, @@ -376,12 +336,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "P3KTiiInzf-a" }, @@ -445,12 +400,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "7t2XrzEOzf-e" }, @@ -463,12 +413,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "dkjWIATszf-h" }, @@ -481,6 +426,32 @@ " return tf.losses.sparse_softmax_cross_entropy(labels=real, logits=preds)" ] }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3K6s6F79P7za" + }, + "source": [ + "## Checkpoints (Object-based saving)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oAGisDdfP9rL" + }, + "outputs": [], + "source": [ + "checkpoint_dir = './training_checkpoints'\n", + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n", + "checkpoint = tf.train.Checkpoint(optimizer=optimizer,\n", + " model=model)" + ] + }, { "cell_type": "markdown", "metadata": { @@ -514,12 +485,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "d4tSNwymzf-q" }, @@ -547,17 +513,44 @@ " loss = loss_function(target, predictions)\n", " \n", " grads = tape.gradient(loss, model.variables)\n", - " optimizer.apply_gradients(zip(grads, model.variables), global_step=tf.train.get_or_create_global_step())\n", + " optimizer.apply_gradients(zip(grads, model.variables))\n", "\n", " if batch % 100 == 0:\n", " print ('Epoch {} Batch {} Loss {:.4f}'.format(epoch+1,\n", " batch,\n", " loss))\n", - " \n", + " # saving (checkpoint) the model every 5 epochs\n", + " if epoch % 5 == 0:\n", + " checkpoint.save(file_prefix = checkpoint_prefix)\n", + "\n", " print ('Epoch {} Loss {:.4f}'.format(epoch+1, loss))\n", " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" ] }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "01AR9vpNQMFF" + }, + "source": [ + "## Restore the latest checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tyvpYomYQQkF" + }, + "outputs": [], + "source": [ + "# restoring the latest checkpoint in checkpoint_dir\n", + "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))" + ] + }, { "cell_type": "markdown", "metadata": { @@ -584,12 +577,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "WvuwZBX5Ogfd" }, @@ -651,12 +639,7 @@ "cell_type": "code", "execution_count": 0, "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, + "colab": {}, "colab_type": "code", "id": "gtEd86sX5cB2" }, @@ -670,13 +653,11 @@ "accelerator": "GPU", "colab": { "collapsed_sections": [], - "default_view": {}, "name": "text_generation.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true, - "version": "0.3.2", - "views": {} + "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index 1ab1b71bd0549e06a1d86611c21faef1f182d740..1d07721e3b65ba0f1774d7e7c8d1997608f390c6 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -1,39 +1,11 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "nmt_with_attention.ipynb", - "version": "0.3.2", - "views": {}, - "default_view": {}, - "provenance": [ - { - "file_id": "1C4fpM7_7IL8ZzF7Gc5abywqQjeQNS2-U", - "timestamp": 1527858391290 - }, - { - "file_id": "1pExo6aUuw0S6MISFWoinfJv0Ftm9V4qv", - "timestamp": 1527776041613 - } - ], - "private_outputs": true, - "collapsed_sections": [], - "toc_visible": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "accelerator": "GPU" - }, "cells": [ { + "cell_type": "markdown", "metadata": { - "id": "AOpGoE2T-YXS", - "colab_type": "text" + "colab_type": "text", + "id": "AOpGoE2T-YXS" }, - "cell_type": "markdown", "source": [ "##### Copyright 2018 The TensorFlow Authors.\n", "\n", @@ -41,19 +13,19 @@ "\n", "# Neural Machine Translation with Attention\n", "\n", - "
\n", - "\n", - " Run in Google Colab \n", - "\n", - "View source on GitHub
" + "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb\"\u003e\n", + " \u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e \n", + "\u003c/td\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb\"\u003e\u003cimg width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\u003c/td\u003e\u003c/table\u003e" ] }, { + "cell_type": "markdown", "metadata": { - "id": "CiwtNgENbx2g", - "colab_type": "text" + "colab_type": "text", + "id": "CiwtNgENbx2g" }, - "cell_type": "markdown", "source": [ "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example that assumes some knowledge of sequence to sequence models.\n", "\n", @@ -61,27 +33,24 @@ "\n", "The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:\n", "\n", - "\"spanish-english\n", + "\u003cimg src=\"https://tensorflow.org/images/spanish-english.png\" alt=\"spanish-english attention plot\"\u003e\n", "\n", "Note: This example takes approximately 10 mintues to run on a single P100 GPU." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "tnxXKDjq3jEL", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "tnxXKDjq3jEL" }, - "cell_type": "code", + "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function\n", "\n", - "# Import TensorFlow >= 1.9 and enable eager execution\n", + "# Import TensorFlow \u003e= 1.10 and enable eager execution\n", "import tensorflow as tf\n", "\n", "tf.enable_eager_execution()\n", @@ -96,16 +65,14 @@ "import time\n", "\n", "print(tf.__version__)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "wfodePkj3jEa", - "colab_type": "text" + "colab_type": "text", + "id": "wfodePkj3jEa" }, - "cell_type": "markdown", "source": [ "## Download and prepare the dataset\n", "\n", @@ -124,17 +91,14 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "kRVATYOgJs1b", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "kRVATYOgJs1b" }, - "cell_type": "code", + "outputs": [], "source": [ "# Download the file\n", "path_to_zip = tf.keras.utils.get_file(\n", @@ -142,22 +106,17 @@ " extract=True)\n", "\n", "path_to_file = os.path.dirname(path_to_zip)+\"/spa-eng/spa.txt\"" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "rd0jw-eC3jEh", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "rd0jw-eC3jEh" }, - "cell_type": "code", + "outputs": [], "source": [ "# Converts the unicode file to ascii\n", "def unicode_to_ascii(s):\n", @@ -169,7 +128,7 @@ " w = unicode_to_ascii(w.lower().strip())\n", " \n", " # creating a space between a word and the punctuation following it\n", - " # eg: \"he is a boy.\" => \"he is a boy .\" \n", + " # eg: \"he is a boy.\" =\u003e \"he is a boy .\" \n", " # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n", " w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n", " w = re.sub(r'[\" \"]+', \" \", w)\n", @@ -181,24 +140,19 @@ " \n", " # adding a start and an end token to the sentence\n", " # so that the model know when to start and stop predicting.\n", - " w = ' ' + w + ' '\n", + " w = '\u003cstart\u003e ' + w + ' \u003cend\u003e'\n", " return w" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "OHn4Dct23jEm", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "OHn4Dct23jEm" }, - "cell_type": "code", + "outputs": [], "source": [ "# 1. Remove the accents\n", "# 2. Clean the sentences\n", @@ -209,25 +163,20 @@ " word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n", " \n", " return word_pairs" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "9xbqO7Iie9bb", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "9xbqO7Iie9bb" }, - "cell_type": "code", + "outputs": [], "source": [ - "# This class creates a word -> index mapping (e.g,. \"dad\" -> 5) and vice-versa \n", - "# (e.g., 5 -> \"dad\") for each language,\n", + "# This class creates a word -\u003e index mapping (e.g,. \"dad\" -\u003e 5) and vice-versa \n", + "# (e.g., 5 -\u003e \"dad\") for each language,\n", "class LanguageIndex():\n", " def __init__(self, lang):\n", " self.lang = lang\n", @@ -243,28 +192,23 @@ " \n", " self.vocab = sorted(self.vocab)\n", " \n", - " self.word2idx[''] = 0\n", + " self.word2idx['\u003cpad\u003e'] = 0\n", " for index, word in enumerate(self.vocab):\n", " self.word2idx[word] = index + 1\n", " \n", " for word, index in self.word2idx.items():\n", " self.idx2word[index] = word" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "eAY9k49G3jE_", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "eAY9k49G3jE_" }, - "cell_type": "code", + "outputs": [], "source": [ "def max_length(tensor):\n", " return max(len(t) for t in tensor)\n", @@ -300,86 +244,71 @@ " padding='post')\n", " \n", " return input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "GOi42V79Ydlr", - "colab_type": "text" + "colab_type": "text", + "id": "GOi42V79Ydlr" }, - "cell_type": "markdown", "source": [ "### Limit the size of the dataset to experiment faster (optional)\n", "\n", - "Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):" + "Training on the complete dataset of \u003e100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "cnxC7q-j3jFD", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "cnxC7q-j3jFD" }, - "cell_type": "code", + "outputs": [], "source": [ "# Try experimenting with the size of that dataset\n", "num_examples = 30000\n", "input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_targ = load_dataset(path_to_file, num_examples)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "4QILQkOs3jFG", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "4QILQkOs3jFG" }, - "cell_type": "code", + "outputs": [], "source": [ "# Creating training and validation sets using an 80-20 split\n", "input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\n", "\n", "# Show length\n", "len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "rgCLkfv5uO3d", - "colab_type": "text" + "colab_type": "text", + "id": "rgCLkfv5uO3d" }, - "cell_type": "markdown", "source": [ "### Create a tf.data dataset" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "TqHsArVZ3jFS", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "TqHsArVZ3jFS" }, - "cell_type": "code", + "outputs": [], "source": [ "BUFFER_SIZE = len(input_tensor_train)\n", "BATCH_SIZE = 64\n", @@ -391,29 +320,27 @@ "\n", "dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\n", "dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "TNfHIF71ulLu", - "colab_type": "text" + "colab_type": "text", + "id": "TNfHIF71ulLu" }, - "cell_type": "markdown", "source": [ "## Write the encoder and decoder model\n", "\n", "Here, we'll implement an encoder-decoder model with attention which you can read about in the TensorFlow [Neural Machine Translation (seq2seq) tutorial](https://www.tensorflow.org/tutorials/seq2seq). This example uses a more recent set of APIs. This notebook implements the [attention equations](https://www.tensorflow.org/tutorials/seq2seq#background_on_the_attention_mechanism) from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.\n", "\n", - "\"attention\n", + "\u003cimg src=\"https://www.tensorflow.org/images/seq2seq/attention_mechanism.jpg\" width=\"500\" alt=\"attention mechanism\"\u003e\n", "\n", "The input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*. \n", "\n", "Here are the equations that are implemented:\n", "\n", - "\"attention\n", - "\"attention\n", + "\u003cimg src=\"https://www.tensorflow.org/images/seq2seq/attention_equation_0.jpg\" alt=\"attention equation 0\" width=\"800\"\u003e\n", + "\u003cimg src=\"https://www.tensorflow.org/images/seq2seq/attention_equation_1.jpg\" alt=\"attention equation 1\" width=\"800\"\u003e\n", "\n", "We're using *Bahdanau attention*. Lets decide on notation before writing the simplified form:\n", "\n", @@ -435,17 +362,14 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "avyJ_4VIUoHb", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "avyJ_4VIUoHb" }, - "cell_type": "code", + "outputs": [], "source": [ "def gru(units):\n", " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", @@ -461,22 +385,17 @@ " return_state=True, \n", " recurrent_activation='sigmoid', \n", " recurrent_initializer='glorot_uniform')" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "nZ2rI24i3jFg", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "nZ2rI24i3jFg" }, - "cell_type": "code", + "outputs": [], "source": [ "class Encoder(tf.keras.Model):\n", " def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\n", @@ -493,22 +412,17 @@ " \n", " def initialize_hidden_state(self):\n", " return tf.zeros((self.batch_sz, self.enc_units))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "yJ_B3mhW3jFk", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "yJ_B3mhW3jFk" }, - "cell_type": "code", + "outputs": [], "source": [ "class Decoder(tf.keras.Model):\n", " def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\n", @@ -562,51 +476,41 @@ " \n", " def initialize_hidden_state(self):\n", " return tf.zeros((self.batch_sz, self.dec_units))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "P5UY8wko3jFp", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "P5UY8wko3jFp" }, - "cell_type": "code", + "outputs": [], "source": [ "encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)\n", "decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "_ch_71VbIRfK", - "colab_type": "text" + "colab_type": "text", + "id": "_ch_71VbIRfK" }, - "cell_type": "markdown", "source": [ "## Define the optimizer and the loss function" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "WmTHr5iV3jFr", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "WmTHr5iV3jFr" }, - "cell_type": "code", + "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer()\n", "\n", @@ -615,16 +519,41 @@ " mask = 1 - np.equal(real, 0)\n", " loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n", " return tf.reduce_mean(loss_)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "hpObfY22IddU", - "colab_type": "text" + "colab_type": "text", + "id": "DMVWzzsfNl4e" }, + "source": [ + "## Checkpoints (Object-based saving)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Zj8bXQTgNwrF" + }, + "outputs": [], + "source": [ + "checkpoint_dir = './training_checkpoints'\n", + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n", + "checkpoint = tf.train.Checkpoint(optimizer=optimizer,\n", + " encoder=encoder,\n", + " decoder=decoder)" + ] + }, + { "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hpObfY22IddU" + }, "source": [ "## Training\n", "\n", @@ -638,17 +567,14 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "ddefjBMa3jF0", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "ddefjBMa3jF0" }, - "cell_type": "code", + "outputs": [], "source": [ "EPOCHS = 10\n", "\n", @@ -666,7 +592,7 @@ " \n", " dec_hidden = enc_hidden\n", " \n", - " dec_input = tf.expand_dims([targ_lang.word2idx['']] * BATCH_SIZE, 1) \n", + " dec_input = tf.expand_dims([targ_lang.word2idx['\u003cstart\u003e']] * BATCH_SIZE, 1) \n", " \n", " # Teacher forcing - feeding the target as the next input\n", " for t in range(1, targ.shape[1]):\n", @@ -686,26 +612,27 @@ " \n", " gradients = tape.gradient(loss, variables)\n", " \n", - " optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())\n", + " optimizer.apply_gradients(zip(gradients, variables))\n", " \n", " if batch % 100 == 0:\n", " print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,\n", " batch,\n", " batch_loss.numpy()))\n", + " # saving (checkpoint) the model every 2 epochs\n", + " if epoch % 2 == 0:\n", + " checkpoint.save(file_prefix = checkpoint_prefix)\n", " \n", " print('Epoch {} Loss {:.4f}'.format(epoch + 1,\n", " total_loss / N_BATCH))\n", " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "mU3Ce8M6I3rz", - "colab_type": "text" + "colab_type": "text", + "id": "mU3Ce8M6I3rz" }, - "cell_type": "markdown", "source": [ "## Translate\n", "\n", @@ -717,17 +644,14 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "EbQpyYs13jF_", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "EbQpyYs13jF_" }, - "cell_type": "code", + "outputs": [], "source": [ "def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", " attention_plot = np.zeros((max_length_targ, max_length_inp))\n", @@ -744,7 +668,7 @@ " enc_out, enc_hidden = encoder(inputs, hidden)\n", "\n", " dec_hidden = enc_hidden\n", - " dec_input = tf.expand_dims([targ_lang.word2idx['']], 0)\n", + " dec_input = tf.expand_dims([targ_lang.word2idx['\u003cstart\u003e']], 0)\n", "\n", " for t in range(max_length_targ):\n", " predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)\n", @@ -757,29 +681,24 @@ "\n", " result += targ_lang.idx2word[predicted_id] + ' '\n", "\n", - " if targ_lang.idx2word[predicted_id] == '':\n", + " if targ_lang.idx2word[predicted_id] == '\u003cend\u003e':\n", " return result, sentence, attention_plot\n", " \n", " # the predicted ID is fed back into the model\n", " dec_input = tf.expand_dims([predicted_id], 0)\n", "\n", " return result, sentence, attention_plot" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "s5hQWlbN3jGF", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "s5hQWlbN3jGF" }, - "cell_type": "code", + "outputs": [], "source": [ "# function for plotting the attention weights\n", "def plot_attention(attention, sentence, predicted_sentence):\n", @@ -793,22 +712,17 @@ " ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n", "\n", " plt.show()" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "sl9zUHzg3jGI", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "sl9zUHzg3jGI" }, - "cell_type": "code", + "outputs": [], "source": [ "def translate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", " result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)\n", @@ -818,89 +732,91 @@ " \n", " attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\n", " plot_attention(attention_plot, sentence.split(' '), result.split(' '))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "WrAM0FDomq3E", + "colab_type": "text", + "id": "n250XbnjOaqP" + }, + "source": [ + "## Restore the latest checkpoint and test" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "UJpT9D5_OgP6" }, + "outputs": [], + "source": [ + "# restoring the latest checkpoint in checkpoint_dir\n", + "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))" + ] + }, + { "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "WrAM0FDomq3E" + }, + "outputs": [], "source": [ "translate('hace mucho frio aqui.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "zSx2iM36EZQZ", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "zSx2iM36EZQZ" }, - "cell_type": "code", + "outputs": [], "source": [ "translate('esta es mi vida.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "A3LLCx3ZE0Ls", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "A3LLCx3ZE0Ls" }, - "cell_type": "code", + "outputs": [], "source": [ "translate('¿todavia estan en casa?', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "DUQVLVqUE1YW", + "colab": {}, "colab_type": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - } + "id": "DUQVLVqUE1YW" }, - "cell_type": "code", + "outputs": [], "source": [ "# wrong translation\n", "translate('trata de averiguarlo.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "RTe5P5ioMJwN", - "colab_type": "text" + "colab_type": "text", + "id": "RTe5P5ioMJwN" }, - "cell_type": "markdown", "source": [ "## Next steps\n", "\n", @@ -908,5 +824,31 @@ "* Experiment with training on a larger dataset, or using more epochs\n" ] } - ] + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "nmt_with_attention.ipynb", + "private_outputs": true, + "provenance": [ + { + "file_id": "1C4fpM7_7IL8ZzF7Gc5abywqQjeQNS2-U", + "timestamp": 1527858391290 + }, + { + "file_id": "1pExo6aUuw0S6MISFWoinfJv0Ftm9V4qv", + "timestamp": 1527776041613 + } + ], + "toc_visible": true, + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/tensorflow/contrib/eager/python/examples/pix2pix/pix2pix_eager.ipynb b/tensorflow/contrib/eager/python/examples/pix2pix/pix2pix_eager.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..acc0f5b6531c39861cd38b4c0cc7683a4c26c6ff --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/pix2pix/pix2pix_eager.ipynb @@ -0,0 +1,810 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0TD5ZrvEMbhZ" + }, + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n", + "\n", + "# Pix2Pix: An example with tf.keras and eager\n", + "\n", + "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/pix2pix/pix2pix_eager.ipynb\"\u003e\n", + " \u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e \n", + "\u003c/td\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/pix2pix/pix2pix_eager.ipynb\"\u003e\u003cimg width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\u003c/td\u003e\u003c/table\u003e" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ITZuApL56Mny" + }, + "source": [ + "This notebook demonstrates image to image translation using conditional GAN's, as described in [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/abs/1611.07004). Using this technique we can colorize black and white photos, convert google maps to google earth, etc. Here, we convert building facades to real buildings. We use [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager) to achieve this.\n", + "\n", + "In example, we will use the [CMP Facade Database](http://cmp.felk.cvut.cz/~tylecr1/facade/), helpfully provided by the [Center for Machine Perception](http://cmp.felk.cvut.cz/) at the [Czech Technical University in Prague](https://www.cvut.cz/). To keep our example short, we will use a preprocessed [copy](https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/) of this dataset, created by the authors of the [paper](https://arxiv.org/abs/1611.07004) above.\n", + "\n", + "Each epoch takes around 58 seconds on a single P100 GPU.\n", + "\n", + "Below is the output generated after training the model for 200 epochs.\n", + "\n", + "\n", + "![sample output_1](https://www.tensorflow.org/images/gan/pix2pix_1.png)\n", + "![sample output_2](https://www.tensorflow.org/images/gan/pix2pix_2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "e1_Y75QXJS6h" + }, + "source": [ + "## Import TensorFlow and enable eager execution" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "YfIk2es3hJEd" + }, + "outputs": [], + "source": [ + "# Import TensorFlow \u003e= 1.10 and enable eager execution\n", + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "import os\n", + "import time\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import PIL\n", + "from IPython.display import clear_output" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "iYn4MdZnKCey" + }, + "source": [ + "## Load the dataset\n", + "\n", + "You can download this dataset and similar datasets from [here](https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets). As mentioned in the [paper](https://arxiv.org/abs/1611.07004) we apply random jittering and mirroring to the training dataset.\n", + "* In random jittering, the image is resized to `286 x 286` and then randomly cropped to `256 x 256`\n", + "* In random mirroring, the image is randomly flipped horizontally i.e left to right." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Kn-k8kTXuAlv" + }, + "outputs": [], + "source": [ + "path_to_zip = tf.keras.utils.get_file('facades.tar.gz',\n", + " cache_subdir=os.path.abspath('.'),\n", + " origin='https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/facades.tar.gz', \n", + " extract=True)\n", + "\n", + "PATH = os.path.join(os.path.dirname(path_to_zip), 'facades/')" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "2CbTEt448b4R" + }, + "outputs": [], + "source": [ + "BUFFER_SIZE = 400\n", + "BATCH_SIZE = 1\n", + "IMG_WIDTH = 256\n", + "IMG_HEIGHT = 256" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tyaP4hLJ8b4W" + }, + "outputs": [], + "source": [ + "def load_image(image_file, is_train):\n", + " image = tf.read_file(image_file)\n", + " image = tf.image.decode_jpeg(image)\n", + "\n", + " w = tf.shape(image)[1]\n", + "\n", + " w = w // 2\n", + " real_image = image[:, :w, :]\n", + " input_image = image[:, w:, :]\n", + "\n", + " input_image = tf.cast(input_image, tf.float32)\n", + " real_image = tf.cast(real_image, tf.float32)\n", + "\n", + " if is_train:\n", + " # random jittering\n", + " \n", + " # resizing to 286 x 286 x 3\n", + " # method = 2 indicates using \"ResizeMethod.NEAREST_NEIGHBOR\"\n", + " input_image = tf.image.resize_images(input_image, [286, 286], \n", + " align_corners=True, method=2)\n", + " real_image = tf.image.resize_images(real_image, [286, 286], \n", + " align_corners=True, method=2)\n", + " \n", + " # randomly cropping to 256 x 256 x 3\n", + " stacked_image = tf.stack([input_image, real_image], axis=0)\n", + " cropped_image = tf.random_crop(stacked_image, size=[2, IMG_HEIGHT, IMG_WIDTH, 3])\n", + " input_image, real_image = cropped_image[0], cropped_image[1]\n", + "\n", + " if np.random.random() \u003e 0.5:\n", + " # random mirroring\n", + " input_image = tf.image.flip_left_right(input_image)\n", + " real_image = tf.image.flip_left_right(real_image)\n", + " else:\n", + " input_image = tf.image.resize_images(input_image, size=[IMG_HEIGHT, IMG_WIDTH], \n", + " align_corners=True, method=2)\n", + " real_image = tf.image.resize_images(real_image, size=[IMG_HEIGHT, IMG_WIDTH], \n", + " align_corners=True, method=2)\n", + " \n", + " # normalizing the images to [-1, 1]\n", + " input_image = (input_image / 127.5) - 1\n", + " real_image = (real_image / 127.5) - 1\n", + "\n", + " return input_image, real_image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PIGN6ouoQxt3" + }, + "source": [ + "## Use tf.data to create batches, map(do preprocessing) and shuffle the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "SQHmYSmk8b4b" + }, + "outputs": [], + "source": [ + "train_dataset = tf.data.Dataset.list_files(PATH+'train/*.jpg')\n", + "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n", + "train_dataset = train_dataset.map(lambda x: load_image(x, True))\n", + "train_dataset = train_dataset.batch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MS9J0yA58b4g" + }, + "outputs": [], + "source": [ + "test_dataset = tf.data.Dataset.list_files(PATH+'test/*.jpg')\n", + "test_dataset = test_dataset.map(lambda x: load_image(x, False))\n", + "test_dataset = test_dataset.batch(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "THY-sZMiQ4UV" + }, + "source": [ + "## Write the generator and discriminator models\n", + "\n", + "* **Generator** \n", + " * The architecture of generator is a modified U-Net.\n", + " * Each block in the encoder is (Conv -\u003e Batchnorm -\u003e Leaky ReLU)\n", + " * Each block in the decoder is (Transposed Conv -\u003e Batchnorm -\u003e Dropout(applied to the first 3 blocks) -\u003e ReLU)\n", + " * There are skip connections between the encoder and decoder (as in U-Net).\n", + " \n", + "* **Discriminator**\n", + " * The Discriminator is a PatchGAN.\n", + " * Each block in the discriminator is (Conv -\u003e BatchNorm -\u003e Leaky ReLU)\n", + " * The shape of the output after the last layer is (batch_size, 30, 30, 1)\n", + " * Each 30x30 patch of the output classifies a 70x70 portion of the input image (such an architecture is called a PatchGAN).\n", + " * Discriminator receives 2 inputs.\n", + " * Input image and the target image, which it should classify as real.\n", + " * Input image and the generated image (output of generator), which it should classify as fake. \n", + " * We concatenate these 2 inputs together in the code (`tf.concat([inp, tar], axis=-1)`)\n", + "\n", + "* Shape of the input travelling through the generator and the discriminator is in the comments in the code.\n", + "\n", + "To learn more about the architecture and the hyperparameters you can refer the [paper](https://arxiv.org/abs/1611.07004).\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tqqvWxlw8b4l" + }, + "outputs": [], + "source": [ + "OUTPUT_CHANNELS = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "lFPI4Nu-8b4q" + }, + "outputs": [], + "source": [ + "class Downsample(tf.keras.Model):\n", + " \n", + " def __init__(self, filters, size, apply_batchnorm=True):\n", + " super(Downsample, self).__init__()\n", + " self.apply_batchnorm = apply_batchnorm\n", + " initializer = tf.random_normal_initializer(0., 0.02)\n", + "\n", + " self.conv1 = tf.keras.layers.Conv2D(filters, \n", + " (size, size), \n", + " strides=2, \n", + " padding='same',\n", + " kernel_initializer=initializer,\n", + " use_bias=False)\n", + " if self.apply_batchnorm:\n", + " self.batchnorm = tf.keras.layers.BatchNormalization()\n", + " \n", + " def call(self, x, training):\n", + " x = self.conv1(x)\n", + " if self.apply_batchnorm:\n", + " x = self.batchnorm(x, training=training)\n", + " x = tf.nn.leaky_relu(x)\n", + " return x \n", + "\n", + "\n", + "class Upsample(tf.keras.Model):\n", + " \n", + " def __init__(self, filters, size, apply_dropout=False):\n", + " super(Upsample, self).__init__()\n", + " self.apply_dropout = apply_dropout\n", + " initializer = tf.random_normal_initializer(0., 0.02)\n", + "\n", + " self.up_conv = tf.keras.layers.Conv2DTranspose(filters, \n", + " (size, size), \n", + " strides=2, \n", + " padding='same',\n", + " kernel_initializer=initializer,\n", + " use_bias=False)\n", + " self.batchnorm = tf.keras.layers.BatchNormalization()\n", + " if self.apply_dropout:\n", + " self.dropout = tf.keras.layers.Dropout(0.5)\n", + "\n", + " def call(self, x1, x2, training):\n", + " x = self.up_conv(x1)\n", + " x = self.batchnorm(x, training=training)\n", + " if self.apply_dropout:\n", + " x = self.dropout(x, training=training)\n", + " x = tf.nn.relu(x)\n", + " x = tf.concat([x, x2], axis=-1)\n", + " return x\n", + "\n", + "\n", + "class Generator(tf.keras.Model):\n", + " \n", + " def __init__(self):\n", + " super(Generator, self).__init__()\n", + " initializer = tf.random_normal_initializer(0., 0.02)\n", + " \n", + " self.down1 = Downsample(64, 4, apply_batchnorm=False)\n", + " self.down2 = Downsample(128, 4)\n", + " self.down3 = Downsample(256, 4)\n", + " self.down4 = Downsample(512, 4)\n", + " self.down5 = Downsample(512, 4)\n", + " self.down6 = Downsample(512, 4)\n", + " self.down7 = Downsample(512, 4)\n", + " self.down8 = Downsample(512, 4)\n", + "\n", + " self.up1 = Upsample(512, 4, apply_dropout=True)\n", + " self.up2 = Upsample(512, 4, apply_dropout=True)\n", + " self.up3 = Upsample(512, 4, apply_dropout=True)\n", + " self.up4 = Upsample(512, 4)\n", + " self.up5 = Upsample(256, 4)\n", + " self.up6 = Upsample(128, 4)\n", + " self.up7 = Upsample(64, 4)\n", + "\n", + " self.last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, \n", + " (4, 4), \n", + " strides=2, \n", + " padding='same',\n", + " kernel_initializer=initializer)\n", + " \n", + " @tf.contrib.eager.defun\n", + " def call(self, x, training):\n", + " # x shape == (bs, 256, 256, 3) \n", + " x1 = self.down1(x, training=training) # (bs, 128, 128, 64)\n", + " x2 = self.down2(x1, training=training) # (bs, 64, 64, 128)\n", + " x3 = self.down3(x2, training=training) # (bs, 32, 32, 256)\n", + " x4 = self.down4(x3, training=training) # (bs, 16, 16, 512)\n", + " x5 = self.down5(x4, training=training) # (bs, 8, 8, 512)\n", + " x6 = self.down6(x5, training=training) # (bs, 4, 4, 512)\n", + " x7 = self.down7(x6, training=training) # (bs, 2, 2, 512)\n", + " x8 = self.down8(x7, training=training) # (bs, 1, 1, 512)\n", + "\n", + " x9 = self.up1(x8, x7, training=training) # (bs, 2, 2, 1024)\n", + " x10 = self.up2(x9, x6, training=training) # (bs, 4, 4, 1024)\n", + " x11 = self.up3(x10, x5, training=training) # (bs, 8, 8, 1024)\n", + " x12 = self.up4(x11, x4, training=training) # (bs, 16, 16, 1024)\n", + " x13 = self.up5(x12, x3, training=training) # (bs, 32, 32, 512)\n", + " x14 = self.up6(x13, x2, training=training) # (bs, 64, 64, 256)\n", + " x15 = self.up7(x14, x1, training=training) # (bs, 128, 128, 128)\n", + "\n", + " x16 = self.last(x15) # (bs, 256, 256, 3)\n", + " x16 = tf.nn.tanh(x16)\n", + "\n", + " return x16" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ll6aNeQx8b4v" + }, + "outputs": [], + "source": [ + "class DiscDownsample(tf.keras.Model):\n", + " \n", + " def __init__(self, filters, size, apply_batchnorm=True):\n", + " super(DiscDownsample, self).__init__()\n", + " self.apply_batchnorm = apply_batchnorm\n", + " initializer = tf.random_normal_initializer(0., 0.02)\n", + "\n", + " self.conv1 = tf.keras.layers.Conv2D(filters, \n", + " (size, size), \n", + " strides=2, \n", + " padding='same',\n", + " kernel_initializer=initializer,\n", + " use_bias=False)\n", + " if self.apply_batchnorm:\n", + " self.batchnorm = tf.keras.layers.BatchNormalization()\n", + " \n", + " def call(self, x, training):\n", + " x = self.conv1(x)\n", + " if self.apply_batchnorm:\n", + " x = self.batchnorm(x, training=training)\n", + " x = tf.nn.leaky_relu(x)\n", + " return x \n", + "\n", + "class Discriminator(tf.keras.Model):\n", + " \n", + " def __init__(self):\n", + " super(Discriminator, self).__init__()\n", + " initializer = tf.random_normal_initializer(0., 0.02)\n", + " \n", + " self.down1 = DiscDownsample(64, 4, False)\n", + " self.down2 = DiscDownsample(128, 4)\n", + " self.down3 = DiscDownsample(256, 4)\n", + " \n", + " # we are zero padding here with 1 because we need our shape to \n", + " # go from (batch_size, 32, 32, 256) to (batch_size, 31, 31, 512)\n", + " self.zero_pad1 = tf.keras.layers.ZeroPadding2D()\n", + " self.conv = tf.keras.layers.Conv2D(512, \n", + " (4, 4), \n", + " strides=1, \n", + " kernel_initializer=initializer, \n", + " use_bias=False)\n", + " self.batchnorm1 = tf.keras.layers.BatchNormalization()\n", + " \n", + " # shape change from (batch_size, 31, 31, 512) to (batch_size, 30, 30, 1)\n", + " self.zero_pad2 = tf.keras.layers.ZeroPadding2D()\n", + " self.last = tf.keras.layers.Conv2D(1, \n", + " (4, 4), \n", + " strides=1,\n", + " kernel_initializer=initializer)\n", + " \n", + " @tf.contrib.eager.defun\n", + " def call(self, inp, tar, training):\n", + " # concatenating the input and the target\n", + " x = tf.concat([inp, tar], axis=-1) # (bs, 256, 256, channels*2)\n", + " x = self.down1(x, training=training) # (bs, 128, 128, 64)\n", + " x = self.down2(x, training=training) # (bs, 64, 64, 128)\n", + " x = self.down3(x, training=training) # (bs, 32, 32, 256)\n", + "\n", + " x = self.zero_pad1(x) # (bs, 34, 34, 256)\n", + " x = self.conv(x) # (bs, 31, 31, 512)\n", + " x = self.batchnorm1(x, training=training)\n", + " x = tf.nn.leaky_relu(x)\n", + " \n", + " x = self.zero_pad2(x) # (bs, 33, 33, 512)\n", + " # don't add a sigmoid activation here since\n", + " # the loss function expects raw logits.\n", + " x = self.last(x) # (bs, 30, 30, 1)\n", + "\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "gDkA05NE6QMs" + }, + "outputs": [], + "source": [ + "# The call function of Generator and Discriminator have been decorated\n", + "# with tf.contrib.eager.defun()\n", + "# We get a performance speedup if defun is used (~25 seconds per epoch)\n", + "generator = Generator()\n", + "discriminator = Discriminator()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0FMYgY_mPfTi" + }, + "source": [ + "## Define the loss functions and the optimizer\n", + "\n", + "* **Discriminator loss**\n", + " * The discriminator loss function takes 2 inputs; **real images, generated images**\n", + " * real_loss is a sigmoid cross entropy loss of the **real images** and an **array of ones(since these are the real images)**\n", + " * generated_loss is a sigmoid cross entropy loss of the **generated images** and an **array of zeros(since these are the fake images)**\n", + " * Then the total_loss is the sum of real_loss and the generated_loss\n", + " \n", + "* **Generator loss**\n", + " * It is a sigmoid cross entropy loss of the generated images and an **array of ones**.\n", + " * The [paper](https://arxiv.org/abs/1611.07004) also includes L1 loss which is MAE (mean absolute error) between the generated image and the target image.\n", + " * This allows the generated image to become structurally similar to the target image.\n", + " * The formula to calculate the total generator loss = gan_loss + LAMBDA * l1_loss, where LAMBDA = 100. This value was decided by the authors of the [paper](https://arxiv.org/abs/1611.07004)." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "cyhxTuvJyIHV" + }, + "outputs": [], + "source": [ + "LAMBDA = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "wkMNfBWlT-PV" + }, + "outputs": [], + "source": [ + "def discriminator_loss(disc_real_output, disc_generated_output):\n", + " real_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels = tf.ones_like(disc_real_output), \n", + " logits = disc_real_output)\n", + " generated_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels = tf.zeros_like(disc_generated_output), \n", + " logits = disc_generated_output)\n", + "\n", + " total_disc_loss = real_loss + generated_loss\n", + "\n", + " return total_disc_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "90BIcCKcDMxz" + }, + "outputs": [], + "source": [ + "def generator_loss(disc_generated_output, gen_output, target):\n", + " gan_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels = tf.ones_like(disc_generated_output),\n", + " logits = disc_generated_output) \n", + " # mean absolute error\n", + " l1_loss = tf.reduce_mean(tf.abs(target - gen_output))\n", + "\n", + " total_gen_loss = gan_loss + (LAMBDA * l1_loss)\n", + "\n", + " return total_gen_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "iWCn_PVdEJZ7" + }, + "outputs": [], + "source": [ + "generator_optimizer = tf.train.AdamOptimizer(2e-4, beta1=0.5)\n", + "discriminator_optimizer = tf.train.AdamOptimizer(2e-4, beta1=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aKUZnDiqQrAh" + }, + "source": [ + "## Checkpoints (Object-based saving)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "WJnftd5sQsv6" + }, + "outputs": [], + "source": [ + "checkpoint_dir = './training_checkpoints'\n", + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n", + "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n", + " discriminator_optimizer=discriminator_optimizer,\n", + " generator=generator,\n", + " discriminator=discriminator)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Rw1fkAczTQYh" + }, + "source": [ + "## Training\n", + "\n", + "* We start by iterating over the dataset\n", + "* The generator gets the input image and we get a generated output.\n", + "* The discriminator receives the input_image and the generated image as the first input. The second input is the input_image and the target_image.\n", + "* Next, we calculate the generator and the discriminator loss.\n", + "* Then, we calculate the gradients of loss with respect to both the generator and the discriminator variables(inputs) and apply those to the optimizer.\n", + "\n", + "## Generate Images\n", + "\n", + "* After training, its time to generate some images!\n", + "* We pass images from the test dataset to the generator.\n", + "* The generator will then translate the input image into the output we expect.\n", + "* Last step is to plot the predictions and **voila!**" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "NS2GWywBbAWo" + }, + "outputs": [], + "source": [ + "EPOCHS = 200" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RmdVsmvhPxyy" + }, + "outputs": [], + "source": [ + "def generate_images(model, test_input, tar):\n", + " # the training=True is intentional here since\n", + " # we want the batch statistics while running the model\n", + " # on the test dataset. If we use training=False, we will get \n", + " # the accumulated statistics learned from the training dataset\n", + " # (which we don't want)\n", + " prediction = model(test_input, training=True)\n", + " plt.figure(figsize=(15,15))\n", + "\n", + " display_list = [test_input[0], tar[0], prediction[0]]\n", + " title = ['Input Image', 'Ground Truth', 'Predicted Image']\n", + "\n", + " for i in range(3):\n", + " plt.subplot(1, 3, i+1)\n", + " plt.title(title[i])\n", + " # getting the pixel values between [0, 1] to plot it.\n", + " plt.imshow(display_list[i] * 0.5 + 0.5)\n", + " plt.axis('off')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "2M7LmLtGEMQJ" + }, + "outputs": [], + "source": [ + "def train(dataset, epochs): \n", + " for epoch in range(epochs):\n", + " start = time.time()\n", + "\n", + " for input_image, target in dataset:\n", + "\n", + " with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n", + " gen_output = generator(input_image, training=True)\n", + "\n", + " disc_real_output = discriminator(input_image, target, training=True)\n", + " disc_generated_output = discriminator(input_image, gen_output, training=True)\n", + "\n", + " gen_loss = generator_loss(disc_generated_output, gen_output, target)\n", + " disc_loss = discriminator_loss(disc_real_output, disc_generated_output)\n", + "\n", + " generator_gradients = gen_tape.gradient(gen_loss, \n", + " generator.variables)\n", + " discriminator_gradients = disc_tape.gradient(disc_loss, \n", + " discriminator.variables)\n", + "\n", + " generator_optimizer.apply_gradients(zip(generator_gradients, \n", + " generator.variables))\n", + " discriminator_optimizer.apply_gradients(zip(discriminator_gradients, \n", + " discriminator.variables))\n", + "\n", + " if epoch % 1 == 0:\n", + " clear_output(wait=True)\n", + " for inp, tar in test_dataset.take(1):\n", + " generate_images(generator, inp, tar)\n", + " \n", + " # saving (checkpoint) the model every 20 epochs\n", + " if epoch % 20 == 0:\n", + " checkpoint.save(file_prefix = checkpoint_prefix)\n", + "\n", + " print ('Time taken for epoch {} is {} sec\\n'.format(epoch + 1,\n", + " time.time()-start))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "a1zZmKmvOH85" + }, + "outputs": [], + "source": [ + "train(train_dataset, EPOCHS)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kz80bY3aQ1VZ" + }, + "source": [ + "## Restore the latest checkpoint and test" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4t4x69adQ5xb" + }, + "outputs": [], + "source": [ + "# restoring the latest checkpoint in checkpoint_dir\n", + "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1RGysMU_BZhx" + }, + "source": [ + "## Testing on the entire test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "KUgSnmy2nqSP" + }, + "outputs": [], + "source": [ + "# Run the trained model on the entire test dataset\n", + "for inp, tar in test_dataset:\n", + " generate_images(generator, inp, tar)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "3AJXOByaZVOf" + }, + "outputs": [], + "source": [ + "" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "pix2pix_eager.ipynb", + "private_outputs": true, + "provenance": [ + { + "file_id": "1eb0NOTQapkYs3X0v-zL1x5_LFKgDISnp", + "timestamp": 1527173385672 + } + ], + "toc_visible": true, + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow/contrib/eager/python/examples/revnet/README.md b/tensorflow/contrib/eager/python/examples/revnet/README.md index 2875d0ffb330c2593a7f293f417a5d1ce8322624..822d86e9c7a7e620da3b84ded9af98b1c1d4b701 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/README.md +++ b/tensorflow/contrib/eager/python/examples/revnet/README.md @@ -1,6 +1,6 @@ # RevNet with TensorFlow eager execution -This folder contains a TensorFlow eager implementation of the [Reversible Residual Network](https://arxiv.org/pdf/1707.04585.pdf) adapted from the released implementation by the authors. The presented implementation can be ran both in eager and graph mode. The code is considerably simplified with `tf.GradientTape`. Moreover, we reduce the step of reconstructing the outputs. This saves us from using `tf.stop_gradient` and makes the model run faster. +This folder contains a TensorFlow eager implementation of the [Reversible Residual Network](https://arxiv.org/pdf/1707.04585.pdf) adapted from the released implementation by the authors. The presented implementation can be ran with both eager and graph execution. The code is considerably simplified with `tf.GradientTape`. Moreover, we reduce the a redundant forward pass in the implementation by the authors. This saves us from using `tf.stop_gradient` and makes the model run faster. ## Content @@ -16,7 +16,7 @@ This folder contains a TensorFlow eager implementation of the [Reversible Residu - `resnet_preprocessing.py`, `imagenet_input.py`: Boilerplate to read ImageNet data from TFRecords. ## Train on CIFAR-10/CIFAR-100 -- Make sure you have installed TensorFlow 1.9+ or the latest `tf-nightly` +- Make sure you have installed TensorFlow 1.10+ or the latest `tf-nightly` or `tf-nightly-gpu` pip package in order to access the eager execution feature. - First run @@ -41,11 +41,13 @@ python main.py --data_dir ${PWD}/cifar - `config`: RevNet configuration. - `use_defun`: Use `tfe.defun` to boost performance. -- To train a model with estimators in graph-mode, run +- To train a model with estimators in graph execution, run ```bash python main_estimator.py --data_dir ${PWD}/cifar ``` +To ensure our code works properly when using the Keras model in an estimator, +`tf-nightly` or `tf-nightly-gpu` is highly recommended as of August 2018. - Optional arguments for `main.py` include - `model_dir`: Directory to store eventfiles and checkpoints. @@ -54,13 +56,19 @@ python main_estimator.py --data_dir ${PWD}/cifar - `export`: Export the model for serving if True. ## Speed up with `tfe.defun` -Even though the speed difference between pure eager execution and graph-mode execution is noticeable, -the difference between fully "defunned" model training and graph-mode +To ensure that `tf.contrib.eager.defun` in our code works properly with all +part of the model during training, the latest `tf-nightly` or `tf-nightly-gpu` +is highly recommended as of August 2018. + +Even though the speed difference between pure eager execution and graph execution is noticeable, +the difference between fully "defunned" model training and graph training is negligible. ## Train on ImageNet with Cloud TPUs -The standard way to train models on Cloud TPUs is via TPU estimators and graph-mode +The standard way to train models on Cloud TPUs is via TPU estimators and graph execution. Models built with the `tf.keras` API are fully compatible with TPU estimators. +To ensure our code works properly in this setting, +`tf-nightly` or `tf-nightly-gpu` is highly recommended as of August 2018. ### Setup a Google Cloud project @@ -96,7 +104,8 @@ python main_estimator_tpu.py \ ``` ## Performance -- With the current implementation, RevNet-38 achieves >92% on CIFAR-10 and >71% on CIFAR-100. +- RevNet-38 achieves >92% and >71% accuracy on CIFAR-10 and CIFAR-100 respectively. +- RevNet-56 achieves <26% top-1 error rate on ImageNet. ## Reference The Reversible Residual Network: Backpropagation Without Storing Activations. diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py index fda9020ddf79cd3fd59611d03c1a4202a4901337..9ff6b605b912772a92ab9e07a0ba5b9325030e43 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py @@ -188,6 +188,40 @@ class RevBlockTest(tf.test.TestCase): self._check_grad_angle(dx_true, dx) self._check_grad_angle(dw_true, dw) + def test_backward_grads_with_nativepy(self): + if not tf.test.is_gpu_available(): + self.skipTest("GPU not available") + + input_shape = (128, 8, 8) + data_shape = (16,) + input_shape + x = tf.random_normal(shape=data_shape, dtype=tf.float64) + dy = tf.random_normal(shape=data_shape, dtype=tf.float64) + dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=1) + block = blocks.RevBlock( + n_res=3, + filters=128, + strides=(1, 1), + input_shape=input_shape, + fused=False, + dtype=tf.float64) + with tf.GradientTape() as tape: + tape.watch(x) + x1, x2 = tf.split(x, num_or_size_splits=2, axis=1) + y1, y2 = block((x1, x2), training=True) + y = tf.concat((y1, y2), axis=1) + + # Compute true grads + dx_true = tape.gradient(y, x, output_gradients=dy) + + # Compute grads from reconstruction + (dx1, dx2), _ = block.backward_grads( + x=(x1, x2), y=(y1, y2), dy=(dy1, dy2), training=True) + dx = tf.concat((dx1, dx2), axis=1) + + thres = 1e-5 + diff_abs = tf.reshape(abs(dx - dx_true), [-1]) + assert all(diff_abs < thres) + class _ResidualTest(tf.test.TestCase): diff --git a/tensorflow/contrib/eager/python/remote_test.py b/tensorflow/contrib/eager/python/remote_test.py new file mode 100644 index 0000000000000000000000000000000000000000..76f48eeb1cab9d1f014adeafe4827cb5d3a8c77d --- /dev/null +++ b/tensorflow/contrib/eager/python/remote_test.py @@ -0,0 +1,178 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for remote eager execution.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import os + +import numpy as np + +from tensorflow.core.protobuf import cluster_pb2 +from tensorflow.core.protobuf import tensorflow_server_pb2 +from tensorflow.python.eager import backprop +from tensorflow.python.eager import context +from tensorflow.python.eager import function +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.platform import test +from tensorflow.python.training import server_lib + +JOB_NAME = "remote_device" +ALT_JOB_NAME = "alt_remote_device" + + +def run_sync_and_async(f): + """Execute all test methods in the given class in sync and async modes.""" + + @functools.wraps(f) + def decorator(self, *args, **kwargs): + with context.execution_mode(context.ASYNC): + f(self, *args, **kwargs) + + with context.execution_mode(context.SYNC): + f(self, *args, **kwargs) + + return decorator + + +def get_server_def(job_name, local_server_port, remote_server_addresses, + task_index): + """Returns a server def with a single job + multiple tasks.""" + cluster_def = cluster_pb2.ClusterDef() + job_def = cluster_def.job.add() + job_def.name = job_name + job_def.tasks[0] = "localhost:%d" % local_server_port + + for i, remote_server_address in enumerate(remote_server_addresses, start=1): + job_def.tasks[i] = remote_server_address + + server_def = tensorflow_server_pb2.ServerDef( + cluster=cluster_def, + job_name=job_name, + task_index=task_index, + protocol="grpc") + + return server_def + + +class RemoteExecutionTest(test.TestCase): + + def __init__(self, methodName="runTest"): # pylint: disable=invalid-name + super(RemoteExecutionTest, self).__init__(methodName) + self._cached_server1 = server_lib.Server.create_local_server() + self._cached_server2 = server_lib.Server.create_local_server() + + os.environ["TF_EAGER_REMOTE_USE_SEND_TENSOR_RPC"] = "1" + + self._cached_server1_target = self._cached_server1.target[len("grpc://"):] + self._cached_server2_target = self._cached_server2.target[len("grpc://"):] + + # Start the local server. + context.set_server_def( + server_def=get_server_def( + JOB_NAME, + local_server_port=0, + remote_server_addresses=[ + self._cached_server1_target, self._cached_server2_target + ], + task_index=0)) + + @run_sync_and_async + def testDefunMatmul(self): + """Basic remote eager execution with defun.""" + + mm_defun = function.defun(math_ops.matmul) + with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME): + x1 = array_ops.ones([2, 2]) + with ops.device("job:%s/replica:0/task:2/device:CPU:0" % JOB_NAME): + x2 = array_ops.ones([2, 2]) + y = mm_defun(x1, x2) + np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy()) + + @run_sync_and_async + def testSimpleMatmul(self): + """Basic remote eager execution.""" + + with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME): + x1 = array_ops.ones([2, 2]) + with ops.device("job:%s/replica:0/task:2/device:CPU:0" % JOB_NAME): + x2 = array_ops.ones([2, 2]) + y = math_ops.matmul(x1, x2) + np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy()) + + @run_sync_and_async + def testSimpleWeightRead(self): + """Basic remote eager weight read.""" + + with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME): + w = resource_variable_ops.ResourceVariable([[2.0]]) + loss = w * w + np.testing.assert_array_equal([[4.0]], loss.numpy()) + + @run_sync_and_async + def testTapeWeightRead(self): + """Remote eager weight read in a tape.""" + + with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME): + w = resource_variable_ops.ResourceVariable([[3.0]]) + with backprop.GradientTape() as tape: + loss = w * w + + grad = tape.gradient(loss, w) + np.testing.assert_array_equal([[9.0]], loss.numpy()) + np.testing.assert_array_equal([[6.0]], grad.numpy()) + + @run_sync_and_async + def testServerDefChanged(self): + """Update server def, and run ops on new cluster.""" + context.set_server_def( + server_def=get_server_def( + ALT_JOB_NAME, + local_server_port=0, + remote_server_addresses=[ + self._cached_server1_target, self._cached_server2_target + ], + task_index=0)) + + with ops.device("job:%s/replica:0/task:1/device:CPU:0" % ALT_JOB_NAME): + x1 = array_ops.ones([2, 2]) + y = math_ops.matmul(x1, x1) + np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy()) + + # Set the server def back to JOB_NAME + context.set_server_def( + server_def=get_server_def( + JOB_NAME, + local_server_port=0, + remote_server_addresses=[ + self._cached_server1_target, self._cached_server2_target + ], + task_index=0)) + + with ops.device("job:%s/replica:0/task:1/device:CPU:0" % JOB_NAME): + x1 = array_ops.ones([2, 2]) + y = math_ops.matmul(x1, x1) + np.testing.assert_array_equal([[2, 2], [2, 2]], y.numpy()) + + +if __name__ == "__main__": + ops.enable_eager_execution() + test.main() diff --git a/tensorflow/contrib/eager/python/saver.py b/tensorflow/contrib/eager/python/saver.py index d70930864784b3e48140da27ca33ff13f593e663..f9c716360c5755ee1902b576545d776725f9966f 100644 --- a/tensorflow/contrib/eager/python/saver.py +++ b/tensorflow/contrib/eager/python/saver.py @@ -161,7 +161,7 @@ class Saver(object): Args: file_prefix: Path prefix where parameters were previously saved. Typically obtained from a previous `save()` call, or from - @{tf.train.latest_checkpoint}. + `tf.train.latest_checkpoint`. """ with ops.device("/device:CPU:0"): self._saver.restore(None, file_prefix) diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 349f48f7f788b458af2639f7ad4cc4cd904465b4..82272bf1207c9b85243bb1c2d92a2c6704a2761e 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -487,6 +487,9 @@ py_test( size = "medium", srcs = ["python/estimator/saved_model_estimator_test.py"], srcs_version = "PY2AND3", + tags = [ + "notsan", + ], deps = [ ":export", ":saved_model_estimator", diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py b/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py index 2eef60c39f54bfb464b7da0eb57a47e9eee9b800..724bc2c82f8289bbaa19a1dbbc1dc81b6e158e02 100644 --- a/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py +++ b/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py @@ -147,7 +147,7 @@ class DNNLinearCombinedEstimator(estimator.Estimator): if a categorical column is multivalent. One of "mean", "sqrtn", and "sum" -- these are effectively different ways to do example-level normalization, which can be useful for bag-of-words features. For more - details, see @{tf.feature_column.linear_model$linear_model}. + details, see `tf.feature_column.linear_model`. Raises: ValueError: If both linear_feature_columns and dnn_features_columns are diff --git a/tensorflow/contrib/estimator/python/estimator/extenders.py b/tensorflow/contrib/estimator/python/estimator/extenders.py index bf08be09e7baf63e507a6a4db6a91e7b6bb20b74..26449b46516fe1d8c93a8e3567f93801c689a65a 100644 --- a/tensorflow/contrib/estimator/python/estimator/extenders.py +++ b/tensorflow/contrib/estimator/python/estimator/extenders.py @@ -34,7 +34,7 @@ _VALID_METRIC_FN_ARGS = set(['features', 'labels', 'predictions', 'config']) def add_metrics(estimator, metric_fn): - """Creates a new @{tf.estimator.Estimator} which has given metrics. + """Creates a new `tf.estimator.Estimator` which has given metrics. Example: @@ -61,7 +61,7 @@ def add_metrics(estimator, metric_fn): ``` Args: - estimator: A @{tf.estimator.Estimator} object. + estimator: A `tf.estimator.Estimator` object. metric_fn: A function which should obey the following signature: - Args: can only have following four arguments in any order: * predictions: Predictions `Tensor` or dict of `Tensor` created by given @@ -79,7 +79,7 @@ def add_metrics(estimator, metric_fn): function, namely a `(metric_tensor, update_op)` tuple. Returns: - A new @{tf.estimator.Estimator} which has a union of original metrics with + A new `tf.estimator.Estimator` which has a union of original metrics with given ones. """ _verify_metric_fn_args(metric_fn) @@ -165,14 +165,14 @@ def forward_features(estimator, keys=None): ``` Args: - estimator: A @{tf.estimator.Estimator} object. + estimator: A `tf.estimator.Estimator` object. keys: a `string` or a `list` of `string`. If it is `None`, all of the `features` in `dict` is forwarded to the `predictions`. If it is a `string`, only given key is forwarded. If it is a `list` of strings, all the given `keys` are forwarded. Returns: - A new @{tf.estimator.Estimator} which forwards features to predictions. + A new `tf.estimator.Estimator` which forwards features to predictions. Raises: ValueError: diff --git a/tensorflow/contrib/estimator/python/estimator/linear.py b/tensorflow/contrib/estimator/python/estimator/linear.py index 62a37abefb1f6ed291df1df3da6de35bfd2b6c52..2b68f24eb2d4c528bc1cb87e7d858014f66c0433 100644 --- a/tensorflow/contrib/estimator/python/estimator/linear.py +++ b/tensorflow/contrib/estimator/python/estimator/linear.py @@ -121,7 +121,7 @@ class LinearEstimator(estimator.Estimator): is multivalent. One of "mean", "sqrtn", and "sum" -- these are effectively different ways to do example-level normalization, which can be useful for bag-of-words features. for more details, see - @{tf.feature_column.linear_model$linear_model}. + `tf.feature_column.linear_model`. """ def _model_fn(features, labels, mode, config): return linear_lib._linear_model_fn( # pylint: disable=protected-access diff --git a/tensorflow/contrib/factorization/BUILD b/tensorflow/contrib/factorization/BUILD index effec42f028fe472593a8d06e15a0831346d6f50..9e1f14f9905d584287864c15d9b6f9c152d17787 100644 --- a/tensorflow/contrib/factorization/BUILD +++ b/tensorflow/contrib/factorization/BUILD @@ -65,7 +65,7 @@ tf_custom_op_py_library( "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/estimator", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/feature_column:feature_column_py", "//third_party/py/numpy", ], @@ -242,7 +242,7 @@ py_test( "//tensorflow/python:platform_benchmark", "//tensorflow/python:random_ops", "//tensorflow/python:training", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/feature_column:feature_column_py", "//third_party/py/numpy", ], diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py index 9ffdd3ba5e8ac496533d0207f2b6846dbc92bc89..4d8d5004fe2262594b909b27ae1c832b7b31a29a 100644 --- a/tensorflow/contrib/factorization/python/ops/kmeans.py +++ b/tensorflow/contrib/factorization/python/ops/kmeans.py @@ -158,12 +158,12 @@ class _ModelFn(object): return either `features` or, equivalently, `(features, None)`. Args: - features: The input points. See @{tf.estimator.Estimator}. - mode: See @{tf.estimator.Estimator}. - config: See @{tf.estimator.Estimator}. + features: The input points. See `tf.estimator.Estimator`. + mode: See `tf.estimator.Estimator`. + config: See `tf.estimator.Estimator`. Returns: - A @{tf.estimator.EstimatorSpec} (see @{tf.estimator.Estimator}) specifying + A `tf.estimator.EstimatorSpec` (see `tf.estimator.Estimator`) specifying this behavior: * `train_op`: Execute one mini-batch or full-batch run of Lloyd's algorithm. @@ -394,7 +394,7 @@ class KMeansClustering(estimator.Estimator): relative_tolerance: A relative tolerance of change in the loss between iterations. Stops learning if the loss changes less than this amount. This may not work correctly if `use_mini_batch=True`. - config: See @{tf.estimator.Estimator}. + config: See `tf.estimator.Estimator`. feature_columns: An optionable iterable containing all the feature columns used by the model. All items in the set should be feature column instances that can be passed to `tf.feature_column.input_layer`. If this @@ -431,7 +431,7 @@ class KMeansClustering(estimator.Estimator): """Finds the index of the closest cluster center to each input point. Args: - input_fn: Input points. See @{tf.estimator.Estimator.predict}. + input_fn: Input points. See `tf.estimator.Estimator.predict`. Yields: The index of the closest cluster center for each input point. @@ -447,7 +447,7 @@ class KMeansClustering(estimator.Estimator): which returns the negative sum. Args: - input_fn: Input points. See @{tf.estimator.Estimator.evaluate}. Only one + input_fn: Input points. See `tf.estimator.Estimator.evaluate`. Only one batch is retrieved. Returns: @@ -465,7 +465,7 @@ class KMeansClustering(estimator.Estimator): sklearn function returns the Euclidean distance. Args: - input_fn: Input points. See @{tf.estimator.Estimator.predict}. + input_fn: Input points. See `tf.estimator.Estimator.predict`. Yields: The distances from each input point to each cluster center. diff --git a/tensorflow/contrib/framework/__init__.py b/tensorflow/contrib/framework/__init__.py index 918a7e2bc772dee226e5ef23d0e3e34309f180f4..20d099fe5d49dac0caec4a28801f09e7bee4f2e2 100644 --- a/tensorflow/contrib/framework/__init__.py +++ b/tensorflow/contrib/framework/__init__.py @@ -100,6 +100,8 @@ See the @{$python/contrib.framework} guide. @@BoundedTensorSpec @@TensorSpec + +@@RecordInput """ from __future__ import absolute_import @@ -119,6 +121,7 @@ from tensorflow.python.framework.smart_cond import smart_cond from tensorflow.python.framework.smart_cond import smart_constant_value from tensorflow.python.framework.tensor_spec import BoundedTensorSpec from tensorflow.python.framework.tensor_spec import TensorSpec +from tensorflow.python.ops.data_flow_ops import RecordInput from tensorflow.python.ops.init_ops import convolutional_delta_orthogonal from tensorflow.python.ops.init_ops import convolutional_orthogonal_1d from tensorflow.python.ops.init_ops import convolutional_orthogonal_2d diff --git a/tensorflow/contrib/framework/python/ops/arg_scope.py b/tensorflow/contrib/framework/python/ops/arg_scope.py index 5b150339953f961c756c0909dd1795341159b9cd..0a02e76a265c8ad25d978e7d610fb50fc0fdfdb1 100644 --- a/tensorflow/contrib/framework/python/ops/arg_scope.py +++ b/tensorflow/contrib/framework/python/ops/arg_scope.py @@ -103,9 +103,8 @@ def _kwarg_names(func): def _add_op(op): - key = arg_scope_func_key(op) - if key not in _DECORATED_OPS: - _DECORATED_OPS[key] = _kwarg_names(op) + key_op = arg_scope_func_key(op) + _DECORATED_OPS[key_op] = _kwarg_names(op) @tf_contextlib.contextmanager diff --git a/tensorflow/contrib/framework/python/ops/arg_scope_test.py b/tensorflow/contrib/framework/python/ops/arg_scope_test.py index 4c3879d4fc08b53ea8be5f1256a830a64fb39af6..bcafc1a3280ba0435f655eacb8173e4e97051154 100644 --- a/tensorflow/contrib/framework/python/ops/arg_scope_test.py +++ b/tensorflow/contrib/framework/python/ops/arg_scope_test.py @@ -38,6 +38,12 @@ def func3(args, a=None, b=1, c=2): """Some cool doc string.""" return (args, a, b, c) +@add_arg_scope +def func4(x='x', y='y'): + if x: + pass + if y: + pass def _key_op(op): return getattr(op, '_key_op', str(op)) @@ -231,6 +237,15 @@ class ArgScopeTest(test.TestCase): self.assertTupleEqual(args, func2_args) self.assertDictEqual(kwargs, func2_kwargs) + def testAddArgScopeRaceCondition(self): + func4_kwargs = ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h') + for i in range(4): + # redefine the function with different args + @add_arg_scope + def func4(a=1, b=2, c=3, d=4, e=5, f=6, g=7, h=8): + pass + self.assertTupleEqual(arg_scoped_arguments(func4), func4_kwargs) + def testDocString(self): self.assertEqual(func3.__doc__, 'Some cool doc string.') diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py index 322d5c335e6a77c46c7ce5dd795e21a2d5a1f8f9..a7acae804a0c71cc19757a48d47fd9cf9022b0e2 100644 --- a/tensorflow/contrib/framework/python/ops/variables.py +++ b/tensorflow/contrib/framework/python/ops/variables.py @@ -241,13 +241,13 @@ def variable(name, use_resource: If `True` use a ResourceVariable instead of a Variable. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. Returns: The created or existing variable. @@ -320,13 +320,13 @@ def model_variable(name, use_resource: If `True` use a ResourceVariable instead of a Variable. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. Returns: The created or existing variable. diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index 053d4e3e977ed1baed8ceeca1a983e999b1ad1ff..82e3bbe3c01fa7e9231ac4038cebf33ef90251e4 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -459,8 +459,7 @@ py_library( ":train", "//tensorflow/python:framework_ops", "//tensorflow/python:util", - "//tensorflow/python/estimator:head", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -477,7 +476,7 @@ py_test( "//tensorflow/python:math_ops", "//tensorflow/python:training", "//tensorflow/python:variable_scope", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -497,8 +496,7 @@ py_library( "//tensorflow/python:metrics", "//tensorflow/python:util", "//tensorflow/python:variable_scope", - "//tensorflow/python/estimator", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -526,8 +524,7 @@ py_test( "//tensorflow/python:training", "//tensorflow/python:training_util", "//tensorflow/python:variable_scope", - "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/estimator:numpy_io", + "//tensorflow/python/estimator:estimator_py", "//third_party/py/numpy", "@absl_py//absl/testing:parameterized", "@six_archive//:six", diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py index 026a3d1200033400472c4fd763a244c04b284a9b..e79ccd8da1f8952758ae322d3a92dec34910a9db 100644 --- a/tensorflow/contrib/graph_editor/transform.py +++ b/tensorflow/contrib/graph_editor/transform.py @@ -129,7 +129,7 @@ def transform_op_if_inside_handler(info, op, keep_if_possible=True): return None -def copy_op_handler(info, op, new_inputs, copy_shape=True, nodedef_fn=None): +def copy_op_handler(info, op, new_inputs, copy_shape=False, nodedef_fn=None): """Copy a `tf.Operation`. Args: diff --git a/tensorflow/contrib/hadoop/BUILD b/tensorflow/contrib/hadoop/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..ccad31efa1dba92d954ff1cb455b6c9c784b29bc --- /dev/null +++ b/tensorflow/contrib/hadoop/BUILD @@ -0,0 +1,117 @@ +package(default_visibility = ["//tensorflow:internal"]) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load( + "//tensorflow:tensorflow.bzl", + "tf_custom_op_library", + "tf_custom_op_py_library", + "tf_gen_op_libs", + "tf_gen_op_wrapper_py", + "tf_kernel_library", + "tf_py_test", +) + +filegroup( + name = "test_data", + srcs = glob(["python/kernel_tests/testdata/*"]), +) + +py_library( + name = "hadoop", + srcs = ["__init__.py"], + srcs_version = "PY2AND3", + deps = [ + ":dataset_ops", + ], +) + +tf_custom_op_library( + name = "_dataset_ops.so", + srcs = ["ops/dataset_ops.cc"], + deps = [ + ":dataset_kernels", + ], +) + +tf_gen_op_libs( + op_lib_names = ["dataset_ops"], +) + +cc_library( + name = "dataset_kernels", + srcs = ["kernels/hadoop_dataset_ops.cc"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//third_party/eigen3", + "@protobuf_archive//:protobuf_headers", + ], + alwayslink = 1, +) + +py_library( + name = "dataset_ops", + srcs = [ + "python/ops/hadoop_dataset_ops.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":hadoop_op_loader", + "//tensorflow/python:dataset_ops_gen", + "//tensorflow/python:util", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/data/util:nest", + ], +) + +tf_gen_op_wrapper_py( + name = "gen_dataset_ops", + out = "python/ops/gen_dataset_ops.py", + deps = ["//tensorflow/contrib/hadoop:dataset_ops_op_lib"], +) + +tf_kernel_library( + name = "dataset_ops_kernels", + deps = [ + ":dataset_kernels", + "//tensorflow/core:framework", + ], + alwayslink = 1, +) + +tf_custom_op_py_library( + name = "hadoop_op_loader", + srcs = ["python/ops/hadoop_op_loader.py"], + dso = ["//tensorflow/contrib/hadoop:_dataset_ops.so"], + kernels = [ + ":dataset_ops_kernels", + "//tensorflow/contrib/hadoop:dataset_ops_op_lib", + ], + srcs_version = "PY2AND3", + deps = [ + ":gen_dataset_ops", + "//tensorflow/contrib/util:util_py", + "//tensorflow/python:platform", + ], +) + +tf_py_test( + name = "hadoop_test", + srcs = ["python/kernel_tests/hadoop_test.py"], + additional_deps = [ + ":hadoop", + "//third_party/py/numpy", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:platform_test", + ], + data = [ + ":test_data", + ], + tags = [ + "notap", + ], +) diff --git a/tensorflow/contrib/hadoop/__init__.py b/tensorflow/contrib/hadoop/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..abf8cd4845f9713ebd8a647af191000061e01ad1 --- /dev/null +++ b/tensorflow/contrib/hadoop/__init__.py @@ -0,0 +1,32 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Sequence File Dataset. + +@@SequenceFileDataset +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.hadoop.python.ops.hadoop_dataset_ops import SequenceFileDataset + +from tensorflow.python.util.all_util import remove_undocumented + +_allowed_symbols = [ + "SequenceFileDataset", +] + +remove_undocumented(__name__) diff --git a/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc b/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..b510994152bf792ab1c36f2e40acbf27c48a7043 --- /dev/null +++ b/tensorflow/contrib/hadoop/kernels/hadoop_dataset_ops.cc @@ -0,0 +1,339 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/dataset.h" +#include "tensorflow/core/lib/io/buffered_inputstream.h" +#include "tensorflow/core/platform/file_system.h" + +namespace tensorflow { +namespace { + +static const size_t kSyncMarkerSize = 16; +static const size_t kSequenceFileBufferSize = 1024 * 1024; + +class SequenceFileReader { + public: + explicit SequenceFileReader(RandomAccessFile* file) + : input_stream_( + new io::BufferedInputStream(file, kSequenceFileBufferSize)) {} + + Status ReadHeader() { + string version; + TF_RETURN_IF_ERROR(input_stream_->ReadNBytes(4, &version)); + if (version.substr(0, 3) != "SEQ" || version[3] != 6) { + return errors::InvalidArgument( + "sequence file header must starts with `SEQ6`, received \"", + version.substr(0, 3), static_cast(version[3]), "\""); + } + TF_RETURN_IF_ERROR(ReadString(&key_class_name_)); + TF_RETURN_IF_ERROR(ReadString(&value_class_name_)); + + // At the moment we only support `org.apache.hadoop.io.Text` for key/value. + // TODO (yongtang): Add more class name support. + if (key_class_name_ != "org.apache.hadoop.io.Text" || + value_class_name_ != "org.apache.hadoop.io.Text") { + return errors::Unimplemented("key/value of '", key_class_name_, "/", + value_class_name_, + "' is currently not supported"); + } + + string buffer; + TF_RETURN_IF_ERROR(input_stream_->ReadNBytes(2, &buffer)); + compression_ = buffer[0]; + block_compression_ = buffer[1]; + if (compression_ || block_compression_) { + TF_RETURN_IF_ERROR(ReadString(&compression_codec_class_name_)); + } + + // At the moment no compression is supported. + // TODO (yongtang): Add compression support. + if (compression_ || block_compression_) { + return errors::Unimplemented("compression is currently not supported"); + } + + // Not interested in metadata for now. + uint32 num_metadata_pairs = 0; + TF_RETURN_IF_ERROR(ReadUInt32(&num_metadata_pairs)); + if (num_metadata_pairs > 1024) { + return errors::InvalidArgument( + "sequence file metadata should have key value pairs < 1024, " + "received ", + num_metadata_pairs); + } + for (int i = 0; i < num_metadata_pairs; i++) { + TF_RETURN_IF_ERROR(ReadString(nullptr)); + TF_RETURN_IF_ERROR(ReadString(nullptr)); + } + + TF_RETURN_IF_ERROR( + input_stream_->ReadNBytes(kSyncMarkerSize, &sync_marker_)); + + return Status::OK(); + } + + Status ReadRecord(string* key, string* value) { + uint32 length = 0; + TF_RETURN_IF_ERROR(ReadUInt32(&length)); + if (length == static_cast(-1)) { + // Sync marker. + string sync_marker; + TF_RETURN_IF_ERROR( + input_stream_->ReadNBytes(kSyncMarkerSize, &sync_marker)); + if (sync_marker != sync_marker_) { + return errors::InvalidArgument( + "sequence file should have sync marker \"", sync_marker_, + "\" at pos ", input_stream_->Tell() - kSyncMarkerSize, + ", received \"", sync_marker, "\""); + } + return ReadRecord(key, value); + } + uint32 key_length = 0; + TF_RETURN_IF_ERROR(ReadUInt32(&key_length)); + if (key_length > length) { + return errors::InvalidArgument("key length (", key_length, + ") should be < record length (", length, + ")"); + } + // At the moment we only support `org.apache.hadoop.io.Text` for key/value. + // TODO (yongtang): Expand supported format. + TF_RETURN_IF_ERROR(ReadString(key)); + TF_RETURN_IF_ERROR(ReadString(value)); + return Status::OK(); + } + + Status ReadString(string* value) { + int64 length = 0; + TF_RETURN_IF_ERROR(ReadVInt(&length)); + if (value == nullptr) { + return input_stream_->SkipNBytes(length); + } + return input_stream_->ReadNBytes(length, value); + } + + Status ReadUInt32(uint32* value) { + string buffer; + TF_RETURN_IF_ERROR(input_stream_->ReadNBytes(4, &buffer)); + *value = ((static_cast(buffer[0]) << 24) | + static_cast(buffer[1]) << 16) | + (static_cast(buffer[2]) << 8) | + static_cast(buffer[3]); + return Status::OK(); + } + + Status ReadVInt(int64* value) { + string buffer; + TF_RETURN_IF_ERROR(input_stream_->ReadNBytes(1, &buffer)); + if (buffer[0] >= -112) { + *value = static_cast(buffer[0]); + return Status::OK(); + } + + int64 remaining = 0; + bool negative = false; + if (buffer[0] >= -120) { + remaining = static_cast(-112) - static_cast(buffer[0]); + } else { + remaining = static_cast(-120) - static_cast(buffer[0]); + negative = true; + } + buffer.clear(); + TF_RETURN_IF_ERROR(input_stream_->ReadNBytes(remaining, &buffer)); + + uint64 v = 0; + for (int i = 0; i < buffer.size(); i++) { + v = (v << 8) | static_cast(buffer[i]); + } + if (negative) { + v = ~v; + } + *value = static_cast(v); + return Status::OK(); + } + + virtual ~SequenceFileReader() = default; + + private: + std::unique_ptr input_stream_; + string key_class_name_; + string value_class_name_; + string sync_marker_; + bool compression_; + bool block_compression_; + string compression_codec_class_name_; + TF_DISALLOW_COPY_AND_ASSIGN(SequenceFileReader); +}; +class SequenceFileDatasetOp : public DatasetOpKernel { + public: + using DatasetOpKernel::DatasetOpKernel; + explicit SequenceFileDatasetOp(OpKernelConstruction* ctx) + : DatasetOpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); + for (const DataType& dt : output_types_) { + OP_REQUIRES(ctx, dt == DT_STRING, + errors::InvalidArgument( + "Each element of `output_types_` must be one of: " + "DT_STRING")); + } + } + void MakeDataset(OpKernelContext* ctx, DatasetBase** output) override { + const Tensor* filenames_tensor; + OP_REQUIRES_OK(ctx, ctx->input("filenames", &filenames_tensor)); + OP_REQUIRES( + ctx, filenames_tensor->dims() <= 1, + errors::InvalidArgument("`filenames` must be a scalar or a vector.")); + + std::vector filenames; + filenames.reserve(filenames_tensor->NumElements()); + for (int i = 0; i < filenames_tensor->NumElements(); ++i) { + filenames.push_back(filenames_tensor->flat()(i)); + } + + *output = new Dataset(ctx, filenames, output_types_); + } + + private: + class Dataset : public GraphDatasetBase { + public: + Dataset(OpKernelContext* ctx, const std::vector& filenames, + const DataTypeVector& output_types) + : GraphDatasetBase(ctx), + filenames_(filenames), + output_types_(output_types) {} + + std::unique_ptr MakeIteratorInternal( + const string& prefix) const override { + return std::unique_ptr( + new Iterator({this, strings::StrCat(prefix, "::SequenceFile")})); + } + + const DataTypeVector& output_dtypes() const override { + return output_types_; + } + + const std::vector& output_shapes() const override { + static std::vector* shapes = + new std::vector({{}, {}}); + return *shapes; + } + + string DebugString() const override { + return "SequenceFileDatasetOp::Dataset"; + } + + protected: + Status AsGraphDefInternal(DatasetGraphDefBuilder* b, + Node** output) const override { + Node* filenames = nullptr; + TF_RETURN_IF_ERROR(b->AddVector(filenames_, &filenames)); + TF_RETURN_IF_ERROR(b->AddDataset(this, {filenames}, output)); + return Status::OK(); + } + + private: + class Iterator : public DatasetIterator { + public: + explicit Iterator(const Params& params) + : DatasetIterator(params) {} + + Status GetNextInternal(IteratorContext* ctx, + std::vector* out_tensors, + bool* end_of_sequence) override { + mutex_lock l(mu_); + do { + // We are currently processing a file, so try to read the next record. + if (reader_) { + string key, value; + Status status = reader_->ReadRecord(&key, &value); + if (!errors::IsOutOfRange(status)) { + TF_RETURN_IF_ERROR(status); + + Tensor key_tensor(ctx->allocator({}), DT_STRING, {}); + key_tensor.scalar()() = key; + out_tensors->emplace_back(std::move(key_tensor)); + + Tensor value_tensor(ctx->allocator({}), DT_STRING, {}); + value_tensor.scalar()() = value; + out_tensors->emplace_back(std::move(value_tensor)); + + *end_of_sequence = false; + return Status::OK(); + } + // We have reached the end of the current file, so maybe + // move on to next file. + ResetStreamsLocked(); + ++current_file_index_; + } + + // Iteration ends when there are no more files to process. + if (current_file_index_ == dataset()->filenames_.size()) { + *end_of_sequence = true; + return Status::OK(); + } + + TF_RETURN_IF_ERROR(SetupStreamsLocked(ctx->env())); + } while (true); + } + + protected: + Status SaveInternal(IteratorStateWriter* writer) override { + return errors::Unimplemented("SaveInternal is currently not supported"); + } + + Status RestoreInternal(IteratorContext* ctx, + IteratorStateReader* reader) override { + return errors::Unimplemented( + "RestoreInternal is currently not supported"); + } + + private: + // Sets up SequenceFile streams to read from the topic at + // `current_file_index_`. + Status SetupStreamsLocked(Env* env) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + if (current_file_index_ >= dataset()->filenames_.size()) { + return errors::InvalidArgument( + "current_file_index_:", current_file_index_, + " >= filenames_.size():", dataset()->filenames_.size()); + } + + // Actually move on to next file. + const string& filename = dataset()->filenames_[current_file_index_]; + TF_RETURN_IF_ERROR(env->NewRandomAccessFile(filename, &file_)); + reader_.reset(new SequenceFileReader(file_.get())); + return reader_->ReadHeader(); + } + + // Resets all Hadoop SequenceFile streams. + void ResetStreamsLocked() EXCLUSIVE_LOCKS_REQUIRED(mu_) { + reader_.reset(); + file_.reset(); + } + + mutex mu_; + size_t current_file_index_ GUARDED_BY(mu_) = 0; + std::unique_ptr file_ GUARDED_BY(mu_); + std::unique_ptr reader_ GUARDED_BY(mu_); + }; + + const std::vector filenames_; + const DataTypeVector output_types_; + }; + DataTypeVector output_types_; +}; +} // namespace + +REGISTER_KERNEL_BUILDER(Name("SequenceFileDataset").Device(DEVICE_CPU), + SequenceFileDatasetOp); + +} // namespace tensorflow diff --git a/tensorflow/contrib/hadoop/ops/dataset_ops.cc b/tensorflow/contrib/hadoop/ops/dataset_ops.cc new file mode 100644 index 0000000000000000000000000000000000000000..66ad549b4756028a45c1ce76db4a2367517f81a5 --- /dev/null +++ b/tensorflow/contrib/hadoop/ops/dataset_ops.cc @@ -0,0 +1,29 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/core/framework/common_shape_fns.h" +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/shape_inference.h" + +namespace tensorflow { + +REGISTER_OP("SequenceFileDataset") + .Input("filenames: string") + .Output("handle: variant") + .Attr("output_types: list(type) >= 1") + .SetIsStateful() + .SetShapeFn(shape_inference::ScalarShape); + +} // namespace tensorflow diff --git a/tensorflow/contrib/hadoop/python/kernel_tests/hadoop_test.py b/tensorflow/contrib/hadoop/python/kernel_tests/hadoop_test.py new file mode 100644 index 0000000000000000000000000000000000000000..d796e43d877e463fa4398741748013b2eb661155 --- /dev/null +++ b/tensorflow/contrib/hadoop/python/kernel_tests/hadoop_test.py @@ -0,0 +1,66 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); you may not +# use this file except in compliance with the License. You may obtain a copy of +# the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT +# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the +# License for the specific language governing permissions and limitations under +# the License. +# ============================================================================== +"""Tests for SequenceFileDataset.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.contrib.hadoop.python.ops import hadoop_dataset_ops +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.platform import resource_loader +from tensorflow.python.platform import test + + +class SequenceFileDatasetTest(test.TestCase): + + def test_sequence_file_dataset(self): + """Test case for SequenceFileDataset. + + The file is generated with `org.apache.hadoop.io.Text` for key/value. + There are 25 records in the file with the format of: + key = XXX + value = VALUEXXX + where XXX is replaced as the line number (starts with 001). + """ + filename = os.path.join(resource_loader.get_data_files_path(), + "testdata", "string.seq") + + filenames = constant_op.constant([filename], dtypes.string) + num_repeats = 2 + + dataset = hadoop_dataset_ops.SequenceFileDataset(filenames).repeat( + num_repeats) + iterator = dataset.make_initializable_iterator() + init_op = iterator.initializer + get_next = iterator.get_next() + + with self.test_session() as sess: + sess.run(init_op) + for _ in range(num_repeats): # Dataset is repeated. + for i in range(25): # 25 records. + v0 = b"%03d" % (i + 1) + v1 = b"VALUE%03d" % (i + 1) + self.assertEqual((v0, v1), sess.run(get_next)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/hadoop/python/kernel_tests/testdata/string.seq b/tensorflow/contrib/hadoop/python/kernel_tests/testdata/string.seq new file mode 100755 index 0000000000000000000000000000000000000000..b7175338af3417a8858d66082ab5a616f87cb234 Binary files /dev/null and b/tensorflow/contrib/hadoop/python/kernel_tests/testdata/string.seq differ diff --git a/tensorflow/contrib/hadoop/python/ops/hadoop_dataset_ops.py b/tensorflow/contrib/hadoop/python/ops/hadoop_dataset_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..6e0e628655fbc32a43fad2dc4883b26c6ad57c48 --- /dev/null +++ b/tensorflow/contrib/hadoop/python/ops/hadoop_dataset_ops.py @@ -0,0 +1,75 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""SequenceFile Dataset.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.hadoop.python.ops import gen_dataset_ops +from tensorflow.contrib.hadoop.python.ops import hadoop_op_loader # pylint: disable=unused-import +from tensorflow.python.data.ops.dataset_ops import Dataset +from tensorflow.python.data.util import nest +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape + + +class SequenceFileDataset(Dataset): + """A Sequence File Dataset that reads the sequence file.""" + + def __init__(self, filenames): + """Create a `SequenceFileDataset`. + + `SequenceFileDataset` allows a user to read data from a hadoop sequence + file. A sequence file consists of (key value) pairs sequentially. At + the moment, `org.apache.hadoop.io.Text` is the only serialization type + being supported, and there is no compression support. + + For example: + + ```python + dataset = tf.contrib.hadoop.SequenceFileDataset("/foo/bar.seq") + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + # Prints the (key, value) pairs inside a hadoop sequence file. + while True: + try: + print(sess.run(next_element)) + except tf.errors.OutOfRangeError: + break + ``` + + Args: + filenames: A `tf.string` tensor containing one or more filenames. + """ + super(SequenceFileDataset, self).__init__() + self._filenames = ops.convert_to_tensor( + filenames, dtype=dtypes.string, name="filenames") + + def _as_variant_tensor(self): + return gen_dataset_ops.sequence_file_dataset( + self._filenames, nest.flatten(self.output_types)) + + @property + def output_classes(self): + return ops.Tensor, ops.Tensor + + @property + def output_shapes(self): + return (tensor_shape.TensorShape([]), tensor_shape.TensorShape([])) + + @property + def output_types(self): + return dtypes.string, dtypes.string diff --git a/tensorflow/contrib/hadoop/python/ops/hadoop_op_loader.py b/tensorflow/contrib/hadoop/python/ops/hadoop_op_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..6dbf1253f3f746de0da9664b4262cb208bee9c98 --- /dev/null +++ b/tensorflow/contrib/hadoop/python/ops/hadoop_op_loader.py @@ -0,0 +1,24 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Python helper for loading hadoop ops and kernels.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.util import loader +from tensorflow.python.platform import resource_loader + +_dataset_ops = loader.load_op_library( + resource_loader.get_path_to_datafile("../../_dataset_ops.so")) diff --git a/tensorflow/contrib/image/kernels/image_ops.cc b/tensorflow/contrib/image/kernels/image_ops.cc index 022e17d13963a14f81d76e683d13060d1f3f8a7e..693724b45751b82f942bc9416e6fb2ba33b30e22 100644 --- a/tensorflow/contrib/image/kernels/image_ops.cc +++ b/tensorflow/contrib/image/kernels/image_ops.cc @@ -71,6 +71,7 @@ class ImageProjectiveTransform : public OpKernel { void Compute(OpKernelContext* ctx) override { const Tensor& images_t = ctx->input(0); const Tensor& transform_t = ctx->input(1); + const Tensor& shape_t = ctx->input(2); OP_REQUIRES(ctx, images_t.shape().dims() == 4, errors::InvalidArgument("Input images must have rank 4")); OP_REQUIRES(ctx, @@ -81,11 +82,28 @@ class ImageProjectiveTransform : public OpKernel { ProjectiveGenerator::kNumParameters), errors::InvalidArgument( "Input transform should be num_images x 8 or 1 x 8")); - auto images = images_t.tensor(); - auto transform = transform_t.matrix(); + OP_REQUIRES(ctx, shape_t.dims() == 1, + errors::InvalidArgument("output shape must be 1-dimensional", + shape_t.shape().DebugString())); + OP_REQUIRES(ctx, shape_t.NumElements() == 2, + errors::InvalidArgument("output shape must have two elements", + shape_t.shape().DebugString())); + auto shape_vec = shape_t.vec(); + int32 out_height = shape_vec(0); + int32 out_width = shape_vec(1); + OP_REQUIRES(ctx, out_height > 0 && out_width > 0, + errors::InvalidArgument("output dimensions must be positive")); + Tensor* output_t; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, images_t.shape(), &output_t)); + OP_REQUIRES_OK(ctx, ctx->allocate_output( + 0, + TensorShape({images_t.dim_size(0), out_height, + out_width, images_t.dim_size(3)}), + &output_t)); auto output = output_t->tensor(); + auto images = images_t.tensor(); + auto transform = transform_t.matrix(); + (FillProjectiveTransform(interpolation_))( ctx->eigen_device(), &output, images, transform); } @@ -129,10 +147,11 @@ TF_CALL_double(DECLARE_FUNCTOR); } // end namespace functor -#define REGISTER(TYPE) \ - REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransform") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("dtype"), \ +#define REGISTER(TYPE) \ + REGISTER_KERNEL_BUILDER(Name("ImageProjectiveTransform") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("dtype") \ + .HostMemory("output_shape"), \ ImageProjectiveTransform) TF_CALL_uint8(REGISTER); diff --git a/tensorflow/contrib/image/kernels/image_ops.h b/tensorflow/contrib/image/kernels/image_ops.h index 209aa24548443bb10c13cd506b8c93c23cfff4a4..6b63eed1303accc330293b3a44cdb9def7881666 100644 --- a/tensorflow/contrib/image/kernels/image_ops.h +++ b/tensorflow/contrib/image/kernels/image_ops.h @@ -167,7 +167,7 @@ struct FillProjectiveTransform { void operator()(const Device& device, OutputType* output, const InputType& images, const TransformsType& transform) const { - output->device(device) = images.generate( + output->device(device) = output->generate( ProjectiveGenerator(images, transform, interpolation_)); } }; diff --git a/tensorflow/contrib/image/ops/image_ops.cc b/tensorflow/contrib/image/ops/image_ops.cc index e59f1bf8443732a4b84fe7461439e3d0ee7dd158..4969ac58f96c8c0b829828ad7617a0bb5520cd6a 100644 --- a/tensorflow/contrib/image/ops/image_ops.cc +++ b/tensorflow/contrib/image/ops/image_ops.cc @@ -19,23 +19,66 @@ limitations under the License. namespace tensorflow { +using shape_inference::DimensionHandle; using shape_inference::InferenceContext; using shape_inference::ShapeHandle; +namespace { + +// Sets output[0] to shape [batch_dim,height,width,channel_dim], where +// height and width come from the size_tensor. +Status SetOutputToSizedImage(InferenceContext* c, DimensionHandle batch_dim, + int size_input_idx, DimensionHandle channel_dim) { + // Verify shape of size input. + ShapeHandle size; + TF_RETURN_IF_ERROR(c->WithRank(c->input(size_input_idx), 1, &size)); + DimensionHandle unused; + TF_RETURN_IF_ERROR(c->WithValue(c->Dim(size, 0), 2, &unused)); + + // Get size values from the size tensor. + const Tensor* size_tensor = c->input_tensor(size_input_idx); + DimensionHandle width; + DimensionHandle height; + if (size_tensor == nullptr) { + width = c->UnknownDim(); + height = c->UnknownDim(); + } else { + // TODO(petewarden) - Remove once we have constant evaluation in C++ only. + if (size_tensor->dtype() != DT_INT32) { + return errors::InvalidArgument( + "Bad size input type for SetOutputToSizedImage: Expected DT_INT32 " + "but got ", + DataTypeString(size_tensor->dtype()), " for input #", size_input_idx, + " in ", c->DebugString()); + } + auto vec = size_tensor->vec(); + height = c->MakeDim(vec(0)); + width = c->MakeDim(vec(1)); + } + c->set_output(0, c->MakeShape({batch_dim, height, width, channel_dim})); + return Status::OK(); +} + +// TODO(qyu): Move this to core/framework/common_shape_fns.h +Status ResizeShapeFn(InferenceContext* c) { + ShapeHandle input; + TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input)); + return SetOutputToSizedImage(c, c->Dim(input, 0), 2 /* size_input_idx */, + c->Dim(input, 3)); +} + +} // namespace + // TODO(ringwalt): Add a "fill_mode" argument with "constant", "mirror", etc. // TODO(ringwalt): Add a "fill_constant" argument for constant mode (default 0). -// TODO(ringwalt): Add an "output_shape" argument. This is sufficient to -// implement "same" and "valid" modes in the Python function. REGISTER_OP("ImageProjectiveTransform") .Input("images: dtype") .Input("transforms: float32") + .Input("output_shape: int32") .Attr("dtype: {uint8, int32, int64, float16, float32, float64}") .Attr("interpolation: string") .Output("transformed_images: dtype") - .SetShapeFn([](InferenceContext* c) { - c->set_output(0, c->input(0)); - return Status::OK(); - }) + .SetShapeFn(ResizeShapeFn) .Doc(R"doc( Applies the given transform to each of the images. @@ -49,7 +92,7 @@ If one row of `transforms` is `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps the *output* point `(x, y)` to a transformed *input* point `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`, where `k = c0 x + c1 y + 1`. If the transformed point lays outside of the input -image, the output pixel is set to 0. The output is the same size as the input, +image, the output pixel is set to 0. images: 4D `Tensor`, input image(s) in NHWC format. transforms: 2D `Tensor`, projective transform(s) to apply to the image(s). diff --git a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py index 62a22dcf3411fb160b3c432bbdd67303697f7262..f588eae923f403f07c7f502821db4ef6acad71d5 100644 --- a/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py +++ b/tensorflow/contrib/image/python/kernel_tests/image_ops_test.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import math_ops +from tensorflow.python.ops import random_ops from tensorflow.python.platform import googletest _DTYPES = set( @@ -194,6 +195,19 @@ class ImageOpsTest(test_util.TensorFlowTestCase): [0.0, 149, 233, 149, 0.0], [0.0, 0.0, 87., 0.0, 0.0]]) + def test_rotate_static_shape(self): + image = array_ops.diag([1., 2., 3.]) + result = image_ops.rotate( + image, random_ops.random_uniform((), -1, 1), interpolation="BILINEAR") + self.assertEqual(image.get_shape(), result.get_shape()) + + def test_transform_static_output_shape(self): + image = constant_op.constant([[1., 2.], [3., 4.]]) + result = image_ops.transform( + image, random_ops.random_uniform([8], -1, 1), + output_shape=constant_op.constant([3, 5])) + self.assertAllEqual([3, 5], result.get_shape()) + def _test_grad(self, shape_to_test): with self.test_session(): test_image_shape = shape_to_test @@ -213,10 +227,40 @@ class ImageOpsTest(test_util.TensorFlowTestCase): x_init_value=test_image) self.assertLess(left_err, 1e-10) + def _test_grad_different_shape(self, input_shape, output_shape): + with self.test_session(): + test_image_shape = input_shape + test_image = np.random.randn(*test_image_shape) + test_image_tensor = constant_op.constant( + test_image, shape=test_image_shape) + test_transform = image_ops.angles_to_projective_transforms( + np.pi / 2, 4, 4) + + if len(output_shape) == 2: + resize_shape = output_shape + elif len(output_shape) == 3: + resize_shape = output_shape[0:2] + elif len(output_shape) == 4: + resize_shape = output_shape[1:3] + output = image_ops.transform( + images=test_image_tensor, + transforms=test_transform, + output_shape=resize_shape) + left_err = gradient_checker.compute_gradient_error( + test_image_tensor, + test_image_shape, + output, + output_shape, + x_init_value=test_image) + self.assertLess(left_err, 1e-10) + def test_grad(self): self._test_grad([16, 16]) self._test_grad([4, 12, 12]) self._test_grad([3, 4, 12, 12]) + self._test_grad_different_shape([16, 16], [8, 8]) + self._test_grad_different_shape([4, 12, 3], [8, 24, 3]) + self._test_grad_different_shape([3, 4, 12, 3], [3, 8, 24, 3]) class BipartiteMatchTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/contrib/image/python/ops/image_ops.py b/tensorflow/contrib/image/python/ops/image_ops.py index 86b0ffe9a0f2236d5ac7d5f846e7b5d2615c9b09..e7a09041adb33981df0a8c8238bc5b9358f14180 100644 --- a/tensorflow/contrib/image/python/ops/image_ops.py +++ b/tensorflow/contrib/image/python/ops/image_ops.py @@ -23,6 +23,7 @@ from tensorflow.python.framework import common_shapes from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import linalg_ops @@ -40,6 +41,9 @@ ops.RegisterShape("ImageConnectedComponents")(common_shapes.call_cpp_shape_fn) ops.RegisterShape("ImageProjectiveTransform")(common_shapes.call_cpp_shape_fn) +# TODO(ringwalt): Support a "reshape" (name used by SciPy) or "expand" (name +# used by PIL, maybe more readable) mode, which determines the correct +# output_shape and translation for the transform. def rotate(images, angles, interpolation="NEAREST", name=None): """Rotate image(s) counterclockwise by the passed angle(s) in radians. @@ -213,7 +217,11 @@ def translations_to_projective_transforms(translations, name=None): axis=1) -def transform(images, transforms, interpolation="NEAREST", name=None): +def transform(images, + transforms, + interpolation="NEAREST", + output_shape=None, + name=None): """Applies the given transform(s) to the image(s). Args: @@ -230,6 +238,10 @@ def transform(images, transforms, interpolation="NEAREST", name=None): the transform mapping input points to output points. Note that gradients are not backpropagated into transformation parameters. interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR". + output_shape: Output dimesion after the transform, [height, width]. + If None, output is the same size as input image. + + name: The name of the op. Returns: Image(s) with the same type and shape as `images`, with the given @@ -238,6 +250,7 @@ def transform(images, transforms, interpolation="NEAREST", name=None): Raises: TypeError: If `image` is an invalid type. + ValueError: If output shape is not 1-D int32 Tensor. """ with ops.name_scope(name, "transform"): image_or_images = ops.convert_to_tensor(images, name="images") @@ -256,6 +269,17 @@ def transform(images, transforms, interpolation="NEAREST", name=None): else: raise TypeError("Images should have rank between 2 and 4.") + if output_shape is None: + output_shape = tensor_util.constant_value( + array_ops.shape(images)[1:3]) or array_ops.shape(images)[1:3] + + output_shape = ops.convert_to_tensor( + output_shape, dtypes.int32, name="output_shape") + + if not output_shape.get_shape().is_compatible_with([2]): + raise ValueError("output_shape must be a 1-D Tensor of 2 elements: " + "new_height, new_width") + if len(transform_or_transforms.get_shape()) == 1: transforms = transform_or_transforms[None] elif transform_or_transforms.get_shape().ndims is None: @@ -265,8 +289,12 @@ def transform(images, transforms, interpolation="NEAREST", name=None): transforms = transform_or_transforms else: raise TypeError("Transforms should have rank 1 or 2.") + output = gen_image_ops.image_projective_transform( - images, transforms, interpolation=interpolation.upper()) + images, + output_shape=output_shape, + transforms=transforms, + interpolation=interpolation.upper()) if len(image_or_images.get_shape()) == 2: return output[0, :, :, 0] elif len(image_or_images.get_shape()) == 3: @@ -376,14 +404,6 @@ def _image_projective_transform_grad(op, grad): if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES: raise TypeError("Invalid dtype %s." % image_or_images.dtype) - if len(image_or_images.get_shape()) == 2: - images = image_or_images[None, :, :, None] - elif len(image_or_images.get_shape()) == 3: - images = image_or_images[None, :, :, :] - elif len(image_or_images.get_shape()) == 4: - images = image_or_images - else: - raise TypeError("Images should have rank between 2 and 4") if len(transform_or_transforms.get_shape()) == 1: transforms = transform_or_transforms[None] elif len(transform_or_transforms.get_shape()) == 2: @@ -396,13 +416,11 @@ def _image_projective_transform_grad(op, grad): inverse = linalg_ops.matrix_inverse(transforms) transforms = matrices_to_flat_transforms(inverse) output = gen_image_ops.image_projective_transform( - grad, transforms, interpolation=interpolation) - if len(image_or_images.get_shape()) == 2: - return [output[0, :, :, 0], None] - elif len(image_or_images.get_shape()) == 3: - return [output[0, :, :, :], None] - else: - return [output, None] + images=grad, + transforms=transforms, + output_shape=array_ops.shape(image_or_images)[1:3], + interpolation=interpolation) + return [output, None, None] def bipartite_match(distance_mat, diff --git a/tensorflow/contrib/image/python/ops/sparse_image_warp.py b/tensorflow/contrib/image/python/ops/sparse_image_warp.py index 54a215d6db6ded56a1a4a018a7e176f35fe6397e..1ea8f705b7e6f522281de6384de0d42efab6a406 100644 --- a/tensorflow/contrib/image/python/ops/sparse_image_warp.py +++ b/tensorflow/contrib/image/python/ops/sparse_image_warp.py @@ -112,10 +112,10 @@ def sparse_image_warp(image, Apply a non-linear warp to the image, where the warp is specified by the source and destination locations of a (potentially small) number of control points. First, we use a polyharmonic spline - (@{tf.contrib.image.interpolate_spline}) to interpolate the displacements + (`tf.contrib.image.interpolate_spline`) to interpolate the displacements between the corresponding control points to a dense flow field. Then, we warp the image using this dense flow field - (@{tf.contrib.image.dense_image_warp}). + (`tf.contrib.image.dense_image_warp`). Let t index our control points. For regularization_weight=0, we have: warped_image[b, dest_control_point_locations[b, t, 0], @@ -126,7 +126,7 @@ def sparse_image_warp(image, For regularization_weight > 0, this condition is met approximately, since regularized interpolation trades off smoothness of the interpolant vs. reconstruction of the interpolant at the control points. - See @{tf.contrib.image.interpolate_spline} for further documentation of the + See `tf.contrib.image.interpolate_spline` for further documentation of the interpolation_order and regularization_weight arguments. diff --git a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc index 2638b25ec424b5b4ef556ff769e94e64da32fec2..92ae79d3c7a1d27bba1ad62c8ea596fb78a326d0 100644 --- a/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc +++ b/tensorflow/contrib/kafka/kernels/kafka_dataset_ops.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/core/framework/dataset.h" -#include "src-cpp/rdkafkacpp.h" +#include "rdkafkacpp.h" namespace tensorflow { @@ -84,7 +84,8 @@ class KafkaDatasetOp : public DatasetOpKernel { string DebugString() const override { return "KafkaDatasetOp::Dataset"; } protected: - Status AsGraphDefInternal(DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { Node* topics = nullptr; TF_RETURN_IF_ERROR(b->AddVector(topics_, &topics)); diff --git a/tensorflow/contrib/keras/__init__.py b/tensorflow/contrib/keras/__init__.py index a162f0cb584038b8df7d1ee6fe8237160ad8f695..cecf1ddcdb1c6e1b6a6f895b83a6c4f2a2aae1f7 100644 --- a/tensorflow/contrib/keras/__init__.py +++ b/tensorflow/contrib/keras/__init__.py @@ -15,7 +15,7 @@ # ============================================================================== """Implementation of the Keras API meant to be a high-level API for TensorFlow. -This module an alias for @{tf.keras}, for backwards compatibility. +This module an alias for `tf.keras`, for backwards compatibility. Detailed documentation and user guides are also available at [keras.io](https://keras.io). diff --git a/tensorflow/contrib/keras/api/keras/preprocessing/image/__init__.py b/tensorflow/contrib/keras/api/keras/preprocessing/image/__init__.py index 1f9e82b41bf09b235e93fa512a50ea4c3047c01b..cb649a37510c301cb3df997f844617e9a4e6c7be 100644 --- a/tensorflow/contrib/keras/api/keras/preprocessing/image/__init__.py +++ b/tensorflow/contrib/keras/api/keras/preprocessing/image/__init__.py @@ -18,10 +18,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.keras.preprocessing.image import apply_transform from tensorflow.python.keras.preprocessing.image import array_to_img from tensorflow.python.keras.preprocessing.image import DirectoryIterator -from tensorflow.python.keras.preprocessing.image import flip_axis from tensorflow.python.keras.preprocessing.image import ImageDataGenerator from tensorflow.python.keras.preprocessing.image import img_to_array from tensorflow.python.keras.preprocessing.image import Iterator diff --git a/tensorflow/contrib/kernel_methods/README.md b/tensorflow/contrib/kernel_methods/README.md index 44ed9670a09ece8fb11e79a3e58725e2a54e513b..1bce3277ff46ac91a8de118db17041a0e424ebc0 100644 --- a/tensorflow/contrib/kernel_methods/README.md +++ b/tensorflow/contrib/kernel_methods/README.md @@ -21,13 +21,15 @@ Currently, there is a [RandomFourierFeatureMapper](https://www.tensorflow.org/co output. More mappers are on the way. ## Kernel-based Estimators -These are estimators inheriting from the @{tf.contrib.learn.Estimator} class and -use kernel mappers internally to discover non-linearities in the data. These -canned estimators map their input features using kernel mapper Ops and then -apply linear models to the mapped features. Combining kernel mappers with linear -models and different loss functions leads to a variety of models: linear and -non-linear SVMs, linear regression (with and without kernels) and (multinomial) -logistic regression (with and without kernels). + +These estimators inherit from the +[`tf.contrib.learn.Estimator`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/estimators/estimator.py) +class and use kernel mappers internally to discover non-linearities in the +data. These canned estimators map their input features using kernel mapper +Ops and then apply linear models to the mapped features. Combining kernel +mappers with linear models and different loss functions leads to a variety of +models: linear and non-linear SVMs, linear regression (with and without +kernels) and (multinomial) logistic regression (with and without kernels). Currently there is a [KernelLinearClassifier](https://www.tensorflow.org/code/tensorflow/contrib/kernel_methods/python/kernel_estimators.py) implemented but more pre-packaged estimators are on the way. diff --git a/tensorflow/contrib/kinesis/kernels/kinesis_dataset_ops.cc b/tensorflow/contrib/kinesis/kernels/kinesis_dataset_ops.cc index 3212279c4c50efb92acc712b82cb3e1a22c76870..7b28bb5e4db0a5bb1d88daf382ed78c485bab609 100644 --- a/tensorflow/contrib/kinesis/kernels/kinesis_dataset_ops.cc +++ b/tensorflow/contrib/kinesis/kernels/kinesis_dataset_ops.cc @@ -194,7 +194,8 @@ class KinesisDatasetOp : public DatasetOpKernel { string DebugString() const override { return "KinesisDatasetOp::Dataset"; } protected: - Status AsGraphDefInternal(DatasetGraphDefBuilder* b, + Status AsGraphDefInternal(SerializationContext* ctx, + DatasetGraphDefBuilder* b, Node** output) const override { Node* stream = nullptr; TF_RETURN_IF_ERROR(b->AddScalar(stream_, &stream)); diff --git a/tensorflow/contrib/layers/python/layers/initializers.py b/tensorflow/contrib/layers/python/layers/initializers.py index 51610f21b24f1d40f26630cc1e69ca723d130639..1192198ec26c9db749a9bd1ee07f52395fd16a0f 100644 --- a/tensorflow/contrib/layers/python/layers/initializers.py +++ b/tensorflow/contrib/layers/python/layers/initializers.py @@ -47,7 +47,7 @@ def xavier_initializer(uniform=True, seed=None, dtype=dtypes.float32): Args: uniform: Whether to use uniform or normal distributed random initialization. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. Returns: @@ -98,7 +98,7 @@ def variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False, mode: String. 'FAN_IN', 'FAN_OUT', 'FAN_AVG'. uniform: Whether to use uniform or normal distributed random initialization. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. Returns: diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index fa334070ad5a78fd08aa5dd255aebee44ee91c5d..04668f112d85b946f313f85e60ee607fe761f63c 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -1584,7 +1584,7 @@ def dropout(inputs, outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. Returns: A tensor representing the output of the operation. @@ -2660,7 +2660,7 @@ def separable_convolution2d( inputs, num_outputs, kernel_size, - depth_multiplier, + depth_multiplier=1, stride=1, padding='SAME', data_format=DATA_FORMAT_NHWC, diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index c5c7269b1f15849956e90654e3bcf8ab0eebc393..51c7abb105a29ff0dfab49d77bc62d5b51517179 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -1189,7 +1189,7 @@ class ConvolutionInPlaneTest(test.TestCase): result = sess.run(horz_gradients) expected = np.zeros((1, 10, 9, 1)) - self.assertAllEqual(result, expected) + self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5) def testHorzConvWithBlankImageAndPlaceholder(self): image = array_ops.placeholder(dtypes.float32, shape=(None, None, None, 1)) @@ -1209,7 +1209,7 @@ class ConvolutionInPlaneTest(test.TestCase): }) expected = np.zeros((1, 10, 9, 1)) - self.assertAllEqual(result, expected) + self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5) def testHorzConvWithRandomImageMultiBatch(self): np.random.seed(1) diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD index b56a88659bbd4467600788fc8e3e9dbf38ce8244..d3aa3fa92c3ca8b67e81c4600c4ccce8a54d5792 100644 --- a/tensorflow/contrib/learn/BUILD +++ b/tensorflow/contrib/learn/BUILD @@ -79,16 +79,7 @@ py_library( "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python:weights_broadcast_ops", - "//tensorflow/python/estimator", "//tensorflow/python/estimator:estimator_py", - "//tensorflow/python/estimator:export_export", - "//tensorflow/python/estimator:export_output", - "//tensorflow/python/estimator:inputs", - "//tensorflow/python/estimator:inputs_queues", - "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/estimator:pandas_io", - "//tensorflow/python/estimator:run_config", "//tensorflow/python/feature_column", "//tensorflow/python/feature_column:feature_column_py", "//tensorflow/python/ops/losses", @@ -171,7 +162,7 @@ tf_py_test( "//tensorflow/python:training", "//tensorflow/python:util", "//tensorflow/python:variables", - "//tensorflow/python/estimator", + "//tensorflow/python/estimator:estimator_py", ], tags = ["no_windows"], # TODO: needs investigation on Windows ) @@ -220,7 +211,7 @@ py_test( "//tensorflow/contrib/training:training_py", "//tensorflow/python:client_testlib", "//tensorflow/python:platform", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -245,7 +236,7 @@ py_test( "//tensorflow/python:summary", "//tensorflow/python:training", "//tensorflow/python:variables", - "//tensorflow/python/estimator", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -259,7 +250,7 @@ py_test( "//tensorflow/core:protos_all_py", "//tensorflow/python:client_testlib", "//tensorflow/python:training", - "//tensorflow/python/estimator:run_config", + "//tensorflow/python/estimator:estimator_py", ], ) @@ -621,7 +612,7 @@ py_test( "//tensorflow/python:control_flow_ops", "//tensorflow/python:session", "//tensorflow/python:training", - "//tensorflow/python/estimator:export_output", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/saved_model:signature_constants", "@six_archive//:six", ], diff --git a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py index 66ebcfd1d81904b9afe5be6bd1a648fe325e1e0b..21f7dcc5e427bf00ffbc71150475d94f5336f8aa 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/kmeans.py +++ b/tensorflow/contrib/learn/python/learn/estimators/kmeans.py @@ -15,9 +15,9 @@ """Implementation of k-means clustering on top of `Estimator` API (deprecated). This module is deprecated. Please use -@{tf.contrib.factorization.KMeansClustering} instead of -@{tf.contrib.learn.KMeansClustering}. It has a similar interface, but uses the -@{tf.estimator.Estimator} API instead of @{tf.contrib.learn.Estimator}. +`tf.contrib.factorization.KMeansClustering` instead of +`tf.contrib.learn.KMeansClustering`. It has a similar interface, but uses the +`tf.estimator.Estimator` API instead of `tf.contrib.learn.Estimator`. """ from __future__ import absolute_import diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py index c36879e0483c92db0cc08dedbb483bcc288d4894..08f23aa2231424887f3c935dbb8368a2aa46cc63 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py +++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py @@ -221,7 +221,7 @@ class ClusterConfig(object): class RunConfig(ClusterConfig, core_run_config.RunConfig): """This class specifies the configurations for an `Estimator` run. - This class is a deprecated implementation of @{tf.estimator.RunConfig} + This class is a deprecated implementation of `tf.estimator.RunConfig` interface. """ _USE_DEFAULT = 0 diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py index 08e907a608b0c6df6e7ac9d9675f7f9e2b84ff5d..4e64efdd959eef0951c9ab782996fc2bd5919cc5 100644 --- a/tensorflow/contrib/learn/python/learn/experiment.py +++ b/tensorflow/contrib/learn/python/learn/experiment.py @@ -162,16 +162,16 @@ class Experiment(object): Args: estimator: Object implementing Estimator interface, which could be a - combination of @{tf.contrib.learn.Trainable} and - @{tf.contrib.learn.Evaluable} (deprecated), or - @{tf.estimator.Estimator}. + combination of `tf.contrib.learn.Trainable` and + `tf.contrib.learn.Evaluable` (deprecated), or + `tf.estimator.Estimator`. train_input_fn: function, returns features and labels for training. eval_input_fn: function, returns features and labels for evaluation. If `eval_steps` is `None`, this should be configured only to produce for a finite number of batches (generally, 1 epoch over the evaluation data). eval_metrics: `dict` of string, metric function. If `None`, default set is used. This should be `None` if the `estimator` is - @{tf.estimator.Estimator}. If metrics are provided they will be + `tf.estimator.Estimator`. If metrics are provided they will be *appended* to the default set. train_steps: Perform this many steps of training. `None`, the default, means train forever. diff --git a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py index 66af6833da1644fa4f73a24987079c9ffc8cecce..4f22054af3077fa5322b52f56e815fe76104f602 100644 --- a/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py +++ b/tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py @@ -415,7 +415,7 @@ def make_export_strategy(serving_input_fn, `InputFnOps`. default_output_alternative_key: the name of the head to serve when an incoming serving request does not explicitly request a specific head. - Must be `None` if the estimator inherits from @{tf.estimator.Estimator} + Must be `None` if the estimator inherits from `tf.estimator.Estimator` or for single-headed models. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the destination @@ -453,7 +453,7 @@ def make_export_strategy(serving_input_fn, The string path to the exported directory. Raises: - ValueError: If `estimator` is a @{tf.estimator.Estimator} instance + ValueError: If `estimator` is a `tf.estimator.Estimator` instance and `default_output_alternative_key` was specified. """ if isinstance(estimator, core_estimator.Estimator): @@ -504,7 +504,7 @@ def make_parsing_export_strategy(feature_columns, that must be provided at serving time (excluding labels!). default_output_alternative_key: the name of the head to serve when an incoming serving request does not explicitly request a specific head. - Must be `None` if the estimator inherits from @{tf.estimator.Estimator} + Must be `None` if the estimator inherits from `tf.estimator.Estimator` or for single-headed models. assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel. Each key should give the destination @@ -767,7 +767,7 @@ def extend_export_strategy(base_export_strategy, The string path to the SavedModel indicated by post_export_fn. Raises: - ValueError: If `estimator` is a @{tf.estimator.Estimator} instance + ValueError: If `estimator` is a `tf.estimator.Estimator` instance and `default_output_alternative_key` was specified or if post_export_fn does not return a valid directory. RuntimeError: If unable to create temporary or final export directory. diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 3f158850d9405d2d0e6567a3cc270c04c19a2a5f..81844756bc7239fa798ff96b8b093afdf9ea9557 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -265,7 +265,7 @@ def generated_test_models(): "prelu", "pow", "reduce_max", - #"reduce_prod", # disabled due to b/111823366 + "reduce_prod", "relu", "relu1", "relu6", diff --git a/tensorflow/contrib/lite/delegates/eager/BUILD b/tensorflow/contrib/lite/delegates/eager/BUILD index 332a871446e273344556d6910dbfa271d3ba3766..bb518becc582b776096fc0d2720042286b0b871e 100644 --- a/tensorflow/contrib/lite/delegates/eager/BUILD +++ b/tensorflow/contrib/lite/delegates/eager/BUILD @@ -7,6 +7,8 @@ package(default_visibility = [ licenses(["notice"]) # Apache 2.0 +load("//tensorflow:tensorflow.bzl", "tf_cc_test") + cc_library( name = "buffer_map", srcs = ["buffer_map.cc"], @@ -21,12 +23,11 @@ cc_library( ], ) -cc_test( +tf_cc_test( name = "buffer_map_test", size = "small", srcs = ["buffer_map_test.cc"], tags = [ - "no_oss", "tflite_not_portable", ], deps = [ @@ -50,6 +51,7 @@ cc_library( ":buffer_map", ":delegate_data", ":kernel", + ":util", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:kernel_api", "//tensorflow/contrib/lite:util", @@ -57,12 +59,11 @@ cc_library( ], ) -cc_test( +tf_cc_test( name = "delegate_test", size = "small", srcs = ["delegate_test.cc"], tags = [ - "no_oss", "tflite_not_portable", ], deps = [ @@ -85,12 +86,11 @@ cc_library( ], ) -cc_test( +tf_cc_test( name = "delegate_data_test", size = "small", srcs = ["delegate_data_test.cc"], tags = [ - "no_oss", "tflite_not_portable", ], deps = [ @@ -111,6 +111,7 @@ cc_library( ":util", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:kernel_api", + "//tensorflow/contrib/lite:string", "//tensorflow/contrib/lite/kernels:kernel_util", "//tensorflow/core:protos_all_cc", "//tensorflow/core/common_runtime/eager:context", @@ -120,12 +121,11 @@ cc_library( ], ) -cc_test( +tf_cc_test( name = "kernel_test", size = "small", srcs = ["kernel_test.cc"], tags = [ - "no_oss", "tflite_not_portable", ], deps = [ @@ -143,6 +143,7 @@ cc_library( hdrs = ["test_util.h"], deps = [ "//tensorflow/c:c_api_internal", + "//tensorflow/contrib/lite:string", "//tensorflow/contrib/lite/kernels:test_util", "@com_google_absl//absl/memory", "@flatbuffers", @@ -154,6 +155,7 @@ cc_library( srcs = ["util.cc"], hdrs = ["util.h"], deps = [ + ":constants", "//tensorflow/c:c_api_internal", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:kernel_api", @@ -162,18 +164,17 @@ cc_library( ], ) -cc_test( +tf_cc_test( name = "util_test", size = "small", srcs = ["util_test.cc"], tags = [ - "no_oss", "tflite_not_portable", ], deps = [ ":util", + "//tensorflow/contrib/lite:string", "//tensorflow/contrib/lite/testing:util", - "//tensorflow/core:lib", "@com_google_googletest//:gtest", ], ) diff --git a/tensorflow/contrib/lite/delegates/eager/buffer_map_test.cc b/tensorflow/contrib/lite/delegates/eager/buffer_map_test.cc index dcb3f6c94150892f565380ff0598a7a28f9399b1..a046943e56d2b80f2670b7fc3dd57b36dc4d2425 100644 --- a/tensorflow/contrib/lite/delegates/eager/buffer_map_test.cc +++ b/tensorflow/contrib/lite/delegates/eager/buffer_map_test.cc @@ -56,8 +56,8 @@ tensorflow::Tensor MakeTensor(const std::vector& shape, return buffer_map.GetTensor(0); } -std::vector GetTensorShape(const tensorflow::Tensor& t) { - std::vector shape(t.dims()); +std::vector GetTensorShape(const tensorflow::Tensor& t) { + std::vector shape(t.dims()); for (int i = 0; i < t.dims(); ++i) { shape[i] = t.dim_size(i); } diff --git a/tensorflow/contrib/lite/delegates/eager/delegate.cc b/tensorflow/contrib/lite/delegates/eager/delegate.cc index 673859da486feb5e2eadbef4dc253321389f696b..7d22b454199e2c0d9b8fea05086a7c62d7cdbe81 100644 --- a/tensorflow/contrib/lite/delegates/eager/delegate.cc +++ b/tensorflow/contrib/lite/delegates/eager/delegate.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/contrib/lite/context_util.h" #include "tensorflow/contrib/lite/delegates/eager/buffer_map.h" #include "tensorflow/contrib/lite/delegates/eager/kernel.h" +#include "tensorflow/contrib/lite/delegates/eager/util.h" #include "tensorflow/contrib/lite/util.h" #include "tensorflow/core/lib/core/status.h" @@ -27,7 +28,7 @@ namespace eager { namespace delegate { TfLiteStatus Prepare(TfLiteContext* context, TfLiteDelegate* delegate) { - // Get the nodes in the current execution plan. + // Get the nodes in the current execution plan. Interpreter owns this array. TfLiteIntArray* plan; TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan)); @@ -39,8 +40,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteDelegate* delegate) { TF_LITE_ENSURE_STATUS(context->GetNodeAndRegistration( context, node_index, &node, ®istration)); - if (registration->custom_name && - strncmp(registration->custom_name, "Eager", 5) == 0) { + if (IsEagerOp(registration->custom_name)) { supported_nodes.push_back(node_index); } } @@ -63,6 +63,7 @@ TfLiteStatus CopyFromBufferHandle(TfLiteDelegate* delegate, BufferMap* buffer_map = reinterpret_cast(delegate->data_)->GetBufferMap(); + // TODO(nupurgarg): Use TfLiteContext's ReportError instead of fprinf. if (!buffer_map->HasTensor(buffer_handle)) { fprintf(stderr, "Invalid tensor index %d.\n", buffer_handle); return kTfLiteError; @@ -83,20 +84,27 @@ TfLiteStatus CopyFromBufferHandle(TfLiteDelegate* delegate, } // namespace delegate } // namespace eager -EagerDelegate::EagerDelegate() { - if (!eager::DelegateData::Create(&delegate_data_).ok()) { - fprintf(stderr, "Unable to initialize TensorFlow context.\n"); - return; +EagerDelegate::EagerDelegate() {} + +EagerDelegate::~EagerDelegate() {} + +TfLiteStatus EagerDelegate::Apply(Interpreter* interpreter) { + if (!delegate_) { + if (!eager::DelegateData::Create(&delegate_data_).ok()) { + fprintf(stderr, "Unable to initialize TensorFlow context.\n"); + return kTfLiteError; + } + + delegate_.reset(new TfLiteDelegate{ + /*data_=*/delegate_data_.get(), + /*nullptr,*/ &eager::delegate::Prepare, + /*CopyFromBufferHandle=*/&eager::delegate::CopyFromBufferHandle, + /*CopyToBufferHandle=*/nullptr, + /*FreeBufferHandle=*/nullptr}); } - delegate_.reset(new TfLiteDelegate{ - /*data_=*/delegate_data_.get(), - /*nullptr,*/ &eager::delegate::Prepare, - /*CopyFromBufferHandle=*/&eager::delegate::CopyFromBufferHandle, - /*CopyToBufferHandle=*/nullptr, - /*FreeBufferHandle=*/nullptr}); + return interpreter->ModifyGraphWithDelegate(delegate_.get(), + /*allow_dynamic_tensors=*/true); } -EagerDelegate::~EagerDelegate() {} - } // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/delegate.h b/tensorflow/contrib/lite/delegates/eager/delegate.h index 6259b35931719e1f2385b66f373e357bc15e7d0f..0defca7c323e81bfb211ac56fd59c8656b320574 100644 --- a/tensorflow/contrib/lite/delegates/eager/delegate.h +++ b/tensorflow/contrib/lite/delegates/eager/delegate.h @@ -30,7 +30,7 @@ namespace tflite { // interpreter. // // Usage: -// EagerDelegate delegate(); +// EagerDelegate delegate; // ... build interpreter ... // // delegate.Apply(interpreter); @@ -42,10 +42,8 @@ class EagerDelegate { EagerDelegate(); ~EagerDelegate(); - TfLiteStatus Apply(Interpreter* interpreter) { - return interpreter->ModifyGraphWithDelegate(delegate_.get(), - /*allow_dynamic_tensors=*/true); - } + // Modifies the graph loaded in the interpreter. + TfLiteStatus Apply(Interpreter* interpreter); private: std::unique_ptr delegate_data_; diff --git a/tensorflow/contrib/lite/delegates/eager/kernel.cc b/tensorflow/contrib/lite/delegates/eager/kernel.cc index 172798180762f87e1c080be7788db661a63208b5..1bd17a3bcae727e8908ce669472f79595a8916a0 100644 --- a/tensorflow/contrib/lite/delegates/eager/kernel.cc +++ b/tensorflow/contrib/lite/delegates/eager/kernel.cc @@ -14,13 +14,14 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/delegates/eager/kernel.h" -#include "third_party/flatbuffers/include/flatbuffers/flexbuffers.h" +#include "flatbuffers/flexbuffers.h" #include "tensorflow/contrib/lite/builtin_ops.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/context_util.h" #include "tensorflow/contrib/lite/delegates/eager/delegate_data.h" #include "tensorflow/contrib/lite/delegates/eager/util.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/string.h" #include "tensorflow/core/common_runtime/eager/context.h" #include "tensorflow/core/common_runtime/eager/execute.h" #include "tensorflow/core/common_runtime/eager/tensor_handle.h" diff --git a/tensorflow/contrib/lite/delegates/eager/test_util.cc b/tensorflow/contrib/lite/delegates/eager/test_util.cc index 80acf5d9955f92ec06844b4bc3b980b3a924ab8f..26d96acc82064ba1046555940e1b1132874ef23e 100644 --- a/tensorflow/contrib/lite/delegates/eager/test_util.cc +++ b/tensorflow/contrib/lite/delegates/eager/test_util.cc @@ -16,7 +16,8 @@ limitations under the License. #include "tensorflow/contrib/lite/delegates/eager/test_util.h" #include "absl/memory/memory.h" -#include "third_party/flatbuffers/include/flatbuffers/flexbuffers.h" +#include "flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/string.h" namespace tflite { namespace eager { diff --git a/tensorflow/contrib/lite/delegates/eager/util.cc b/tensorflow/contrib/lite/delegates/eager/util.cc index 4426c653e6ff80aac52b50e06a3005173490433d..c8aa0b7f69f8f6bd3bff52b13f3cc7d689a514da 100644 --- a/tensorflow/contrib/lite/delegates/eager/util.cc +++ b/tensorflow/contrib/lite/delegates/eager/util.cc @@ -13,10 +13,16 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/delegates/eager/util.h" +#include "tensorflow/contrib/lite/delegates/eager/constants.h" namespace tflite { namespace eager { +bool IsEagerOp(const char* custom_name) { + return custom_name && strncmp(custom_name, kCustomCodePrefix, + strlen(kCustomCodePrefix)) == 0; +} + TfLiteStatus ConvertStatus(TfLiteContext* context, const tensorflow::Status& status) { if (!status.ok()) { diff --git a/tensorflow/contrib/lite/delegates/eager/util.h b/tensorflow/contrib/lite/delegates/eager/util.h index a9407be071192e9b7f25f95df9e76a5f44e7c9e3..b7363361bec47f30e0741e3a76a5a375d7d9aeb1 100644 --- a/tensorflow/contrib/lite/delegates/eager/util.h +++ b/tensorflow/contrib/lite/delegates/eager/util.h @@ -23,6 +23,10 @@ limitations under the License. namespace tflite { namespace eager { +// Checks whether the prefix of the custom name indicates the operation is an +// Eager operation. +bool IsEagerOp(const char* custom_name); + // Converts a tensorflow:Status into a TfLiteStatus. If the original status // represented an error, reports it using the given 'context'. TfLiteStatus ConvertStatus(TfLiteContext* context, diff --git a/tensorflow/contrib/lite/delegates/eager/util_test.cc b/tensorflow/contrib/lite/delegates/eager/util_test.cc index c4fbf5412776a2c5743e8d72fc6729cfd709c545..541d0b170197f7ac657cccfb79769522887e87e5 100644 --- a/tensorflow/contrib/lite/delegates/eager/util_test.cc +++ b/tensorflow/contrib/lite/delegates/eager/util_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/contrib/lite/string.h" #include "tensorflow/contrib/lite/testing/util.h" namespace tflite { @@ -102,6 +103,16 @@ TEST(UtilTest, TypeConversions) { EXPECT_EQ(TF_BOOL, GetTensorFlowDataType(kTfLiteBool)); } +TEST(UtilTest, IsEagerOp) { + EXPECT_TRUE(IsEagerOp("Eager")); + EXPECT_TRUE(IsEagerOp("EagerOp")); + EXPECT_FALSE(IsEagerOp("eager")); + EXPECT_FALSE(IsEagerOp("Eage")); + EXPECT_FALSE(IsEagerOp("OpEager")); + EXPECT_FALSE(IsEagerOp(nullptr)); + EXPECT_FALSE(IsEagerOp("")); +} + } // namespace } // namespace eager } // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/nnapi/BUILD b/tensorflow/contrib/lite/delegates/nnapi/BUILD index 091f8fbce734b466de33bb4b84e5e0fc3e4a71ef..954955f24b87f79a8dbe2863f608d532e25902c6 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/BUILD +++ b/tensorflow/contrib/lite/delegates/nnapi/BUILD @@ -22,7 +22,10 @@ tf_cc_test( name = "nnapi_delegate_test", size = "small", srcs = ["nnapi_delegate_test.cc"], - tags = ["no_oss"], + tags = [ + "no_oss", + "noasan", # TODO(b/112326936): re-enable for asan once fixed. + ], deps = [ ":nnapi_delegate", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc index 60855eb8edc4fb708d76b1e3a4ac37d462a64465..e6cc3dd99c2e18bf297f8fac244e5d809954a01a 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc @@ -27,7 +27,9 @@ limitations under the License. #include "tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h" #ifdef __ANDROID__ +#include #include +#include #endif namespace tflite { @@ -80,6 +82,44 @@ struct NNFreeCompilation { } }; +// Manage NNAPI shared memory handle +class NNMemory { + public: + NNMemory(const char* name, size_t size) { +#ifdef __ANDROID__ + byte_size_ = size; + fd_ = ASharedMemory_create(name, size); + data_ptr_ = reinterpret_cast( + mmap(nullptr, size, PROT_READ | PROT_WRITE, MAP_SHARED, fd_, 0)); + ANeuralNetworksMemory_createFromFd(size, PROT_READ | PROT_WRITE, fd_, 0, + &nn_memory_handle_); +#endif + } + + ~NNMemory() { +#ifdef __ANDROID__ + if (data_ptr_) { + munmap(data_ptr_, byte_size_); + } + if (nn_memory_handle_) { + ANeuralNetworksMemory_free(nn_memory_handle_); + } + if (fd_ > 0) close(fd_); +#endif + } + + ANeuralNetworksMemory* get_handle() { return nn_memory_handle_; } + uint8_t* get_data_ptr() { return data_ptr_; } + + private: +#ifdef __ANDROID__ + int fd_ = 0; + size_t byte_size_ = 0; +#endif + uint8_t* data_ptr_ = nullptr; + ANeuralNetworksMemory* nn_memory_handle_ = nullptr; +}; // namespace + // Track tensor indices to NN API tensor indices mapping. class OperandMapping { public: @@ -142,6 +182,12 @@ class NNAPIOpBuilder { ANEURALNETWORKS_TENSOR_INT32); } + TfLiteStatus AddVectorFloat32Operand(const float* values, + uint32_t num_values) { + return AddVectorOperand(values, num_values, + ANEURALNETWORKS_TENSOR_FLOAT32); + } + TfLiteStatus AddPoolingParams(void* data) { auto builtin = reinterpret_cast(data); AddScalarInt32Operand(builtin->padding); @@ -167,6 +213,37 @@ class NNAPIOpBuilder { return kTfLiteOk; } + TfLiteStatus AddAdditionalFloat32OutputTensor(uint32_t dimension_count) { + std::vector dims(dimension_count, 0); + ANeuralNetworksOperandType operand_type{ + .type = ANEURALNETWORKS_TENSOR_FLOAT32, + .dimensionCount = dimension_count, + .dimensions = dims.data()}; + CHECK_NN(context_, + ANeuralNetworksModel_addOperand(nn_model_, &operand_type)); + int ann_operand = operand_mapping_->add_new_non_tensor_operand(); + augmented_outputs_.push_back(ann_operand); + return kTfLiteOk; + } + + TfLiteStatus AddStateFloat32Tensor(int tensor_index, + int* ann_tensor_index_out) { + TfLiteTensor* tensor = &context_->tensors[tensor_index]; + int ann_index = operand_mapping_->add_new_non_tensor_operand(); + + ANeuralNetworksOperandType operand_type{ + ANEURALNETWORKS_TENSOR_FLOAT32, + static_cast(tensor->dims->size), + reinterpret_cast(tensor->dims->data), tensor->params.scale, + tensor->params.zero_point}; + CHECK_NN(context_, + ANeuralNetworksModel_addOperand(nn_model_, &operand_type)); + augmented_inputs_.push_back(ann_index); + + *ann_tensor_index_out = ann_index; + return kTfLiteOk; + } + // Adds a new NN API tensor that shadows the TF Lite tensor `tensor_index`. // This returns the NN API tensor index corresponding to the created tensor. // If another caller previously created a NN API tensor for `tensor_index` @@ -198,6 +275,10 @@ class NNAPIOpBuilder { nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; scale = tensor->params.scale; zeroPoint = tensor->params.zero_point; + if (scale == 0) { + // TENSOR_QUANT8_ASYMM with zero scale is not valid in NNAPI. + scale = 1; + } break; case kTfLiteInt32: nn_type = ANEURALNETWORKS_TENSOR_INT32; @@ -285,14 +366,21 @@ class NNAPIOpBuilder { std::vector augmented_outputs_; }; +struct NNAPIOpMappingArgs { + TfLiteContext* context; + NNAPIOpBuilder* builder; + TfLiteNode* node; + std::vector* model_state_inputs; + std::vector* model_state_tfl_outputs; +}; + // The kernel that represents the subgraph of TF Lite being run on NN API. class NNAPIDelegateKernel { public: NNAPIDelegateKernel() = default; - typedef ANeuralNetworksOperationType (*MappingFn)(TfLiteContext*, - NNAPIOpBuilder* builder, - TfLiteNode* node); + typedef ANeuralNetworksOperationType (*MappingFn)( + const NNAPIOpMappingArgs& mapping_args); // Return a function that knows how to translate a node into its operands // when called. You can use this function to see if a node is supported @@ -302,11 +390,11 @@ class NNAPIDelegateKernel { switch (builtin_code) { case kTfLiteBuiltinAdd: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->activation); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); return ANEURALNETWORKS_ADD; }; } else { @@ -315,11 +403,11 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinMul: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->activation); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); return ANEURALNETWORKS_MUL; }; } else { @@ -328,9 +416,10 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinAveragePool2d: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - builder->AddPoolingParams(node->builtin_data); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + mapping_args.builder->AddPoolingParams( + mapping_args.node->builtin_data); return ANEURALNETWORKS_AVERAGE_POOL_2D; }; } else { @@ -339,9 +428,10 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinMaxPool2d: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - builder->AddPoolingParams(node->builtin_data); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + mapping_args.builder->AddPoolingParams( + mapping_args.node->builtin_data); return ANEURALNETWORKS_MAX_POOL_2D; }; } else { @@ -350,9 +440,10 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinL2Pool2d: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - builder->AddPoolingParams(node->builtin_data); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + mapping_args.builder->AddPoolingParams( + mapping_args.node->builtin_data); return ANEURALNETWORKS_L2_POOL_2D; }; } else { @@ -368,14 +459,14 @@ class NNAPIDelegateKernel { // NNAPI does not support dilated Conv2D. return nullptr; } - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->padding); - builder->AddScalarInt32Operand(builtin->stride_width); - builder->AddScalarInt32Operand(builtin->stride_height); - builder->AddScalarInt32Operand(builtin->activation); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->padding); + mapping_args.builder->AddScalarInt32Operand(builtin->stride_width); + mapping_args.builder->AddScalarInt32Operand(builtin->stride_height); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); return ANEURALNETWORKS_CONV_2D; }; } else { @@ -384,15 +475,16 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinDepthwiseConv2d: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { auto builtin = reinterpret_cast( - node->builtin_data); - builder->AddScalarInt32Operand(builtin->padding); - builder->AddScalarInt32Operand(builtin->stride_width); - builder->AddScalarInt32Operand(builtin->stride_height); - builder->AddScalarInt32Operand(builtin->depth_multiplier); - builder->AddScalarInt32Operand(builtin->activation); + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->padding); + mapping_args.builder->AddScalarInt32Operand(builtin->stride_width); + mapping_args.builder->AddScalarInt32Operand(builtin->stride_height); + mapping_args.builder->AddScalarInt32Operand( + builtin->depth_multiplier); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); return ANEURALNETWORKS_DEPTHWISE_CONV_2D; }; } else { @@ -401,11 +493,11 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinFullyConnected: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { auto builtin = reinterpret_cast( - node->builtin_data); - builder->AddScalarInt32Operand(builtin->activation); + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); return ANEURALNETWORKS_FULLY_CONNECTED; }; } else { @@ -414,11 +506,11 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinSoftmax: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarFloat32Operand(builtin->beta); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarFloat32Operand(builtin->beta); return ANEURALNETWORKS_SOFTMAX; }; } else { @@ -427,8 +519,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinReshape: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_RESHAPE; }; } else { @@ -437,13 +529,13 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinSqueeze: if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); // Note that we add the squeeze dimensions even if the dimensions // were unspecified (empty), as NNAPI requires the operand. - builder->AddVectorInt32Operand( + mapping_args.builder->AddVectorInt32Operand( builtin->squeeze_dims, static_cast(builtin->num_squeeze_dims)); return ANEURALNETWORKS_SQUEEZE; @@ -458,21 +550,21 @@ class NNAPIDelegateKernel { // NNAPI does not support activations return nullptr; } - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_L2_NORMALIZATION; }; } case kTfLiteBuiltinLocalResponseNormalization: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { auto builtin = reinterpret_cast( - node->builtin_data); - builder->AddScalarInt32Operand(builtin->radius); - builder->AddScalarFloat32Operand(builtin->bias); - builder->AddScalarFloat32Operand(builtin->alpha); - builder->AddScalarFloat32Operand(builtin->beta); + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->radius); + mapping_args.builder->AddScalarFloat32Operand(builtin->bias); + mapping_args.builder->AddScalarFloat32Operand(builtin->alpha); + mapping_args.builder->AddScalarFloat32Operand(builtin->beta); return ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION; }; } else { @@ -488,11 +580,11 @@ class NNAPIDelegateKernel { ->type == kTfLiteLshProjectionSparse) { return nullptr; } - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { auto builtin = reinterpret_cast( - node->builtin_data); - builder->AddScalarInt32Operand(builtin->type); + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->type); return ANEURALNETWORKS_LSH_PROJECTION; }; } else { @@ -515,11 +607,11 @@ class NNAPIDelegateKernel { } } } - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { auto builtin = reinterpret_cast( - node->builtin_data); - builder->AddScalarInt32Operand(builtin->axis); + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->axis); return ANEURALNETWORKS_CONCATENATION; }; } else { @@ -528,8 +620,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinDequantize: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_DEQUANTIZE; }; } else { @@ -538,8 +630,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinFloor: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_FLOOR; }; } else { @@ -548,8 +640,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinRelu: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_RELU; }; } else { @@ -558,8 +650,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinReluN1To1: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_RELU1; }; } else { @@ -568,8 +660,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinRelu6: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_RELU6; }; } else { @@ -578,8 +670,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinLogistic: if (version == 1) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_LOGISTIC; }; } else { @@ -591,8 +683,8 @@ class NNAPIDelegateKernel { if (version == 1 && context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { // NNAPI only support float tanh. - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_TANH; }; } else { @@ -603,11 +695,11 @@ class NNAPIDelegateKernel { if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { // NNAPI only support float sub. - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->activation); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); return ANEURALNETWORKS_SUB; }; } else { @@ -618,11 +710,11 @@ class NNAPIDelegateKernel { if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { // NNAPI only support float div. - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->activation); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); return ANEURALNETWORKS_DIV; }; } else { @@ -636,8 +728,8 @@ class NNAPIDelegateKernel { // NNAPI does not support specifying the padding value. // NNAPI pads physical zero for quantized tensors, so only delegate // float pad to NNAPI. - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_PAD; }; } else { @@ -646,8 +738,8 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinSpaceToBatchNd: if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_SPACE_TO_BATCH_ND; }; } else { @@ -656,13 +748,14 @@ class NNAPIDelegateKernel { break; case kTfLiteBuiltinStridedSlice: if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { - auto builtin = - reinterpret_cast(node->builtin_data); - builder->AddScalarInt32Operand(builtin->begin_mask); - builder->AddScalarInt32Operand(builtin->end_mask); - builder->AddScalarInt32Operand(builtin->shrink_axis_mask); + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->begin_mask); + mapping_args.builder->AddScalarInt32Operand(builtin->end_mask); + mapping_args.builder->AddScalarInt32Operand( + builtin->shrink_axis_mask); return ANEURALNETWORKS_STRIDED_SLICE; }; } else { @@ -678,14 +771,146 @@ class NNAPIDelegateKernel { (node->inputs->size > 1) && (context->tensors[node->inputs->data[1]].allocation_type == kTfLiteMmapRo)) { - return [](TfLiteContext* context, NNAPIOpBuilder* builder, - TfLiteNode* node) -> ANeuralNetworksOperationType { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { return ANEURALNETWORKS_TRANSPOSE; }; } else { return nullptr; } break; + case kTfLiteBuiltinRnn: + // NNAPI only support float32 weights. + // TODO(miaowang): check the number of inputs before accessing it. + if (version == 1 && + context->tensors[node->inputs->data[/*kWeightsTensor*/ 1]].type == + kTfLiteFloat32) { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + // NNAPI need both state_in and state_out. + int ann_index; + mapping_args.builder->AddStateFloat32Tensor( + mapping_args.node->outputs->data[/*kHiddenStateTensor*/ 0], + &ann_index); + mapping_args.model_state_inputs->push_back(ann_index); + mapping_args.model_state_tfl_outputs->push_back( + mapping_args.node->outputs->data[/*kHiddenStateTensor*/ 0]); + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_RNN; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSvdf: + // NNAPI only support float32 weights. + if (version == 1 && + context->tensors[node->inputs->data[/*kWeightsFeatureTensor*/ 1]] + .type == kTfLiteFloat32) { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + // NNAPI need both state_in and state_out. + int ann_index; + mapping_args.builder->AddStateFloat32Tensor( + mapping_args.node->outputs->data[/*kStateTensor*/ 0], + &ann_index); + mapping_args.model_state_inputs->push_back(ann_index); + mapping_args.model_state_tfl_outputs->push_back( + mapping_args.node->outputs->data[/*kStateTensor*/ 0]); + + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->rank); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_SVDF; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinLstm: + // NNAPI only support float32 weights. + // TODO(miaowang): add loggings to indicate why the op is rejected. + if (version == 1 && node->inputs->size == 18 && + context->tensors[node->inputs + ->data[/*kInputToOutputWeightsTensor*/ 4]] + .type == kTfLiteFloat32) { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + // NNAPI need both state_in and state_out for cell_state and + // output_state. + int ann_index; + mapping_args.builder->AddStateFloat32Tensor( + mapping_args.node->outputs->data[/*kOutputStateTensor*/ 0], + &ann_index); + mapping_args.model_state_inputs->push_back(ann_index); + mapping_args.model_state_tfl_outputs->push_back( + mapping_args.node->outputs->data[/*kOutputStateTensor*/ 0]); + mapping_args.builder->AddStateFloat32Tensor( + mapping_args.node->outputs->data[/*kCellStateTensor*/ 1], + &ann_index); + mapping_args.model_state_inputs->push_back(ann_index); + mapping_args.model_state_tfl_outputs->push_back( + mapping_args.node->outputs->data[/*kCellStateTensor*/ 1]); + + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + mapping_args.builder->AddScalarInt32Operand(builtin->activation); + mapping_args.builder->AddScalarFloat32Operand(builtin->cell_clip); + mapping_args.builder->AddScalarFloat32Operand(builtin->proj_clip); + + // Current NNAPI implementation requires the sratch_buffer as + // output. + mapping_args.builder->AddAdditionalFloat32OutputTensor(2); + return ANEURALNETWORKS_LSTM; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinMean: + // NNAPI does not support generating a scalar as output for MEAN. + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32 && + context->tensors[node->outputs->data[0]].dims->size > 0) { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + mapping_args.node->builtin_data); + int32_t keep_dims = 0; + if (builtin->keep_dims) keep_dims = 1; + mapping_args.builder->AddScalarInt32Operand(keep_dims); + return ANEURALNETWORKS_MEAN; + }; + } else { + return nullptr; + } + case kTfLiteBuiltinEmbeddingLookup: + // NNAPI only support float32 values. + if (version == 1 && + context->tensors[node->inputs->data[1]].type == kTfLiteFloat32) { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_EMBEDDING_LOOKUP; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinHashtableLookup: + // NNAPI only support float32 output. + if (version == 1 && + context->tensors[node->outputs->data[0]].type == kTfLiteFloat32) { + return [](const NNAPIOpMappingArgs& mapping_args) + -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_HASHTABLE_LOOKUP; + }; + } else { + return nullptr; + } + break; default: return nullptr; } @@ -725,27 +950,56 @@ class NNAPIDelegateKernel { // Set the input tensor buffers. Note: we access tflite tensors using // absolute indices but NN api indices inputs by relative indices. int relative_input_index = 0; + int num_optional_tensors = 0; + + size_t input_offset = 0; for (auto absolute_input_index : TfLiteIntArrayView(node->inputs)) { + if (absolute_input_index == kOptionalTensor) { + num_optional_tensors++; + continue; + } TfLiteTensor* tensor = &context->tensors[absolute_input_index]; // TODO(miaowang): make sure the delegation works with dequantized weights // as intermediate tensors. if (tensor->allocation_type != kTfLiteMmapRo) { - CHECK_NN(context, ANeuralNetworksExecution_setInput( + // copy data to pre-allocated shared memory. + memcpy(nn_input_memory_->get_data_ptr() + input_offset, + tensor->data.raw, tensor->bytes); + CHECK_NN(context, ANeuralNetworksExecution_setInputFromMemory( execution, relative_input_index, nullptr, - tensor->data.raw, tensor->bytes)); + nn_input_memory_->get_handle(), input_offset, + tensor->bytes)); + input_offset += tensor->bytes; relative_input_index++; } } // Set the output tensor buffers. int relative_output_index = 0; + size_t output_offset = 0; for (auto output_index : TfLiteIntArrayView(node->outputs)) { TfLiteTensor* tensor = &context->tensors[output_index]; - CHECK_NN(context, ANeuralNetworksExecution_setOutput( + CHECK_NN(context, ANeuralNetworksExecution_setOutputFromMemory( execution, relative_output_index, nullptr, - tensor->data.raw, tensor->bytes)); + nn_output_memory_->get_handle(), output_offset, + tensor->bytes)); + output_offset += tensor->bytes; relative_output_index++; } + + // The state_out of previous invocation need to be mapped to state_in of + // current invocation. + for (size_t i = 0; i < model_state_tfl_outputs_.size(); i++) { + int state_tensor_idx = model_state_tfl_outputs_[i]; + TfLiteTensor* tensor = &context->tensors[state_tensor_idx]; + // Here we are using a deep copy for state_in tensors so that we are not + // reading and writing into the same buffer during a invocation. + // TODO(110369471): using double shared buffer to minimize the copies. + CHECK_NN(context, + ANeuralNetworksExecution_setInput( + execution, i + node->inputs->size - num_optional_tensors, + nullptr, tensor->data.raw, tensor->bytes)); + } // Invoke ANN in blocking fashion. ANeuralNetworksEvent* event = nullptr; CHECK_NN(context, ANeuralNetworksExecution_startCompute(execution, &event)); @@ -753,6 +1007,15 @@ class NNAPIDelegateKernel { ANeuralNetworksEvent_free(event); ANeuralNetworksExecution_free(execution); + // copy results from shared memory to the destination. + output_offset = 0; + for (auto output_index : TfLiteIntArrayView(node->outputs)) { + TfLiteTensor* tensor = &context->tensors[output_index]; + memcpy(tensor->data.raw, + nn_output_memory_->get_data_ptr() + output_offset, tensor->bytes); + output_offset += tensor->bytes; + } + return kTfLiteOk; } @@ -767,6 +1030,12 @@ class NNAPIDelegateKernel { // Track indices we use OperandMapping operand_mapping_; + std::vector model_state_inputs_; + std::vector model_state_tfl_outputs_; + + std::unique_ptr nn_input_memory_; + std::unique_ptr nn_output_memory_; + TfLiteStatus AddOpsAndTensors(TfLiteContext* context) { // The operand builder allows creating a single op. We create it at this // reduced power position rather than in the for loop to avoid reallocating @@ -781,11 +1050,22 @@ class NNAPIDelegateKernel { context->GetNodeAndRegistration(context, node_index, &node, ®); // Map inputs to NN API tensor indices. for (auto input_index : TfLiteIntArrayView(node->inputs)) { - TF_LITE_ENSURE_STATUS(builder.AddTensorInput(input_index)); + if (input_index == kOptionalTensor && + (reg->builtin_code == kTfLiteBuiltinLstm || + reg->builtin_code == kTfLiteBuiltinSvdf)) { + // properly handle the optional tensor for LSTM and SVDF. + // currently only support float32. + // TODO(miaowang): make sure this is also able to handle quantized + // tensor when supported by NNAPI. + TF_LITE_ENSURE_STATUS(builder.AddVectorFloat32Operand(nullptr, 0)); + } else { + TF_LITE_ENSURE_STATUS(builder.AddTensorInput(input_index)); + } } // Get op type and operands - int nn_op_type = Map(context, reg->builtin_code, reg->version, node)( - context, &builder, node); + int nn_op_type = Map(context, reg->builtin_code, reg->version, + node)({context, &builder, node, &model_state_inputs_, + &model_state_tfl_outputs_}); // Map outputs to NN API tensor indices. for (auto output_index : TfLiteIntArrayView(node->outputs)) { TF_LITE_ENSURE_STATUS(builder.AddTensorOutput(output_index)); @@ -806,15 +1086,29 @@ class NNAPIDelegateKernel { inputs.reserve(input_tensors->size); std::vector outputs; outputs.reserve(output_tensors->size); + + size_t total_input_byte_size = 0; // Make the TensorFlow lite inputs and outputs to ann_indices. for (int i : TfLiteIntArrayView(input_tensors)) { // Constant tensors are not NNAPI inputs. - if (context->tensors[i].allocation_type != kTfLiteMmapRo) { + if (i != kOptionalTensor && + context->tensors[i].allocation_type != kTfLiteMmapRo) { inputs.push_back(operand_mapping_.lite_index_to_ann(i)); + total_input_byte_size += context->tensors[i].bytes; } } - for (int i : TfLiteIntArrayView(output_tensors)) + + // Add state input tensors as model inputs + for (int i : model_state_inputs_) { + inputs.push_back(i); + } + + size_t total_output_byte_size = 0; + for (int i : TfLiteIntArrayView(output_tensors)) { outputs.push_back(operand_mapping_.lite_index_to_ann(i)); + total_output_byte_size += context->tensors[i].bytes; + } + // Tell ANN to declare inputs/outputs CHECK_NN(context, ANeuralNetworksModel_identifyInputsAndOutputs( nn_model_.get(), inputs.size(), inputs.data(), @@ -822,6 +1116,11 @@ class NNAPIDelegateKernel { // Finalize the model CHECK_NN(context, ANeuralNetworksModel_finish(nn_model_.get())); + // Create shared memory pool for inputs and outputs. + nn_input_memory_.reset(new NNMemory("input_pool", total_input_byte_size)); + nn_output_memory_.reset( + new NNMemory("output_pool", total_output_byte_size)); + return kTfLiteOk; } }; diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc index b7b159c59f2f81b055d5d06436b70331cff3dea8..3224b23a0c3bc8456bd75f2923d16f0eed7d53ff 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc @@ -1623,6 +1623,1898 @@ TEST(NNAPIDelegate, StridedSliceIn2D_ShrinkAxisMask) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); } +static float rnn_input[] = { + 0.23689353, 0.285385, 0.037029743, -0.19858193, -0.27569133, + 0.43773448, 0.60379338, 0.35562468, -0.69424844, -0.93421471, + -0.87287879, 0.37144363, -0.62476718, 0.23791671, 0.40060222, + 0.1356622, -0.99774903, -0.98858172, -0.38952237, -0.47685933, + 0.31073618, 0.71511042, -0.63767755, -0.31729108, 0.33468103, + 0.75801885, 0.30660987, -0.37354088, 0.77002847, -0.62747043, + -0.68572164, 0.0069220066, 0.65791464, 0.35130811, 0.80834007, + -0.61777675, -0.21095741, 0.41213346, 0.73784804, 0.094794154, + 0.47791874, 0.86496925, -0.53376222, 0.85315156, 0.10288584, + 0.86684, -0.011186242, 0.10513687, 0.87825835, 0.59929144, + 0.62827742, 0.18899453, 0.31440187, 0.99059987, 0.87170351, + -0.35091716, 0.74861872, 0.17831337, 0.2755419, 0.51864719, + 0.55084288, 0.58982027, -0.47443086, 0.20875752, -0.058871567, + -0.66609079, 0.59098077, 0.73017097, 0.74604273, 0.32882881, + -0.17503482, 0.22396147, 0.19379807, 0.29120302, 0.077113032, + -0.70331609, 0.15804303, -0.93407321, 0.40182066, 0.036301374, + 0.66521823, 0.0300982, -0.7747041, -0.02038002, 0.020698071, + -0.90300065, 0.62870288, -0.23068321, 0.27531278, -0.095755219, + -0.712036, -0.17384434, -0.50593495, -0.18646687, -0.96508682, + 0.43519354, 0.14744234, 0.62589407, 0.1653645, -0.10651493, + -0.045277178, 0.99032974, -0.88255352, -0.85147917, 0.28153265, + 0.19455957, -0.55479527, -0.56042433, 0.26048636, 0.84702539, + 0.47587705, -0.074295521, -0.12287641, 0.70117295, 0.90532446, + 0.89782166, 0.79817224, 0.53402734, -0.33286154, 0.073485017, + -0.56172788, -0.044897556, 0.89964068, -0.067662835, 0.76863563, + 0.93455386, -0.6324693, -0.083922029}; + +static float rnn_golden_output[] = { + 0.496726, 0, 0.965996, 0, 0.0584254, 0, + 0, 0.12315, 0, 0, 0.612266, 0.456601, + 0, 0.52286, 1.16099, 0.0291232, + + 0, 0, 0.524901, 0, 0, 0, + 0, 1.02116, 0, 1.35762, 0, 0.356909, + 0.436415, 0.0355727, 0, 0, + + 0, 0, 0, 0.262335, 0, 0, + 0, 1.33992, 0, 2.9739, 0, 0, + 1.31914, 2.66147, 0, 0, + + 0.942568, 0, 0, 0, 0.025507, 0, + 0, 0, 0.321429, 0.569141, 1.25274, 1.57719, + 0.8158, 1.21805, 0.586239, 0.25427, + + 1.04436, 0, 0.630725, 0, 0.133801, 0.210693, + 0.363026, 0, 0.533426, 0, 1.25926, 0.722707, + 0, 1.22031, 1.30117, 0.495867, + + 0.222187, 0, 0.72725, 0, 0.767003, 0, + 0, 0.147835, 0, 0, 0, 0.608758, + 0.469394, 0.00720298, 0.927537, 0, + + 0.856974, 0.424257, 0, 0, 0.937329, 0, + 0, 0, 0.476425, 0, 0.566017, 0.418462, + 0.141911, 0.996214, 1.13063, 0, + + 0.967899, 0, 0, 0, 0.0831304, 0, + 0, 1.00378, 0, 0, 0, 1.44818, + 1.01768, 0.943891, 0.502745, 0, + + 0.940135, 0, 0, 0, 0, 0, + 0, 2.13243, 0, 0.71208, 0.123918, 1.53907, + 1.30225, 1.59644, 0.70222, 0, + + 0.804329, 0, 0.430576, 0, 0.505872, 0.509603, + 0.343448, 0, 0.107756, 0.614544, 1.44549, 1.52311, + 0.0454298, 0.300267, 0.562784, 0.395095, + + 0.228154, 0, 0.675323, 0, 1.70536, 0.766217, + 0, 0, 0, 0.735363, 0.0759267, 1.91017, + 0.941888, 0, 0, 0, + + 0, 0, 1.5909, 0, 0, 0, + 0, 0.5755, 0, 0.184687, 0, 1.56296, + 0.625285, 0, 0, 0, + + 0, 0, 0.0857888, 0, 0, 0, + 0, 0.488383, 0.252786, 0, 0, 0, + 1.02817, 1.85665, 0, 0, + + 0.00981836, 0, 1.06371, 0, 0, 0, + 0, 0, 0, 0.290445, 0.316406, 0, + 0.304161, 1.25079, 0.0707152, 0, + + 0.986264, 0.309201, 0, 0, 0, 0, + 0, 1.64896, 0.346248, 0, 0.918175, 0.78884, + 0.524981, 1.92076, 2.07013, 0.333244, + + 0.415153, 0.210318, 0, 0, 0, 0, + 0, 2.02616, 0, 0.728256, 0.84183, 0.0907453, + 0.628881, 3.58099, 1.49974, 0}; + +static std::initializer_list rnn_weights = { + 0.461459, 0.153381, 0.529743, -0.00371218, 0.676267, -0.211346, + 0.317493, 0.969689, -0.343251, 0.186423, 0.398151, 0.152399, + 0.448504, 0.317662, 0.523556, -0.323514, 0.480877, 0.333113, + -0.757714, -0.674487, -0.643585, 0.217766, -0.0251462, 0.79512, + -0.595574, -0.422444, 0.371572, -0.452178, -0.556069, -0.482188, + -0.685456, -0.727851, 0.841829, 0.551535, -0.232336, 0.729158, + -0.00294906, -0.69754, 0.766073, -0.178424, 0.369513, -0.423241, + 0.548547, -0.0152023, -0.757482, -0.85491, 0.251331, -0.989183, + 0.306261, -0.340716, 0.886103, -0.0726757, -0.723523, -0.784303, + 0.0354295, 0.566564, -0.485469, -0.620498, 0.832546, 0.697884, + -0.279115, 0.294415, -0.584313, 0.548772, 0.0648819, 0.968726, + 0.723834, -0.0080452, -0.350386, -0.272803, 0.115121, -0.412644, + -0.824713, -0.992843, -0.592904, -0.417893, 0.863791, -0.423461, + -0.147601, -0.770664, -0.479006, 0.654782, 0.587314, -0.639158, + 0.816969, -0.337228, 0.659878, 0.73107, 0.754768, -0.337042, + 0.0960841, 0.368357, 0.244191, -0.817703, -0.211223, 0.442012, + 0.37225, -0.623598, -0.405423, 0.455101, 0.673656, -0.145345, + -0.511346, -0.901675, -0.81252, -0.127006, 0.809865, -0.721884, + 0.636255, 0.868989, -0.347973, -0.10179, -0.777449, 0.917274, + 0.819286, 0.206218, -0.00785118, 0.167141, 0.45872, 0.972934, + -0.276798, 0.837861, 0.747958, -0.0151566, -0.330057, -0.469077, + 0.277308, 0.415818}; + +static std::initializer_list rnn_recurrent_weights = { + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.1}; + +static std::initializer_list rnn_bias = { + 0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068, -0.23566568, + -0.389184, 0.47481549, -0.4791103, 0.29931796, 0.10463274, 0.83918178, + 0.37197268, 0.61957061, 0.3956964, -0.37609905}; + +class RNNOpModel : public SingleOpModelWithNNAPI { + public: + RNNOpModel(int batches, int units, int size, + const TensorType& weights = TensorType_FLOAT32, + const TensorType& recurrent_weights = TensorType_FLOAT32) + : batches_(batches), units_(units), input_size_(size) { + input_ = AddInput(TensorType_FLOAT32); + weights_ = AddInput(weights); + recurrent_weights_ = AddInput(recurrent_weights); + bias_ = AddInput(TensorType_FLOAT32); + hidden_state_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp( + BuiltinOperator_RNN, BuiltinOptions_RNNOptions, + CreateRNNOptions(builder_, ActivationFunctionType_RELU).Union()); + BuildInterpreter({{batches_, input_size_}, + {units_, input_size_}, + {units_, units_}, + {units_}}); + } + + void SetBias(std::initializer_list f) { PopulateTensor(bias_, f); } + + void SetWeights(std::initializer_list f) { + PopulateTensor(weights_, f); + } + + void SetRecurrentWeights(std::initializer_list f) { + PopulateTensor(recurrent_weights_, f); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + void ResetHiddenState() { + const int zero_buffer_size = units_ * batches_; + std::unique_ptr zero_buffer(new float[zero_buffer_size]); + memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float)); + PopulateTensor(hidden_state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + int input_size() { return input_size_; } + int num_units() { return units_; } + int num_batches() { return batches_; } + + protected: + int input_; + int weights_; + int recurrent_weights_; + int bias_; + int hidden_state_; + int output_; + + int batches_; + int units_; + int input_size_; +}; + +TEST(NNAPIDelegate, RnnBlackBoxTest) { + RNNOpModel rnn(2, 16, 8); + rnn.SetWeights(rnn_weights); + rnn.SetBias(rnn_bias); + rnn.SetRecurrentWeights(rnn_recurrent_weights); + + rnn.ResetHiddenState(); + const int input_sequence_size = sizeof(rnn_input) / sizeof(float) / + (rnn.input_size() * rnn.num_batches()); + + for (int i = 0; i < input_sequence_size; i++) { + float* batch_start = rnn_input + i * rnn.input_size(); + float* batch_end = batch_start + rnn.input_size(); + rnn.SetInput(0, batch_start, batch_end); + rnn.SetInput(rnn.input_size(), batch_start, batch_end); + + rnn.Invoke(); + + float* golden_start = rnn_golden_output + i * rnn.num_units(); + float* golden_end = golden_start + rnn.num_units(); + std::vector expected; + expected.insert(expected.end(), golden_start, golden_end); + expected.insert(expected.end(), golden_start, golden_end); + + EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); + } +} + +static float svdf_input[] = { + 0.12609188, -0.46347019, -0.89598465, + 0.35867718, 0.36897406, 0.73463392, + + 0.14278367, -1.64410412, -0.75222826, + -0.57290924, 0.12729003, 0.7567004, + + 0.49837467, 0.19278903, 0.26584083, + 0.17660543, 0.52949083, -0.77931279, + + -0.11186574, 0.13164264, -0.05349274, + -0.72674477, -0.5683046, 0.55900657, + + -0.68892461, 0.37783599, 0.18263303, + -0.63690937, 0.44483393, -0.71817774, + + -0.81299269, -0.86831826, 1.43940818, + -0.95760226, 1.82078898, 0.71135032, + + -1.45006323, -0.82251364, -1.69082689, + -1.65087092, -1.89238167, 1.54172635, + + 0.03966608, -0.24936394, -0.77526885, + 2.06740379, -1.51439476, 1.43768692, + + 0.11771342, -0.23761693, -0.65898693, + 0.31088525, -1.55601168, -0.87661445, + + -0.89477462, 1.67204106, -0.53235275, + -0.6230064, 0.29819036, 1.06939757, +}; + +static float svdf_golden_output_rank_1[] = { + 0.014899, -0.0517661, -0.143725, -0.00271883, + -0.03004015, 0.09565311, 0.1587342, 0.00784263, + + 0.068281, -0.162217, -0.152268, 0.00323521, + 0.01582633, 0.03858774, -0.03001583, -0.02671271, + + -0.0317821, -0.0333089, 0.0609602, 0.0333759, + -0.01432795, 0.05524484, 0.1101355, -0.02382665, + + -0.00623099, -0.077701, -0.391193, -0.0136691, + -0.02333033, 0.02293761, 0.12338032, 0.04326871, + + 0.201551, -0.164607, -0.179462, -0.0592739, + 0.01064911, -0.17503069, 0.07821996, -0.00224009, + + 0.0886511, -0.0875401, -0.269283, 0.0281379, + -0.02282338, 0.09741908, 0.32973239, 0.12281385, + + -0.201174, -0.586145, -0.628624, -0.0330412, + 0.24780814, -0.39304617, -0.22473189, 0.02589256, + + -0.0839096, -0.299329, 0.108746, 0.109808, + 0.10084175, -0.06416984, 0.28936723, 0.0026358, + + 0.419114, -0.237824, -0.422627, 0.175115, + -0.2314795, -0.18584411, -0.4228974, -0.12928449, + + 0.36726, -0.522303, -0.456502, -0.175475, + 0.17012937, -0.34447709, 0.38505614, -0.28158101, +}; + +static float svdf_golden_output_rank_2[] = { + -0.09623547, -0.10193135, 0.11083051, -0.0347917, + 0.1141196, 0.12965347, -0.12652366, 0.01007236, + + -0.16396809, -0.21247184, 0.11259045, -0.04156673, + 0.10132131, -0.06143532, -0.00924693, 0.10084561, + + 0.01257364, 0.0506071, -0.19287863, -0.07162561, + -0.02033747, 0.22673416, 0.15487903, 0.02525555, + + -0.1411963, -0.37054959, 0.01774767, 0.05867489, + 0.09607603, -0.0141301, -0.08995658, 0.12867066, + + -0.27142537, -0.16955489, 0.18521598, -0.12528358, + 0.00331409, 0.11167502, 0.02218599, -0.07309391, + + 0.09593632, -0.28361851, -0.0773851, 0.17199151, + -0.00075242, 0.33691186, -0.1536046, 0.16572715, + + -0.27916506, -0.27626723, 0.42615682, 0.3225764, + -0.37472126, -0.55655634, -0.05013514, 0.289112, + + -0.24418658, 0.07540751, -0.1940318, -0.08911639, + 0.00732617, 0.46737891, 0.26449674, 0.24888524, + + -0.17225097, -0.54660404, -0.38795233, 0.08389944, + 0.07736043, -0.28260678, 0.15666828, 1.14949894, + + -0.57454878, -0.64704704, 0.73235172, -0.34616736, + 0.21120001, -0.22927976, 0.02455296, -0.35906726, +}; + +class BaseSVDFOpModel : public SingleOpModelWithNNAPI { + public: + BaseSVDFOpModel(int batches, int units, int input_size, int memory_size, + int rank, + TensorType weights_feature_type = TensorType_FLOAT32, + TensorType weights_time_type = TensorType_FLOAT32) + : batches_(batches), + units_(units), + input_size_(input_size), + memory_size_(memory_size), + rank_(rank) { + input_ = AddInput(TensorType_FLOAT32); + weights_feature_ = AddInput(weights_feature_type); + weights_time_ = AddInput(weights_time_type); + bias_ = AddNullInput(); + state_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp( + BuiltinOperator_SVDF, BuiltinOptions_SVDFOptions, + CreateSVDFOptions(builder_, rank, ActivationFunctionType_NONE).Union()); + BuildInterpreter({ + {batches_, input_size_}, // Input tensor + {units_ * rank, input_size_}, // weights_feature tensor + {units_ * rank, memory_size_}, // weights_time tensor + {units_} // bias tensor + }); + } + + // Populates the weights_feature tensor. + void SetWeightsFeature(std::initializer_list f) { + PopulateTensor(weights_feature_, f); + } + + // Populates the weights_time tensor. + void SetWeightsTime(std::initializer_list f) { + PopulateTensor(weights_time_, f); + } + + // Populates the input tensor. + void SetInput(int offset, float* begin, float* end) { + PopulateTensor(input_, offset, begin, end); + } + + // Resets the state of SVDF op by filling it with 0's. + void ResetState() { + const int zero_buffer_size = rank_ * units_ * batches_ * memory_size_; + std::unique_ptr zero_buffer(new float[zero_buffer_size]); + memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float)); + PopulateTensor(state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + } + + // Extracts the output tensor from the SVDF op. + std::vector GetOutput() { return ExtractVector(output_); } + + int input_size() { return input_size_; } + int num_units() { return units_; } + int num_batches() { return batches_; } + + protected: + int input_; + int weights_feature_; + int weights_time_; + int bias_; + int state_; + int output_; + + int batches_; + int units_; + int input_size_; + int memory_size_; + int rank_; +}; + +class SVDFOpModel : public BaseSVDFOpModel { + public: + using BaseSVDFOpModel::BaseSVDFOpModel; + + void VerifyGoldens(float golden_input[], float golden_output[], + int golden_size, float tolerance = 1e-5) { + const int svdf_num_batches = num_batches(); + const int svdf_input_size = input_size(); + const int svdf_num_units = num_units(); + const int input_sequence_size = + golden_size / sizeof(float) / (svdf_input_size * svdf_num_batches); + // Going over each input batch, setting the input tensor, invoking the SVDF + // op and checking the output with the expected golden values. + for (int i = 0; i < input_sequence_size; i++) { + float* batch_start = + golden_input + i * svdf_input_size * svdf_num_batches; + float* batch_end = batch_start + svdf_input_size * svdf_num_batches; + SetInput(0, batch_start, batch_end); + + Invoke(); + + const float* golden_start = + golden_output + i * svdf_num_units * svdf_num_batches; + const float* golden_end = + golden_start + svdf_num_units * svdf_num_batches; + std::vector expected; + expected.insert(expected.end(), golden_start, golden_end); + + EXPECT_THAT(GetOutput(), + ElementsAreArray(ArrayFloatNear(expected, tolerance))); + } + } +}; + +TEST(NNAPIDelegate, SVDFBlackBoxTestRank1) { + SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, + /*memory_size=*/10, /*rank=*/1); + svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347, + 0.22197971, 0.12416199, 0.27901134, 0.27557442, + 0.3905206, -0.36137494, -0.06634006, -0.10640851}); + + svdf.SetWeightsTime( + {-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, + 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, + + 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, + -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, + + -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, + 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, + + -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, + -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657}); + + svdf.ResetState(); + svdf.VerifyGoldens(svdf_input, svdf_golden_output_rank_1, sizeof(svdf_input)); +} + +TEST(NNAPIDelegate, SVDFBlackBoxTestRank2) { + SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, + /*memory_size=*/10, /*rank=*/2); + svdf.SetWeightsFeature({-0.31930989, 0.0079667, 0.39296314, 0.37613347, + 0.12416199, 0.15785322, 0.27901134, 0.3905206, + 0.21931258, -0.36137494, -0.10640851, 0.31053296, + -0.36118156, -0.0976817, -0.36916667, 0.22197971, + 0.15294972, 0.38031587, 0.27557442, 0.39635518, + -0.21580373, -0.06634006, -0.02702999, 0.27072677}); + + svdf.SetWeightsTime( + {-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, + 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, + + 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, + -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, + + -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, + 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, + + -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, + -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657, + + -0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486, + 0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187, + + -0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589, + 0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836, + + -0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277, + -0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214, + + 0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326, + 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763}); + + svdf.ResetState(); + svdf.VerifyGoldens(svdf_input, svdf_golden_output_rank_2, sizeof(svdf_input)); +} + +class LSTMOpModel : public SingleOpModelWithNNAPI { + public: + LSTMOpModel(int n_batch, int n_input, int n_cell, int n_output, bool use_cifg, + bool use_peephole, bool use_projection_weights, + bool use_projection_bias, float cell_clip, float proj_clip, + const std::vector>& input_shapes, + const TensorType& weight_type = TensorType_FLOAT32) + : n_batch_(n_batch), + n_input_(n_input), + n_cell_(n_cell), + n_output_(n_output) { + input_ = AddInput(TensorType_FLOAT32); + + if (use_cifg) { + input_to_input_weights_ = AddNullInput(); + } else { + input_to_input_weights_ = AddInput(weight_type); + } + + input_to_forget_weights_ = AddInput(weight_type); + input_to_cell_weights_ = AddInput(weight_type); + input_to_output_weights_ = AddInput(weight_type); + + if (use_cifg) { + recurrent_to_input_weights_ = AddNullInput(); + } else { + recurrent_to_input_weights_ = AddInput(weight_type); + } + + recurrent_to_forget_weights_ = AddInput(weight_type); + recurrent_to_cell_weights_ = AddInput(weight_type); + recurrent_to_output_weights_ = AddInput(weight_type); + + if (use_peephole) { + if (use_cifg) { + cell_to_input_weights_ = AddNullInput(); + } else { + cell_to_input_weights_ = AddInput(weight_type); + } + cell_to_forget_weights_ = AddInput(weight_type); + cell_to_output_weights_ = AddInput(weight_type); + } else { + cell_to_input_weights_ = AddNullInput(); + cell_to_forget_weights_ = AddNullInput(); + cell_to_output_weights_ = AddNullInput(); + } + + if (use_cifg) { + input_gate_bias_ = AddNullInput(); + } else { + input_gate_bias_ = AddInput(TensorType_FLOAT32); + } + forget_gate_bias_ = AddInput(TensorType_FLOAT32); + cell_bias_ = AddInput(TensorType_FLOAT32); + output_gate_bias_ = AddInput(TensorType_FLOAT32); + + if (use_projection_weights) { + projection_weights_ = AddInput(weight_type); + if (use_projection_bias) { + projection_bias_ = AddInput(TensorType_FLOAT32); + } else { + projection_bias_ = AddNullInput(); + } + } else { + projection_weights_ = AddNullInput(); + projection_bias_ = AddNullInput(); + } + + output_state_ = AddOutput(TensorType_FLOAT32); + cell_state_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + + SetBuiltinOp(BuiltinOperator_LSTM, BuiltinOptions_LSTMOptions, + CreateLSTMOptions(builder_, ActivationFunctionType_TANH, + cell_clip, proj_clip) + .Union()); + BuildInterpreter(input_shapes); + } + + void SetInputToInputWeights(std::initializer_list f) { + PopulateTensor(input_to_input_weights_, f); + } + + void SetInputToForgetWeights(std::initializer_list f) { + PopulateTensor(input_to_forget_weights_, f); + } + + void SetInputToCellWeights(std::initializer_list f) { + PopulateTensor(input_to_cell_weights_, f); + } + + void SetInputToOutputWeights(std::initializer_list f) { + PopulateTensor(input_to_output_weights_, f); + } + + void SetRecurrentToInputWeights(std::initializer_list f) { + PopulateTensor(recurrent_to_input_weights_, f); + } + + void SetRecurrentToForgetWeights(std::initializer_list f) { + PopulateTensor(recurrent_to_forget_weights_, f); + } + + void SetRecurrentToCellWeights(std::initializer_list f) { + PopulateTensor(recurrent_to_cell_weights_, f); + } + + void SetRecurrentToOutputWeights(std::initializer_list f) { + PopulateTensor(recurrent_to_output_weights_, f); + } + + void SetCellToInputWeights(std::initializer_list f) { + PopulateTensor(cell_to_input_weights_, f); + } + + void SetCellToForgetWeights(std::initializer_list f) { + PopulateTensor(cell_to_forget_weights_, f); + } + + void SetCellToOutputWeights(std::initializer_list f) { + PopulateTensor(cell_to_output_weights_, f); + } + + void SetInputGateBias(std::initializer_list f) { + PopulateTensor(input_gate_bias_, f); + } + + void SetForgetGateBias(std::initializer_list f) { + PopulateTensor(forget_gate_bias_, f); + } + + void SetCellBias(std::initializer_list f) { + PopulateTensor(cell_bias_, f); + } + + void SetOutputGateBias(std::initializer_list f) { + PopulateTensor(output_gate_bias_, f); + } + + void SetProjectionWeights(std::initializer_list f) { + PopulateTensor(projection_weights_, f); + } + + void SetProjectionBias(std::initializer_list f) { + PopulateTensor(projection_bias_, f); + } + + void ResetOutputState() { + const int zero_buffer_size = n_cell_ * n_batch_; + std::unique_ptr zero_buffer(new float[zero_buffer_size]); + memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float)); + PopulateTensor(output_state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + } + + void ResetCellState() { + const int zero_buffer_size = n_cell_ * n_batch_; + std::unique_ptr zero_buffer(new float[zero_buffer_size]); + memset(zero_buffer.get(), 0, zero_buffer_size * sizeof(float)); + PopulateTensor(cell_state_, 0, zero_buffer.get(), + zero_buffer.get() + zero_buffer_size); + } + + void SetInput(int offset, const float* begin, const float* end) { + PopulateTensor(input_, offset, const_cast(begin), + const_cast(end)); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + int num_inputs() { return n_input_; } + int num_outputs() { return n_output_; } + int num_cells() { return n_cell_; } + int num_batches() { return n_batch_; } + + protected: + int input_; + int input_to_input_weights_; + int input_to_forget_weights_; + int input_to_cell_weights_; + int input_to_output_weights_; + + int recurrent_to_input_weights_; + int recurrent_to_forget_weights_; + int recurrent_to_cell_weights_; + int recurrent_to_output_weights_; + + int cell_to_input_weights_; + int cell_to_forget_weights_; + int cell_to_output_weights_; + + int input_gate_bias_; + int forget_gate_bias_; + int cell_bias_; + int output_gate_bias_; + + int projection_weights_; + int projection_bias_; + int input_activation_state_; + int input_cell_state_; + + int output_; + int output_state_; + int cell_state_; + + int n_batch_; + int n_input_; + int n_cell_; + int n_output_; +}; + +class BaseLstmTest : public ::testing::Test { + protected: + // Weights of the LSTM model. Some are optional. + std::initializer_list input_to_input_weights_; + std::initializer_list input_to_cell_weights_; + std::initializer_list input_to_forget_weights_; + std::initializer_list input_to_output_weights_; + std::initializer_list input_gate_bias_; + std::initializer_list cell_gate_bias_; + std::initializer_list forget_gate_bias_; + std::initializer_list output_gate_bias_; + std::initializer_list recurrent_to_input_weights_; + std::initializer_list recurrent_to_cell_weights_; + std::initializer_list recurrent_to_forget_weights_; + std::initializer_list recurrent_to_output_weights_; + std::initializer_list cell_to_input_weights_; + std::initializer_list cell_to_forget_weights_; + std::initializer_list cell_to_output_weights_; + std::initializer_list projection_weights_; + + // LSTM input is stored as num_batch x num_inputs vector. + std::vector> lstm_input_; + // LSTM output is stored as num_batch x num_outputs vector. + std::vector> lstm_golden_output_; + + // Compares output up to tolerance to the result of the lstm given the input. + void VerifyGoldens(const std::vector>& input, + const std::vector>& output, + LSTMOpModel* lstm, float tolerance = 1e-5) { + const int num_batches = input.size(); + EXPECT_GT(num_batches, 0); + const int num_inputs = lstm->num_inputs(); + EXPECT_GT(num_inputs, 0); + const int input_sequence_size = input[0].size() / num_inputs; + EXPECT_GT(input_sequence_size, 0); + for (int i = 0; i < input_sequence_size; ++i) { + for (int b = 0; b < num_batches; ++b) { + const float* batch_start = input[b].data() + i * num_inputs; + const float* batch_end = batch_start + num_inputs; + + lstm->SetInput(b * lstm->num_inputs(), batch_start, batch_end); + } + + lstm->Invoke(); + + const int num_outputs = lstm->num_outputs(); + std::vector expected; + for (int b = 0; b < num_batches; ++b) { + const float* golden_start_batch = output[b].data() + i * num_outputs; + const float* golden_end_batch = golden_start_batch + num_outputs; + expected.insert(expected.end(), golden_start_batch, golden_end_batch); + } + EXPECT_THAT(lstm->GetOutput(), + ElementsAreArray(ArrayFloatNear(expected, tolerance))); + } + } +}; + +class NoCifgNoPeepholeNoProjectionNoClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_input_weights_ = {-0.45018822, -0.02338299, -0.0870589, + -0.34550029, 0.04266912, -0.15680569, + -0.34856534, 0.43890524}; + input_to_cell_weights_ = {-0.50013041, 0.1370284, 0.11810488, 0.2013163, + -0.20583314, 0.44344562, 0.22077113, -0.29909778}; + input_to_forget_weights_ = {0.09701663, 0.20334584, -0.50592935, + -0.31343272, -0.40032279, 0.44781327, + 0.01387155, -0.35593212}; + input_to_output_weights_ = {-0.25065863, -0.28290087, 0.04613829, + 0.40525138, 0.44272184, 0.03897077, + -0.1556896, 0.19487578}; + input_gate_bias_ = {0., 0., 0., 0.}; + cell_gate_bias_ = {0., 0., 0., 0.}; + forget_gate_bias_ = {1., 1., 1., 1.}; + output_gate_bias_ = {0., 0., 0., 0.}; + + recurrent_to_input_weights_ = { + -0.0063535, -0.2042388, 0.31454784, -0.35746509, + 0.28902304, 0.08183324, -0.16555229, 0.02286911, + -0.13566875, 0.03034258, 0.48091322, -0.12528998, + 0.24077177, -0.51332325, -0.33502164, 0.10629296}; + + recurrent_to_cell_weights_ = { + -0.3407414, 0.24443203, -0.2078532, 0.26320225, + 0.05695659, -0.00123841, -0.4744786, -0.35869038, + -0.06418842, -0.13502428, -0.501764, 0.22830659, + -0.46367589, 0.26016325, -0.03894562, -0.16368064}; + + recurrent_to_forget_weights_ = { + -0.48684245, -0.06655136, 0.42224967, 0.2112639, + 0.27654213, 0.20864892, -0.07646349, 0.45877004, + 0.00141793, -0.14609534, 0.36447752, 0.09196436, + 0.28053468, 0.01560611, -0.20127171, -0.01140004}; + + recurrent_to_output_weights_ = { + 0.43385774, -0.17194885, 0.2718237, 0.09215671, + 0.24107647, -0.39835793, 0.18212086, 0.01301402, + 0.48572797, -0.50656658, 0.20047462, -0.20607421, + -0.51818722, -0.15390486, 0.0468148, 0.39922136}; + + lstm_input_ = {{2., 3., 3., 4., 1., 1.}}; + lstm_golden_output_ = {{-0.02973187, 0.1229473, 0.20885126, -0.15358765, + -0.03716109, 0.12507336, 0.41193449, -0.20860538, + -0.15053082, 0.09120187, 0.24278517, -0.12222792}}; + } +}; + +TEST_F(NoCifgNoPeepholeNoProjectionNoClippingLstmTest, LstmBlackBoxTest) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; + + LSTMOpModel lstm(n_batch, n_input, n_cell, n_output, + /*use_cifg=*/false, /*use_peephole=*/false, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {n_batch, n_input}, // input tensor + + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight_tensor + {n_cell, n_output}, // recurrent_to_forget_weight_tensor + {n_cell, n_output}, // recurrent_to_cell_weight_tensor + {n_cell, n_output}, // recurrent_to_output_weight_tensor + + {0}, // cell_to_input_weight tensor + {0}, // cell_to_forget_weight tensor + {0}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); + + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} + +class CifgNoPeepholeNoProjectionNoClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_cell_weights_ = {-0.49770179, -0.27711356, -0.09624726, + 0.05100781, 0.04717243, 0.48944736, + -0.38535351, -0.17212132}; + + input_to_forget_weights_ = {-0.55291498, -0.42866567, 0.13056988, + -0.3633365, -0.22755712, 0.28253698, + 0.24407166, 0.33826375}; + + input_to_output_weights_ = {0.10725588, -0.02335852, -0.55932593, + -0.09426838, -0.44257352, 0.54939759, + 0.01533556, 0.42751634}; + cell_gate_bias_ = {0., 0., 0., 0.}; + forget_gate_bias_ = {1., 1., 1., 1.}; + output_gate_bias_ = {0., 0., 0., 0.}; + + recurrent_to_cell_weights_ = { + 0.54066205, -0.32668582, -0.43562764, -0.56094903, + 0.42957711, 0.01841056, -0.32764608, -0.33027974, + -0.10826075, 0.20675004, 0.19069612, -0.03026325, + -0.54532051, 0.33003211, 0.44901288, 0.21193194}; + + recurrent_to_forget_weights_ = { + -0.13832897, -0.0515101, -0.2359007, -0.16661474, + -0.14340827, 0.36986142, 0.23414481, 0.55899, + 0.10798943, -0.41174671, 0.17751795, -0.34484994, + -0.35874045, -0.11352962, 0.27268326, 0.54058349}; + + recurrent_to_output_weights_ = { + 0.41613156, 0.42610586, -0.16495961, -0.5663873, + 0.30579174, -0.05115908, -0.33941799, 0.23364776, + 0.11178309, 0.09481031, -0.26424935, 0.46261835, + 0.50248802, 0.26114327, -0.43736315, 0.33149987}; + + cell_to_forget_weights_ = {0.47485286, -0.51955009, -0.24458408, + 0.31544167}; + cell_to_output_weights_ = {-0.17135078, 0.82760304, 0.85573703, + -0.77109635}; + + lstm_input_ = {{2., 3., 3., 4., 1., 1.}}; + lstm_golden_output_ = {{-0.36444446, -0.00352185, 0.12886585, -0.05163646, + -0.42312205, -0.01218222, 0.24201041, -0.08124574, + -0.358325, -0.04621704, 0.21641694, -0.06471302}}; + } +}; + +TEST_F(CifgNoPeepholeNoProjectionNoClippingLstmTest, LstmBlackBoxTest) { + const int n_batch = 1; + const int n_input = 2; + // n_cell and n_output have the same size when there is no projection. + const int n_cell = 4; + const int n_output = 4; + + LSTMOpModel lstm(n_batch, n_input, n_cell, n_output, + /*use_cifg=*/true, /*use_peephole=*/true, + /*use_projection_weights=*/false, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {n_batch, n_input}, // input tensor + + {0, 0}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {0, 0}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {0}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {0}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {0, 0}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); + + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); + + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} + +class NoCifgPeepholeProjectionClippingLstmTest : public BaseLstmTest { + void SetUp() override { + input_to_input_weights_ = { + 0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, + 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, + -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, + -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, + -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, + -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, + -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, + 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, + 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, + 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, + -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, + 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, + -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682, + -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988, + -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764, + 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476, + -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012, + -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604, + -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654, + -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677}; + + input_to_forget_weights_ = { + -0.0018401089, -0.004852237, 0.03698424, 0.014181704, + 0.028273236, -0.016726194, -0.05249759, -0.10204261, + 0.00861066, -0.040979505, -0.009899187, 0.01923892, + -0.028177269, -0.08535103, -0.14585495, 0.10662567, + -0.01909731, -0.017883534, -0.0047269356, -0.045103323, + 0.0030784295, 0.076784775, 0.07463696, 0.094531395, + 0.0814421, -0.12257899, -0.033945758, -0.031303465, + 0.045630626, 0.06843887, -0.13492945, -0.012480007, + -0.0811829, -0.07224499, -0.09628791, 0.045100946, + 0.0012300825, 0.013964662, 0.099372394, 0.02543059, + 0.06958324, 0.034257296, 0.0482646, 0.06267997, + 0.052625068, 0.12784666, 0.07077897, 0.025725935, + 0.04165009, 0.07241905, 0.018668644, -0.037377294, + -0.06277783, -0.08833636, -0.040120605, -0.011405586, + -0.007808335, -0.010301386, -0.005102167, 0.027717464, + 0.05483423, 0.11449111, 0.11289652, 0.10939839, + 0.13396506, -0.08402166, -0.01901462, -0.044678304, + -0.07720565, 0.014350063, -0.11757958, -0.0652038, + -0.08185733, -0.076754324, -0.092614375, 0.10405491, + 0.052960336, 0.035755895, 0.035839386, -0.012540553, + 0.036881298, 0.02913376, 0.03420159, 0.05448447, + -0.054523353, 0.02582715, 0.02327355, -0.011857179, + -0.0011980024, -0.034641717, -0.026125094, -0.17582615, + -0.15923657, -0.27486774, -0.0006143371, 0.0001771948, + -8.470171e-05, 0.02651807, 0.045790765, 0.06956496}; + + input_to_cell_weights_ = { + -0.04580283, -0.09549462, -0.032418985, -0.06454633, + -0.043528453, 0.043018587, -0.049152344, -0.12418144, + -0.078985475, -0.07596889, 0.019484362, -0.11434962, + -0.0074034138, -0.06314844, -0.092981495, 0.0062155537, + -0.025034338, -0.0028890965, 0.048929527, 0.06235075, + 0.10665918, -0.032036792, -0.08505916, -0.10843358, + -0.13002433, -0.036816437, -0.02130134, -0.016518239, + 0.0047691227, -0.0025825808, 0.066017866, 0.029991534, + -0.10652836, -0.1037554, -0.13056071, -0.03266643, + -0.033702414, -0.006473424, -0.04611692, 0.014419339, + -0.025174323, 0.0396852, 0.081777506, 0.06157468, + 0.10210095, -0.009658194, 0.046511717, 0.03603906, + 0.0069369148, 0.015960095, -0.06507666, 0.09551598, + 0.053568836, 0.06408714, 0.12835667, -0.008714329, + -0.20211966, -0.12093674, 0.029450472, 0.2849013, + -0.029227901, 0.1164364, -0.08560263, 0.09941786, + -0.036999565, -0.028842626, -0.0033637602, -0.017012902, + -0.09720865, -0.11193351, -0.029155117, -0.017936034, + -0.009768936, -0.04223324, -0.036159635, 0.06505112, + -0.021742892, -0.023377212, -0.07221364, -0.06430552, + 0.05453865, 0.091149814, 0.06387331, 0.007518393, + 0.055960953, 0.069779344, 0.046411168, 0.10509911, + 0.07463894, 0.0075130584, 0.012850982, 0.04555431, + 0.056955688, 0.06555285, 0.050801456, -0.009862683, + 0.00826772, -0.026555609, -0.0073611983, -0.0014897042}; + + input_to_output_weights_ = { + -0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918, + -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534, + 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722, + -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761, + -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394, + 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154, + -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135, + -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564, + -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047, + -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304, + 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946, + 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646, + 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813, + -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403, + 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415, + 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495, + -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158, + 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295, + -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739, + -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956}; + + input_gate_bias_ = {0.02234832, 0.14757581, 0.18176508, 0.10380666, + 0.053110216, -0.06928846, -0.13942584, -0.11816189, 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-0.12131499, + 0.031935584, 0.09180414, -0.08876437, -0.08049874, + 0.008753825, 0.03498998, 0.030215185, 0.03907079, + 0.089751154, 0.029194152, -0.03337423, -0.019092513, + 0.04331237, 0.04299654, -0.036394123, -0.12915532, + 0.09793732, 0.07512415, -0.11319543, -0.032502122, + 0.15661901, 0.07671967, -0.005491124, -0.19379048, + -0.218606, 0.21448623, 0.017840758, 0.1416943, + -0.07051762, 0.19488361, 0.02664691, -0.18104725, + -0.09334311, 0.15026465, -0.15493552, -0.057762887, + -0.11604192, -0.262013, -0.01391798, 0.012185008, + 0.11156489, -0.07483202, 0.06693364, -0.26151478, + 0.046425626, 0.036540434, -0.16435726, 0.17338543, + -0.21401681, -0.11385144, -0.08283257, -0.069031075, + 0.030635102, 0.010969227, 0.11109743, 0.010919218, + 0.027526086, 0.13519906, 0.01891392, -0.046839405, + -0.040167913, 0.017953383, -0.09700955, 0.0061885654, + -0.07000971, 0.026893595, -0.038844477, 0.14543656}; + + lstm_input_ = { + {// Batch0: 4 (input_sequence_size) * 5 (n_input) + 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, // step 0 + 0.596268, 0.998386, 0.568695, 0.864524, 0.571277, // step 1 + 0.073204, 0.296072, 0.743333, 0.069199, 0.045348, // step 2 + 0.867394, 0.291279, 0.013714, 0.482521, 0.626339}, // step 3 + + {// Batch1: 4 (input_sequence_size) * 5 (n_input) + 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, // step 0 + 0.642421, 0.524260, 0.134799, 0.003639, 0.162482, // step 1 + 0.640394, 0.930399, 0.050782, 0.432485, 0.988078, // step 2 + 0.082922, 0.563329, 0.865614, 0.333232, 0.259916} // step 3 + }; + + lstm_golden_output_ = { + {// Batch0: 4 (input_sequence_size) * 16 (n_output) + -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, + -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, + -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, + 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363, + -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322, + -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308, + 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794, + 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474, + 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827, + 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512, + -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407, + -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193, + 0.0286833, 0.00824207, 0.0264887, 0.0305169}, + {// Batch1: 4 (input_sequence_size) * 16 (n_output) + -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, + -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, + 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, + 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507, + -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039, + -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233, + 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378, + 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034, + 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789, + 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855, + -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679, + -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181, + 0.0412031, 0.0118723, 0.0239643, 0.0394009}}; + } +}; + +TEST_F(NoCifgPeepholeProjectionClippingLstmTest, LstmBlackBoxTest) { + const int n_batch = 2; + const int n_input = 5; + const int n_cell = 20; + const int n_output = 16; + + LSTMOpModel lstm(n_batch, n_input, n_cell, n_output, + /*use_cifg=*/false, /*use_peephole=*/true, + /*use_projection_weights=*/true, + /*use_projection_bias=*/false, + /*cell_clip=*/0.0, /*proj_clip=*/0.0, + { + {n_batch, n_input}, // input tensor + + {n_cell, n_input}, // input_to_input_weight tensor + {n_cell, n_input}, // input_to_forget_weight tensor + {n_cell, n_input}, // input_to_cell_weight tensor + {n_cell, n_input}, // input_to_output_weight tensor + + {n_cell, n_output}, // recurrent_to_input_weight tensor + {n_cell, n_output}, // recurrent_to_forget_weight tensor + {n_cell, n_output}, // recurrent_to_cell_weight tensor + {n_cell, n_output}, // recurrent_to_output_weight tensor + + {n_cell}, // cell_to_input_weight tensor + {n_cell}, // cell_to_forget_weight tensor + {n_cell}, // cell_to_output_weight tensor + + {n_cell}, // input_gate_bias tensor + {n_cell}, // forget_gate_bias tensor + {n_cell}, // cell_bias tensor + {n_cell}, // output_gate_bias tensor + + {n_output, n_cell}, // projection_weight tensor + {0}, // projection_bias tensor + }); + + lstm.SetInputToInputWeights(input_to_input_weights_); + lstm.SetInputToCellWeights(input_to_cell_weights_); + lstm.SetInputToForgetWeights(input_to_forget_weights_); + lstm.SetInputToOutputWeights(input_to_output_weights_); + + lstm.SetInputGateBias(input_gate_bias_); + lstm.SetCellBias(cell_gate_bias_); + lstm.SetForgetGateBias(forget_gate_bias_); + lstm.SetOutputGateBias(output_gate_bias_); + + lstm.SetRecurrentToInputWeights(recurrent_to_input_weights_); + lstm.SetRecurrentToCellWeights(recurrent_to_cell_weights_); + lstm.SetRecurrentToForgetWeights(recurrent_to_forget_weights_); + lstm.SetRecurrentToOutputWeights(recurrent_to_output_weights_); + + lstm.SetCellToInputWeights(cell_to_input_weights_); + lstm.SetCellToForgetWeights(cell_to_forget_weights_); + lstm.SetCellToOutputWeights(cell_to_output_weights_); + + lstm.SetProjectionWeights(projection_weights_); + + // Resetting cell_state and output_state + lstm.ResetCellState(); + lstm.ResetOutputState(); + + VerifyGoldens(lstm_input_, lstm_golden_output_, &lstm); +} + +class BaseReduceOpModel : public SingleOpModelWithNNAPI { + public: + void SetAxis(const std::vector& data) { PopulateTensor(axis_, data); } + + template + void SetInput(std::vector data) { + PopulateTensor(input_, data); + } + + template + std::vector GetOutput() { + return ExtractVector(output_); + } + + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } + + std::vector GetOutputShape() { return GetTensorShape(output_); } + + int Input() { return input_; } + + protected: + int input_; + int axis_; + int output_; +}; + +// Model for the tests case where axis is a const tensor. +class MeanOpConstModel : public BaseReduceOpModel { + public: + MeanOpConstModel(const TensorData& input, const TensorData& output, + std::initializer_list axis_shape, + std::initializer_list axis, bool keep_dims) { + input_ = AddInput(input); + axis_ = AddConstInput(TensorType_INT32, axis, axis_shape); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_MEAN, BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder_, keep_dims).Union()); + BuildInterpreter({GetShape(input_)}); + } +}; + +// Tests for reduce_mean +TEST(NNAPIDelegate, MeanFloatNotKeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {2}}, + {4}, {1, 0, -3, -3}, false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({12, 13}))); +} + +TEST(NNAPIDelegate, MeanFloatKeepDims) { + std::vector data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + MeanOpConstModel m({TensorType_FLOAT32, {4, 3, 2}}, {TensorType_FLOAT32, {3}}, + {2}, {0, 2}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); +} + +class BaseEmbeddingLookupOpModel : public SingleOpModelWithNNAPI { + public: + BaseEmbeddingLookupOpModel(std::initializer_list index_shape, + std::initializer_list weight_shape, + TensorType weight_type = TensorType_FLOAT32) { + input_ = AddInput(TensorType_INT32); + weight_ = AddInput(weight_type); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_EMBEDDING_LOOKUP, BuiltinOptions_NONE, 0); + BuildInterpreter({index_shape, weight_shape}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input_; + int weight_; + int output_; +}; + +class EmbeddingLookupOpModel : public BaseEmbeddingLookupOpModel { + public: + using BaseEmbeddingLookupOpModel::BaseEmbeddingLookupOpModel; + + void Set3DWeightMatrix(const std::function& function) { + TfLiteTensor* tensor = interpreter_->tensor(weight_); + int rows = tensor->dims->data[0]; + int columns = tensor->dims->data[1]; + int features = tensor->dims->data[2]; + for (int i = 0; i < rows; i++) { + for (int j = 0; j < columns; j++) { + for (int k = 0; k < features; k++) { + tensor->data.f[(i * columns + j) * features + k] = function(i, j, k); + } + } + } + } +}; + +TEST(NNAPIDelegate, EmbeddingLookupSimpleTest) { + EmbeddingLookupOpModel m({3}, {3, 2, 4}); + m.SetInput({1, 0, 2}); + m.Set3DWeightMatrix( + [](int i, int j, int k) { return i + j / 10.0f + k / 100.0f; }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({ + 1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1 + 0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0 + 2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2 + }))); +} + +class HashtableLookupOpModel : public SingleOpModelWithNNAPI { + public: + HashtableLookupOpModel(std::initializer_list lookup_shape, + std::initializer_list key_shape, + std::initializer_list value_shape, + TensorType type) { + lookup_ = AddInput(TensorType_INT32); + key_ = AddInput(TensorType_INT32); + value_ = AddInput(type); + output_ = AddOutput(type); + hit_ = AddOutput(TensorType_UINT8); + SetBuiltinOp(BuiltinOperator_HASHTABLE_LOOKUP, BuiltinOptions_NONE, 0); + BuildInterpreter({lookup_shape, key_shape, value_shape}); + } + + void SetLookup(std::initializer_list data) { + PopulateTensor(lookup_, data); + } + + void SetHashtableKey(std::initializer_list data) { + PopulateTensor(key_, data); + } + + void SetHashtableValue(const std::vector& content) { + PopulateStringTensor(value_, content); + } + + void SetHashtableValue(const std::function& function) { + TfLiteTensor* tensor = interpreter_->tensor(value_); + int rows = tensor->dims->data[0]; + for (int i = 0; i < rows; i++) { + tensor->data.f[i] = function(i); + } + } + + void SetHashtableValue(const std::function& function) { + TfLiteTensor* tensor = interpreter_->tensor(value_); + int rows = tensor->dims->data[0]; + int features = tensor->dims->data[1]; + for (int i = 0; i < rows; i++) { + for (int j = 0; j < features; j++) { + tensor->data.f[i * features + j] = function(i, j); + } + } + } + + std::vector GetStringOutput() { + TfLiteTensor* output = interpreter_->tensor(output_); + int num = GetStringCount(output); + std::vector result(num); + for (int i = 0; i < num; i++) { + auto ref = GetString(output, i); + result[i] = string(ref.str, ref.len); + } + return result; + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetHit() { return ExtractVector(hit_); } + + private: + int lookup_; + int key_; + int value_; + int output_; + int hit_; +}; + +TEST(NNAPIDelegate, HashtableLookupTest2DInput) { + HashtableLookupOpModel m({4}, {3}, {3, 2}, TensorType_FLOAT32); + + m.SetLookup({1234, -292, -11, 0}); + m.SetHashtableKey({-11, 0, 1234}); + m.SetHashtableValue([](int i, int j) { return i + j / 10.0f; }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 2.0, 2.1, // 2-nd item + 0, 0, // Not found + 0.0, 0.1, // 0-th item + 1.0, 1.1, // 1-st item + }))); + EXPECT_THAT(m.GetHit(), ElementsAreArray({ + 1, + 0, + 1, + 1, + })); +} + +TEST(NNAPIDelegate, HashtableLookupTest1DInput) { + HashtableLookupOpModel m({4}, {3}, {3}, TensorType_FLOAT32); + + m.SetLookup({1234, -292, -11, 0}); + m.SetHashtableKey({-11, 0, 1234}); + m.SetHashtableValue([](int i) { return i * i / 10.0f; }); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 0.4, // 2-nd item + 0, // Not found + 0.0, // 0-th item + 0.1, // 1-st item + }))); + EXPECT_THAT(m.GetHit(), ElementsAreArray({ + 1, + 0, + 1, + 1, + })); +} } // namespace } // namespace tflite diff --git a/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm b/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm index d74e275f0439b1ce56b29e0eadff5f211f6a4faa..30fee64a6f621016446eff58c305e88fda01fa76 100644 --- a/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm +++ b/tensorflow/contrib/lite/examples/ios/camera/CameraExampleViewController.mm @@ -315,7 +315,7 @@ static void GetTopN(const uint8_t* prediction, const int prediction_size, const labelLayers = [[NSMutableArray alloc] init]; oldPredictionValues = [[NSMutableDictionary alloc] init]; - NSString* graph_path = FilePathForResourceName(model_file_name, @"tflite"); + NSString* graph_path = FilePathForResourceName(model_file_name, model_file_type); model = tflite::FlatBufferModel::BuildFromFile([graph_path UTF8String]); if (!model) { LOG(FATAL) << "Failed to mmap model " << graph_path; diff --git a/tensorflow/contrib/lite/examples/ios/camera/Podfile b/tensorflow/contrib/lite/examples/ios/camera/Podfile index c7d3b1c966eaa0de71f5c37a6a77b3881e30ddd7..cd8c39043f6df61ed83e75e80a42156fdba68642 100644 --- a/tensorflow/contrib/lite/examples/ios/camera/Podfile +++ b/tensorflow/contrib/lite/examples/ios/camera/Podfile @@ -2,4 +2,4 @@ platform :ios, '8.0' inhibit_all_warnings! target 'tflite_camera_example' - pod 'TensorFlowLite' + pod 'TensorFlowLite', '0.1.7' diff --git a/tensorflow/contrib/lite/examples/ios/simple/Podfile b/tensorflow/contrib/lite/examples/ios/simple/Podfile index e4aca2be82d437a0225d2c15d3e486b0344aa978..c885398f44456bc1b7429b4f6605237bbc64e654 100644 --- a/tensorflow/contrib/lite/examples/ios/simple/Podfile +++ b/tensorflow/contrib/lite/examples/ios/simple/Podfile @@ -2,4 +2,4 @@ platform :ios, '8.0' inhibit_all_warnings! target 'tflite_simple_example' - pod 'TensorFlowLite' + pod 'TensorFlowLite', '0.1.7' diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.cc b/tensorflow/contrib/lite/examples/label_image/label_image.cc index 86d7d1cc4a625243791d5e7d5b746526a58efb6d..7c6f523041ad5a516f348c1b4f66683128838228 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.cc +++ b/tensorflow/contrib/lite/examples/label_image/label_image.cc @@ -213,22 +213,23 @@ void RunInference(Settings* s) { } } - const int output_size = 1000; - const size_t num_results = 5; const float threshold = 0.001f; std::vector> top_results; int output = interpreter->outputs()[0]; + TfLiteIntArray* output_dims = interpreter->tensor(output)->dims; + // assume output dims to be something like (1, 1, ... ,size) + auto output_size = output_dims->data[output_dims->size - 1]; switch (interpreter->tensor(output)->type) { case kTfLiteFloat32: get_top_n(interpreter->typed_output_tensor(0), output_size, - num_results, threshold, &top_results, true); + s->number_of_results, threshold, &top_results, true); break; case kTfLiteUInt8: get_top_n(interpreter->typed_output_tensor(0), - output_size, num_results, threshold, &top_results, - false); + output_size, s->number_of_results, threshold, + &top_results, false); break; default: LOG(FATAL) << "cannot handle output type " @@ -259,6 +260,7 @@ void display_usage() { << "--labels, -l: labels for the model\n" << "--tflite_model, -m: model_name.tflite\n" << "--profiling, -p: [0|1], profiling or not\n" + << "--num_results, -r: number of results to show\n" << "--threads, -t: number of threads\n" << "--verbose, -v: [0|1] print more information\n" << "\n"; @@ -280,12 +282,13 @@ int Main(int argc, char** argv) { {"threads", required_argument, nullptr, 't'}, {"input_mean", required_argument, nullptr, 'b'}, {"input_std", required_argument, nullptr, 's'}, + {"num_results", required_argument, nullptr, 'r'}, {nullptr, 0, nullptr, 0}}; /* getopt_long stores the option index here. */ int option_index = 0; - c = getopt_long(argc, argv, "a:b:c:f:i:l:m:p:s:t:v:", long_options, + c = getopt_long(argc, argv, "a:b:c:f:i:l:m:p:r:s:t:v:", long_options, &option_index); /* Detect the end of the options. */ @@ -315,6 +318,10 @@ int Main(int argc, char** argv) { s.profiling = strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn) break; + case 'r': + s.number_of_results = + strtol(optarg, nullptr, 10); // NOLINT(runtime/deprecated_fn) + break; case 's': s.input_std = strtod(optarg, nullptr); break; diff --git a/tensorflow/contrib/lite/examples/label_image/label_image.h b/tensorflow/contrib/lite/examples/label_image/label_image.h index 4b48014e1c77eca1eca081f0fe906441a5dcce22..34c223f713b9fe7692440a6b7538f00be995ad11 100644 --- a/tensorflow/contrib/lite/examples/label_image/label_image.h +++ b/tensorflow/contrib/lite/examples/label_image/label_image.h @@ -34,6 +34,7 @@ struct Settings { string labels_file_name = "./labels.txt"; string input_layer_type = "uint8_t"; int number_of_threads = 4; + int number_of_results = 5; }; } // namespace label_image diff --git a/tensorflow/contrib/lite/examples/python/BUILD b/tensorflow/contrib/lite/examples/python/BUILD new file mode 100644 index 0000000000000000000000000000000000000000..d337c3ddc43a23e50a5afdab93b16c0f61ccd538 --- /dev/null +++ b/tensorflow/contrib/lite/examples/python/BUILD @@ -0,0 +1,13 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//tensorflow:internal"]) + +py_binary( + name = "label_image", + srcs = ["label_image.py"], + main = "label_image.py", + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/contrib/lite/python:lite", + ], +) diff --git a/tensorflow/contrib/lite/examples/python/label_image.md b/tensorflow/contrib/lite/examples/python/label_image.md new file mode 100644 index 0000000000000000000000000000000000000000..e81192a96c142f2b3e7e85d160166fdd37ccdc53 --- /dev/null +++ b/tensorflow/contrib/lite/examples/python/label_image.md @@ -0,0 +1,50 @@ + +With model, input image (grace_hopper.bmp), and labels file (labels.txt) +in /tmp. + +The example input image and labels file are from TensorFlow repo and +MobileNet V1 model files. + +``` +curl https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/contrib/lite/examples/label_image/testdata/grace_hopper.bmp > /tmp/grace_hopper.bmp + +curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz | tar xzv -C /tmp mobilenet_v1_1.0_224/labels.txt +mv /tmp/mobilenet_v1_1.0_224/labels.txt /tmp/ + +``` + +Run + +``` +curl http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz | tar xzv -C /tmp +bazel run --config opt //tensorflow/contrib/lite/examples/python:label_image +``` + +We can get results like + +``` +0.470588: military uniform +0.337255: Windsor tie +0.047059: bow tie +0.031373: mortarboard +0.019608: suit +``` + +Run + +``` +curl http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp +bazel run --config opt //tensorflow/contrib/lite/examples/python:label_image \ +-- --model_file /tmp/mobilenet_v1_1.0_224.tflite +``` + +We can get results like +``` +0.728693: military uniform +0.116163: Windsor tie +0.035517: bow tie +0.014874: mortarboard +0.011758: bolo tie +``` + +Check [models](../../g3doc/models.md) for models hosted by Google. diff --git a/tensorflow/contrib/lite/examples/python/label_image.py b/tensorflow/contrib/lite/examples/python/label_image.py new file mode 100644 index 0000000000000000000000000000000000000000..282118a1d2b43a08930b24366110a021fc634b5e --- /dev/null +++ b/tensorflow/contrib/lite/examples/python/label_image.py @@ -0,0 +1,86 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""label_image for tflite""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import numpy as np + +from PIL import Image + +from tensorflow.contrib.lite.python import interpreter as interpreter_wrapper + +def load_labels(filename): + my_labels = [] + input_file = open(filename, 'r') + for l in input_file: + my_labels.append(l.strip()) + return my_labels + +if __name__ == "__main__": + floating_model = False + + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--image", default="/tmp/grace_hopper.bmp", \ + help="image to be classified") + parser.add_argument("-m", "--model_file", \ + default="/tmp/mobilenet_v1_1.0_224_quant.tflite", \ + help=".tflite model to be executed") + parser.add_argument("-l", "--label_file", default="/tmp/labels.txt", \ + help="name of file containing labels") + parser.add_argument("--input_mean", default=127.5, help="input_mean") + parser.add_argument("--input_std", default=127.5, \ + help="input standard deviation") + args = parser.parse_args() + + interpreter = interpreter_wrapper.Interpreter(model_path=args.model_file) + interpreter.allocate_tensors() + + input_details = interpreter.get_input_details() + output_details = interpreter.get_output_details() + + # check the type of the input tensor + if input_details[0]['dtype'] == np.float32: + floating_model = True + + # NxHxWxC, H:1, W:2 + height = input_details[0]['shape'][1] + width = input_details[0]['shape'][2] + img = Image.open(args.image) + img = img.resize((width, height)) + + # add N dim + input_data = np.expand_dims(img, axis=0) + + if floating_model: + input_data = (np.float32(input_data) - args.input_mean) / args.input_std + + interpreter.set_tensor(input_details[0]['index'], input_data) + + interpreter.invoke() + + output_data = interpreter.get_tensor(output_details[0]['index']) + results = np.squeeze(output_data) + + top_k = results.argsort()[-5:][::-1] + labels = load_labels(args.label_file) + for i in top_k: + if floating_model: + print('{0:08.6f}'.format(float(results[i]))+":", labels[i]) + else: + print('{0:08.6f}'.format(float(results[i]/255.0))+":", labels[i]) diff --git a/tensorflow/contrib/lite/experimental/c/BUILD b/tensorflow/contrib/lite/experimental/c/BUILD index 50f8da66d06abaf0637866e85c04e80fee042071..8fc07e8eb7eb1b53cc94eed75093c49c29679d77 100644 --- a/tensorflow/contrib/lite/experimental/c/BUILD +++ b/tensorflow/contrib/lite/experimental/c/BUILD @@ -26,17 +26,33 @@ tflite_cc_shared_object( }), deps = [ ":c_api", + ":c_api_experimental", ":exported_symbols.lds", ":version_script.lds", ], ) +cc_library( + name = "c_api_internal", + srcs = ["c_api.h"], + hdrs = ["c_api_internal.h"], + copts = tflite_copts(), + visibility = [ + "//tensorflow/contrib/lite/experimental/c:__subpackages__", + ], + deps = [ + "//tensorflow/contrib/lite:context", + "//tensorflow/contrib/lite:framework", + ], +) + cc_library( name = "c_api", srcs = ["c_api.cc"], hdrs = ["c_api.h"], copts = tflite_copts(), deps = [ + ":c_api_internal", "//tensorflow/contrib/lite:context", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:schema_fbs_version", @@ -44,6 +60,17 @@ cc_library( ], ) +cc_library( + name = "c_api_experimental", + srcs = ["c_api_experimental.cc"], + hdrs = ["c_api_experimental.h"], + copts = tflite_copts(), + deps = [ + ":c_api", + ":c_api_internal", + ], +) + cc_test( name = "c_api_test", size = "small", @@ -51,9 +78,21 @@ cc_test( data = ["//tensorflow/contrib/lite:testdata/add.bin"], deps = [ ":c_api", - "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:kernel_api", "//tensorflow/contrib/lite/testing:util", "@com_google_googletest//:gtest", ], ) + +cc_test( + name = "c_api_experimental_test", + size = "small", + srcs = ["c_api_experimental_test.cc"], + data = ["//tensorflow/contrib/lite:testdata/add.bin"], + deps = [ + ":c_api", + ":c_api_experimental", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) diff --git a/tensorflow/contrib/lite/experimental/c/c_api.cc b/tensorflow/contrib/lite/experimental/c/c_api.cc index 9d29e8b3e055e86a9e68285d81de742e36452215..a4ab0e8c306b5b1e514e1ddf0c166ba0b43d75d1 100644 --- a/tensorflow/contrib/lite/experimental/c/c_api.cc +++ b/tensorflow/contrib/lite/experimental/c/c_api.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/contrib/lite/experimental/c/c_api.h" #include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/experimental/c/c_api_internal.h" #include "tensorflow/contrib/lite/interpreter.h" #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" @@ -23,28 +24,55 @@ limitations under the License. extern "C" { #endif // __cplusplus -struct _TFL_Interpreter { - std::unique_ptr impl; -}; - // LINT.IfChange -TFL_Interpreter* TFL_NewInterpreter(const void* model_data, - int32_t model_size) { +TFL_Model* TFL_NewModel(const void* model_data, size_t model_size) { auto model = tflite::FlatBufferModel::BuildFromBuffer( - static_cast(model_data), static_cast(model_size)); - if (!model) { + static_cast(model_data), model_size); + return model ? new TFL_Model{std::move(model)} : nullptr; +} + +TFL_Model* TFL_NewModelFromFile(const char* model_path) { + auto model = tflite::FlatBufferModel::BuildFromFile(model_path); + return model ? new TFL_Model{std::move(model)} : nullptr; +} + +void TFL_DeleteModel(TFL_Model* model) { delete model; } + +TFL_InterpreterOptions* TFL_NewInterpreterOptions() { + return new TFL_InterpreterOptions{}; +} + +void TFL_DeleteInterpreterOptions(TFL_InterpreterOptions* options) { + delete options; +} + +void TFL_InterpreterOptionsSetNumThreads(TFL_InterpreterOptions* options, + int32_t num_threads) { + options->num_threads = num_threads; +} + +TFL_Interpreter* TFL_NewInterpreter( + const TFL_Model* model, const TFL_InterpreterOptions* optional_options) { + if (!model || !model->impl) { return nullptr; } tflite::ops::builtin::BuiltinOpResolver resolver; - tflite::InterpreterBuilder builder(*model, resolver); - std::unique_ptr interpreter_impl; - if (builder(&interpreter_impl) != kTfLiteOk) { + tflite::InterpreterBuilder builder(*model->impl, resolver); + std::unique_ptr interpreter; + if (builder(&interpreter) != kTfLiteOk) { return nullptr; } - return new TFL_Interpreter{std::move(interpreter_impl)}; + if (optional_options) { + if (optional_options->num_threads != + TFL_InterpreterOptions::kDefaultNumThreads) { + interpreter->SetNumThreads(optional_options->num_threads); + } + } + + return new TFL_Interpreter{std::move(interpreter)}; } void TFL_DeleteInterpreter(TFL_Interpreter* interpreter) { delete interpreter; } @@ -97,9 +125,13 @@ int32_t TFL_TensorDim(const TFL_Tensor* tensor, int32_t dim_index) { size_t TFL_TensorByteSize(const TFL_Tensor* tensor) { return tensor->bytes; } +void* TFL_TensorData(const TFL_Tensor* tensor) { + return static_cast(tensor->data.raw); +} + TFL_Status TFL_TensorCopyFromBuffer(TFL_Tensor* tensor, const void* input_data, - int32_t input_data_size) { - if (tensor->bytes != static_cast(input_data_size)) { + size_t input_data_size) { + if (tensor->bytes != input_data_size) { return kTfLiteError; } memcpy(tensor->data.raw, input_data, input_data_size); @@ -107,8 +139,8 @@ TFL_Status TFL_TensorCopyFromBuffer(TFL_Tensor* tensor, const void* input_data, } TFL_Status TFL_TensorCopyToBuffer(const TFL_Tensor* tensor, void* output_data, - int32_t output_data_size) { - if (tensor->bytes != static_cast(output_data_size)) { + size_t output_data_size) { + if (tensor->bytes != output_data_size) { return kTfLiteError; } memcpy(output_data, tensor->data.raw, output_data_size); diff --git a/tensorflow/contrib/lite/experimental/c/c_api.h b/tensorflow/contrib/lite/experimental/c/c_api.h index 070f1add13c9904e1a2b3736001ada0e274fdc55..3757349b5510ea3c3ac876b50b5c8c7db14688c9 100644 --- a/tensorflow/contrib/lite/experimental/c/c_api.h +++ b/tensorflow/contrib/lite/experimental/c/c_api.h @@ -30,6 +30,9 @@ limitations under the License. // // Conventions: // * We use the prefix TFL_ for everything in the API. +// * size_t is used to represent byte sizes of objects that are +// materialized in the address space of the calling process. +// * int is used as an index into arrays. #ifdef SWIG #define TFL_CAPI_EXPORT @@ -53,16 +56,51 @@ typedef TfLiteTensor TFL_Tensor; typedef TfLiteStatus TFL_Status; typedef TfLiteType TFL_Type; +// -------------------------------------------------------------------------- +// TFL_Model wraps a loaded TensorFlow Lite model. +typedef struct TFL_Model TFL_Model; + +// Returns a model from the provided buffer, or null on failure. +TFL_CAPI_EXPORT extern TFL_Model* TFL_NewModel(const void* model_data, + size_t model_size); + +// Returns a model from the provided file, or null on failure. +TFL_CAPI_EXPORT extern TFL_Model* TFL_NewModelFromFile(const char* model_path); + +// Destroys the model instance. +TFL_CAPI_EXPORT extern void TFL_DeleteModel(TFL_Model* model); + +// -------------------------------------------------------------------------- +// TFL_InterpreterOptions allows customized interpreter configuration. +typedef struct TFL_InterpreterOptions TFL_InterpreterOptions; + +// Returns a new interpreter options instances. +TFL_CAPI_EXPORT extern TFL_InterpreterOptions* TFL_NewInterpreterOptions(); + +// Destroys the interpreter options instance. +TFL_CAPI_EXPORT extern void TFL_DeleteInterpreterOptions( + TFL_InterpreterOptions* options); + +// Sets the number of CPU threads to use for the interpreter. +TFL_CAPI_EXPORT extern void TFL_InterpreterOptionsSetNumThreads( + TFL_InterpreterOptions* options, int32_t num_threads); + // -------------------------------------------------------------------------- // TFL_Interpreter provides inference from a provided model. -typedef struct _TFL_Interpreter TFL_Interpreter; +typedef struct TFL_Interpreter TFL_Interpreter; -// Returns an interpreter for the provided model, or null on failure. +// Returns a new interpreter using the provided model and options, or null on +// failure. +// +// * `model` must be a valid model instance. The caller retains ownership of the +// object, and can destroy it immediately after creating the interpreter. +// * `optional_options` may be null. The caller retains ownership of the object, +// and can safely destroy it immediately after creating the interpreter. // // NOTE: The client *must* explicitly allocate tensors before attempting to // access input tensor data or invoke the interpreter. TFL_CAPI_EXPORT extern TFL_Interpreter* TFL_NewInterpreter( - const void* model_data, int32_t model_size); + const TFL_Model* model, const TFL_InterpreterOptions* optional_options); // Destroys the interpreter. TFL_CAPI_EXPORT extern void TFL_DeleteInterpreter(TFL_Interpreter* interpreter); @@ -76,7 +114,8 @@ TFL_CAPI_EXPORT extern int TFL_InterpreterGetInputTensorCount( TFL_CAPI_EXPORT extern TFL_Tensor* TFL_InterpreterGetInputTensor( const TFL_Interpreter* interpreter, int32_t input_index); -// Attempts to resize the specified input tensor. +// Resizes the specified input tensor. +// // NOTE: After a resize, the client *must* explicitly allocate tensors before // attempting to access the resized tensor data or invoke the interpreter. // REQUIRES: 0 <= input_index < TFL_InterpreterGetInputTensorCount(tensor) @@ -131,16 +170,24 @@ TFL_CAPI_EXPORT extern int32_t TFL_TensorDim(const TFL_Tensor* tensor, // Returns the size of the underlying data in bytes. TFL_CAPI_EXPORT extern size_t TFL_TensorByteSize(const TFL_Tensor* tensor); +// Returns a pointer to the underlying data buffer. +// +// Note: The result may be null if tensors have not yet been allocated, e.g., +// if the Tensor has just been created or resized and `TFL_AllocateTensors()` +// has yet to be called, or if the output tensor is dynamically sized and the +// interpreter hasn't been invoked. +TFL_CAPI_EXPORT extern void* TFL_TensorData(const TFL_Tensor* tensor); + // Copies from the provided input buffer into the tensor's buffer. // REQUIRES: input_data_size == TFL_TensorByteSize(tensor) TFL_CAPI_EXPORT extern TFL_Status TFL_TensorCopyFromBuffer( - TFL_Tensor* tensor, const void* input_data, int32_t input_data_size); + TFL_Tensor* tensor, const void* input_data, size_t input_data_size); // Copies to the provided output buffer from the tensor's buffer. // REQUIRES: output_data_size == TFL_TensorByteSize(tensor) TFL_CAPI_EXPORT extern TFL_Status TFL_TensorCopyToBuffer( const TFL_Tensor* output_tensor, void* output_data, - int32_t output_data_size); + size_t output_data_size); #ifdef __cplusplus } // extern "C" diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/contrib/lite/experimental/c/c_api_experimental.cc similarity index 62% rename from tensorflow/compiler/xla/client/xla_client/xla_builder.h rename to tensorflow/contrib/lite/experimental/c/c_api_experimental.cc index ce2a8afd4cb1e7037e68a02670af707f3ff9252c..c4dbc55cbf6b116df46553411be5337f83ceb4e7 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h +++ b/tensorflow/contrib/lite/experimental/c/c_api_experimental.cc @@ -13,9 +13,19 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ -#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ +#include "tensorflow/contrib/lite/experimental/c/c_api_experimental.h" -#include "tensorflow/compiler/xla/client/xla_builder.h" +#include "tensorflow/contrib/lite/experimental/c/c_api_internal.h" -#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +TFL_Status TFL_InterpreterResetVariableTensorsToZero( + TFL_Interpreter* interpreter) { + return interpreter->impl->ResetVariableTensorsToZero(); +} + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus diff --git a/tensorflow/contrib/lite/experimental/c/c_api_experimental.h b/tensorflow/contrib/lite/experimental/c/c_api_experimental.h new file mode 100644 index 0000000000000000000000000000000000000000..b0ac258dcf9bf4ab603ba847f1b111a89cf2f29b --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api_experimental.h @@ -0,0 +1,32 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_EXPERIMENTAL_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_EXPERIMENTAL_H_ + +#include "tensorflow/contrib/lite/experimental/c/c_api.h" + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +// Resets all variable tensors to zero. +TFL_CAPI_EXPORT extern TFL_Status TFL_InterpreterResetVariableTensorsToZero( + TFL_Interpreter* interpreter); + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_EXPERIMENTAL_H_ diff --git a/tensorflow/contrib/lite/experimental/c/c_api_experimental_test.cc b/tensorflow/contrib/lite/experimental/c/c_api_experimental_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..db6e5251de518d2e754f853edbfb1c1edc425a83 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api_experimental_test.cc @@ -0,0 +1,46 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/c/c_api_experimental.h" + +#include +#include "tensorflow/contrib/lite/experimental/c/c_api.h" +#include "tensorflow/contrib/lite/testing/util.h" + +namespace { + +TEST(CApiExperimentalSimple, Smoke) { + TFL_Model* model = TFL_NewModelFromFile( + "tensorflow/contrib/lite/testdata/add.bin"); + ASSERT_NE(model, nullptr); + + TFL_Interpreter* interpreter = + TFL_NewInterpreter(model, /*optional_options=*/nullptr); + ASSERT_NE(interpreter, nullptr); + ASSERT_EQ(TFL_InterpreterAllocateTensors(interpreter), kTfLiteOk); + + EXPECT_EQ(TFL_InterpreterResetVariableTensorsToZero(interpreter), kTfLiteOk); + + TFL_DeleteModel(model); + TFL_DeleteInterpreter(interpreter); +} + +} // namespace + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/experimental/c/c_api_internal.h b/tensorflow/contrib/lite/experimental/c/c_api_internal.h new file mode 100644 index 0000000000000000000000000000000000000000..c5c612a4c6d3f8ccc49697961fd87b81bc00b6a8 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api_internal.h @@ -0,0 +1,41 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_INTERNAL_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_INTERNAL_H_ + +#include "tensorflow/contrib/lite/experimental/c/c_api.h" + +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/model.h" + +// Internal structures used by the C API. These are likely to change and should +// not be depended on. + +struct TFL_Model { + std::unique_ptr impl; +}; + +struct TFL_InterpreterOptions { + enum { + kDefaultNumThreads = -1, + }; + int num_threads = kDefaultNumThreads; +}; + +struct TFL_Interpreter { + std::unique_ptr impl; +}; + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_INTERNAL_H_ diff --git a/tensorflow/contrib/lite/experimental/c/c_api_test.cc b/tensorflow/contrib/lite/experimental/c/c_api_test.cc index bc925e00a6096c5e8abcc0fa68b335c4db4401c3..a631dae8906a2f5ab10b4125454f2eafb937823f 100644 --- a/tensorflow/contrib/lite/experimental/c/c_api_test.cc +++ b/tensorflow/contrib/lite/experimental/c/c_api_test.cc @@ -18,22 +18,28 @@ limitations under the License. #include "tensorflow/contrib/lite/experimental/c/c_api.h" #include -#include "tensorflow/contrib/lite/allocation.h" #include "tensorflow/contrib/lite/context.h" #include "tensorflow/contrib/lite/testing/util.h" namespace { TEST(CApiSimple, Smoke) { - tflite::FileCopyAllocation model_file( - "tensorflow/contrib/lite/testdata/add.bin", - tflite::DefaultErrorReporter()); + TFL_Model* model = TFL_NewModelFromFile( + "tensorflow/contrib/lite/testdata/add.bin"); + ASSERT_NE(model, nullptr); - TFL_Interpreter* interpreter = - TFL_NewInterpreter(model_file.base(), model_file.bytes()); + TFL_InterpreterOptions* options = TFL_NewInterpreterOptions(); + ASSERT_NE(options, nullptr); + TFL_InterpreterOptionsSetNumThreads(options, 2); + + TFL_Interpreter* interpreter = TFL_NewInterpreter(model, options); ASSERT_NE(interpreter, nullptr); - ASSERT_EQ(TFL_InterpreterAllocateTensors(interpreter), kTfLiteOk); + // The options/model can be deleted immediately after interpreter creation. + TFL_DeleteInterpreterOptions(options); + TFL_DeleteModel(model); + + ASSERT_EQ(TFL_InterpreterAllocateTensors(interpreter), kTfLiteOk); ASSERT_EQ(TFL_InterpreterGetInputTensorCount(interpreter), 1); ASSERT_EQ(TFL_InterpreterGetOutputTensorCount(interpreter), 1); diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs index ab966bae2efb9431e2f9f35dc818d130aabd71f6..b6905b5fbfe5b49e30d79b372b3be35d90fe252a 100644 --- a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs @@ -16,6 +16,8 @@ using System; using System.Runtime.InteropServices; using TFL_Interpreter = System.IntPtr; +using TFL_InterpreterOptions = System.IntPtr; +using TFL_Model = System.IntPtr; using TFL_Tensor = System.IntPtr; namespace TensorFlowLite @@ -32,7 +34,9 @@ namespace TensorFlowLite public Interpreter(byte[] modelData) { GCHandle modelDataHandle = GCHandle.Alloc(modelData, GCHandleType.Pinned); IntPtr modelDataPtr = modelDataHandle.AddrOfPinnedObject(); - handle = TFL_NewInterpreter(modelDataPtr, modelData.Length); + TFL_Model model = TFL_NewModel(modelDataPtr, modelData.Length); + handle = TFL_NewInterpreter(model, /*options=*/IntPtr.Zero); + TFL_DeleteModel(model); if (handle == IntPtr.Zero) throw new Exception("Failed to create TensorFlowLite Interpreter"); } @@ -88,10 +92,16 @@ namespace TensorFlowLite #region Externs + [DllImport (TensorFlowLibrary)] + private static extern unsafe TFL_Interpreter TFL_NewModel(IntPtr model_data, int model_size); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe TFL_Interpreter TFL_DeleteModel(TFL_Model model); + [DllImport (TensorFlowLibrary)] private static extern unsafe TFL_Interpreter TFL_NewInterpreter( - IntPtr model_data, - int model_size); + TFL_Model model, + TFL_InterpreterOptions optional_options); [DllImport (TensorFlowLibrary)] private static extern unsafe void TFL_DeleteInterpreter(TFL_Interpreter interpreter); diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md index c0dcb090b466e134c77635919b5be09f45f842f3..f480c49cd050de2192e9673f72c9e4d5c3c6ceff 100644 --- a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md @@ -1,6 +1,6 @@ # TF Lite Experimental Unity Plugin -This directoryy contains an experimental sample Unity (2017) Plugin, based on +This directory contains an experimental sample Unity (2017) Plugin, based on the experimental TF Lite C API. The sample demonstrates running inference within Unity by way of a C# `Interpreter` wrapper. @@ -25,3 +25,5 @@ bazel build -c opt --cxxopt=--std=c++11 \ If you encounter issues with native plugin discovery on Mac ("Darwin") platforms, try renaming `libtensorflowlite_c.so` to `tensorflowlite_c.bundle`. +Similarly, on Windows you'll likely need to rename `libtensorflowlite_c.so` to +`tensorflowlite_c.dll`. diff --git a/tensorflow/contrib/lite/g3doc/rpi.md b/tensorflow/contrib/lite/g3doc/rpi.md index cdc9172d873bfd32811ca69901ed2e4eedf902a3..9fcf79ba004d85566b64ce35b3693e01c4b0e2cf 100644 --- a/tensorflow/contrib/lite/g3doc/rpi.md +++ b/tensorflow/contrib/lite/g3doc/rpi.md @@ -20,7 +20,7 @@ Clone this Tensorflow repository, Run this script at the root of the repository ```bash ./tensorflow/contrib/lite/download_dependencies.sh ``` -Note than you only need to to this once. +Note that you only need to do this once. You should then be able to compile: ```bash @@ -42,7 +42,7 @@ First, clone this TensorFlow repository. Run this at the root of the repository: ```bash ./tensorflow/contrib/lite/download_dependencies.sh ``` -Note than you only need to to this once. +Note that you only need to do this once. You should then be able to compile: ```bash diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index be149a8cc0e642d10b270ba617cd8d6be29430b2..159ff7bc20a1e1261cdfd746312279bd59e3b1a4 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -165,7 +165,7 @@ class Interpreter { return SetTensorParametersReadOnly(tensor_index, type, name, dims.size(), dims.data(), quantization, buffer, bytes, allocation); - }; + } TfLiteStatus SetTensorParametersReadOnly( int tensor_index, TfLiteType type, const char* name, const size_t rank, @@ -527,12 +527,13 @@ class Interpreter { TfLiteRegistration** registration); // WARNING: This is an experimental interface that is subject to change. - // Gets an TfLiteIntArray* representing the execution plan. The caller owns - // this memory and must free it with TfLiteIntArrayFree(). + // Gets an TfLiteIntArray* representing the execution plan. The interpreter + // owns this memory and it is only guaranteed to exist during the invocation + // of the delegate prepare. TfLiteStatus GetExecutionPlan(TfLiteIntArray** execution_plan); // WARNING: This is an experimental interface that is subject to change. - // Entry point for C node plugin API to get the execution plan + // Entry point for C node plugin API to get the execution plan. static TfLiteStatus GetExecutionPlan(struct TfLiteContext* context, TfLiteIntArray** execution_plan); diff --git a/tensorflow/contrib/lite/java/demo/.gitignore b/tensorflow/contrib/lite/java/demo/.gitignore index 39fb081a42a86ccf8f9cf99dbccc8bdf7c828bce..d245ab61095a6f9b6d2077aac934f9b13e66d85e 100644 --- a/tensorflow/contrib/lite/java/demo/.gitignore +++ b/tensorflow/contrib/lite/java/demo/.gitignore @@ -1,9 +1,29 @@ +# This file is based on https://github.com/github/gitignore/blob/master/Android.gitignore *.iml +.idea/compiler.xml +.idea/copyright +.idea/dictionaries +.idea/gradle.xml +.idea/libraries +.idea/inspectionProfiles +.idea/misc.xml +.idea/modules.xml +.idea/runConfigurations.xml +.idea/tasks.xml +.idea/workspace.xml .gradle -/local.properties -/.idea/workspace.xml -/.idea/libraries +local.properties .DS_Store -/build +build/ +gradleBuild/ +*.apk +*.ap_ +*.dex +*.class +bin/ +gen/ +out/ +*.log +.navigation/ /captures .externalNativeBuild diff --git a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java index c23521c0774ebab01f38db8b416020ae5755cee9..38b740021bb5037fc8980c75ca6aac2a9cc20c4e 100644 --- a/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java +++ b/tensorflow/contrib/lite/java/src/testhelper/java/org/tensorflow/lite/TestHelper.java @@ -65,6 +65,25 @@ public class TestHelper { } } + /** + * Gets the string name of the data type of an input. + * + * @param interpreter an instance of {@code Interpreter}. If it is not initialized, an {@code + * IllegalArgumentException} will be thrown. + * @param index an integer index of the input. If it is invalid, an {@code + * IllegalArgumentException} will be thrown. + * @return string name of the data type. Possible values include "float", "int", "byte", and + * "long". + */ + public static String getInputDataType(Interpreter interpreter, int index) { + if (interpreter != null && interpreter.wrapper != null) { + return interpreter.wrapper.getInputTensor(index).dataType().toStringName(); + } else { + throw new IllegalArgumentException( + "Interpreter has not initialized;" + " Failed to get input data type."); + } + } + /** * Gets the string name of the data type of an output. * diff --git a/tensorflow/contrib/lite/kernels/concatenation.cc b/tensorflow/contrib/lite/kernels/concatenation.cc index ad211e9c67eed9ca70fcdd51171fdb70bd89b27c..605a20ac3e7c8346db2bcf64e9422132b433b3da 100644 --- a/tensorflow/contrib/lite/kernels/concatenation.cc +++ b/tensorflow/contrib/lite/kernels/concatenation.cc @@ -57,7 +57,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE(context, t0->dims->size <= 4); TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone); TF_LITE_ENSURE(context, - input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8); + input_type == kTfLiteFloat32 || input_type == kTfLiteUInt8 || + input_type == kTfLiteInt16 || input_type == kTfLiteInt32 || + input_type == kTfLiteInt64); // Output dimensions will match input dimensions, except 'axis', which // will be the sum of inputs @@ -121,6 +123,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_CONCATENATION(optimized_ops, float); } break; + case kTfLiteInt32: + if (kernel_type == kReference) { + TF_LITE_CONCATENATION(reference_ops, int32); + } else { + TF_LITE_CONCATENATION(optimized_ops, int32); + } + break; case kTfLiteUInt8: if (kernel_type == kReference) { TF_LITE_CONCATENATION_QUANTIZED(reference_ops); @@ -128,6 +137,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_CONCATENATION_QUANTIZED(optimized_ops); } break; + case kTfLiteInt64: + if (kernel_type == kReference) { + TF_LITE_CONCATENATION(reference_ops, int64_t); + } else { + TF_LITE_CONCATENATION(optimized_ops, int64_t); + } + break; + default: context->ReportError(context, "Only float32 and uint8 are currently supported."); diff --git a/tensorflow/contrib/lite/kernels/dequantize.cc b/tensorflow/contrib/lite/kernels/dequantize.cc index 672b2170e4990f0a7ca9755071d9d086f5ae5c2b..2b0f04489a48cd4402e7574ecc5eeecfd8c6234f 100644 --- a/tensorflow/contrib/lite/kernels/dequantize.cc +++ b/tensorflow/contrib/lite/kernels/dequantize.cc @@ -36,6 +36,21 @@ struct OpContext { TfLiteTensor* output; }; +struct OpData { + // This boolean value is only used when the input tensor is constant. + bool float_dequantized_weights_initialized; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* op_data = new OpData(); + op_data->float_dequantized_weights_initialized = false; + return op_data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); @@ -45,12 +60,22 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8); op_context.output->type = kTfLiteFloat32; + // If the input tensor is constant, we can persist the dequantized value in + // the output tensor. Otherwise we run dequantize upon each eval. + if (IsConstantTensor(op_context.input)) { + op_context.output->allocation_type = kTfLiteArenaRwPersistent; + } return context->ResizeTensor(context, op_context.output, TfLiteIntArrayCopy(op_context.input->dims)); } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OpData* op_data = reinterpret_cast(node->user_data); OpContext op_context(context, node); + if (IsConstantTensor(op_context.input) && + op_data->float_dequantized_weights_initialized) { + return kTfLiteOk; + } auto zero_point = op_context.input->params.zero_point; auto scale = op_context.input->params.scale; @@ -59,14 +84,19 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetTensorDims(op_context.input), zero_point, scale, GetTensorData(op_context.output), GetTensorDims(op_context.output)); + + if (IsConstantTensor(op_context.input)) { + op_data->float_dequantized_weights_initialized = true; + } + return kTfLiteOk; } } // namespace dequantize TfLiteRegistration* Register_DEQUANTIZE_OPT() { - static TfLiteRegistration r = {nullptr, nullptr, dequantize::Prepare, - dequantize::Eval}; + static TfLiteRegistration r = {dequantize::Init, dequantize::Free, + dequantize::Prepare, dequantize::Eval}; return &r; } diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc index bc370608c092eeb5312dc40b56f47740f473c8ae..eaf5a67d6787b9113bd0835d436b459e00ed7fff 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected.cc @@ -121,10 +121,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { double real_multiplier = 0.0; TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( context, input, filter, bias, output, &real_multiplier)); - TF_LITE_ENSURE(context, real_multiplier < 1.0); - QuantizeMultiplierSmallerThanOneExp( - real_multiplier, &data->output_multiplier, &data->output_shift); - data->output_shift *= -1; + int exponent; + QuantizeMultiplier(real_multiplier, &data->output_multiplier, &exponent); + data->output_shift = -exponent; TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( context, params->activation, output, &data->output_activation_min, &data->output_activation_max)); diff --git a/tensorflow/contrib/lite/kernels/fully_connected_test.cc b/tensorflow/contrib/lite/kernels/fully_connected_test.cc index ec949056971ccb5f7a6f93fa9f236a93625ca6ad..08b43209466a1b85613ae41d5aa776194f992c60 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected_test.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected_test.cc @@ -423,6 +423,37 @@ TEST_P(QuantizedFullyConnectedOpTest, SimpleTestQuantized) { ElementsAre(151, 152, 153, 185, 186, 187)); } +TEST_P(QuantizedFullyConnectedOpTest, + SimpleTestQuantizedOutputMultiplierGreaterThan1) { + // real_multiplier = 2. + QuantizedFullyConnectedOpModel m( + GetRegistration(), /*units=*/3, /*batches*/ 2, + /*input=*/{TensorType_UINT8, {2, 10}, -127, 128}, + /*output=*/{TensorType_UINT8, {}, -63.5, 64}); + + m.SetWeights({ + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 2 + }); + m.SetBias({1, 2, 3}); + + m.SetInput({ + 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0 + 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 24, 25, 26, // first batch + 58, 59, 60, // second batch + }))); + EXPECT_THAT(m.GetOutput(), + ElementsAre(175, 177, 179, 243, 245, 247)); +} + void SimpleTestQuantizedInt16OutputCase( TfLiteRegistration* registration, int input_depth, int output_depth, int batches, FullyConnectedOptionsWeightsFormat weights_format) { @@ -631,6 +662,37 @@ TEST_P(QuantizedFullyConnectedOpTest, SimpleTest4dInputQuantized) { ElementsAre(151, 152, 153, 185, 186, 187)); } +TEST_P(QuantizedFullyConnectedOpTest, + SimpleTest4dInputQuantizedOutputMultiplierGreaterThan1) { + // real_multiplier = 2. + QuantizedFullyConnectedOpModel m( + GetRegistration(), /*units=*/3, /*batches=*/2, + /*input=*/{TensorType_UINT8, {4, 1, 5, 1}, -127, 128}, + /*output=*/{TensorType_UINT8, {}, -63.5, 64}); + + m.SetWeights({ + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + }); + m.SetBias({1, 2, 3}); + + m.SetInput({ + 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0 + 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1 + }); + + m.Invoke(); + + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 24, 25, 26, // first batch + 58, 59, 60, // second batch + }))); + EXPECT_THAT(m.GetOutput(), + ElementsAre(175, 177, 179, 243, 245, 247)); +} + INSTANTIATE_TEST_CASE_P( FloatFullyConnectedOpTest, FloatFullyConnectedOpTest, ::testing::ValuesIn(SingleOpTest::GetKernelTags(*kKernelMap))); diff --git a/tensorflow/contrib/lite/kernels/internal/BUILD b/tensorflow/contrib/lite/kernels/internal/BUILD index 0d424071da23010afe5f15a61e0ea6e45b4e6742..87155e4ba428f885b68e1106ea5501d0bbaaeced 100644 --- a/tensorflow/contrib/lite/kernels/internal/BUILD +++ b/tensorflow/contrib/lite/kernels/internal/BUILD @@ -496,6 +496,7 @@ cc_library( hdrs = ["test_util.h"], deps = [ ":types", + "//tensorflow/contrib/lite:string", ], ) @@ -576,6 +577,7 @@ cc_test( ":quantization_util", ":reference_base", ":test_util", + "//tensorflow/contrib/lite:string", "@com_google_googletest//:gtest_main", ], ) @@ -595,6 +597,7 @@ cc_test( ":quantization_util", ":reference_base", ":test_util", + "//tensorflow/contrib/lite:string", "@com_google_googletest//:gtest_main", ], ) @@ -606,6 +609,7 @@ cc_test( deps = [ ":optimized_base", ":reference_base", + "//tensorflow/contrib/lite:string", "@com_google_googletest//:gtest_main", ], ) diff --git a/tensorflow/contrib/lite/kernels/internal/log_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/log_quantized_test.cc index 7e9ff5242a43a8b54e0e6ae167cdcf7a341c918e..8963abb9afd9d51473fe5a22d8e88d314b385ad9 100644 --- a/tensorflow/contrib/lite/kernels/internal/log_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/log_quantized_test.cc @@ -29,8 +29,9 @@ limitations under the License. #include #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h" #include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/string.h" -namespace { +namespace tflite { class NumberGenerator { public: @@ -330,4 +331,4 @@ TEST_F(LogQuantizedTest, SelectedIntegerBits) { &generator_); } -} // namespace +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc index d2f1103e14b40b81c59c8053bcdbee30c85e5c78..3624c20ae3bbf5f8eb5cb5fb51aadcde7327fd55 100644 --- a/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/logsoftmax_quantized_test.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" #include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" #include "tensorflow/contrib/lite/kernels/internal/test_util.h" +#include "tensorflow/contrib/lite/string.h" namespace tflite { namespace { diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 6adb879c71e6a02007dacd5ed9f91b04b2094fe7..b87078977234fd856cb0fcd96363ba92ddb3ad74 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -893,6 +893,7 @@ inline void FullyConnectedAsGEMV( const int input_size = FlatSizeSkipDim(input_dims, 3); const int output_size = MatchingArraySize(filter_dims, 1, output_dims, 0); static constexpr int kPeel = 4; + const bool shift_left = (output_shift <= 0); for (int k = 0; k < input_size; k += 64) { optimized_ops_preload_l1_stream(input_data + k); } @@ -1004,11 +1005,17 @@ inline void FullyConnectedAsGEMV( int32x4_t bias_vec = vld1q_s32(bias_ptr); bias_ptr += 4; reduced = vaddq_s32(reduced, bias_vec); - // Multiply by the fixed-point multiplier. - reduced = vqrdmulhq_n_s32(reduced, output_multiplier); - // Rounding-shift-right. - using gemmlowp::RoundingDivideByPOT; - reduced = RoundingDivideByPOT(reduced, output_shift); + if (shift_left) { + const int32 multiplier_power_of_two = 1 << -output_shift; + reduced = vmulq_n_s32(reduced, multiplier_power_of_two); + reduced = vqrdmulhq_n_s32(reduced, output_multiplier); + } else { + // Multiply by the fixed-point multiplier. + reduced = vqrdmulhq_n_s32(reduced, output_multiplier); + // Rounding-shift-right. + using gemmlowp::RoundingDivideByPOT; + reduced = RoundingDivideByPOT(reduced, output_shift); + } // Add the output offset. const int32x4_t output_offset_vec = vdupq_n_s32(output_offset); reduced = vaddq_s32(reduced, output_offset_vec); diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc index 94773b47d3817d7ed7240f74545ad04e7fa4bd52..00fc3e91dc90254ca68d637941e5a2482e2832a8 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc @@ -130,22 +130,22 @@ void RunSafeCastTests() { } TEST(QuantizationUtilTest, SafeCast) { - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); - RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); + RunSafeCastTests(); } // Example taken from http://www.tensorflow.org/performance/quantization diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc index e6ccd7a32cbad1d2faf72e7e4fe375e2ff1fa5e6..aa93e857d7a9f98aa06e91ff3d6c743b00b17137 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc @@ -73,10 +73,12 @@ void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, for (int b = 0; b < n_batch; b++) { const float* matrix_ptr = matrix; for (int r = 0; r < m_rows; r++) { + float dot_prod = 0.0f; const float* vector_in_batch = vector + b * m_cols; for (int c = 0; c < m_cols; c++) { - *result_in_batch += *matrix_ptr++ * *vector_in_batch++; + dot_prod += *matrix_ptr++ * *vector_in_batch++; } + *result_in_batch += dot_prod; result_in_batch += result_stride; } } @@ -84,9 +86,8 @@ void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, void PortableMatrixBatchVectorMultiplyAccumulate( const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, - const float* __restrict__ scaling_factors, int n_batch, - float* __restrict__ result, int result_stride) { + const int8_t* __restrict__ vectors, const float* scaling_factors, + int n_batch, float* __restrict__ result, int result_stride) { int batch, row, col; for (batch = 0; batch < n_batch; ++batch, vectors += m_cols) { const float batch_scaling_factor = scaling_factors[batch]; diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index ace3af2da06b31cea7f3d7e60d086f9ff6d7c0ce..f4176e474e738d83783379fff0e45722396f24a6 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -546,8 +546,8 @@ inline void FullyConnected(const uint8* input_data, const Dims<4>& input_dims, if (bias_data) { acc += bias_data[Offset(bias_dims, out_c, 0, 0, 0)]; } - acc = MultiplyByQuantizedMultiplierSmallerThanOneExp( - acc, output_multiplier, kReverseShift * output_shift); + acc = MultiplyByQuantizedMultiplier(acc, output_multiplier, + kReverseShift * output_shift); acc += output_offset; acc = std::max(acc, output_activation_min); acc = std::min(acc, output_activation_max); diff --git a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc index a7dad3c14e60fac9da9c0bcfd5d1d4c8f10b71c7..ca94e7740eb18e9d2d36c676e1db2766d7050852 100644 --- a/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/softmax_quantized_test.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" #include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" #include "tensorflow/contrib/lite/kernels/internal/test_util.h" +#include "tensorflow/contrib/lite/string.h" namespace tflite { namespace { diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc index 372a6efec5c69e53d558edf8c822f638a4d33d81..e8343f1223b2137a7df9cc264c56100bc66f9fc1 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils_test.cc @@ -72,7 +72,7 @@ TEST(uKernels, SymmetricQuantizeFloatsTest) { static float input[kVectorSize] = {-640, -635.0, -630, 10.0, 2.0, -5.0, -10.0, 0.0, 1000.0}; - int8 output[kVectorSize]; + int8_t output[kVectorSize]; float min, max, scaling_factor; SymmetricQuantizeFloats(input, kVectorSize, output, &min, &max, &scaling_factor); @@ -89,7 +89,7 @@ TEST(uKernels, SymmetricQuantizeFloatsAllZerosTest) { constexpr int kVectorSize = 9; static float input[kVectorSize] = {0, 0, 0, 0, 0, 0, 0, 0, 0}; - int8 output[kVectorSize]; + int8_t output[kVectorSize]; float min, max, scaling_factor; SymmetricQuantizeFloats(input, kVectorSize, output, &min, &max, &scaling_factor); @@ -105,7 +105,7 @@ TEST(uKernels, SymmetricQuantizeFloatsAllAlmostZeroTest) { static float input[kVectorSize] = {-1e-5, 3e-5, -7e-6, -9e-5, 1e-6, 4e-5, 9e-6, 2e-4, 0}; - int8 output[kVectorSize]; + int8_t output[kVectorSize]; float min, max, scaling_factor; SymmetricQuantizeFloats(input, kVectorSize, output, &min, &max, &scaling_factor); @@ -143,6 +143,7 @@ TEST(uKernels, MatrixBatchVectorMultiplyAccumulateTest) { -1., 3., 7., 3., 23., 3.}))); } +#ifdef __ANDROID__ TEST(uKernels, MatrixBatchVectorMultiplyAccumulateSymmetricQuantizedTest) { // Note we use 29 columns as this exercises all the neon kernel: the // 16-block SIMD code, the 8-block postamble, and the leftover postamble. @@ -166,13 +167,13 @@ TEST(uKernels, MatrixBatchVectorMultiplyAccumulateSymmetricQuantizedTest) { -13.13, 14.14, -15.15, 16.16, -17.17, 18.18, -19.19, 20.2, -21.21, 22.22, -23.23, 24.24, -25.25, 26.26, -27.27, 28.28, 0}; - int8* a_int8_data = reinterpret_cast( + int8_t* a_int8_data = reinterpret_cast( aligned_malloc(a_rows * a_cols, kWeightsPerUint32)); float a_min, a_max; float scaling_factor_a; SymmetricQuantizeFloats(a_float_data, a_rows * a_cols, a_int8_data, &a_min, &a_max, &scaling_factor_a); - const int8 expected_a_int8_data[] = { + const int8_t expected_a_int8_data[] = { /* 1st row */ 5, 10, @@ -363,7 +364,7 @@ TEST(uKernels, MatrixBatchVectorMultiplyAccumulateSymmetricQuantizedTest) { }; // Quantized values of B: - int8 b_int8_data[b_rows * b_cols * batches]; + int8_t b_int8_data[b_rows * b_cols * batches]; float b_min, b_max; float scaling_factor_b[batches]; SymmetricQuantizeFloats(b_float_data, b_rows * b_cols, b_int8_data, &b_min, @@ -372,7 +373,7 @@ TEST(uKernels, MatrixBatchVectorMultiplyAccumulateSymmetricQuantizedTest) { &b_int8_data[b_rows * b_cols], &b_min, &b_max, &scaling_factor_b[1]); - const int8 expected_b_int8_data[] = { + const int8_t expected_b_int8_data[] = { /* batch 1 */ 127, -127, @@ -465,6 +466,7 @@ TEST(uKernels, MatrixBatchVectorMultiplyAccumulateSymmetricQuantizedTest) { aligned_free(a_int8_data); } +#endif // __ANDROID__ TEST(uKernels, VectorVectorCwiseProductTest) { constexpr int kVectorSize = 10; diff --git a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h index becd1f615f04a806cba9c494323285c004ec41df..42b8163445d252c766491e7bcd2fd7eea0dd7571 100644 --- a/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h +++ b/tensorflow/contrib/lite/nnapi/NeuralNetworksShim.h @@ -44,6 +44,19 @@ inline void* loadLibrary(const char* name) { return handle; } +typedef int (*ASharedMemory_create_fn)(const char* name, size_t size); + +// ASharedMemory_create was added in Android 8.0, so safe to use with NNAPI +// which was added in 8.1. +inline int ASharedMemory_create(const char* name, size_t size) { + static void* handle = loadLibrary("libandroid.so"); + static ASharedMemory_create_fn fn = + handle != nullptr ? reinterpret_cast( + dlsym(handle, "ASharedMemory_create")) + : nullptr; + return fn(name, size); +} + inline void* getLibraryHandle() { static void* handle = loadLibrary("libneuralnetworks.so"); return handle; diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index c91f4881754968aef8115b1b3743e1e4b19eb0fa..13325a8c7c62142fcbc8ca37af1216e8943b905b 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -568,9 +568,17 @@ TfLiteStatus AddOpsAndParams( "NNAPI does not support L2Normalization with fused activations"); } break; + case tflite::BuiltinOperator_HASHTABLE_LOOKUP: + if (interpreter->tensor(node.outputs->data[0])->type != + kTfLiteFloat32) { + logError("NNAPI only support HASHTABLE_LOOKUP with float32 output", + builtin); + return kTfLiteError; + } + nn_op_type = ANEURALNETWORKS_HASHTABLE_LOOKUP; + break; case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: case tflite::BuiltinOperator_LSH_PROJECTION: - case tflite::BuiltinOperator_HASHTABLE_LOOKUP: case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: diff --git a/tensorflow/contrib/lite/python/convert.py b/tensorflow/contrib/lite/python/convert.py index ec49738fb5365a16c41cc6737198b5707508a3e2..11d4bdbe82295bff9a7a457e2fd5ca1f8fe04036 100644 --- a/tensorflow/contrib/lite/python/convert.py +++ b/tensorflow/contrib/lite/python/convert.py @@ -54,7 +54,7 @@ def toco_convert_protos(model_flags_str, toco_flags_str, input_data_str): """Convert `input_data_str` according to model and toco parameters. Unless you know what you are doing consider using - the more friendly @{tf.contrib.lite.toco_convert}}. + the more friendly `tf.contrib.lite.toco_convert`. Args: model_flags_str: Serialized proto describing model properties, see diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index 3243bddac879b8eb0ca7a03d28b2f6094f905983..1be61fe05343a0e7d39f2808c78672698e0d767f 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -54,6 +54,10 @@ class Interpreter(object): if not self._interpreter: raise ValueError('Failed to open {}'.format(model_path)) elif model_content and not model_path: + # Take a reference, so the pointer remains valid. + # Since python strings are immutable then PyString_XX functions + # will always return the same pointer. + self._model_content = model_content self._interpreter = ( _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer( model_content)) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index 3e03751da40064c64ab646d0b976a2ff5ca9c250..641dd93db5b9df292e03e9704a218299f48b14fb 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -15,12 +15,15 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_PYTHON_INTERPRETER_WRAPPER_INTERPRETER_WRAPPER_H_ #define TENSORFLOW_CONTRIB_LITE_PYTHON_INTERPRETER_WRAPPER_INTERPRETER_WRAPPER_H_ -// Place `` before to avoid build failures in macOS. -#include #include #include #include +// Place `` before to avoid build failures in macOS. +#include + +// The empty line above is on purpose as otherwise clang-format will +// automatically move before . #include // We forward declare TFLite classes here to avoid exposing them to SWIG. diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 2f9b9d469a27cc8910cb61c0da14769e5ff0baf0..52ef43d71f2c0ce138efe324ea60298e9cd10513 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -54,7 +54,6 @@ from tensorflow.python import keras as _keras from tensorflow.python.client import session as _session from tensorflow.python.framework import graph_util as _tf_graph_util from tensorflow.python.framework.importer import import_graph_def as _import_graph_def -from tensorflow.python.ops.variables import global_variables_initializer as _global_variables_initializer from tensorflow.python.saved_model import signature_constants as _signature_constants from tensorflow.python.saved_model import tag_constants as _tag_constants @@ -195,8 +194,6 @@ class TocoConverter(object): input_arrays or output_arrays contains an invalid tensor name. """ with _session.Session() as sess: - sess.run(_global_variables_initializer()) - # Read GraphDef from file. graph_def = _graph_pb2.GraphDef() with open(graph_def_file, "rb") as f: @@ -427,7 +424,6 @@ def _freeze_graph(sess, output_tensors): Frozen GraphDef. """ if not _is_frozen_graph(sess): - sess.run(_global_variables_initializer()) output_arrays = [_tensor_name(tensor) for tensor in output_tensors] return _tf_graph_util.convert_variables_to_constants( sess, sess.graph_def, output_arrays) diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py index ca2af5aaed3ee4f4fce5f0d31eaa61df0e11f364..2f1368422842846aa616eaa7bc1e60ee6b0deaaf 100644 --- a/tensorflow/contrib/lite/python/lite_test.py +++ b/tensorflow/contrib/lite/python/lite_test.py @@ -33,6 +33,7 @@ from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.ops.variables import global_variables_initializer as _global_variables_initializer from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.saved_model import saved_model @@ -198,6 +199,7 @@ class FromSessionTest(test_util.TensorFlowTestCase): 'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32) out_tensor = in_tensor + var sess = session.Session() + sess.run(_global_variables_initializer()) # Convert model and ensure model is not None. converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor]) @@ -655,9 +657,7 @@ class FromKerasFile(test_util.TensorFlowTestCase): tflite_model = converter.convert() self.assertTrue(tflite_model) - os.remove(keras_file) - - # Check values from converted model. + # Check tensor details of converted model. interpreter = Interpreter(model_content=tflite_model) interpreter.allocate_tensors() @@ -675,6 +675,18 @@ class FromKerasFile(test_util.TensorFlowTestCase): self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all()) self.assertEqual((0., 0.), output_details[0]['quantization']) + # Check inference of converted model. + input_data = np.array([[1, 2, 3]], dtype=np.float32) + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + tflite_result = interpreter.get_tensor(output_details[0]['index']) + + keras_model = keras.models.load_model(keras_file) + keras_result = keras_model.predict(input_data) + + np.testing.assert_almost_equal(tflite_result, keras_result, 5) + os.remove(keras_file) + def testSequentialModelInputArray(self): """Test a Sequential tf.keras model testing input arrays argument.""" keras_file = self._getSequentialModel() @@ -755,17 +767,17 @@ class FromKerasFile(test_util.TensorFlowTestCase): model.predict(x) fd, keras_file = tempfile.mkstemp('.h5') - keras.models.save_model(model, keras_file) + try: + keras.models.save_model(model, keras_file) + finally: + os.close(fd) # Convert to TFLite model. converter = lite.TocoConverter.from_keras_model_file(keras_file) tflite_model = converter.convert() self.assertTrue(tflite_model) - os.close(fd) - os.remove(keras_file) - - # Check values from converted model. + # Check tensor details of converted model. interpreter = Interpreter(model_content=tflite_model) interpreter.allocate_tensors() @@ -783,6 +795,18 @@ class FromKerasFile(test_util.TensorFlowTestCase): self.assertTrue(([1, 3] == output_details[0]['shape']).all()) self.assertEqual((0., 0.), output_details[0]['quantization']) + # Check inference of converted model. + input_data = np.array([[1, 2, 3]], dtype=np.float32) + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + tflite_result = interpreter.get_tensor(output_details[0]['index']) + + keras_model = keras.models.load_model(keras_file) + keras_result = keras_model.predict(input_data) + + np.testing.assert_almost_equal(tflite_result, keras_result, 5) + os.remove(keras_file) + def testFunctionalModelMultipleInputs(self): """Test a Functional tf.keras model with multiple inputs and outputs.""" a = keras.layers.Input(shape=(3,), name='input_a') @@ -865,17 +889,17 @@ class FromKerasFile(test_util.TensorFlowTestCase): model.predict(x) fd, keras_file = tempfile.mkstemp('.h5') - keras.models.save_model(model, keras_file) + try: + keras.models.save_model(model, keras_file) + finally: + os.close(fd) # Convert to TFLite model. converter = lite.TocoConverter.from_keras_model_file(keras_file) tflite_model = converter.convert() self.assertTrue(tflite_model) - os.close(fd) - os.remove(keras_file) - - # Check values from converted model. + # Check tensor details of converted model. interpreter = Interpreter(model_content=tflite_model) interpreter.allocate_tensors() @@ -893,6 +917,18 @@ class FromKerasFile(test_util.TensorFlowTestCase): self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all()) self.assertEqual((0., 0.), output_details[0]['quantization']) + # Check inference of converted model. + input_data = np.array([[1, 2, 3]], dtype=np.float32) + interpreter.set_tensor(input_details[0]['index'], input_data) + interpreter.invoke() + tflite_result = interpreter.get_tensor(output_details[0]['index']) + + keras_model = keras.models.load_model(keras_file) + keras_result = keras_model.predict(input_data) + + np.testing.assert_almost_equal(tflite_result, keras_result, 5) + os.remove(keras_file) + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py index d17482e60113da5bad3a76fa2ab634ae0ffb89fd..a76cc3963580767ab8bd745a9bcd7c9c780ec2b5 100644 --- a/tensorflow/contrib/lite/python/tflite_convert.py +++ b/tensorflow/contrib/lite/python/tflite_convert.py @@ -203,8 +203,9 @@ def _check_flags(flags, unparsed): raise ValueError("--default_ranges_min and --default_ranges_max must be " "used together") - if flags.dump_graphviz_video and not flags.dump_graphviz: - raise ValueError("--dump_graphviz_video must be used with --dump_graphviz") + if flags.dump_graphviz_video and not flags.dump_graphviz_dir: + raise ValueError("--dump_graphviz_video must be used with " + "--dump_graphviz_dir") def run_main(_): diff --git a/tensorflow/contrib/lite/schema/upgrade_schema.py b/tensorflow/contrib/lite/schema/upgrade_schema.py index e0b36d3d3ee94b00cccd3968d14c63fe19c3c27c..a2ddf6295014f3b29fa584f2bb367a7e0a4399e7 100644 --- a/tensorflow/contrib/lite/schema/upgrade_schema.py +++ b/tensorflow/contrib/lite/schema/upgrade_schema.py @@ -99,9 +99,9 @@ class Converter(object): # dispatch function table. self._schemas.sort() self._new_version, self._new_schema = self._schemas[-1][:2] - self._upgrade_dispatch = dict( - (version, dispatch) - for version, unused1, unused2, dispatch in self._schemas) + self._upgrade_dispatch = { + version: dispatch + for version, unused1, unused2, dispatch in self._schemas} def _Read(self, input_file, schema, raw_binary=False): """Read a tflite model assuming the given flatbuffer schema. diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 3d1f8c07d288539ffbab73bd0349091a208cb983..52ef0d5b86524d605b2f5d6dbae98d4c343ad6a0 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -90,8 +90,6 @@ TEST_INPUT_DEPTH = 3 # matching the expression will be considered due to the corresponding bug. KNOWN_BUGS = { # TOCO doesn't support scalars as input. - r"relu.*input_shape=\[\]": "67587484", - r"sigmoid.*input_shape=\[\]": "67645668", # Concat doesn't work with a single input tensor r"concat.*num_tensors=1": "67378344", # Transposition in MatMul is not fully supported. @@ -104,8 +102,6 @@ KNOWN_BUGS = { r"div.*int32": "72051395", # No support for SplitV r"split.*num_or_size_splits=\[2,2\]": "73377559", - # Scalar constants don't work. - r"constant.*shape=\[\]": "109811500", } @@ -230,6 +226,7 @@ _TF_TYPE_INFO = { tf.float16: (np.float16, "FLOAT"), tf.int32: (np.int32, "INT32"), tf.uint8: (np.uint8, "QUANTIZED_UINT8"), + tf.int16: (np.int16, "QUANTIZED_INT16"), tf.int64: (np.int64, "INT64"), tf.bool: (np.bool, "BOOL"), } @@ -243,7 +240,7 @@ def create_tensor_data(dtype, shape, min_value=-100, max_value=100): if dtype in (tf.float32, tf.float16): value = (max_value-min_value)*np.random.random_sample(shape)+min_value - elif dtype in (tf.int32, tf.uint8, tf.int64): + elif dtype in (tf.int32, tf.uint8, tf.int64, tf.int16): value = np.random.randint(min_value, max_value+1, shape) elif dtype == tf.bool: value = np.random.choice([True, False], size=shape) @@ -259,7 +256,7 @@ def create_scalar_data(dtype, min_value=-100, max_value=100): if dtype in (tf.float32, tf.float16): value = (max_value - min_value) * np.random.random() + min_value - elif dtype in (tf.int32, tf.uint8, tf.int64): + elif dtype in (tf.int32, tf.uint8, tf.int64, tf.int16): value = np.random.randint(min_value, max_value + 1) return np.array(value, dtype=dtype) @@ -824,11 +821,13 @@ def make_binary_op_tests(zip_path, binary_operator): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) -def make_reduce_tests(reduce_op): +def make_reduce_tests(reduce_op, min_value=-10, max_value=10): """Make a set of tests to do reduce operation. Args: reduce_op: TensorFlow reduce operation to test, i.e. `tf.reduce_mean`. + min_value: min value for created tensor data. + max_value: max value for created tensor data. Returns: a function representing the true generator with `reduce_op_in` curried. @@ -891,10 +890,12 @@ def make_reduce_tests(reduce_op): def build_inputs(parameters, sess, inputs, outputs): values = [ - create_tensor_data(parameters["input_dtype"], - parameters["input_shape"], - min_value=-10, - max_value=10)] + create_tensor_data( + parameters["input_dtype"], + parameters["input_shape"], + min_value=min_value, + max_value=max_value) + ] if not parameters["const_axis"]: values.append(np.array(parameters["axis"])) return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) @@ -916,7 +917,8 @@ def make_sum_tests(zip_path): def make_reduce_prod_tests(zip_path): """Make a set of tests to do prod.""" - return make_reduce_tests(tf.reduce_prod)(zip_path) + # set min max value to be -2, 2 to avoid overflow. + return make_reduce_tests(tf.reduce_prod, -2, 2)(zip_path) def make_reduce_max_tests(zip_path): @@ -1355,6 +1357,7 @@ def make_concat_tests(zip_path): "base_shape": [[1, 3, 4, 3], [3, 4]], "num_tensors": [1, 2, 3, 4, 5, 6], "axis": [0, 1, 2, 3, -3, -2, -1], + "type": [tf.float32, tf.uint8, tf.int32, tf.int64], }] def get_shape(parameters, delta): @@ -1370,7 +1373,8 @@ def make_concat_tests(zip_path): def build_graph(parameters): all_tensors = [] for n in range(0, parameters["num_tensors"]): - input_tensor = tf.placeholder(dtype=tf.float32, name=("input%d" % n), + input_tensor = tf.placeholder(dtype=parameters["type"], + name=("input%d" % n), shape=get_shape(parameters, n)) all_tensors.append(input_tensor) out = tf.concat(all_tensors, parameters["axis"]) @@ -1379,8 +1383,8 @@ def make_concat_tests(zip_path): def build_inputs(parameters, sess, inputs, outputs): all_values = [] for n in range(0, parameters["num_tensors"]): - input_values = create_tensor_data(np.float32, - get_shape(parameters, n)) + input_values = create_tensor_data( + parameters["type"], get_shape(parameters, n)) all_values.append(input_values) return all_values, sess.run( outputs, feed_dict=dict(zip(inputs, all_values))) @@ -1669,7 +1673,7 @@ def make_shape_tests(zip_path): }] def build_graph(parameters): - """Build the topk op testing graph.""" + """Build the shape op testing graph.""" # Note that we intentionally leave out the shape from the input placeholder # to prevent the Shape operation from being optimized out during conversion. input_value = tf.placeholder(dtype=parameters["input_dtype"], name="input") @@ -2317,6 +2321,7 @@ def make_topk_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32, tf.int32], "input_shape": [[10], [5, 20]], + "input_k": [None, 1, 3], }] def build_graph(parameters): @@ -2325,15 +2330,23 @@ def make_topk_tests(zip_path): dtype=parameters["input_dtype"], name="input", shape=parameters["input_shape"]) - k = tf.constant(3, name="k") + if parameters["input_k"] is not None: + k = tf.placeholder(dtype=tf.int32, name="input_k", shape=[]) + else: + k = tf.constant(3, name="k") out = tf.nn.top_k(input_value, k) - return [input_value], [out[1]] + return [input_value, k], [out[1]] def build_inputs(parameters, sess, inputs, outputs): input_value = create_tensor_data(parameters["input_dtype"], parameters["input_shape"]) - return [input_value], sess.run( - outputs, feed_dict=dict(zip(inputs, [input_value]))) + if parameters["input_k"] is not None: + k = np.array(parameters["input_k"], dtype=np.int32) + return [input_value, k], sess.run( + outputs, feed_dict=dict(zip(inputs, [input_value, k]))) + else: + return [input_value], sess.run( + outputs, feed_dict=dict(zip(inputs, [input_value]))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) diff --git a/tensorflow/contrib/lite/toco/BUILD b/tensorflow/contrib/lite/toco/BUILD index c88079717ddc9bf39850762dffe711f0d2832d38..aa4a4d885402580926009fd395e9143202be9164 100644 --- a/tensorflow/contrib/lite/toco/BUILD +++ b/tensorflow/contrib/lite/toco/BUILD @@ -11,6 +11,7 @@ load( "//tensorflow:tensorflow.bzl", "tf_cc_binary", "tf_cc_test", + "tf_copts", ) tf_proto_library_cc( @@ -305,7 +306,7 @@ cc_library( "tensorflow_util.h", "toco_tooling.h", ], - copts = select({ + copts = tf_copts() + select({ "//tensorflow:darwin": ["-DTOCO_SUPPORT_PORTABLE_PROTOS=0"], "//conditions:default": [], }), @@ -360,6 +361,7 @@ cc_library( "dump_graphviz.h", "tooling_util.h", ], + copts = tf_copts(), visibility = ["//visibility:public"], deps = [ ":model", diff --git a/tensorflow/contrib/lite/toco/dump_graphviz.cc b/tensorflow/contrib/lite/toco/dump_graphviz.cc index 6877fb237c0514a972589ac0301647104f5ed7ed..30525efd2391bb63afd7035b8134e5858add45f2 100644 --- a/tensorflow/contrib/lite/toco/dump_graphviz.cc +++ b/tensorflow/contrib/lite/toco/dump_graphviz.cc @@ -167,7 +167,7 @@ NodeProperties GetPropertiesForArray(const Model& model, node_properties.label += "]"; int buffer_size = 0; - if (IsValid(array.shape())) { + if (IsNonEmpty(array.shape())) { buffer_size = RequiredBufferSizeForShape(array.shape()); node_properties.log2_buffer_size = std::log2(static_cast(buffer_size)); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc index 527013bfa3540c34c9d86d96fd8fbb3a7dcc558b..d26c3b2878b8499fcbabc5448de9ec045eb07879 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/hardcode_min_max.cc @@ -371,6 +371,7 @@ bool HardcodeMinMax::Run(Model* model, std::size_t op_index) { case OperatorType::kStridedSlice: case OperatorType::kSqueeze: case OperatorType::kReshape: + case OperatorType::kExpandDims: case OperatorType::kPad: case OperatorType::kGather: case OperatorType::kTranspose: diff --git a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc index 3c9379fd878ea350064c6b0f562ae11e9a713365..91e290439ae4bfd491c8201b02b161fe2caf2f8d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -1082,27 +1082,23 @@ void ProcessTopkV2Operator(Model* model, TopKV2Operator* op) { } // Yield until input dims have been resolved. - if (!input_values.has_shape()) { + if (!input_values.has_shape() || !input_k.has_shape()) { return; } - const auto& input_values_shape = input_values.shape(); - auto output_indexes_dims = output_indexes.mutable_shape()->mutable_dims(); - auto output_values_dims = output_values.mutable_shape()->mutable_dims(); - for (int dim = 0; dim < input_values_shape.dimensions_count() - 1; dim++) { - output_indexes_dims->push_back(input_values_shape.dims(dim)); - output_values_dims->push_back(input_values_shape.dims(dim)); - } // If the value is initialized, we can specify the last dimension, otherwise // unknown. if (input_k.buffer) { + const auto& input_values_shape = input_values.shape(); + auto output_indexes_dims = output_indexes.mutable_shape()->mutable_dims(); + auto output_values_dims = output_values.mutable_shape()->mutable_dims(); + for (int dim = 0; dim < input_values_shape.dimensions_count() - 1; dim++) { + output_indexes_dims->push_back(input_values_shape.dims(dim)); + output_values_dims->push_back(input_values_shape.dims(dim)); + } const int32_t k_value = input_k.GetBuffer().data[0]; output_indexes_dims->push_back(k_value); output_values_dims->push_back(k_value); - - } else { - output_indexes_dims->push_back(0); - output_values_dims->push_back(0); } } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index b5a6554c1d87459ce2d06a32c3e66ae592bfe841..8d22ae2eb1356b8c9c9430c517acddfc971b9f57 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -62,7 +62,7 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kLessEqual || type == OperatorType::kSelect || type == OperatorType::kArgMax || type == OperatorType::kRelu || type == OperatorType::kRelu1 || type == OperatorType::kRelu6 || - type == OperatorType::kShape; + type == OperatorType::kShape || type == OperatorType::kExpandDims; } // The quantized op allows output arrays of type float using diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc index da8e7a2d1c06cf89b9708b404da7667565245f8f..8bef440afd21572d7014e4f376be3aba2d80127d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_tensorflow_switch.cc @@ -92,7 +92,9 @@ bool ResolveTensorFlowSwitch::Run(Model* model, std::size_t op_index) { if (*input_it == switch_op->outputs[nonselected_output_index]) { // Let us guard our assumption that only Merge nodes consume the outputs // of Switch nodes: - CHECK(other_op->type == OperatorType::kMerge); + CHECK(other_op->type == OperatorType::kMerge) + << "Found " << HelpfulOperatorTypeName(*other_op) + << " as non-selected output from Switch, but only Merge supported."; input_it = other_op->inputs.erase(input_it); } else { ++input_it; diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index d8d331f3d4afa214298d739fde813f72ea2fcbba..b7fffbce2223a71ac1e16ec1ce18ba9f610cc2ac 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -1223,11 +1223,10 @@ tensorflow::Status ConvertGatherOperator( return tensorflow::Status::OK(); } -template +template tensorflow::Status ConvertArgMinMaxOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { - CHECK_EQ(node.op(), op_name); TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 2)); const auto axis_data_type = HasAttr(node, "Tidx") ? GetDataTypeAttr(node, "Tidx") : DT_INT32; @@ -1245,6 +1244,20 @@ tensorflow::Status ConvertArgMinMaxOperator( return tensorflow::Status::OK(); } +tensorflow::Status ConvertArgMaxOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "ArgMax"); + return ConvertArgMinMaxOperator(node, tf_import_flags, model); +} + +tensorflow::Status ConvertArgMinOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "ArgMin"); + return ConvertArgMinMaxOperator(node, tf_import_flags, model); +} + tensorflow::Status ConvertResizeBilinearOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -1899,17 +1912,14 @@ using ConverterType = tensorflow::Status (*)( Model* model); using ConverterMapType = std::unordered_map; -constexpr char kArgMax[] = "ArgMax"; -constexpr char kArgMin[] = "ArgMin"; - ConverterMapType GetTensorFlowNodeConverterMap() { return std::unordered_map({ {"Add", ConvertSimpleOperator}, {"AddN", ConvertSimpleOperator}, {"All", ConvertSimpleOperator}, {"Any", ConvertAnyOperator}, - {"ArgMax", ConvertArgMinMaxOperator}, - {"ArgMin", ConvertArgMinMaxOperator}, + {"ArgMax", ConvertArgMaxOperator}, + {"ArgMin", ConvertArgMinOperator}, {"Assert", ConvertSimpleOperator}, {"AvgPool", ConvertAvgPoolOperator}, {"BatchMatMul", ConvertBatchMatMulOperator}, diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 18c78e32d0c927d79ef882630aa8dcef557b5c7b..412e14c4ada3280dafcd2fcfa59e2908dd785f9f 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -2071,7 +2071,7 @@ class Model { std::size_t transient_data_size = 0; // For code-generation only: required alignment of the transient_data buffer std::size_t transient_data_alignment = 0; - // Arithmatic operations performed in the model. + // Arithmetic operations performed in the model. int64 ops_count = 0; private: diff --git a/tensorflow/contrib/lite/toco/tflite/BUILD b/tensorflow/contrib/lite/toco/tflite/BUILD index 83e977d7b3b0a4d572faee3ba7e36690896ac8e8..709c53606b1081111fb2e2f8971ba71e5d38b629 100644 --- a/tensorflow/contrib/lite/toco/tflite/BUILD +++ b/tensorflow/contrib/lite/toco/tflite/BUILD @@ -27,6 +27,7 @@ cc_library( "//tensorflow/contrib/lite/toco:graph_transformations", "//tensorflow/contrib/lite/toco:model", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:ptr_util", "@com_google_absl//absl/memory", "@flatbuffers", ], diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 9ff89e9a653173fa1edc691b17b60f709af0a435..75808f2b690fb6699f86d61a3078ef458db6d295 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -21,9 +21,9 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/tflite/custom_operator.h" #include "tensorflow/contrib/lite/toco/tflite/simple_operator.h" #include "tensorflow/contrib/lite/toco/tflite/types.h" - #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/node_def.pb.h" +#include "tensorflow/core/util/ptr_util.h" namespace toco { @@ -1235,162 +1235,175 @@ namespace { // Build a vector containing all the known operators. std::vector> BuildOperatorList() { std::vector> ops; - + using tensorflow::MakeUnique; // Builtin Operators. - ops.emplace_back(new Add(::tflite::BuiltinOperator_ADD, OperatorType::kAdd)); - ops.emplace_back(new Div(::tflite::BuiltinOperator_DIV, OperatorType::kDiv)); - ops.emplace_back(new Sub(::tflite::BuiltinOperator_SUB, OperatorType::kSub)); - ops.emplace_back(new AveragePool(::tflite::BuiltinOperator_AVERAGE_POOL_2D, - OperatorType::kAveragePool)); - ops.emplace_back( - new SpaceToBatchND(::tflite::BuiltinOperator_SPACE_TO_BATCH_ND, - OperatorType::kSpaceToBatchND)); - ops.emplace_back( - new BatchToSpaceND(::tflite::BuiltinOperator_BATCH_TO_SPACE_ND, - OperatorType::kBatchToSpaceND)); - ops.emplace_back(new Concatenation(::tflite::BuiltinOperator_CONCATENATION, - OperatorType::kConcatenation)); - ops.emplace_back( - new Convolution(::tflite::BuiltinOperator_CONV_2D, OperatorType::kConv)); - ops.emplace_back( - new DepthwiseConvolution(::tflite::BuiltinOperator_DEPTHWISE_CONV_2D, - OperatorType::kDepthwiseConv)); - ops.emplace_back(new FullyConnected(::tflite::BuiltinOperator_FULLY_CONNECTED, - OperatorType::kFullyConnected)); - ops.emplace_back( - new Gather(::tflite::BuiltinOperator_GATHER, OperatorType::kGather)); - ops.emplace_back( - new L2Normalization(::tflite::BuiltinOperator_L2_NORMALIZATION, - OperatorType::kL2Normalization)); - ops.emplace_back( - new L2Pool(::tflite::BuiltinOperator_L2_POOL_2D, OperatorType::kL2Pool)); - ops.emplace_back(new LocalResponseNormalization( + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_ADD, OperatorType::kAdd)); + ops.push_back( + MakeUnique
(::tflite::BuiltinOperator_DIV, OperatorType::kDiv)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_SUB, OperatorType::kSub)); + ops.push_back(MakeUnique( + ::tflite::BuiltinOperator_AVERAGE_POOL_2D, OperatorType::kAveragePool)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_SPACE_TO_BATCH_ND, + OperatorType::kSpaceToBatchND)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_BATCH_TO_SPACE_ND, + OperatorType::kBatchToSpaceND)); + ops.push_back(MakeUnique( + ::tflite::BuiltinOperator_CONCATENATION, OperatorType::kConcatenation)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_CONV_2D, + OperatorType::kConv)); + ops.push_back(MakeUnique( + ::tflite::BuiltinOperator_DEPTHWISE_CONV_2D, + OperatorType::kDepthwiseConv)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_FULLY_CONNECTED, + OperatorType::kFullyConnected)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_GATHER, + OperatorType::kGather)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_L2_NORMALIZATION, + OperatorType::kL2Normalization)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_L2_POOL_2D, + OperatorType::kL2Pool)); + ops.push_back(MakeUnique( ::tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, OperatorType::kLocalResponseNormalization)); - ops.emplace_back(new MaxPool(::tflite::BuiltinOperator_MAX_POOL_2D, - OperatorType::kMaxPool)); - ops.emplace_back(new Mul(::tflite::BuiltinOperator_MUL, OperatorType::kMul)); - ops.emplace_back(new Pad(::tflite::BuiltinOperator_PAD, OperatorType::kPad)); - ops.emplace_back( - new PadV2(::tflite::BuiltinOperator_PADV2, OperatorType::kPadV2)); - ops.emplace_back( - new Reshape(::tflite::BuiltinOperator_RESHAPE, OperatorType::kReshape)); - ops.emplace_back( - new Softmax(::tflite::BuiltinOperator_SOFTMAX, OperatorType::kSoftmax)); - ops.emplace_back(new SpaceToDepth(::tflite::BuiltinOperator_SPACE_TO_DEPTH, - OperatorType::kSpaceToDepth)); - ops.emplace_back( - new Svdf(::tflite::BuiltinOperator_SVDF, OperatorType::kSvdf)); - ops.emplace_back(new Transpose(::tflite::BuiltinOperator_TRANSPOSE, - OperatorType::kTranspose)); - ops.emplace_back( - new Mean(::tflite::BuiltinOperator_MEAN, OperatorType::kMean)); - ops.emplace_back(new Sum(::tflite::BuiltinOperator_SUM, OperatorType::kSum)); - ops.emplace_back(new ReduceProd(::tflite::BuiltinOperator_REDUCE_PROD, - OperatorType::kReduceProd)); - ops.emplace_back(new ReduceMax(::tflite::BuiltinOperator_REDUCE_MAX, - OperatorType::kReduceMax)); - ops.emplace_back(new ResizeBilinear(::tflite::BuiltinOperator_RESIZE_BILINEAR, - OperatorType::kResizeBilinear)); - ops.emplace_back( - new Squeeze(::tflite::BuiltinOperator_SQUEEZE, OperatorType::kSqueeze)); - ops.emplace_back( - new Split(::tflite::BuiltinOperator_SPLIT, OperatorType::kSplit)); - ops.emplace_back(new StridedSlice(::tflite::BuiltinOperator_STRIDED_SLICE, - OperatorType::kStridedSlice)); - ops.emplace_back( - new TopK_V2(::tflite::BuiltinOperator_TOPK_V2, OperatorType::kTopK_V2)); - ops.emplace_back( - new Lstm(::tflite::BuiltinOperator_LSTM, OperatorType::kLstmCell)); - ops.emplace_back( - new Cast(::tflite::BuiltinOperator_CAST, OperatorType::kCast)); - ops.emplace_back( - new ArgMax(::tflite::BuiltinOperator_ARG_MAX, OperatorType::kArgMax)); - ops.emplace_back( - new ArgMin(::tflite::BuiltinOperator_ARG_MIN, OperatorType::kArgMin)); - ops.emplace_back( - new Tile(::tflite::BuiltinOperator_TILE, OperatorType::kTile)); - ops.emplace_back(new ExpandDims(::tflite::BuiltinOperator_EXPAND_DIMS, - OperatorType::kExpandDims)); - ops.emplace_back(new TransposeConv(::tflite::BuiltinOperator_TRANSPOSE_CONV, - OperatorType::kTransposeConv)); - ops.emplace_back(new SparseToDense(::tflite::BuiltinOperator_SPARSE_TO_DENSE, - OperatorType::kSparseToDense)); - ops.emplace_back( - new Shape(::tflite::BuiltinOperator_SHAPE, OperatorType::kShape)); - ops.emplace_back(new FakeQuant(::tflite::BuiltinOperator_FAKE_QUANT, - OperatorType::kFakeQuant)); - ops.emplace_back( - new Pack(::tflite::BuiltinOperator_PACK, OperatorType::kPack)); - ops.emplace_back( - new OneHot(::tflite::BuiltinOperator_ONE_HOT, OperatorType::kOneHot)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_MAX_POOL_2D, + OperatorType::kMaxPool)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_MUL, OperatorType::kMul)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_PAD, OperatorType::kPad)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_PADV2, OperatorType::kPadV2)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_RESHAPE, + OperatorType::kReshape)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_SOFTMAX, + OperatorType::kSoftmax)); + ops.push_back(MakeUnique( + ::tflite::BuiltinOperator_SPACE_TO_DEPTH, OperatorType::kSpaceToDepth)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_SVDF, OperatorType::kSvdf)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_TRANSPOSE, + OperatorType::kTranspose)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_MEAN, OperatorType::kMean)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_SUM, OperatorType::kSum)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_REDUCE_PROD, + OperatorType::kReduceProd)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_REDUCE_MAX, + OperatorType::kReduceMax)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_RESIZE_BILINEAR, + OperatorType::kResizeBilinear)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_SQUEEZE, + OperatorType::kSqueeze)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_SPLIT, OperatorType::kSplit)); + ops.push_back(MakeUnique( + ::tflite::BuiltinOperator_STRIDED_SLICE, OperatorType::kStridedSlice)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_TOPK_V2, + OperatorType::kTopK_V2)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_LSTM, + OperatorType::kLstmCell)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_CAST, OperatorType::kCast)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_ARG_MAX, + OperatorType::kArgMax)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_ARG_MIN, + OperatorType::kArgMin)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_TILE, OperatorType::kTile)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_EXPAND_DIMS, + OperatorType::kExpandDims)); + ops.push_back(MakeUnique( + ::tflite::BuiltinOperator_TRANSPOSE_CONV, OperatorType::kTransposeConv)); + ops.push_back(MakeUnique( + ::tflite::BuiltinOperator_SPARSE_TO_DENSE, OperatorType::kSparseToDense)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_SHAPE, OperatorType::kShape)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_FAKE_QUANT, + OperatorType::kFakeQuant)); + ops.push_back( + MakeUnique(::tflite::BuiltinOperator_PACK, OperatorType::kPack)); + ops.push_back(MakeUnique(::tflite::BuiltinOperator_ONE_HOT, + OperatorType::kOneHot)); // Custom Operators. - ops.emplace_back( - new DepthToSpace("DEPTH_TO_SPACE", OperatorType::kDepthToSpace)); - ops.emplace_back(new CTCBeamSearchDecoder( + ops.push_back( + MakeUnique("DEPTH_TO_SPACE", OperatorType::kDepthToSpace)); + ops.push_back(MakeUnique( "CTC_BEAM_SEARCH_DECODER", OperatorType::kCTCBeamSearchDecoder)); - ops.emplace_back(new TensorFlowUnsupported("TENSORFLOW_UNSUPPORTED", - OperatorType::kUnsupported)); + ops.push_back(MakeUnique("TENSORFLOW_UNSUPPORTED", + OperatorType::kUnsupported)); // There operators are supported by Toco, but not by TF Lite, and has no // attributes. - ops.emplace_back( - new SimpleOperator("ADDN", OperatorType::kAddN)); + ops.push_back( + MakeUnique>("ADDN", OperatorType::kAddN)); // Simple Operators. - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( "DEQUANTIZE", OperatorType::kDequantize)); - ops.emplace_back( - new SimpleOperator("FLOOR", OperatorType::kFloor)); - ops.emplace_back( - new SimpleOperator("RELU", OperatorType::kRelu)); - ops.emplace_back( - new SimpleOperator("RELU_N1_TO_1", OperatorType::kRelu1)); - ops.emplace_back( - new SimpleOperator("RELU6", OperatorType::kRelu6)); - ops.emplace_back( - new SimpleOperator("PRELU", OperatorType::kPRelu)); - ops.emplace_back(new SimpleOperator( + ops.push_back( + MakeUnique>("FLOOR", OperatorType::kFloor)); + ops.push_back( + MakeUnique>("RELU", OperatorType::kRelu)); + ops.push_back(MakeUnique>( + "RELU_N1_TO_1", OperatorType::kRelu1)); + ops.push_back( + MakeUnique>("RELU6", OperatorType::kRelu6)); + ops.push_back( + MakeUnique>("PRELU", OperatorType::kPRelu)); + ops.push_back(MakeUnique>( "LOGISTIC", OperatorType::kLogistic)); - ops.emplace_back( - new SimpleOperator("TANH", OperatorType::kTanh)); - ops.emplace_back(new SimpleOperator("EXP", OperatorType::kExp)); - ops.emplace_back(new SimpleOperator( + ops.push_back( + MakeUnique>("TANH", OperatorType::kTanh)); + ops.push_back( + MakeUnique>("EXP", OperatorType::kExp)); + ops.push_back(MakeUnique>( "LOG_SOFTMAX", OperatorType::kLogSoftmax)); - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( "MAXIMUM", OperatorType::kMaximum)); // Element-wise Maximum - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( "MINIMUM", OperatorType::kMinimum)); // Element-wise Minimum - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( "GREATER", OperatorType::kGreater)); - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( "GREATER_EQUAL", OperatorType::kGreaterEqual)); - ops.emplace_back( - new SimpleOperator("LESS", OperatorType::kLess)); - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( + "LESS", OperatorType::kLess)); + ops.push_back(MakeUnique>( "LESS_EQUAL", OperatorType::kLessEqual)); - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( "EQUAL", OperatorType::kEqual)); - ops.emplace_back(new SimpleOperator( + ops.push_back(MakeUnique>( "NOT_EQUAL", OperatorType::kNotEqual)); - ops.emplace_back(new SimpleOperator("NEG", OperatorType::kNeg)); - ops.emplace_back( - new SimpleOperator("SELECT", OperatorType::kSelect)); - ops.emplace_back( - new SimpleOperator("SLICE", OperatorType::kSlice)); - ops.emplace_back(new SimpleOperator("POW", OperatorType::kPow)); - ops.emplace_back(new SimpleOperator( + ops.push_back( + MakeUnique>("NEG", OperatorType::kNeg)); + ops.push_back(MakeUnique>( + "SELECT", OperatorType::kSelect)); + ops.push_back( + MakeUnique>("SLICE", OperatorType::kSlice)); + ops.push_back( + MakeUnique>("POW", OperatorType::kPow)); + ops.push_back(MakeUnique>( "LOGICAL_OR", OperatorType::kLogicalOr)); ops.emplace_back(new SimpleOperator( "LOGICAL_AND", OperatorType::kLogicalAnd)); ops.emplace_back(new SimpleOperator( "LOGICAL_NOT", OperatorType::kLogicalNot)); // Element-wise operator - ops.emplace_back(new SimpleOperator("SIN", OperatorType::kSin)); - ops.emplace_back(new SimpleOperator("LOG", OperatorType::kLog)); - ops.emplace_back( - new SimpleOperator("SQRT", OperatorType::kSqrt)); - ops.emplace_back(new SimpleOperator( + ops.push_back( + MakeUnique>("SIN", OperatorType::kSin)); + ops.push_back( + MakeUnique>("LOG", OperatorType::kLog)); + ops.push_back(MakeUnique>( + "SQRT", OperatorType::kSqrt)); + ops.push_back(MakeUnique>( "RSQRT", OperatorType::kRsqrt)); return ops; diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 80df09eb081f471fa8843bf7bc9c1e005903c229..2ad27198119b4a8150a7381c047a4edb51aebfe6 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -602,14 +602,33 @@ void UnextendShape(Shape* shape, int new_shape_size) { shape_dims.erase(shape_dims.begin(), shape_dims.begin() + size_reduction); } -bool IsValid(const Shape& shape) { +// In general, zero-sized dimensions are disallowed, but there are exceptions, +// e.g., if the tensor data itself represents a scalar (rank 0) shape, its +// shape will have dimensions [0]. CheckNonEmptyShapeDimensions is more +// strict, and is appropriate for ops and comparisons where an empty shape +// doesn't make sense. +template +void CheckValidShapeDimensions(const Dims& dims) { + if (dims.size() == 1 && dims[0] == 0) { + return; + } + for (const auto& dim : dims) { + CHECK_GE(dim, 1); + } +} + +void CheckValidShape(const Shape& shape) { + CheckValidShapeDimensions(shape.dims()); +} + +bool IsNonEmpty(const Shape& shape) { for (int i = 0; i < shape.dimensions_count(); ++i) { if (shape.dims(i) < 1) return false; } return true; } -void CheckShapeDimensions(const Shape& shape) { +void CheckNonEmptyShapeDimensions(const Shape& shape) { for (int i = 0; i < shape.dimensions_count(); ++i) { CHECK_GE(shape.dims()[i], 1) << "shape has dimension 0 at index << " << i << ". shape = " << ShapeToString(shape); @@ -617,8 +636,8 @@ void CheckShapeDimensions(const Shape& shape) { } bool ShapesAgreeUpToBroadcasting(const Shape& shape0, const Shape& shape1) { - CheckShapeDimensions(shape0); - CheckShapeDimensions(shape1); + CheckNonEmptyShapeDimensions(shape0); + CheckNonEmptyShapeDimensions(shape1); const Shape* longer = &shape0; const Shape* shorter = &shape1; @@ -645,8 +664,8 @@ bool ShapesAgreeUpToBroadcasting(const Shape& shape0, const Shape& shape1) { } bool ShapesAgreeUpToExtending(const Shape& shape0, const Shape& shape1) { - CheckShapeDimensions(shape0); - CheckShapeDimensions(shape1); + CheckNonEmptyShapeDimensions(shape0); + CheckNonEmptyShapeDimensions(shape1); const Shape* longer = &shape0; const Shape* shorter = &shape1; @@ -683,9 +702,9 @@ bool ShapesAgreeUpToExtending(const Shape& shape0, const Shape& shape1) { } int RequiredBufferSizeForShape(const Shape& shape) { + CheckValidShape(shape); int max_offset = 1; for (const auto& dim : shape.dims()) { - CHECK_GE(dim, 1); max_offset *= dim; } return max_offset; @@ -946,13 +965,7 @@ void CheckEachArray(const Model& model) { // shape. CHECK(array->has_shape()); // Constant buffer should has a valid shape. - bool is_scalar = - array->shape().dimensions_count() == 1 && array->shape().dims(0) == 0; - if (!is_scalar) { - for (int d : array->shape().dims()) { - CHECK_GE(d, 1); - } - } + CheckValidShape(array->shape()); // The shape flat-size should agree with the buffer length. CHECK_EQ(array->buffer->Length(), RequiredBufferSizeForShape(array->shape())); @@ -1544,8 +1557,8 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { if (!input_array.has_shape()) { if (input_array_proto.has_shape()) { auto& input_array_dims = *input_array.mutable_shape()->mutable_dims(); + CheckValidShapeDimensions(input_array_proto.shape().dims()); for (auto dim : input_array_proto.shape().dims()) { - CHECK_GE(dim, 1); input_array_dims.push_back(dim); } } diff --git a/tensorflow/contrib/lite/toco/tooling_util.h b/tensorflow/contrib/lite/toco/tooling_util.h index 5dbfa54fa0369676dce638aec171b409a468da9f..b99e6111fe92be178b5ff8b83477f1ce10c20926 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.h +++ b/tensorflow/contrib/lite/toco/tooling_util.h @@ -115,10 +115,9 @@ void ExtendShape(Shape* shape, int new_shape_size); // TODO(b/36075966): Clean up when dims superseded by array shape. void UnextendShape(Shape* shape, int new_shape_size); -// Checks that all dimensions of 'shape' are at least 1. -bool IsValid(const Shape& shape); -// Same as above, but reports error using CHECK. -void CheckShapeDimensions(const Shape& shape); +// Checks that all dimensions of 'shape' are at least 1. Note that scalars, +// lacking dimensions, satisfy this condition and are considered non-empty. +bool IsNonEmpty(const Shape& shape); // Given two shapes with potentially different dimensionality and dimension // arrays d0 and d1. Without loss of generality, assume that shape0 may have diff --git a/tensorflow/contrib/lite/tools/visualize.py b/tensorflow/contrib/lite/tools/visualize.py index e07f899e4d8c249cb03d4251a722df0614007fed..597dede63b0c089da21f4b0ede065189d8bbe1d8 100644 --- a/tensorflow/contrib/lite/tools/visualize.py +++ b/tensorflow/contrib/lite/tools/visualize.py @@ -334,7 +334,7 @@ def CreateHtmlFile(tflite_input, html_output): for key, mapping in toplevel_stuff: if not mapping: mapping = lambda x: x - html += "%s%s\n" % (key, mapping(data[key])) + html += "%s%s\n" % (key, mapping(data.get(key))) html += "\n" # Spec on what keys to display diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh index 448ae6d22e65fcd9129e27e6321d3081abf7d1ac..dc9b17a62783817ec9a2998c4d5548c0f05e073b 100755 --- a/tensorflow/contrib/makefile/download_dependencies.sh +++ b/tensorflow/contrib/makefile/download_dependencies.sh @@ -35,7 +35,9 @@ NSYNC_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/nsync/.*tar\. # process. For now we're hardcoding to the version which is used by # TensorFlow 1.9. PROTOBUF_URL="https://mirror.bazel.build/github.com/google/protobuf/archive/396336eb961b75f03b25824fe86cf6490fb75e3a.tar.gz" -RE2_URL="$(grep -o 'https://mirror.bazel.build/github.com/google/re2/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" +# TODO (yongtang): Replace the following with 'https://mirror.bazel.build/github.com/google/re2/.*tar\.gz' once +# the archive has been propagated in mirror.bazel.build. +RE2_URL="$(grep -o 'https://github.com/google/re2/.*tar\.gz' "${BZL_FILE_PATH}" | head -n1)" FFT2D_URL="$(grep -o 'http.*fft\.tgz' "${BZL_FILE_PATH}" | grep -v bazel-mirror | head -n1)" DOUBLE_CONVERSION_URL="$(grep -o "https.*google/double-conversion.*\.zip" "${BZL_FILE_PATH}" | head -n1)" ABSL_URL="$(grep -o 'https://github.com/abseil/abseil-cpp/.*tar.gz' "${BZL_FILE_PATH}" | head -n1)" diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py b/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py index be7377b1519f3bdab8755411af3de7aa0c2dc9eb..eba505881fb648cf4993e2b8ce7d935dca0f4830 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_manager.py @@ -41,12 +41,12 @@ class LossScaleManager(object): applied on variables. This class is used together with - @{tf.contrib.mixed_precision.LossScaleOptimizer} for mixed precision training + `tf.contrib.mixed_precision.LossScaleOptimizer` for mixed precision training (float32 variables and float16 ops) on Nvidia GPUs in order to achieve the same model quality as single precision training, with the benefits of potential higher throughput. - See @{tf.contrib.mixed_precision.LossScaleOptimizer} for more details. + See `tf.contrib.mixed_precision.LossScaleOptimizer` for more details. """ @abc.abstractmethod diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py index 93050a3ae373603c516c7eb72c22f327f4a60a00..fcce52a07a88547af437382c3ec060b23c9d334e 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer.py @@ -103,7 +103,7 @@ class LossScaleOptimizer(optimizer.Optimizer): Args: opt: The actual optimizer that will be used to compute and apply the - gradients. Must be an implementation of the @{tf.train.Optimizer} + gradients. Must be an implementation of the `tf.train.Optimizer` interface. loss_scale_manager: A LossScaleManager object. """ @@ -117,7 +117,7 @@ class LossScaleOptimizer(optimizer.Optimizer): aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None): - """Compute gradients. See base class @{tf.train.Optimizer}.""" + """Compute gradients. See base class `tf.train.Optimizer`.""" loss_scale = self._loss_scale_manager.get_loss_scale() if context.executing_eagerly(): @@ -141,7 +141,7 @@ class LossScaleOptimizer(optimizer.Optimizer): return self._down_scale(grads_and_vars, loss_scale) def apply_gradients(self, grads_and_vars, global_step=None, name=None): - """Apply gradients. See base class @{tf.train.Optimizer}.""" + """Apply gradients. See base class `tf.train.Optimizer`.""" grads = [g for (g, _) in grads_and_vars] is_finite_grad = [] diff --git a/tensorflow/contrib/model_pruning/BUILD b/tensorflow/contrib/model_pruning/BUILD index 54bd39afacbec07f054f61b72eda0a3654858aa7..16ddc38f5a5ba88485e18b136b2b1081b0e2ff0f 100644 --- a/tensorflow/contrib/model_pruning/BUILD +++ b/tensorflow/contrib/model_pruning/BUILD @@ -95,6 +95,22 @@ py_library( ], ) +py_library( + name = "strip_pruning_vars_lib", + srcs = ["python/strip_pruning_vars_lib.py"], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":pruning", + "//tensorflow/python:client", + "//tensorflow/python:framework", + "//tensorflow/python:platform", + "//tensorflow/python:training", + "//third_party/py/numpy", + "@six_archive//:six", + ], +) + py_test( name = "pruning_utils_test", size = "small", @@ -129,6 +145,31 @@ py_test( ], ) +py_test( + name = "strip_pruning_vars_test", + size = "small", + srcs = ["python/strip_pruning_vars_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":layers", + ":pruning", + ":rnn_cells", + ":strip_pruning_vars_lib", + "//tensorflow/python:client_testlib", + ], +) + +py_binary( + name = "strip_pruning_vars", + srcs = ["python/strip_pruning_vars.py"], + srcs_version = "PY2AND3", + visibility = ["//visibility:public"], + deps = [ + ":strip_pruning_vars_lib", + "//tensorflow/python:platform", + ], +) + py_library( name = "init_py", srcs = ["__init__.py"], @@ -145,5 +186,6 @@ py_library( ":learning", ":pruning", ":rnn_cells", + ":strip_pruning_vars_lib", ], ) diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md index dbe4e124fd3e4566a4ca76f5d449af98af2b0b87..a5267fd90482287a65a4c38ae257a0af349523e8 100644 --- a/tensorflow/contrib/model_pruning/README.md +++ b/tensorflow/contrib/model_pruning/README.md @@ -4,7 +4,15 @@ This document describes the API that facilitates magnitude-based pruning of neural network's weight tensors. The API helps inject necessary tensorflow op into the training graph so the model can be pruned while it is being trained. -### Model creation +## Table of contents +1. [Model creation](#model-creation) +2. [Hyperparameters for pruning](#hyperparameters) + - [Block sparsity](#block-sparsity) +3. [Adding pruning ops to the training graph](#adding-pruning-ops) +4. [Removing pruning ops from trained model](#remove) +5. [Example](#example) + +### Model creation The first step involves adding mask and threshold variables to the layers that need to undergo pruning. The variable mask is the same shape as the layer's @@ -33,7 +41,7 @@ auxiliary variables built-in (see * [rnn_cells.MaskedLSTMCell](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/model_pruning/python/layers/rnn_cells.py?l=154) -### Adding pruning ops to the training graph +### Pruning-related hyperparameters The pruning library allows for specification of the following hyper parameters: @@ -64,7 +72,13 @@ is divided into $$n$$ intervals of size equal to the pruning_frequency ($$\Delta t$$). $$s_f$$ is the target_sparsity, $$s_i$$ is the initial_sparsity, $$t_0$$ is the sparsity_function_begin_step. In this equation, the sparsity_function_exponent is set to 3. -### Adding pruning ops to the training graph + +#### Block Sparsity + +For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is only supported for weight tensors which can be squeezed to rank 2. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter). +The convolution layer tensors are always pruned used block dimensions of [1,1]. + +### Adding pruning ops to the training graph The final step involves adding ops to the training graph that monitor the distribution of the layer's weight magnitudes and determine the layer threshold, @@ -105,7 +119,19 @@ with tf.graph.as_default(): ``` Ensure that `global_step` is being [incremented](https://www.tensorflow.org/api_docs/python/tf/train/Optimizer#minimize), otherwise pruning will not work! -## Example: Pruning and training deep CNNs on the cifar10 dataset +### Removing pruning ops from the trained graph +Once the model is trained, it is necessary to remove the auxiliary variables (mask, threshold) and pruning ops added to the graph in the steps above. This can be accomplished using the `strip_pruning_vars` utility. + +This utility generates a binary GraphDef in which the variables have been converted to constants. In particular, the threshold variables are removed from the graph and the mask variable is fused with the corresponding weight tensor to produce a `masked_weight` tensor. This tensor is sparse, has the same size as the weight tensor, and the sparsity is as set by the `target_sparsity` or the `weight_sparsity_map` hyperparameters above. + +```shell +$ bazel build -c opt contrib/model_pruning:strip_pruning_vars +$ bazel-bin/contrib/model_pruning/strip_pruning_vars --checkpoint_dir=/path/to/checkpoints/ --output_node_names=graph_node1,graph_node2 --output_dir=/tmp --filename=pruning_stripped.pb +``` + +For now, it is assumed that the underlying hardware platform will provide mechanisms for compressing the sparse tensors and/or accelerating the sparse tensor computations. + +## Example: Pruning and training deep CNNs on the cifar10 dataset Please see https://www.tensorflow.org/tutorials/deep_cnn for details on neural network architecture, setting up inputs etc. The additional changes needed to @@ -121,7 +147,7 @@ incorporate pruning are captured in the following: To train the pruned version of cifar10: -```bash +```shell $ examples_dir=contrib/model_pruning/examples $ bazel build -c opt $examples_dir/cifar10:cifar10_{train,eval} $ bazel-bin/$examples_dir/cifar10/cifar10_train --pruning_hparams=name=cifar10_pruning,begin_pruning_step=10000,end_pruning_step=100000,target_sparsity=0.9,sparsity_function_begin_step=10000,sparsity_function_end_step=100000 @@ -133,10 +159,14 @@ Eval: $ bazel-bin/$examples_dir/cifar10/cifar10_eval --run_once ``` -### Block Sparsity +Removing pruning nodes from the trained graph: -For some hardware architectures, it may be beneficial to induce spatially correlated sparsity. To train models in which the weight tensors have block sparse structure, set *block_height* and *block_width* hyperparameters to the desired block configuration (2x2, 4x4, 4x1, 1x8, etc). Currently, block sparsity is only supported for weight tensors which can be squeezed to rank 2. The matrix is partitioned into non-overlapping blocks of size *[block_height, block_dim]* and the either the average or max absolute value in this block is taken as a proxy for the entire block (set by *block_pooling_function* hyperparameter). -The convolution layer tensors are always pruned used block dimensions of [1,1]. +```shell +$ bazel build -c opt contrib/model_pruning:strip_pruning_vars +$ bazel-bin/contrib/model_pruning/strip_pruning_vars --checkpoint_path=/tmp/cifar10_train --output_node_names=softmax_linear/softmax_linear_2 --filename=cifar_pruned.pb +``` + +The generated GraphDef (cifar_pruned.pb) may be visualized using the [`import_pb_to_tensorboard`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/tools/import_pb_to_tensorboard.py) utility ## References diff --git a/tensorflow/contrib/model_pruning/__init__.py b/tensorflow/contrib/model_pruning/__init__.py index d32bedbcd6b63bc8e473a9e9d1c8e0753877e6f8..6eca54aaee186f5873a84ef2cb3ff3c7cfb42cd4 100644 --- a/tensorflow/contrib/model_pruning/__init__.py +++ b/tensorflow/contrib/model_pruning/__init__.py @@ -33,6 +33,9 @@ from tensorflow.contrib.model_pruning.python.pruning import get_thresholds from tensorflow.contrib.model_pruning.python.pruning import get_weight_sparsity from tensorflow.contrib.model_pruning.python.pruning import get_weights from tensorflow.contrib.model_pruning.python.pruning import Pruning +from tensorflow.contrib.model_pruning.python.strip_pruning_vars_lib import graph_def_from_checkpoint +from tensorflow.contrib.model_pruning.python.strip_pruning_vars_lib import strip_pruning_vars_fn + # pylint: enable=unused-import from tensorflow.python.util.all_util import remove_undocumented @@ -41,7 +44,8 @@ _allowed_symbols = [ 'masked_convolution', 'masked_conv2d', 'masked_fully_connected', 'MaskedBasicLSTMCell', 'MaskedLSTMCell', 'train', 'apply_mask', 'get_masked_weights', 'get_masks', 'get_pruning_hparams', 'get_thresholds', - 'get_weights', 'get_weight_sparsity', 'Pruning' + 'get_weights', 'get_weight_sparsity', 'Pruning', 'strip_pruning_vars_fn', + 'graph_def_from_checkpoint' ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/model_pruning/python/layers/layers.py b/tensorflow/contrib/model_pruning/python/layers/layers.py index 466daf204a1ae86a7f37107342046305ea7249fc..d453e350f05c8e66df13c3861959980d69a564e8 100644 --- a/tensorflow/contrib/model_pruning/python/layers/layers.py +++ b/tensorflow/contrib/model_pruning/python/layers/layers.py @@ -139,7 +139,7 @@ def masked_convolution(inputs, with "NC". num_outputs: Integer, the number of output filters. kernel_size: A sequence of N positive integers specifying the spatial - dimensions of of the filters. Can be a single integer to specify the same + dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. stride: A sequence of N positive integers specifying the stride at which to compute output. Can be a single integer to specify the same value for all diff --git a/tensorflow/contrib/model_pruning/python/layers/rnn_cells.py b/tensorflow/contrib/model_pruning/python/layers/rnn_cells.py index a5b050d25d00b298a20f7ce6abdda7c1d00db899..5f6c6aea74f2965ccfe552a58cde290b5506ef12 100644 --- a/tensorflow/contrib/model_pruning/python/layers/rnn_cells.py +++ b/tensorflow/contrib/model_pruning/python/layers/rnn_cells.py @@ -48,7 +48,7 @@ class MaskedBasicLSTMCell(tf_rnn.BasicLSTMCell): It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline. - For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell} + For advanced models, please use the full `tf.nn.rnn_cell.LSTMCell` that follows. """ diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index 723dab936937a876a3881a98fabf4aac0905e7fa..cd58526ed3620d4bd880cf36d806afac70c4bff7 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -237,6 +237,9 @@ class Pruning(object): # Pruning specification self._spec = spec if spec else get_pruning_hparams() + # Sanity check for pruning hparams + self._validate_spec() + # A tensorflow variable that tracks the sparsity function. # If not provided as input, the graph must already contain the global_step # variable before calling this constructor. @@ -262,6 +265,34 @@ class Pruning(object): # Mapping of weight names and target sparsity self._weight_sparsity_map = self._get_weight_sparsity_map() + def _validate_spec(self): + spec = self._spec + if spec.begin_pruning_step < 0: + raise ValueError('Illegal value for begin_pruning_step') + + if spec.begin_pruning_step >= spec.end_pruning_step: + if spec.end_pruning_step != -1: + raise ValueError( + 'Pruning must begin before it can end. begin_step=%d, end_step=%d.' + 'Set end_pruning_step to -1 if pruning is required till training' + 'stops' % (spec.begin_pruning_step, spec.end_pruning_step)) + + if spec.sparsity_function_begin_step < 0: + raise ValueError('Illegal value for sparsity_function_begin_step') + + if spec.sparsity_function_begin_step >= spec.sparsity_function_end_step: + raise ValueError( + 'Sparsity function requires begin_step < end_step') + + if not 0.0 <= spec.threshold_decay < 1.0: + raise ValueError('threshold_decay must be in range [0,1)') + + if not 0.0 <= spec.initial_sparsity < 1.0: + raise ValueError('initial_sparsity must be in range [0,1)') + + if not 0.0 <= spec.target_sparsity < 1.0: + raise ValueError('target_sparsity must be in range [0,1)') + def _setup_global_step(self, global_step): graph_global_step = global_step if graph_global_step is None: @@ -276,11 +307,6 @@ class Pruning(object): target_sparsity = self._spec.target_sparsity exponent = self._spec.sparsity_function_exponent - if begin_step >= end_step: - raise ValueError( - 'Pruning must begin before it can end. begin_step=%d, end_step=%d' % - (begin_step, end_step)) - with ops.name_scope(self._spec.name): p = math_ops.minimum( 1.0, diff --git a/tensorflow/contrib/model_pruning/python/pruning_test.py b/tensorflow/contrib/model_pruning/python/pruning_test.py index 5b67656e9f877e16c73483e5ea34c8e45da19c6d..33c4ad58bd7f57422935fc839ddfc64d5e1f00f5 100644 --- a/tensorflow/contrib/model_pruning/python/pruning_test.py +++ b/tensorflow/contrib/model_pruning/python/pruning_test.py @@ -60,7 +60,6 @@ class PruningHParamsTest(test.TestCase): self.assertEqual(p._weight_sparsity_map["conv1"], 0.8) self.assertEqual(p._weight_sparsity_map["conv2/kernel"], 0.8) - def testInitWithExternalSparsity(self): with self.test_session(): p = pruning.Pruning(spec=self.pruning_hparams, sparsity=self.sparsity) diff --git a/tensorflow/contrib/model_pruning/python/strip_pruning_vars.py b/tensorflow/contrib/model_pruning/python/strip_pruning_vars.py new file mode 100644 index 0000000000000000000000000000000000000000..3385103807f6dbdab2d27882c670a3ccf6a26e9d --- /dev/null +++ b/tensorflow/contrib/model_pruning/python/strip_pruning_vars.py @@ -0,0 +1,103 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +r"""Removes the auxiliary variables and ops added by the pruning library. + +Usage: + +bazel build tensorflow/contrib/model_pruning:strip_pruning_vars && \ +bazel-bin/tensorflow/contrib/model_pruning/strip_pruning_vars \ +--checkpoint_dir=/tmp/model_ckpts \ +--output_node_names=softmax \ +--output_dir=/tmp \ +--filename=pruning_stripped.pb +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import os +import sys + +from tensorflow.contrib.model_pruning.python import strip_pruning_vars_lib +from tensorflow.python.framework import graph_io +from tensorflow.python.platform import app +from tensorflow.python.platform import tf_logging as logging + +FLAGS = None + + +def strip_pruning_vars(checkpoint_dir, output_node_names, output_dir, filename): + """Remove pruning-related auxiliary variables and ops from the graph. + + Accepts training checkpoints and produces a GraphDef in which the pruning vars + and ops have been removed. + + Args: + checkpoint_dir: Path to the checkpoints. + output_node_names: The name of the output nodes, comma separated. + output_dir: Directory where to write the graph. + filename: Output GraphDef file name. + + Returns: + None + + Raises: + ValueError: if output_nodes_names are not provided. + """ + if not output_node_names: + raise ValueError( + 'Need to specify atleast 1 output node through output_node_names flag') + output_node_names = output_node_names.replace(' ', '').split(',') + + initial_graph_def = strip_pruning_vars_lib.graph_def_from_checkpoint( + checkpoint_dir, output_node_names) + + final_graph_def = strip_pruning_vars_lib.strip_pruning_vars_fn( + initial_graph_def, output_node_names) + graph_io.write_graph(final_graph_def, output_dir, filename, as_text=False) + logging.info('\nFinal graph written to %s', os.path.join( + output_dir, filename)) + + +def main(unused_args): + return strip_pruning_vars(FLAGS.checkpoint_dir, FLAGS.output_node_names, + FLAGS.output_dir, FLAGS.filename) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.register('type', 'bool', lambda v: v.lower() == 'true') + parser.add_argument( + '--checkpoint_dir', type=str, default='', help='Path to the checkpoints.') + parser.add_argument( + '--output_node_names', + type=str, + default='', + help='The name of the output nodes, comma separated.') + parser.add_argument( + '--output_dir', + type=str, + default='/tmp', + help='Directory where to write the graph.') + parser.add_argument( + '--filename', + type=str, + default='pruning_stripped.pb', + help='Output \'GraphDef\' file name.') + + FLAGS, unparsed = parser.parse_known_args() + app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/contrib/model_pruning/python/strip_pruning_vars_lib.py b/tensorflow/contrib/model_pruning/python/strip_pruning_vars_lib.py new file mode 100644 index 0000000000000000000000000000000000000000..fc4b10863f7c46235059f948fbbfcfcf83d3e15b --- /dev/null +++ b/tensorflow/contrib/model_pruning/python/strip_pruning_vars_lib.py @@ -0,0 +1,142 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Utilities to remove pruning-related ops and variables from a GraphDef. +""" + +# pylint: disable=missing-docstring +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.framework import attr_value_pb2 +from tensorflow.core.framework import graph_pb2 +from tensorflow.core.framework import node_def_pb2 +from tensorflow.python.client import session +from tensorflow.python.framework import graph_util +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import saver as saver_lib + + +def _node_name(tensor_name): + """Remove the trailing ':0' from the variable name.""" + if ':' not in tensor_name: + return tensor_name + + return tensor_name.split(':')[0] + + +def _tensor_name(node_name): + """Appends the :0 in the op name to get the canonical tensor name.""" + if ':' in node_name: + return node_name + + return node_name + ':0' + + +def _get_masked_weights(input_graph_def): + """Extracts masked_weights from the graph as a dict of {var_name:ndarray}.""" + input_graph = ops.Graph() + with input_graph.as_default(): + importer.import_graph_def(input_graph_def, name='') + + with session.Session(graph=input_graph) as sess: + masked_weights_dict = {} + for node in input_graph_def.node: + if 'masked_weight' in node.name: + masked_weight_val = sess.run( + sess.graph.get_tensor_by_name(_tensor_name(node.name))) + logging.info( + '%s has %d values, %1.2f%% zeros \n', node.name, + np.size(masked_weight_val), + 100 - float(100 * np.count_nonzero(masked_weight_val)) / + np.size(masked_weight_val)) + masked_weights_dict.update({node.name: masked_weight_val}) + return masked_weights_dict + + +def strip_pruning_vars_fn(input_graph_def, output_node_names): + """Removes mask variable from the graph. + + Replaces the masked_weight tensor with element-wise multiplication of mask + and the corresponding weight variable. + + Args: + input_graph_def: A GraphDef in which the variables have been converted to + constants. This is typically the output of + tf.graph_util.convert_variables_to_constant() + output_node_names: List of name strings for the result nodes of the graph + + Returns: + A GraphDef in which pruning-related variables have been removed + """ + masked_weights_dict = _get_masked_weights(input_graph_def) + pruned_graph_def = graph_pb2.GraphDef() + + # Replace masked_weight with a const op containing the + # result of tf.multiply(mask,weight) + for node in input_graph_def.node: + output_node = node_def_pb2.NodeDef() + if 'masked_weight' in node.name: + output_node.op = 'Const' + output_node.name = node.name + dtype = node.attr['T'] + data = masked_weights_dict[node.name] + output_node.attr['dtype'].CopyFrom(dtype) + output_node.attr['value'].CopyFrom( + attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(data))) + + else: + output_node.CopyFrom(node) + pruned_graph_def.node.extend([output_node]) + + # Remove stranded nodes: mask and weights + return graph_util.extract_sub_graph(pruned_graph_def, output_node_names) + + +def graph_def_from_checkpoint(checkpoint_dir, output_node_names): + """Converts checkpoint data to GraphDef. + + Reads the latest checkpoint data and produces a GraphDef in which the + variables have been converted to constants. + + Args: + checkpoint_dir: Path to the checkpoints. + output_node_names: List of name strings for the result nodes of the graph. + + Returns: + A GraphDef from the latest checkpoint + + Raises: + ValueError: if no checkpoint is found + """ + checkpoint_path = saver_lib.latest_checkpoint(checkpoint_dir) + if checkpoint_path is None: + raise ValueError('Could not find a checkpoint at: {0}.' + .format(checkpoint_dir)) + + saver_for_restore = saver_lib.import_meta_graph( + checkpoint_path + '.meta', clear_devices=True) + with session.Session() as sess: + saver_for_restore.restore(sess, checkpoint_path) + graph_def = ops.get_default_graph().as_graph_def() + output_graph_def = graph_util.convert_variables_to_constants( + sess, graph_def, output_node_names) + + return output_graph_def diff --git a/tensorflow/contrib/model_pruning/python/strip_pruning_vars_test.py b/tensorflow/contrib/model_pruning/python/strip_pruning_vars_test.py new file mode 100644 index 0000000000000000000000000000000000000000..255daa036099c0d3ef2dbc5eb37fdb0c31c71383 --- /dev/null +++ b/tensorflow/contrib/model_pruning/python/strip_pruning_vars_test.py @@ -0,0 +1,232 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for strip_pruning_vars.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import re + +from tensorflow.contrib.model_pruning.python import pruning +from tensorflow.contrib.model_pruning.python import strip_pruning_vars_lib +from tensorflow.contrib.model_pruning.python.layers import layers +from tensorflow.contrib.model_pruning.python.layers import rnn_cells +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import graph_util +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import random_ops +from tensorflow.python.ops import rnn +from tensorflow.python.ops import rnn_cell as tf_rnn_cells +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import training_util + + +def _get_number_pruning_vars(graph_def): + number_vars = 0 + for node in graph_def.node: + if re.match(r"^.*(mask$)|(threshold$)", node.name): + number_vars += 1 + return number_vars + + +def _get_node_names(tensor_names): + return [ + strip_pruning_vars_lib._node_name(tensor_name) + for tensor_name in tensor_names + ] + + +class StripPruningVarsTest(test.TestCase): + + def setUp(self): + param_list = [ + "pruning_frequency=1", "begin_pruning_step=1", "end_pruning_step=10", + "nbins=2048", "threshold_decay=0.0" + ] + self.initial_graph = ops.Graph() + self.initial_graph_def = None + self.final_graph = ops.Graph() + self.final_graph_def = None + self.pruning_spec = ",".join(param_list) + with self.initial_graph.as_default(): + self.sparsity = variables.Variable(0.5, name="sparsity") + self.global_step = training_util.get_or_create_global_step() + self.increment_global_step = state_ops.assign_add(self.global_step, 1) + self.mask_update_op = None + + def _build_convolutional_model(self, number_of_layers): + # Create a graph with several conv2d layers + kernel_size = 3 + base_depth = 4 + depth_step = 7 + height, width = 7, 9 + with variable_scope.variable_scope("conv_model"): + input_tensor = array_ops.ones((8, height, width, base_depth)) + top_layer = input_tensor + for ix in range(number_of_layers): + top_layer = layers.masked_conv2d( + top_layer, + base_depth + (ix + 1) * depth_step, + kernel_size, + scope="Conv_" + str(ix)) + + return top_layer + + def _build_fully_connected_model(self, number_of_layers): + base_depth = 4 + depth_step = 7 + + input_tensor = array_ops.ones((8, base_depth)) + + top_layer = input_tensor + + with variable_scope.variable_scope("fc_model"): + for ix in range(number_of_layers): + top_layer = layers.masked_fully_connected( + top_layer, base_depth + (ix + 1) * depth_step) + + return top_layer + + def _build_lstm_model(self, number_of_layers): + batch_size = 8 + dim = 10 + inputs = variables.Variable(random_ops.random_normal([batch_size, dim])) + + def lstm_cell(): + return rnn_cells.MaskedBasicLSTMCell( + dim, forget_bias=0.0, state_is_tuple=True, reuse=False) + + cell = tf_rnn_cells.MultiRNNCell( + [lstm_cell() for _ in range(number_of_layers)], state_is_tuple=True) + + outputs = rnn.static_rnn( + cell, [inputs], + initial_state=cell.zero_state(batch_size, dtypes.float32)) + + return outputs + + def _prune_model(self, session): + pruning_hparams = pruning.get_pruning_hparams().parse(self.pruning_spec) + p = pruning.Pruning(pruning_hparams, sparsity=self.sparsity) + self.mask_update_op = p.conditional_mask_update_op() + + variables.global_variables_initializer().run() + for _ in range(20): + session.run(self.mask_update_op) + session.run(self.increment_global_step) + + def _get_outputs(self, session, input_graph, tensors_list, graph_prefix=None): + outputs = [] + + for output_tensor in tensors_list: + if graph_prefix: + output_tensor = graph_prefix + "/" + output_tensor + outputs.append( + session.run(session.graph.get_tensor_by_name(output_tensor))) + + return outputs + + def _get_initial_outputs(self, output_tensor_names_list): + with self.test_session(graph=self.initial_graph) as sess1: + self._prune_model(sess1) + reference_outputs = self._get_outputs(sess1, self.initial_graph, + output_tensor_names_list) + + self.initial_graph_def = graph_util.convert_variables_to_constants( + sess1, sess1.graph.as_graph_def(), + _get_node_names(output_tensor_names_list)) + return reference_outputs + + def _get_final_outputs(self, output_tensor_names_list): + self.final_graph_def = strip_pruning_vars_lib.strip_pruning_vars_fn( + self.initial_graph_def, _get_node_names(output_tensor_names_list)) + _ = importer.import_graph_def(self.final_graph_def, name="final") + + with self.test_session(self.final_graph) as sess2: + final_outputs = self._get_outputs( + sess2, + self.final_graph, + output_tensor_names_list, + graph_prefix="final") + return final_outputs + + def _check_removal_of_pruning_vars(self, number_masked_layers): + self.assertEqual( + _get_number_pruning_vars(self.initial_graph_def), number_masked_layers) + self.assertEqual(_get_number_pruning_vars(self.final_graph_def), 0) + + def _check_output_equivalence(self, initial_outputs, final_outputs): + for initial_output, final_output in zip(initial_outputs, final_outputs): + self.assertAllEqual(initial_output, final_output) + + def testConvolutionalModel(self): + with self.initial_graph.as_default(): + number_masked_conv_layers = 5 + top_layer = self._build_convolutional_model(number_masked_conv_layers) + output_tensor_names = [top_layer.name] + initial_outputs = self._get_initial_outputs(output_tensor_names) + + # Remove pruning-related nodes. + with self.final_graph.as_default(): + final_outputs = self._get_final_outputs(output_tensor_names) + + # Check that the final graph has no pruning-related vars + self._check_removal_of_pruning_vars(number_masked_conv_layers) + + # Check that outputs remain the same after removal of pruning-related nodes + self._check_output_equivalence(initial_outputs, final_outputs) + + def testFullyConnectedModel(self): + with self.initial_graph.as_default(): + number_masked_fc_layers = 3 + top_layer = self._build_fully_connected_model(number_masked_fc_layers) + output_tensor_names = [top_layer.name] + initial_outputs = self._get_initial_outputs(output_tensor_names) + + # Remove pruning-related nodes. + with self.final_graph.as_default(): + final_outputs = self._get_final_outputs(output_tensor_names) + + # Check that the final graph has no pruning-related vars + self._check_removal_of_pruning_vars(number_masked_fc_layers) + + # Check that outputs remain the same after removal of pruning-related nodes + self._check_output_equivalence(initial_outputs, final_outputs) + + def testLSTMModel(self): + with self.initial_graph.as_default(): + number_masked_lstm_layers = 2 + outputs = self._build_lstm_model(number_masked_lstm_layers) + output_tensor_names = [outputs[0][0].name] + initial_outputs = self._get_initial_outputs(output_tensor_names) + + # Remove pruning-related nodes. + with self.final_graph.as_default(): + final_outputs = self._get_final_outputs(output_tensor_names) + + # Check that the final graph has no pruning-related vars + self._check_removal_of_pruning_vars(number_masked_lstm_layers) + + # Check that outputs remain the same after removal of pruning-related nodes + self._check_output_equivalence(initial_outputs, final_outputs) + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/nn/python/ops/alpha_dropout.py b/tensorflow/contrib/nn/python/ops/alpha_dropout.py index 2f92d05ba81f30a91f68f3c3ec51b6695d3d0371..98f4264fe0813d421f559594efae73608e53ca62 100644 --- a/tensorflow/contrib/nn/python/ops/alpha_dropout.py +++ b/tensorflow/contrib/nn/python/ops/alpha_dropout.py @@ -43,7 +43,7 @@ def alpha_dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylin noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. name: A name for this operation (optional). Returns: diff --git a/tensorflow/contrib/nn/python/ops/sampling_ops.py b/tensorflow/contrib/nn/python/ops/sampling_ops.py index e65925610c5f5125c2d2e92edc1cf708c54255d4..de71b0845e292b3ee03848afc6cc05c15286d9e8 100644 --- a/tensorflow/contrib/nn/python/ops/sampling_ops.py +++ b/tensorflow/contrib/nn/python/ops/sampling_ops.py @@ -123,15 +123,15 @@ def rank_sampled_softmax_loss(weights, """Computes softmax loss using rank-based adaptive resampling. This has been shown to improve rank loss after training compared to - @{tf.nn.sampled_softmax_loss}. For a description of the algorithm and some + `tf.nn.sampled_softmax_loss`. For a description of the algorithm and some experimental results, please see: [TAPAS: Two-pass Approximate Adaptive Sampling for Softmax](https://arxiv.org/abs/1707.03073). Sampling follows two phases: * In the first phase, `num_sampled` classes are selected using - @{tf.nn.learned_unigram_candidate_sampler} or supplied `sampled_values`. + `tf.nn.learned_unigram_candidate_sampler` or supplied `sampled_values`. The logits are calculated on those sampled classes. This phases is - similar to @{tf.nn.sampled_softmax_loss}. + similar to `tf.nn.sampled_softmax_loss`. * In the second phase, the `num_resampled` classes with highest predicted probability are kept. Probabilities are `LogSumExp(logits / resampling_temperature)`, where the sum is over @@ -142,7 +142,7 @@ def rank_sampled_softmax_loss(weights, picks more candidates close to the predicted classes. A common strategy is to decrease the temperature as training proceeds. - See @{tf.nn.sampled_softmax_loss} for more documentation on sampling and + See `tf.nn.sampled_softmax_loss` for more documentation on sampling and for typical default values for some of the parameters. This operation is for training only. It is generally an underestimate of @@ -197,7 +197,7 @@ def rank_sampled_softmax_loss(weights, where a sampled class equals one of the target classes. partition_strategy: A string specifying the partitioning strategy, relevant if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported. - See @{tf.nn.embedding_lookup} for more details. + See `tf.nn.embedding_lookup` for more details. name: A name for the operation (optional). Returns: diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD index 280d4a54922ccc76188abe19263e1f9c22c24d8b..778b710d78a2095b8a1315018641c67419c26b98 100644 --- a/tensorflow/contrib/opt/BUILD +++ b/tensorflow/contrib/opt/BUILD @@ -348,6 +348,7 @@ py_test( py_test( name = "shampoo_test", + size = "large", srcs = ["python/training/shampoo_test.py"], srcs_version = "PY2AND3", deps = [ @@ -361,5 +362,6 @@ py_test( "//tensorflow/python:resource_variable_ops", "//tensorflow/python:variables", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/opt/python/training/shampoo.py b/tensorflow/contrib/opt/python/training/shampoo.py index 7afa0998f4208852d727fef38bd16445dbf6c080..294627f42a839f399f747bcd6ba50968aadb35a1 100644 --- a/tensorflow/contrib/opt/python/training/shampoo.py +++ b/tensorflow/contrib/opt/python/training/shampoo.py @@ -66,8 +66,9 @@ class ShampooOptimizer(optimizer.Optimizer): a lambda function that depends on step. """ - def __init__(self, global_step=0, - max_matrix_size=500, + def __init__(self, + global_step=0, + max_matrix_size=768, gbar_decay=0.0, gbar_weight=1.0, mat_gbar_decay=1.0, @@ -138,7 +139,7 @@ class ShampooOptimizer(optimizer.Optimizer): shape = np.array(v.get_shape()) for i, d in enumerate(shape): d_tensor = ops.convert_to_tensor(d) - if d < self._max_matrix_size: + if d <= self._max_matrix_size: mat_g_init = array_ops.zeros_like(linalg_ops.eye(d_tensor)) if self._svd_interval > 1: _ = self._get_or_make_slot(v, linalg_ops.eye(d_tensor), @@ -149,18 +150,27 @@ class ShampooOptimizer(optimizer.Optimizer): _ = self._get_or_make_slot(v, mat_g_init, "Gbar_" + str(i), self._name) + def _resource_apply_dense(self, grad, var): + return self._apply_dense(grad, var) + def _apply_dense(self, grad, var): return self._apply_gradient(grad, var) + def _resource_apply_sparse(self, grad_values, var, grad_indices): + return self._apply_sparse_shared(grad_values, grad_indices, var) + def _apply_sparse(self, grad, var): - if var.get_shape()[0] < self._max_matrix_size or self._gbar_decay != 0.0: + return self._apply_sparse_shared(grad.values, grad.indices, var) + + def _apply_sparse_shared(self, grad_values, grad_indices, var): + if var.get_shape()[0] <= self._max_matrix_size or self._gbar_decay != 0.0: # The dimension is small enough, we can make the variable dense and # do a dense update dense_grad = array_ops.scatter_nd( - array_ops.expand_dims(grad.indices, axis=1), - grad.values, array_ops.shape(var, out_type=grad.indices.dtype)) + array_ops.expand_dims(grad_indices, axis=1), grad_values, + array_ops.shape(var, out_type=grad_indices.dtype)) return self._apply_gradient(dense_grad, var) - return self._apply_gradient(grad.values, var, grad.indices) + return self._apply_gradient(grad_values, var, grad_indices) def _weighted_average(self, var, weight, weight_t, rest): """Computes exponential weighted average: var = weight_t * var + rest. @@ -304,7 +314,7 @@ class ShampooOptimizer(optimizer.Optimizer): mat_h = math_ops.pow(mat_g + self._epsilon, alpha) else: damped_mat_g = mat_g + self._epsilon * identity - z = (1 - 1/alpha) / (2 * linalg_ops.norm(damped_mat_g, ord=2)) + z = (1 - 1 / alpha) / (2 * linalg_ops.norm(damped_mat_g)) # The best value for z is # (1 - 1/alpha) * (c_max^{-alpha} - c_min^{-alpha}) / # (c_max^{1-alpha} - c_min^{1-alpha}) @@ -326,12 +336,13 @@ class ShampooOptimizer(optimizer.Optimizer): def _compute_power(self, var, mat_g, mat_g_size, alpha, mat_h_slot_name=None): """Just a switch between the iterative power vs svd.""" - if self._use_iterative_root: - return self._compute_power_iter(var, mat_g, mat_g_size, alpha, - mat_h_slot_name) - else: - return self._compute_power_svd(var, mat_g, mat_g_size, alpha, - mat_h_slot_name) + with ops.name_scope("matrix_iterative_power"): + if self._use_iterative_root: + return self._compute_power_iter(var, mat_g, mat_g_size, alpha, + mat_h_slot_name) + else: + return self._compute_power_svd(var, mat_g, mat_g_size, alpha, + mat_h_slot_name) def _apply_gradient(self, grad, var, indices=None): """The main function to update a variable. @@ -397,7 +408,7 @@ class ShampooOptimizer(optimizer.Optimizer): for i, mat_g in enumerate(mat_g_list): # axes is the list of indices to reduce - everything but the current i. axes = list(range(i)) + list(range(i+1, v_rank)) - if shape[i] < self._max_matrix_size: + if shape[i] <= self._max_matrix_size: # If the tensor size is sufficiently small perform full Shampoo update # Note if precond_update_interval > 1 and mat_gbar_decay_t != 1, this # is not strictly correct. However we will use it for now, and @@ -455,8 +466,8 @@ class ShampooOptimizer(optimizer.Optimizer): # Update the variable based on the Shampoo update learning_rate_t = GetParam(self._learning_rate, global_step) if indices is not None: - var_updated = state_ops.scatter_sub(var, indices, - learning_rate_t * preconditioned_grad) + var_updated = state_ops.scatter_add( + var, indices, -learning_rate_t * preconditioned_grad) else: var_updated = state_ops.assign_sub(var, learning_rate_t * preconditioned_grad) diff --git a/tensorflow/contrib/opt/python/training/shampoo_test.py b/tensorflow/contrib/opt/python/training/shampoo_test.py index 3148d022962f91251ddf072cb092dcfec7737176..2e0a202ae293664d85ece884a505096455cde73c 100644 --- a/tensorflow/contrib/opt/python/training/shampoo_test.py +++ b/tensorflow/contrib/opt/python/training/shampoo_test.py @@ -19,6 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np from tensorflow.contrib.opt.python.training import shampoo @@ -40,9 +41,10 @@ def np_power(mat_g, alpha): return np.dot(np.dot(mat_u, np.diag(diag_d)), mat_v) -class ShampooTest(test.TestCase): +class ShampooTest(test.TestCase, parameterized.TestCase): - def testBasicVector(self): + @parameterized.named_parameters(('Var', False), ('ResourceVar', True)) + def testBasicVector(self, use_resource_var): """Similar to the full Adagrad update.""" size = 20 @@ -51,8 +53,10 @@ class ShampooTest(test.TestCase): grad_np_2 = np.random.rand(size) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = constant_op.constant(grad_np, dtype=dtypes.float32) grad_2 = constant_op.constant(grad_np_2, dtype=dtypes.float32) @@ -91,7 +95,8 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def testBasicMatrix(self): + @parameterized.named_parameters(('Var', False), ('ResourceVar', True)) + def testBasicMatrix(self, use_resource_var): """Check update when gradient is a matrix.""" size = [10, 5] init_var_np = np.zeros(size) @@ -99,8 +104,10 @@ class ShampooTest(test.TestCase): grad_np_2 = np.random.rand(size[0], size[1]) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = constant_op.constant(grad_np, dtype=dtypes.float32) grad_2 = constant_op.constant(grad_np_2, dtype=dtypes.float32) @@ -143,16 +150,23 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def _testBasicTensor(self, use_iterative_root): - """Check update when gradient is a tensor.""" + def _testBasicTensor(self, use_iterative_root, use_resource_var): + """Check update when gradient is a tensor. + + Args: + use_iterative_root: use iterative power method or SVD to find nth roots. + use_resource_var: use resource var as variables. + """ size = [10, 5, 7] init_var_np = np.zeros(size) grad_np = np.random.rand(size[0], size[1], size[2]) grad_np_2 = np.random.rand(size[0], size[1], size[2]) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = constant_op.constant(grad_np, dtype=dtypes.float32) grad_2 = constant_op.constant(grad_np_2, dtype=dtypes.float32) @@ -208,11 +222,17 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def testBasicTensor(self): - for use_iterative_root in [True, False]: - self._testBasicTensor(use_iterative_root) - - def testLargeVector(self): + @parameterized.named_parameters( + ('SVDWithVar', False, False), + ('SVDWithResourceVar', False, True), + ('IterRootWithVar', True, False), + ('IterRootWithResourceVar', True, True), + ) + def testBasicTensor(self, use_iterative_root, use_resource_var): + self._testBasicTensor(use_iterative_root, use_resource_var) + + @parameterized.named_parameters(('Var', False), ('ResourceVar', True)) + def testLargeVector(self, use_resource_var): """This is just the diagonal Adagrad update.""" size = 2000 @@ -221,8 +241,10 @@ class ShampooTest(test.TestCase): grad_np_2 = np.random.rand(size) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = constant_op.constant(grad_np, dtype=dtypes.float32) grad_2 = constant_op.constant(grad_np_2, dtype=dtypes.float32) @@ -257,10 +279,14 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val) - def testLargeMatrix(self): + @parameterized.named_parameters(('Var', False), ('ResourceVar', True)) + def testLargeMatrix(self, use_resource_var): """Gradient is a matrix, one of whose dimensions is large. We do diagonal updates for large dimensions. + + Args: + use_resource_var: use resource var as variables. """ size = [2000, 3] @@ -269,8 +295,10 @@ class ShampooTest(test.TestCase): grad_np_2 = np.random.rand(size[0], size[1]) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = constant_op.constant(grad_np, dtype=dtypes.float32) grad_2 = constant_op.constant(grad_np_2, dtype=dtypes.float32) @@ -316,12 +344,15 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def testSparseUpdateLarge(self): + @parameterized.named_parameters(('Var', False)) + def testSparseUpdateLarge(self, use_resource_var): """Check update when gradient is of type IndexSlices. We do diagonal updates for the first dimension, unless it is very small. - """ + Args: + use_resource_var: use resource var as variables. + """ size = [2000, 3] sample_size_1 = 100 init_var_np = np.zeros(size) @@ -335,8 +366,10 @@ class ShampooTest(test.TestCase): grad_np_2 = np.random.rand(sample_size_2, size[1]) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = ops.IndexedSlices( constant_op.constant(grad_np, dtype=dtypes.float32), constant_op.constant(grad_indices), @@ -395,13 +428,14 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def _testSparseUpdateSmall(self, use_iterative_root): + def _testSparseUpdateSmall(self, use_iterative_root, use_resource_var): """Gradient is of type IndexSlices, but the first dimension is small. We create dense gradient and do the full update with SVD etc. Args: use_iterative_root: use iterative power method or SVD to find nth roots. + use_resource_var: use resource var as variables. """ size = [100, 3, 5] @@ -412,8 +446,10 @@ class ShampooTest(test.TestCase): grad_np = np.random.rand(sample_size, size[1], size[2]) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = ops.IndexedSlices( constant_op.constant(grad_np, dtype=dtypes.float32), constant_op.constant(grad_indices), @@ -453,15 +489,21 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def testSparseUpdateSmall(self): - for use_iterative_root in [True, False]: - self._testSparseUpdateSmall(use_iterative_root) + @parameterized.named_parameters( + ('SVDWithVar', False, False), + ('SVDWithResourceVar', False, True), + ('IterRootWithVar', True, False), + ('IterRootWithResourceVar', True, True), + ) + def testSparseUpdateSmall(self, use_iterative_root, use_resource_var): + self._testSparseUpdateSmall(use_iterative_root, use_resource_var) - def _testBasicTensorWithMomentum(self, use_iterative_root): + def _testBasicTensorWithMomentum(self, use_iterative_root, use_resource_var): """Check update with momentum when gradient is a tensor. Args: use_iterative_root: use iterative power method or SVD to find nth roots. + use_resource_var: use resource var as variables. """ size = [10, 5, 7] init_var_np = np.zeros(size) @@ -471,8 +513,10 @@ class ShampooTest(test.TestCase): gbar_weight = 0.1 with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = constant_op.constant(grad_np, dtype=dtypes.float32) grad_2 = constant_op.constant(grad_np_2, dtype=dtypes.float32) @@ -528,15 +572,21 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def testBasicTensorWithMomentum(self): - for use_iterative_root in [True, False]: - self._testBasicTensorWithMomentum(use_iterative_root) + @parameterized.named_parameters( + ('SVDWithVar', False, False), + ('SVDWithResourceVar', False, True), + ('IterRootWithVar', True, False), + ('IterRootWithResourceVar', True, True), + ) + def testBasicTensorWithMomentum(self, use_iterative_root, use_resource_var): + self._testBasicTensorWithMomentum(use_iterative_root, use_resource_var) - def _testDelayedSVD(self, use_iterative_root): + def _testDelayedSVD(self, use_iterative_root, use_resource_var): """Performing the SVD every nth step. Args: use_iterative_root: use iterative power method or SVD to find nth roots. + use_resource_var: use resource var as variables. """ size = [10, 5, 7] init_var_np = np.zeros(size).astype(np.float32) @@ -552,8 +602,10 @@ class ShampooTest(test.TestCase): mat_g3 = np.zeros_like(mat_g3_a) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = array_ops.placeholder(dtypes.float32, shape=size) opt = shampoo.ShampooOptimizer(global_step, svd_interval=svd_interval, @@ -590,15 +642,21 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def testDelayedSVD(self): - for use_iterative_root in [True, False]: - self._testDelayedSVD(use_iterative_root) + @parameterized.named_parameters( + ('SVDWithVar', False, False), + ('SVDWithResourceVar', False, True), + ('IterRootWithVar', True, False), + ('IterRootWithResourceVar', True, True), + ) + def testDelayedSVD(self, use_iterative_root, use_resource_var): + self._testDelayedSVD(use_iterative_root, use_resource_var) - def _testDelayedPrecondUpdate(self, use_iterative_root): + def _testDelayedPrecondUpdate(self, use_iterative_root, use_resource_var): """Update the squared sum every nth step, drop the other steps. Args: use_iterative_root: use iterative power method or SVD to find nth roots. + use_resource_var: use resource var as variables. """ size = [10, 5, 7] init_var_np = np.zeros(size).astype(np.float32) @@ -615,8 +673,10 @@ class ShampooTest(test.TestCase): mat_g3 = np.zeros_like(mat_g3_a) with self.test_session() as sess: - global_step = variables.Variable(0, dtype=dtypes.int64) - var = variables.Variable(init_var_np, dtype=dtypes.float32) + global_step = variables.Variable( + 0, dtype=dtypes.int64, use_resource=use_resource_var) + var = variables.Variable( + init_var_np, dtype=dtypes.float32, use_resource=use_resource_var) grad = array_ops.placeholder(dtypes.float32, shape=size) opt = shampoo.ShampooOptimizer( @@ -660,9 +720,14 @@ class ShampooTest(test.TestCase): self.assertAllCloseAccordingToType(new_val_np, new_val, atol=TOLERANCE, rtol=TOLERANCE) - def testDelayedPrecondUpdate(self): - for use_iterative_root in [True, False]: - self._testDelayedPrecondUpdate(use_iterative_root) + @parameterized.named_parameters( + ('SVDWithVar', False, False), + ('SVDWithResourceVar', False, True), + ('IterRootWithVar', True, False), + ('IterRootWithResourceVar', True, True), + ) + def testDelayedPrecondUpdate(self, use_iterative_root, use_resource_var): + self._testDelayedPrecondUpdate(use_iterative_root, use_resource_var) if __name__ == '__main__': diff --git a/tensorflow/contrib/predictor/BUILD b/tensorflow/contrib/predictor/BUILD index 36e21af618f5af744ce793509813eaf36e1b8479..72ea777ca7036bad91b15d8d2163fdee842b1e32 100644 --- a/tensorflow/contrib/predictor/BUILD +++ b/tensorflow/contrib/predictor/BUILD @@ -60,7 +60,7 @@ py_library( ":base_predictor", "//tensorflow/python:framework_ops", "//tensorflow/python:training", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/saved_model:signature_constants", ], ) @@ -90,9 +90,7 @@ py_library( "//tensorflow/python:framework_ops", "//tensorflow/python:math_ops", "//tensorflow/python/estimator", - "//tensorflow/python/estimator:export", - "//tensorflow/python/estimator:export_output", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/saved_model:signature_constants", ], ) diff --git a/tensorflow/contrib/quantize/python/quant_ops_test.py b/tensorflow/contrib/quantize/python/quant_ops_test.py index c2a8def48012c808da18587c8ff462fa33a363c0..a45840009b758881c14fb64b2d39af6cd4ec4bc4 100644 --- a/tensorflow/contrib/quantize/python/quant_ops_test.py +++ b/tensorflow/contrib/quantize/python/quant_ops_test.py @@ -75,7 +75,7 @@ class QuantOpsTest(googletest.TestCase): self.assertGreater(max_value, 0.0) self.assertLess(max_value, 1.0) - def testVariablesNotParitioned_LastValue(self): + def testVariablesNotPartitioned_LastValue(self): # Variables added should not use a default partiioner since they are # scalar. There would be a tensorflow error thrown if the partitioner was # respected by the rewrite. @@ -90,7 +90,7 @@ class QuantOpsTest(googletest.TestCase): is_training=True, vars_collection=_MIN_MAX_VARS) - def testVariablesNotParitioned_MovingAvg(self): + def testVariablesNotPartitioned_MovingAvg(self): # Variables added should not use a default partiioner since they are # scalar. There would be a tensorflow error thrown if the partitioner was # respected by the rewrite. diff --git a/tensorflow/contrib/recurrent/python/kernel_tests/functional_rnn_test.py b/tensorflow/contrib/recurrent/python/kernel_tests/functional_rnn_test.py index 0f19ac7dbe0cee2eb6c780ec5ea6266bc847abd7..f23194a6f2e64e0619049bac51891d6d6099831f 100644 --- a/tensorflow/contrib/recurrent/python/kernel_tests/functional_rnn_test.py +++ b/tensorflow/contrib/recurrent/python/kernel_tests/functional_rnn_test.py @@ -61,10 +61,17 @@ class FunctionalRnnTest(test_util.TensorFlowTestCase): func, args = self._CELLDEFS[celldef_name] return func(*args) - def _CreateInputs(self): - inputs = np.random.random([FunctionalRnnTest._BATCH_SIZE, - FunctionalRnnTest._TOTAL_TIME, - FunctionalRnnTest._INPUT_SIZE]) + def _CreateInputs(self, time_major=False): + if time_major: + inputs = np.random.random([ + FunctionalRnnTest._TOTAL_TIME, FunctionalRnnTest._BATCH_SIZE, + FunctionalRnnTest._INPUT_SIZE + ]) + else: + inputs = np.random.random([ + FunctionalRnnTest._BATCH_SIZE, FunctionalRnnTest._TOTAL_TIME, + FunctionalRnnTest._INPUT_SIZE + ]) # Always leave one time slot empty, to check max_length behavior. sequence_length = np.random.randint( 0, high=FunctionalRnnTest._TOTAL_TIME - 1, @@ -72,15 +79,51 @@ class FunctionalRnnTest(test_util.TensorFlowTestCase): dtype=np.int) return (inputs, sequence_length) - def _CreateRnnGraph(self, create_rnn_computation_func, cell, tf_inputs, - tf_sequence_length, initial_state=None, - time_major=None, scope=None): - tf_result = create_rnn_computation_func(cell=cell, inputs=tf_inputs, - sequence_length=tf_sequence_length, - initial_state=initial_state, - dtype=dtypes.float32, - time_major=time_major, - scope=scope) + def _CreateSymmetricInputs(self): + # total time = batch size + inputs = np.zeros( + (FunctionalRnnTest._BATCH_SIZE, FunctionalRnnTest._BATCH_SIZE, + FunctionalRnnTest._INPUT_SIZE)) + for i in range(FunctionalRnnTest._BATCH_SIZE): + for j in range(i, FunctionalRnnTest._BATCH_SIZE): + inputs[i][j] = np.random.random([FunctionalRnnTest._INPUT_SIZE]) + inputs[j][i] = inputs[i][j] + + # Always leave one time slot empty, to check max_length behavior. + sequence_length = np.random.randint( + 0, + high=FunctionalRnnTest._BATCH_SIZE - 1, + size=FunctionalRnnTest._BATCH_SIZE, + dtype=np.int) + return (inputs, sequence_length) + + def _CreateRnnGraph(self, + create_rnn_computation_func, + cell, + tf_inputs, + tf_sequence_length, + is_bidirectional, + initial_state=None, + time_major=None, + scope=None): + if is_bidirectional: + tf_result = create_rnn_computation_func( + cell_fw=cell, + cell_bw=cell, + inputs=tf_inputs, + sequence_length=tf_sequence_length, + dtype=dtypes.float32, + time_major=time_major, + scope=scope) + else: + tf_result = create_rnn_computation_func( + cell=cell, + inputs=tf_inputs, + sequence_length=tf_sequence_length, + initial_state=initial_state, + dtype=dtypes.float32, + time_major=time_major, + scope=scope) grad = gradients_impl.gradients(tf_result, variables.trainable_variables()) return {'inference': tf_result, 'grad': grad} @@ -102,15 +145,26 @@ class FunctionalRnnTest(test_util.TensorFlowTestCase): variable_cache[n] = v def _RunRnn(self, numpy_inputs, numpy_slen, cell_name, variable_cache, - is_dynamic): + is_dynamic, time_major=None, is_bidirectional=False): with ops.Graph().as_default() as graph: tf_inputs = array_ops.placeholder( dtypes.float32, shape=numpy_inputs.shape) tf_slen = array_ops.placeholder(dtypes.int32) feeds = {tf_inputs: numpy_inputs, tf_slen: numpy_slen} cell = self._CreateCell(cell_name) - fn = rnn_lib.dynamic_rnn if is_dynamic else functional_rnn.functional_rnn - fetches = self._CreateRnnGraph(fn, cell, tf_inputs, tf_slen) + if is_dynamic: + if is_bidirectional: + fn = rnn_lib.bidirectional_dynamic_rnn + else: + fn = rnn_lib.dynamic_rnn + else: + if is_bidirectional: + fn = functional_rnn.bidirectional_functional_rnn + else: + fn = functional_rnn.functional_rnn + + fetches = self._CreateRnnGraph( + fn, cell, tf_inputs, tf_slen, is_bidirectional, time_major=time_major) with self.test_session(graph=graph) as sess: sess.run(variables.global_variables_initializer()) # Note that cell.trainable_variables it not always set. @@ -158,6 +212,78 @@ class FunctionalRnnTest(test_util.TensorFlowTestCase): self.assertAllClose(dyn_rnn['inference'], func_rnn['inference']) self.assertAllClose(dyn_rnn['grad'], func_rnn['grad']) + def testLstmWithTimeMajorInputs(self): + """Checks an LSTM against the reference implementation, with time_major.""" + time_major = True + np_inputs, np_slen = self._CreateInputs(time_major=True) + var_cache = {} + args = [np_inputs, np_slen, 'lstm', var_cache] + _, func_rnn = self._RunRnn(*(args + [False]), time_major=time_major) + _, dyn_rnn = self._RunRnn(*(args + [True]), time_major=time_major) + self.assertAllClose(dyn_rnn['inference'], func_rnn['inference']) + self.assertAllClose(dyn_rnn['grad'], func_rnn['grad']) + + def testBidirectionalLstmWithTimeMajorInputs(self): + """Checks a bi-directional LSTM with time-major inputs.""" + time_major = True + np_inputs, np_slen = self._CreateInputs(time_major) + var_cache = {} + args = [np_inputs, np_slen, 'lstm', var_cache] + _, func_rnn = self._RunRnn( + *(args + [False]), time_major=time_major, is_bidirectional=True) + _, dyn_rnn = self._RunRnn( + *(args + [True]), time_major=time_major, is_bidirectional=True) + self.assertAllClose(dyn_rnn['inference'], func_rnn['inference']) + # TODO(b/112170761): comment out this line after the bug is fixed. + # self.assertAllClose(dyn_rnn['grad'], func_rnn['grad']) + + def testBidirectionalLstm(self): + """Checks time-major and batch-major rnn produce consistent results.""" + time_major_inputs, np_slen = self._CreateInputs(True) + batch_major_inputs = np.transpose(time_major_inputs, [1, 0, 2]) + var_cache = {} + args = [np_slen, 'lstm', var_cache, False] + _, time_major_rnn = self._RunRnn( + *([time_major_inputs] + args), time_major=True, is_bidirectional=True) + _, batch_major_rnn = self._RunRnn( + *([batch_major_inputs]+ args), time_major=False, is_bidirectional=True) + # Convert the batch-major outputs to be time-major before the comparasion. + outputs, state = batch_major_rnn['inference'] + outputs = [np.transpose(x, [1, 0, 2]) for x in outputs] + batch_major_rnn['inference'] = [outputs, state] + self.assertAllClose(time_major_rnn['inference'], + batch_major_rnn['inference']) + self.assertAllClose(time_major_rnn['grad'], batch_major_rnn['grad']) + + def testBidirectionalLstmWithSymmetricInputs(self): + """Checks a bi-directional LSTM with symmetric inputs. + + time-major and batch-major rnn produce the same result with symmetric + inputs. + """ + np_inputs, np_slen = self._CreateSymmetricInputs() + var_cache = {} + args = [np_inputs, np_slen, 'lstm', var_cache] + _, time_major_func_rnn = self._RunRnn( + *(args + [False]), time_major=True, is_bidirectional=True) + _, batch_major_func_rnn = self._RunRnn( + *(args + [False]), time_major=False, is_bidirectional=True) + _, time_major_dyn_rnn = self._RunRnn( + *(args + [True]), time_major=True, is_bidirectional=True) + _, batch_major_dyn_rnn = self._RunRnn( + *(args + [True]), time_major=False, is_bidirectional=True) + self.assertAllClose(time_major_func_rnn['inference'], + batch_major_func_rnn['inference']) + self.assertAllClose(time_major_func_rnn['grad'], + batch_major_func_rnn['grad']) + self.assertAllClose(time_major_dyn_rnn['inference'], + batch_major_dyn_rnn['inference']) + self.assertAllClose(time_major_dyn_rnn['grad'], batch_major_dyn_rnn['grad']) + self.assertAllClose(time_major_func_rnn['inference'], + batch_major_dyn_rnn['inference']) + self.assertAllClose(time_major_func_rnn['grad'], + batch_major_dyn_rnn['grad']) + if __name__ == '__main__': test_lib.main() diff --git a/tensorflow/contrib/recurrent/python/ops/functional_rnn.py b/tensorflow/contrib/recurrent/python/ops/functional_rnn.py index a085474c1bf6117ba5663139c78d8f08f71392d3..67a8f59c3c03d01a5957a9eff8bd026e70770a45 100644 --- a/tensorflow/contrib/recurrent/python/ops/functional_rnn.py +++ b/tensorflow/contrib/recurrent/python/ops/functional_rnn.py @@ -206,7 +206,7 @@ def _PickFinalStateFromHistory(acc_state, sequence_length): lengths = array_ops.tile(array_ops.reshape(sequence_length, [-1, 1]), [1, max_time]) last_idx = math_ops.cast(math_ops.equal(output_time, lengths - 1), - dtype=dtypes.float32) + dtype=state_var.dtype) last_idx = array_ops.transpose(last_idx) last_idx_for_bcast = array_ops.expand_dims(last_idx, -1) sliced = math_ops.multiply(last_idx_for_bcast, state_var) @@ -284,8 +284,13 @@ def functional_rnn(cell, inputs, sequence_length=None, inputs=inputs, cell_fn=func_cell.cell_step, use_tpu=use_tpu) - return _PostProcessOutput(extended_acc_state, extended_final_state, - func_cell, inputs_flat[0].shape[0], sequence_length) + tf_output, tf_state = _PostProcessOutput( + extended_acc_state, extended_final_state, func_cell, + inputs_flat[0].shape[0], sequence_length) + + if time_major: + tf_output = array_ops.transpose(tf_output, [1, 0, 2]) + return tf_output, tf_state def bidirectional_functional_rnn( diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py index 1816b469ee5bf338453a82d18663f97f6565dc0c..f74c95f96299cf132a9a1d8ab8b238a532e2695b 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py +++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py @@ -3276,7 +3276,7 @@ class IndyLSTMCell(rnn_cell_impl.LayerRNNCell): It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline. - For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell} + For advanced models, please use the full `tf.nn.rnn_cell.LSTMCell` that follows. TODO(gonnet): Write a paper describing this and add a reference here. diff --git a/tensorflow/contrib/seq2seq/BUILD b/tensorflow/contrib/seq2seq/BUILD index 1a1591d798f6f904e23987d9d7a60193c124c20e..18b56cd21942e28cb0dc3210df0bb04d55c1e16f 100644 --- a/tensorflow/contrib/seq2seq/BUILD +++ b/tensorflow/contrib/seq2seq/BUILD @@ -177,7 +177,7 @@ cuda_py_test( cuda_py_test( name = "beam_search_decoder_test", - size = "small", + size = "medium", srcs = ["python/kernel_tests/beam_search_decoder_test.py"], additional_deps = [ ":seq2seq_py", diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py index 1c9d179e3c55ad07fcf709f66028c91c20e8eea0..0ba32cd3bf8a374f5f55bdc6b2325b03443cd545 100644 --- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py +++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py @@ -382,8 +382,8 @@ class LuongAttention(_BaseAttentionMechanism): for values past the respective sequence lengths. scale: Python boolean. Whether to scale the energy term. probability_fn: (optional) A `callable`. Converts the score to - probabilities. The default is @{tf.nn.softmax}. Other options include - @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}. + probabilities. The default is `tf.nn.softmax`. Other options include + `tf.contrib.seq2seq.hardmax` and `tf.contrib.sparsemax.sparsemax`. Its signature should be: `probabilities = probability_fn(score)`. score_mask_value: (optional) The mask value for score before passing into `probability_fn`. The default is -inf. Only used if @@ -529,8 +529,8 @@ class BahdanauAttention(_BaseAttentionMechanism): for values past the respective sequence lengths. normalize: Python boolean. Whether to normalize the energy term. probability_fn: (optional) A `callable`. Converts the score to - probabilities. The default is @{tf.nn.softmax}. Other options include - @{tf.contrib.seq2seq.hardmax} and @{tf.contrib.sparsemax.sparsemax}. + probabilities. The default is `tf.nn.softmax`. Other options include + `tf.contrib.seq2seq.hardmax` and `tf.contrib.sparsemax.sparsemax`. Its signature should be: `probabilities = probability_fn(score)`. score_mask_value: (optional): The mask value for score before passing into `probability_fn`. The default is -inf. Only used if @@ -1091,7 +1091,7 @@ class AttentionWrapper(rnn_cell_impl.RNNCell): `AttentionWrapper`, then you must ensure that: - The encoder output has been tiled to `beam_width` via - @{tf.contrib.seq2seq.tile_batch} (NOT `tf.tile`). + `tf.contrib.seq2seq.tile_batch` (NOT `tf.tile`). - The `batch_size` argument passed to the `zero_state` method of this wrapper is equal to `true_batch_size * beam_width`. - The initial state created with `zero_state` above contains a diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py index f17dbb0fe3c13c3a43f043b82772949737dfb2de..74741a7bd6306181c248af50e9784f45dfc41c55 100644 --- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py @@ -234,7 +234,7 @@ class BeamSearchDecoder(decoder.Decoder): `AttentionWrapper`, then you must ensure that: - The encoder output has been tiled to `beam_width` via - @{tf.contrib.seq2seq.tile_batch} (NOT `tf.tile`). + `tf.contrib.seq2seq.tile_batch` (NOT `tf.tile`). - The `batch_size` argument passed to the `zero_state` method of this wrapper is equal to `true_batch_size * beam_width`. - The initial state created with `zero_state` above contains a diff --git a/tensorflow/contrib/signal/python/kernel_tests/test_util.py b/tensorflow/contrib/signal/python/kernel_tests/test_util.py index 7d6289532addfd4b4b867bf64d9113253bd1c76d..b4422a49887378187a2be46275d4dabf1fbd40a1 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/test_util.py +++ b/tensorflow/contrib/signal/python/kernel_tests/test_util.py @@ -27,15 +27,15 @@ def grappler_optimize(graph, fetches=None, rewriter_config=None): """Tries to optimize the provided graph using grappler. Args: - graph: A @{tf.Graph} instance containing the graph to optimize. + graph: A `tf.Graph` instance containing the graph to optimize. fetches: An optional list of `Tensor`s to fetch (i.e. not optimize away). Grappler uses the 'train_op' collection to look for fetches, so if not provided this collection should be non-empty. - rewriter_config: An optional @{tf.RewriterConfig} to use when rewriting the + rewriter_config: An optional `tf.RewriterConfig` to use when rewriting the graph. Returns: - A @{tf.GraphDef} containing the rewritten graph. + A `tf.GraphDef` containing the rewritten graph. """ if rewriter_config is None: rewriter_config = rewriter_config_pb2.RewriterConfig() diff --git a/tensorflow/contrib/signal/python/ops/mel_ops.py b/tensorflow/contrib/signal/python/ops/mel_ops.py index 062d84aea183ab61501a8b07521adb1a1a17c63c..ecc2fedb9f82151511bab3f3c0496bc4e290903f 100644 --- a/tensorflow/contrib/signal/python/ops/mel_ops.py +++ b/tensorflow/contrib/signal/python/ops/mel_ops.py @@ -108,7 +108,7 @@ def linear_to_mel_weight_matrix(num_mel_bins=20, # `M` has shape [frames, num_mel_bins] M = tf.matmul(S, A) - The matrix can be used with @{tf.tensordot} to convert an arbitrary rank + The matrix can be used with `tf.tensordot` to convert an arbitrary rank `Tensor` of linear-scale spectral bins into the mel scale. # S has shape [..., num_spectrogram_bins]. diff --git a/tensorflow/contrib/signal/python/ops/reconstruction_ops.py b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py index 653c030a04c2bbc7e3ee49b9c85a781fb49de8d0..4db8dc2ca090534f2cda66bd55c30dfa389b860a 100644 --- a/tensorflow/contrib/signal/python/ops/reconstruction_ops.py +++ b/tensorflow/contrib/signal/python/ops/reconstruction_ops.py @@ -90,22 +90,28 @@ def overlap_and_add(signal, frame_step, name=None): raise ValueError("frame_step must be an integer. Got %s" % frame_step.dtype) - # If frame_length and frame_step are known at graph construction time, check - # frame_step is less than or equal to frame_length. - frame_step_static = tensor_util.constant_value(frame_step) - if (frame_step_static is not None and signal.shape.ndims is not None and - signal.shape[-1].value is not None and - frame_step_static > signal.shape[-1].value): - raise ValueError( - "frame_step (%d) must be less than or equal to frame_length (%d)" % ( - frame_step_static, signal.shape[-1].value)) - signal_shape = array_ops.shape(signal) # All dimensions that are not part of the overlap-and-add. Can be empty for # rank 2 inputs. outer_dimensions = signal_shape[:-2] + # If frame_length and frame_step are known at graph construction time, check + # frame_step is less than or equal to frame_length. + frame_step_static = tensor_util.constant_value(frame_step) + if (frame_step_static is not None and signal.shape.ndims is not None and + signal.shape[-1].value is not None): + if frame_step_static > signal.shape[-1].value: + raise ValueError( + "frame_step (%d) must be less than or equal to " + "frame_length (%d)" % ( + frame_step_static, signal.shape[-1].value)) + # If frame_length is equal to frame_step, there's no overlap so just + # reshape the tensor. + if frame_step_static == signal.shape[-1].value: + return array_ops.reshape(signal, array_ops.concat( + [outer_dimensions, [-1]], 0)) + signal_rank = array_ops.rank(signal) frames = signal_shape[-2] frame_length = signal_shape[-1] diff --git a/tensorflow/contrib/slim/python/slim/evaluation.py b/tensorflow/contrib/slim/python/slim/evaluation.py index 5cfd5ee82e2a0fce33311a8783d2d4ceb031544d..0feb3925eb8ec4eca7c7fd527510f45ceb83091b 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation.py +++ b/tensorflow/contrib/slim/python/slim/evaluation.py @@ -22,7 +22,8 @@ modules using a variety of metrics and summarizing the results. ********************** In the simplest use case, we use a model to create the predictions, then specify -the metrics and finally call the `evaluation` method: +the metrics and choose one model checkpoint, finally call the`evaluation_once` +method: # Create model and obtain the predictions: images, labels = LoadData(...) @@ -34,20 +35,24 @@ the metrics and finally call the `evaluation` method: "mse": slim.metrics.mean_squared_error(predictions, labels), }) + checkpoint_path = '/tmp/my_model_dir/my_checkpoint' + log_dir = '/tmp/my_model_eval/' + initial_op = tf.group( tf.global_variables_initializer(), tf.local_variables_initializer()) - with tf.Session() as sess: - metric_values = slim.evaluation( - sess, - num_evals=1, - initial_op=initial_op, - eval_op=names_to_updates.values(), - final_op=name_to_values.values()) + metric_values = slim.evaluate_once( + master='', + checkpoint_path=checkpoint_path, + log_dir=log_dir, + num_evals=1, + initial_op=initial_op, + eval_op=names_to_updates.values(), + final_op=name_to_values.values()) - for metric, value in zip(names_to_values.keys(), metric_values): - logging.info('Metric %s has value: %f', metric, value) + for metric, value in zip(names_to_values.keys(), metric_values): + logging.info('Metric %s has value: %f', metric, value) ************************************************ * Evaluating a Checkpointed Model with Metrics * diff --git a/tensorflow/contrib/summary/summary.py b/tensorflow/contrib/summary/summary.py index d22b80ac88a9ced541a952fcbb58c50366464075..42898e797cc351e3de290cc65fc825f1406c739d 100644 --- a/tensorflow/contrib/summary/summary.py +++ b/tensorflow/contrib/summary/summary.py @@ -17,7 +17,7 @@ The operations in this package are safe to use with eager execution turned on or off. It has a more flexible API that allows summaries to be written directly from ops to places other than event log files, rather than propagating protos -from @{tf.summary.merge_all} to @{tf.summary.FileWriter}. +from `tf.summary.merge_all` to `tf.summary.FileWriter`. To use with eager execution enabled, write your code as follows: diff --git a/tensorflow/contrib/timeseries/python/timeseries/BUILD b/tensorflow/contrib/timeseries/python/timeseries/BUILD index 0e96c1fbd43ef45e9ff1e090a6d5489ab186484a..c230919168b937b26c68e141e15f0762ad70f3e6 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/BUILD +++ b/tensorflow/contrib/timeseries/python/timeseries/BUILD @@ -94,7 +94,6 @@ py_library( "//tensorflow/python:training", "//tensorflow/python:util", "//tensorflow/python/estimator:estimator_py", - "//tensorflow/python/estimator:export", "//tensorflow/python/feature_column", ], ) @@ -149,9 +148,6 @@ py_library( "//tensorflow/python:util", "//tensorflow/python:variable_scope", "//tensorflow/python/estimator:estimator_py", - "//tensorflow/python/estimator:export", - "//tensorflow/python/estimator:head", - "//tensorflow/python/estimator:metric_keys", ], ) diff --git a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py index 63f5d3568bc208e1ce0ae69abb3a93132163c860..5eb4deefb9494566bc31b2b8a72aab4f04f2980e 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/ar_model_test.py @@ -195,7 +195,7 @@ class ARModelTest(test.TestCase): self.train_helper(input_window_size=10, loss=ar_model.ARModel.NORMAL_LIKELIHOOD_LOSS, train_steps=300, - max_loss=1.5, + max_loss=2.5, anomaly_distribution=None) def test_autoregression_normal_multiple_periods(self): diff --git a/tensorflow/contrib/timeseries/python/timeseries/head.py b/tensorflow/contrib/timeseries/python/timeseries/head.py index d2484d0ef57c4e324289481fb726ff57ae48aa72..32194e400e6ada594ef2a067bf612826a6e4acd3 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head.py @@ -321,6 +321,14 @@ class OneShotPredictionHead(TimeSeriesRegressionHead): feature_keys.TrainEvalFeatures.VALUES, ])) + def _evaluate_ops(self, features): + """Add ops for evaluation (aka filtering) to the graph.""" + spec = super(OneShotPredictionHead, self)._evaluate_ops(features) + # No state is fed to OneShotPredictionHead, so we don't return it; it being + # a tuple can cause issues for downstream infrastructure. + del spec.eval_metric_ops[feature_keys.State.STATE_TUPLE] + return spec + def _serving_ops(self, features): """Add ops for serving to the graph.""" with variable_scope.variable_scope("model", use_resource=True): diff --git a/tensorflow/contrib/timeseries/python/timeseries/head_test.py b/tensorflow/contrib/timeseries/python/timeseries/head_test.py index 857e7c563528a5b551bb76e07f6d251996675d8d..bda3b53aca0d0156e542e2bedcadf5caa6b3d2cf 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/head_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/head_test.py @@ -397,6 +397,8 @@ class OneShotTests(parameterized.TestCase): input_pipeline.NumpyReader(train_features), shuffle_seed=2, num_threads=1, batch_size=16, window_size=16) estimator.train(input_fn=train_input_fn, steps=5) + result = estimator.evaluate(input_fn=train_input_fn, steps=1) + self.assertNotIn(feature_keys.State.STATE_TUPLE, result) input_receiver_fn = estimator.build_raw_serving_input_receiver_fn() export_location = estimator.export_savedmodel(_new_temp_dir(), input_receiver_fn) diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index f5d852908a8a2b557b40f93fcffdeb1b06379781..2abf402e6cf566ee09a73b3d654f7ee2aa7b0436 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -41,7 +41,6 @@ py_library( "python/tpu/tpu_config.py", "python/tpu/tpu_context.py", "python/tpu/tpu_estimator.py", - "python/tpu/tpu_system_metadata.py", "python/tpu/util.py", ], srcs_version = "PY2AND3", @@ -63,10 +62,7 @@ py_library( "//tensorflow/python:training", "//tensorflow/python:variable_scope", "//tensorflow/python:variables", - "//tensorflow/python/estimator", - "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/estimator:run_config", - "//tensorflow/python/estimator:util", + "//tensorflow/python/estimator:estimator_py", "@six_archive//:six", ], ) @@ -196,7 +192,7 @@ py_library( "//tensorflow/python:tensor_spec", "//tensorflow/python:variable_scope", "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/keras:backend", "//tensorflow/python/keras:engine", "//tensorflow/python/keras:layers", @@ -217,6 +213,7 @@ py_library( "python/tpu/tpu_function.py", "python/tpu/tpu_optimizer.py", "python/tpu/tpu_sharding.py", + "python/tpu/tpu_system_metadata.py", "python/tpu/training_loop.py", ], srcs_version = "PY2AND3", diff --git a/tensorflow/contrib/tpu/__init__.py b/tensorflow/contrib/tpu/__init__.py index d0a37eb0ed246faf47e83018f8e135ed17796906..537d94b7979af3e4bd3fb7392c8dcc5a210e98af 100644 --- a/tensorflow/contrib/tpu/__init__.py +++ b/tensorflow/contrib/tpu/__init__.py @@ -18,6 +18,10 @@ @@cross_replica_sum @@infeed_dequeue @@infeed_dequeue_tuple +@@infeed_enqueue +@@infeed_enqueue_tuple +@@outfeed_dequeue +@@outfeed_dequeue_tuple @@outfeed_enqueue @@outfeed_enqueue_tuple diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index 7994c2c6c74c2d5dfef9a193404c35e0606839a7..7fa06d6d560a4b6ffa6d9a3fd0fa208b4c60ee7f 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -1015,6 +1015,19 @@ _BLACKLISTED_INFERENCE_OPS = set([ ]) +def under_tpu_inference_context(): + """Check if it is currently under `tpu.rewrite_for_inference()`.""" + graph = ops.get_default_graph() + + context = graph._get_control_flow_context() # pylint: disable=protected-access + while context: + if isinstance(context, _TPUInferenceContext): + return True + context = context.outer_context + + return False + + class _TPUInferenceContext(control_flow_ops.XLAControlFlowContext): """A `ControlFlowContext` for nodes inside a TPU inference computation. diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index 2c054360a4bf0377f8b76c535aca2e6e3ef4062c..806ae1c4c9918be0bf0af8579c12386c0a18aff0 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -232,11 +232,16 @@ class _InternalTPUContext(object): if tpu_system_metadata is not None: return tpu_system_metadata + cluster_def = None + if (self._config.session_config and + self._config.session_config.cluster_def.job): + cluster_def = self._config.session_config.cluster_def + # pylint: disable=protected-access tpu_system_metadata = ( tpu_system_metadata_lib._query_tpu_system_metadata( master, - run_config=self._config, + cluster_def=cluster_def, query_topology=self.model_parallelism_enabled)) self._lazy_tpu_system_metadata_dict[master] = tpu_system_metadata diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index c104b2403c69529625bf7a6d921a952150b31b3a..029492b489ea2b790660d7a02dfd189451acf26c 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -224,7 +224,7 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote `metric_fn` runs on CPU to generate metrics and `tensors` represents the `Tensor`s transferred from TPU system to CPU host and passed to `metric_fn`. To be precise, TPU evaluation expects a slightly different signature from the - @{tf.estimator.Estimator}. While `EstimatorSpec.eval_metric_ops` expects a + `tf.estimator.Estimator`. While `EstimatorSpec.eval_metric_ops` expects a dict, `TPUEstimatorSpec.eval_metrics` is a tuple of `metric_fn` and `tensors`. The `tensors` could be a list of `Tensor`s or dict of names to `Tensor`s. The `tensors` usually specify the model logits, which are transferred back from @@ -247,7 +247,7 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote sending tensors from TPU to CPU. To reduce the overhead, try reducing the size of the tensors. The `tensors` are concatenated along their major (batch) dimension, and so must be >= rank 1. The `host_call` is useful for writing - summaries with @{tf.contrib.summary.create_file_writer}. + summaries with `tf.contrib.summary.create_file_writer`. """ def __new__(cls, diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py index 894f21d0635ca47d3da1c0d2c3f5c37bac690920..ec682e5829c4df536a043334b74200f0b6259df3 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_system_metadata.py @@ -45,7 +45,7 @@ _TPUSystemMetadata = collections.namedtuple('_TPUSystemMetadata', [ ]) -def _query_tpu_system_metadata(master_address, run_config, +def _query_tpu_system_metadata(master_address, cluster_def=None, query_topology=False): """Automatically detects the TPU system metadata in the system.""" tpu_core_count = 0 @@ -61,7 +61,8 @@ def _query_tpu_system_metadata(master_address, run_config, with session_lib.Session( master_address, config=get_session_config_with_timeout( - _PINGING_MASTER_TIMEOUT_IN_MS, run_config)) as sess: + _PINGING_MASTER_TIMEOUT_IN_MS, + cluster_def)) as sess: devices = sess.list_devices() for device in devices: match = _TPU_DEVICE_REG.match(device.name) @@ -105,7 +106,7 @@ def _query_tpu_system_metadata(master_address, run_config, 'TPU worker has some problems. Available devices: {}'.format( master_address, devices)) - topology = _obtain_topology(master_address, run_config) + topology = _obtain_topology(master_address, cluster_def) metadata = _TPUSystemMetadata( num_cores=tpu_core_count, @@ -127,14 +128,15 @@ def _query_tpu_system_metadata(master_address, run_config, return metadata -def _obtain_topology(master_address, run_config): +def _obtain_topology(master_address, cluster_def): + """Obtains TPU fabric topology.""" try: logging.info('Initializing TPU system (master: %s) to fetch topology ' 'for model parallelism. This might take a while.', master_address) with ops.Graph().as_default(): session_config = get_session_config_with_timeout( - _INITIAL_TPU_SYSTEM_TIMEOUT_IN_MS, run_config) + _INITIAL_TPU_SYSTEM_TIMEOUT_IN_MS, cluster_def) with session_lib.Session( master_address, config=session_config) as sess: topology = sess.run(tpu.initialize_system()) @@ -146,11 +148,8 @@ def _obtain_topology(master_address, run_config): master_address)) -def get_session_config_with_timeout(timeout_in_secs, run_config): - cluster_def = None - if run_config.session_config and run_config.session_config.cluster_def.job: - cluster_def = run_config.session_config.cluster_def - +def get_session_config_with_timeout(timeout_in_secs, cluster_def): + """Returns a session given a timeout and a cluster configuration.""" config = config_pb2.ConfigProto( operation_timeout_in_ms=timeout_in_secs, cluster_def=cluster_def) return config diff --git a/tensorflow/contrib/training/BUILD b/tensorflow/contrib/training/BUILD index 76927e62e82d02de172a0851819716dc63180371..ddf8365d6130dcb4c8234ac60c91955d007e2410 100644 --- a/tensorflow/contrib/training/BUILD +++ b/tensorflow/contrib/training/BUILD @@ -61,7 +61,7 @@ py_library( "//tensorflow/python:variable_scope", "//tensorflow/python:variables", "//tensorflow/python/data", - "//tensorflow/python/estimator:inputs_queues", + "//tensorflow/python/estimator:estimator_py", "//third_party/py/numpy", "@six_archive//:six", ], @@ -133,7 +133,7 @@ py_test( "//tensorflow/python:framework_ops", "//tensorflow/python:session", "//tensorflow/python:training", - "//tensorflow/python/estimator:inputs_queues", + "//tensorflow/python/estimator:estimator_py", "//third_party/py/numpy", ], ) diff --git a/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py b/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py index 39d75a080604e3a7ae93391652d4c03be9857218..53e4f23a7cd940c026e462dc7fb55cf9f175bf02 100644 --- a/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py +++ b/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py @@ -988,14 +988,14 @@ class SequenceQueueingStateSaver(object): assert isinstance(sequences, dict) assert isinstance(context, dict) assert isinstance(states, dict) - self._name_to_index = dict( - (name, ix) + self._name_to_index = { + name: ix for (ix, name) in enumerate([ "__length", "__total_length", "__next_key", "__sequence", "__sequence_count" ] + ["__sequence__%s" % k for k in sequences.keys()] + [ "__context__%s" % k for k in context.keys() - ] + ["__state__%s" % k for k in states.keys()])) + ] + ["__state__%s" % k for k in states.keys()])} self._index_to_name = [ name for (name, _) in sorted( diff --git a/tensorflow/contrib/training/python/training/tensor_queue_dataset.py b/tensorflow/contrib/training/python/training/tensor_queue_dataset.py index a2444934bc21d58ed57d15494b3548a31ce3a2df..f46d03209ce7b111415b61181906c496f8181e71 100644 --- a/tensorflow/contrib/training/python/training/tensor_queue_dataset.py +++ b/tensorflow/contrib/training/python/training/tensor_queue_dataset.py @@ -156,7 +156,7 @@ def prepend_from_queue_and_padded_batch_dataset(batch_size, Returns: A `Dataset` transformation function, which can be passed to - @{tf.data.Dataset.apply}. + `tf.data.Dataset.apply`. """ def _apply_fn(dataset): diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 1423c7fbcb227c3a00d74fb62c8e2b547d93c41c..084f128674db02e4ec4278cd6c307518e473dc0b 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2037,6 +2037,7 @@ cc_library( "//tensorflow:freebsd": [], "//tensorflow:windows": [], "//tensorflow:windows_msvc": [], + "//tensorflow:android": [], "//conditions:default": [ "-ldl", "-lpthread", @@ -4581,6 +4582,8 @@ filegroup( # PNG data "lib/png/testdata/lena_gray.png", "lib/png/testdata/lena_rgba.png", + "lib/png/testdata/lena_palette.png", + "lib/png/testdata/lena_palette_trns.png", # JPEG data "lib/jpeg/testdata/jpeg_merge_test1.jpg", "lib/jpeg/testdata/jpeg_merge_test1_cmyk.jpg", diff --git a/tensorflow/core/api_def/base_api/api_def_GatherNd.pbtxt b/tensorflow/core/api_def/base_api/api_def_GatherNd.pbtxt index 342a1f6b0504046ae837e5e1ad1c91aaa2da95fc..a0e42dd02c5b570e34fb22867af53dcfce3a0f1d 100644 --- a/tensorflow/core/api_def/base_api/api_def_GatherNd.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_GatherNd.pbtxt @@ -27,7 +27,7 @@ slice of `params`: output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]] -Whereas in @{tf.gather} `indices` defines slices into the first +Whereas in `tf.gather` `indices` defines slices into the first dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the first `N` dimensions of `params`, where `N = indices.shape[-1]`. diff --git a/tensorflow/core/api_def/base_api/api_def_Igamma.pbtxt b/tensorflow/core/api_def/base_api/api_def_Igamma.pbtxt index e7bc5ddae237deb226606dc96141845e3efcc859..40d7d371ca2fbcd5ed886816b3cc8e2e0e11c27e 100644 --- a/tensorflow/core/api_def/base_api/api_def_Igamma.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_Igamma.pbtxt @@ -1,6 +1,6 @@ op { graph_op_name: "Igamma" - summary: "Compute the lower regularized incomplete Gamma function `Q(a, x)`." + summary: "Compute the lower regularized incomplete Gamma function `P(a, x)`." description: <
The helpers in `tf.train` that create these queues and enqueuing operations add -a @{tf.train.QueueRunner} to the +a `tf.train.QueueRunner` to the graph using the -@{tf.train.add_queue_runner} +`tf.train.add_queue_runner` function. Each `QueueRunner` is responsible for one stage, and holds the list of enqueue operations that need to be run in threads. Once the graph is constructed, the -@{tf.train.start_queue_runners} +`tf.train.start_queue_runners` function asks each QueueRunner in the graph to start its threads running the enqueuing operations. If all goes well, you can now run your training steps and the queues will be filled by the background threads. If you have set an epoch limit, at some point an attempt to dequeue examples will get an -@{tf.errors.OutOfRangeError}. This +`tf.errors.OutOfRangeError`. This is the TensorFlow equivalent of "end of file" (EOF) -- this means the epoch limit has been reached and no more examples are available. The last ingredient is the -@{tf.train.Coordinator}. This is responsible +`tf.train.Coordinator`. This is responsible for letting all the threads know if anything has signaled a shut down. Most commonly this would be because an exception was raised, for example one of the threads got an error when running some operation (or an ordinary Python @@ -396,21 +396,21 @@ associated with a single QueueRunner. If this isn't the last thread in the QueueRunner, the `OutOfRange` error just causes the one thread to exit. This allows the other threads, which are still finishing up their last file, to proceed until they finish as well. (Assuming you are using a -@{tf.train.Coordinator}, +`tf.train.Coordinator`, other types of errors will cause all the threads to stop.) Once all the reader threads hit the `OutOfRange` error, only then does the next queue, the example queue, gets closed. Again, the example queue will have some elements queued, so training will continue until those are exhausted. If the example queue is a -@{tf.RandomShuffleQueue}, say +`tf.RandomShuffleQueue`, say because you are using `shuffle_batch` or `shuffle_batch_join`, it normally will avoid ever having fewer than its `min_after_dequeue` attr elements buffered. However, once the queue is closed that restriction will be lifted and the queue will eventually empty. At that point the actual training threads, when they try and dequeue from example queue, will start getting `OutOfRange` errors and exiting. Once all the training threads are done, -@{tf.train.Coordinator.join} +`tf.train.Coordinator.join` will return and you can exit cleanly. ### Filtering records or producing multiple examples per record @@ -426,7 +426,7 @@ when calling one of the batching functions (such as `shuffle_batch` or SparseTensors don't play well with queues. If you use SparseTensors you have to decode the string records using -@{tf.parse_example} **after** +`tf.parse_example` **after** batching (instead of using `tf.parse_single_example` before batching). ## Preloaded data @@ -475,11 +475,11 @@ update it when training. Setting `collections=[]` keeps the variable out of the `GraphKeys.GLOBAL_VARIABLES` collection used for saving and restoring checkpoints. Either way, -@{tf.train.slice_input_producer} +`tf.train.slice_input_producer` can be used to produce a slice at a time. This shuffles the examples across an entire epoch, so further shuffling when batching is undesirable. So instead of using the `shuffle_batch` functions, we use the plain -@{tf.train.batch} function. To use +`tf.train.batch` function. To use multiple preprocessing threads, set the `num_threads` parameter to a number bigger than 1. @@ -500,7 +500,7 @@ sessions, maybe in separate processes: * The evaluation process restores the checkpoint files into an inference model that reads validation input data. -This is what is done @{tf.estimator$estimators} and manually in +This is what is done `tf.estimator` and manually in @{$deep_cnn#save-and-restore-checkpoints$the example CIFAR-10 model}. This has a couple of benefits: @@ -517,6 +517,6 @@ that allow the user to change the input pipeline without rebuilding the graph or session. Note: Regardless of the implementation, many -operations (like @{tf.layers.batch_normalization}, and @{tf.layers.dropout}) +operations (like `tf.layers.batch_normalization`, and `tf.layers.dropout`) need to know if they are in training or evaluation mode, and you must be careful to set this appropriately if you change the data source. diff --git a/tensorflow/docs_src/api_guides/python/regression_examples.md b/tensorflow/docs_src/api_guides/python/regression_examples.md index 7de2be05521d9293e33664cdbbd7bf16b9ad7c52..f8abbf0f9741e379cd628f0ee3cf18fdb8152a0b 100644 --- a/tensorflow/docs_src/api_guides/python/regression_examples.md +++ b/tensorflow/docs_src/api_guides/python/regression_examples.md @@ -8,25 +8,25 @@ to implement regression in Estimators: linear_regression.py - Use the @{tf.estimator.LinearRegressor} Estimator to train a + Use the `tf.estimator.LinearRegressor` Estimator to train a regression model on numeric data. linear_regression_categorical.py - Use the @{tf.estimator.LinearRegressor} Estimator to train a + Use the `tf.estimator.LinearRegressor` Estimator to train a regression model on categorical data. dnn_regression.py - Use the @{tf.estimator.DNNRegressor} Estimator to train a + Use the `tf.estimator.DNNRegressor` Estimator to train a regression model on discrete data with a deep neural network. custom_regression.py - Use @{tf.estimator.Estimator} to train a customized dnn + Use `tf.estimator.Estimator` to train a customized dnn regression model. @@ -219,7 +219,7 @@ The `custom_regression.py` example also trains a model that predicts the price of a car based on mixed real-valued and categorical input features, described by feature_columns. Unlike `linear_regression_categorical.py`, and `dnn_regression.py` this example does not use a pre-made estimator, but defines -a custom model using the base @{tf.estimator.Estimator$`Estimator`} class. The +a custom model using the base `tf.estimator.Estimator` class. The custom model is quite similar to the model defined by `dnn_regression.py`. The custom model is defined by the `model_fn` argument to the constructor. The @@ -227,6 +227,6 @@ customization is made more reusable through `params` dictionary, which is later passed through to the `model_fn` when the `model_fn` is called. The `model_fn` returns an -@{tf.estimator.EstimatorSpec$`EstimatorSpec`} which is a simple structure +`tf.estimator.EstimatorSpec` which is a simple structure indicating to the `Estimator` which operations should be run to accomplish various tasks. diff --git a/tensorflow/docs_src/api_guides/python/session_ops.md b/tensorflow/docs_src/api_guides/python/session_ops.md index 5176e3549c38e07d789401c5e684c16449d84a8a..5f41bcf209b13b4f3a4a14322cf20e82cc3d27d8 100644 --- a/tensorflow/docs_src/api_guides/python/session_ops.md +++ b/tensorflow/docs_src/api_guides/python/session_ops.md @@ -1,7 +1,7 @@ # Tensor Handle Operations Note: Functions taking `Tensor` arguments can also take anything accepted by -@{tf.convert_to_tensor}. +`tf.convert_to_tensor`. [TOC] @@ -10,6 +10,6 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by TensorFlow provides several operators that allows the user to keep tensors "in-place" across run calls. -* @{tf.get_session_handle} -* @{tf.get_session_tensor} -* @{tf.delete_session_tensor} +* `tf.get_session_handle` +* `tf.get_session_tensor` +* `tf.delete_session_tensor` diff --git a/tensorflow/docs_src/api_guides/python/sparse_ops.md b/tensorflow/docs_src/api_guides/python/sparse_ops.md index 19d5faba05a6ac79229b721ab6e45e4e36fd9f7a..b360055ed0ed0cde59a68c89f0a0f4ae1d5758ab 100644 --- a/tensorflow/docs_src/api_guides/python/sparse_ops.md +++ b/tensorflow/docs_src/api_guides/python/sparse_ops.md @@ -1,7 +1,7 @@ # Sparse Tensors Note: Functions taking `Tensor` arguments can also take anything accepted by -@{tf.convert_to_tensor}. +`tf.convert_to_tensor`. [TOC] @@ -12,34 +12,34 @@ in multiple dimensions. Contrast this representation with `IndexedSlices`, which is efficient for representing tensors that are sparse in their first dimension, and dense along all other dimensions. -* @{tf.SparseTensor} -* @{tf.SparseTensorValue} +* `tf.SparseTensor` +* `tf.SparseTensorValue` ## Conversion -* @{tf.sparse_to_dense} -* @{tf.sparse_tensor_to_dense} -* @{tf.sparse_to_indicator} -* @{tf.sparse_merge} +* `tf.sparse_to_dense` +* `tf.sparse_tensor_to_dense` +* `tf.sparse_to_indicator` +* `tf.sparse_merge` ## Manipulation -* @{tf.sparse_concat} -* @{tf.sparse_reorder} -* @{tf.sparse_reshape} -* @{tf.sparse_split} -* @{tf.sparse_retain} -* @{tf.sparse_reset_shape} -* @{tf.sparse_fill_empty_rows} -* @{tf.sparse_transpose} +* `tf.sparse_concat` +* `tf.sparse_reorder` +* `tf.sparse_reshape` +* `tf.sparse_split` +* `tf.sparse_retain` +* `tf.sparse_reset_shape` +* `tf.sparse_fill_empty_rows` +* `tf.sparse_transpose` ## Reduction -* @{tf.sparse_reduce_sum} -* @{tf.sparse_reduce_sum_sparse} +* `tf.sparse_reduce_sum` +* `tf.sparse_reduce_sum_sparse` ## Math Operations -* @{tf.sparse_add} -* @{tf.sparse_softmax} -* @{tf.sparse_tensor_dense_matmul} -* @{tf.sparse_maximum} -* @{tf.sparse_minimum} +* `tf.sparse_add` +* `tf.sparse_softmax` +* `tf.sparse_tensor_dense_matmul` +* `tf.sparse_maximum` +* `tf.sparse_minimum` diff --git a/tensorflow/docs_src/api_guides/python/spectral_ops.md b/tensorflow/docs_src/api_guides/python/spectral_ops.md index dd13802f009185a48fe0f10dc5ae502b98a1772a..f6d109a3a080b467eb8606f36671b449fb6e5c4d 100644 --- a/tensorflow/docs_src/api_guides/python/spectral_ops.md +++ b/tensorflow/docs_src/api_guides/python/spectral_ops.md @@ -2,25 +2,25 @@ [TOC] -The @{tf.spectral} module supports several spectral decomposition operations +The `tf.spectral` module supports several spectral decomposition operations that you can use to transform Tensors of real and complex signals. ## Discrete Fourier Transforms -* @{tf.spectral.fft} -* @{tf.spectral.ifft} -* @{tf.spectral.fft2d} -* @{tf.spectral.ifft2d} -* @{tf.spectral.fft3d} -* @{tf.spectral.ifft3d} -* @{tf.spectral.rfft} -* @{tf.spectral.irfft} -* @{tf.spectral.rfft2d} -* @{tf.spectral.irfft2d} -* @{tf.spectral.rfft3d} -* @{tf.spectral.irfft3d} +* `tf.spectral.fft` +* `tf.spectral.ifft` +* `tf.spectral.fft2d` +* `tf.spectral.ifft2d` +* `tf.spectral.fft3d` +* `tf.spectral.ifft3d` +* `tf.spectral.rfft` +* `tf.spectral.irfft` +* `tf.spectral.rfft2d` +* `tf.spectral.irfft2d` +* `tf.spectral.rfft3d` +* `tf.spectral.irfft3d` ## Discrete Cosine Transforms -* @{tf.spectral.dct} -* @{tf.spectral.idct} +* `tf.spectral.dct` +* `tf.spectral.idct` diff --git a/tensorflow/docs_src/api_guides/python/state_ops.md b/tensorflow/docs_src/api_guides/python/state_ops.md index ec2d8773860f0595cabe91d591a5fdc025e99b83..fc55ea14813ef0a20b0a30fbb35888777c5f152f 100644 --- a/tensorflow/docs_src/api_guides/python/state_ops.md +++ b/tensorflow/docs_src/api_guides/python/state_ops.md @@ -1,68 +1,68 @@ # Variables Note: Functions taking `Tensor` arguments can also take anything accepted by -@{tf.convert_to_tensor}. +`tf.convert_to_tensor`. [TOC] ## Variables -* @{tf.Variable} +* `tf.Variable` ## Variable helper functions TensorFlow provides a set of functions to help manage the set of variables collected in the graph. -* @{tf.global_variables} -* @{tf.local_variables} -* @{tf.model_variables} -* @{tf.trainable_variables} -* @{tf.moving_average_variables} -* @{tf.global_variables_initializer} -* @{tf.local_variables_initializer} -* @{tf.variables_initializer} -* @{tf.is_variable_initialized} -* @{tf.report_uninitialized_variables} -* @{tf.assert_variables_initialized} -* @{tf.assign} -* @{tf.assign_add} -* @{tf.assign_sub} +* `tf.global_variables` +* `tf.local_variables` +* `tf.model_variables` +* `tf.trainable_variables` +* `tf.moving_average_variables` +* `tf.global_variables_initializer` +* `tf.local_variables_initializer` +* `tf.variables_initializer` +* `tf.is_variable_initialized` +* `tf.report_uninitialized_variables` +* `tf.assert_variables_initialized` +* `tf.assign` +* `tf.assign_add` +* `tf.assign_sub` ## Saving and Restoring Variables -* @{tf.train.Saver} -* @{tf.train.latest_checkpoint} -* @{tf.train.get_checkpoint_state} -* @{tf.train.update_checkpoint_state} +* `tf.train.Saver` +* `tf.train.latest_checkpoint` +* `tf.train.get_checkpoint_state` +* `tf.train.update_checkpoint_state` ## Sharing Variables TensorFlow provides several classes and operations that you can use to create variables contingent on certain conditions. -* @{tf.get_variable} -* @{tf.get_local_variable} -* @{tf.VariableScope} -* @{tf.variable_scope} -* @{tf.variable_op_scope} -* @{tf.get_variable_scope} -* @{tf.make_template} -* @{tf.no_regularizer} -* @{tf.constant_initializer} -* @{tf.random_normal_initializer} -* @{tf.truncated_normal_initializer} -* @{tf.random_uniform_initializer} -* @{tf.uniform_unit_scaling_initializer} -* @{tf.zeros_initializer} -* @{tf.ones_initializer} -* @{tf.orthogonal_initializer} +* `tf.get_variable` +* `tf.get_local_variable` +* `tf.VariableScope` +* `tf.variable_scope` +* `tf.variable_op_scope` +* `tf.get_variable_scope` +* `tf.make_template` +* `tf.no_regularizer` +* `tf.constant_initializer` +* `tf.random_normal_initializer` +* `tf.truncated_normal_initializer` +* `tf.random_uniform_initializer` +* `tf.uniform_unit_scaling_initializer` +* `tf.zeros_initializer` +* `tf.ones_initializer` +* `tf.orthogonal_initializer` ## Variable Partitioners for Sharding -* @{tf.fixed_size_partitioner} -* @{tf.variable_axis_size_partitioner} -* @{tf.min_max_variable_partitioner} +* `tf.fixed_size_partitioner` +* `tf.variable_axis_size_partitioner` +* `tf.min_max_variable_partitioner` ## Sparse Variable Updates @@ -73,38 +73,38 @@ only a small subset of embedding vectors change in any given step. Since a sparse update of a large tensor may be generated automatically during gradient computation (as in the gradient of -@{tf.gather}), -an @{tf.IndexedSlices} class is provided that encapsulates a set +`tf.gather`), +an `tf.IndexedSlices` class is provided that encapsulates a set of sparse indices and values. `IndexedSlices` objects are detected and handled automatically by the optimizers in most cases. -* @{tf.scatter_update} -* @{tf.scatter_add} -* @{tf.scatter_sub} -* @{tf.scatter_mul} -* @{tf.scatter_div} -* @{tf.scatter_min} -* @{tf.scatter_max} -* @{tf.scatter_nd_update} -* @{tf.scatter_nd_add} -* @{tf.scatter_nd_sub} -* @{tf.sparse_mask} -* @{tf.IndexedSlices} +* `tf.scatter_update` +* `tf.scatter_add` +* `tf.scatter_sub` +* `tf.scatter_mul` +* `tf.scatter_div` +* `tf.scatter_min` +* `tf.scatter_max` +* `tf.scatter_nd_update` +* `tf.scatter_nd_add` +* `tf.scatter_nd_sub` +* `tf.sparse_mask` +* `tf.IndexedSlices` ### Read-only Lookup Tables -* @{tf.initialize_all_tables} -* @{tf.tables_initializer} +* `tf.initialize_all_tables` +* `tf.tables_initializer` ## Exporting and Importing Meta Graphs -* @{tf.train.export_meta_graph} -* @{tf.train.import_meta_graph} +* `tf.train.export_meta_graph` +* `tf.train.import_meta_graph` # Deprecated functions (removed after 2017-03-02). Please don't use them. -* @{tf.all_variables} -* @{tf.initialize_all_variables} -* @{tf.initialize_local_variables} -* @{tf.initialize_variables} +* `tf.all_variables` +* `tf.initialize_all_variables` +* `tf.initialize_local_variables` +* `tf.initialize_variables` diff --git a/tensorflow/docs_src/api_guides/python/string_ops.md b/tensorflow/docs_src/api_guides/python/string_ops.md index e9be4f156a9b40fac41dfee16e3265464e940d7e..24a3aad642d16eaef25f427ae0223b884ef884d7 100644 --- a/tensorflow/docs_src/api_guides/python/string_ops.md +++ b/tensorflow/docs_src/api_guides/python/string_ops.md @@ -1,7 +1,7 @@ # Strings Note: Functions taking `Tensor` arguments can also take anything accepted by -@{tf.convert_to_tensor}. +`tf.convert_to_tensor`. [TOC] @@ -10,30 +10,30 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by String hashing ops take a string input tensor and map each element to an integer. -* @{tf.string_to_hash_bucket_fast} -* @{tf.string_to_hash_bucket_strong} -* @{tf.string_to_hash_bucket} +* `tf.string_to_hash_bucket_fast` +* `tf.string_to_hash_bucket_strong` +* `tf.string_to_hash_bucket` ## Joining String joining ops concatenate elements of input string tensors to produce a new string tensor. -* @{tf.reduce_join} -* @{tf.string_join} +* `tf.reduce_join` +* `tf.string_join` ## Splitting -* @{tf.string_split} -* @{tf.substr} +* `tf.string_split` +* `tf.substr` ## Conversion -* @{tf.as_string} -* @{tf.string_to_number} +* `tf.as_string` +* `tf.string_to_number` -* @{tf.decode_raw} -* @{tf.decode_csv} +* `tf.decode_raw` +* `tf.decode_csv` -* @{tf.encode_base64} -* @{tf.decode_base64} +* `tf.encode_base64` +* `tf.decode_base64` diff --git a/tensorflow/docs_src/api_guides/python/summary.md b/tensorflow/docs_src/api_guides/python/summary.md index eda119ab24edf2caeb6d2de01abc541b590289f4..e290703b7d844504291bd3f6fc9819f7e6782d45 100644 --- a/tensorflow/docs_src/api_guides/python/summary.md +++ b/tensorflow/docs_src/api_guides/python/summary.md @@ -7,17 +7,17 @@ then accessible in tools such as @{$summaries_and_tensorboard$TensorBoard}. ## Generation of Summaries ### Class for writing Summaries -* @{tf.summary.FileWriter} -* @{tf.summary.FileWriterCache} +* `tf.summary.FileWriter` +* `tf.summary.FileWriterCache` ### Summary Ops -* @{tf.summary.tensor_summary} -* @{tf.summary.scalar} -* @{tf.summary.histogram} -* @{tf.summary.audio} -* @{tf.summary.image} -* @{tf.summary.merge} -* @{tf.summary.merge_all} +* `tf.summary.tensor_summary` +* `tf.summary.scalar` +* `tf.summary.histogram` +* `tf.summary.audio` +* `tf.summary.image` +* `tf.summary.merge` +* `tf.summary.merge_all` ## Utilities -* @{tf.summary.get_summary_description} +* `tf.summary.get_summary_description` diff --git a/tensorflow/docs_src/api_guides/python/test.md b/tensorflow/docs_src/api_guides/python/test.md index 5dc88124e7e1c26237c5c150b624486ab0df1283..b6e0a332b9d2e906af96d36d4ef856199e485a05 100644 --- a/tensorflow/docs_src/api_guides/python/test.md +++ b/tensorflow/docs_src/api_guides/python/test.md @@ -23,25 +23,25 @@ which adds methods relevant to TensorFlow tests. Here is an example: ``` `tf.test.TestCase` inherits from `unittest.TestCase` but adds a few additional -methods. See @{tf.test.TestCase} for details. +methods. See `tf.test.TestCase` for details. -* @{tf.test.main} -* @{tf.test.TestCase} -* @{tf.test.test_src_dir_path} +* `tf.test.main` +* `tf.test.TestCase` +* `tf.test.test_src_dir_path` ## Utilities Note: `tf.test.mock` is an alias to the python `mock` or `unittest.mock` depending on the python version. -* @{tf.test.assert_equal_graph_def} -* @{tf.test.get_temp_dir} -* @{tf.test.is_built_with_cuda} -* @{tf.test.is_gpu_available} -* @{tf.test.gpu_device_name} +* `tf.test.assert_equal_graph_def` +* `tf.test.get_temp_dir` +* `tf.test.is_built_with_cuda` +* `tf.test.is_gpu_available` +* `tf.test.gpu_device_name` ## Gradient checking -@{tf.test.compute_gradient} and @{tf.test.compute_gradient_error} perform +`tf.test.compute_gradient` and `tf.test.compute_gradient_error` perform numerical differentiation of graphs for comparison against registered analytic gradients. diff --git a/tensorflow/docs_src/api_guides/python/tfdbg.md b/tensorflow/docs_src/api_guides/python/tfdbg.md index 2212a2da0e8c4f339120453c15d5b61b4574f8ee..9778cdc0b0a6bdf4acecce95e19deb99490d669e 100644 --- a/tensorflow/docs_src/api_guides/python/tfdbg.md +++ b/tensorflow/docs_src/api_guides/python/tfdbg.md @@ -8,9 +8,9 @@ Public Python API of TensorFlow Debugger (tfdbg). These functions help you modify `RunOptions` to specify which `Tensor`s are to be watched when the TensorFlow graph is executed at runtime. -* @{tfdbg.add_debug_tensor_watch} -* @{tfdbg.watch_graph} -* @{tfdbg.watch_graph_with_blacklists} +* `tfdbg.add_debug_tensor_watch` +* `tfdbg.watch_graph` +* `tfdbg.watch_graph_with_blacklists` ## Classes for debug-dump data and directories @@ -18,13 +18,13 @@ be watched when the TensorFlow graph is executed at runtime. These classes allow you to load and inspect tensor values dumped from TensorFlow graphs during runtime. -* @{tfdbg.DebugTensorDatum} -* @{tfdbg.DebugDumpDir} +* `tfdbg.DebugTensorDatum` +* `tfdbg.DebugDumpDir` ## Functions for loading debug-dump data -* @{tfdbg.load_tensor_from_event_file} +* `tfdbg.load_tensor_from_event_file` ## Tensor-value predicates @@ -32,7 +32,7 @@ TensorFlow graphs during runtime. Built-in tensor-filter predicates to support conditional breakpoint between runs. See `DebugDumpDir.find()` for more details. -* @{tfdbg.has_inf_or_nan} +* `tfdbg.has_inf_or_nan` ## Session wrapper class and `SessionRunHook` implementations @@ -44,7 +44,7 @@ These classes allow you to * generate `SessionRunHook` objects to debug `tf.contrib.learn` models (see `DumpingDebugHook` and `LocalCLIDebugHook`). -* @{tfdbg.DumpingDebugHook} -* @{tfdbg.DumpingDebugWrapperSession} -* @{tfdbg.LocalCLIDebugHook} -* @{tfdbg.LocalCLIDebugWrapperSession} +* `tfdbg.DumpingDebugHook` +* `tfdbg.DumpingDebugWrapperSession` +* `tfdbg.LocalCLIDebugHook` +* `tfdbg.LocalCLIDebugWrapperSession` diff --git a/tensorflow/docs_src/api_guides/python/threading_and_queues.md b/tensorflow/docs_src/api_guides/python/threading_and_queues.md index 8ad4c4c07512d04d1df43062954f2e64b1d8e177..48f0778b732919c4d70154f0200d0f065139bac3 100644 --- a/tensorflow/docs_src/api_guides/python/threading_and_queues.md +++ b/tensorflow/docs_src/api_guides/python/threading_and_queues.md @@ -25,7 +25,7 @@ longer holds, the queue will unblock the step and allow execution to proceed. TensorFlow implements several classes of queue. The principal difference between these classes is the order that items are removed from the queue. To get a feel for queues, let's consider a simple example. We will create a "first in, first -out" queue (@{tf.FIFOQueue}) and fill it with zeros. Then we'll construct a +out" queue (`tf.FIFOQueue`) and fill it with zeros. Then we'll construct a graph that takes an item off the queue, adds one to that item, and puts it back on the end of the queue. Slowly, the numbers on the queue increase. @@ -47,8 +47,8 @@ Now that you have a bit of a feel for queues, let's dive into the details... ## Queue usage overview -Queues, such as @{tf.FIFOQueue} -and @{tf.RandomShuffleQueue}, +Queues, such as `tf.FIFOQueue` +and `tf.RandomShuffleQueue`, are important TensorFlow objects that aid in computing tensors asynchronously in a graph. @@ -59,11 +59,11 @@ prepare inputs for training a model as follows: * A training thread executes a training op that dequeues mini-batches from the queue -We recommend using the @{tf.data.Dataset.shuffle$`shuffle`} -and @{tf.data.Dataset.batch$`batch`} methods of a -@{tf.data.Dataset$`Dataset`} to accomplish this. However, if you'd prefer +We recommend using the `tf.data.Dataset.shuffle` +and `tf.data.Dataset.batch` methods of a +`tf.data.Dataset` to accomplish this. However, if you'd prefer to use a queue-based version instead, you can find a full implementation in the -@{tf.train.shuffle_batch} function. +`tf.train.shuffle_batch` function. For demonstration purposes a simplified implementation is given below. @@ -93,8 +93,8 @@ def simple_shuffle_batch(source, capacity, batch_size=10): return queue.dequeue_many(batch_size) ``` -Once started by @{tf.train.start_queue_runners}, or indirectly through -@{tf.train.MonitoredSession}, the `QueueRunner` will launch the +Once started by `tf.train.start_queue_runners`, or indirectly through +`tf.train.MonitoredSession`, the `QueueRunner` will launch the threads in the background to fill the queue. Meanwhile the main thread will execute the `dequeue_many` op to pull data from it. Note how these ops do not depend on each other, except indirectly through the internal state of the queue. @@ -126,7 +126,7 @@ with tf.train.MonitoredSession() as sess: ``` For most use cases, the automatic thread startup and management provided -by @{tf.train.MonitoredSession} is sufficient. In the rare case that it is not, +by `tf.train.MonitoredSession` is sufficient. In the rare case that it is not, TensorFlow provides tools for manually managing your threads and queues. ## Manual Thread Management @@ -139,8 +139,8 @@ threads must be able to stop together, exceptions must be caught and reported, and queues must be properly closed when stopping. TensorFlow provides two classes to help: -@{tf.train.Coordinator} and -@{tf.train.QueueRunner}. These two classes +`tf.train.Coordinator` and +`tf.train.QueueRunner`. These two classes are designed to be used together. The `Coordinator` class helps multiple threads stop together and report exceptions to a program that waits for them to stop. The `QueueRunner` class is used to create a number of threads cooperating to @@ -148,14 +148,14 @@ enqueue tensors in the same queue. ### Coordinator -The @{tf.train.Coordinator} class manages background threads in a TensorFlow +The `tf.train.Coordinator` class manages background threads in a TensorFlow program and helps multiple threads stop together. Its key methods are: -* @{tf.train.Coordinator.should_stop}: returns `True` if the threads should stop. -* @{tf.train.Coordinator.request_stop}: requests that threads should stop. -* @{tf.train.Coordinator.join}: waits until the specified threads have stopped. +* `tf.train.Coordinator.should_stop`: returns `True` if the threads should stop. +* `tf.train.Coordinator.request_stop`: requests that threads should stop. +* `tf.train.Coordinator.join`: waits until the specified threads have stopped. You first create a `Coordinator` object, and then create a number of threads that use the coordinator. The threads typically run loops that stop when @@ -191,11 +191,11 @@ coord.join(threads) Obviously, the coordinator can manage threads doing very different things. They don't have to be all the same as in the example above. The coordinator -also has support to capture and report exceptions. See the @{tf.train.Coordinator} documentation for more details. +also has support to capture and report exceptions. See the `tf.train.Coordinator` documentation for more details. ### QueueRunner -The @{tf.train.QueueRunner} class creates a number of threads that repeatedly +The `tf.train.QueueRunner` class creates a number of threads that repeatedly run an enqueue op. These threads can use a coordinator to stop together. In addition, a queue runner will run a *closer operation* that closes the queue if an exception is reported to the coordinator. diff --git a/tensorflow/docs_src/api_guides/python/train.md b/tensorflow/docs_src/api_guides/python/train.md index cbc50529469b32afbb9c0646a0cfd27627563f87..a118123665e42cdee28819a86e5b24a2a106f5df 100644 --- a/tensorflow/docs_src/api_guides/python/train.md +++ b/tensorflow/docs_src/api_guides/python/train.md @@ -1,7 +1,7 @@ # Training [TOC] -@{tf.train} provides a set of classes and functions that help train models. +`tf.train` provides a set of classes and functions that help train models. ## Optimizers @@ -12,19 +12,19 @@ optimization algorithms such as GradientDescent and Adagrad. You never instantiate the Optimizer class itself, but instead instantiate one of the subclasses. -* @{tf.train.Optimizer} -* @{tf.train.GradientDescentOptimizer} -* @{tf.train.AdadeltaOptimizer} -* @{tf.train.AdagradOptimizer} -* @{tf.train.AdagradDAOptimizer} -* @{tf.train.MomentumOptimizer} -* @{tf.train.AdamOptimizer} -* @{tf.train.FtrlOptimizer} -* @{tf.train.ProximalGradientDescentOptimizer} -* @{tf.train.ProximalAdagradOptimizer} -* @{tf.train.RMSPropOptimizer} +* `tf.train.Optimizer` +* `tf.train.GradientDescentOptimizer` +* `tf.train.AdadeltaOptimizer` +* `tf.train.AdagradOptimizer` +* `tf.train.AdagradDAOptimizer` +* `tf.train.MomentumOptimizer` +* `tf.train.AdamOptimizer` +* `tf.train.FtrlOptimizer` +* `tf.train.ProximalGradientDescentOptimizer` +* `tf.train.ProximalAdagradOptimizer` +* `tf.train.RMSPropOptimizer` -See @{tf.contrib.opt} for more optimizers. +See `tf.contrib.opt` for more optimizers. ## Gradient Computation @@ -34,10 +34,10 @@ optimizer classes automatically compute derivatives on your graph, but creators of new Optimizers or expert users can call the lower-level functions below. -* @{tf.gradients} -* @{tf.AggregationMethod} -* @{tf.stop_gradient} -* @{tf.hessians} +* `tf.gradients` +* `tf.AggregationMethod` +* `tf.stop_gradient` +* `tf.hessians` ## Gradient Clipping @@ -47,22 +47,22 @@ functions to your graph. You can use these functions to perform general data clipping, but they're particularly useful for handling exploding or vanishing gradients. -* @{tf.clip_by_value} -* @{tf.clip_by_norm} -* @{tf.clip_by_average_norm} -* @{tf.clip_by_global_norm} -* @{tf.global_norm} +* `tf.clip_by_value` +* `tf.clip_by_norm` +* `tf.clip_by_average_norm` +* `tf.clip_by_global_norm` +* `tf.global_norm` ## Decaying the learning rate -* @{tf.train.exponential_decay} -* @{tf.train.inverse_time_decay} -* @{tf.train.natural_exp_decay} -* @{tf.train.piecewise_constant} -* @{tf.train.polynomial_decay} -* @{tf.train.cosine_decay} -* @{tf.train.linear_cosine_decay} -* @{tf.train.noisy_linear_cosine_decay} +* `tf.train.exponential_decay` +* `tf.train.inverse_time_decay` +* `tf.train.natural_exp_decay` +* `tf.train.piecewise_constant` +* `tf.train.polynomial_decay` +* `tf.train.cosine_decay` +* `tf.train.linear_cosine_decay` +* `tf.train.noisy_linear_cosine_decay` ## Moving Averages @@ -70,7 +70,7 @@ Some training algorithms, such as GradientDescent and Momentum often benefit from maintaining a moving average of variables during optimization. Using the moving averages for evaluations often improve results significantly. -* @{tf.train.ExponentialMovingAverage} +* `tf.train.ExponentialMovingAverage` ## Coordinator and QueueRunner @@ -79,61 +79,61 @@ for how to use threads and queues. For documentation on the Queue API, see @{$python/io_ops#queues$Queues}. -* @{tf.train.Coordinator} -* @{tf.train.QueueRunner} -* @{tf.train.LooperThread} -* @{tf.train.add_queue_runner} -* @{tf.train.start_queue_runners} +* `tf.train.Coordinator` +* `tf.train.QueueRunner` +* `tf.train.LooperThread` +* `tf.train.add_queue_runner` +* `tf.train.start_queue_runners` ## Distributed execution See @{$distributed$Distributed TensorFlow} for more information about how to configure a distributed TensorFlow program. -* @{tf.train.Server} -* @{tf.train.Supervisor} -* @{tf.train.SessionManager} -* @{tf.train.ClusterSpec} -* @{tf.train.replica_device_setter} -* @{tf.train.MonitoredTrainingSession} -* @{tf.train.MonitoredSession} -* @{tf.train.SingularMonitoredSession} -* @{tf.train.Scaffold} -* @{tf.train.SessionCreator} -* @{tf.train.ChiefSessionCreator} -* @{tf.train.WorkerSessionCreator} +* `tf.train.Server` +* `tf.train.Supervisor` +* `tf.train.SessionManager` +* `tf.train.ClusterSpec` +* `tf.train.replica_device_setter` +* `tf.train.MonitoredTrainingSession` +* `tf.train.MonitoredSession` +* `tf.train.SingularMonitoredSession` +* `tf.train.Scaffold` +* `tf.train.SessionCreator` +* `tf.train.ChiefSessionCreator` +* `tf.train.WorkerSessionCreator` ## Reading Summaries from Event Files See @{$summaries_and_tensorboard$Summaries and TensorBoard} for an overview of summaries, event files, and visualization in TensorBoard. -* @{tf.train.summary_iterator} +* `tf.train.summary_iterator` ## Training Hooks Hooks are tools that run in the process of training/evaluation of the model. -* @{tf.train.SessionRunHook} -* @{tf.train.SessionRunArgs} -* @{tf.train.SessionRunContext} -* @{tf.train.SessionRunValues} -* @{tf.train.LoggingTensorHook} -* @{tf.train.StopAtStepHook} -* @{tf.train.CheckpointSaverHook} -* @{tf.train.NewCheckpointReader} -* @{tf.train.StepCounterHook} -* @{tf.train.NanLossDuringTrainingError} -* @{tf.train.NanTensorHook} -* @{tf.train.SummarySaverHook} -* @{tf.train.GlobalStepWaiterHook} -* @{tf.train.FinalOpsHook} -* @{tf.train.FeedFnHook} +* `tf.train.SessionRunHook` +* `tf.train.SessionRunArgs` +* `tf.train.SessionRunContext` +* `tf.train.SessionRunValues` +* `tf.train.LoggingTensorHook` +* `tf.train.StopAtStepHook` +* `tf.train.CheckpointSaverHook` +* `tf.train.NewCheckpointReader` +* `tf.train.StepCounterHook` +* `tf.train.NanLossDuringTrainingError` +* `tf.train.NanTensorHook` +* `tf.train.SummarySaverHook` +* `tf.train.GlobalStepWaiterHook` +* `tf.train.FinalOpsHook` +* `tf.train.FeedFnHook` ## Training Utilities -* @{tf.train.global_step} -* @{tf.train.basic_train_loop} -* @{tf.train.get_global_step} -* @{tf.train.assert_global_step} -* @{tf.train.write_graph} +* `tf.train.global_step` +* `tf.train.basic_train_loop` +* `tf.train.get_global_step` +* `tf.train.assert_global_step` +* `tf.train.write_graph` diff --git a/tensorflow/docs_src/community/index.md b/tensorflow/docs_src/community/index.md index eec2e51a8706b73abcedb8329df3ad03e3b349c3..0aa8e7612a6dfd96dc3f59403e2691df00418cb5 100644 --- a/tensorflow/docs_src/community/index.md +++ b/tensorflow/docs_src/community/index.md @@ -54,7 +54,7 @@ with content from the TensorFlow team and the best articles from the community. ### YouTube -Our [YouTube Channel](http://youtube.com/tensorflow/) focuses on machine learing +Our [YouTube Channel](http://youtube.com/tensorflow/) focuses on machine learning and AI with TensorFlow. On it we have a number of new shows, including: - TensorFlow Meets: meet with community contributors to learn and share what they're doing diff --git a/tensorflow/docs_src/community/lists.md b/tensorflow/docs_src/community/lists.md index 7450ab36c436538dd584541fb0dafb5a2c6067b3..bc2f573c29ca445cc1770a3a2c520a7b60e52855 100644 --- a/tensorflow/docs_src/community/lists.md +++ b/tensorflow/docs_src/community/lists.md @@ -32,6 +32,8 @@ These projects inside the TensorFlow GitHub organization have lists dedicated to and peer support for TensorFlow.js. * [tflite](https://groups.google.com/a/tensorflow.org/d/forum/tflite) - Discussion and peer support for TensorFlow Lite. +* [tfprobability](https://groups.google.com/a/tensorflow.org/d/forum/tfprobability) - Discussion and + peer support for TensorFlow Probability. * [tpu-users](https://groups.google.com/a/tensorflow.org/d/forum/tpu-users) - Community discussion and support for TPU users. diff --git a/tensorflow/docs_src/community/style_guide.md b/tensorflow/docs_src/community/style_guide.md index c9268790a71fad9328f60f6a889c19c32117497e..daf0d2fdc042509972f7ab7446bb5876bb218657 100644 --- a/tensorflow/docs_src/community/style_guide.md +++ b/tensorflow/docs_src/community/style_guide.md @@ -47,27 +47,7 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) ``` -* At the end of every BUILD file, should contain: -``` -filegroup( - name = "all_files", - srcs = glob( - ["**/*"], - exclude = [ - "**/METADATA", - "**/OWNERS", - ], - ), - visibility = ["//tensorflow:__subpackages__"], -) -``` - -* When adding new BUILD file, add this line to `tensorflow/BUILD` file into `all_opensource_files` target. - -``` -"//tensorflow/:all_files", -``` * For all Python BUILD targets (libraries and tests) add next line: @@ -80,6 +60,9 @@ srcs_version = "PY2AND3", * Operations that deal with batches may assume that the first dimension of a Tensor is the batch dimension. +* In most models the *last dimension* is the number of channels. + +* Dimensions excluding the first and last usually make up the "space" dimensions: Sequence-length or Image-size. ## Python operations @@ -148,37 +131,6 @@ Usage: ## Layers -A *Layer* is a Python operation that combines variable creation and/or one or many -other graph operations. Follow the same requirements as for regular Python -operation. - -* If a layer creates one or more variables, the layer function - should take next arguments also following order: - - `initializers`: Optionally allow to specify initializers for the variables. - - `regularizers`: Optionally allow to specify regularizers for the variables. - - `trainable`: which control if their variables are trainable or not. - - `scope`: `VariableScope` object that variable will be put under. - - `reuse`: `bool` indicator if the variable should be reused if - it's present in the scope. - -* Layers that behave differently during training should take: - - `is_training`: `bool` indicator to conditionally choose different - computation paths (e.g. using `tf.cond`) during execution. - -Example: - - def conv2d(inputs, - num_filters_out, - kernel_size, - stride=1, - padding='SAME', - activation_fn=tf.nn.relu, - normalization_fn=add_bias, - normalization_params=None, - initializers=None, - regularizers=None, - trainable=True, - scope=None, - reuse=None): - ... see implementation at tensorflow/contrib/layers/python/layers/layers.py ... +Use `tf.keras.layers`, not `tf.layers`. +See `tf.keras.layers` and [the Keras guide](../guide/keras.md#custom_layers) for details on how to sub-class layers. diff --git a/tensorflow/docs_src/deploy/distributed.md b/tensorflow/docs_src/deploy/distributed.md index fc3a60603f57ba565783a2d37a5d491dccdf60db..6a760f53c878a38d69e3edb8706b20b67aabf5dd 100644 --- a/tensorflow/docs_src/deploy/distributed.md +++ b/tensorflow/docs_src/deploy/distributed.md @@ -21,7 +21,7 @@ $ python ``` The -@{tf.train.Server.create_local_server} +`tf.train.Server.create_local_server` method creates a single-process cluster, with an in-process server. ## Create a cluster @@ -55,7 +55,7 @@ the following: The cluster specification dictionary maps job names to lists of network addresses. Pass this dictionary to -the @{tf.train.ClusterSpec} +the `tf.train.ClusterSpec` constructor. For example: @@ -84,10 +84,10 @@ tf.train.ClusterSpec({ ### Create a `tf.train.Server` instance in each task -A @{tf.train.Server} object contains a +A `tf.train.Server` object contains a set of local devices, a set of connections to other tasks in its `tf.train.ClusterSpec`, and a -@{tf.Session} that can use these +`tf.Session` that can use these to perform a distributed computation. Each server is a member of a specific named job and has a task index within that job. A server can communicate with any other server in the cluster. @@ -117,7 +117,7 @@ which you'd like to see support, please raise a ## Specifying distributed devices in your model To place operations on a particular process, you can use the same -@{tf.device} +`tf.device` function that is used to specify whether ops run on the CPU or GPU. For example: ```python @@ -165,7 +165,7 @@ simplify the work of specifying a replicated model. Possible approaches include: for each `/job:worker` task, typically in the same process as the worker task. Each client builds a similar graph containing the parameters (pinned to `/job:ps` as before using - @{tf.train.replica_device_setter} + `tf.train.replica_device_setter` to map them deterministically to the same tasks); and a single copy of the compute-intensive part of the model, pinned to the local task in `/job:worker`. @@ -180,7 +180,7 @@ simplify the work of specifying a replicated model. Possible approaches include: gradient averaging as in the [CIFAR-10 multi-GPU trainer](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10/cifar10_multi_gpu_train.py)), and between-graph replication (e.g. using the - @{tf.train.SyncReplicasOptimizer}). + `tf.train.SyncReplicasOptimizer`). ### Putting it all together: example trainer program @@ -318,7 +318,7 @@ A TensorFlow cluster comprises one or more "jobs", each divided into lists of one or more "tasks". A cluster is typically dedicated to a particular high-level objective, such as training a neural network, using many machines in parallel. A cluster is defined by -a @{tf.train.ClusterSpec} object. +a `tf.train.ClusterSpec` object. **Job** @@ -344,7 +344,7 @@ to a single process. A task belongs to a particular "job" and is identified by its index within that job's list of tasks. **TensorFlow server** A process running -a @{tf.train.Server} instance, which is +a `tf.train.Server` instance, which is a member of a cluster, and exports a "master service" and "worker service". **Worker service** diff --git a/tensorflow/docs_src/deploy/s3.md b/tensorflow/docs_src/deploy/s3.md index 7028249e94f68a6990eaae9b3a4fb9d19069bfc5..079c796aa7766377c46f47087268e47b41356a12 100644 --- a/tensorflow/docs_src/deploy/s3.md +++ b/tensorflow/docs_src/deploy/s3.md @@ -40,7 +40,7 @@ AWS_SECRET_ACCESS_KEY=XXXXX AWS_REGION=us-east-1 # Region for the S3 bucket, this is not always needed. Default is us-east-1. S3_ENDPOINT=s3.us-east-1.amazonaws.com # The S3 API Endpoint to connect to. This is specified in a HOST:PORT format. S3_USE_HTTPS=1 # Whether or not to use HTTPS. Disable with 0. -S3_VERIFY_SSL=1 # If HTTPS is used, conterols if SSL should be enabled. Disable with 0. +S3_VERIFY_SSL=1 # If HTTPS is used, controls if SSL should be enabled. Disable with 0. ``` ## Usage diff --git a/tensorflow/docs_src/extend/adding_an_op.md b/tensorflow/docs_src/extend/adding_an_op.md index 1b028be4ea16af89b8aac8a8a73e9ceca9e842c5..fbf5c0b90d57bfcd23ea8a09611d43b395c36c09 100644 --- a/tensorflow/docs_src/extend/adding_an_op.md +++ b/tensorflow/docs_src/extend/adding_an_op.md @@ -46,7 +46,7 @@ To incorporate your custom op you'll need to: 4. Write a function to compute gradients for the op (optional). 5. Test the op. We usually do this in Python for convenience, but you can also test the op in C++. If you define gradients, you can verify them with the - Python @{tf.test.compute_gradient_error$gradient checker}. + Python `tf.test.compute_gradient_error`. See [`relu_op_test.py`](https://www.tensorflow.org/code/tensorflow/python/kernel_tests/relu_op_test.py) as an example that tests the forward functions of Relu-like operators and @@ -388,7 +388,7 @@ $ bazel build --config opt //tensorflow/core/user_ops:zero_out.so ## Use the op in Python TensorFlow Python API provides the -@{tf.load_op_library} function to +`tf.load_op_library` function to load the dynamic library and register the op with the TensorFlow framework. `load_op_library` returns a Python module that contains the Python wrappers for the op and the kernel. Thus, once you have built the op, you can @@ -538,7 +538,7 @@ REGISTER_OP("ZeroOut") ``` (Note that the set of [attribute types](#attr_types) is different from the -@{tf.DType$tensor types} used for inputs and outputs.) +`tf.DType` used for inputs and outputs.) Your kernel can then access this attr in its constructor via the `context` parameter: @@ -615,7 +615,7 @@ define an attr with constraints, you can use the following ``s: * `{, }`: The value is of type `type`, and must be one of `` or ``, where `` and `` are supported - @{tf.DType$tensor types}. You don't specify + `tf.DType`. You don't specify that the type of the attr is `type`. This is implied when you have a list of types in `{...}`. For example, in this case the attr `t` is a type that must be an `int32`, a `float`, or a `bool`: @@ -649,7 +649,7 @@ define an attr with constraints, you can use the following ``s: ``` Lists can be combined with other lists and single types. The following - op allows attr `t` to be any of the numberic types, or the bool type: + op allows attr `t` to be any of the numeric types, or the bool type: ```c++ REGISTER_OP("NumberOrBooleanType") @@ -714,7 +714,7 @@ REGISTER_OP("AttrDefaultExampleForAllTypes") ``` Note in particular that the values of type `type` -use @{tf.DType$the `DT_*` names for the types}. +use `tf.DType`. #### Polymorphism @@ -1056,7 +1056,7 @@ expressions: `string`). This specifies a single tensor of the given type. See - @{tf.DType$the list of supported Tensor types}. + `tf.DType`. ```c++ REGISTER_OP("BuiltInTypesExample") @@ -1098,8 +1098,7 @@ expressions: * For a sequence of tensors with the same type: ` * `, where `` is the name of an [Attr](#attrs) with type `int`. The `` can - either be - @{tf.DType$a specific type like `int32` or `float`}, + either be a `tf.DType`, or the name of an attr with type `type`. As an example of the first, this op accepts a list of `int32` tensors: @@ -1202,7 +1201,7 @@ There are several examples of kernels with GPU support in Notice some kernels have a CPU version in a `.cc` file, a GPU version in a file ending in `_gpu.cu.cc`, and some code shared in common in a `.h` file. -For example, the @{tf.pad} has +For example, the `tf.pad` has everything but the GPU kernel in [`tensorflow/core/kernels/pad_op.cc`][pad_op]. The GPU kernel is in [`tensorflow/core/kernels/pad_op_gpu.cu.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/pad_op_gpu.cu.cc), @@ -1307,16 +1306,16 @@ def _zero_out_grad(op, grad): ``` Details about registering gradient functions with -@{tf.RegisterGradient}: +`tf.RegisterGradient`: * For an op with one output, the gradient function will take an - @{tf.Operation} `op` and a - @{tf.Tensor} `grad` and build new ops + `tf.Operation` `op` and a + `tf.Tensor` `grad` and build new ops out of the tensors [`op.inputs[i]`](../../api_docs/python/framework.md#Operation.inputs), [`op.outputs[i]`](../../api_docs/python/framework.md#Operation.outputs), and `grad`. Information about any attrs can be found via - @{tf.Operation.get_attr}. + `tf.Operation.get_attr`. * If the op has multiple outputs, the gradient function will take `op` and `grads`, where `grads` is a list of gradients with respect to each output. diff --git a/tensorflow/docs_src/extend/architecture.md b/tensorflow/docs_src/extend/architecture.md index 84435a57f226e0d90a3cb3bbf83863e85309116b..83d70c9468e940b4b347d0d5652327c226ecffe4 100644 --- a/tensorflow/docs_src/extend/architecture.md +++ b/tensorflow/docs_src/extend/architecture.md @@ -81,7 +81,7 @@ implementation from all client languages. Most of the training libraries are still Python-only, but C++ does have support for efficient inference. The client creates a session, which sends the graph definition to the -distributed master as a @{tf.GraphDef} +distributed master as a `tf.GraphDef` protocol buffer. When the client evaluates a node or nodes in the graph, the evaluation triggers a call to the distributed master to initiate computation. @@ -96,7 +96,7 @@ feature vector (x), adds a bias term (b) and saves the result in a variable ### Code -* @{tf.Session} +* `tf.Session` ## Distributed master diff --git a/tensorflow/docs_src/extend/index.md b/tensorflow/docs_src/extend/index.md index d48340a777d38551cdb882e7b85ba002f6ff5215..0e4bfd1dc46a2f669902dca30dfab512356705f3 100644 --- a/tensorflow/docs_src/extend/index.md +++ b/tensorflow/docs_src/extend/index.md @@ -17,7 +17,7 @@ TensorFlow: Python is currently the only language supported by TensorFlow's API stability promises. However, TensorFlow also provides functionality in C++, Go, Java and -[JavaScript](https://js.tensorflow.org) (incuding +[JavaScript](https://js.tensorflow.org) (including [Node.js](https://github.com/tensorflow/tfjs-node)), plus community support for [Haskell](https://github.com/tensorflow/haskell) and [Rust](https://github.com/tensorflow/rust). If you'd like to create or diff --git a/tensorflow/docs_src/extend/new_data_formats.md b/tensorflow/docs_src/extend/new_data_formats.md index abbf47910e691590c008dd37fe328a4fb75bee05..47a8344b70adade03612532d6fab340b2576bed7 100644 --- a/tensorflow/docs_src/extend/new_data_formats.md +++ b/tensorflow/docs_src/extend/new_data_formats.md @@ -15,25 +15,24 @@ We divide the task of supporting a file format into two pieces: * Record formats: We use decoder or parsing ops to turn a string record into tensors usable by TensorFlow. -For example, to read a -[CSV file](https://en.wikipedia.org/wiki/Comma-separated_values), we use -@{tf.data.TextLineDataset$a dataset for reading text files line-by-line} -and then @{tf.data.Dataset.map$map} an -@{tf.decode_csv$op} that parses CSV data from each line of text in the dataset. +For example, to re-implement `tf.contrib.data.make_csv_dataset` function, we +could use `tf.data.TextLineDataset` to extract the records, and then +use `tf.data.Dataset.map` and `tf.decode_csv` to parses the CSV records from +each line of text in the dataset. [TOC] ## Writing a `Dataset` for a file format -A @{tf.data.Dataset} represents a sequence of *elements*, which can be the +A `tf.data.Dataset` represents a sequence of *elements*, which can be the individual records in a file. There are several examples of "reader" datasets that are already built into TensorFlow: -* @{tf.data.TFRecordDataset} +* `tf.data.TFRecordDataset` ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc)) -* @{tf.data.FixedLengthRecordDataset} +* `tf.data.FixedLengthRecordDataset` ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc)) -* @{tf.data.TextLineDataset} +* `tf.data.TextLineDataset` ([source in `kernels/data/reader_dataset_ops.cc`](https://www.tensorflow.org/code/tensorflow/core/kernels/data/reader_dataset_ops.cc)) Each of these implementations comprises three related classes: @@ -64,7 +63,7 @@ need to: that implement the reading logic. 2. In C++, register a new reader op and kernel with the name `"MyReaderDataset"`. -3. In Python, define a subclass of @{tf.data.Dataset} called `MyReaderDataset`. +3. In Python, define a subclass of `tf.data.Dataset` called `MyReaderDataset`. You can put all the C++ code in a single file, such as `my_reader_dataset_op.cc`. It will help if you are @@ -230,7 +229,7 @@ REGISTER_KERNEL_BUILDER(Name("MyReaderDataset").Device(tensorflow::DEVICE_CPU), The last step is to build the C++ code and add a Python wrapper. The easiest way to do this is by @{$adding_an_op#build_the_op_library$compiling a dynamic library} (e.g. called `"my_reader_dataset_op.so"`), and adding a Python class -that subclasses @{tf.data.Dataset} to wrap it. An example Python program is +that subclasses `tf.data.Dataset` to wrap it. An example Python program is given here: ```python @@ -293,14 +292,14 @@ track down where the bad data came from. Examples of Ops useful for decoding records: -* @{tf.parse_single_example} (and @{tf.parse_example}) -* @{tf.decode_csv} -* @{tf.decode_raw} +* `tf.parse_single_example` (and `tf.parse_example`) +* `tf.decode_csv` +* `tf.decode_raw` Note that it can be useful to use multiple Ops to decode a particular record format. For example, you may have an image saved as a string in [a `tf.train.Example` protocol buffer](https://www.tensorflow.org/code/tensorflow/core/example/example.proto). Depending on the format of that image, you might take the corresponding output -from a @{tf.parse_single_example} op and call @{tf.image.decode_jpeg}, -@{tf.image.decode_png}, or @{tf.decode_raw}. It is common to take the output -of `tf.decode_raw` and use @{tf.slice} and @{tf.reshape} to extract pieces. +from a `tf.parse_single_example` op and call `tf.image.decode_jpeg`, +`tf.image.decode_png`, or `tf.decode_raw`. It is common to take the output +of `tf.decode_raw` and use `tf.slice` and `tf.reshape` to extract pieces. diff --git a/tensorflow/docs_src/guide/checkpoints.md b/tensorflow/docs_src/guide/checkpoints.md index dfb2626b8675ccc3db293498314fcc3e417bc1bd..e1add298527f27c063cc7622c26e5f3cc28e863d 100644 --- a/tensorflow/docs_src/guide/checkpoints.md +++ b/tensorflow/docs_src/guide/checkpoints.md @@ -129,7 +129,7 @@ in the `model_dir` according to the following schedule: You may alter the default schedule by taking the following steps: -1. Create a @{tf.estimator.RunConfig$`RunConfig`} object that defines the +1. Create a `tf.estimator.RunConfig` object that defines the desired schedule. 2. When instantiating the Estimator, pass that `RunConfig` object to the Estimator's `config` argument. diff --git a/tensorflow/docs_src/guide/custom_estimators.md b/tensorflow/docs_src/guide/custom_estimators.md index 6e4ef2e0f2c1e5bba4908ba9197a243ce9c60096..199a0e93de1b8bf1b00b3539e975278481781cb1 100644 --- a/tensorflow/docs_src/guide/custom_estimators.md +++ b/tensorflow/docs_src/guide/custom_estimators.md @@ -2,9 +2,9 @@ # Creating Custom Estimators This document introduces custom Estimators. In particular, this document -demonstrates how to create a custom @{tf.estimator.Estimator$Estimator} that +demonstrates how to create a custom `tf.estimator.Estimator` that mimics the behavior of the pre-made Estimator -@{tf.estimator.DNNClassifier$`DNNClassifier`} in solving the Iris problem. See +`tf.estimator.DNNClassifier` in solving the Iris problem. See the @{$premade_estimators$Pre-Made Estimators chapter} for details on the Iris problem. @@ -34,7 +34,7 @@ with ## Pre-made vs. custom As the following figure shows, pre-made Estimators are subclasses of the -@{tf.estimator.Estimator} base class, while custom Estimators are an instance +`tf.estimator.Estimator` base class, while custom Estimators are an instance of tf.estimator.Estimator:
@@ -144,7 +144,7 @@ The caller may pass `params` to an Estimator's constructor. Any `params` passed to the constructor are in turn passed on to the `model_fn`. In [`custom_estimator.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/custom_estimator.py) the following lines create the estimator and set the params to configure the -model. This configuration step is similar to how we configured the @{tf.estimator.DNNClassifier} in +model. This configuration step is similar to how we configured the `tf.estimator.DNNClassifier` in @{$premade_estimators}. ```python @@ -178,7 +178,7 @@ The basic deep neural network model must define the following three sections: ### Define the input layer -The first line of the `model_fn` calls @{tf.feature_column.input_layer} to +The first line of the `model_fn` calls `tf.feature_column.input_layer` to convert the feature dictionary and `feature_columns` into input for your model, as follows: @@ -202,7 +202,7 @@ creating the model's input layer. If you are creating a deep neural network, you must define one or more hidden layers. The Layers API provides a rich set of functions to define all types of hidden layers, including convolutional, pooling, and dropout layers. For Iris, -we're simply going to call @{tf.layers.dense} to create hidden layers, with +we're simply going to call `tf.layers.dense` to create hidden layers, with dimensions defined by `params['hidden_layers']`. In a `dense` layer each node is connected to every node in the preceding layer. Here's the relevant code: @@ -231,14 +231,14 @@ simplicity, the figure does not show all the units in each layer. src="../images/custom_estimators/add_hidden_layer.png">
-Note that @{tf.layers.dense} provides many additional capabilities, including +Note that `tf.layers.dense` provides many additional capabilities, including the ability to set a multitude of regularization parameters. For the sake of simplicity, though, we're going to simply accept the default values of the other parameters. ### Output Layer -We'll define the output layer by calling @{tf.layers.dense} yet again, this +We'll define the output layer by calling `tf.layers.dense` yet again, this time without an activation function: ```python @@ -265,7 +265,7 @@ score, or "logit", calculated for the associated class of Iris: Setosa, Versicolor, or Virginica, respectively. Later on, these logits will be transformed into probabilities by the -@{tf.nn.softmax} function. +`tf.nn.softmax` function. ## Implement training, evaluation, and prediction {#modes} @@ -290,9 +290,9 @@ function with the mode parameter set as follows: | Estimator method | Estimator Mode | |:---------------------------------|:------------------| -|@{tf.estimator.Estimator.train$`train()`} |@{tf.estimator.ModeKeys.TRAIN$`ModeKeys.TRAIN`} | -|@{tf.estimator.Estimator.evaluate$`evaluate()`} |@{tf.estimator.ModeKeys.EVAL$`ModeKeys.EVAL`} | -|@{tf.estimator.Estimator.predict$`predict()`}|@{tf.estimator.ModeKeys.PREDICT$`ModeKeys.PREDICT`} | +|`tf.estimator.Estimator.train` |`tf.estimator.ModeKeys.TRAIN` | +|`tf.estimator.Estimator.evaluate` |`tf.estimator.ModeKeys.EVAL` | +|`tf.estimator.Estimator.predict`|`tf.estimator.ModeKeys.PREDICT` | For example, suppose you instantiate a custom Estimator to generate an object named `classifier`. Then, you make the following call: @@ -350,8 +350,8 @@ The `predictions` holds the following three key/value pairs: * `logit` holds the raw logit values (in this example, -1.3, 2.6, and -0.9) We return that dictionary to the caller via the `predictions` parameter of the -@{tf.estimator.EstimatorSpec}. The Estimator's -@{tf.estimator.Estimator.predict$`predict`} method will yield these +`tf.estimator.EstimatorSpec`. The Estimator's +`tf.estimator.Estimator.predict` method will yield these dictionaries. ### Calculate the loss @@ -361,7 +361,7 @@ model's loss. This is the [objective](https://developers.google.com/machine-learning/glossary/#objective) that will be optimized. -We can calculate the loss by calling @{tf.losses.sparse_softmax_cross_entropy}. +We can calculate the loss by calling `tf.losses.sparse_softmax_cross_entropy`. The value returned by this function will be approximately 0 at lowest, when the probability of the correct class (at index `label`) is near 1.0. The loss value returned is progressively larger as the probability of the @@ -382,12 +382,12 @@ When the Estimator's `evaluate` method is called, the `model_fn` receives or more metrics. Although returning metrics is optional, most custom Estimators do return at -least one metric. TensorFlow provides a Metrics module @{tf.metrics} to +least one metric. TensorFlow provides a Metrics module `tf.metrics` to calculate common metrics. For brevity's sake, we'll only return accuracy. The -@{tf.metrics.accuracy} function compares our predictions against the +`tf.metrics.accuracy` function compares our predictions against the true values, that is, against the labels provided by the input function. The -@{tf.metrics.accuracy} function requires the labels and predictions to have the -same shape. Here's the call to @{tf.metrics.accuracy}: +`tf.metrics.accuracy` function requires the labels and predictions to have the +same shape. Here's the call to `tf.metrics.accuracy`: ``` python # Compute evaluation metrics. @@ -396,7 +396,7 @@ accuracy = tf.metrics.accuracy(labels=labels, name='acc_op') ``` -The @{tf.estimator.EstimatorSpec$`EstimatorSpec`} returned for evaluation +The `tf.estimator.EstimatorSpec` returned for evaluation typically contains the following information: * `loss`, which is the model's loss @@ -416,7 +416,7 @@ if mode == tf.estimator.ModeKeys.EVAL: mode, loss=loss, eval_metric_ops=metrics) ``` -The @{tf.summary.scalar} will make accuracy available to TensorBoard +The `tf.summary.scalar` will make accuracy available to TensorBoard in both `TRAIN` and `EVAL` modes. (More on this later). ### Train @@ -426,7 +426,7 @@ with `mode = ModeKeys.TRAIN`. In this case, the model function must return an `EstimatorSpec` that contains the loss and a training operation. Building the training operation will require an optimizer. We will use -@{tf.train.AdagradOptimizer} because we're mimicking the `DNNClassifier`, which +`tf.train.AdagradOptimizer` because we're mimicking the `DNNClassifier`, which also uses `Adagrad` by default. The `tf.train` package provides many other optimizers—feel free to experiment with them. @@ -437,14 +437,14 @@ optimizer = tf.train.AdagradOptimizer(learning_rate=0.1) ``` Next, we build the training operation using the optimizer's -@{tf.train.Optimizer.minimize$`minimize`} method on the loss we calculated +`tf.train.Optimizer.minimize` method on the loss we calculated earlier. The `minimize` method also takes a `global_step` parameter. TensorFlow uses this parameter to count the number of training steps that have been processed (to know when to end a training run). Furthermore, the `global_step` is essential for TensorBoard graphs to work correctly. Simply call -@{tf.train.get_global_step} and pass the result to the `global_step` +`tf.train.get_global_step` and pass the result to the `global_step` argument of `minimize`. Here's the code to train the model: @@ -453,7 +453,7 @@ Here's the code to train the model: train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) ``` -The @{tf.estimator.EstimatorSpec$`EstimatorSpec`} returned for training +The `tf.estimator.EstimatorSpec` returned for training must have the following fields set: * `loss`, which contains the value of the loss function. diff --git a/tensorflow/docs_src/guide/datasets.md b/tensorflow/docs_src/guide/datasets.md index 8b69860a68461e849a445f5c01c2e9b71d614a46..bb18e8b79cef8cd9958fa77ac20819d1dc7675e1 100644 --- a/tensorflow/docs_src/guide/datasets.md +++ b/tensorflow/docs_src/guide/datasets.md @@ -1,6 +1,6 @@ # Importing Data -The @{tf.data} API enables you to build complex input pipelines from +The `tf.data` API enables you to build complex input pipelines from simple, reusable pieces. For example, the pipeline for an image model might aggregate data from files in a distributed file system, apply random perturbations to each image, and merge randomly selected images into a batch @@ -51,7 +51,7 @@ Once you have a `Dataset` object, you can *transform* it into a new `Dataset` by chaining method calls on the `tf.data.Dataset` object. For example, you can apply per-element transformations such as `Dataset.map()` (to apply a function to each element), and multi-element transformations such as -`Dataset.batch()`. See the documentation for @{tf.data.Dataset} +`Dataset.batch()`. See the documentation for `tf.data.Dataset` for a complete list of transformations. The most common way to consume values from a `Dataset` is to make an @@ -211,13 +211,13 @@ for _ in range(20): sess.run(next_element) ``` -A **feedable** iterator can be used together with @{tf.placeholder} to select -what `Iterator` to use in each call to @{tf.Session.run}, via the familiar +A **feedable** iterator can be used together with `tf.placeholder` to select +what `Iterator` to use in each call to `tf.Session.run`, via the familiar `feed_dict` mechanism. It offers the same functionality as a reinitializable iterator, but it does not require you to initialize the iterator from the start of a dataset when you switch between iterators. For example, using the same training and validation example from above, you can use -@{tf.data.Iterator.from_string_handle} to define a feedable iterator +`tf.data.Iterator.from_string_handle` to define a feedable iterator that allows you to switch between the two datasets: ```python @@ -329,12 +329,12 @@ of an iterator will include all components in a single expression. ### Saving iterator state -The @{tf.contrib.data.make_saveable_from_iterator} function creates a +The `tf.contrib.data.make_saveable_from_iterator` function creates a `SaveableObject` from an iterator, which can be used to save and restore the current state of the iterator (and, effectively, the whole input -pipeline). A saveable object thus created can be added to @{tf.train.Saver} +pipeline). A saveable object thus created can be added to `tf.train.Saver` variables list or the `tf.GraphKeys.SAVEABLE_OBJECTS` collection for saving and -restoring in the same manner as a @{tf.Variable}. Refer to +restoring in the same manner as a `tf.Variable`. Refer to @{$saved_model$Saving and Restoring} for details on how to save and restore variables. @@ -488,7 +488,7 @@ dataset = dataset.flat_map( ### Consuming CSV data The CSV file format is a popular format for storing tabular data in plain text. -The @{tf.contrib.data.CsvDataset} class provides a way to extract records from +The `tf.contrib.data.CsvDataset` class provides a way to extract records from one or more CSV files that comply with [RFC 4180](https://tools.ietf.org/html/rfc4180). Given one or more filenames and a list of defaults, a `CsvDataset` will produce a tuple of elements whose types correspond to the types of the defaults @@ -757,9 +757,9 @@ dataset = dataset.repeat() ### Using high-level APIs -The @{tf.train.MonitoredTrainingSession} API simplifies many aspects of running +The `tf.train.MonitoredTrainingSession` API simplifies many aspects of running TensorFlow in a distributed setting. `MonitoredTrainingSession` uses the -@{tf.errors.OutOfRangeError} to signal that training has completed, so to use it +`tf.errors.OutOfRangeError` to signal that training has completed, so to use it with the `tf.data` API, we recommend using `Dataset.make_one_shot_iterator()`. For example: @@ -782,7 +782,7 @@ with tf.train.MonitoredTrainingSession(...) as sess: sess.run(training_op) ``` -To use a `Dataset` in the `input_fn` of a @{tf.estimator.Estimator}, we also +To use a `Dataset` in the `input_fn` of a `tf.estimator.Estimator`, we also recommend using `Dataset.make_one_shot_iterator()`. For example: ```python diff --git a/tensorflow/docs_src/guide/datasets_for_estimators.md b/tensorflow/docs_src/guide/datasets_for_estimators.md index b55a5731a46fab8bd904ffef9e4f5ef5f4d11848..969ea579f7e85fc296f928c6ab71ea94d47d0fb5 100644 --- a/tensorflow/docs_src/guide/datasets_for_estimators.md +++ b/tensorflow/docs_src/guide/datasets_for_estimators.md @@ -1,6 +1,6 @@ # Datasets for Estimators -The @{tf.data} module contains a collection of classes that allows you to +The `tf.data` module contains a collection of classes that allows you to easily load data, manipulate it, and pipe it into your model. This document introduces the API by walking through two simple examples: @@ -73,8 +73,8 @@ Let's walk through the `train_input_fn()`. ### Slices -The function starts by using the @{tf.data.Dataset.from_tensor_slices} function -to create a @{tf.data.Dataset} representing slices of the array. The array is +The function starts by using the `tf.data.Dataset.from_tensor_slices` function +to create a `tf.data.Dataset` representing slices of the array. The array is sliced across the first dimension. For example, an array containing the MNIST training data has a shape of `(60000, 28, 28)`. Passing this to `from_tensor_slices` returns a `Dataset` object containing 60000 slices, each one @@ -170,15 +170,15 @@ function takes advantage of several of these methods: dataset = dataset.shuffle(1000).repeat().batch(batch_size) ``` -The @{tf.data.Dataset.shuffle$`shuffle`} method uses a fixed-size buffer to +The `tf.data.Dataset.shuffle` method uses a fixed-size buffer to shuffle the items as they pass through. In this case the `buffer_size` is greater than the number of examples in the `Dataset`, ensuring that the data is completely shuffled (The Iris data set only contains 150 examples). -The @{tf.data.Dataset.repeat$`repeat`} method restarts the `Dataset` when +The `tf.data.Dataset.repeat` method restarts the `Dataset` when it reaches the end. To limit the number of epochs, set the `count` argument. -The @{tf.data.Dataset.batch$`batch`} method collects a number of examples and +The `tf.data.Dataset.batch` method collects a number of examples and stacks them, to create batches. This adds a dimension to their shape. The new dimension is added as the first dimension. The following code uses the `batch` method on the MNIST `Dataset`, from earlier. This results in a @@ -234,7 +234,7 @@ The `labels` can/should be omitted when using the `predict` method. ## Reading a CSV File The most common real-world use case for the `Dataset` class is to stream data -from files on disk. The @{tf.data} module includes a variety of +from files on disk. The `tf.data` module includes a variety of file readers. Let's see how parsing the Iris dataset from the csv file looks using a `Dataset`. @@ -255,9 +255,9 @@ from the local files. ### Build the `Dataset` -We start by building a @{tf.data.TextLineDataset$`TextLineDataset`} object to +We start by building a `tf.data.TextLineDataset` object to read the file one line at a time. Then, we call the -@{tf.data.Dataset.skip$`skip`} method to skip over the first line of the file, which contains a header, not an example: +`tf.data.Dataset.skip` method to skip over the first line of the file, which contains a header, not an example: ``` python ds = tf.data.TextLineDataset(train_path).skip(1) @@ -268,11 +268,11 @@ ds = tf.data.TextLineDataset(train_path).skip(1) We will start by building a function to parse a single line. The following `iris_data.parse_line` function accomplishes this task using the -@{tf.decode_csv} function, and some simple python code: +`tf.decode_csv` function, and some simple python code: We must parse each of the lines in the dataset in order to generate the necessary `(features, label)` pairs. The following `_parse_line` function -calls @{tf.decode_csv} to parse a single line into its features +calls `tf.decode_csv` to parse a single line into its features and the label. Since Estimators require that features be represented as a dictionary, we rely on Python's built-in `dict` and `zip` functions to build that dictionary. The feature names are the keys of that dictionary. @@ -301,7 +301,7 @@ def _parse_line(line): ### Parse the lines Datasets have many methods for manipulating the data while it is being piped -to a model. The most heavily-used method is @{tf.data.Dataset.map$`map`}, which +to a model. The most heavily-used method is `tf.data.Dataset.map`, which applies a transformation to each element of the `Dataset`. The `map` method takes a `map_func` argument that describes how each item in the @@ -311,7 +311,7 @@ The `map` method takes a `map_func` argument that describes how each item in the
-The @{tf.data.Dataset.map$`map`} method applies the `map_func` to +The `tf.data.Dataset.map` method applies the `map_func` to transform each item in the Dataset.
diff --git a/tensorflow/docs_src/guide/debugger.md b/tensorflow/docs_src/guide/debugger.md index f0e465214e0b8fc5e2dabd0a31b9830f77c26bb9..4c4a04a88af19ec2d3b1fc0b093a38153666d2de 100644 --- a/tensorflow/docs_src/guide/debugger.md +++ b/tensorflow/docs_src/guide/debugger.md @@ -89,7 +89,7 @@ control the execution and inspect the graph's internal state. the diagnosis of issues. In this example, we have already registered a tensor filter called -@{tfdbg.has_inf_or_nan}, +`tfdbg.has_inf_or_nan`, which simply determines if there are any `nan` or `inf` values in any intermediate tensors (tensors that are neither inputs or outputs of the `Session.run()` call, but are in the path leading from the inputs to the @@ -98,13 +98,11 @@ we ship it with the @{$python/tfdbg#Classes_for_debug_dump_data_and_directories$`debug_data`} module. -Note: You can also write your own custom filters. See -the @{tfdbg.DebugDumpDir.find$API documentation} -of `DebugDumpDir.find()` for additional information. +Note: You can also write your own custom filters. See `tfdbg.DebugDumpDir.find` +for additional information. ## Debugging Model Training with tfdbg - Let's try training the model again, but with the `--debug` flag added this time: ```none @@ -429,9 +427,9 @@ described in the preceding sections inapplicable. Fortunately, you can still debug them by using special `hook`s provided by `tfdbg`. `tfdbg` can debug the -@{tf.estimator.Estimator.train$`train()`}, -@{tf.estimator.Estimator.evaluate$`evaluate()`} and -@{tf.estimator.Estimator.predict$`predict()`} +`tf.estimator.Estimator.train`, +`tf.estimator.Estimator.evaluate` and +`tf.estimator.Estimator.predict` methods of tf-learn `Estimator`s. To debug `Estimator.train()`, create a `LocalCLIDebugHook` and supply it in the `hooks` argument. For example: @@ -473,7 +471,7 @@ python -m tensorflow.python.debug.examples.debug_tflearn_iris --debug The `LocalCLIDebugHook` also allows you to configure a `watch_fn` that can be used to flexibly specify what `Tensor`s to watch on different `Session.run()` calls, as a function of the `fetches` and `feed_dict` and other states. See -@{tfdbg.DumpingDebugWrapperSession.__init__$this API doc} +`tfdbg.DumpingDebugWrapperSession.__init__` for more details. ## Debugging Keras Models with TFDBG @@ -556,7 +554,7 @@ and the higher-level `Estimator` API. If you interact directly with the `tf.Session` API in `python`, you can configure the `RunOptions` proto that you call your `Session.run()` method -with, by using the method @{tfdbg.watch_graph}. +with, by using the method `tfdbg.watch_graph`. This will cause the intermediate tensors and runtime graphs to be dumped to a shared storage location of your choice when the `Session.run()` call occurs (at the cost of slower performance). For example: @@ -629,7 +627,7 @@ hooks = [tf_debug.DumpingDebugHook("/shared/storage/location/tfdbg_dumps_1")] Then this `hook` can be used in the same way as the `LocalCLIDebugHook` examples described earlier in this document. -As the training, evalution or prediction happens with `Estimator`, +As the training, evaluation or prediction happens with `Estimator`, tfdbg creates directories having the following name pattern: `/shared/storage/location/tfdbg_dumps_1/run__`. Each directory corresponds to a `Session.run()` call that underlies @@ -715,7 +713,7 @@ You might encounter this problem in any of the following situations: * models with many intermediate tensors * very large intermediate tensors -* many @{tf.while_loop} iterations +* many `tf.while_loop` iterations There are three possible workarounds or solutions: @@ -770,12 +768,12 @@ sess.run(b) **A**: The reason why you see no data dumped is because every node in the executed TensorFlow graph is constant-folded by the TensorFlow runtime. - In this exapmle, `a` is a constant tensor; therefore, the fetched + In this example, `a` is a constant tensor; therefore, the fetched tensor `b` is effectively also a constant tensor. TensorFlow's graph optimization folds the graph that contains `a` and `b` into a single node to speed up future runs of the graph, which is why `tfdbg` does not generate any intermediate tensor dumps. However, if `a` were a - @{tf.Variable}, as in the following example: + `tf.Variable`, as in the following example: ``` python import numpy as np diff --git a/tensorflow/docs_src/guide/eager.md b/tensorflow/docs_src/guide/eager.md index 3b54d6d2bbc15180fee96a8d3be4537905740667..017fdaf81e2f9631f74f397842dbf314aa542206 100644 --- a/tensorflow/docs_src/guide/eager.md +++ b/tensorflow/docs_src/guide/eager.md @@ -193,8 +193,7 @@ class MNISTModel(tf.keras.Model): def call(self, input): """Run the model.""" result = self.dense1(input) - result = self.dense2(result) - result = self.dense2(result) # reuse variables from dense2 layer + result = self.dense2(result) # reuse variables from dense1 layer return result model = MNISTModel() @@ -727,7 +726,13 @@ def measure(x, steps): start = time.time() for i in range(steps): x = tf.matmul(x, x) - _ = x.numpy() # Make sure to execute op and not just enqueue it + # tf.matmul can return before completing the matrix multiplication + # (e.g., can return after enqueing the operation on a CUDA stream). + # The x.numpy() call below will ensure that all enqueued operations + # have completed (and will also copy the result to host memory, + # so we're including a little more than just the matmul operation + # time). + _ = x.numpy() end = time.time() return end - start @@ -751,8 +756,8 @@ Output (exact numbers depend on hardware): ``` Time to multiply a (1000, 1000) matrix by itself 200 times: -CPU: 4.614904403686523 secs -GPU: 0.5581181049346924 secs +CPU: 1.46628093719 secs +GPU: 0.0593810081482 secs ``` A `tf.Tensor` object can be copied to a different device to execute its diff --git a/tensorflow/docs_src/guide/estimators.md b/tensorflow/docs_src/guide/estimators.md index 78b30c3040f646e4ae1bf97246666e8585e18057..7b54e3de29a9f215f5b9396b25b78fc848d2d7e7 100644 --- a/tensorflow/docs_src/guide/estimators.md +++ b/tensorflow/docs_src/guide/estimators.md @@ -1,6 +1,6 @@ # Estimators -This document introduces @{tf.estimator$**Estimators**}--a high-level TensorFlow +This document introduces `tf.estimator`--a high-level TensorFlow API that greatly simplifies machine learning programming. Estimators encapsulate the following actions: @@ -11,10 +11,13 @@ the following actions: You may either use the pre-made Estimators we provide or write your own custom Estimators. All Estimators--whether pre-made or custom--are -classes based on the @{tf.estimator.Estimator} class. +classes based on the `tf.estimator.Estimator` class. + +For a quick example try [Estimator tutorials]](../tutorials/estimators/linear). +To see each sub-topic in depth, see the [Estimator guides](premade_estimators). Note: TensorFlow also includes a deprecated `Estimator` class at -@{tf.contrib.learn.Estimator}, which you should not use. +`tf.contrib.learn.Estimator`, which you should not use. ## Advantages of Estimators @@ -29,14 +32,14 @@ Estimators provide the following benefits: * You can develop a state of the art model with high-level intuitive code. In short, it is generally much easier to create models with Estimators than with the low-level TensorFlow APIs. -* Estimators are themselves built on @{tf.layers}, which +* Estimators are themselves built on `tf.keras.layers`, which simplifies customization. * Estimators build the graph for you. * Estimators provide a safe distributed training loop that controls how and when to: * build the graph * initialize variables - * start queues + * load data * handle exceptions * create checkpoint files and recover from failures * save summaries for TensorBoard @@ -52,9 +55,9 @@ Pre-made Estimators enable you to work at a much higher conceptual level than the base TensorFlow APIs. You no longer have to worry about creating the computational graph or sessions since Estimators handle all the "plumbing" for you. That is, pre-made Estimators create and manage -@{tf.Graph$`Graph`} and @{tf.Session$`Session`} objects for you. Furthermore, +`tf.Graph` and `tf.Session` objects for you. Furthermore, pre-made Estimators let you experiment with different model architectures by -making only minimal code changes. @{tf.estimator.DNNClassifier$`DNNClassifier`}, +making only minimal code changes. `tf.estimator.DNNClassifier`, for example, is a pre-made Estimator class that trains classification models based on dense, feed-forward neural networks. @@ -83,7 +86,7 @@ of the following four steps: (See @{$guide/datasets} for full details.) -2. **Define the feature columns.** Each @{tf.feature_column} +2. **Define the feature columns.** Each `tf.feature_column` identifies a feature name, its type, and any input pre-processing. For example, the following snippet creates three feature columns that hold integer or floating-point data. The first two @@ -155,7 +158,7 @@ We recommend the following workflow: You can convert existing Keras models to Estimators. Doing so enables your Keras model to access Estimator's strengths, such as distributed training. Call -@{tf.keras.estimator.model_to_estimator} as in the +`tf.keras.estimator.model_to_estimator` as in the following sample: ```python @@ -190,4 +193,4 @@ and similarly, the predicted output names can be obtained from `keras_inception_v3.output_names`. For more details, please refer to the documentation for -@{tf.keras.estimator.model_to_estimator}. +`tf.keras.estimator.model_to_estimator`. diff --git a/tensorflow/docs_src/guide/faq.md b/tensorflow/docs_src/guide/faq.md index b6291a9fface404406829d8d7ce5cc36980661a3..8370097560c01d10cba038be63bd1f152115e7f5 100644 --- a/tensorflow/docs_src/guide/faq.md +++ b/tensorflow/docs_src/guide/faq.md @@ -28,13 +28,13 @@ See also the #### Why does `c = tf.matmul(a, b)` not execute the matrix multiplication immediately? In the TensorFlow Python API, `a`, `b`, and `c` are -@{tf.Tensor} objects. A `Tensor` object is +`tf.Tensor` objects. A `Tensor` object is a symbolic handle to the result of an operation, but does not actually hold the values of the operation's output. Instead, TensorFlow encourages users to build up complicated expressions (such as entire neural networks and its gradients) as a dataflow graph. You then offload the computation of the entire dataflow graph (or a subgraph of it) to a TensorFlow -@{tf.Session}, which is able to execute the +`tf.Session`, which is able to execute the whole computation much more efficiently than executing the operations one-by-one. @@ -46,7 +46,7 @@ device, and `"/device:GPU:i"` (or `"/gpu:i"`) for the *i*th GPU device. #### How do I place operations on a particular device? To place a group of operations on a device, create them within a -@{tf.device$`with tf.device(name):`} context. See +`tf.device` context. See the how-to documentation on @{$using_gpu$using GPUs with TensorFlow} for details of how TensorFlow assigns operations to devices, and the @@ -63,17 +63,17 @@ See also the Feeding is a mechanism in the TensorFlow Session API that allows you to substitute different values for one or more tensors at run time. The `feed_dict` -argument to @{tf.Session.run} is a -dictionary that maps @{tf.Tensor} objects to +argument to `tf.Session.run` is a +dictionary that maps `tf.Tensor` objects to numpy arrays (and some other types), which will be used as the values of those tensors in the execution of a step. #### What is the difference between `Session.run()` and `Tensor.eval()`? -If `t` is a @{tf.Tensor} object, -@{tf.Tensor.eval} is shorthand for -@{tf.Session.run}, where `sess` is the -current @{tf.get_default_session}. The +If `t` is a `tf.Tensor` object, +`tf.Tensor.eval` is shorthand for +`tf.Session.run`, where `sess` is the +current `tf.get_default_session`. The two following snippets of code are equivalent: ```python @@ -99,11 +99,11 @@ sessions, it may be more straightforward to make explicit calls to #### Do Sessions have a lifetime? What about intermediate tensors? Sessions can own resources, such as -@{tf.Variable}, -@{tf.QueueBase}, and -@{tf.ReaderBase}. These resources can sometimes use +`tf.Variable`, +`tf.QueueBase`, and +`tf.ReaderBase`. These resources can sometimes use a significant amount of memory, and can be released when the session is closed by calling -@{tf.Session.close}. +`tf.Session.close`. The intermediate tensors that are created as part of a call to @{$python/client$`Session.run()`} will be freed at or before the @@ -120,7 +120,7 @@ dimensions: devices, which makes it possible to speed up @{$deep_cnn$CIFAR-10 training using multiple GPUs}. * The Session API allows multiple concurrent steps (i.e. calls to - @{tf.Session.run} in parallel). This + `tf.Session.run` in parallel). This enables the runtime to get higher throughput, if a single step does not use all of the resources in your computer. @@ -151,8 +151,8 @@ than 3.5. #### Why does `Session.run()` hang when using a reader or a queue? -The @{tf.ReaderBase} and -@{tf.QueueBase} classes provide special operations that +The `tf.ReaderBase` and +`tf.QueueBase` classes provide special operations that can *block* until input (or free space in a bounded queue) becomes available. These operations allow you to build sophisticated @{$reading_data$input pipelines}, at the cost of making the @@ -169,9 +169,9 @@ See also the how-to documentation on @{$variables$variables} and #### What is the lifetime of a variable? A variable is created when you first run the -@{tf.Variable.initializer} +`tf.Variable.initializer` operation for that variable in a session. It is destroyed when that -@{tf.Session.close}. +`tf.Session.close`. #### How do variables behave when they are concurrently accessed? @@ -179,32 +179,31 @@ Variables allow concurrent read and write operations. The value read from a variable may change if it is concurrently updated. By default, concurrent assignment operations to a variable are allowed to run with no mutual exclusion. To acquire a lock when assigning to a variable, pass `use_locking=True` to -@{tf.Variable.assign}. +`tf.Variable.assign`. ## Tensor shapes See also the -@{tf.TensorShape}. +`tf.TensorShape`. #### How can I determine the shape of a tensor in Python? In TensorFlow, a tensor has both a static (inferred) shape and a dynamic (true) shape. The static shape can be read using the -@{tf.Tensor.get_shape} +`tf.Tensor.get_shape` method: this shape is inferred from the operations that were used to create the -tensor, and may be -@{tf.TensorShape$partially complete}. If the static -shape is not fully defined, the dynamic shape of a `Tensor` `t` can be -determined by evaluating @{tf.shape$`tf.shape(t)`}. +tensor, and may be partially complete (the static-shape may contain `None`). If +the static shape is not fully defined, the dynamic shape of a `tf.Tensor`, `t` +can be determined using `tf.shape(t)`. #### What is the difference between `x.set_shape()` and `x = tf.reshape(x)`? -The @{tf.Tensor.set_shape} method updates +The `tf.Tensor.set_shape` method updates the static shape of a `Tensor` object, and it is typically used to provide additional shape information when this cannot be inferred directly. It does not change the dynamic shape of the tensor. -The @{tf.reshape} operation creates +The `tf.reshape` operation creates a new tensor with a different dynamic shape. #### How do I build a graph that works with variable batch sizes? @@ -212,9 +211,9 @@ a new tensor with a different dynamic shape. It is often useful to build a graph that works with variable batch sizes so that the same code can be used for (mini-)batch training, and single-instance inference. The resulting graph can be -@{tf.Graph.as_graph_def$saved as a protocol buffer} +`tf.Graph.as_graph_def` and -@{tf.import_graph_def$imported into another program}. +`tf.import_graph_def`. When building a variable-size graph, the most important thing to remember is not to encode the batch size as a Python constant, but instead to use a symbolic @@ -224,7 +223,7 @@ to encode the batch size as a Python constant, but instead to use a symbolic to extract the batch dimension from a `Tensor` called `input`, and store it in a `Tensor` called `batch_size`. -* Use @{tf.reduce_mean} instead +* Use `tf.reduce_mean` instead of `tf.reduce_sum(...) / batch_size`. @@ -259,19 +258,19 @@ See the how-to documentation for There are three main options for dealing with data in a custom format. The easiest option is to write parsing code in Python that transforms the data -into a numpy array. Then, use @{tf.data.Dataset.from_tensor_slices} to +into a numpy array. Then, use `tf.data.Dataset.from_tensor_slices` to create an input pipeline from the in-memory data. If your data doesn't fit in memory, try doing the parsing in the Dataset pipeline. Start with an appropriate file reader, like -@{tf.data.TextLineDataset}. Then convert the dataset by mapping -@{tf.data.Dataset.map$mapping} appropriate operations over it. -Prefer predefined TensorFlow operations such as @{tf.decode_raw}, -@{tf.decode_csv}, @{tf.parse_example}, or @{tf.image.decode_png}. +`tf.data.TextLineDataset`. Then convert the dataset by mapping +`tf.data.Dataset.map` appropriate operations over it. +Prefer predefined TensorFlow operations such as `tf.decode_raw`, +`tf.decode_csv`, `tf.parse_example`, or `tf.image.decode_png`. If your data is not easily parsable with the built-in TensorFlow operations, consider converting it, offline, to a format that is easily parsable, such -as @{tf.python_io.TFRecordWriter$`TFRecord`} format. +as `tf.python_io.TFRecordWriter` format. The most efficient method to customize the parsing behavior is to @{$adding_an_op$add a new op written in C++} that parses your diff --git a/tensorflow/docs_src/guide/feature_columns.md b/tensorflow/docs_src/guide/feature_columns.md index 41080e050b34896c4926df9f1e0ca11d71d0c5b7..b189c4334ed5a5428de223f92de8d93f4ef052ba 100644 --- a/tensorflow/docs_src/guide/feature_columns.md +++ b/tensorflow/docs_src/guide/feature_columns.md @@ -6,10 +6,10 @@ enabling you to transform a diverse range of raw data into formats that Estimators can use, allowing easy experimentation. In @{$premade_estimators$Premade Estimators}, we used the premade -Estimator, @{tf.estimator.DNNClassifier$`DNNClassifier`} to train a model to +Estimator, `tf.estimator.DNNClassifier` to train a model to predict different types of Iris flowers from four input features. That example created only numerical feature columns (of type -@{tf.feature_column.numeric_column}). Although numerical feature columns model +`tf.feature_column.numeric_column`). Although numerical feature columns model the lengths of petals and sepals effectively, real world data sets contain all kinds of features, many of which are non-numerical. @@ -59,7 +59,7 @@ Feature columns bridge raw data with the data your model needs. To create feature columns, call functions from the -@{tf.feature_column} module. This document explains nine of the functions in +`tf.feature_column` module. This document explains nine of the functions in that module. As the following figure shows, all nine functions return either a Categorical-Column or a Dense-Column object, except `bucketized_column`, which inherits from both classes: @@ -75,7 +75,7 @@ Let's look at these functions in more detail. ### Numeric column -The Iris classifier calls the @{tf.feature_column.numeric_column} function for +The Iris classifier calls the `tf.feature_column.numeric_column` function for all input features: * `SepalLength` @@ -119,7 +119,7 @@ matrix_feature_column = tf.feature_column.numeric_column(key="MyMatrix", Often, you don't want to feed a number directly into the model, but instead split its value into different categories based on numerical ranges. To do so, -create a @{tf.feature_column.bucketized_column$bucketized column}. For +create a `tf.feature_column.bucketized_column`. For example, consider raw data that represents the year a house was built. Instead of representing that year as a scalar numeric column, we could split the year into the following four buckets: @@ -194,7 +194,7 @@ value. That is: * `1="electronics"` * `2="sport"` -Call @{tf.feature_column.categorical_column_with_identity} to implement a +Call `tf.feature_column.categorical_column_with_identity` to implement a categorical identity column. For example: ``` python @@ -230,8 +230,8 @@ As you can see, categorical vocabulary columns are kind of an enum version of categorical identity columns. TensorFlow provides two different functions to create categorical vocabulary columns: -* @{tf.feature_column.categorical_column_with_vocabulary_list} -* @{tf.feature_column.categorical_column_with_vocabulary_file} +* `tf.feature_column.categorical_column_with_vocabulary_list` +* `tf.feature_column.categorical_column_with_vocabulary_file` `categorical_column_with_vocabulary_list` maps each string to an integer based on an explicit vocabulary list. For example: @@ -281,7 +281,7 @@ categories can be so big that it's not possible to have individual categories for each vocabulary word or integer because that would consume too much memory. For these cases, we can instead turn the question around and ask, "How many categories am I willing to have for my input?" In fact, the -@{tf.feature_column.categorical_column_with_hash_bucket} function enables you +`tf.feature_column.categorical_column_with_hash_bucket` function enables you to specify the number of categories. For this type of feature column the model calculates a hash value of the input, then puts it into one of the `hash_bucket_size` categories using the modulo operator, as in the following @@ -289,7 +289,7 @@ pseudocode: ```python # pseudocode -feature_id = hash(raw_feature) % hash_buckets_size +feature_id = hash(raw_feature) % hash_bucket_size ``` The code to create the `feature_column` might look something like this: @@ -298,7 +298,7 @@ The code to create the `feature_column` might look something like this: hashed_feature_column = tf.feature_column.categorical_column_with_hash_bucket( key = "some_feature", - hash_buckets_size = 100) # The number of categories + hash_bucket_size = 100) # The number of categories ``` At this point, you might rightfully think: "This is crazy!" After all, we are forcing the different input values to a smaller set of categories. This means @@ -349,7 +349,7 @@ equal size. For the solution, we used a combination of the `bucketized_column` we looked at -earlier, with the @{tf.feature_column.crossed_column} function. +earlier, with the `tf.feature_column.crossed_column` function. @@ -440,7 +440,7 @@ Representing data in indicator columns. Here's how you create an indicator column by calling -@{tf.feature_column.indicator_column}: +`tf.feature_column.indicator_column`: ``` python categorical_column = ... # Create any type of categorical column. @@ -521,7 +521,7 @@ number of dimensions is 3: Note that this is just a general guideline; you can set the number of embedding dimensions as you please. -Call @{tf.feature_column.embedding_column} to create an `embedding_column` as +Call `tf.feature_column.embedding_column` to create an `embedding_column` as suggested by the following snippet: ``` python @@ -543,15 +543,15 @@ columns. As the following list indicates, not all Estimators permit all types of `feature_columns` argument(s): -* @{tf.estimator.LinearClassifier$`LinearClassifier`} and - @{tf.estimator.LinearRegressor$`LinearRegressor`}: Accept all types of +* `tf.estimator.LinearClassifier` and + `tf.estimator.LinearRegressor`: Accept all types of feature column. -* @{tf.estimator.DNNClassifier$`DNNClassifier`} and - @{tf.estimator.DNNRegressor$`DNNRegressor`}: Only accept dense columns. Other +* `tf.estimator.DNNClassifier` and + `tf.estimator.DNNRegressor`: Only accept dense columns. Other column types must be wrapped in either an `indicator_column` or `embedding_column`. -* @{tf.estimator.DNNLinearCombinedClassifier$`DNNLinearCombinedClassifier`} and - @{tf.estimator.DNNLinearCombinedRegressor$`DNNLinearCombinedRegressor`}: +* `tf.estimator.DNNLinearCombinedClassifier` and + `tf.estimator.DNNLinearCombinedRegressor`: * The `linear_feature_columns` argument accepts any feature column type. * The `dnn_feature_columns` argument only accepts dense columns. diff --git a/tensorflow/docs_src/guide/graph_viz.md b/tensorflow/docs_src/guide/graph_viz.md index a8876da5a5b8e989196f3bb28526d27e9d7d32af..97b0e2d4de8e8658f6cde787bc030fe074e59d49 100644 --- a/tensorflow/docs_src/guide/graph_viz.md +++ b/tensorflow/docs_src/guide/graph_viz.md @@ -15,7 +15,7 @@ variable names can be scoped and the visualization uses this information to define a hierarchy on the nodes in the graph. By default, only the top of this hierarchy is shown. Here is an example that defines three operations under the `hidden` name scope using -@{tf.name_scope}: +`tf.name_scope`: ```python import tensorflow as tf diff --git a/tensorflow/docs_src/guide/graphs.md b/tensorflow/docs_src/guide/graphs.md index 492f97c19143315c54e10711c7cb1e1993e99fd7..2bb44fbb327d14fe2650bfa8adb1740312f136f0 100644 --- a/tensorflow/docs_src/guide/graphs.md +++ b/tensorflow/docs_src/guide/graphs.md @@ -7,7 +7,7 @@ TensorFlow **session** to run parts of the graph across a set of local and remote devices. This guide will be most useful if you intend to use the low-level programming -model directly. Higher-level APIs such as @{tf.estimator.Estimator} and Keras +model directly. Higher-level APIs such as `tf.estimator.Estimator` and Keras hide the details of graphs and sessions from the end user, but this guide may also be useful if you want to understand how these APIs are implemented. @@ -18,12 +18,12 @@ also be useful if you want to understand how these APIs are implemented. [Dataflow](https://en.wikipedia.org/wiki/Dataflow_programming) is a common programming model for parallel computing. In a dataflow graph, the nodes represent units of computation, and the edges represent the data consumed or -produced by a computation. For example, in a TensorFlow graph, the @{tf.matmul} +produced by a computation. For example, in a TensorFlow graph, the `tf.matmul` operation would correspond to a single node with two incoming edges (the matrices to be multiplied) and one outgoing edge (the result of the multiplication). - + Dataflow has several advantages that TensorFlow leverages when executing your programs: @@ -48,9 +48,9 @@ programs: low-latency inference. -## What is a @{tf.Graph}? +## What is a `tf.Graph`? -A @{tf.Graph} contains two relevant kinds of information: +A `tf.Graph` contains two relevant kinds of information: * **Graph structure.** The nodes and edges of the graph, indicating how individual operations are composed together, but not prescribing how they @@ -59,78 +59,78 @@ A @{tf.Graph} contains two relevant kinds of information: context that source code conveys. * **Graph collections.** TensorFlow provides a general mechanism for storing - collections of metadata in a @{tf.Graph}. The @{tf.add_to_collection} function - enables you to associate a list of objects with a key (where @{tf.GraphKeys} - defines some of the standard keys), and @{tf.get_collection} enables you to + collections of metadata in a `tf.Graph`. The `tf.add_to_collection` function + enables you to associate a list of objects with a key (where `tf.GraphKeys` + defines some of the standard keys), and `tf.get_collection` enables you to look up all objects associated with a key. Many parts of the TensorFlow - library use this facility: for example, when you create a @{tf.Variable}, it + library use this facility: for example, when you create a `tf.Variable`, it is added by default to collections representing "global variables" and - "trainable variables". When you later come to create a @{tf.train.Saver} or - @{tf.train.Optimizer}, the variables in these collections are used as the + "trainable variables". When you later come to create a `tf.train.Saver` or + `tf.train.Optimizer`, the variables in these collections are used as the default arguments. -## Building a @{tf.Graph} +## Building a `tf.Graph` Most TensorFlow programs start with a dataflow graph construction phase. In this -phase, you invoke TensorFlow API functions that construct new @{tf.Operation} -(node) and @{tf.Tensor} (edge) objects and add them to a @{tf.Graph} +phase, you invoke TensorFlow API functions that construct new `tf.Operation` +(node) and `tf.Tensor` (edge) objects and add them to a `tf.Graph` instance. TensorFlow provides a **default graph** that is an implicit argument to all API functions in the same context. For example: -* Calling `tf.constant(42.0)` creates a single @{tf.Operation} that produces the - value `42.0`, adds it to the default graph, and returns a @{tf.Tensor} that +* Calling `tf.constant(42.0)` creates a single `tf.Operation` that produces the + value `42.0`, adds it to the default graph, and returns a `tf.Tensor` that represents the value of the constant. -* Calling `tf.matmul(x, y)` creates a single @{tf.Operation} that multiplies - the values of @{tf.Tensor} objects `x` and `y`, adds it to the default graph, - and returns a @{tf.Tensor} that represents the result of the multiplication. +* Calling `tf.matmul(x, y)` creates a single `tf.Operation` that multiplies + the values of `tf.Tensor` objects `x` and `y`, adds it to the default graph, + and returns a `tf.Tensor` that represents the result of the multiplication. -* Executing `v = tf.Variable(0)` adds to the graph a @{tf.Operation} that will - store a writeable tensor value that persists between @{tf.Session.run} calls. - The @{tf.Variable} object wraps this operation, and can be used [like a +* Executing `v = tf.Variable(0)` adds to the graph a `tf.Operation` that will + store a writeable tensor value that persists between `tf.Session.run` calls. + The `tf.Variable` object wraps this operation, and can be used [like a tensor](#tensor-like_objects), which will read the current value of the - stored value. The @{tf.Variable} object also has methods such as - @{tf.Variable.assign$`assign`} and @{tf.Variable.assign_add$`assign_add`} that - create @{tf.Operation} objects that, when executed, update the stored value. + stored value. The `tf.Variable` object also has methods such as + `tf.Variable.assign` and `tf.Variable.assign_add` that + create `tf.Operation` objects that, when executed, update the stored value. (See @{$guide/variables} for more information about variables.) -* Calling @{tf.train.Optimizer.minimize} will add operations and tensors to the - default graph that calculates gradients, and return a @{tf.Operation} that, +* Calling `tf.train.Optimizer.minimize` will add operations and tensors to the + default graph that calculates gradients, and return a `tf.Operation` that, when run, will apply those gradients to a set of variables. Most programs rely solely on the default graph. However, see [Dealing with multiple graphs](#programming_with_multiple_graphs) for more -advanced use cases. High-level APIs such as the @{tf.estimator.Estimator} API +advanced use cases. High-level APIs such as the `tf.estimator.Estimator` API manage the default graph on your behalf, and--for example--may create different graphs for training and evaluation. Note: Calling most functions in the TensorFlow API merely adds operations and tensors to the default graph, but **does not** perform the actual -computation. Instead, you compose these functions until you have a @{tf.Tensor} -or @{tf.Operation} that represents the overall computation--such as performing -one step of gradient descent--and then pass that object to a @{tf.Session} to -perform the computation. See the section "Executing a graph in a @{tf.Session}" +computation. Instead, you compose these functions until you have a `tf.Tensor` +or `tf.Operation` that represents the overall computation--such as performing +one step of gradient descent--and then pass that object to a `tf.Session` to +perform the computation. See the section "Executing a graph in a `tf.Session`" for more details. ## Naming operations -A @{tf.Graph} object defines a **namespace** for the @{tf.Operation} objects it +A `tf.Graph` object defines a **namespace** for the `tf.Operation` objects it contains. TensorFlow automatically chooses a unique name for each operation in your graph, but giving operations descriptive names can make your program easier to read and debug. The TensorFlow API provides two ways to override the name of an operation: -* Each API function that creates a new @{tf.Operation} or returns a new - @{tf.Tensor} accepts an optional `name` argument. For example, - `tf.constant(42.0, name="answer")` creates a new @{tf.Operation} named - `"answer"` and returns a @{tf.Tensor} named `"answer:0"`. If the default graph +* Each API function that creates a new `tf.Operation` or returns a new + `tf.Tensor` accepts an optional `name` argument. For example, + `tf.constant(42.0, name="answer")` creates a new `tf.Operation` named + `"answer"` and returns a `tf.Tensor` named `"answer:0"`. If the default graph already contains an operation named `"answer"`, then TensorFlow would append `"_1"`, `"_2"`, and so on to the name, in order to make it unique. -* The @{tf.name_scope} function makes it possible to add a **name scope** prefix +* The `tf.name_scope` function makes it possible to add a **name scope** prefix to all operations created in a particular context. The current name scope - prefix is a `"/"`-delimited list of the names of all active @{tf.name_scope} + prefix is a `"/"`-delimited list of the names of all active `tf.name_scope` context managers. If a name scope has already been used in the current context, TensorFlow appends `"_1"`, `"_2"`, and so on. For example: @@ -160,7 +160,7 @@ The graph visualizer uses name scopes to group operations and reduce the visual complexity of a graph. See [Visualizing your graph](#visualizing-your-graph) for more information. -Note that @{tf.Tensor} objects are implicitly named after the @{tf.Operation} +Note that `tf.Tensor` objects are implicitly named after the `tf.Operation` that produces the tensor as output. A tensor name has the form `":"` where: @@ -171,7 +171,7 @@ where: ## Placing operations on different devices If you want your TensorFlow program to use multiple different devices, the -@{tf.device} function provides a convenient way to request that all operations +`tf.device` function provides a convenient way to request that all operations created in a particular context are placed on the same device (or type of device). @@ -186,7 +186,7 @@ where: * `` is an alpha-numeric string that does not start with a number. * `` is a registered device type (such as `GPU` or `CPU`). * `` is a non-negative integer representing the index of the task - in the job named ``. See @{tf.train.ClusterSpec} for an explanation + in the job named ``. See `tf.train.ClusterSpec` for an explanation of jobs and tasks. * `` is a non-negative integer representing the index of the device, for example, to distinguish between different GPU devices used in the @@ -194,7 +194,7 @@ where: You do not need to specify every part of a device specification. For example, if you are running in a single-machine configuration with a single GPU, you -might use @{tf.device} to pin some operations to the CPU and GPU: +might use `tf.device` to pin some operations to the CPU and GPU: ```python # Operations created outside either context will run on the "best possible" @@ -229,13 +229,13 @@ with tf.device("/job:worker"): layer_2 = tf.matmul(train_batch, weights_2) + biases_2 ``` -@{tf.device} gives you a lot of flexibility to choose placements for individual +`tf.device` gives you a lot of flexibility to choose placements for individual operations or broad regions of a TensorFlow graph. In many cases, there are simple heuristics that work well. For example, the -@{tf.train.replica_device_setter} API can be used with @{tf.device} to place +`tf.train.replica_device_setter` API can be used with `tf.device` to place operations for **data-parallel distributed training**. For example, the -following code fragment shows how @{tf.train.replica_device_setter} applies -different placement policies to @{tf.Variable} objects and other operations: +following code fragment shows how `tf.train.replica_device_setter` applies +different placement policies to `tf.Variable` objects and other operations: ```python with tf.device(tf.train.replica_device_setter(ps_tasks=3)): @@ -253,41 +253,41 @@ with tf.device(tf.train.replica_device_setter(ps_tasks=3)): ## Tensor-like objects -Many TensorFlow operations take one or more @{tf.Tensor} objects as arguments. -For example, @{tf.matmul} takes two @{tf.Tensor} objects, and @{tf.add_n} takes -a list of `n` @{tf.Tensor} objects. For convenience, these functions will accept -a **tensor-like object** in place of a @{tf.Tensor}, and implicitly convert it -to a @{tf.Tensor} using the @{tf.convert_to_tensor} method. Tensor-like objects +Many TensorFlow operations take one or more `tf.Tensor` objects as arguments. +For example, `tf.matmul` takes two `tf.Tensor` objects, and `tf.add_n` takes +a list of `n` `tf.Tensor` objects. For convenience, these functions will accept +a **tensor-like object** in place of a `tf.Tensor`, and implicitly convert it +to a `tf.Tensor` using the `tf.convert_to_tensor` method. Tensor-like objects include elements of the following types: -* @{tf.Tensor} -* @{tf.Variable} +* `tf.Tensor` +* `tf.Variable` * [`numpy.ndarray`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html) * `list` (and lists of tensor-like objects) * Scalar Python types: `bool`, `float`, `int`, `str` You can register additional tensor-like types using -@{tf.register_tensor_conversion_function}. +`tf.register_tensor_conversion_function`. -Note: By default, TensorFlow will create a new @{tf.Tensor} each time you use +Note: By default, TensorFlow will create a new `tf.Tensor` each time you use the same tensor-like object. If the tensor-like object is large (e.g. a `numpy.ndarray` containing a set of training examples) and you use it multiple times, you may run out of memory. To avoid this, manually call -@{tf.convert_to_tensor} on the tensor-like object once and use the returned -@{tf.Tensor} instead. +`tf.convert_to_tensor` on the tensor-like object once and use the returned +`tf.Tensor` instead. -## Executing a graph in a @{tf.Session} +## Executing a graph in a `tf.Session` -TensorFlow uses the @{tf.Session} class to represent a connection between the +TensorFlow uses the `tf.Session` class to represent a connection between the client program---typically a Python program, although a similar interface is -available in other languages---and the C++ runtime. A @{tf.Session} object +available in other languages---and the C++ runtime. A `tf.Session` object provides access to devices in the local machine, and remote devices using the distributed TensorFlow runtime. It also caches information about your -@{tf.Graph} so that you can efficiently run the same computation multiple times. +`tf.Graph` so that you can efficiently run the same computation multiple times. -### Creating a @{tf.Session} +### Creating a `tf.Session` -If you are using the low-level TensorFlow API, you can create a @{tf.Session} +If you are using the low-level TensorFlow API, you can create a `tf.Session` for the current default graph as follows: ```python @@ -300,50 +300,50 @@ with tf.Session("grpc://example.org:2222"): # ... ``` -Since a @{tf.Session} owns physical resources (such as GPUs and +Since a `tf.Session` owns physical resources (such as GPUs and network connections), it is typically used as a context manager (in a `with` block) that automatically closes the session when you exit the block. It is also possible to create a session without using a `with` block, but you should -explicitly call @{tf.Session.close} when you are finished with it to free the +explicitly call `tf.Session.close` when you are finished with it to free the resources. -Note: Higher-level APIs such as @{tf.train.MonitoredTrainingSession} or -@{tf.estimator.Estimator} will create and manage a @{tf.Session} for you. These +Note: Higher-level APIs such as `tf.train.MonitoredTrainingSession` or +`tf.estimator.Estimator` will create and manage a `tf.Session` for you. These APIs accept optional `target` and `config` arguments (either directly, or as -part of a @{tf.estimator.RunConfig} object), with the same meaning as +part of a `tf.estimator.RunConfig` object), with the same meaning as described below. -@{tf.Session.__init__} accepts three optional arguments: +`tf.Session.__init__` accepts three optional arguments: * **`target`.** If this argument is left empty (the default), the session will only use devices in the local machine. However, you may also specify a `grpc://` URL to specify the address of a TensorFlow server, which gives the session access to all devices on machines that this server controls. See - @{tf.train.Server} for details of how to create a TensorFlow + `tf.train.Server` for details of how to create a TensorFlow server. For example, in the common **between-graph replication** - configuration, the @{tf.Session} connects to a @{tf.train.Server} in the same + configuration, the `tf.Session` connects to a `tf.train.Server` in the same process as the client. The [distributed TensorFlow](../deploy/distributed.md) deployment guide describes other common scenarios. -* **`graph`.** By default, a new @{tf.Session} will be bound to---and only able +* **`graph`.** By default, a new `tf.Session` will be bound to---and only able to run operations in---the current default graph. If you are using multiple graphs in your program (see [Programming with multiple graphs](#programming_with_multiple_graphs) for more details), you can specify - an explicit @{tf.Graph} when you construct the session. + an explicit `tf.Graph` when you construct the session. -* **`config`.** This argument allows you to specify a @{tf.ConfigProto} that +* **`config`.** This argument allows you to specify a `tf.ConfigProto` that controls the behavior of the session. For example, some of the configuration options include: * `allow_soft_placement`. Set this to `True` to enable a "soft" device - placement algorithm, which ignores @{tf.device} annotations that attempt + placement algorithm, which ignores `tf.device` annotations that attempt to place CPU-only operations on a GPU device, and places them on the CPU instead. * `cluster_def`. When using distributed TensorFlow, this option allows you to specify what machines to use in the computation, and provide a mapping between job names, task indices, and network addresses. See - @{tf.train.ClusterSpec.as_cluster_def} for details. + `tf.train.ClusterSpec.as_cluster_def` for details. * `graph_options.optimizer_options`. Provides control over the optimizations that TensorFlow performs on your graph before executing it. @@ -353,21 +353,21 @@ described below. rather than allocating most of the memory at startup. -### Using @{tf.Session.run} to execute operations +### Using `tf.Session.run` to execute operations -The @{tf.Session.run} method is the main mechanism for running a @{tf.Operation} -or evaluating a @{tf.Tensor}. You can pass one or more @{tf.Operation} or -@{tf.Tensor} objects to @{tf.Session.run}, and TensorFlow will execute the +The `tf.Session.run` method is the main mechanism for running a `tf.Operation` +or evaluating a `tf.Tensor`. You can pass one or more `tf.Operation` or +`tf.Tensor` objects to `tf.Session.run`, and TensorFlow will execute the operations that are needed to compute the result. -@{tf.Session.run} requires you to specify a list of **fetches**, which determine -the return values, and may be a @{tf.Operation}, a @{tf.Tensor}, or -a [tensor-like type](#tensor-like_objects) such as @{tf.Variable}. These fetches -determine what **subgraph** of the overall @{tf.Graph} must be executed to +`tf.Session.run` requires you to specify a list of **fetches**, which determine +the return values, and may be a `tf.Operation`, a `tf.Tensor`, or +a [tensor-like type](#tensor-like_objects) such as `tf.Variable`. These fetches +determine what **subgraph** of the overall `tf.Graph` must be executed to produce the result: this is the subgraph that contains all operations named in the fetch list, plus all operations whose outputs are used to compute the value of the fetches. For example, the following code fragment shows how different -arguments to @{tf.Session.run} cause different subgraphs to be executed: +arguments to `tf.Session.run` cause different subgraphs to be executed: ```python x = tf.constant([[37.0, -23.0], [1.0, 4.0]]) @@ -390,8 +390,8 @@ with tf.Session() as sess: y_val, output_val = sess.run([y, output]) ``` -@{tf.Session.run} also optionally takes a dictionary of **feeds**, which is a -mapping from @{tf.Tensor} objects (typically @{tf.placeholder} tensors) to +`tf.Session.run` also optionally takes a dictionary of **feeds**, which is a +mapping from `tf.Tensor` objects (typically `tf.placeholder` tensors) to values (typically Python scalars, lists, or NumPy arrays) that will be substituted for those tensors in the execution. For example: @@ -415,7 +415,7 @@ with tf.Session() as sess: sess.run(y, {x: 37.0}) ``` -@{tf.Session.run} also accepts an optional `options` argument that enables you +`tf.Session.run` also accepts an optional `options` argument that enables you to specify options about the call, and an optional `run_metadata` argument that enables you to collect metadata about the execution. For example, you can use these options together to collect tracing information about the execution: @@ -447,8 +447,8 @@ with tf.Session() as sess: TensorFlow includes tools that can help you to understand the code in a graph. The **graph visualizer** is a component of TensorBoard that renders the structure of your graph visually in a browser. The easiest way to create a -visualization is to pass a @{tf.Graph} when creating the -@{tf.summary.FileWriter}: +visualization is to pass a `tf.Graph` when creating the +`tf.summary.FileWriter`: ```python # Build your graph. @@ -471,7 +471,7 @@ with tf.Session() as sess: writer.close() ``` -Note: If you are using a @{tf.estimator.Estimator}, the graph (and any +Note: If you are using a `tf.estimator.Estimator`, the graph (and any summaries) will be logged automatically to the `model_dir` that you specified when creating the estimator. @@ -495,8 +495,8 @@ graph for training your model, and a separate graph for evaluating or performing inference with a trained model. In many cases, the inference graph will be different from the training graph: for example, techniques like dropout and batch normalization use different operations in each case. Furthermore, by -default utilities like @{tf.train.Saver} use the names of @{tf.Variable} objects -(which have names based on an underlying @{tf.Operation}) to identify each +default utilities like `tf.train.Saver` use the names of `tf.Variable` objects +(which have names based on an underlying `tf.Operation`) to identify each variable in a saved checkpoint. When programming this way, you can either use completely separate Python processes to build and execute the graphs, or you can use multiple graphs in the same process. This section describes how to use @@ -507,21 +507,21 @@ to all API functions in the same context. For many applications, a single graph is sufficient. However, TensorFlow also provides methods for manipulating the default graph, which can be useful in more advanced use cases. For example: -* A @{tf.Graph} defines the namespace for @{tf.Operation} objects: each +* A `tf.Graph` defines the namespace for `tf.Operation` objects: each operation in a single graph must have a unique name. TensorFlow will "uniquify" the names of operations by appending `"_1"`, `"_2"`, and so on to their names if the requested name is already taken. Using multiple explicitly created graphs gives you more control over what name is given to each operation. -* The default graph stores information about every @{tf.Operation} and - @{tf.Tensor} that was ever added to it. If your program creates a large number +* The default graph stores information about every `tf.Operation` and + `tf.Tensor` that was ever added to it. If your program creates a large number of unconnected subgraphs, it may be more efficient to use a different - @{tf.Graph} to build each subgraph, so that unrelated state can be garbage + `tf.Graph` to build each subgraph, so that unrelated state can be garbage collected. -You can install a different @{tf.Graph} as the default graph, using the -@{tf.Graph.as_default} context manager: +You can install a different `tf.Graph` as the default graph, using the +`tf.Graph.as_default` context manager: ```python g_1 = tf.Graph() @@ -548,8 +548,8 @@ assert d.graph is g_2 assert sess_2.graph is g_2 ``` -To inspect the current default graph, call @{tf.get_default_graph}, which -returns a @{tf.Graph} object: +To inspect the current default graph, call `tf.get_default_graph`, which +returns a `tf.Graph` object: ```python # Print all of the operations in the default graph. diff --git a/tensorflow/docs_src/guide/index.md b/tensorflow/docs_src/guide/index.md index f78dfc9a89451440e12303ecd42ffef801a96601..1c920e7d700c29b2851927beafa5ca4207787a09 100644 --- a/tensorflow/docs_src/guide/index.md +++ b/tensorflow/docs_src/guide/index.md @@ -9,14 +9,13 @@ works. The units are as follows: training deep learning models. * @{$guide/eager}, an API for writing TensorFlow code imperatively, like you would use Numpy. - * @{$guide/estimators}, a high-level API that provides - fully-packaged models ready for large-scale training and production. * @{$guide/datasets}, easy input pipelines to bring your data into your TensorFlow program. + * @{$guide/estimators}, a high-level API that provides + fully-packaged models ready for large-scale training and production. ## Estimators -* @{$estimators}, learn how to use Estimators for machine learning. * @{$premade_estimators}, the basics of premade Estimators. * @{$checkpoints}, save training progress and resume where you left off. * @{$feature_columns}, handle a variety of input data types without changes to the model. diff --git a/tensorflow/docs_src/guide/leftnav_files b/tensorflow/docs_src/guide/leftnav_files index c4e235b41a0e7708ded4c0e571833aefe01c4fb2..8e227e0c8fc5cf7a30ed222706f89db9af482ec0 100644 --- a/tensorflow/docs_src/guide/leftnav_files +++ b/tensorflow/docs_src/guide/leftnav_files @@ -4,9 +4,9 @@ index.md keras.md eager.md datasets.md +estimators.md: Introduction to Estimators ### Estimators -estimators.md: Introduction to Estimators premade_estimators.md checkpoints.md feature_columns.md diff --git a/tensorflow/docs_src/guide/low_level_intro.md b/tensorflow/docs_src/guide/low_level_intro.md index 665a5568b49a4cf3ee47d60617116f73e0db364f..dc6cb9ee0dfec37ce56f2c791f99f3f4917cf4f9 100644 --- a/tensorflow/docs_src/guide/low_level_intro.md +++ b/tensorflow/docs_src/guide/low_level_intro.md @@ -63,17 +63,17 @@ TensorFlow uses numpy arrays to represent tensor **values**. You might think of TensorFlow Core programs as consisting of two discrete sections: -1. Building the computational graph (a @{tf.Graph}). -2. Running the computational graph (using a @{tf.Session}). +1. Building the computational graph (a `tf.Graph`). +2. Running the computational graph (using a `tf.Session`). ### Graph A **computational graph** is a series of TensorFlow operations arranged into a graph. The graph is composed of two types of objects. - * @{tf.Operation$Operations} (or "ops"): The nodes of the graph. + * `tf.Operation` (or "ops"): The nodes of the graph. Operations describe calculations that consume and produce tensors. - * @{tf.Tensor$Tensors}: The edges in the graph. These represent the values + * `tf.Tensor`: The edges in the graph. These represent the values that will flow through the graph. Most TensorFlow functions return `tf.Tensors`. @@ -149,7 +149,7 @@ For more about TensorBoard's graph visualization tools see @{$graph_viz}. ### Session -To evaluate tensors, instantiate a @{tf.Session} object, informally known as a +To evaluate tensors, instantiate a `tf.Session` object, informally known as a **session**. A session encapsulates the state of the TensorFlow runtime, and runs TensorFlow operations. If a `tf.Graph` is like a `.py` file, a `tf.Session` is like the `python` executable. @@ -232,7 +232,7 @@ z = x + y The preceding three lines are a bit like a function in which we define two input parameters (`x` and `y`) and then an operation on them. We can evaluate this graph with multiple inputs by using the `feed_dict` argument of -the @{tf.Session.run$run method} to feed concrete values to the placeholders: +the `tf.Session.run` method to feed concrete values to the placeholders: ```python print(sess.run(z, feed_dict={x: 3, y: 4.5})) @@ -251,15 +251,15 @@ that placeholders throw an error if no value is fed to them. ## Datasets -Placeholders work for simple experiments, but @{tf.data$Datasets} are the +Placeholders work for simple experiments, but `tf.data` are the preferred method of streaming data into a model. To get a runnable `tf.Tensor` from a Dataset you must first convert it to a -@{tf.data.Iterator}, and then call the Iterator's -@{tf.data.Iterator.get_next$`get_next`} method. +`tf.data.Iterator`, and then call the Iterator's +`tf.data.Iterator.get_next` method. The simplest way to create an Iterator is with the -@{tf.data.Dataset.make_one_shot_iterator$`make_one_shot_iterator`} method. +`tf.data.Dataset.make_one_shot_iterator` method. For example, in the following code the `next_item` tensor will return a row from the `my_data` array on each `run` call: @@ -275,7 +275,7 @@ next_item = slices.make_one_shot_iterator().get_next() ``` Reaching the end of the data stream causes `Dataset` to throw an -@{tf.errors.OutOfRangeError$`OutOfRangeError`}. For example, the following code +`tf.errors.OutOfRangeError`. For example, the following code reads the `next_item` until there is no more data to read: ``` python @@ -308,7 +308,7 @@ For more details on Datasets and Iterators see: @{$guide/datasets}. ## Layers A trainable model must modify the values in the graph to get new outputs with -the same input. @{tf.layers$Layers} are the preferred way to add trainable +the same input. `tf.layers` are the preferred way to add trainable parameters to a graph. Layers package together both the variables and the operations that act @@ -321,7 +321,7 @@ The connection weights and biases are managed by the layer object. ### Creating Layers -The following code creates a @{tf.layers.Dense$`Dense`} layer that takes a +The following code creates a `tf.layers.Dense` layer that takes a batch of input vectors, and produces a single output value for each. To apply a layer to an input, call the layer as if it were a function. For example: @@ -375,8 +375,8 @@ will generate a two-element output vector such as the following: ### Layer Function shortcuts -For each layer class (like @{tf.layers.Dense}) TensorFlow also supplies a -shortcut function (like @{tf.layers.dense}). The only difference is that the +For each layer class (like `tf.layers.Dense`) TensorFlow also supplies a +shortcut function (like `tf.layers.dense`). The only difference is that the shortcut function versions create and run the layer in a single call. For example, the following code is equivalent to the earlier version: @@ -390,17 +390,17 @@ sess.run(init) print(sess.run(y, {x: [[1, 2, 3], [4, 5, 6]]})) ``` -While convenient, this approach allows no access to the @{tf.layers.Layer} +While convenient, this approach allows no access to the `tf.layers.Layer` object. This makes introspection and debugging more difficult, and layer reuse impossible. ## Feature columns The easiest way to experiment with feature columns is using the -@{tf.feature_column.input_layer} function. This function only accepts +`tf.feature_column.input_layer` function. This function only accepts @{$feature_columns$dense columns} as inputs, so to view the result of a categorical column you must wrap it in an -@{tf.feature_column.indicator_column}. For example: +`tf.feature_column.indicator_column`. For example: ``` python features = { @@ -422,9 +422,9 @@ inputs = tf.feature_column.input_layer(features, columns) Running the `inputs` tensor will parse the `features` into a batch of vectors. Feature columns can have internal state, like layers, so they often need to be -initialized. Categorical columns use @{tf.contrib.lookup$lookup tables} +initialized. Categorical columns use `tf.contrib.lookup` internally and these require a separate initialization op, -@{tf.tables_initializer}. +`tf.tables_initializer`. ``` python var_init = tf.global_variables_initializer() @@ -501,7 +501,7 @@ To optimize a model, you first need to define the loss. We'll use the mean square error, a standard loss for regression problems. While you could do this manually with lower level math operations, -the @{tf.losses} module provides a set of common loss functions. You can use it +the `tf.losses` module provides a set of common loss functions. You can use it to calculate the mean square error as follows: ``` python @@ -520,10 +520,10 @@ This will produce a loss value, something like: TensorFlow provides [**optimizers**](https://developers.google.com/machine-learning/glossary/#optimizer) implementing standard optimization algorithms. These are implemented as -sub-classes of @{tf.train.Optimizer}. They incrementally change each +sub-classes of `tf.train.Optimizer`. They incrementally change each variable in order to minimize the loss. The simplest optimization algorithm is [**gradient descent**](https://developers.google.com/machine-learning/glossary/#gradient_descent), -implemented by @{tf.train.GradientDescentOptimizer}. It modifies each +implemented by `tf.train.GradientDescentOptimizer`. It modifies each variable according to the magnitude of the derivative of loss with respect to that variable. For example: diff --git a/tensorflow/docs_src/guide/premade_estimators.md b/tensorflow/docs_src/guide/premade_estimators.md index 3e910c1fe2ebfdffc25044f15b3558407d407ef1..dc38f0c1d38d8ffed8abb820eadf7f093307d01b 100644 --- a/tensorflow/docs_src/guide/premade_estimators.md +++ b/tensorflow/docs_src/guide/premade_estimators.md @@ -175,9 +175,9 @@ handles the details of initialization, logging, saving and restoring, and many other features so you can concentrate on your model. For more details see @{$guide/estimators}. -An Estimator is any class derived from @{tf.estimator.Estimator}. TensorFlow +An Estimator is any class derived from `tf.estimator.Estimator`. TensorFlow provides a collection of -@{tf.estimator$pre-made Estimators} +`tf.estimator` (for example, `LinearRegressor`) to implement common ML algorithms. Beyond those, you may write your own @{$custom_estimators$custom Estimators}. @@ -200,7 +200,7 @@ Let's see how those tasks are implemented for Iris classification. You must create input functions to supply data for training, evaluating, and prediction. -An **input function** is a function that returns a @{tf.data.Dataset} object +An **input function** is a function that returns a `tf.data.Dataset` object which outputs the following two-element tuple: * [`features`](https://developers.google.com/machine-learning/glossary/#feature) - A Python dictionary in which: @@ -271,7 +271,7 @@ A [**feature column**](https://developers.google.com/machine-learning/glossary/# is an object describing how the model should use raw input data from the features dictionary. When you build an Estimator model, you pass it a list of feature columns that describes each of the features you want the model to use. -The @{tf.feature_column} module provides many options for representing data +The `tf.feature_column` module provides many options for representing data to the model. For Iris, the 4 raw features are numeric values, so we'll build a list of @@ -299,10 +299,10 @@ features, we can build the estimator. The Iris problem is a classic classification problem. Fortunately, TensorFlow provides several pre-made classifier Estimators, including: -* @{tf.estimator.DNNClassifier} for deep models that perform multi-class +* `tf.estimator.DNNClassifier` for deep models that perform multi-class classification. -* @{tf.estimator.DNNLinearCombinedClassifier} for wide & deep models. -* @{tf.estimator.LinearClassifier} for classifiers based on linear models. +* `tf.estimator.DNNLinearCombinedClassifier` for wide & deep models. +* `tf.estimator.LinearClassifier` for classifiers based on linear models. For the Iris problem, `tf.estimator.DNNClassifier` seems like the best choice. Here's how we instantiated this Estimator: diff --git a/tensorflow/docs_src/guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md index 717488e7cc2643d03394f01ff6e18963fae80e31..c260da79668807eaefb3811fd475151571cb69bf 100644 --- a/tensorflow/docs_src/guide/saved_model.md +++ b/tensorflow/docs_src/guide/saved_model.md @@ -1,8 +1,8 @@ # Save and Restore -The @{tf.train.Saver} class provides methods to save and restore models. The -@{tf.saved_model.simple_save} function is an easy way to build a -@{tf.saved_model$saved model} suitable for serving. [Estimators](./estimators) +The `tf.train.Saver` class provides methods to save and restore models. The +`tf.saved_model.simple_save` function is an easy way to build a +`tf.saved_model` suitable for serving. [Estimators](./estimators) automatically save and restore variables in the `model_dir`. ## Save and restore variables @@ -145,13 +145,13 @@ Notes: * If you only restore a subset of the model variables at the start of a session, you have to run an initialize op for the other variables. See - @{tf.variables_initializer} for more information. + `tf.variables_initializer` for more information. * To inspect the variables in a checkpoint, you can use the [`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library, particularly the `print_tensors_in_checkpoint_file` function. -* By default, `Saver` uses the value of the @{tf.Variable.name} property +* By default, `Saver` uses the value of the `tf.Variable.name` property for each variable. However, when you create a `Saver` object, you may optionally choose names for the variables in the checkpoint files. @@ -196,15 +196,15 @@ Use `SavedModel` to save and load your model—variables, the graph, and the graph's metadata. This is a language-neutral, recoverable, hermetic serialization format that enables higher-level systems and tools to produce, consume, and transform TensorFlow models. TensorFlow provides several ways to -interact with `SavedModel`, including the @{tf.saved_model} APIs, -@{tf.estimator.Estimator}, and a command-line interface. +interact with `SavedModel`, including the `tf.saved_model` APIs, +`tf.estimator.Estimator`, and a command-line interface. ## Build and load a SavedModel ### Simple save -The easiest way to create a `SavedModel` is to use the @{tf.saved_model.simple_save} +The easiest way to create a `SavedModel` is to use the `tf.saved_model.simple_save` function: ```python @@ -218,14 +218,14 @@ This configures the `SavedModel` so it can be loaded by [TensorFlow serving](/serving/serving_basic) and supports the [Predict API](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/predict.proto). To access the classify, regress, or multi-inference APIs, use the manual -`SavedModel` builder APIs or an @{tf.estimator.Estimator}. +`SavedModel` builder APIs or an `tf.estimator.Estimator`. ### Manually build a SavedModel -If your use case isn't covered by @{tf.saved_model.simple_save}, use the manual -@{tf.saved_model.builder$builder APIs} to create a `SavedModel`. +If your use case isn't covered by `tf.saved_model.simple_save`, use the manual +`tf.saved_model.builder` to create a `SavedModel`. -The @{tf.saved_model.builder.SavedModelBuilder} class provides functionality to +The `tf.saved_model.builder.SavedModelBuilder` class provides functionality to save multiple `MetaGraphDef`s. A **MetaGraph** is a dataflow graph, plus its associated variables, assets, and signatures. A **`MetaGraphDef`** is the protocol buffer representation of a MetaGraph. A **signature** is @@ -272,16 +272,16 @@ builder.save() Following the guidance below gives you forward compatibility only if the set of Ops has not changed. -The @{tf.saved_model.builder.SavedModelBuilder$`SavedModelBuilder`} class allows +The `tf.saved_model.builder.SavedModelBuilder` class allows users to control whether default-valued attributes must be stripped from the @{$extend/tool_developers#nodes$`NodeDefs`} while adding a meta graph to the SavedModel bundle. Both -@{tf.saved_model.builder.SavedModelBuilder.add_meta_graph_and_variables$`SavedModelBuilder.add_meta_graph_and_variables`} -and @{tf.saved_model.builder.SavedModelBuilder.add_meta_graph$`SavedModelBuilder.add_meta_graph`} +`tf.saved_model.builder.SavedModelBuilder.add_meta_graph_and_variables` +and `tf.saved_model.builder.SavedModelBuilder.add_meta_graph` methods accept a Boolean flag `strip_default_attrs` that controls this behavior. -If `strip_default_attrs` is `False`, the exported @{tf.MetaGraphDef} will have -the default valued attributes in all its @{tf.NodeDef} instances. +If `strip_default_attrs` is `False`, the exported `tf.MetaGraphDef` will have +the default valued attributes in all its `tf.NodeDef` instances. This can break forward compatibility with a sequence of events such as the following: @@ -304,7 +304,7 @@ for more information. ### Loading a SavedModel in Python The Python version of the SavedModel -@{tf.saved_model.loader$loader} +`tf.saved_model.loader` provides load and restore capability for a SavedModel. The `load` operation requires the following information: @@ -423,20 +423,20 @@ the model. This function has the following purposes: * To add any additional ops needed to convert data from the input format into the feature `Tensor`s expected by the model. -The function returns a @{tf.estimator.export.ServingInputReceiver} object, +The function returns a `tf.estimator.export.ServingInputReceiver` object, which packages the placeholders and the resulting feature `Tensor`s together. A typical pattern is that inference requests arrive in the form of serialized `tf.Example`s, so the `serving_input_receiver_fn()` creates a single string placeholder to receive them. The `serving_input_receiver_fn()` is then also -responsible for parsing the `tf.Example`s by adding a @{tf.parse_example} op to +responsible for parsing the `tf.Example`s by adding a `tf.parse_example` op to the graph. When writing such a `serving_input_receiver_fn()`, you must pass a parsing -specification to @{tf.parse_example} to tell the parser what feature names to +specification to `tf.parse_example` to tell the parser what feature names to expect and how to map them to `Tensor`s. A parsing specification takes the -form of a dict from feature names to @{tf.FixedLenFeature}, @{tf.VarLenFeature}, -and @{tf.SparseFeature}. Note this parsing specification should not include +form of a dict from feature names to `tf.FixedLenFeature`, `tf.VarLenFeature`, +and `tf.SparseFeature`. Note this parsing specification should not include any label or weight columns, since those will not be available at serving time—in contrast to a parsing specification used in the `input_fn()` at training time. @@ -457,7 +457,7 @@ def serving_input_receiver_fn(): return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) ``` -The @{tf.estimator.export.build_parsing_serving_input_receiver_fn} utility +The `tf.estimator.export.build_parsing_serving_input_receiver_fn` utility function provides that input receiver for the common case. > Note: when training a model to be served using the Predict API with a local @@ -468,7 +468,7 @@ Even if you require no parsing or other input processing—that is, if the serving system will feed feature `Tensor`s directly—you must still provide a `serving_input_receiver_fn()` that creates placeholders for the feature `Tensor`s and passes them through. The -@{tf.estimator.export.build_raw_serving_input_receiver_fn} utility provides for +`tf.estimator.export.build_raw_serving_input_receiver_fn` utility provides for this. If these utilities do not meet your needs, you are free to write your own @@ -488,7 +488,7 @@ By contrast, the *output* portion of the signature is determined by the model. ### Specify the outputs of a custom model When writing a custom `model_fn`, you must populate the `export_outputs` element -of the @{tf.estimator.EstimatorSpec} return value. This is a dict of +of the `tf.estimator.EstimatorSpec` return value. This is a dict of `{name: output}` describing the output signatures to be exported and used during serving. @@ -498,9 +498,9 @@ is represented by an entry in this dict. In this case the `name` is a string of your choice that can be used to request a specific head at serving time. Each `output` value must be an `ExportOutput` object such as -@{tf.estimator.export.ClassificationOutput}, -@{tf.estimator.export.RegressionOutput}, or -@{tf.estimator.export.PredictOutput}. +`tf.estimator.export.ClassificationOutput`, +`tf.estimator.export.RegressionOutput`, or +`tf.estimator.export.PredictOutput`. These output types map straightforwardly to the [TensorFlow Serving APIs](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/prediction_service.proto), @@ -520,7 +520,7 @@ does not specify one. ### Perform the export To export your trained Estimator, call -@{tf.estimator.Estimator.export_savedmodel} with the export base path and +`tf.estimator.Estimator.export_savedmodel` with the export base path and the `serving_input_receiver_fn`. ```py diff --git a/tensorflow/docs_src/guide/summaries_and_tensorboard.md b/tensorflow/docs_src/guide/summaries_and_tensorboard.md index fadfa03e78349801d69e0045991a8fa9a0a59df9..6177c3393b203620842d88f9a18bfdde2239f369 100644 --- a/tensorflow/docs_src/guide/summaries_and_tensorboard.md +++ b/tensorflow/docs_src/guide/summaries_and_tensorboard.md @@ -41,7 +41,7 @@ data from, and decide which nodes you would like to annotate with For example, suppose you are training a convolutional neural network for recognizing MNIST digits. You'd like to record how the learning rate varies over time, and how the objective function is changing. Collect these by -attaching @{tf.summary.scalar} ops +attaching `tf.summary.scalar` ops to the nodes that output the learning rate and loss respectively. Then, give each `scalar_summary` a meaningful `tag`, like `'learning rate'` or `'loss function'`. @@ -49,7 +49,7 @@ function'`. Perhaps you'd also like to visualize the distributions of activations coming off a particular layer, or the distribution of gradients or weights. Collect this data by attaching -@{tf.summary.histogram} ops to +`tf.summary.histogram` ops to the gradient outputs and to the variable that holds your weights, respectively. For details on all of the summary operations available, check out the docs on @@ -60,13 +60,13 @@ depends on their output. And the summary nodes that we've just created are peripheral to your graph: none of the ops you are currently running depend on them. So, to generate summaries, we need to run all of these summary nodes. Managing them by hand would be tedious, so use -@{tf.summary.merge_all} +`tf.summary.merge_all` to combine them into a single op that generates all the summary data. Then, you can just run the merged summary op, which will generate a serialized `Summary` protobuf object with all of your summary data at a given step. Finally, to write this summary data to disk, pass the summary protobuf to a -@{tf.summary.FileWriter}. +`tf.summary.FileWriter`. The `FileWriter` takes a logdir in its constructor - this logdir is quite important, it's the directory where all of the events will be written out. diff --git a/tensorflow/docs_src/guide/tensors.md b/tensorflow/docs_src/guide/tensors.md index 7227260f1a4ee08309f42d21bab8eaa3c77e3297..6b5a110a1c3e59b2b9d18c8c43d56c4323bdbf55 100644 --- a/tensorflow/docs_src/guide/tensors.md +++ b/tensorflow/docs_src/guide/tensors.md @@ -176,7 +176,7 @@ Rank | Shape | Dimension number | Example n | [D0, D1, ... Dn-1] | n-D | A tensor with shape [D0, D1, ... Dn-1]. Shapes can be represented via Python lists / tuples of ints, or with the -@{tf.TensorShape}. +`tf.TensorShape`. ### Getting a `tf.Tensor` object's shape diff --git a/tensorflow/docs_src/guide/using_tpu.md b/tensorflow/docs_src/guide/using_tpu.md index 41d80d9d60694c87675f07d8045713d9a117c7f1..90a663b75ed87e724009897045abac7bb338e911 100644 --- a/tensorflow/docs_src/guide/using_tpu.md +++ b/tensorflow/docs_src/guide/using_tpu.md @@ -17,9 +17,9 @@ This doc is aimed at users who: ## TPUEstimator -@{tf.estimator.Estimator$Estimators} are TensorFlow's model-level abstraction. +`tf.estimator.Estimator` are TensorFlow's model-level abstraction. Standard `Estimators` can drive models on CPU and GPUs. You must use -@{tf.contrib.tpu.TPUEstimator} to drive a model on TPUs. +`tf.contrib.tpu.TPUEstimator` to drive a model on TPUs. Refer to TensorFlow's Getting Started section for an introduction to the basics of using a @{$premade_estimators$pre-made `Estimator`}, and @@ -44,10 +44,10 @@ my_estimator = tf.estimator.Estimator( model_fn=my_model_fn) ``` -The changes required to use a @{tf.contrib.tpu.TPUEstimator} on your local +The changes required to use a `tf.contrib.tpu.TPUEstimator` on your local machine are relatively minor. The constructor requires two additional arguments. You should set the `use_tpu` argument to `False`, and pass a -@{tf.contrib.tpu.RunConfig} as the `config` argument, as shown below: +`tf.contrib.tpu.RunConfig` as the `config` argument, as shown below: ``` python my_tpu_estimator = tf.contrib.tpu.TPUEstimator( @@ -117,7 +117,7 @@ my_tpu_run_config = tf.contrib.tpu.RunConfig( ) ``` -Then you must pass the @{tf.contrib.tpu.RunConfig} to the constructor: +Then you must pass the `tf.contrib.tpu.RunConfig` to the constructor: ``` python my_tpu_estimator = tf.contrib.tpu.TPUEstimator( @@ -137,7 +137,7 @@ training locally to training on a cloud TPU you would need to: ## Optimizer When training on a cloud TPU you **must** wrap the optimizer in a -@{tf.contrib.tpu.CrossShardOptimizer}, which uses an `allreduce` to aggregate +`tf.contrib.tpu.CrossShardOptimizer`, which uses an `allreduce` to aggregate gradients and broadcast the result to each shard (each TPU core). The `CrossShardOptimizer` is not compatible with local training. So, to have @@ -200,7 +200,7 @@ Build your evaluation metrics dictionary in a stand-alone `metric_fn`. Evaluation metrics are an essential part of training a model. These are fully supported on Cloud TPUs, but with a slightly different syntax. -A standard @{tf.metrics} returns two tensors. The first returns the running +A standard `tf.metrics` returns two tensors. The first returns the running average of the metric value, while the second updates the running average and returns the value for this batch: @@ -242,15 +242,15 @@ An `Estimator`'s `model_fn` must return an `EstimatorSpec`. An `EstimatorSpec` is a simple structure of named fields containing all the `tf.Tensors` of the model that the `Estimator` may need to interact with. -`TPUEstimators` use a @{tf.contrib.tpu.TPUEstimatorSpec}. There are a few -differences between it and a standard @{tf.estimator.EstimatorSpec}: +`TPUEstimators` use a `tf.contrib.tpu.TPUEstimatorSpec`. There are a few +differences between it and a standard `tf.estimator.EstimatorSpec`: * The `eval_metric_ops` must be wrapped into a `metrics_fn`, this field is renamed `eval_metrics` ([see above](#metrics)). -* The @{tf.train.SessionRunHook$hooks} are unsupported, so these fields are +* The `tf.train.SessionRunHook` are unsupported, so these fields are omitted. -* The @{tf.train.Scaffold$`scaffold`}, if used, must also be wrapped in a +* The `tf.train.Scaffold`, if used, must also be wrapped in a function. This field is renamed to `scaffold_fn`. `Scaffold` and `Hooks` are for advanced usage, and can typically be omitted. @@ -304,7 +304,7 @@ In many cases the batch size is the only unknown dimension. A typical input pipeline, using `tf.data`, will usually produce batches of a fixed size. The last batch of a finite `Dataset`, however, is typically smaller, containing just the remaining elements. Since a `Dataset` does not know its own -length or finiteness, the standard @{tf.data.Dataset.batch$`batch`} method +length or finiteness, the standard `tf.data.Dataset.batch` method cannot determine if all batches will have a fixed size batch on its own: ``` @@ -317,7 +317,7 @@ cannot determine if all batches will have a fixed size batch on its own: ``` The most straightforward fix is to -@{tf.data.Dataset.apply$apply} @{tf.contrib.data.batch_and_drop_remainder} +`tf.data.Dataset.apply` `tf.contrib.data.batch_and_drop_remainder` as follows: ``` @@ -346,19 +346,19 @@ TPU, as it is impossible to use the Cloud TPU's unless you can feed it data quickly enough. See @{$datasets_performance} for details on dataset performance. For all but the simplest experimentation (using -@{tf.data.Dataset.from_tensor_slices} or other in-graph data) you will need to +`tf.data.Dataset.from_tensor_slices` or other in-graph data) you will need to store all data files read by the `TPUEstimator`'s `Dataset` in Google Cloud Storage Buckets. For most use-cases, we recommend converting your data into `TFRecord` -format and using a @{tf.data.TFRecordDataset} to read it. This, however, is not +format and using a `tf.data.TFRecordDataset` to read it. This, however, is not a hard requirement and you can use other dataset readers (`FixedLengthRecordDataset` or `TextLineDataset`) if you prefer. Small datasets can be loaded entirely into memory using -@{tf.data.Dataset.cache}. +`tf.data.Dataset.cache`. Regardless of the data format used, it is strongly recommended that you @{$performance_guide#use_large_files$use large files}, on the order of diff --git a/tensorflow/docs_src/guide/variables.md b/tensorflow/docs_src/guide/variables.md index cd8c4b5b9a026f01af4957ade0e132477b0066a5..5d5d73394c6f2529c9af5513e2e8d661a1f8a147 100644 --- a/tensorflow/docs_src/guide/variables.md +++ b/tensorflow/docs_src/guide/variables.md @@ -119,7 +119,7 @@ It is particularly important for variables to be in the correct device in distributed settings. Accidentally putting variables on workers instead of parameter servers, for example, can severely slow down training or, in the worst case, let each worker blithely forge ahead with its own independent copy of each -variable. For this reason we provide @{tf.train.replica_device_setter}, which +variable. For this reason we provide `tf.train.replica_device_setter`, which can automatically place variables in parameter servers. For example: ``` python @@ -211,7 +211,7 @@ sess.run(assignment) # or assignment.op.run(), or assignment.eval() Most TensorFlow optimizers have specialized ops that efficiently update the values of variables according to some gradient descent-like algorithm. See -@{tf.train.Optimizer} for an explanation of how to use optimizers. +`tf.train.Optimizer` for an explanation of how to use optimizers. Because variables are mutable it's sometimes useful to know what version of a variable's value is being used at any point in time. To force a re-read of the diff --git a/tensorflow/docs_src/guide/version_compat.md b/tensorflow/docs_src/guide/version_compat.md index d2e5e41190d17f811ce862a086b5a97a47438182..29ac066e6f2b94fa456a3af2c851a5e87be765da 100644 --- a/tensorflow/docs_src/guide/version_compat.md +++ b/tensorflow/docs_src/guide/version_compat.md @@ -66,7 +66,7 @@ patch versions. The public APIs consist of Some API functions are explicitly marked as "experimental" and can change in backward incompatible ways between minor releases. These include: -* **Experimental APIs**: The @{tf.contrib} module and its submodules in Python +* **Experimental APIs**: The `tf.contrib` module and its submodules in Python and any functions in the C API or fields in protocol buffers that are explicitly commented as being experimental. In particular, any field in a protocol buffer which is called "experimental" and all its fields and @@ -79,6 +79,7 @@ backward incompatible ways between minor releases. These include: [`tensorflow/cc`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/cc)). - [Java](../api_docs/java/reference/org/tensorflow/package-summary), - [Go](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go) + - [JavaScript](https://js.tensorflow.org) * **Details of composite ops:** Many public functions in Python expand to several primitive ops in the graph, and these details will be part of any @@ -252,13 +253,13 @@ ops has not changed: 1. If forward compatibility is desired, set `strip_default_attrs` to `True` while exporting the model using either the - @{tf.saved_model.builder.SavedModelBuilder.add_meta_graph_and_variables$`add_meta_graph_and_variables`} - and @{tf.saved_model.builder.SavedModelBuilder.add_meta_graph$`add_meta_graph`} + `tf.saved_model.builder.SavedModelBuilder.add_meta_graph_and_variables` + and `tf.saved_model.builder.SavedModelBuilder.add_meta_graph` methods of the `SavedModelBuilder` class, or - @{tf.estimator.Estimator.export_savedmodel$`Estimator.export_savedmodel`} + `tf.estimator.Estimator.export_savedmodel` 2. This strips off the default valued attributes at the time of producing/exporting the models. This makes sure that the exported - @{tf.MetaGraphDef} does not contain the new op-attribute when the default + `tf.MetaGraphDef` does not contain the new op-attribute when the default value is used. 3. Having this control could allow out-of-date consumers (for example, serving binaries that lag behind training binaries) to continue loading the models diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 5e26facaba1066dc56af6d496fcfc8c4b69cd1d0..4a63f11fcac03a1b56f900fc29b1950bdba2e4bf 100644 --- a/tensorflow/docs_src/install/install_c.md +++ b/tensorflow/docs_src/install/install_c.md @@ -38,7 +38,7 @@ enable TensorFlow for C: OS="linux" # Change to "darwin" for macOS TARGET_DIRECTORY="/usr/local" curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.10.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.10.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index a59c2741e1ea55518f57eda39dc7387327343e18..f0f8436777ea17885b6ccd2b0f75fbb9e900d15f 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -6,7 +6,7 @@ a Go application. This guide explains how to install and set up the [TensorFlow Go package](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go). Warning: The TensorFlow Go API is *not* covered by the TensorFlow -[API stability guarantees](../guide/version_semantics.md). +[API stability guarantees](../guide/version_compat.md). ## Supported Platforms @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.10.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.10.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index e9c6650c929a5a2b207e491b3118f77768168786..c131a2ea766625a57af6df60ad425cc46bf7cad2 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.10.0-rc1 + 1.10.0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.10.0-rc1 + 1.10.0 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.10.0-rc1 + 1.10.0 org.tensorflow libtensorflow_jni_gpu - 1.10.0-rc1 + 1.10.0 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.10.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.10.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.10.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.10.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,10 +175,10 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: Take the following steps to install TensorFlow for Java on Windows: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.10.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.10.0.jar), which is the TensorFlow Java Archive (JAR). 2. Download the following Java Native Interface (JNI) file appropriate for - [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.10.0-rc1.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.10.0.zip). 3. Extract this .zip file. __Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. @@ -227,7 +227,7 @@ must be part of your `classpath`. For example, you can include the downloaded `.jar` in your `classpath` by using the `-cp` compilation flag as follows: -
javac -cp libtensorflow-1.10.0-rc1.jar HelloTF.java
+
javac -cp libtensorflow-1.10.0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.10.0-rc1.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.10.0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.10.0-rc1.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.10.0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, you've successfully installed TensorFlow for Java and are ready to use the API. If the program diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 005ad437bc984de48e19065d79faece8de591be6..0febdee99fd267947858cea2b2a3fcbfc59f986d 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -436,7 +436,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-cp34-cp34m-linux_x86_64.whl @@ -650,13 +650,13 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -667,13 +667,13 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -684,13 +684,13 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-cp35-cp35m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -701,13 +701,13 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.10.0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.10.0-cp36-cp36m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 3a8637bfb125d1b2de0c249cce41530f9573cd6b..c4d63cc10716b2f399df15bd462c3551944375b6 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -119,7 +119,7 @@ Take the following steps to install TensorFlow with Virtualenv: TensorFlow in the active Virtualenv is as follows: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0-py2-none-any.whl @@ -517,7 +517,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0-py2-none-any.whl
@@ -525,5 +525,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py2-none- 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0rc1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.10.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_raspbian.md b/tensorflow/docs_src/install/install_raspbian.md index 58a5285c78be9bc187ae4679c79213ae40df2f30..cf6b6b4f79113fee7fde6e83522af4fe6d9d7f43 100644 --- a/tensorflow/docs_src/install/install_raspbian.md +++ b/tensorflow/docs_src/install/install_raspbian.md @@ -60,7 +60,7 @@ If it gives the error "Command not found", then the package has not been installed yet. To install if for the first time, run: 
$ sudo apt-get install python3-pip # for Python 3.n
-sudo apt-get install python-pip # for Python 2.7
+$ sudo apt-get install python-pip # for Python 2.7 You can find more help on installing and upgrading pip in [the Raspberry Pi documentation](https://www.raspberrypi.org/documentation/linux/software/python.md). @@ -78,8 +78,8 @@ your system, run the following command: Assuming the prerequisite software is installed on your Pi, install TensorFlow by invoking **one** of the following commands: - 
 $ pip3 install tensorflow     # Python 3.n
-     $ pip install tensorflow      # Python 2.7
+
$ pip3 install tensorflow     # Python 3.n
+$ pip install tensorflow      # Python 2.7
This can take some time on certain platforms like the Pi Zero, where some Python packages like scipy that TensorFlow depends on need to be compiled before the diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index a7c0b6970abe2a2d954c8b29f91c1d7f17c0aafb..dfd9fbce4b53dce2a981526b1794d6b359312e40 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -168,6 +168,7 @@ If bazel is not installed on your system, install it now by following To build TensorFlow, you must install the following packages: * six +* mock * numpy, which is a numerical processing package that TensorFlow requires. * wheel, which enables you to manage Python compressed packages in the wheel (.whl) format. @@ -179,7 +180,10 @@ If you follow these instructions, you will not need to disable SIP. After installing pip, invoke the following commands: -
 $ sudo pip install six numpy wheel
+
 $ sudo pip install six numpy wheel mock h5py
+ $ sudo pip install keras_applications==1.0.4 --no-deps
+ $ sudo pip install keras_preprocessing==1.0.2 --no-deps
+
Note: These are just the minimum requirements to _build_ tensorflow. Installing the pip package will download additional packages required to _run_ it. If you @@ -374,10 +378,10 @@ Invoke `pip install` to install that pip package. The filename of the `.whl` file depends on your platform. For example, the following command will install the pip package -for TensorFlow 1.10.0rc1 on Linux: +for TensorFlow 1.10.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.10.0rc1-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.10.0-py2-none-any.whl
## Validate your installation diff --git a/tensorflow/docs_src/performance/datasets_performance.md b/tensorflow/docs_src/performance/datasets_performance.md index 46b43b7673c561679e89fff0ae738b0e751fcff5..5d9e4ba392558b6a621808961102e5958e2cbe74 100644 --- a/tensorflow/docs_src/performance/datasets_performance.md +++ b/tensorflow/docs_src/performance/datasets_performance.md @@ -38,9 +38,9 @@ the heavy lifting of training your model. In addition, viewing input pipelines as an ETL process provides structure that facilitates the application of performance optimizations. -When using the @{tf.estimator.Estimator} API, the first two phases (Extract and +When using the `tf.estimator.Estimator` API, the first two phases (Extract and Transform) are captured in the `input_fn` passed to -@{tf.estimator.Estimator.train}. In code, this might look like the following +`tf.estimator.Estimator.train`. In code, this might look like the following (naive, sequential) implementation: ``` @@ -99,7 +99,7 @@ With pipelining, idle time diminishes significantly: ![with pipelining](/images/datasets_with_pipelining.png) The `tf.data` API provides a software pipelining mechanism through the -@{tf.data.Dataset.prefetch} transformation, which can be used to decouple the +`tf.data.Dataset.prefetch` transformation, which can be used to decouple the time data is produced from the time it is consumed. In particular, the transformation uses a background thread and an internal buffer to prefetch elements from the input dataset ahead of the time they are requested. Thus, to @@ -130,7 +130,7 @@ The preceding recommendation is simply the most common application. ### Parallelize Data Transformation When preparing a batch, input elements may need to be pre-processed. To this -end, the `tf.data` API offers the @{tf.data.Dataset.map} transformation, which +end, the `tf.data` API offers the `tf.data.Dataset.map` transformation, which applies a user-defined function (for example, `parse_fn` from the running example) to each element of the input dataset. Because input elements are independent of one another, the pre-processing can be parallelized across @@ -164,7 +164,7 @@ dataset = dataset.map(map_func=parse_fn, num_parallel_calls=FLAGS.num_parallel_c Furthermore, if your batch size is in the hundreds or thousands, your pipeline will likely additionally benefit from parallelizing the batch creation. To this -end, the `tf.data` API provides the @{tf.contrib.data.map_and_batch} +end, the `tf.data` API provides the `tf.contrib.data.map_and_batch` transformation, which effectively "fuses" the map and batch transformations. To apply this change to our running example, change: @@ -205,7 +205,7 @@ is stored locally or remotely, but can be worse in the remote case if data is not prefetched effectively. To mitigate the impact of the various data extraction overheads, the `tf.data` -API offers the @{tf.contrib.data.parallel_interleave} transformation. Use this +API offers the `tf.contrib.data.parallel_interleave` transformation. Use this transformation to parallelize the execution of and interleave the contents of other datasets (such as data file readers). The number of datasets to overlap can be specified by the `cycle_length` argument. @@ -232,7 +232,7 @@ dataset = files.apply(tf.contrib.data.parallel_interleave( The throughput of remote storage systems can vary over time due to load or network events. To account for this variance, the `parallel_interleave` transformation can optionally use prefetching. (See -@{tf.contrib.data.parallel_interleave} for details). +`tf.contrib.data.parallel_interleave` for details). By default, the `parallel_interleave` transformation provides a deterministic ordering of elements to aid reproducibility. As an alternative to prefetching @@ -261,7 +261,7 @@ function (that is, have it operate over a batch of inputs at once) and apply the ### Map and Cache -The @{tf.data.Dataset.cache} transformation can cache a dataset, either in +The `tf.data.Dataset.cache` transformation can cache a dataset, either in memory or on local storage. If the user-defined function passed into the `map` transformation is expensive, apply the cache transformation after the map transformation as long as the resulting dataset can still fit into memory or @@ -281,9 +281,9 @@ performance (for example, to enable fusing of the map and batch transformations) ### Repeat and Shuffle -The @{tf.data.Dataset.repeat} transformation repeats the input data a finite (or +The `tf.data.Dataset.repeat` transformation repeats the input data a finite (or infinite) number of times; each repetition of the data is typically referred to -as an _epoch_. The @{tf.data.Dataset.shuffle} transformation randomizes the +as an _epoch_. The `tf.data.Dataset.shuffle` transformation randomizes the order of the dataset's examples. If the `repeat` transformation is applied before the `shuffle` transformation, @@ -296,7 +296,7 @@ internal state of the `shuffle` transformation. In other words, the former (`shuffle` before `repeat`) provides stronger ordering guarantees. When possible, we recommend using the fused -@{tf.contrib.data.shuffle_and_repeat} transformation, which combines the best of +`tf.contrib.data.shuffle_and_repeat` transformation, which combines the best of both worlds (good performance and strong ordering guarantees). Otherwise, we recommend shuffling before repeating. diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md index dafacbe37974f80c85131509824956ea1c5c8426..df703095688097123d0c46bdbfcf0c0f92457871 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -94,7 +94,7 @@ sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) #### Fused decode and crop If inputs are JPEG images that also require cropping, use fused -@{tf.image.decode_and_crop_jpeg} to speed up preprocessing. +`tf.image.decode_and_crop_jpeg` to speed up preprocessing. `tf.image.decode_and_crop_jpeg` only decodes the part of the image within the crop window. This significantly speeds up the process if the crop window is much smaller than the full image. For imagenet data, this @@ -187,14 +187,14 @@ some models makes up a large percentage of the operation time. Using fused batch norm can result in a 12%-30% speedup. There are two commonly used batch norms and both support fusing. The core -@{tf.layers.batch_normalization} added fused starting in TensorFlow 1.3. +`tf.layers.batch_normalization` added fused starting in TensorFlow 1.3. ```python bn = tf.layers.batch_normalization( input_layer, fused=True, data_format='NCHW') ``` -The contrib @{tf.contrib.layers.batch_norm} method has had fused as an option +The contrib `tf.contrib.layers.batch_norm` method has had fused as an option since before TensorFlow 1.0. ```python @@ -205,43 +205,43 @@ bn = tf.contrib.layers.batch_norm(input_layer, fused=True, data_format='NCHW') There are many ways to specify an RNN computation in TensorFlow and they have trade-offs with respect to model flexibility and performance. The -@{tf.nn.rnn_cell.BasicLSTMCell} should be considered a reference implementation +`tf.nn.rnn_cell.BasicLSTMCell` should be considered a reference implementation and used only as a last resort when no other options will work. When using one of the cells, rather than the fully fused RNN layers, you have a -choice of whether to use @{tf.nn.static_rnn} or @{tf.nn.dynamic_rnn}. There +choice of whether to use `tf.nn.static_rnn` or `tf.nn.dynamic_rnn`. There shouldn't generally be a performance difference at runtime, but large unroll -amounts can increase the graph size of the @{tf.nn.static_rnn} and cause long -compile times. An additional advantage of @{tf.nn.dynamic_rnn} is that it can +amounts can increase the graph size of the `tf.nn.static_rnn` and cause long +compile times. An additional advantage of `tf.nn.dynamic_rnn` is that it can optionally swap memory from the GPU to the CPU to enable training of very long sequences. Depending on the model and hardware configuration, this can come at a performance cost. It is also possible to run multiple iterations of -@{tf.nn.dynamic_rnn} and the underlying @{tf.while_loop} construct in parallel, +`tf.nn.dynamic_rnn` and the underlying `tf.while_loop` construct in parallel, although this is rarely useful with RNN models as they are inherently sequential. -On NVIDIA GPUs, the use of @{tf.contrib.cudnn_rnn} should always be preferred +On NVIDIA GPUs, the use of `tf.contrib.cudnn_rnn` should always be preferred unless you want layer normalization, which it doesn't support. It is often at -least an order of magnitude faster than @{tf.contrib.rnn.BasicLSTMCell} and -@{tf.contrib.rnn.LSTMBlockCell} and uses 3-4x less memory than -@{tf.contrib.rnn.BasicLSTMCell}. +least an order of magnitude faster than `tf.contrib.rnn.BasicLSTMCell` and +`tf.contrib.rnn.LSTMBlockCell` and uses 3-4x less memory than +`tf.contrib.rnn.BasicLSTMCell`. If you need to run one step of the RNN at a time, as might be the case in reinforcement learning with a recurrent policy, then you should use the -@{tf.contrib.rnn.LSTMBlockCell} with your own environment interaction loop -inside a @{tf.while_loop} construct. Running one step of the RNN at a time and +`tf.contrib.rnn.LSTMBlockCell` with your own environment interaction loop +inside a `tf.while_loop` construct. Running one step of the RNN at a time and returning to Python is possible, but it will be slower. -On CPUs, mobile devices, and if @{tf.contrib.cudnn_rnn} is not available on +On CPUs, mobile devices, and if `tf.contrib.cudnn_rnn` is not available on your GPU, the fastest and most memory efficient option is -@{tf.contrib.rnn.LSTMBlockFusedCell}. +`tf.contrib.rnn.LSTMBlockFusedCell`. -For all of the less common cell types like @{tf.contrib.rnn.NASCell}, -@{tf.contrib.rnn.PhasedLSTMCell}, @{tf.contrib.rnn.UGRNNCell}, -@{tf.contrib.rnn.GLSTMCell}, @{tf.contrib.rnn.Conv1DLSTMCell}, -@{tf.contrib.rnn.Conv2DLSTMCell}, @{tf.contrib.rnn.LayerNormBasicLSTMCell}, +For all of the less common cell types like `tf.contrib.rnn.NASCell`, +`tf.contrib.rnn.PhasedLSTMCell`, `tf.contrib.rnn.UGRNNCell`, +`tf.contrib.rnn.GLSTMCell`, `tf.contrib.rnn.Conv1DLSTMCell`, +`tf.contrib.rnn.Conv2DLSTMCell`, `tf.contrib.rnn.LayerNormBasicLSTMCell`, etc., one should be aware that they are implemented in the graph like -@{tf.contrib.rnn.BasicLSTMCell} and as such will suffer from the same poor +`tf.contrib.rnn.BasicLSTMCell` and as such will suffer from the same poor performance and high memory usage. One should consider whether or not those trade-offs are worth it before using these cells. For example, while layer normalization can speed up convergence, because cuDNN is 20x faster the fastest diff --git a/tensorflow/docs_src/performance/performance_models.md b/tensorflow/docs_src/performance/performance_models.md index 359b0e904dba1aea92f30604ff3b8abb81d432b1..66bf684d5b195a0e303aeaa2534c293777b4709c 100644 --- a/tensorflow/docs_src/performance/performance_models.md +++ b/tensorflow/docs_src/performance/performance_models.md @@ -10,8 +10,8 @@ incorporated into high-level APIs. ## Input Pipeline The @{$performance_guide$Performance Guide} explains how to identify possible -input pipeline issues and best practices. We found that using @{tf.FIFOQueue} -and @{tf.train.queue_runner} could not saturate multiple current generation GPUs +input pipeline issues and best practices. We found that using `tf.FIFOQueue` +and `tf.train.queue_runner` could not saturate multiple current generation GPUs when using large inputs and processing with higher samples per second, such as training ImageNet with [AlexNet](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf). This is due to the use of Python threads as its underlying implementation. The @@ -29,7 +29,7 @@ implementation is made up of 3 stages: The dominant part of each stage is executed in parallel with the other stages using `data_flow_ops.StagingArea`. `StagingArea` is a queue-like operator -similar to @{tf.FIFOQueue}. The difference is that `StagingArea` does not +similar to `tf.FIFOQueue`. The difference is that `StagingArea` does not guarantee FIFO ordering, but offers simpler functionality and can be executed on both CPU and GPU in parallel with other stages. Breaking the input pipeline into 3 stages that operate independently in parallel is scalable and takes full @@ -62,10 +62,10 @@ and executed in parallel. The image preprocessing ops include operations such as image decoding, distortion, and resizing. Once the images are through preprocessing, they are concatenated together into 8 -tensors each with a batch-size of 32. Rather than using @{tf.concat} for this +tensors each with a batch-size of 32. Rather than using `tf.concat` for this purpose, which is implemented as a single op that waits for all the inputs to be -ready before concatenating them together, @{tf.parallel_stack} is used. -@{tf.parallel_stack} allocates an uninitialized tensor as an output, and each +ready before concatenating them together, `tf.parallel_stack` is used. +`tf.parallel_stack` allocates an uninitialized tensor as an output, and each input tensor is written to its designated portion of the output tensor as soon as the input is available. @@ -94,7 +94,7 @@ the GPU, all the tensors are already available. With all the stages capable of being driven by different processors, `data_flow_ops.StagingArea` is used between them so they run in parallel. -`StagingArea` is a queue-like operator similar to @{tf.FIFOQueue} that offers +`StagingArea` is a queue-like operator similar to `tf.FIFOQueue` that offers simpler functionalities that can be executed on both CPU and GPU. Before the model starts running all the stages, the input pipeline stages are @@ -153,7 +153,7 @@ weights obtained from training. The default batch-normalization in TensorFlow is implemented as composite operations. This is very general, but often leads to suboptimal performance. An alternative is to use fused batch-normalization which often has much better -performance on GPU. Below is an example of using @{tf.contrib.layers.batch_norm} +performance on GPU. Below is an example of using `tf.contrib.layers.batch_norm` to implement fused batch-normalization. ```python @@ -301,7 +301,7 @@ In order to broadcast variables and aggregate gradients across different GPUs within the same host machine, we can use the default TensorFlow implicit copy mechanism. -However, we can instead use the optional NCCL (@{tf.contrib.nccl}) support. NCCL +However, we can instead use the optional NCCL (`tf.contrib.nccl`) support. NCCL is an NVIDIA® library that can efficiently broadcast and aggregate data across different GPUs. It schedules a cooperating kernel on each GPU that knows how to best utilize the underlying hardware topology; this kernel uses a single SM of diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index c97f74139c6ee852bf29724a3ac335d349a73fd3..4499f5715cd58ff846d49f3ed4560ded319c883c 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -163,7 +163,7 @@ bazel build tensorflow/contrib/lite/toco:toco && \ --std_value=127.5 --mean_value=127.5 ``` -See the documentation for @{tf.contrib.quantize} and +See the documentation for `tf.contrib.quantize` and [TensorFlow Lite](/mobile/tflite/). ## Quantized accuracy diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index edc777a3c70d5b89aa43a1de73f209468404eccb..e24a7cda733febd98f0cf7af1c86893d9a8f91dc 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -13,6 +13,79 @@ arbitrary-dimensional array. For convenience, special cases have more specific and familiar names; for example a *vector* is a 1-dimensional array and a *matrix* is a 2-dimensional array. +## AllToAll + +See also +[`XlaBuilder::AllToAll`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). + +Alltoall is a collective operation that sends data from all cores to all cores. +It has two phases: + +1. the scatter phase. On each core, the operand is split into `split_count` + number of blocks along the `split_dimensions`, and the blocks are scattered + to all cores, e.g., the ith block is send to the ith core. +2. the gather phase. Each core concatenates the received blocks along the + `concat_dimension`. + +The participating cores can be configured by: + +- `replica_groups`: each ReplicaGroup contains a list of replica id. If empty, + all replicas belong to one group in the order of 0 - (n-1). Alltoall will be + applied within subgroups in the specified order. For example, replica + groups = {{1,2,3},{4,5,0}} means, an Alltoall will be applied within replica + 1, 2, 3, and in the gather phase, the received blocks will be concatenated + in the order of 1, 2, 3; another Alltoall will be applied within replica 4, + 5, 0, and the concatenation order is 4, 5, 0. + +Prerequisites: + +- The dimension size of the operand on the split_dimension is divisible by + split_count. +- The operand's shape is not tuple. + + `AllToAll(operand, split_dimension, concat_dimension, split_count, +replica_groups)` + + +| Arguments | Type | Semantics | +| ------------------ | --------------------- | ------------------------------- | +| `operand` | `XlaOp` | n dimensional input array | +| `split_dimension` | `int64` | A value in the interval `[0, | +: : : n)` that names the dimension : +: : : along which the operand is : +: : : split : +| `concat_dimension` | `int64` | a value in the interval `[0, | +: : : n)` that names the dimension : +: : : along which the split blocks : +: : : are concatenated : +| `split_count` | `int64` | the number of cores that | +: : : participate this operation. If : +: : : `replica_groups` is empty, this : +: : : should be the number of : +: : : replicas; otherwise, this : +: : : should be equal to the number : +: : : of replicas in each group. : +| `replica_groups` | `ReplicaGroup` vector | each group contains a list of | +: : : replica id. : + +Below shows an example of Alltoall. + +``` +XlaBuilder b("alltoall"); +auto x = Parameter(&b, 0, ShapeUtil::MakeShape(F32, {4, 16}), "x"); +AllToAll(x, /*split_dimension=*/1, /*concat_dimension=*/0, /*split_count=*/4); +``` + +
+ +
+ +In this example, there are 4 cores participating the Alltoall. On each core, the +operand is split into 4 parts along dimension 0, so each part has shape +f32[4,4]. The 4 parts are scattered to all cores. Then each core concatenates +the received parts along dimension 1, in the order or core 0-4. So the output on +each core has shape f32[16,4]. + ## BatchNormGrad See also @@ -270,7 +343,7 @@ Clamp(min, operand, max) = s32[3]{0, 5, 6}; See also [`XlaBuilder::Collapse`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) -and the @{tf.reshape} operation. +and the `tf.reshape` operation. Collapses dimensions of an array into one dimension. @@ -291,7 +364,7 @@ same position in the dimension sequence as those they replace, with the new dimension size equal to the product of original dimension sizes. The lowest dimension number in `dimensions` is the slowest varying dimension (most major) in the loop nest which collapses these dimension, and the highest dimension -number is fastest varying (most minor). See the @{tf.reshape} operator +number is fastest varying (most minor). See the `tf.reshape` operator if more general collapse ordering is needed. For example, let v be an array of 24 elements: @@ -490,8 +563,8 @@ array. The holes are filled with a no-op value, which for convolution means zeroes. Dilation of the rhs is also called atrous convolution. For more details, see -@{tf.nn.atrous_conv2d}. Dilation of the lhs is also called transposed -convolution. For more details, see @{tf.nn.conv2d_transpose}. +`tf.nn.atrous_conv2d`. Dilation of the lhs is also called transposed +convolution. For more details, see `tf.nn.conv2d_transpose`. The output shape has these dimensions, in this order: @@ -1270,7 +1343,7 @@ let t: (f32[10], s32) = tuple(v, s); let element_1: s32 = gettupleelement(t, 1); // Inferred shape matches s32. ``` -See also @{tf.tuple}. +See also `tf.tuple`. ## Infeed @@ -1804,19 +1877,19 @@ See also [`XlaBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output of a given shape with random numbers generated following -the $$N(\mu, \sigma)$$ normal distribution. The parameters `mu` and `sigma`, and -output shape have to have elemental type F32. The parameters furthermore have to -be scalar valued. +the $$N(\mu, \sigma)$$ normal distribution. The parameters $$\mu$$ and +$$\sigma$$, and output shape have to have a floating point elemental type. The +parameters furthermore have to be scalar valued. -`RngNormal(mean, sigma, shape)` +`RngNormal(mu, sigma, shape)` | Arguments | Type | Semantics | | --------- | ------- | --------------------------------------------------- | -| `mu` | `XlaOp` | Scalar of type F32 specifying mean of generated | -: : : numbers : -| `sigma` | `XlaOp` | Scalar of type F32 specifying standard deviation of | +| `mu` | `XlaOp` | Scalar of type T specifying mean of generated | +: : : numbers : +| `sigma` | `XlaOp` | Scalar of type T specifying standard deviation of | : : : generated numbers : -| `shape` | `Shape` | Output shape of type F32 | +| `shape` | `Shape` | Output shape of type T | ## RngUniform @@ -1825,9 +1898,11 @@ See also Constructs an output of a given shape with random numbers generated following the uniform distribution over the interval $$[a,b)$$. The parameters and output -shape may be either F32, S32 or U32, but the types have to be consistent. -Furthermore, the parameters need to be scalar valued. If $$b <= a$$ the result -is implementation-defined. +element type have to be a boolean type, an integral type or a floating point +types, and the types have to be consistent. The CPU and GPU backends currently +only support F64, F32, F16, BF16, S64, U64, S32 and U32. Furthermore, the +parameters need to be scalar valued. If $$b <= a$$ the result is +implementation-defined. `RngUniform(a, b, shape)` @@ -1847,7 +1922,7 @@ tensor `operand`, with several slices (at indices specified by `update_computation`. See also -[`XlaBuilder::Scatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Scatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `scatter(operand, scatter_indices, updates, update_computation, index_vector_dim, update_window_dims, inserted_window_dims, scatter_dims_to_operand_dims)` @@ -2250,7 +2325,7 @@ element types. ## Transpose -See also the @{tf.reshape} operation. +See also the `tf.reshape` operation. `Transpose(operand)` @@ -2310,8 +2385,6 @@ restrictions listed below. last execution of the `body`. * The shape of the type `T` is statically determined and must be the same across all iterations. -* `While` nodes are not allowed to be nested. (This restriction may be lifted - in the future on some targets.) The T parameters of the computations are initialized with the `init` value in the first iteration and are automatically updated to the new result from `body` diff --git a/tensorflow/docs_src/tutorials/_toc.yaml b/tensorflow/docs_src/tutorials/_toc.yaml index d33869af6ee7fffe39874f690b154b92034675a2..0e25208a000b7bb196462c2904c3dfba5adead6c 100644 --- a/tensorflow/docs_src/tutorials/_toc.yaml +++ b/tensorflow/docs_src/tutorials/_toc.yaml @@ -37,9 +37,30 @@ toc: status: external - title: "Custom training: walkthrough" path: /tutorials/eager/custom_training_walkthrough + - title: Text generation + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb + status: external - title: Translation with attention path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb status: external + - title: Image captioning + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb + status: external + - title: Neural Style Transfer + path: https://github.com/tensorflow/models/blob/master/research/nst_blogpost/4_Neural_Style_Transfer_with_Eager_Execution.ipynb + status: external + - title: DCGAN + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb + status: external + - title: VAE + path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/generative_examples/cvae.ipynb + status: external + - title: Pix2Pix + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/pix2pix/pix2pix_eager.ipynb + status: external + - title: Image Segmentation + path: https://github.com/tensorflow/models/blob/master/samples/outreach/blogs/segmentation_blogpost/image_segmentation.ipynb + status: external - title: ML at production scale style: accordion diff --git a/tensorflow/docs_src/tutorials/estimators/cnn.md b/tensorflow/docs_src/tutorials/estimators/cnn.md index 12a215b50c54f276f3c084885810c7a496769681..100f501cc215a624212cdfe15555cd6db5da1e20 100644 --- a/tensorflow/docs_src/tutorials/estimators/cnn.md +++ b/tensorflow/docs_src/tutorials/estimators/cnn.md @@ -1,6 +1,6 @@ # Build a Convolutional Neural Network using Estimators -The TensorFlow @{tf.layers$`layers` module} provides a high-level API that makes +The `tf.layers` module provides a high-level API that makes it easy to construct a neural network. It provides methods that facilitate the creation of dense (fully connected) layers and convolutional layers, adding activation functions, and applying dropout regularization. In this tutorial, @@ -118,8 +118,8 @@ output from one layer-creation method and supply it as input to another. Open `cnn_mnist.py` and add the following `cnn_model_fn` function, which conforms to the interface expected by TensorFlow's Estimator API (more on this later in [Create the Estimator](#create-the-estimator)). `cnn_mnist.py` takes -MNIST feature data, labels, and -@{tf.estimator.ModeKeys$model mode} (`TRAIN`, `EVAL`, `PREDICT`) as arguments; +MNIST feature data, labels, and mode (from +`tf.estimator.ModeKeys`: `TRAIN`, `EVAL`, `PREDICT`) as arguments; configures the CNN; and returns predictions, loss, and a training operation: ```python @@ -277,7 +277,7 @@ a 5x5 convolution over a 28x28 tensor will produce a 24x24 tensor, as there are The `activation` argument specifies the activation function to apply to the output of the convolution. Here, we specify ReLU activation with -@{tf.nn.relu}. +`tf.nn.relu`. Our output tensor produced by `conv2d()` has a shape of [batch_size, 28, 28, 32]: the same height and width @@ -423,7 +423,7 @@ raw values into two different formats that our model function can return: For a given example, our predicted class is the element in the corresponding row of the logits tensor with the highest raw value. We can find the index of this -element using the @{tf.argmax} +element using the `tf.argmax` function: ```python @@ -438,7 +438,7 @@ value along the dimension with index of 1, which corresponds to our predictions 10]). We can derive probabilities from our logits layer by applying softmax activation -using @{tf.nn.softmax}: +using `tf.nn.softmax`: ```python tf.nn.softmax(logits, name="softmax_tensor") @@ -572,8 +572,8 @@ feel free to change to another directory of your choice). ### Set Up a Logging Hook {#set_up_a_logging_hook} Since CNNs can take a while to train, let's set up some logging so we can track -progress during training. We can use TensorFlow's @{tf.train.SessionRunHook} to create a -@{tf.train.LoggingTensorHook} +progress during training. We can use TensorFlow's `tf.train.SessionRunHook` to create a +`tf.train.LoggingTensorHook` that will log the probability values from the softmax layer of our CNN. Add the following to `main()`: diff --git a/tensorflow/docs_src/tutorials/images/deep_cnn.md b/tensorflow/docs_src/tutorials/images/deep_cnn.md index 27963575f5a02eb8a91b490fdfcc33d35749963c..42ad484bbfe0b34383648197a8c88c2fa097c342 100644 --- a/tensorflow/docs_src/tutorials/images/deep_cnn.md +++ b/tensorflow/docs_src/tutorials/images/deep_cnn.md @@ -31,26 +31,26 @@ new ideas and experimenting with new techniques. The CIFAR-10 tutorial demonstrates several important constructs for designing larger and more sophisticated models in TensorFlow: -* Core mathematical components including @{tf.nn.conv2d$convolution} +* Core mathematical components including `tf.nn.conv2d` ([wiki](https://en.wikipedia.org/wiki/Convolution)), -@{tf.nn.relu$rectified linear activations} +`tf.nn.relu` ([wiki](https://en.wikipedia.org/wiki/Rectifier_(neural_networks))), -@{tf.nn.max_pool$max pooling} +`tf.nn.max_pool` ([wiki](https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer)) -and @{tf.nn.local_response_normalization$local response normalization} +and `tf.nn.local_response_normalization` (Chapter 3.3 in [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)). * @{$summaries_and_tensorboard$Visualization} of network activities during training, including input images, losses and distributions of activations and gradients. * Routines for calculating the -@{tf.train.ExponentialMovingAverage$moving average} +`tf.train.ExponentialMovingAverage` of learned parameters and using these averages during evaluation to boost predictive performance. * Implementation of a -@{tf.train.exponential_decay$learning rate schedule} +`tf.train.exponential_decay` that systematically decrements over time. -* Prefetching @{tf.train.shuffle_batch$queues} +* Prefetching `tf.train.shuffle_batch` for input data to isolate the model from disk latency and expensive image pre-processing. @@ -113,27 +113,27 @@ gradients, variable updates and visualization summaries. The input part of the model is built by the functions `inputs()` and `distorted_inputs()` which read images from the CIFAR-10 binary data files. These files contain fixed byte length records, so we use -@{tf.FixedLengthRecordReader}. +`tf.FixedLengthRecordReader`. See @{$reading_data#reading-from-files$Reading Data} to learn more about how the `Reader` class works. The images are processed as follows: * They are cropped to 24 x 24 pixels, centrally for evaluation or - @{tf.random_crop$randomly} for training. -* They are @{tf.image.per_image_standardization$approximately whitened} + `tf.random_crop` for training. +* They are `tf.image.per_image_standardization` to make the model insensitive to dynamic range. For training, we additionally apply a series of random distortions to artificially increase the data set size: -* @{tf.image.random_flip_left_right$Randomly flip} the image from left to right. -* Randomly distort the @{tf.image.random_brightness$image brightness}. -* Randomly distort the @{tf.image.random_contrast$image contrast}. +* `tf.image.random_flip_left_right` the image from left to right. +* Randomly distort the `tf.image.random_brightness`. +* Randomly distort the `tf.image.random_contrast`. Please see the @{$python/image$Images} page for the list of available distortions. We also attach an -@{tf.summary.image} to the images +`tf.summary.image` to the images so that we may visualize them in @{$summaries_and_tensorboard$TensorBoard}. This is a good practice to verify that inputs are built correctly. @@ -144,7 +144,7 @@ This is a good practice to verify that inputs are built correctly. Reading images from disk and distorting them can use a non-trivial amount of processing time. To prevent these operations from slowing down training, we run them inside 16 separate threads which continuously fill a TensorFlow -@{tf.train.shuffle_batch$queue}. +`tf.train.shuffle_batch`. ### Model Prediction @@ -154,12 +154,12 @@ the model is organized as follows: Layer Name | Description --- | --- -`conv1` | @{tf.nn.conv2d$convolution} and @{tf.nn.relu$rectified linear} activation. -`pool1` | @{tf.nn.max_pool$max pooling}. -`norm1` | @{tf.nn.local_response_normalization$local response normalization}. -`conv2` | @{tf.nn.conv2d$convolution} and @{tf.nn.relu$rectified linear} activation. -`norm2` | @{tf.nn.local_response_normalization$local response normalization}. -`pool2` | @{tf.nn.max_pool$max pooling}. +`conv1` | `tf.nn.conv2d` and `tf.nn.relu` activation. +`pool1` | `tf.nn.max_pool`. +`norm1` | `tf.nn.local_response_normalization`. +`conv2` | `tf.nn.conv2d` and `tf.nn.relu` activation. +`norm2` | `tf.nn.local_response_normalization`. +`pool2` | `tf.nn.max_pool`. `local3` | @{$python/nn$fully connected layer with rectified linear activation}. `local4` | @{$python/nn$fully connected layer with rectified linear activation}. `softmax_linear` | linear transformation to produce logits. @@ -172,7 +172,7 @@ Here is a graph generated from TensorBoard describing the inference operation: > **EXERCISE**: The output of `inference` are un-normalized logits. Try editing the network architecture to return normalized predictions using -@{tf.nn.softmax}. +`tf.nn.softmax`. The `inputs()` and `inference()` functions provide all the components necessary to perform an evaluation of a model. We now shift our focus towards @@ -190,16 +190,16 @@ architecture in the top layer. The usual method for training a network to perform N-way classification is [multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression), aka. *softmax regression*. Softmax regression applies a -@{tf.nn.softmax$softmax} nonlinearity to the +`tf.nn.softmax` nonlinearity to the output of the network and calculates the -@{tf.nn.sparse_softmax_cross_entropy_with_logits$cross-entropy} +`tf.nn.sparse_softmax_cross_entropy_with_logits` between the normalized predictions and the label index. For regularization, we also apply the usual -@{tf.nn.l2_loss$weight decay} losses to all learned +`tf.nn.l2_loss` losses to all learned variables. The objective function for the model is the sum of the cross entropy loss and all these weight decay terms, as returned by the `loss()` function. -We visualize it in TensorBoard with a @{tf.summary.scalar}: +We visualize it in TensorBoard with a `tf.summary.scalar`: ![CIFAR-10 Loss](https://www.tensorflow.org/images/cifar_loss.png "CIFAR-10 Total Loss") @@ -207,14 +207,14 @@ We train the model using standard [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) algorithm (see @{$python/train$Training} for other methods) with a learning rate that -@{tf.train.exponential_decay$exponentially decays} +`tf.train.exponential_decay` over time. ![CIFAR-10 Learning Rate Decay](https://www.tensorflow.org/images/cifar_lr_decay.png "CIFAR-10 Learning Rate Decay") The `train()` function adds the operations needed to minimize the objective by calculating the gradient and updating the learned variables (see -@{tf.train.GradientDescentOptimizer} +`tf.train.GradientDescentOptimizer` for details). It returns an operation that executes all the calculations needed to train and update the model for one batch of images. @@ -263,7 +263,7 @@ training step can take so long. Try decreasing the number of images that initially fill up the queue. Search for `min_fraction_of_examples_in_queue` in `cifar10_input.py`. -`cifar10_train.py` periodically @{tf.train.Saver$saves} +`cifar10_train.py` periodically uses a `tf.train.Saver` to save all model parameters in @{$guide/saved_model$checkpoint files} but it does *not* evaluate the model. The checkpoint file @@ -285,7 +285,7 @@ how the model is training. We want more insight into the model during training: @{$summaries_and_tensorboard$TensorBoard} provides this functionality, displaying data exported periodically from `cifar10_train.py` via a -@{tf.summary.FileWriter}. +`tf.summary.FileWriter`. For instance, we can watch how the distribution of activations and degree of sparsity in `local3` features evolve during training: @@ -300,7 +300,7 @@ interesting to track over time. However, the loss exhibits a considerable amount of noise due to the small batch size employed by training. In practice we find it extremely useful to visualize their moving averages in addition to their raw values. See how the scripts use -@{tf.train.ExponentialMovingAverage} +`tf.train.ExponentialMovingAverage` for this purpose. ## Evaluating a Model @@ -336,8 +336,8 @@ exports summaries that may be visualized in TensorBoard. These summaries provide additional insight into the model during evaluation. The training script calculates the -@{tf.train.ExponentialMovingAverage$moving average} -version of all learned variables. The evaluation script substitutes +`tf.train.ExponentialMovingAverage` of all learned variables. +The evaluation script substitutes all learned model parameters with the moving average version. This substitution boosts model performance at evaluation time. @@ -401,17 +401,17 @@ gradients for a single model replica. In the code we term this abstraction a "tower". We must set two attributes for each tower: * A unique name for all operations within a tower. -@{tf.name_scope} provides +`tf.name_scope` provides this unique name by prepending a scope. For instance, all operations in the first tower are prepended with `tower_0`, e.g. `tower_0/conv1/Conv2D`. * A preferred hardware device to run the operation within a tower. -@{tf.device} specifies this. For +`tf.device` specifies this. For instance, all operations in the first tower reside within `device('/device:GPU:0')` scope indicating that they should be run on the first GPU. All variables are pinned to the CPU and accessed via -@{tf.get_variable} +`tf.get_variable` in order to share them in a multi-GPU version. See how-to on @{$variables$Sharing Variables}. diff --git a/tensorflow/docs_src/tutorials/images/image_recognition.md b/tensorflow/docs_src/tutorials/images/image_recognition.md index d545de73df57a7bc775a83cc1fc41ffa185874c5..83a8d97cf04ca0442c6b670d144c3dcf5443bfc8 100644 --- a/tensorflow/docs_src/tutorials/images/image_recognition.md +++ b/tensorflow/docs_src/tutorials/images/image_recognition.md @@ -253,7 +253,7 @@ definition with the `ToGraphDef()` function. TF_RETURN_IF_ERROR(session->Run({}, {output_name}, {}, out_tensors)); return Status::OK(); ``` -Then we create a @{tf.Session} +Then we create a `tf.Session` object, which is the interface to actually running the graph, and run it, specifying which node we want to get the output from, and where to put the output data. diff --git a/tensorflow/docs_src/tutorials/representation/kernel_methods.md b/tensorflow/docs_src/tutorials/representation/kernel_methods.md index f3c232c51155927a4b8e5abdd6e1e04403f8caa4..71e87f4d3e986ad552ccabc33d41266c3e0f871b 100644 --- a/tensorflow/docs_src/tutorials/representation/kernel_methods.md +++ b/tensorflow/docs_src/tutorials/representation/kernel_methods.md @@ -1,9 +1,8 @@ # Improving Linear Models Using Explicit Kernel Methods -Note: This document uses a deprecated version of @{tf.estimator}, -which has a @{tf.contrib.learn.Estimator$different interface}. -It also uses other `contrib` methods whose -@{$version_compat#not_covered$API may not be stable}. +Note: This document uses a deprecated version of `tf.estimator`, +`tf.contrib.learn.Estimator`, which has a different interface. It also uses +other `contrib` methods whose @{$version_compat#not_covered$API may not be stable}. In this tutorial, we demonstrate how combining (explicit) kernel methods with linear models can drastically increase the latters' quality of predictions @@ -90,7 +89,7 @@ eval_input_fn = get_input_fn(data.validation, batch_size=5000) ## Training a simple linear model We can now train a linear model over the MNIST dataset. We will use the -@{tf.contrib.learn.LinearClassifier} estimator with 10 classes representing the +`tf.contrib.learn.LinearClassifier` estimator with 10 classes representing the 10 digits. The input features form a 784-dimensional dense vector which can be specified as follows: @@ -195,7 +194,7 @@ much higher dimensional space than the original one. See for more details. ### Kernel classifier -@{tf.contrib.kernel_methods.KernelLinearClassifier} is a pre-packaged +`tf.contrib.kernel_methods.KernelLinearClassifier` is a pre-packaged `tf.contrib.learn` estimator that combines the power of explicit kernel mappings with linear models. Its constructor is almost identical to that of the LinearClassifier estimator with the additional option to specify a list of diff --git a/tensorflow/docs_src/tutorials/representation/linear.md b/tensorflow/docs_src/tutorials/representation/linear.md index 1b418cf065a141dc46833bb0d3c2048658efc388..014409c617ea7c836e472cab1aa828fa497bd412 100644 --- a/tensorflow/docs_src/tutorials/representation/linear.md +++ b/tensorflow/docs_src/tutorials/representation/linear.md @@ -1,6 +1,6 @@ # Large-scale Linear Models with TensorFlow -@{tf.estimator$Estimators} provides (among other things) a rich set of tools for +`tf.estimator` provides (among other things) a rich set of tools for working with linear models in TensorFlow. This document provides an overview of those tools. It explains: diff --git a/tensorflow/docs_src/tutorials/representation/word2vec.md b/tensorflow/docs_src/tutorials/representation/word2vec.md index 0a1c41c84a3971cb6237e37ccaaa884e53de2aae..7964650e199d0d8f156feb74ee95bc0c33593661 100644 --- a/tensorflow/docs_src/tutorials/representation/word2vec.md +++ b/tensorflow/docs_src/tutorials/representation/word2vec.md @@ -317,7 +317,7 @@ optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0).minimize(loss) Training the model is then as simple as using a `feed_dict` to push data into the placeholders and calling -@{tf.Session.run} with this new data +`tf.Session.run` with this new data in a loop. ```python diff --git a/tensorflow/examples/android/.gitignore b/tensorflow/examples/android/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d245ab61095a6f9b6d2077aac934f9b13e66d85e --- /dev/null +++ b/tensorflow/examples/android/.gitignore @@ -0,0 +1,29 @@ +# This file is based on https://github.com/github/gitignore/blob/master/Android.gitignore +*.iml +.idea/compiler.xml +.idea/copyright +.idea/dictionaries +.idea/gradle.xml +.idea/libraries +.idea/inspectionProfiles +.idea/misc.xml +.idea/modules.xml +.idea/runConfigurations.xml +.idea/tasks.xml +.idea/workspace.xml +.gradle +local.properties +.DS_Store +build/ +gradleBuild/ +*.apk +*.ap_ +*.dex +*.class +bin/ +gen/ +out/ +*.log +.navigation/ +/captures +.externalNativeBuild diff --git a/tensorflow/examples/android/README.md b/tensorflow/examples/android/README.md index 30a26d13c5734c5cf4a3b565c793db3e093c8271..dac9b7ab82c97d4d694374fea82d4d6fda85e0a0 100644 --- a/tensorflow/examples/android/README.md +++ b/tensorflow/examples/android/README.md @@ -45,11 +45,7 @@ on API >= 14 devices. ## Prebuilt Components: -If you just want the fastest path to trying the demo, you may download the -nightly build -[here](https://ci.tensorflow.org/view/Nightly/job/nightly-android/). Expand the -"View" and then the "out" folders under "Last Successful Artifacts" to find -tensorflow_demo.apk. +The fastest path to trying the demo is to download the [prebuilt demo APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk). Also available are precompiled native libraries, and a jcenter package that you may simply drop into your own applications. See @@ -113,8 +109,7 @@ protobuf compilation. NOTE: Bazel does not currently support building for Android on Windows. Full support for gradle/cmake builds is coming soon, but in the meantime we suggest -that Windows users download the [prebuilt -binaries](https://ci.tensorflow.org/view/Nightly/job/nightly-android/) instead. +that Windows users download the [prebuilt demo APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk) instead. ##### Install Bazel and Android Prerequisites diff --git a/tensorflow/examples/ios/README.md b/tensorflow/examples/ios/README.md index 5d7bd36837b2a2c33ab4bc311a582c174666dcd5..64412d25a00f55543f011b4ae3aaa85f03894ab5 100644 --- a/tensorflow/examples/ios/README.md +++ b/tensorflow/examples/ios/README.md @@ -190,8 +190,5 @@ increase you see in your own app is similar, and if it's larger, look at the "Other Linker Flags" used in the Simple Xcode project settings to strip the executable. -After that, you can manually look at modifying the list of kernels -included in tensorflow/contrib/makefile/tf_op_files.txt to reduce the number of -implementations to the ones you're actually using in your own model. We're -hoping to automate this step in the future, but for now manually removing them -is the best approach. +For further optimization, please refer to the ["Optimization" section](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/makefile#optimization) +of the makefile instructions. diff --git a/tensorflow/g3doc/README.txt b/tensorflow/g3doc/README.txt index ed648f8b6b8895010be84becd4fda25ded5859fb..515a9e9a025d9b974d4ba0cf81c3c8319f38a877 100644 --- a/tensorflow/g3doc/README.txt +++ b/tensorflow/g3doc/README.txt @@ -22,12 +22,12 @@ When authoring docs, note that we have some new syntax for references -- at least for docs coming from Python docstrings or tensorflow/docs_src/. Use: -* @{tf.symbol} to make a link to the reference page for a Python +* `tf.symbol` to make a link to the reference page for a Python symbol. Note that class members don't get their own page, but the - syntax still works, since @{tf.MyClass.method} links to the right + syntax still works, since `tf.MyClass.method` links to the right part of the tf.MyClass page. -* @{tensorflow::symbol} to make a link to the reference page for a C++ +* `tensorflow::symbol` to make a link to the reference page for a C++ symbol. (This only works for a few symbols but will work for more soon.) * @{$doc_page} to make a link to another (not an API reference) doc diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index ca1521e641a0d376ad4ffa377211e59897768b0b..9015cd616c39c4b577f5f3e898eebac6664aba83 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -2618,70 +2618,6 @@ func Reverse(scope *Scope, tensor tf.Output, dims tf.Output) (output tf.Output) return op.Output(0) } -// Copy a tensor setting everything outside a central band in each innermost matrix -// -// to zero. -// -// The `band` part is computed as follows: -// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a -// tensor with the same shape where -// -// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. -// -// The indicator function -// -// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && -// (num_upper < 0 || (n-m) <= num_upper)`. -// -// For example: -// -// ``` -// # if 'input' is [[ 0, 1, 2, 3] -// [-1, 0, 1, 2] -// [-2, -1, 0, 1] -// [-3, -2, -1, 0]], -// -// tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] -// [-1, 0, 1, 2] -// [ 0, -1, 0, 1] -// [ 0, 0, -1, 0]], -// -// tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] -// [-1, 0, 1, 0] -// [-2, -1, 0, 1] -// [ 0, -2, -1, 0]] -// ``` -// -// Useful special cases: -// -// ``` -// tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. -// tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. -// tf.matrix_band_part(input, 0, 0) ==> Diagonal. -// ``` -// -// Arguments: -// input: Rank `k` tensor. -// num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire -// lower triangle. -// num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep -// entire upper triangle. -// -// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor. -func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "MatrixBandPart", - Input: []tf.Input{ - input, num_lower, num_upper, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Returns the batched diagonal part of a batched tensor. // // This operation returns a tensor with the `diagonal` part @@ -3383,6 +3319,42 @@ func Unbatch(scope *Scope, batched_tensor tf.Output, batch_index tf.Output, id t return op.Output(0) } +// Elementwise computes the bitwise left-shift of `x` and `y`. +// +// If `y` is negative, or greater than or equal to the width of `x` in bits the +// result is implementation defined. +func LeftShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "LeftShift", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// Elementwise computes the bitwise XOR of `x` and `y`. +// +// The result will have those bits set, that are different in `x` and `y`. The +// computation is performed on the underlying representations of `x` and `y`. +func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "BitwiseXor", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // Computes the mean along sparse segments of a tensor. // // Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of @@ -12594,6 +12566,65 @@ func ResourceSparseApplyAdagrad(scope *Scope, var_ tf.Output, accum tf.Output, l return scope.AddOperation(opspec) } +// Elementwise computes the bitwise right-shift of `x` and `y`. +// +// Performs a logical shift for unsigned integer types, and an arithmetic shift +// for signed integer types. +// +// If `y` is negative, or greater than or equal to than the width of `x` in bits +// the result is implementation defined. +func RightShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "RightShift", + Input: []tf.Input{ + x, y, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + +// TensorListStackAttr is an optional argument to TensorListStack. +type TensorListStackAttr func(optionalAttr) + +// TensorListStackNumElements sets the optional num_elements attribute to value. +// If not specified, defaults to -1 +func TensorListStackNumElements(value int64) TensorListStackAttr { + return func(m optionalAttr) { + m["num_elements"] = value + } +} + +// Stacks all tensors in the list. +// +// Requires that all tensors have the same shape. +// +// input_handle: the input list +// tensor: the gathered result +// num_elements: optional. If not -1, the number of elements in the list. +// +func TensorListStack(scope *Scope, input_handle tf.Output, element_dtype tf.DataType, optional ...TensorListStackAttr) (tensor tf.Output) { + if scope.Err() != nil { + return + } + attrs := map[string]interface{}{"element_dtype": element_dtype} + for _, a := range optional { + a(attrs) + } + opspec := tf.OpSpec{ + Type: "TensorListStack", + Input: []tf.Input{ + input_handle, + }, + Attrs: attrs, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // StatelessRandomUniformAttr is an optional argument to StatelessRandomUniform. type StatelessRandomUniformAttr func(optionalAttr) @@ -12670,24 +12701,6 @@ func Fact(scope *Scope) (fact tf.Output) { return op.Output(0) } -// Elementwise computes the bitwise XOR of `x` and `y`. -// -// The result will have those bits set, that are different in `x` and `y`. The -// computation is performed on the underlying representations of `x` and `y`. -func BitwiseXor(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "BitwiseXor", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - // Deserialize `SparseTensor` objects. // // The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where @@ -19393,6 +19406,70 @@ func LogMatrixDeterminant(scope *Scope, input tf.Output) (sign tf.Output, log_ab return op.Output(0), op.Output(1) } +// Copy a tensor setting everything outside a central band in each innermost matrix +// +// to zero. +// +// The `band` part is computed as follows: +// Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a +// tensor with the same shape where +// +// `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. +// +// The indicator function +// +// `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && +// (num_upper < 0 || (n-m) <= num_upper)`. +// +// For example: +// +// ``` +// # if 'input' is [[ 0, 1, 2, 3] +// [-1, 0, 1, 2] +// [-2, -1, 0, 1] +// [-3, -2, -1, 0]], +// +// tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] +// [-1, 0, 1, 2] +// [ 0, -1, 0, 1] +// [ 0, 0, -1, 0]], +// +// tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] +// [-1, 0, 1, 0] +// [-2, -1, 0, 1] +// [ 0, -2, -1, 0]] +// ``` +// +// Useful special cases: +// +// ``` +// tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. +// tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. +// tf.matrix_band_part(input, 0, 0) ==> Diagonal. +// ``` +// +// Arguments: +// input: Rank `k` tensor. +// num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire +// lower triangle. +// num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep +// entire upper triangle. +// +// Returns Rank `k` tensor of the same shape as input. The extracted banded tensor. +func MatrixBandPart(scope *Scope, input tf.Output, num_lower tf.Output, num_upper tf.Output) (band tf.Output) { + if scope.Err() != nil { + return + } + opspec := tf.OpSpec{ + Type: "MatrixBandPart", + Input: []tf.Input{ + input, num_lower, num_upper, + }, + } + op := scope.AddOperation(opspec) + return op.Output(0) +} + // SumAttr is an optional argument to Sum. type SumAttr func(optionalAttr) @@ -31839,80 +31916,3 @@ func BitwiseAnd(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { op := scope.AddOperation(opspec) return op.Output(0) } - -// Elementwise computes the bitwise left-shift of `x` and `y`. -// -// If `y` is negative, or greater than or equal to the width of `x` in bits the -// result is implementation defined. -func LeftShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "LeftShift", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// TensorListStackAttr is an optional argument to TensorListStack. -type TensorListStackAttr func(optionalAttr) - -// TensorListStackNumElements sets the optional num_elements attribute to value. -// If not specified, defaults to -1 -func TensorListStackNumElements(value int64) TensorListStackAttr { - return func(m optionalAttr) { - m["num_elements"] = value - } -} - -// Stacks all tensors in the list. -// -// Requires that all tensors have the same shape. -// -// input_handle: the input list -// tensor: the gathered result -// num_elements: optional. If not -1, the number of elements in the list. -// -func TensorListStack(scope *Scope, input_handle tf.Output, element_dtype tf.DataType, optional ...TensorListStackAttr) (tensor tf.Output) { - if scope.Err() != nil { - return - } - attrs := map[string]interface{}{"element_dtype": element_dtype} - for _, a := range optional { - a(attrs) - } - opspec := tf.OpSpec{ - Type: "TensorListStack", - Input: []tf.Input{ - input_handle, - }, - Attrs: attrs, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} - -// Elementwise computes the bitwise right-shift of `x` and `y`. -// -// Performs a logical shift for unsigned integer types, and an arithmetic shift -// for signed integer types. -// -// If `y` is negative, or greater than or equal to than the width of `x` in bits -// the result is implementation defined. -func RightShift(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) { - if scope.Err() != nil { - return - } - opspec := tf.OpSpec{ - Type: "RightShift", - Input: []tf.Input{ - x, y, - }, - } - op := scope.AddOperation(opspec) - return op.Output(0) -} diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index 58a002c776545614ea5844ee64d90e8519da5f28..28f26ad27e151d579a43d9d282bfb51066f79d38 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -724,7 +724,7 @@ class BaseSession(SessionInterface): """Returns a context manager that makes this object the default session. Use with the `with` keyword to specify that calls to - @{tf.Operation.run} or @{tf.Tensor.eval} should be executed in + `tf.Operation.run` or `tf.Tensor.eval` should be executed in this session. ```python @@ -736,7 +736,7 @@ class BaseSession(SessionInterface): print(c.eval()) ``` - To get the current default session, use @{tf.get_default_session}. + To get the current default session, use `tf.get_default_session`. *N.B.* The `as_default` context manager *does not* close the session when you exit the context, and you must close the session @@ -765,7 +765,7 @@ class BaseSession(SessionInterface): *N.B.* Entering a `with sess.as_default():` block does not affect the current default graph. If you are using multiple graphs, and - `sess.graph` is different from the value of @{tf.get_default_graph}, + `sess.graph` is different from the value of `tf.get_default_graph`, you must explicitly enter a `with sess.graph.as_default():` block to make `sess.graph` the default graph. @@ -786,14 +786,14 @@ class BaseSession(SessionInterface): nested list, tuple, namedtuple, dict, or OrderedDict containing graph elements at its leaves. A graph element can be one of the following types: - * An @{tf.Operation}. + * An `tf.Operation`. The corresponding fetched value will be `None`. - * A @{tf.Tensor}. + * A `tf.Tensor`. The corresponding fetched value will be a numpy ndarray containing the value of that tensor. - * A @{tf.SparseTensor}. + * A `tf.SparseTensor`. The corresponding fetched value will be a - @{tf.SparseTensorValue} + `tf.SparseTensorValue` containing the value of that sparse tensor. * A `get_tensor_handle` op. The corresponding fetched value will be a numpy ndarray containing the handle of that tensor. @@ -829,16 +829,16 @@ class BaseSession(SessionInterface): the value of tensors in the graph. Each key in `feed_dict` can be one of the following types: - * If the key is a @{tf.Tensor}, the + * If the key is a `tf.Tensor`, the value may be a Python scalar, string, list, or numpy ndarray that can be converted to the same `dtype` as that tensor. Additionally, if the key is a - @{tf.placeholder}, the shape of + `tf.placeholder`, the shape of the value will be checked for compatibility with the placeholder. * If the key is a - @{tf.SparseTensor}, + `tf.SparseTensor`, the value should be a - @{tf.SparseTensorValue}. + `tf.SparseTensorValue`. * If the key is a nested tuple of `Tensor`s or `SparseTensor`s, the value should be a nested tuple with the same structure that maps to their corresponding values as above. @@ -1120,7 +1120,7 @@ class BaseSession(SessionInterface): For example, if element `i` of `feed_list` is a `tf.Tensor`, the `i`th argument to the returned callable must be a numpy ndarray (or something convertible to an ndarray) with matching element type and shape. See - @{tf.Session.run} for details of the allowable feed key and value types. + `tf.Session.run` for details of the allowable feed key and value types. The returned callable will have the same return type as `tf.Session.run(fetches, ...)`. For example, if `fetches` is a `tf.Tensor`, @@ -1128,14 +1128,14 @@ class BaseSession(SessionInterface): it will return `None`. Args: - fetches: A value or list of values to fetch. See @{tf.Session.run} + fetches: A value or list of values to fetch. See `tf.Session.run` for details of the allowable fetch types. feed_list: (Optional.) A list of `feed_dict` keys. See - @{tf.Session.run} for details of the allowable feed key types. + `tf.Session.run` for details of the allowable feed key types. accept_options: (Optional.) Iff `True`, the returned `Callable` will be - able to accept @{tf.RunOptions} and @{tf.RunMetadata} as optional + able to accept `tf.RunOptions` and `tf.RunMetadata` as optional keyword arguments `options` and `run_metadata`, respectively, with - the same syntax and semantics as @{tf.Session.run}, which is useful + the same syntax and semantics as `tf.Session.run`, which is useful for certain use cases (profiling and debugging) but will result in measurable slowdown of the `Callable`'s performance. Default: `False`. @@ -1145,7 +1145,7 @@ class BaseSession(SessionInterface): Raises: TypeError: If `fetches` or `feed_list` cannot be interpreted - as arguments to @{tf.Session.run}. + as arguments to `tf.Session.run`. """ if feed_list is not None: if not isinstance(feed_list, (list, tuple)): @@ -1453,10 +1453,10 @@ class Session(BaseSession): ``` A session may own resources, such as - @{tf.Variable}, @{tf.QueueBase}, - and @{tf.ReaderBase}. It is important to release + `tf.Variable`, `tf.QueueBase`, + and `tf.ReaderBase`. It is important to release these resources when they are no longer required. To do this, either - invoke the @{tf.Session.close} method on the session, or use + invoke the `tf.Session.close` method on the session, or use the session as a context manager. The following two examples are equivalent: @@ -1592,8 +1592,8 @@ class InteractiveSession(BaseSession): The only difference with a regular `Session` is that an `InteractiveSession` installs itself as the default session on construction. - The methods @{tf.Tensor.eval} - and @{tf.Operation.run} + The methods `tf.Tensor.eval` + and `tf.Operation.run` will use that session to run ops. This is convenient in interactive shells and [IPython diff --git a/tensorflow/python/compat/compat.py b/tensorflow/python/compat/compat.py index af47ff69c9e382f8d9ff146d2354eec229a0fe90..f33e881d87fa398b2d7222a1aa3989e966653167 100644 --- a/tensorflow/python/compat/compat.py +++ b/tensorflow/python/compat/compat.py @@ -26,7 +26,7 @@ import datetime from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import tf_export -_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 8, 6) +_FORWARD_COMPATIBILITY_HORIZON = datetime.date(2018, 8, 11) @tf_export("compat.forward_compatible") diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 6cda2a77ccbc5e60a7b10082de9638f21b56c966..8ba98cb88d8d879762cf00cfd8fa19f00b09f82e 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -222,7 +222,7 @@ class Dataset(object): Note that if `tensors` contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more - @{tf.constant} operations. For large datasets (> 1 GB), this can waste + `tf.constant` operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in @{$guide/datasets#consuming_numpy_arrays$this guide}. @@ -241,7 +241,7 @@ class Dataset(object): Note that if `tensors` contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more - @{tf.constant} operations. For large datasets (> 1 GB), this can waste + `tf.constant` operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in @{$guide/datasets#consuming_numpy_arrays$this guide}. @@ -331,7 +331,7 @@ class Dataset(object): ``` NOTE: The current implementation of `Dataset.from_generator()` uses - @{tf.py_func} and inherits the same constraints. In particular, it + `tf.py_func` and inherits the same constraints. In particular, it requires the `Dataset`- and `Iterator`-related operations to be placed on a device in the same process as the Python program that called `Dataset.from_generator()`. The body of `generator` will not be @@ -641,7 +641,7 @@ class Dataset(object): Defaults to `True`. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. Returns: Dataset: A `Dataset` of strings corresponding to file names. @@ -706,7 +706,7 @@ class Dataset(object): dataset will sample. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. reshuffle_each_iteration: (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to `True`.) @@ -863,7 +863,7 @@ class Dataset(object): This transformation combines multiple consecutive elements of the input dataset into a single element. - Like @{tf.data.Dataset.batch}, the tensors in the resulting element will + Like `tf.data.Dataset.batch`, the tensors in the resulting element will have an additional outer dimension, which will be `batch_size` (or `N % batch_size` for the last element if `batch_size` does not divide the number of input elements `N` evenly and `drop_remainder` is `False`). If @@ -871,7 +871,7 @@ class Dataset(object): should set the `drop_remainder` argument to `True` to prevent the smaller batch from being produced. - Unlike @{tf.data.Dataset.batch}, the input elements to be batched may have + Unlike `tf.data.Dataset.batch`, the input elements to be batched may have different shapes, and this transformation will pad each component to the respective shape in `padding_shapes`. The `padding_shapes` argument determines the resulting shape for each dimension of each component in an @@ -883,8 +883,8 @@ class Dataset(object): will be padded out to the maximum length of all elements in that dimension. - See also @{tf.contrib.data.dense_to_sparse_batch}, which combines elements - that may have different shapes into a @{tf.SparseTensor}. + See also `tf.contrib.data.dense_to_sparse_batch`, which combines elements + that may have different shapes into a `tf.SparseTensor`. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of @@ -1039,7 +1039,7 @@ class Dataset(object): elements are produced. `cycle_length` controls the number of input elements that are processed concurrently. If you set `cycle_length` to 1, this transformation will handle one input element at a time, and will produce - identical results = to @{tf.data.Dataset.flat_map}. In general, + identical results = to `tf.data.Dataset.flat_map`. In general, this transformation will apply `map_func` to `cycle_length` input elements, open iterators on the returned `Dataset` objects, and cycle through them producing `block_length` consecutive elements from each iterator, and @@ -1306,7 +1306,7 @@ class _NestedDatasetComponent(object): class _VariantDataset(Dataset): - """A Dataset wrapper around a @{tf.variant}-typed function argument.""" + """A Dataset wrapper around a `tf.variant`-typed function argument.""" def __init__(self, dataset_variant, structure): super(_VariantDataset, self).__init__() @@ -1342,20 +1342,20 @@ class StructuredFunctionWrapper(object): func: A function from a nested structure to another nested structure. transformation_name: Human-readable name of the transformation in which this function is being instantiated, for error messages. - dataset: (Optional.) A @{tf.data.Dataset}. If given, the structure of this + dataset: (Optional.) A `tf.data.Dataset`. If given, the structure of this dataset will be assumed as the structure for `func` arguments; otherwise `input_classes`, `input_shapes`, and `input_types` must be defined. input_classes: (Optional.) A nested structure of `type`. If given, this argument defines the Python types for `func` arguments. - input_shapes: (Optional.) A nested structure of @{tf.TensorShape}. If + input_shapes: (Optional.) A nested structure of `tf.TensorShape`. If given, this argument defines the shapes and structure for `func` arguments. - input_types: (Optional.) A nested structure of @{tf.DType}. If given, this + input_types: (Optional.) A nested structure of `tf.DType`. If given, this argument defines the element types and structure for `func` arguments. add_to_graph: (Optional.) If `True`, the function will be added to the default graph. experimental_nested_dataset_support: (Optional.) If `True`, the function - will support @{tf.data.Dataset} objects as arguments and return values. + will support `tf.data.Dataset` objects as arguments and return values. Raises: ValueError: If an invalid combination of `dataset`, `input_classes`, @@ -1478,7 +1478,7 @@ class StructuredFunctionWrapper(object): self._function._create_definition_if_needed() # pylint: disable=protected-access def _defun_args(self): - """Returns a flat list of @{tf.DType} for the input element structure.""" + """Returns a flat list of `tf.DType` for the input element structure.""" ret = [] for input_type, input_class in zip(nest.flatten(self._input_types), nest.flatten(self._input_classes)): @@ -1523,7 +1523,7 @@ def flat_structure(dataset): `**flat_structure(self)` to the op constructor. Args: - dataset: A @{tf.data.Dataset}. + dataset: A `tf.data.Dataset`. Returns: A dictionary of keyword arguments that can be passed to many Dataset op @@ -1846,7 +1846,7 @@ class ShuffleDataset(Dataset): dataset will sample. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. reshuffle_each_iteration: (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to `True`.) diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index 83c541c2f720db1f7b40dcac6ab2c624c397b1da..8f8e026df92c3fd430a2c1d6211668cad2a20a4c 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -220,9 +220,9 @@ class Iterator(checkpointable.CheckpointableBase): """Creates a new, uninitialized `Iterator` based on the given handle. This method allows you to define a "feedable" iterator where you can choose - between concrete iterators by feeding a value in a @{tf.Session.run} call. - In that case, `string_handle` would a @{tf.placeholder}, and you would feed - it with the value of @{tf.data.Iterator.string_handle} in each step. + between concrete iterators by feeding a value in a `tf.Session.run` call. + In that case, `string_handle` would be a `tf.placeholder`, and you would + feed it with the value of `tf.data.Iterator.string_handle` in each step. For example, if you had two iterators that marked the current position in a training dataset and a test dataset, you could choose which to use in @@ -362,9 +362,9 @@ class Iterator(checkpointable.CheckpointableBase): In graph mode, you should typically call this method *once* and use its result as the input to another computation. A typical loop will then call - @{tf.Session.run} on the result of that computation. The loop will terminate + `tf.Session.run` on the result of that computation. The loop will terminate when the `Iterator.get_next()` operation raises - @{tf.errors.OutOfRangeError}. The following skeleton shows how to use + `tf.errors.OutOfRangeError`. The following skeleton shows how to use this method when building a training loop: ```python diff --git a/tensorflow/python/data/ops/optional_ops.py b/tensorflow/python/data/ops/optional_ops.py index 1d3007ef76e4eaec9693141098e7449c726e66fc..b75b98dc72975bb30cfb3e56f3ed1845b4d5c370 100644 --- a/tensorflow/python/data/ops/optional_ops.py +++ b/tensorflow/python/data/ops/optional_ops.py @@ -33,8 +33,8 @@ class Optional(object): An `Optional` can represent the result of an operation that may fail as a value, rather than raising an exception and halting execution. For example, - @{tf.contrib.data.get_next_as_optional} returns an `Optional` that either - contains the next value from a @{tf.data.Iterator} if one exists, or a "none" + `tf.contrib.data.get_next_as_optional` returns an `Optional` that either + contains the next value from a `tf.data.Iterator` if one exists, or a "none" value that indicates the end of the sequence has been reached. """ @@ -55,7 +55,7 @@ class Optional(object): """Returns a nested structure of values wrapped by this optional. If this optional does not have a value (i.e. `self.has_value()` evaluates - to `False`), this operation will raise @{tf.errors.InvalidArgumentError} + to `False`), this operation will raise `tf.errors.InvalidArgumentError` at runtime. Args: diff --git a/tensorflow/python/data/util/convert.py b/tensorflow/python/data/util/convert.py index 746b3d66de082d59e8c1e316c51e2a9ab7670e6d..ba297900b0c9834d856d1fea866c01313473ad0a 100644 --- a/tensorflow/python/data/util/convert.py +++ b/tensorflow/python/data/util/convert.py @@ -36,11 +36,11 @@ def optional_param_to_tensor(argument_name, def partial_shape_to_tensor(shape_like): - """Returns a @{tf.Tensor} that represents the given shape. + """Returns a `tf.Tensor` that represents the given shape. Args: - shape_like: A value that can be converted to a @{tf.TensorShape} or a - @{tf.Tensor}. + shape_like: A value that can be converted to a `tf.TensorShape` or a + `tf.Tensor`. Returns: A 1-D `tf.Tensor` of `tf.int64` elements representing the given shape, where diff --git a/tensorflow/python/data/util/random_seed.py b/tensorflow/python/data/util/random_seed.py index e2c9d8672f94587fd3164f25f97b44a97526be07..d5169f7a53e815f7ab4e1a2e973414ead4b7c71d 100644 --- a/tensorflow/python/data/util/random_seed.py +++ b/tensorflow/python/data/util/random_seed.py @@ -29,14 +29,14 @@ from tensorflow.python.ops import math_ops def get_seed(seed): """Returns the local seeds an operation should use given an op-specific seed. - See @{tf.get_seed} for more details. This wrapper adds support for the case + See `tf.get_seed` for more details. This wrapper adds support for the case where `seed` may be a tensor. Args: - seed: An integer or a @{tf.int64} scalar tensor. + seed: An integer or a `tf.int64` scalar tensor. Returns: - A tuple of two @{tf.int64} scalar tensors that should be used for the local + A tuple of two `tf.int64` scalar tensors that should be used for the local seed of the calling dataset. """ seed, seed2 = random_seed.get_seed(seed) diff --git a/tensorflow/python/debug/lib/debug_gradients.py b/tensorflow/python/debug/lib/debug_gradients.py index 589a13db7f798aef3bb82dfbd442deabfbcf2a41..5e95bcba479a4365d3a140ab85ad7492a13a2482 100644 --- a/tensorflow/python/debug/lib/debug_gradients.py +++ b/tensorflow/python/debug/lib/debug_gradients.py @@ -69,7 +69,7 @@ class GradientsDebugger(object): """Gradients Debugger. Allows retrieval of gradient tensors created by TensorFlow's automatic - differentiation algorithm, i.e., @{tf.gradients} and optimizer classes that + differentiation algorithm, i.e., `tf.gradients` and optimizer classes that use it. """ # TODO(cais): Add examples code in the doc string? @@ -142,8 +142,8 @@ class GradientsDebugger(object): Args: input_tensor: the input `tf.Tensor` object whose related gradient tensors are to be reigstered with this `GradientsDebugger` instance when they - are created, e.g., during @{tf.gradients} calls or the construction - of optimization (training) op that uses @{tf.gradients}. + are created, e.g., during `tf.gradients` calls or the construction + of optimization (training) op that uses `tf.gradients`. Returns: A forwarded identity of `input_tensor`, as a `tf.Tensor`. diff --git a/tensorflow/python/debug/wrappers/dumping_wrapper.py b/tensorflow/python/debug/wrappers/dumping_wrapper.py index 3fac2e59717a828424a808b770812afc7772bfe2..c02d5f66ec96d3428ee36e68b69d103af8fc1352 100644 --- a/tensorflow/python/debug/wrappers/dumping_wrapper.py +++ b/tensorflow/python/debug/wrappers/dumping_wrapper.py @@ -45,7 +45,7 @@ class DumpingDebugWrapperSession(framework.NonInteractiveDebugWrapperSession): session_root: (`str`) Path to the session root directory. Must be a directory that does not exist or an empty directory. If the directory does not exist, it will be created by the debugger core during debug - @{tf.Session.run} + `tf.Session.run` calls. As the `run()` calls occur, subdirectories will be added to `session_root`. The subdirectories' names has the following pattern: diff --git a/tensorflow/python/distribute/BUILD b/tensorflow/python/distribute/BUILD index 2bd0b4320afb500afad30e3d5cb0000711e1b664..68d8b8d13b1327cb5c540e868aaddc8bde684082 100644 --- a/tensorflow/python/distribute/BUILD +++ b/tensorflow/python/distribute/BUILD @@ -22,7 +22,7 @@ py_library( py_test( name = "distribute_coordinator_test", - size = "small", + size = "large", srcs = ["distribute_coordinator_test.py"], srcs_version = "PY2AND3", tags = ["no_pip"], diff --git a/tensorflow/python/distribute/distribute_coordinator.py b/tensorflow/python/distribute/distribute_coordinator.py index dab1ed43cafb2d1f5b90604f9776c475d3ad39e9..fc9ca4ac4a3fb159261cb8b5cbae36f0a4e5c97f 100644 --- a/tensorflow/python/distribute/distribute_coordinator.py +++ b/tensorflow/python/distribute/distribute_coordinator.py @@ -32,6 +32,23 @@ class _TaskType(object): WORKER = "worker" CHIEF = "chief" EVALUATOR = "evaluator" + CLIENT = "client" + + +# TODO(yuefengz): support another mode where the client colocates with one +# worker. +class CoordinatorMode(object): + """Specify how distribute coordinator runs.""" + # The default mode where distribute coordinator will run as a standalone + # client and connects to remote servers for training. Each remote server can + # use the distribute coordinator binary with task_type set correctly which + # will then turn into standard servers. + SPLIT_CLIENT = 0 + + # The distribute coordinator runs on each worker. It will run a standard + # server on each worker and optionally run the `worker_fn` that is configured + # to talk to its standard server. + INDEPENDENT_WORKER = 1 _worker_context = threading.local() @@ -99,7 +116,6 @@ class _WorkerContext(object): cluster_spec, task_type, task_id, - between_graph=False, rpc_layer="grpc", worker_barrier=None): """Initialize the worker context object. @@ -108,27 +124,15 @@ class _WorkerContext(object): cluster_spec: a ClusterSpec object. It can be empty or None in the local training case. task_type: a string indicating the role of the corresponding task, such as - "worker" or "ps". It can be None if it is local training or - `between_graph` is False. + "worker" or "ps". It can be None if it is local training or in-graph + replicated training. task_id: an integer indicating id of the corresponding task. It can be - None if it is local training or `between_graph` is False. - between_graph: whether it is between-graph replication or not. + None if it is local training or in-graph replicated training. rpc_layer: optional string specifying the RPC protocol for communication with worker masters. If None or empty, hosts in the `cluster_spec` will be used directly. worker_barrier: optional, the barrier object for worker synchronization. - - Raises: - ValueError: if task_type or task_id is Node or empty and it is distributed - between-graph replicated training. """ - if cluster_spec and between_graph: - if not task_type or task_id is None: - raise ValueError("`task_type` and `task_id` must be set in the " - "distributed between-graph replicated training.") - if task_type not in cluster_spec.jobs: - raise ValueError("`task_type` %r not found in the `cluster_spec` %r" % - (task_type, cluster_spec)) self._cluster_spec = cluster_spec self._task_type = task_type self._task_id = task_id @@ -138,11 +142,16 @@ class _WorkerContext(object): self._num_workers = _get_num_workers(cluster_spec) self._is_chief_node = self._is_chief() + def _debug_message(self): + return "[cluster_spec: %r, task_type: %r, task_id: %r]" % ( + self._cluster_spec, self.task_type, self.task_id) + def __enter__(self): old_context = get_current_worker_context() if old_context: raise ValueError( - "You cannot run distribute coordinator in a `worker_fn`.") + "You cannot run distribute coordinator in a `worker_fn`.\t" + + self._debug_message()) _worker_context.current = self def __exit__(self, unused_exception_type, unused_exception_value, @@ -159,7 +168,6 @@ class _WorkerContext(object): # case we use the chief or first worker's master target. if not self._task_type: if _TaskType.CHIEF in self._cluster_spec.jobs: - assert not self.between_graph task_type = _TaskType.CHIEF task_id = 0 else: @@ -177,7 +185,8 @@ class _WorkerContext(object): def _is_chief(self): """Return whether the task is the chief worker.""" - if (not self._cluster_spec or self._task_type in [_TaskType.CHIEF, None]): + if (not self._cluster_spec or + self._task_type in [_TaskType.CHIEF, _TaskType.EVALUATOR, None]): return True # If not local and chief not in the cluster_spec, use the first worker as @@ -194,14 +203,19 @@ class _WorkerContext(object): ValueError: if `worker_barrier` is not passed to the __init__ method. """ if not self._worker_barrier: - raise ValueError( - "`worker_barrier is not set in the worker context.`") + raise ValueError("`worker_barrier is not set in the worker context.` \t" + + self._debug_message()) self._worker_barrier.wait() + @property + def has_barrier(self): + """Whether the barrier is set or not.""" + return self._worker_barrier is not None + @property def distributed_mode(self): """Whether it is distributed training or not.""" - return bool(self._cluster_spec) + return bool(self._cluster_spec) and self._task_type != _TaskType.EVALUATOR @property def cluster_spec(self): @@ -234,24 +248,110 @@ class _WorkerContext(object): return self._num_workers -def _run(worker_fn, cluster_spec, task_type, task_id, between_graph, rpc_layer, - worker_barrier): - with _WorkerContext(cluster_spec, task_type, task_id, between_graph, - rpc_layer, worker_barrier): +def _run_single_worker(worker_fn, + cluster_spec, + task_type, + task_id, + rpc_layer, + worker_barrier=None): + """Runs a single worker by calling `worker_fn` under context.""" + with _WorkerContext( + cluster_spec, + task_type, + task_id, + rpc_layer=rpc_layer, + worker_barrier=worker_barrier): worker_fn() +def _run_std_server(cluster_spec=None, + task_type=None, + task_id=None, + session_config=None, + rpc_layer=None): + """Runs a standard server.""" + server = server_lib.Server( + cluster_spec, + job_name=task_type, + task_index=task_id, + config=session_config, + protocol=rpc_layer) + server.start() + return server + + +def _run_between_graph_client(worker_fn, cluster_spec, rpc_layer): + """Runs a standalone client for between-graph replication.""" + eval_thread = None + if _TaskType.EVALUATOR in cluster_spec.jobs: + eval_thread = threading.Thread( + target=_run_single_worker, + args=(worker_fn, cluster_spec, _TaskType.EVALUATOR, 0), + kwargs={ + "rpc_layer": rpc_layer, + }) + eval_thread.start() + + threads = [] + worker_barrier = _Barrier(_get_num_workers(cluster_spec)) + for task_type in [_TaskType.CHIEF, _TaskType.WORKER]: + for task_id in range(len(cluster_spec.as_dict().get(task_type, []))): + t = threading.Thread( + target=_run_single_worker, + args=(worker_fn, cluster_spec, task_type, task_id), + kwargs={ + "rpc_layer": rpc_layer, + "worker_barrier": worker_barrier + }) + t.start() + threads.append(t) + + # TODO(yuefengz): wrap threads into thread coordinator? + for t in threads: + t.join() + + # TODO(yuefengz): is it necessary to join eval thread? + if eval_thread: + eval_thread.join() + + +def _run_in_graph_client(worker_fn, cluster_spec, rpc_layer): + """Runs a standalone client for in-graph replication.""" + eval_thread = None + if _TaskType.EVALUATOR in cluster_spec.jobs: + eval_thread = threading.Thread( + target=_run_single_worker, + args=(worker_fn, cluster_spec, _TaskType.EVALUATOR, 0), + kwargs={ + "rpc_layer": rpc_layer, + }) + eval_thread.start() + + _run_single_worker(worker_fn, cluster_spec, None, None, rpc_layer) + if eval_thread: + eval_thread.join() + + +# TODO(yuefengz): propagate cluster_spec in the SPLIT_CLIENT mode. +# TODO(yuefengz): we may need a smart way to figure out whether the current task +# is the special task when we support cluster_spec propagation. def run_distribute_coordinator(worker_fn, + mode=CoordinatorMode.SPLIT_CLIENT, cluster_spec=None, + task_type=None, + task_id=None, between_graph=False, - rpc_layer=None): - """Run the coordinator for distributed TensorFlow. - - This function runs a unified and split coordinator for distributed TensorFlow. - Given a `cluster_spec` specifying server addresses and their roles in a - cluster, this coordinator will figure out how to set them up, give the - underlying function the right targets for master sessions and coordinate their - training. + rpc_layer="grpc"): + """Runs the coordinator for distributed TensorFlow. + + This function runs a split coordinator for distributed TensorFlow in its + default mode, i.e the SPLIT_CLIENT mode. Given a `cluster_spec` specifying + server addresses and their roles in a cluster, this coordinator will figure + out how to set them up, give the underlying function the right targets for + master sessions via a scope object and coordinate their training. The cluster + consisting of standard servers needs to be brought up either with the standard + server binary or with a binary running distribute coordinator with `task_type` + set to non-client type which will then turn into standard servers. In addition to be the distribute coordinator, this is also the source of configurations for each job in the distributed training. As there are multiple @@ -261,9 +361,14 @@ def run_distribute_coordinator(worker_fn, In the between-graph replicated training, this coordinator will create multiple threads and each calls the `worker_fn` which is supposed to create - its own graph and connect to one worker master given by its coordinator - context. In the in-graph replicated training, it has only one thread calling - this `worker_fn`. + its own graph and connect to one worker master given by its context object. In + the in-graph replicated training, it has only one thread calling this + `worker_fn`. + + Another mode is the INDEPENDENT_WORKER mode where each server runs a + distribute coordinator which will start a standard server and optionally runs + `worker_fn` depending whether it is between-graph training or in-graph + replicated training. The `worker_fn` defines the training logic and is called under a its own worker context which can be accessed to via `get_current_worker_context`. A @@ -274,13 +379,14 @@ def run_distribute_coordinator(worker_fn, `worker_fn` or to define different environment variables for different `worker_fn`s. - The `worker_fn` for the between-graph replication is defined as if there are - only one worker corresponding to the `worker_fn` and possibly ps jobs. It - assigns variables to parameter servers and all other operations to that - worker. In the in-graph replication case, the `worker_fn` has to define - operations for all worker jobs. Using a distribution strategy can simplify the - `worker_fn` by not having to worry about the replication and device assignment - of variables and operations. + The `worker_fn` for the between-graph replication is defined as if there is + only one worker corresponding to the `worker_fn` and possibly ps jobs. For + example, when training with parameter servers, it assigns variables to + parameter servers and all other operations to that worker. In the in-graph + replication case, the `worker_fn` has to define operations for all worker + jobs. Using a distribution strategy can simplify the `worker_fn` by not having + to worry about the replication and device assignment of variables and + operations. This method is intended to be invoked by high-level APIs so that users don't have to explictly call it to run this coordinator. For those who don't use @@ -309,8 +415,11 @@ def run_distribute_coordinator(worker_fn, Args: worker_fn: the function to be called and given the access to a coordinator context object. + mode: in which mode this distribute coordinator runs. cluster_spec: a dict, ClusterDef or ClusterSpec specifying servers and roles in a cluster. If not set or empty, fall back to local training. + task_type: the current task type, optional if this is a client. + task_id: the current task id, optional if this is a client. between_graph: a boolean. It is only useful when `cluster_spec` is set and not empty. If true, it will use between-graph replicated training; otherwise it will use in-graph replicated training. @@ -320,9 +429,13 @@ def run_distribute_coordinator(worker_fn, ValueError: if `cluster_spec` is supplied but not a dict or a ClusterDef or a ClusterSpec. """ + tf_config = json.loads(os.environ.get("TF_CONFIG", "{}")) if not cluster_spec: - tf_config = json.loads(os.environ.get("TF_CONFIG", "{}")) cluster_spec = tf_config.get("cluster", {}) + task_env = tf_config.get("task", {}) + if task_env: + task_type = task_env.get("type", task_type) + task_id = int(task_env.get("index", task_id)) if cluster_spec: if isinstance(cluster_spec, (dict, cluster_pb2.ClusterDef)): @@ -333,29 +446,45 @@ def run_distribute_coordinator(worker_fn, "`tf.train.ClusterDef` object") # TODO(yuefengz): validate cluster_spec. - threads = [] - if cluster_spec and _TaskType.EVALUATOR in cluster_spec.jobs: - t = threading.Thread( - target=_run, - args=(worker_fn, cluster_spec, _TaskType.EVALUATOR, 0, between_graph, - rpc_layer, None)) - t.start() - threads.append(t) - - if cluster_spec and between_graph: - worker_barrier = _Barrier(_get_num_workers(cluster_spec)) - for task_type in [_TaskType.CHIEF, _TaskType.WORKER]: - for task_id in range(len(cluster_spec.as_dict().get(task_type, []))): - t = threading.Thread( - target=_run, - args=(worker_fn, cluster_spec, task_type, task_id, between_graph, - rpc_layer, worker_barrier)) - t.start() - threads.append(t) + if not cluster_spec: + # `mode` is ignored in the local case. + _run_single_worker(worker_fn, None, None, None, rpc_layer) + elif mode == CoordinatorMode.SPLIT_CLIENT: + # The client must know the cluster but servers in the cluster don't have to + # know the client. + if task_type in [_TaskType.CLIENT, None]: + if between_graph: + _run_between_graph_client(worker_fn, cluster_spec, rpc_layer) + else: + _run_in_graph_client(worker_fn, cluster_spec, rpc_layer) + else: + # If not a client job, run the standard server. + server = _run_std_server( + cluster_spec=cluster_spec, task_type=task_type, task_id=task_id) + server.join() else: - # Local or in-graph replicated training. - _run(worker_fn, cluster_spec, None, None, between_graph, rpc_layer, None) - - # TODO(yuefengz): wrapper threads into thread coordinator? - for t in threads: - t.join() + if mode != CoordinatorMode.INDEPENDENT_WORKER: + raise ValueError("Unexpected coordinator mode: %r" % mode) + + # Every one starts a standard server. + server = _run_std_server( + cluster_spec=cluster_spec, task_type=task_type, task_id=task_id) + + if task_type in [_TaskType.CHIEF, _TaskType.WORKER]: + if between_graph: + # All jobs run `worker_fn` if between-graph. + _run_single_worker(worker_fn, cluster_spec, task_type, task_id, + rpc_layer) + else: + # Only one node runs `worker_fn` if in-graph. + context = _WorkerContext(cluster_spec, task_type, task_id, rpc_layer) + if context.is_chief: + _run_single_worker(worker_fn, cluster_spec, None, None, rpc_layer) + else: + server.join() + elif task_type == _TaskType.EVALUATOR: + _run_single_worker(worker_fn, cluster_spec, task_type, task_id, rpc_layer) + else: + if task_type != _TaskType.PS: + raise ValueError("Unexpected task_type: %r" % task_type) + server.join() diff --git a/tensorflow/python/distribute/distribute_coordinator_test.py b/tensorflow/python/distribute/distribute_coordinator_test.py index d7ffeb56a5eeb93007c2ccce1083daaa40a75a5f..319c29ba2fae9829526fde03e02f5a77b8c4d46c 100644 --- a/tensorflow/python/distribute/distribute_coordinator_test.py +++ b/tensorflow/python/distribute/distribute_coordinator_test.py @@ -20,9 +20,20 @@ from __future__ import print_function import contextlib import copy +import os +import sys import threading import six +# pylint: disable=invalid-name +_portpicker_import_error = None +try: + import portpicker # pylint: disable=g-import-not-at-top +except ImportError as _error: + _portpicker_import_error = _error + portpicker = None +# pylint: enable=invalid-name + from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.distribute import distribute_coordinator @@ -39,6 +50,11 @@ WORKER = distribute_coordinator._TaskType.WORKER PS = distribute_coordinator._TaskType.PS EVALUATOR = distribute_coordinator._TaskType.EVALUATOR +SPLIT_CLIENT = distribute_coordinator.CoordinatorMode.SPLIT_CLIENT +INDEPENDENT_WORKER = distribute_coordinator.CoordinatorMode.INDEPENDENT_WORKER + +RUN_STD_SERVER_METHOD = "tensorflow.python.distribute.distribute_coordinator._run_std_server" + NUM_WORKERS = 3 NUM_PS = 2 @@ -50,7 +66,29 @@ def _bytes_to_str(maybe_bytes): return str(maybe_bytes, "utf-8") -class DistributeCoordinatorTest(test.TestCase): +def _strip_protocol(target): + # cluster_spec expects "host:port" strings. + if "//" in target: + return target.split("//")[1] + else: + return target + + +class MockServer(object): + + def __init__(self): + self._joined = False + + def join(self): + assert not self._joined + self._joined = True + + @property + def joined(self): + return self._joined + + +class DistributeCoordinatorTestBase(test.TestCase): @classmethod def setUpClass(cls): @@ -60,14 +98,18 @@ class DistributeCoordinatorTest(test.TestCase): cls._workers, cls._ps = test_util.create_local_cluster( NUM_WORKERS, num_ps=NUM_PS) cls._cluster_spec = { - WORKER: [_bytes_to_str(w.target) for w in cls._workers], - PS: [_bytes_to_str(ps.target) for ps in cls._ps] + WORKER: [ + _strip_protocol(_bytes_to_str(w.target)) for w in cls._workers + ], + PS: [_strip_protocol(_bytes_to_str(ps.target)) for ps in cls._ps] } def setUp(self): self._result_correct = 0 self._lock = threading.Lock() self._worker_context = {} + self._std_servers = {} + self._barrier = distribute_coordinator._Barrier(NUM_WORKERS) @contextlib.contextmanager def _test_session(self, target): @@ -76,6 +118,30 @@ class DistributeCoordinatorTest(test.TestCase): with session.Session(graph=None, config=config, target=target) as sess: yield sess + def _create_cluster_spec(self, + has_chief=False, + num_workers=1, + num_ps=0, + has_eval=False): + if _portpicker_import_error: + raise _portpicker_import_error # pylint: disable=raising-bad-type + + cluster_spec = {} + if has_chief: + cluster_spec[CHIEF] = ["localhost:%s" % portpicker.pick_unused_port()] + if num_workers: + cluster_spec[WORKER] = [ + "localhost:%s" % portpicker.pick_unused_port() + for _ in range(num_workers) + ] + if num_ps: + cluster_spec[PS] = [ + "localhost:%s" % portpicker.pick_unused_port() for _ in range(num_ps) + ] + if has_eval: + cluster_spec[EVALUATOR] = ["localhost:%s" % portpicker.pick_unused_port()] + return cluster_spec + def _in_graph_worker_fn(self): context = distribute_coordinator.get_current_worker_context() self.assertTrue(context is not None) @@ -98,13 +164,28 @@ class DistributeCoordinatorTest(test.TestCase): if result_value == expected: self._result_correct += 1 - def testInGraph(self): - """Test it runs in-graph replicated training correctly.""" - distribute_coordinator.run_distribute_coordinator( - self._in_graph_worker_fn, - cluster_spec=self._cluster_spec, - between_graph=False) - self.assertEqual(self._result_correct, 1) + def _run_coordinator_in_thread(self, worker_fn, **kwargs): + t = threading.Thread( + target=distribute_coordinator.run_distribute_coordinator, + args=(worker_fn,), + kwargs=kwargs) + t.start() + return t + + def _run_multiple_coordinator_in_threads(self, worker_fn, cluster_spec, + **kwargs): + threads = {} + for task_type in cluster_spec.keys(): + threads[task_type] = [] + for task_id in range(len(cluster_spec[task_type])): + t = self._run_coordinator_in_thread( + worker_fn, + cluster_spec=cluster_spec, + task_type=task_type, + task_id=task_id, + **kwargs) + threads[task_type].append(t) + return threads def _between_graph_worker_fn(self): context = distribute_coordinator.get_current_worker_context() @@ -127,13 +208,23 @@ class DistributeCoordinatorTest(test.TestCase): variables.global_variables_initializer().run() # Synchronize workers after initializaton. - context.wait_for_other_workers() + if context.has_barrier: + context.wait_for_other_workers() + else: + while True: + uninit_vars = sess.run(variables.report_uninitialized_variables()) + # pylint: disable=g-explicit-length-test + if len(uninit_vars) == 0: + break sess.run(train_op) # Synchronize workers after one step to make sure they all have finished # training. - context.wait_for_other_workers() + if context.has_barrier: + context.wait_for_other_workers() + else: + self._barrier.wait() x_val, y_val = sess.run([x, y]) @@ -143,16 +234,6 @@ class DistributeCoordinatorTest(test.TestCase): with self._lock: self._result_correct += 1 - def testBetweenGraph(self): - """Test it runs between-graph replicated training correctly.""" - distribute_coordinator.run_distribute_coordinator( - self._between_graph_worker_fn, - cluster_spec=self._cluster_spec, - between_graph=True) - - # Each finished worker will increment self._result_correct. - self.assertEqual(self._result_correct, NUM_WORKERS) - def _dump_worker_context(self): """Dumps the propoerties of each worker context. @@ -174,6 +255,45 @@ class DistributeCoordinatorTest(test.TestCase): context.is_chief, context.distributed_mode) + def _run_mock_std_server(self, + session_config=None, + cluster_spec=None, + task_type=None, + task_id=None, + rpc_layer=None): + task_type = str(task_type) + task_id = task_id or 0 + with self._lock: + if task_type not in self._std_servers: + self._std_servers[task_type] = [] + while len(self._std_servers[task_type]) <= task_id: + self._std_servers[task_type].append(None) + + server = MockServer() + self._std_servers[task_type][task_id] = server + return server + + +class DistributeCoordinatorTestSplitMode(DistributeCoordinatorTestBase): + + def testInGraphSplitMode(self): + """Test it runs in-graph replication in split client mode.""" + distribute_coordinator.run_distribute_coordinator( + self._in_graph_worker_fn, + cluster_spec=self._cluster_spec, + between_graph=False) + self.assertEqual(self._result_correct, 1) + + def testBetweenGraph(self): + """Test it runs between-graph replication in split client mode.""" + distribute_coordinator.run_distribute_coordinator( + self._between_graph_worker_fn, + cluster_spec=self._cluster_spec, + between_graph=True) + + # Each finished worker will increment self._result_correct. + self.assertEqual(self._result_correct, NUM_WORKERS) + def testBetweenGraphContext(self): # Dumps the task contexts to the self._worker_context dict. distribute_coordinator.run_distribute_coordinator( @@ -253,15 +373,15 @@ class DistributeCoordinatorTest(test.TestCase): # and distributed_mode. self.assertEqual(self._worker_context[CHIEF][0], ("grpc://fake_chief", 4, True, True)) - self.assertEqual(self._worker_context[WORKER][0], - ("grpc://" + _bytes_to_str(self._workers[0].target), - NUM_WORKERS + 1, False, True)) - self.assertEqual(self._worker_context[WORKER][1], - ("grpc://" + _bytes_to_str(self._workers[1].target), - NUM_WORKERS + 1, False, True)) - self.assertEqual(self._worker_context[WORKER][2], - ("grpc://" + _bytes_to_str(self._workers[2].target), - NUM_WORKERS + 1, False, True)) + self.assertEqual( + self._worker_context[WORKER][0], + (_bytes_to_str(self._workers[0].target), NUM_WORKERS + 1, False, True)) + self.assertEqual( + self._worker_context[WORKER][1], + (_bytes_to_str(self._workers[1].target), NUM_WORKERS + 1, False, True)) + self.assertEqual( + self._worker_context[WORKER][2], + (_bytes_to_str(self._workers[2].target), NUM_WORKERS + 1, False, True)) def testInGraphContextWithEval(self): # Adds a EVALUATOR job. @@ -272,7 +392,140 @@ class DistributeCoordinatorTest(test.TestCase): distribute_coordinator.run_distribute_coordinator( self._dump_worker_context, cluster_spec=cluster_spec, - between_graph=False) + between_graph=False, + rpc_layer=None) + + # There are one "None" task and one EVALUATOR task. + self.assertEqual(len(self._worker_context), 2) + self.assertTrue("None" in self._worker_context) + self.assertTrue(EVALUATOR in self._worker_context) + self.assertEqual(len(self._worker_context["None"]), 1) + self.assertEqual(len(self._worker_context[EVALUATOR]), 1) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual(self._worker_context["None"][0], (_strip_protocol( + _bytes_to_str(self._workers[0].target)), 3, True, True)) + self.assertEqual(self._worker_context[EVALUATOR][0], + ("fake_evaluator", 3, True, False)) + + +class DistributeCoordinatorTestInpendentWorkerMode( + DistributeCoordinatorTestBase): + + def testInGraph(self): + cluster_spec = self._create_cluster_spec(num_workers=NUM_WORKERS) + threads = self._run_multiple_coordinator_in_threads( + self._in_graph_worker_fn, + cluster_spec, + between_graph=False, + mode=INDEPENDENT_WORKER) + threads[WORKER][0].join() + self.assertEqual(self._result_correct, 1) + + def testBetweenGraph(self): + cluster_spec = self._create_cluster_spec( + num_workers=NUM_WORKERS, num_ps=NUM_PS) + threads = self._run_multiple_coordinator_in_threads( + self._between_graph_worker_fn, + cluster_spec, + between_graph=True, + mode=INDEPENDENT_WORKER) + for task_id in range(NUM_WORKERS): + threads[WORKER][task_id].join() + + # Each finished worker will increment self._result_correct. + self.assertEqual(self._result_correct, NUM_WORKERS) + + def testBetweenGraphContext(self): + cluster_spec = self._create_cluster_spec(num_workers=NUM_WORKERS) + # Dumps the task contexts and std server arguments. + with test.mock.patch.object(distribute_coordinator, "_run_std_server", + self._run_mock_std_server): + threads = self._run_multiple_coordinator_in_threads( + self._dump_worker_context, + cluster_spec, + mode=INDEPENDENT_WORKER, + between_graph=True, + rpc_layer=None) + for task_id in range(NUM_WORKERS): + threads[WORKER][task_id].join() + + # There is only one type of task and three such tasks. + self.assertEqual(len(self._worker_context), 1) + self.assertTrue(WORKER in self._worker_context) + self.assertEqual(len(self._worker_context[WORKER]), NUM_WORKERS) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual( + self._worker_context[WORKER][0], + (_bytes_to_str(cluster_spec[WORKER][0]), NUM_WORKERS, True, True)) + self.assertEqual( + self._worker_context[WORKER][1], + (_bytes_to_str(cluster_spec[WORKER][1]), NUM_WORKERS, False, True)) + self.assertEqual( + self._worker_context[WORKER][2], + (_bytes_to_str(cluster_spec[WORKER][2]), NUM_WORKERS, False, True)) + + # Make sure each worker runs a std server. + self.assertEqual(len(self._std_servers), 1) + self.assertTrue(WORKER in self._std_servers) + self.assertEqual(len(self._std_servers[WORKER]), 3) + self.assertFalse(self._std_servers[WORKER][0].joined) + self.assertFalse(self._std_servers[WORKER][1].joined) + self.assertFalse(self._std_servers[WORKER][2].joined) + + def testInGraphContext(self): + cluster_spec = self._create_cluster_spec(num_workers=NUM_WORKERS) + # Dumps the task contexts and std server arguments. + with test.mock.patch.object(distribute_coordinator, "_run_std_server", + self._run_mock_std_server): + threads = self._run_multiple_coordinator_in_threads( + self._dump_worker_context, + cluster_spec, + mode=INDEPENDENT_WORKER, + between_graph=False, + rpc_layer=None) + for task_id in range(NUM_WORKERS): + threads[WORKER][task_id].join() + + # There is only a "None" task in the dumped task context. + self.assertEqual(len(self._worker_context), 1) + self.assertTrue("None" in self._worker_context) + self.assertEqual(len(self._worker_context["None"]), 1) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual( + self._worker_context["None"][0], + (_bytes_to_str(cluster_spec[WORKER][0]), NUM_WORKERS, True, True)) + + # Make sure each worker runs a std server. + self.assertEqual(len(self._std_servers), 1) + self.assertTrue(WORKER in self._std_servers) + self.assertEqual(len(self._std_servers[WORKER]), 3) + self.assertFalse(self._std_servers[WORKER][0].joined) + self.assertTrue(self._std_servers[WORKER][1].joined) + self.assertTrue(self._std_servers[WORKER][2].joined) + + def testInGraphContextWithEval(self): + # Adds a EVALUATOR job. + cluster_spec = self._create_cluster_spec( + num_workers=NUM_WORKERS, has_eval=True) + + # Dumps the task contexts and std server arguments. + with test.mock.patch.object(distribute_coordinator, "_run_std_server", + self._run_mock_std_server): + threads = self._run_multiple_coordinator_in_threads( + self._dump_worker_context, + cluster_spec, + mode=INDEPENDENT_WORKER, + between_graph=False, + rpc_layer=None) + for task_id in range(NUM_WORKERS): + threads[WORKER][task_id].join() + threads[EVALUATOR][0].join() # There are one "None" task and one EVALUATOR task. self.assertEqual(len(self._worker_context), 2) @@ -284,10 +537,23 @@ class DistributeCoordinatorTest(test.TestCase): # Check whether each task has the right master_target, num_workers, is_chief # and distributed_mode. self.assertEqual(self._worker_context["None"][0], - (_bytes_to_str(self._workers[0].target), 3, True, True)) + (_bytes_to_str(cluster_spec[WORKER][0]), 3, True, True)) self.assertEqual(self._worker_context[EVALUATOR][0], - ("fake_evaluator", 3, False, True)) + (cluster_spec[EVALUATOR][0], 3, True, False)) + + # Make sure each worker runs a std server. + self.assertEqual(len(self._std_servers), 2) + self.assertTrue(WORKER in self._std_servers) + self.assertTrue(EVALUATOR in self._std_servers) + self.assertEqual(len(self._std_servers[WORKER]), 3) + self.assertEqual(len(self._std_servers[EVALUATOR]), 1) + self.assertFalse(self._std_servers[WORKER][0].joined) + self.assertTrue(self._std_servers[WORKER][1].joined) + self.assertTrue(self._std_servers[WORKER][2].joined) + self.assertFalse(self._std_servers[EVALUATOR][0].joined) if __name__ == "__main__": - test.main() + # TODO(yuefengz): find a smart way to terminite std server threads. + with test.mock.patch.object(sys, "exit", os._exit): + test.main() diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index 5f60f62874dbec76ad23a69ab9cce128b6e58d4d..553f761a1409112773537b0a1eb9b7b5399533b9 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -646,7 +646,7 @@ class GradientTape(object): Operations are recorded if they are executed within this context manager and at least one of their inputs is being "watched". - Trainable variables (created by `tf.Variable` or @{tf.get_variable}, + Trainable variables (created by `tf.Variable` or `tf.get_variable`, trainable=True is default in both cases) are automatically watched. Tensors can be manually watched by invoking the `watch` method on this context manager. @@ -705,6 +705,7 @@ class GradientTape(object): self._tape = None self._persistent = persistent self._recording = False + context.context().start_step() def __enter__(self): """Enters a context inside which operations are recorded on this tape.""" @@ -733,6 +734,9 @@ class GradientTape(object): tape.pop_tape(self._tape) self._recording = False + def __del__(self): + context.context().end_step() + def watch(self, tensor): """Ensures that `tensor` is being traced by this tape. diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index c79294895bc0be8a579583a4918c401dcfe9f90a..6a327bd010f5d00c403c09a4f5b6fe6572fc6d9a 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -265,7 +265,7 @@ class Context(object): pywrap_tensorflow.TFE_DeleteContextOptions(opts) if self._server_def is not None: server_def_str = self._server_def.SerializeToString() - pywrap_tensorflow.TFE_ContextSetServerDef(self._context_handle, + pywrap_tensorflow.TFE_ContextSetServerDef(self._context_handle, 600, server_def_str) self._initialize_devices() @@ -275,7 +275,7 @@ class Context(object): self.ones_rank_cache().flush() self.zeros_cache().flush() - def set_server_def(self, server_def): + def set_server_def(self, server_def, keep_alive_secs=600): """Allow setting a server_def on the context. When a server def is replaced, it effectively clears a bunch of caches @@ -285,6 +285,11 @@ class Context(object): Args: server_def: A tensorflow::ServerDef proto. Enables execution on remote devices. + keep_alive_secs: Num. seconds after which the remote end will hang up. + As long as the client is still alive, the server state for the context + will be kept alive. If the client is killed (or there is some failure), + the server will clean up its context keep_alive_secs after the final RPC + it receives. Raises: ValueError: if server_def is None. @@ -296,7 +301,7 @@ class Context(object): else: server_def_str = server_def.SerializeToString() pywrap_tensorflow.TFE_ContextSetServerDef(self._context_handle, - server_def_str) + keep_alive_secs, server_def_str) # Clear all the caches in case there are remote tensors in them. self._clear_caches() @@ -603,6 +608,12 @@ class Context(object): """Returns a stack of context switches.""" return self._context_switches + def start_step(self): + pywrap_tensorflow.TFE_ContextStartStep(self._handle) + + def end_step(self): + pywrap_tensorflow.TFE_ContextEndStep(self._handle) + _context = None _context_lock = threading.Lock() @@ -652,7 +663,7 @@ def internal_operation_seed(): def executing_eagerly(): """Returns True if the current thread has eager execution enabled. - Eager execution is typically enabled via @{tf.enable_eager_execution}, + Eager execution is typically enabled via `tf.enable_eager_execution`, but may also be enabled within the context of a Python function via tf.contrib.eager.py_func. """ diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index f315fa296c4df426cae37113cc1166e2b887b7ba..f87d88040f10e8e145d3eac79faee08952df6c2e 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -83,26 +83,30 @@ def create_substitute_placeholder(value, name, dtype=None): def capture_value(tensor_map, value, dtype, name): """Capture a value from outside the function, to pass in as an extra arg.""" - captured_tuple = tensor_map.get(ops.tensor_id(value), None) - if captured_tuple is None: + captured_value = tensor_map.get(value, None) + if captured_value is None: captured_value = create_substitute_placeholder(value, name=name, dtype=dtype) - tensor_map[ops.tensor_id(value)] = (value, captured_value) - else: - captured_value = captured_tuple[1] + tensor_map[value] = captured_value tape.record_operation("captured_value", [captured_value], [value], lambda x: [x]) return captured_value class CapturingGraph(ops.Graph): - """Graph used when constructing eager functions.""" + """Graph that can capture tensors from other graphs. + + Attributes: + captures: Maps external tensor -> internal tensor (e.g. input placeholder). + The entries are in the order they were captured. + """ def __init__(self): super(CapturingGraph, self).__init__() + + self.captures = collections.OrderedDict() self._building_function = True - # Maps external tensor id -> internal tensor (e.g. input placeholder). - self.captures = {} + # Map from resource tensor name to last op (in program order) which uses # this tensor. Used to enforce that execution order matches program order # for resource tensors. @@ -115,7 +119,22 @@ class CapturingGraph(ops.Graph): def clear_resource_control_flow_state(self): self._last_op_using_resource_tensor = {} + # TODO(skyewm): get rid of name and use the name of `tensor`. def capture(self, tensor, name=None): + """Capture `tensor` if it's external to this graph. + + If `tensor` is from a different graph, returns a placeholder for it. + `tensor` and the placeholder will also appears in self.captures. Multiple + calls to this method with the same `tensor` argument will return the same + placeholder. If `tensor` is from this graph, returns `tensor`. + + Args: + tensor: Tensor. May be from this FuncGraph or a different graph. + name: Optional name if a placeholder is created. + + Returns: + Tensor from this FuncGraph. + """ if isinstance(tensor, ops.EagerTensor): if name is None: name = str(ops.uid()) @@ -137,6 +156,7 @@ class CapturingGraph(ops.Graph): op_def=None, compute_shapes=True, compute_device=True): + """Captures an external inputs before calling Graph.capture_op.""" # This capturing logic interacts poorly with control flow contexts which # want to replace inputs of ops far too late in the process. This can lead # the context to get confused and try to create an Enter for an Enter. We @@ -159,6 +179,70 @@ class CapturingGraph(ops.Graph): compute_device=compute_device) +class FuncGraph(CapturingGraph): + """Graph representing a function body. + + Attributes: + name: The name of the function. + + inputs: Placeholder tensors representing the inputs to this function. The + tensors are in this FuncGraph. This represents "regular" inputs as well as + captured inputs (i.e. the values of self.captures), with the regular + inputs coming first. + outputs: Tensors that will be returned by this function. The tensors are in + this FuncGraph. + structured_outputs: A possibly-nested python object which will be returned + by this function. The Tensors in this structure are the same as those of + self.outputs. Note that this structure might contain Python `None`s. + variables: Variables that should be watched during function execution. + seed: The graph-level random seed. + """ + + def __init__(self, name, graph=None): + """Construct a new FuncGraph. + + Args: + name: the name of the function. + graph: if specified, this FuncGraph will inherit its graph key, + collections, and seed from `graph`. + """ + super(FuncGraph, self).__init__() + + self.name = name + self.inputs = [] + self.outputs = [] + self.structured_outputs = None + self.variables = [] + + if graph is not None: + # Inherit the graph key, since this is used for matching variables in + # optimizers. + self._graph_key = graph._graph_key # pylint: disable=protected-access + + # Copy the graph collections to ensure summaries and other things work. + # This lets the function access (but not mutate) collections of the + # containing graph, such as the global step and the summary writer + # collections. + for collection in graph.collections: + self.get_collection_ref(collection)[:] = graph.get_collection( + collection) + + if context.executing_eagerly(): + self.seed = context.global_seed() + else: + self.seed = graph.seed + + def capture(self, tensor, name=None): + """Calls CapturingGraph.capture and updates self.inputs if necessary.""" + new_capture = tensor not in self.captures + internal_tensor = super(FuncGraph, self).capture(tensor, name) + + if new_capture and tensor is not internal_tensor: + self.inputs.append(internal_tensor) + + return internal_tensor + + # pylint: disable=invalid-name class HelperContext(object): """ControlFlowContext with a customizable AddOp method.""" @@ -502,6 +586,7 @@ class GraphModeFunction(object): def _construct_backprop_function(self): """Constructs the backprop function object for this function.""" filtered_outputs = [x for x in self._python_returns if x is not None] + # TODO(skyewm): use FuncGraph backwards_graph = CapturingGraph() backwards_graph._graph_key = self._graph._graph_key # pylint: disable=protected-access for collection in self._graph.collections: @@ -521,13 +606,8 @@ class GraphModeFunction(object): grad for grad in _flatten(in_gradients) if grad is not None) output_shapes = tuple(grad.shape for grad in backward_outputs) - captures = backwards_graph.captures - ids = list(sorted(captures.keys())) - if ids: - extra_inputs, extra_placeholders = zip(*[captures[x] for x in ids]) - else: - extra_inputs = [] - extra_placeholders = [] + extra_inputs = backwards_graph.captures.keys() + extra_placeholders = backwards_graph.captures.values() forward_name = _forward_name(self._func_name) # Note: we cannot have placeholder ops in the graph or the TPU compilation @@ -744,23 +824,13 @@ def _trace_and_define_function(name, python_func, compiled, args, kwds, Returns: A GraphModeFunction. + + Raises: + TypeError: If any of `python_func`'s return values is neither `None` nor a + `Tensor`. """ - graph_key = ops.get_default_graph()._graph_key # pylint: disable=protected-access - func_graph = CapturingGraph() - # Inherit the graph key, since this is used for matching variables in - # optimizers. - func_graph._graph_key = graph_key # pylint: disable=protected-access - # Copy the graph collections to ensure summaries and other things work. This - # lets the function access (but not mutate) collections of the containing - # graph, such as the global step and the summary writer collections. - curr_graph = ops.get_default_graph() - for collection in curr_graph.collections: - func_graph.get_collection_ref(collection)[:] = curr_graph.get_collection( - collection) - if context.executing_eagerly(): - func_graph.seed = context.global_seed() - else: - func_graph.seed = curr_graph.seed + func_graph = FuncGraph(_inference_name(name), graph=ops.get_default_graph()) + with func_graph.as_default(), AutomaticControlDependencies() as a: if signature is None: func_args = _get_defun_inputs_from_args(args) @@ -769,15 +839,29 @@ def _trace_and_define_function(name, python_func, compiled, args, kwds, func_args = _get_defun_inputs_from_signature(signature) func_kwds = {} + # Note: `nest.flatten` sorts by keys, as does `_deterministic_dict_values`. + func_graph.inputs.extend( + x for x in nest.flatten(func_args) + nest.flatten(func_kwds) + if isinstance(x, ops.Tensor) + ) + # Variables to help check whether mutation happens in calling the function # Copy the recursive list, tuple and map structure, but not base objects func_args_before = nest.pack_sequence_as(func_args, nest.flatten(func_args)) func_kwds_before = nest.pack_sequence_as(func_kwds, nest.flatten(func_kwds)) def convert(x): + """Converts an argument to a Tensor.""" if x is None: return None - x = ops.convert_to_tensor_or_indexed_slices(x) + try: + x = ops.convert_to_tensor_or_indexed_slices(x) + except (ValueError, TypeError): + raise TypeError( + "To be compatible with tf.contrib.eager.defun, Python functions " + "must return zero or more Tensors; in compilation of %s, found " + "return value of type %s, which is not a Tensor." % + (str(python_func), type(x))) x = a.mark_as_return(x) return x @@ -806,6 +890,7 @@ def _trace_and_define_function(name, python_func, compiled, args, kwds, finally: tape.pop_tape(this_tape) + func_graph.structured_outputs = func_outputs variables = list(this_tape.watched_variables()) # Some variables captured by the tape can come from a DistributedValue. @@ -818,33 +903,20 @@ def _trace_and_define_function(name, python_func, compiled, args, kwds, # If variable is not distributed value_container returns itself. variables[i] = strategy.value_container(variable) + func_graph.variables = variables + # Returning a closed-over tensor as an output does not trigger a # call to convert_to_tensor, so we manually capture all such tensors. - outputs_list = _flatten(func_outputs) - func_def_outputs = [ - func_graph.capture(x) for x in outputs_list + func_graph.outputs.extend( + func_graph.capture(x) for x in _flatten(func_graph.structured_outputs) if x is not None - ] + ) - captures = func_graph.captures - ids = list(sorted(captures.keys())) - if ids: - extra_inputs, extra_placeholders = zip(* [captures[x] for x in ids]) - else: - extra_inputs = [] - extra_placeholders = [] output_shapes = tuple( x.shape if isinstance(x, ops.Tensor) else None - for x in func_def_outputs) + for x in func_graph.outputs) - # Note: `nest.flatten` sorts by keys, as does `_deterministic_dict_values`. - flat_inputs = [ - x for x in nest.flatten(func_args) + nest.flatten(func_kwds) - if isinstance(x, ops.Tensor) - ] - all_inputs = flat_inputs + list(extra_placeholders) - all_ignored_ops = frozenset(x.op for x in all_inputs) - fname = _inference_name(name) + all_ignored_ops = frozenset(x.op for x in func_graph.inputs) operations = tuple(x for x in func_graph.get_operations() if x not in all_ignored_ops) # Register any other functions defined in the graph @@ -859,8 +931,9 @@ def _trace_and_define_function(name, python_func, compiled, args, kwds, attrs[_xla_compile_attr] = attr_value_pb2.AttrValue(b=True) return GraphModeFunction( - fname, all_inputs, extra_inputs, func_graph, operations, func_def_outputs, - func_outputs, output_shapes, variables, attrs) + func_graph.name, func_graph.inputs, func_graph.captures.keys(), + func_graph, operations, func_graph.outputs, func_graph.structured_outputs, + output_shapes, func_graph.variables, attrs) _TensorType = collections.namedtuple("_TensorType", ["dtype", "shape"]) @@ -1148,7 +1221,7 @@ def defun(func=None, input_signature=None, compiled=False): """Compiles a Python function into a callable TensorFlow graph. `defun` (short for "define function") trace-compiles a Python function - composed of TensorFlow operations into a callable that executes a @{tf.Graph} + composed of TensorFlow operations into a callable that executes a `tf.Graph` containing those operations. The callable produced by `defun` contains only the subgraph of TensorFlow operations that were executed when the Python function was called with a particular input signature, defined as a list @@ -1171,9 +1244,9 @@ def defun(func=None, input_signature=None, compiled=False): For a Python function to be compatible with `defun`, all of its arguments must be hashable Python objects or lists thereof. The function itself may not modify the list/map structure of its arguments. Additionally, it must return - zero or more @{tf.Tensor} objects. If the Python function returns - a @{tf.Variable}, its compiled version will return the value of that variable - as a @{tf.Tensor}. + zero or more `tf.Tensor` objects. If the Python function returns + a `tf.Variable`, its compiled version will return the value of that variable + as a `tf.Tensor`. Executing a graph generated by `defun` respects device annotations (i.e., all `with tf.device` directives present in a Python function will also be @@ -1242,7 +1315,7 @@ def defun(func=None, input_signature=None, compiled=False): When using `defun`, there are subtleties regarding inputs, Python control flow, and variable creation that one should be aware of. For concreteness, let - `f` be a Python function that returns zero or more @{tf.Tensor} objects and + `f` be a Python function that returns zero or more `tf.Tensor` objects and let `F = defun(f)`. `F` builds a graph for each unique input signature it sees, Python control flow is baked into graphs, and operations related to variable initialization are automatically lifted out of the graphs that `F` @@ -1325,10 +1398,10 @@ def defun(func=None, input_signature=None, compiled=False): On the other hand, because `defun` generates graphs by tracing and not by source code analysis, it fully unrolls Python `for` and `while` loops, potentially creating large graphs. If your Python function has native loops - that run for many iterations, consider replacing them with @{tf.while_loop} + that run for many iterations, consider replacing them with `tf.while_loop` operations. - When constructing graphs, @{tf.Tensor} objects cannot be used as Python + When constructing graphs, `tf.Tensor` objects cannot be used as Python `bool` objects. This means, for example, that you should replace code in `f` resembling @@ -1347,7 +1420,7 @@ def defun(func=None, input_signature=None, compiled=False): automatically lifted out of the graphs generated by `defun`. In practice, this implies that variable creation and initialization only happen the first time `F` is called, and that variables are reused every time thereafter. Many - TensorFlow APIs, like @{tf.keras.layers.Layer} objects, create variables the + TensorFlow APIs, like `tf.keras.layers.Layer` objects, create variables the first time they are called and reuse them thereafter. Automatic variable lifting makes it possible to compile these APIs without extra effort, at the cost of introducing a discrepancy between the semantics of executing Python @@ -1386,7 +1459,7 @@ def defun(func=None, input_signature=None, compiled=False): to reference the same set of variables, add logic to your Python function that ensures that variables are only created the first time it is called and are reused for every subsequent invocation; note that this is precisely what - @{tf.keras.layers.Layer} objects do, so we recommend using them to represent + `tf.keras.layers.Layer` objects do, so we recommend using them to represent variable-bearing computations whenever possible. Args: diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index b7c9334c3346176a82d0dc85cc8c88001b78abb8..0488dc97521576dfb4465fdb8c2370749dd91b79 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import collections import functools +from multiprocessing.pool import ThreadPool import sys from tensorflow.core.protobuf import config_pb2 @@ -143,6 +144,61 @@ class FunctionTest(test.TestCase): out = sq_op(t) self.assertAllEqual(out, math_ops.matmul(t, t).numpy()) + def testExecutingStatelessDefunConcurrently(self): + + @function.defun + def stateless(x): + return math_ops.multiply(2.0, x) + + pool = ThreadPool() + inputs = [constant_op.constant(1.0 * x) for x in range(100)] + outputs = [float(out) for out in pool.map(stateless, inputs)] + expected = [float(2.0 * x) for x in inputs] + self.assertSequenceEqual(outputs, expected) + + def testExecutingManyStatelessDefunsConcurrently(self): + + @function.defun + def stateless(x): + del x + return math_ops.multiply(2.0, 2.0) + + pool = ThreadPool() + # `pool.map` below instantiates 100 functions, one for each object. + outputs = [ + float(out) + for out in pool.map(stateless, [object() for _ in range(100)]) + ] + expected = [4.0] * 100 + self.assertSequenceEqual(outputs, expected) + + def testExecutingStatefulDefunConcurrently(self): + + v = resource_variable_ops.ResourceVariable(1.0) + + @function.defun + def stateful(x): + v.assign(x) + + pool = ThreadPool() + inputs = [constant_op.constant(0.0)] * 100 + pool.map(stateful, inputs) + self.assertEqual(float(v.read_value()), 0.0) + + def testExecutingManyStatefulDefunsConcurrently(self): + + v = resource_variable_ops.ResourceVariable(1.0) + + @function.defun + def stateful(x): + del x + return v.assign(0.0) + + pool = ThreadPool() + # `pool.map` below instantiates 100 functions, one for each object. + pool.map(stateful, [object() for _ in range(100)]) + self.assertEqual(float(v.read_value()), 0.0) + def disabled_testRandomSeed(self): @function.defun @@ -232,8 +288,6 @@ class FunctionTest(test.TestCase): @test_util.run_in_graph_and_eager_modes() def testGraphLoopGradient(self): - if context.executing_eagerly(): - self.skipTest('TODO(apassos): support loops in defuns in eager') @function.defun def f(x): @@ -878,9 +932,12 @@ class FunctionTest(test.TestCase): y = model(x) self.assertAllEqual([[[[4.0]]]], y.numpy()) + # Note: The ConfigProto below unfortunately only configures graph + # construction. Eager's configuration is controlled in `__main__`. @test_util.run_in_graph_and_eager_modes( - config=config_pb2.ConfigProto(device_count={'CPU': 3})) + config=config_pb2.ConfigProto(device_count={'CPU': 4})) def testDeviceAnnotationsRespected(self): + @function.defun def multi_device_fn(): with ops.device('/cpu:0'): @@ -892,12 +949,28 @@ class FunctionTest(test.TestCase): with ops.device('/cpu:2'): s3 = iterator_ops.Iterator.from_structure( (dtypes.float32,)).string_handle() - return s1, s2, s3 + with ops.device(''): + # TODO(akshayka): This is unfortunate and brittle. It prevents + # `Iterator.from_structure` from assigning the iterator op to 'cpu:0'. + # Remove this hack once we have a way of obtaining metadata about + # function execution. + s4 = iterator_ops.Iterator.from_structure( + (dtypes.float32,)).string_handle() + return s1, s2, s3, s4 - outputs = multi_device_fn() - self.assertTrue(compat.as_bytes('CPU:0') in self.evaluate(outputs[0])) - self.assertTrue(compat.as_bytes('CPU:1') in self.evaluate(outputs[1])) - self.assertTrue(compat.as_bytes('CPU:2') in self.evaluate(outputs[2])) + with ops.device('/cpu:3'): + outputs = self.evaluate(multi_device_fn()) + self.assertIn(compat.as_bytes('CPU:0'), outputs[0]) + self.assertIn(compat.as_bytes('CPU:1'), outputs[1]) + self.assertIn(compat.as_bytes('CPU:2'), outputs[2]) + self.assertIn(compat.as_bytes('CPU:3'), outputs[3]) + + with ops.device('/cpu:0'): + outputs = self.evaluate(multi_device_fn()) + self.assertIn(compat.as_bytes('CPU:0'), outputs[0]) + self.assertIn(compat.as_bytes('CPU:1'), outputs[1]) + self.assertIn(compat.as_bytes('CPU:2'), outputs[2]) + self.assertIn(compat.as_bytes('CPU:0'), outputs[3]) def testVariablesAreTracked(self): v = resource_variable_ops.ResourceVariable(1.0) @@ -1464,6 +1537,18 @@ class AutomaticControlDependenciesTest(test.TestCase): value = train() self.assertEqual(value.numpy(), -1.0) + def testReturningNonTensorRaisesError(self): + optimizer = momentum.MomentumOptimizer(learning_rate=1.0, momentum=1.0) + optimizer.apply_gradients = function.defun(optimizer.apply_gradients) + v = resource_variable_ops.ResourceVariable(1.0) + grad = backprop.implicit_grad(lambda v: v**2)(v) + + with self.assertRaisesRegexp(TypeError, + '.*must return zero or more Tensors.*'): + # TODO(akshayka): We might want to allow defun-ing Python functions + # that return operations (and just execute the op instead of running it). + optimizer.apply_gradients(grad) + # TODO(b/111663004): This should work when the outer context is graph # building. def testOptimizerNonSlotVarsInDefunNoError(self): @@ -1667,5 +1752,5 @@ class AutomaticControlDependenciesTest(test.TestCase): if __name__ == '__main__': ops.enable_eager_execution( - config=config_pb2.ConfigProto(device_count={'CPU': 3})) + config=config_pb2.ConfigProto(device_count={'CPU': 4})) test.main() diff --git a/tensorflow/python/eager/graph_callable.py b/tensorflow/python/eager/graph_callable.py index 9200396c8aff5c123a3d7b869c30327475c923a1..7105d2e399b373e962526802228f7ffe5af4a55a 100644 --- a/tensorflow/python/eager/graph_callable.py +++ b/tensorflow/python/eager/graph_callable.py @@ -330,13 +330,9 @@ def _graph_callable_internal(func, shape_and_dtypes): sorted_variables = sorted(variable_captures.variables.values(), key=lambda x: x.name) - captures = tmp_graph.captures - ids = list(sorted(captures.keys())) - if ids: - extra_inputs, extra_placeholders = zip(*[captures[x] for x in ids]) - else: - extra_inputs = [] - extra_placeholders = [] + + extra_inputs = tmp_graph.captures.keys() + extra_placeholders = tmp_graph.captures.values() flat_inputs = [x for x in nest.flatten(func_inputs) if isinstance(x, tf_ops.Tensor)] diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 0eabea321c120e1eb483b7161e88f964340db76d..2d54555cd37e630697b1721d526e50c285687e88 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -1726,7 +1726,6 @@ bool OpDoesntRequireOutput(const string& op_name) { "BiasAdd", "BiasAddV1", "BiasAddGrad", - "Relu6", "Softplus", "SoftplusGrad", "Softsign", @@ -1799,6 +1798,7 @@ bool OpDoesntRequireInput(const string& op_name) { "LogSoftmax", "BiasAdd", "Relu", + "Relu6", "Elu", "Selu", "SparseSoftmaxCrossEntropyWithLogits", diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py index efa7812452427a6cdd7854b50b7d95a9a003abbb..4945c3ba11cadf95d8dd4f066a115ba9791461c8 100644 --- a/tensorflow/python/estimator/canned/dnn_linear_combined.py +++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py @@ -388,7 +388,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator): if a categorical column is multivalent. One of "mean", "sqrtn", and "sum" -- these are effectively different ways to do example-level normalization, which can be useful for bag-of-words features. For more - details, see @{tf.feature_column.linear_model$linear_model}. + details, see `tf.feature_column.linear_model`. Raises: ValueError: If both linear_feature_columns and dnn_features_columns are @@ -586,7 +586,7 @@ class DNNLinearCombinedRegressor(estimator.Estimator): if a categorical column is multivalent. One of "mean", "sqrtn", and "sum" -- these are effectively different ways to do example-level normalization, which can be useful for bag-of-words features. For more - details, see @{tf.feature_column.linear_model$linear_model}. + details, see `tf.feature_column.linear_model`. Raises: ValueError: If both linear_feature_columns and dnn_features_columns are diff --git a/tensorflow/python/estimator/canned/linear.py b/tensorflow/python/estimator/canned/linear.py index 58a71603488198373bc4d1fd716538c2cee4d86f..115dd185185adb049d7ce04592fa8dac1e7e4f82 100644 --- a/tensorflow/python/estimator/canned/linear.py +++ b/tensorflow/python/estimator/canned/linear.py @@ -306,7 +306,7 @@ class LinearClassifier(estimator.Estimator): is multivalent. One of "mean", "sqrtn", and "sum" -- these are effectively different ways to do example-level normalization, which can be useful for bag-of-words features. for more details, see - @{tf.feature_column.linear_model$linear_model}. + `tf.feature_column.linear_model`. Returns: A `LinearClassifier` estimator. @@ -472,7 +472,7 @@ class LinearRegressor(estimator.Estimator): is multivalent. One of "mean", "sqrtn", and "sum" -- these are effectively different ways to do example-level normalization, which can be useful for bag-of-words features. for more details, see - @{tf.feature_column.linear_model$linear_model}. + `tf.feature_column.linear_model`. """ head = head_lib._regression_head( # pylint: disable=protected-access label_dimension=label_dimension, weight_column=weight_column, diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 43deb8bc6c7ecff28b9dc85edcbc122404977a69..b8cd55c8061c01daed0f3bb3dd8c86e2e93c4319 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -50,9 +50,10 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import builder as saved_model_builder -from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import utils_impl as saved_model_utils from tensorflow.python.summary import summary from tensorflow.python.summary.writer import writer_cache +from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import checkpoint_management from tensorflow.python.training import device_setter from tensorflow.python.training import distribute as distribute_lib @@ -104,7 +105,7 @@ class Estimator(object): constructor enforces this). Subclasses should use `model_fn` to configure the base class, and may add methods implementing specialized functionality. - @compatbility(eager) + @compatibility(eager) Calling methods of `Estimator` will work while eager execution is enabled. However, the `model_fn` and `input_fn` is not executed eagerly, `Estimator` will switch to graph model before calling all user-provided functions (incl. @@ -128,7 +129,7 @@ class Estimator(object): ``` For more details on warm-start configuration, see - @{tf.estimator.WarmStartSettings$WarmStartSettings}. + `tf.estimator.WarmStartSettings`. Args: model_fn: Model function. Follows the signature: @@ -345,7 +346,23 @@ class Estimator(object): return self def _convert_train_steps_to_hooks(self, steps, max_steps): + """Create hooks to run correct number of steps in training. + + Args: + steps: number of steps to run during training. + max_steps: maximum number of steps to be run during training. It'll be + the maximum number of steps the model will train to after restoring + from checkpoint even across multiple estimator.train calls. + + Returns: + List of hooks to be passed to the estimator. + """ if steps is not None or max_steps is not None: + if self._train_distribution: + steps_per_run = getattr(self._train_distribution, 'steps_per_run', 1) + if steps_per_run > 1: + return [basic_session_run_hooks._MultiStepStopAtStepHook( # pylint: disable=protected-access + steps, max_steps, steps_per_run)] return [training.StopAtStepHook(steps, max_steps)] else: return [] @@ -988,7 +1005,7 @@ class Estimator(object): def _get_features_and_labels_from_input_fn(self, input_fn, mode, distribution=None): """Extracts the `features` and labels from return values of `input_fn`.""" - if distribution is not None and mode == model_fn_lib.ModeKeys.TRAIN: + if distribution is not None: result = distribution.distribute_dataset( lambda: self._call_input_fn(input_fn, mode)) else: @@ -1027,7 +1044,7 @@ class Estimator(object): """Creates the global step tensor in graph. The global step tensor must be an integer type with name 'global_step' and - be added to the collection @{tf.GraphKeys.GLOBAL_STEP}. + be added to the collection `tf.GraphKeys.GLOBAL_STEP`. Args: graph: The graph in which to create the global step tensor. @@ -1184,6 +1201,10 @@ class Estimator(object): worker_hooks = [] with ops.Graph().as_default() as g: + # We want to create the iterations variable outside the distribution scope + # as that is just stored on the host and mainly used to drive the loop + # and doesn't need to be a Mirrored/Device variable. + steps_per_run_variable = training.get_or_create_steps_per_run_variable() with self._train_distribution.scope(): random_seed.set_random_seed(self._config.tf_random_seed) @@ -1215,19 +1236,21 @@ class Estimator(object): labels, model_fn_lib.ModeKeys.TRAIN, self.config) - ctx.last_step_outputs = estimator_spec.loss - ctx.non_tensor_outputs = {'estimator_spec': estimator_spec} - with ops.control_dependencies([estimator_spec.train_op]): - return array_ops.identity(estimator_spec.loss) + ctx.set_last_step_output( + name='loss', + output=estimator_spec.loss, + aggregation=distribute_lib.get_loss_reduction()) + ctx.set_non_tensor_output( + name='estimator_spec', output=estimator_spec) + return estimator_spec.train_op # Create new train_op post graph rewrites - # TODO(sourabhbajaj): Make sure train_steps and tpu_iterations - # work correctly. Currently hardcoded at 2 initial_training_loss = constant_op.constant(1e7) - distributed_train_op, tpu_result, ctx = \ - self._train_distribution._run_steps_on_dataset( # pylint: disable=protected-access - step_fn, iterator, iterations=2, - initial_loop_values=initial_training_loss) + ctx = self._train_distribution.run_steps_on_dataset( + step_fn, iterator, iterations=steps_per_run_variable, + initial_loop_values={'loss': initial_training_loss}) + distributed_train_op = ctx.run_op + tpu_result = ctx.last_step_outputs grouped_estimator_spec = ctx.non_tensor_outputs['estimator_spec'] else: features, labels, input_hooks = ( @@ -1263,22 +1286,22 @@ class Estimator(object): # TODO(sourabhbajaj): Merge the two code paths and clean up the code if is_tpu_strategy: - distributed_loss = tpu_result + loss = tpu_result['loss'] worker_hooks.append( estimator_util.StrategyInitFinalizeHook( - self._train_distribution.get_initialization_ops, - self._train_distribution.get_finalize_ops)) + self._train_distribution.initialize, + self._train_distribution.finalize)) else: - distributed_loss = grouped_estimator_spec.loss + loss = self._train_distribution.unwrap( + self._train_distribution.reduce( + distribute_lib.get_loss_reduction(), + grouped_estimator_spec.loss, + destinations='/device:CPU:0'))[0] distributed_train_op = grouped_estimator_spec.train_op estimator_spec = model_fn_lib.EstimatorSpec( mode=grouped_estimator_spec.mode, - loss=self._train_distribution.unwrap( - self._train_distribution.reduce( - distribute_lib.get_loss_reduction(), - distributed_loss, - destinations='/device:CPU:0'))[0], + loss=loss, train_op=self._train_distribution.group(distributed_train_op), training_hooks=training_hooks, training_chief_hooks=training_chief_hooks, @@ -1783,10 +1806,24 @@ def _write_dict_to_summary(output_dir, logging.warn('Skipping summary for %s, cannot parse string to Summary.', key) continue + elif isinstance(dictionary[key], np.ndarray): + value = summary_proto.value.add() + value.tag = key + value.node_name = key + tensor_proto = tensor_util.make_tensor_proto(dictionary[key]) + value.tensor.CopyFrom(tensor_proto) + # pylint: disable=line-too-long + logging.info( + 'Summary for np.ndarray is not visible in Tensorboard by default. ' + 'Consider using a Tensorboard plugin for visualization (see ' + 'https://github.com/tensorflow/tensorboard-plugin-example/blob/master/README.md' + ' for more information).') + # pylint: enable=line-too-long else: logging.warn( 'Skipping summary for %s, must be a float, np.float32, np.int64, ' - 'np.int32 or int or a serialized string of Summary.', key) + 'np.int32 or int or np.ndarray or a serialized string of Summary.', + key) summary_writer.add_summary(summary_proto, current_global_step) summary_writer.flush() @@ -2001,14 +2038,11 @@ class WarmStartSettings( def _get_saved_model_ckpt(saved_model_dir): """Return path to variables checkpoint in a SavedModel directory.""" if not gfile.Exists( - os.path.join(compat.as_bytes(saved_model_dir), - compat.as_bytes('variables/variables.index'))): + os.path.join(saved_model_utils.get_variables_dir(saved_model_dir), + compat.as_text('variables.index'))): raise ValueError('Directory provided has an invalid SavedModel format: %s' % saved_model_dir) - return os.path.join( - compat.as_bytes(saved_model_dir), - compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY, - constants.VARIABLES_FILENAME))) + return saved_model_utils.get_variables_path(saved_model_dir) def _get_default_warm_start_settings(warm_start_from): @@ -2030,12 +2064,15 @@ def _get_default_warm_start_settings(warm_start_from): if isinstance(warm_start_from, (six.string_types, six.binary_type)): # Infer that this is a SavedModel if export_path + # 'variables/variables.index' exists, and if so, construct the - # WarmStartSettings pointing to export_path + 'variables/variables'. - if gfile.Exists(os.path.join(compat.as_bytes(warm_start_from), - compat.as_bytes('variables/variables.index'))): + # WarmStartSettings pointing to the variables path + # (export_path + 'variables/variables'). + if gfile.Exists(os.path.join( + saved_model_utils.get_variables_dir(warm_start_from), + compat.as_text('variables.index'))): logging.info('Warm-starting from a SavedModel') return WarmStartSettings( - ckpt_to_initialize_from=_get_saved_model_ckpt(warm_start_from)) + ckpt_to_initialize_from=saved_model_utils.get_variables_path( + warm_start_from)) return WarmStartSettings(ckpt_to_initialize_from=warm_start_from) elif isinstance(warm_start_from, WarmStartSettings): return warm_start_from diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index e8552092e09d2b59ceca6ce12a85e10680733d01..e3f22d9010cccb9c9f4a7937270e4aff030df910 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -1458,6 +1458,48 @@ class EstimatorEvaluateTest(test.TestCase): self.assertProtoEquals(expected_tensor_proto, next(summaries).value[0].tensor) + def test_summary_writing_with_tensor(self): + + def model_fn_with_prediction_mean_tensor_eval_metric_ops( + features, labels, mode, params): + _, _ = features, labels + global_step = training.get_global_step() + + metric_name = params.get('metric_name') or 'metric' + predictions = constant_op.constant([1., .5, 0.]) + eval_metric_ops = {metric_name: metrics_lib.mean_tensor(predictions)} + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant(1.), + predictions={'predictions': predictions}, + train_op=state_ops.assign_add(global_step, 1), + eval_metric_ops=eval_metric_ops) + + metric_key = 'PMT' + params = { + 'metric_name': metric_key, + } + est = estimator.Estimator( + model_fn=model_fn_with_prediction_mean_tensor_eval_metric_ops, + params=params, + config=run_config.RunConfig(save_summary_steps=1)) + est.train(input_fn=dummy_input_fn, steps=10) + est.evaluate( + input_fn=dummy_input_fn, + steps=10, + ) + + writer_cache.FileWriterCache.clear() + + self.assertTrue( + check_eventfile_for_keyword(metric_key, est.eval_dir()), + '{} should be part of reported summaries.'.format(metric_key)) + + summaries = summaries_with_matching_keyword(metric_key, est.eval_dir()) + for value in next(summaries).value: + if value.tag == metric_key: + self.assertTrue(value.HasField('tensor')) + class EstimatorPredictTest(test.TestCase): @@ -2641,6 +2683,7 @@ class EstimatorExportTest(test.TestCase): _, _ = features, labels my_int = variables.Variable(1, name='my_int', collections=[ops.GraphKeys.LOCAL_VARIABLES]) + _ = training.get_or_create_steps_per_run_variable() scores = constant_op.constant([3.]) with ops.control_dependencies([ variables.local_variables_initializer(), diff --git a/tensorflow/python/estimator/export/export.py b/tensorflow/python/estimator/export/export.py index 529e7a8b873d18130b70b3e8ee97c0c45ed76f2c..3d171f78119e10f700e2b98811ba169a8e037938 100644 --- a/tensorflow/python/estimator/export/export.py +++ b/tensorflow/python/estimator/export/export.py @@ -288,9 +288,8 @@ def build_parsing_serving_input_receiver_fn(feature_spec, def _placeholder_from_tensor(t, default_batch_size=None): - shape_list = t.get_shape().as_list() - shape_list[0] = default_batch_size - shape = tensor_shape.TensorShape(shape_list) + batch_shape = tensor_shape.TensorShape([default_batch_size]) + shape = batch_shape.concatenate(t.get_shape()[1:]) # Reuse the feature tensor's op name (t.op.name) for the placeholder, # excluding the index from the tensor's name (t.name): diff --git a/tensorflow/python/estimator/export/export_test.py b/tensorflow/python/estimator/export/export_test.py index d2ac7f0b3b7b9050bfaf05a253e5fc2c2104e0c4..1d475adb4396e58abd16c5b4bec1ad5ede925335 100644 --- a/tensorflow/python/estimator/export/export_test.py +++ b/tensorflow/python/estimator/export/export_test.py @@ -31,6 +31,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import parsing_ops @@ -378,6 +379,20 @@ class ExportTest(test_util.TensorFlowTestCase): v = serving_input_receiver_fn() self.assertTrue(isinstance(v, export.ServingInputReceiver)) + def test_build_raw_serving_input_receiver_fn_without_shape(self): + """Test case for issue #21178.""" + f = {"feature_1": array_ops.placeholder(dtypes.float32), + "feature_2": array_ops.placeholder(dtypes.int32)} + serving_input_receiver_fn = export.build_raw_serving_input_receiver_fn(f) + v = serving_input_receiver_fn() + self.assertTrue(isinstance(v, export.ServingInputReceiver)) + self.assertEqual( + tensor_shape.unknown_shape(), + v.receiver_tensors["feature_1"].shape) + self.assertEqual( + tensor_shape.unknown_shape(), + v.receiver_tensors["feature_2"].shape) + def test_build_raw_serving_input_receiver_fn(self): features = {"feature_1": constant_op.constant(["hello"]), "feature_2": constant_op.constant([42])} diff --git a/tensorflow/python/estimator/inputs/numpy_io_test.py b/tensorflow/python/estimator/inputs/numpy_io_test.py index 81b201cc5c5f3d6b8211030d17006f89a545793e..4e7b00b3075fc10b9d8320008be8d23bd5092755 100644 --- a/tensorflow/python/estimator/inputs/numpy_io_test.py +++ b/tensorflow/python/estimator/inputs/numpy_io_test.py @@ -19,9 +19,15 @@ from __future__ import division from __future__ import print_function import numpy as np - +from tensorflow.python.client import session as session_lib from tensorflow.python.estimator.inputs import numpy_io +from tensorflow.python.feature_column import feature_column_lib as fc +from tensorflow.python.feature_column.feature_column import _LinearModel from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +from tensorflow.python.ops import lookup_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables as variables_lib from tensorflow.python.platform import test from tensorflow.python.training import coordinator from tensorflow.python.training import monitored_session @@ -456,5 +462,159 @@ class NumpyIoTest(test.TestCase): self.assertAllEqual(res_arr[1], res_dict[1]) +class FeatureColumnIntegrationTest(test.TestCase): + + def _initialized_session(self, config=None): + sess = session_lib.Session(config=config) + sess.run(variables_lib.global_variables_initializer()) + sess.run(lookup_ops.tables_initializer()) + return sess + + def _get_linear_model_bias(self, name='linear_model'): + with variable_scope.variable_scope(name, reuse=True): + return variable_scope.get_variable('bias_weights') + + def _get_linear_model_column_var(self, column, name='linear_model'): + return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, + name + '/' + column.name)[0] + + def _get_keras_linear_model_predictions( + self, + features, + feature_columns, + units=1, + sparse_combiner='sum', + weight_collections=None, + trainable=True, + cols_to_vars=None): + keras_linear_model = _LinearModel( + feature_columns, + units, + sparse_combiner, + weight_collections, + trainable, + name='linear_model') + retval = keras_linear_model(features) # pylint: disable=not-callable + if cols_to_vars is not None: + cols_to_vars.update(keras_linear_model.cols_to_vars()) + return retval + + def test_linear_model_numpy_input_fn(self): + price = fc.numeric_column('price') + price_buckets = fc.bucketized_column(price, boundaries=[0., 10., 100.,]) + body_style = fc.categorical_column_with_vocabulary_list( + 'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan']) + + input_fn = numpy_io.numpy_input_fn( + x={ + 'price': np.array([-1., 2., 13., 104.]), + 'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']), + }, + batch_size=2, + shuffle=False) + features = input_fn() + net = fc.linear_model(features, [price_buckets, body_style]) + # self.assertEqual(1 + 3 + 5, net.shape[1]) + with self._initialized_session() as sess: + coord = coordinator.Coordinator() + threads = queue_runner_impl.start_queue_runners(sess, coord=coord) + + bias = self._get_linear_model_bias() + price_buckets_var = self._get_linear_model_column_var(price_buckets) + body_style_var = self._get_linear_model_column_var(body_style) + + sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]])) + sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]])) + sess.run(bias.assign([5.])) + + self.assertAllClose([[10 - 1000 + 5.], [100 - 10 + 5.]], sess.run(net)) + + coord.request_stop() + coord.join(threads) + + def test_linear_model_impl_numpy_input_fn(self): + price = fc.numeric_column('price') + price_buckets = fc.bucketized_column( + price, boundaries=[ + 0., + 10., + 100., + ]) + body_style = fc.categorical_column_with_vocabulary_list( + 'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan']) + + input_fn = numpy_io.numpy_input_fn( + x={ + 'price': np.array([-1., 2., 13., 104.]), + 'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']), + }, + batch_size=2, + shuffle=False) + features = input_fn() + net = self._get_keras_linear_model_predictions( + features, [price_buckets, body_style]) + # self.assertEqual(1 + 3 + 5, net.shape[1]) + with self._initialized_session() as sess: + coord = coordinator.Coordinator() + threads = queue_runner_impl.start_queue_runners(sess, coord=coord) + + bias = self._get_linear_model_bias() + price_buckets_var = self._get_linear_model_column_var(price_buckets) + body_style_var = self._get_linear_model_column_var(body_style) + + sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]])) + sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]])) + sess.run(bias.assign([5.])) + + self.assertAllClose([[10 - 1000 + 5.], [100 - 10 + 5.]], sess.run(net)) + + coord.request_stop() + coord.join(threads) + + def test_functional_input_layer_with_numpy_input_fn(self): + embedding_values = ( + (1., 2., 3., 4., 5.), # id 0 + (6., 7., 8., 9., 10.), # id 1 + (11., 12., 13., 14., 15.) # id 2 + ) + def _initializer(shape, dtype, partition_info): + del shape, dtype, partition_info + return embedding_values + + # price has 1 dimension in input_layer + price = fc.numeric_column('price') + body_style = fc.categorical_column_with_vocabulary_list( + 'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan']) + # one_hot_body_style has 3 dims in input_layer. + one_hot_body_style = fc.indicator_column(body_style) + # embedded_body_style has 5 dims in input_layer. + embedded_body_style = fc.embedding_column(body_style, dimension=5, + initializer=_initializer) + + input_fn = numpy_io.numpy_input_fn( + x={ + 'price': np.array([11., 12., 13., 14.]), + 'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']), + }, + batch_size=2, + shuffle=False) + features = input_fn() + net = fc.input_layer(features, + [price, one_hot_body_style, embedded_body_style]) + self.assertEqual(1 + 3 + 5, net.shape[1]) + with self._initialized_session() as sess: + coord = coordinator.Coordinator() + threads = queue_runner_impl.start_queue_runners(sess, coord=coord) + + # Each row is formed by concatenating `embedded_body_style`, + # `one_hot_body_style`, and `price` in order. + self.assertAllEqual( + [[11., 12., 13., 14., 15., 0., 0., 1., 11.], + [1., 2., 3., 4., 5., 1., 0., 0., 12]], + sess.run(net)) + + coord.request_stop() + coord.join(threads) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index c91204a35f331df611412a46c68b948205c15aff..a5f07fea3bbf15a6f7a4b8be5df871e60da9df4e 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -396,7 +396,7 @@ def _create_keras_model_fn(keras_model, custom_objects=None): loss = model.total_loss if model.metrics: - # TODO(fchollet): support stateful metrics + # TODO(psv/fchollet): support stateful metrics eval_metric_ops = {} # When each metric maps to an output if isinstance(model.metrics, dict): diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index a01b2300ddbe8bf131f70de435a4d7509849bae9..bb1305767f8d8d565943bf1aa6f5e1784463d58e 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -323,6 +323,10 @@ def train_and_evaluate(estimator, train_spec, eval_spec): tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec) ``` + Note that in current implementation `estimator.evaluate` will be called + multiple times. This means that evaluation graph (including eval_input_fn) + will be re-created for each `evaluate` call. `estimator.train` will be called + only once. Example of distributed training: diff --git a/tensorflow/python/feature_column/BUILD b/tensorflow/python/feature_column/BUILD index 80707030e6eb3c423a1b8ae38624ddad3e87fb04..1017d4ba475bc0c1f74c1628fc2a23d9195fde27 100644 --- a/tensorflow/python/feature_column/BUILD +++ b/tensorflow/python/feature_column/BUILD @@ -122,7 +122,6 @@ py_test( "//tensorflow/python:variables", "//tensorflow/python/eager:backprop", "//tensorflow/python/eager:context", - "//tensorflow/python/estimator:numpy_io", ], ) diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index d091d2fe0ac688773b27d80f37fbf3083b8ffa1f..2246d2f3e99a2a80311e7e5b5b4f97f3b6ccfd45 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -16,7 +16,7 @@ FeatureColumns provide a high level abstraction for ingesting and representing features. FeatureColumns are also the primary way of encoding features for -canned @{tf.estimator.Estimator}s. +canned `tf.estimator.Estimator`s. When using FeatureColumns with `Estimators`, the type of feature column you should choose depends on (1) the feature type and (2) the model type. @@ -1936,7 +1936,7 @@ class _FeatureColumn(object): It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other - supported objects. Please check documentation of @{tf.parse_example} for all + supported objects. Please check documentation of `tf.parse_example` for all supported spec objects. Let's say a Feature column depends on raw feature ('raw') and another @@ -1995,7 +1995,7 @@ class _DenseColumn(_FeatureColumn): weight_collections: List of graph collections to which Variables (if any will be created) are added. trainable: If `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.Variable}). + `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). Returns: `Tensor` of shape [batch_size] + `_variable_shape`. @@ -2062,7 +2062,7 @@ class _CategoricalColumn(_FeatureColumn): WARNING: Do not subclass this layer unless you know what you are doing: the API is subject to future changes. - A categorical feature typically handled with a @{tf.SparseTensor} of IDs. + A categorical feature typically handled with a `tf.SparseTensor` of IDs. """ __metaclass__ = abc.ABCMeta @@ -2097,7 +2097,7 @@ class _CategoricalColumn(_FeatureColumn): weight_collections: List of graph collections to which variables (if any will be created) are added. trainable: If `True` also add variables to the graph collection - `GraphKeys.TRAINABLE_VARIABLES` (see @{tf.get_variable}). + `GraphKeys.TRAINABLE_VARIABLES` (see `tf.get_variable`). """ pass diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py index 5bb47bfa47cf8fe0311d63f325198bcb7ecd5f9c..6be930be87b5db4e8b0ce261bf2956dd2999c606 100644 --- a/tensorflow/python/feature_column/feature_column_test.py +++ b/tensorflow/python/feature_column/feature_column_test.py @@ -30,7 +30,6 @@ from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.eager import backprop from tensorflow.python.eager import context -from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.feature_column import feature_column_lib as fc from tensorflow.python.feature_column.feature_column import _CategoricalColumn from tensorflow.python.feature_column.feature_column import _DenseColumn @@ -52,8 +51,6 @@ from tensorflow.python.ops import partitioned_variables from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as variables_lib from tensorflow.python.platform import test -from tensorflow.python.training import coordinator -from tensorflow.python.training import queue_runner_impl def _initialized_session(config=None): @@ -1803,39 +1800,6 @@ class LinearModelTest(test.TestCase): features['price2']: [[1.], [5.]], }) - def test_with_numpy_input_fn(self): - price = fc.numeric_column('price') - price_buckets = fc.bucketized_column(price, boundaries=[0., 10., 100.,]) - body_style = fc.categorical_column_with_vocabulary_list( - 'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan']) - - input_fn = numpy_io.numpy_input_fn( - x={ - 'price': np.array([-1., 2., 13., 104.]), - 'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']), - }, - batch_size=2, - shuffle=False) - features = input_fn() - net = fc.linear_model(features, [price_buckets, body_style]) - # self.assertEqual(1 + 3 + 5, net.shape[1]) - with _initialized_session() as sess: - coord = coordinator.Coordinator() - threads = queue_runner_impl.start_queue_runners(sess, coord=coord) - - bias = get_linear_model_bias() - price_buckets_var = get_linear_model_column_var(price_buckets) - body_style_var = get_linear_model_column_var(body_style) - - sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]])) - sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]])) - sess.run(bias.assign([5.])) - - self.assertAllClose([[10 - 1000 + 5.], [100 - 10 + 5.]], sess.run(net)) - - coord.request_stop() - coord.join(threads) - def test_with_1d_sparse_tensor(self): price = fc.numeric_column('price') price_buckets = fc.bucketized_column(price, boundaries=[0., 10., 100.,]) @@ -2458,45 +2422,6 @@ class _LinearModelTest(test.TestCase): features['price2']: [[1.], [5.]], }) - def test_with_numpy_input_fn(self): - price = fc.numeric_column('price') - price_buckets = fc.bucketized_column( - price, boundaries=[ - 0., - 10., - 100., - ]) - body_style = fc.categorical_column_with_vocabulary_list( - 'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan']) - - input_fn = numpy_io.numpy_input_fn( - x={ - 'price': np.array([-1., 2., 13., 104.]), - 'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']), - }, - batch_size=2, - shuffle=False) - features = input_fn() - net = get_keras_linear_model_predictions(features, - [price_buckets, body_style]) - # self.assertEqual(1 + 3 + 5, net.shape[1]) - with _initialized_session() as sess: - coord = coordinator.Coordinator() - threads = queue_runner_impl.start_queue_runners(sess, coord=coord) - - bias = get_linear_model_bias() - price_buckets_var = get_linear_model_column_var(price_buckets) - body_style_var = get_linear_model_column_var(body_style) - - sess.run(price_buckets_var.assign([[10.], [100.], [1000.], [10000.]])) - sess.run(body_style_var.assign([[-10.], [-100.], [-1000.]])) - sess.run(bias.assign([5.])) - - self.assertAllClose([[10 - 1000 + 5.], [100 - 10 + 5.]], sess.run(net)) - - coord.request_stop() - coord.join(threads) - def test_with_1d_sparse_tensor(self): price = fc.numeric_column('price') price_buckets = fc.bucketized_column( @@ -3043,51 +2968,6 @@ class FunctionalInputLayerTest(test.TestCase): ['input_layer/aaa_bbb_shared_embedding/embedding_weights:0'], [v.name for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)]) - def test_with_numpy_input_fn(self): - embedding_values = ( - (1., 2., 3., 4., 5.), # id 0 - (6., 7., 8., 9., 10.), # id 1 - (11., 12., 13., 14., 15.) # id 2 - ) - def _initializer(shape, dtype, partition_info): - del shape, dtype, partition_info - return embedding_values - - # price has 1 dimension in input_layer - price = fc.numeric_column('price') - body_style = fc.categorical_column_with_vocabulary_list( - 'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan']) - # one_hot_body_style has 3 dims in input_layer. - one_hot_body_style = fc.indicator_column(body_style) - # embedded_body_style has 5 dims in input_layer. - embedded_body_style = fc.embedding_column(body_style, dimension=5, - initializer=_initializer) - - input_fn = numpy_io.numpy_input_fn( - x={ - 'price': np.array([11., 12., 13., 14.]), - 'body-style': np.array(['sedan', 'hardtop', 'wagon', 'sedan']), - }, - batch_size=2, - shuffle=False) - features = input_fn() - net = fc.input_layer(features, - [price, one_hot_body_style, embedded_body_style]) - self.assertEqual(1 + 3 + 5, net.shape[1]) - with _initialized_session() as sess: - coord = coordinator.Coordinator() - threads = queue_runner_impl.start_queue_runners(sess, coord=coord) - - # Each row is formed by concatenating `embedded_body_style`, - # `one_hot_body_style`, and `price` in order. - self.assertAllEqual( - [[11., 12., 13., 14., 15., 0., 0., 1., 11.], - [1., 2., 3., 4., 5., 1., 0., 0., 12]], - sess.run(net)) - - coord.request_stop() - coord.join(threads) - def test_with_1d_sparse_tensor(self): embedding_values = ( (1., 2., 3., 4., 5.), # id 0 diff --git a/tensorflow/python/feature_column/feature_column_v2.py b/tensorflow/python/feature_column/feature_column_v2.py index b4dd23f58de60bacae68f9b67ed30c5d4ae49b15..b6bf516286a824ac829ef230c78dd1bbd432fbac 100644 --- a/tensorflow/python/feature_column/feature_column_v2.py +++ b/tensorflow/python/feature_column/feature_column_v2.py @@ -16,7 +16,7 @@ FeatureColumns provide a high level abstraction for ingesting and representing features. FeatureColumns are also the primary way of encoding features for -canned @{tf.estimator.Estimator}s. +canned `tf.estimator.Estimator`s. When using FeatureColumns with `Estimators`, the type of feature column you should choose depends on (1) the feature type and (2) the model type. @@ -1904,7 +1904,7 @@ class FeatureColumn(object): It is used for get_parsing_spec for `tf.parse_example`. Returned spec is a dict from keys ('string') to `VarLenFeature`, `FixedLenFeature`, and other - supported objects. Please check documentation of @{tf.parse_example} for all + supported objects. Please check documentation of `tf.parse_example` for all supported spec objects. Let's say a Feature column depends on raw feature ('raw') and another @@ -2025,7 +2025,7 @@ def _create_dense_column_weighted_sum(column, class CategoricalColumn(FeatureColumn): """Represents a categorical feature. - A categorical feature typically handled with a @{tf.SparseTensor} of IDs. + A categorical feature typically handled with a `tf.SparseTensor` of IDs. """ __metaclass__ = abc.ABCMeta diff --git a/tensorflow/python/framework/errors_impl.py b/tensorflow/python/framework/errors_impl.py index 84106c32c673e15832ff747a7fededdfbfb94ed8..9f973de4004cf69921b551c90a7c4068edaa2029 100644 --- a/tensorflow/python/framework/errors_impl.py +++ b/tensorflow/python/framework/errors_impl.py @@ -63,9 +63,9 @@ class OpError(Exception): *N.B.* If the failed op was synthesized at runtime, e.g. a `Send` or `Recv` op, there will be no corresponding - @{tf.Operation} + `tf.Operation` object. In that case, this will return `None`, and you should - instead use the @{tf.OpError.node_def} to + instead use the `tf.OpError.node_def` to discover information about the op. Returns: @@ -181,10 +181,10 @@ class CancelledError(OpError): """Raised when an operation or step is cancelled. For example, a long-running operation (e.g. - @{tf.QueueBase.enqueue} may be + `tf.QueueBase.enqueue` may be cancelled by running another operation (e.g. - @{tf.QueueBase.close}, - or by @{tf.Session.close}. + `tf.QueueBase.close`, + or by `tf.Session.close`. A step that is running such a long-running operation will fail by raising `CancelledError`. @@ -221,9 +221,9 @@ class InvalidArgumentError(OpError): This may occur, for example, if an operation is receives an input tensor that has an invalid value or shape. For example, the - @{tf.matmul} op will raise this + `tf.matmul` op will raise this error if it receives an input that is not a matrix, and the - @{tf.reshape} op will raise + `tf.reshape` op will raise this error if the new shape does not match the number of elements in the input tensor. @@ -256,7 +256,7 @@ class NotFoundError(OpError): """Raised when a requested entity (e.g., a file or directory) was not found. For example, running the - @{tf.WholeFileReader.read} + `tf.WholeFileReader.read` operation could raise `NotFoundError` if it receives the name of a file that does not exist. @@ -273,7 +273,7 @@ class AlreadyExistsError(OpError): """Raised when an entity that we attempted to create already exists. For example, running an operation that saves a file - (e.g. @{tf.train.Saver.save}) + (e.g. `tf.train.Saver.save`) could potentially raise this exception if an explicit filename for an existing file was passed. @@ -291,7 +291,7 @@ class PermissionDeniedError(OpError): """Raised when the caller does not have permission to run an operation. For example, running the - @{tf.WholeFileReader.read} + `tf.WholeFileReader.read` operation could raise `PermissionDeniedError` if it receives the name of a file for which the user does not have the read file permission. @@ -340,7 +340,7 @@ class FailedPreconditionError(OpError): """Operation was rejected because the system is not in a state to execute it. This exception is most commonly raised when running an operation - that reads a @{tf.Variable} + that reads a `tf.Variable` before it has been initialized. @@__init__ @@ -357,9 +357,9 @@ class AbortedError(OpError): """The operation was aborted, typically due to a concurrent action. For example, running a - @{tf.QueueBase.enqueue} + `tf.QueueBase.enqueue` operation may raise `AbortedError` if a - @{tf.QueueBase.close} operation + `tf.QueueBase.close` operation previously ran. @@__init__ @@ -375,9 +375,9 @@ class OutOfRangeError(OpError): """Raised when an operation iterates past the valid input range. This exception is raised in "end-of-file" conditions, such as when a - @{tf.QueueBase.dequeue} + `tf.QueueBase.dequeue` operation is blocked on an empty queue, and a - @{tf.QueueBase.close} + `tf.QueueBase.close` operation executes. @@__init__ @@ -395,7 +395,7 @@ class UnimplementedError(OpError): Some operations may raise this error when passed otherwise-valid arguments that it does not currently support. For example, running - the @{tf.nn.max_pool} operation + the `tf.nn.max_pool` operation would raise this error if pooling was requested on the batch dimension, because this is not yet supported. @@ -443,7 +443,7 @@ class DataLossError(OpError): """Raised when unrecoverable data loss or corruption is encountered. For example, this may be raised by running a - @{tf.WholeFileReader.read} + `tf.WholeFileReader.read` operation, if the file is truncated while it is being read. @@__init__ @@ -475,8 +475,8 @@ _CODE_TO_EXCEPTION_CLASS = { c_api.PyExceptionRegistry_Init(_CODE_TO_EXCEPTION_CLASS) -_EXCEPTION_CLASS_TO_CODE = dict(( - (class_, code) for (code, class_) in _CODE_TO_EXCEPTION_CLASS.items())) +_EXCEPTION_CLASS_TO_CODE = { + class_: code for code, class_ in _CODE_TO_EXCEPTION_CLASS.items()} @tf_export("errors.exception_type_from_error_code") diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 12bf03c5fa2e81abc24bd6a1a5c9bdb8cb17a1b3..f47c0d8a5e3867e9a1bdf2fb8bd00a635fd8d622 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -665,7 +665,7 @@ class _FuncGraph(ops.Graph): def container(self, container_name): """Returns a context manager that specifies the resource container to use. - Overridden from @{tf.Graph} to update both the init_scope container + Overridden from `tf.Graph` to update both the init_scope container and the present inner container. This is necessary to make sure setting containers applies correctly both to created variables and to stateful ops. diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 687bfebd4306596233df8db6a639e65df2f85980..e48e67c8a13aea7bb070f4b216cdc8081c711da4 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -344,9 +344,9 @@ def import_graph_def(graph_def, This function provides a way to import a serialized TensorFlow [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto) protocol buffer, and extract individual objects in the `GraphDef` as - @{tf.Tensor} and @{tf.Operation} objects. Once extracted, + `tf.Tensor` and `tf.Operation` objects. Once extracted, these objects are placed into the current default `Graph`. See - @{tf.Graph.as_graph_def} for a way to create a `GraphDef` + `tf.Graph.as_graph_def` for a way to create a `GraphDef` proto. Args: diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index ed0bf1afe076a2b9ea14297ded23159183018f39..98a1802490aea1a75bb69ddf252ffc9b3f48081a 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -229,7 +229,7 @@ class Tensor(_TensorLike): A `Tensor` is a symbolic handle to one of the outputs of an `Operation`. It does not hold the values of that operation's output, but instead provides a means of computing those values in a - TensorFlow @{tf.Session}. + TensorFlow `tf.Session`. This class has two primary purposes: @@ -240,7 +240,7 @@ class Tensor(_TensorLike): 2. After the graph has been launched in a session, the value of the `Tensor` can be computed by passing it to - @{tf.Session.run}. + `tf.Session.run`. `t.eval()` is a shortcut for calling `tf.get_default_session().run(t)`. @@ -365,7 +365,7 @@ class Tensor(_TensorLike): The shape is computed using shape inference functions that are registered in the Op for each `Operation`. See - @{tf.TensorShape} + `tf.TensorShape` for more details of what a shape represents. The inferred shape of a tensor is used to provide shape @@ -695,7 +695,7 @@ class Tensor(_TensorLike): Args: feed_dict: A dictionary that maps `Tensor` objects to feed values. - See @{tf.Session.run} for a + See `tf.Session.run` for a description of the valid feed values. session: (Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used. @@ -1455,10 +1455,10 @@ class IndexedSlices(_TensorLike): The `IndexedSlices` class is used principally in the definition of gradients for operations that have sparse gradients - (e.g. @{tf.gather}). + (e.g. `tf.gather`). Contrast this representation with - @{tf.SparseTensor}, + `tf.SparseTensor`, which uses multi-dimensional indices and scalar values. """ @@ -1619,8 +1619,8 @@ class Operation(object): more `Tensor` objects as input, and produces zero or more `Tensor` objects as output. Objects of type `Operation` are created by calling a Python op constructor (such as - @{tf.matmul}) - or @{tf.Graph.create_op}. + `tf.matmul`) + or `tf.Graph.create_op`. For example `c = tf.matmul(a, b)` creates an `Operation` of type "MatMul" that takes tensors `a` and `b` as input, and produces `c` @@ -1628,7 +1628,7 @@ class Operation(object): After the graph has been launched in a session, an `Operation` can be executed by passing it to - @{tf.Session.run}. + `tf.Session.run`. `op.run()` is a shortcut for calling `tf.get_default_session().run(op)`. """ @@ -2338,7 +2338,7 @@ class Operation(object): Args: feed_dict: A dictionary that maps `Tensor` objects to feed values. - See @{tf.Session.run} + See `tf.Session.run` for a description of the valid feed values. session: (Optional.) The `Session` to be used to run to this operation. If none, the default session will be used. @@ -2727,13 +2727,13 @@ class Graph(object): """A TensorFlow computation, represented as a dataflow graph. A `Graph` contains a set of - @{tf.Operation} objects, + `tf.Operation` objects, which represent units of computation; and - @{tf.Tensor} objects, which represent + `tf.Tensor` objects, which represent the units of data that flow between operations. A default `Graph` is always registered, and accessible by calling - @{tf.get_default_graph}. + `tf.get_default_graph`. To add an operation to the default graph, simply call one of the functions that defines a new `Operation`: @@ -2743,7 +2743,7 @@ class Graph(object): ``` Another typical usage involves the - @{tf.Graph.as_default} + `tf.Graph.as_default` context manager, which overrides the current default graph for the lifetime of the context: @@ -2764,7 +2764,7 @@ class Graph(object): that are identified by name. For convenience when building a large graph, collections can store groups of related objects: for example, the `tf.Variable` uses a collection (named - @{tf.GraphKeys.GLOBAL_VARIABLES}) for + `tf.GraphKeys.GLOBAL_VARIABLES`) for all variables that are created during the construction of a graph. The caller may define additional collections by specifying a new name. """ @@ -2941,7 +2941,7 @@ class Graph(object): """Returns a version number that increases as ops are added to the graph. Note that this is unrelated to the - @{tf.Graph.graph_def_versions}. + `tf.Graph.graph_def_versions`. Returns: An integer version that increases as ops are added to the graph. @@ -2991,7 +2991,7 @@ class Graph(object): After calling `g.finalize()`, no new operations can be added to `g`. This method is used to ensure that no operations are added to a graph when it is shared between multiple threads, for example - when using a @{tf.train.QueueRunner}. + when using a `tf.train.QueueRunner`. """ self._finalized = True @@ -3040,7 +3040,7 @@ class Graph(object): """Returns a serialized `GraphDef` representation of this graph. The serialized `GraphDef` can be imported into another `Graph` - (using @{tf.import_graph_def}) or used with the + (using `tf.import_graph_def`) or used with the [C++ Session API](../../../../api_docs/cc/index.md). This method is thread-safe. @@ -3086,7 +3086,7 @@ class Graph(object): """Returns a serialized `GraphDef` representation of this graph. The serialized `GraphDef` can be imported into another `Graph` - (using @{tf.import_graph_def}) or used with the + (using `tf.import_graph_def`) or used with the [C++ Session API](../../api_docs/cc/index.md). This method is thread-safe. @@ -4884,7 +4884,7 @@ def device(device_name_or_function): """Wrapper for `Graph.device()` using the default graph. See - @{tf.Graph.device} + `tf.Graph.device` for more details. Args: @@ -4950,7 +4950,7 @@ def colocate_with(op, ignore_existing=False): def control_dependencies(control_inputs): """Wrapper for `Graph.control_dependencies()` using the default graph. - See @{tf.Graph.control_dependencies} + See `tf.Graph.control_dependencies` for more details. When eager execution is enabled, any callable object in the `control_inputs` @@ -5316,7 +5316,7 @@ def enable_eager_execution(config=None, Eager execution provides an imperative interface to TensorFlow. With eager execution enabled, TensorFlow functions execute operations immediately (as - opposed to adding to a graph to be executed later in a @{tf.Session}) and + opposed to adding to a graph to be executed later in a `tf.Session`) and return concrete values (as opposed to symbolic references to a node in a computational graph). @@ -5336,9 +5336,9 @@ def enable_eager_execution(config=None, both with and without eager execution). Args: - config: (Optional.) A @{tf.ConfigProto} to use to configure the environment - in which operations are executed. Note that @{tf.ConfigProto} is also - used to configure graph execution (via @{tf.Session}) and many options + config: (Optional.) A `tf.ConfigProto` to use to configure the environment + in which operations are executed. Note that `tf.ConfigProto` is also + used to configure graph execution (via `tf.Session`) and many options within `tf.ConfigProto` are not implemented (or are irrelevant) when eager execution is enabled. device_policy: (Optional.) Policy controlling how operations requiring @@ -5638,7 +5638,7 @@ class GraphKeys(object): * `GLOBAL_VARIABLES`: the default collection of `Variable` objects, shared across distributed environment (model variables are subset of these). See - @{tf.global_variables} + `tf.global_variables` for more details. Commonly, all `TRAINABLE_VARIABLES` variables will be in `MODEL_VARIABLES`, and all `MODEL_VARIABLES` variables will be in `GLOBAL_VARIABLES`. @@ -5650,19 +5650,19 @@ class GraphKeys(object): `tf.contrib.framework.model_variable` to add to this collection. * `TRAINABLE_VARIABLES`: the subset of `Variable` objects that will be trained by an optimizer. See - @{tf.trainable_variables} + `tf.trainable_variables` for more details. * `SUMMARIES`: the summary `Tensor` objects that have been created in the graph. See - @{tf.summary.merge_all} + `tf.summary.merge_all` for more details. * `QUEUE_RUNNERS`: the `QueueRunner` objects that are used to produce input for a computation. See - @{tf.train.start_queue_runners} + `tf.train.start_queue_runners` for more details. * `MOVING_AVERAGE_VARIABLES`: the subset of `Variable` objects that will also keep moving averages. See - @{tf.moving_average_variables} + `tf.moving_average_variables` for more details. * `REGULARIZATION_LOSSES`: regularization losses collected during graph construction. @@ -5776,7 +5776,7 @@ class GraphKeys(object): def add_to_collection(name, value): """Wrapper for `Graph.add_to_collection()` using the default graph. - See @{tf.Graph.add_to_collection} + See `tf.Graph.add_to_collection` for more details. Args: @@ -5795,7 +5795,7 @@ def add_to_collection(name, value): def add_to_collections(names, value): """Wrapper for `Graph.add_to_collections()` using the default graph. - See @{tf.Graph.add_to_collections} + See `tf.Graph.add_to_collections` for more details. Args: @@ -5815,7 +5815,7 @@ def add_to_collections(names, value): def get_collection_ref(key): """Wrapper for `Graph.get_collection_ref()` using the default graph. - See @{tf.Graph.get_collection_ref} + See `tf.Graph.get_collection_ref` for more details. Args: @@ -5839,7 +5839,7 @@ def get_collection_ref(key): def get_collection(key, scope=None): """Wrapper for `Graph.get_collection()` using the default graph. - See @{tf.Graph.get_collection} + See `tf.Graph.get_collection` for more details. Args: @@ -5882,7 +5882,7 @@ class name_scope(object): # pylint: disable=invalid-name This context manager validates that the given `values` are from the same graph, makes that graph the default graph, and pushes a name scope in that graph (see - @{tf.Graph.name_scope} + `tf.Graph.name_scope` for more details on that). For example, to define a new Python op called `my_op`: diff --git a/tensorflow/python/framework/random_seed.py b/tensorflow/python/framework/random_seed.py index b724432e00b0d11de86a0fff9ff31758ad36479f..2f9504889afd07dd9e3fa73e3290efa4b3e0b752 100644 --- a/tensorflow/python/framework/random_seed.py +++ b/tensorflow/python/framework/random_seed.py @@ -43,7 +43,7 @@ def get_seed(op_seed): graph, or for only specific operations. For details on how the graph-level seed interacts with op seeds, see - @{tf.set_random_seed}. + `tf.set_random_seed`. Args: op_seed: integer. diff --git a/tensorflow/python/framework/sparse_tensor.py b/tensorflow/python/framework/sparse_tensor.py index 6a5c6468f77382b2b7e62a6a49d4fb637fed4dc0..a45581190fc1db3dd0ca2df88763f1cd6ae11537 100644 --- a/tensorflow/python/framework/sparse_tensor.py +++ b/tensorflow/python/framework/sparse_tensor.py @@ -205,7 +205,7 @@ class SparseTensor(_TensorLike): Args: feed_dict: A dictionary that maps `Tensor` objects to feed values. - See @{tf.Session.run} for a + See `tf.Session.run` for a description of the valid feed values. session: (Optional.) The `Session` to be used to evaluate this sparse tensor. If none, the default session will be used. diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py index c9be3d50056b2838e8cf39c3a17e1cff14e67ea0..bd0f691a619e35a59d152046cfb1e80b74dc1f66 100644 --- a/tensorflow/python/framework/tensor_shape.py +++ b/tensorflow/python/framework/tensor_shape.py @@ -500,7 +500,7 @@ class TensorShape(object): may be inferred if there is a registered shape function for `"Foo"`. See @{$adding_an_op#shape-functions-in-c$`Shape functions in C++`} for details of shape functions and how to register them. Alternatively, - the shape may be set explicitly using @{tf.Tensor.set_shape}. + the shape may be set explicitly using `tf.Tensor.set_shape`. """ def __init__(self, dims): diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 764e8bfacb01677f94f8e3e5628b1898cad4e24b..9be6391b0436b9508d0d6930b5fd5de560c05dd3 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -659,10 +659,10 @@ def run_in_graph_and_eager_modes(func=None, """Execute the decorated test with and without enabling eager execution. This function returns a decorator intended to be applied to test methods in - a @{tf.test.TestCase} class. Doing so will cause the contents of the test + a `tf.test.TestCase` class. Doing so will cause the contents of the test method to be executed twice - once normally, and once with eager execution enabled. This allows unittests to confirm the equivalence between eager - and graph execution (see @{tf.enable_eager_execution}). + and graph execution (see `tf.enable_eager_execution`). For example, consider the following unittest: diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index 1706158c6526270bc7afebb90fa4753d1cf3d220..e04d0e93e25d3aff3c155e8647d689443b25a2c0 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -25,6 +25,7 @@ py_library( "applications/inception_resnet_v2.py", "applications/inception_v3.py", "applications/mobilenet.py", + "applications/mobilenet_v2.py", "applications/nasnet.py", "applications/resnet50.py", "applications/vgg16.py", @@ -860,13 +861,14 @@ py_test( py_test( name = "sequential_test", - size = "small", + size = "medium", srcs = ["engine/sequential_test.py"], srcs_version = "PY2AND3", deps = [ ":keras", "//tensorflow/python:client_testlib", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/python/keras/applications/__init__.py b/tensorflow/python/keras/applications/__init__.py index 062135266dd8b11c489b7dff83b46ae29a0d21e6..51cc51998c44352fbf8f14db06ed55aa02c12156 100644 --- a/tensorflow/python/keras/applications/__init__.py +++ b/tensorflow/python/keras/applications/__init__.py @@ -13,17 +13,33 @@ # limitations under the License. # ============================================================================== """Keras Applications are canned architectures with pre-trained weights.""" - +# pylint: disable=g-import-not-at-top from __future__ import absolute_import from __future__ import division from __future__ import print_function +import keras_applications + +from tensorflow.python.keras import backend +from tensorflow.python.keras import engine +from tensorflow.python.keras import layers +from tensorflow.python.keras import models +from tensorflow.python.keras import utils + +keras_applications.set_keras_submodules( + backend=backend, + engine=engine, + layers=layers, + models=models, + utils=utils) + from tensorflow.python.keras.applications.densenet import DenseNet121 from tensorflow.python.keras.applications.densenet import DenseNet169 from tensorflow.python.keras.applications.densenet import DenseNet201 from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2 from tensorflow.python.keras.applications.inception_v3 import InceptionV3 from tensorflow.python.keras.applications.mobilenet import MobileNet +from tensorflow.python.keras.applications.mobilenet_v2 import MobileNetV2 from tensorflow.python.keras.applications.nasnet import NASNetLarge from tensorflow.python.keras.applications.nasnet import NASNetMobile from tensorflow.python.keras.applications.resnet50 import ResNet50 diff --git a/tensorflow/python/keras/applications/densenet.py b/tensorflow/python/keras/applications/densenet.py index 8df6d086111c4b179d2f0c7b5c1130a6cd95aaab..fbdcc66d2d75bb1d580c106a2fb3101522d75095 100644 --- a/tensorflow/python/keras/applications/densenet.py +++ b/tensorflow/python/keras/applications/densenet.py @@ -13,342 +13,25 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """DenseNet models for Keras. - -# Reference paper - -- [Densely Connected Convolutional Networks] - (https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.applications import imagenet_utils -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.layers import Activation -from tensorflow.python.keras.layers import AveragePooling2D -from tensorflow.python.keras.layers import BatchNormalization -from tensorflow.python.keras.layers import Concatenate -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.layers import ZeroPadding2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file +from keras_applications import densenet from tensorflow.python.util.tf_export import tf_export - -DENSENET121_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels.h5' -DENSENET121_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5' -DENSENET169_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels.h5' -DENSENET169_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5' -DENSENET201_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels.h5' -DENSENET201_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5' - - -def dense_block(x, blocks, name): - """A dense block. - - Arguments: - x: input tensor. - blocks: integer, the number of building blocks. - name: string, block label. - - Returns: - output tensor for the block. - """ - for i in range(blocks): - x = conv_block(x, 32, name=name + '_block' + str(i + 1)) - return x - - -def transition_block(x, reduction, name): - """A transition block. - - Arguments: - x: input tensor. - reduction: float, compression rate at transition layers. - name: string, block label. - - Returns: - output tensor for the block. - """ - bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 - x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) - x = Activation('relu', name=name + '_relu')(x) - x = Conv2D( - int(K.int_shape(x)[bn_axis] * reduction), - 1, - use_bias=False, - name=name + '_conv')( - x) - x = AveragePooling2D(2, strides=2, name=name + '_pool')(x) - return x - - -def conv_block(x, growth_rate, name): - """A building block for a dense block. - - Arguments: - x: input tensor. - growth_rate: float, growth rate at dense layers. - name: string, block label. - - Returns: - output tensor for the block. - """ - bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 - x1 = BatchNormalization( - axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')( - x) - x1 = Activation('relu', name=name + '_0_relu')(x1) - x1 = Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1) - x1 = BatchNormalization( - axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')( - x1) - x1 = Activation('relu', name=name + '_1_relu')(x1) - x1 = Conv2D( - growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')( - x1) - x = Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) - return x - - -def DenseNet(blocks, - include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - """Instantiates the DenseNet architecture. - - Optionally loads weights pre-trained - on ImageNet. Note that when using TensorFlow, - for best performance you should set - `image_data_format='channels_last'` in your Keras config - at ~/.keras/keras.json. - - The model and the weights are compatible with - TensorFlow, Theano, and CNTK. The data format - convention used by the model is the one - specified in your Keras config file. - - Arguments: - blocks: numbers of building blocks for the four dense layers. - include_top: whether to include the fully-connected - layer at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) - to use as image input for the model. - input_shape: optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(224, 224, 3)` (with `channels_last` data format) - or `(3, 224, 224)` (with `channels_first` data format). - It should have exactly 3 inputs channels. - pooling: optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model will be - the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a 2D tensor. - - `max` means that global max pooling will - be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - """ - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as imagenet with `include_top`' - ' as true, `classes` should be 1000') - - # Determine proper input shape - input_shape = _obtain_input_shape( - input_shape, - default_size=224, - min_size=221, - data_format=K.image_data_format(), - require_flatten=include_top, - weights=weights) - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - - bn_axis = 3 if K.image_data_format() == 'channels_last' else 1 - - x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input) - x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) - x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x) - x = Activation('relu', name='conv1/relu')(x) - x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x) - x = MaxPooling2D(3, strides=2, name='pool1')(x) - - x = dense_block(x, blocks[0], name='conv2') - x = transition_block(x, 0.5, name='pool2') - x = dense_block(x, blocks[1], name='conv3') - x = transition_block(x, 0.5, name='pool3') - x = dense_block(x, blocks[2], name='conv4') - x = transition_block(x, 0.5, name='pool4') - x = dense_block(x, blocks[3], name='conv5') - - x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x) - - if include_top: - x = GlobalAveragePooling2D(name='avg_pool')(x) - x = Dense(classes, activation='softmax', name='fc1000')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D(name='avg_pool')(x) - elif pooling == 'max': - x = GlobalMaxPooling2D(name='max_pool')(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - - # Create model. - if blocks == [6, 12, 24, 16]: - model = Model(inputs, x, name='densenet121') - elif blocks == [6, 12, 32, 32]: - model = Model(inputs, x, name='densenet169') - elif blocks == [6, 12, 48, 32]: - model = Model(inputs, x, name='densenet201') - else: - model = Model(inputs, x, name='densenet') - - # Load weights. - if weights == 'imagenet': - if include_top: - if blocks == [6, 12, 24, 16]: - weights_path = get_file( - 'densenet121_weights_tf_dim_ordering_tf_kernels.h5', - DENSENET121_WEIGHT_PATH, - cache_subdir='models', - file_hash='0962ca643bae20f9b6771cb844dca3b0') - elif blocks == [6, 12, 32, 32]: - weights_path = get_file( - 'densenet169_weights_tf_dim_ordering_tf_kernels.h5', - DENSENET169_WEIGHT_PATH, - cache_subdir='models', - file_hash='bcf9965cf5064a5f9eb6d7dc69386f43') - elif blocks == [6, 12, 48, 32]: - weights_path = get_file( - 'densenet201_weights_tf_dim_ordering_tf_kernels.h5', - DENSENET201_WEIGHT_PATH, - cache_subdir='models', - file_hash='7bb75edd58cb43163be7e0005fbe95ef') - else: - if blocks == [6, 12, 24, 16]: - weights_path = get_file( - 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5', - DENSENET121_WEIGHT_PATH_NO_TOP, - cache_subdir='models', - file_hash='4912a53fbd2a69346e7f2c0b5ec8c6d3') - elif blocks == [6, 12, 32, 32]: - weights_path = get_file( - 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5', - DENSENET169_WEIGHT_PATH_NO_TOP, - cache_subdir='models', - file_hash='50662582284e4cf834ce40ab4dfa58c6') - elif blocks == [6, 12, 48, 32]: - weights_path = get_file( - 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5', - DENSENET201_WEIGHT_PATH_NO_TOP, - cache_subdir='models', - file_hash='1c2de60ee40562448dbac34a0737e798') - model.load_weights(weights_path) - elif weights is not None: - model.load_weights(weights) - - return model - - -@tf_export('keras.applications.DenseNet121', - 'keras.applications.densenet.DenseNet121') -def DenseNet121(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - return DenseNet([6, 12, 24, 16], include_top, weights, input_tensor, - input_shape, pooling, classes) - - -@tf_export('keras.applications.DenseNet169', - 'keras.applications.densenet.DenseNet169') -def DenseNet169(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - return DenseNet([6, 12, 32, 32], include_top, weights, input_tensor, - input_shape, pooling, classes) - - -@tf_export('keras.applications.DenseNet201', - 'keras.applications.densenet.DenseNet201') -def DenseNet201(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - return DenseNet([6, 12, 48, 32], include_top, weights, input_tensor, - input_shape, pooling, classes) - - -@tf_export('keras.applications.densenet.preprocess_input') -def preprocess_input(x, data_format=None): - """Preprocesses a numpy array encoding a batch of images. - - Arguments: - x: a 3D or 4D numpy array consists of RGB values within [0, 255]. - data_format: data format of the image tensor. - - Returns: - Preprocessed array. - """ - return imagenet_utils.preprocess_input(x, data_format, mode='torch') - - -setattr(DenseNet121, '__doc__', DenseNet.__doc__) -setattr(DenseNet169, '__doc__', DenseNet.__doc__) -setattr(DenseNet201, '__doc__', DenseNet.__doc__) +DenseNet121 = densenet.DenseNet121 +DenseNet169 = densenet.DenseNet169 +DenseNet201 = densenet.DenseNet201 +decode_predictions = densenet.decode_predictions +preprocess_input = densenet.preprocess_input + +tf_export('keras.applications.densenet.DenseNet121', + 'keras.applications.DenseNet121')(DenseNet121) +tf_export('keras.applications.densenet.DenseNet169', + 'keras.applications.DenseNet169')(DenseNet169) +tf_export('keras.applications.densenet.DenseNet201', + 'keras.applications.DenseNet201')(DenseNet201) +tf_export('keras.applications.densenet.preprocess_input')(preprocess_input) diff --git a/tensorflow/python/keras/applications/imagenet_utils.py b/tensorflow/python/keras/applications/imagenet_utils.py index 0d8ccca1b5c2a6c05f0d933a8f0fe176ea62c2a3..70f8f6fb32cfd0fe397c75ad4d3237919e7b0fad 100644 --- a/tensorflow/python/keras/applications/imagenet_utils.py +++ b/tensorflow/python/keras/applications/imagenet_utils.py @@ -18,322 +18,28 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import json - -import numpy as np - -from tensorflow.python.framework import constant_op -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.ops import math_ops -from tensorflow.python.platform import tf_logging as logging +from keras_applications import imagenet_utils from tensorflow.python.util.tf_export import tf_export - -CLASS_INDEX = None -CLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json' - -# Global tensor of imagenet mean for preprocessing symbolic inputs -_IMAGENET_MEAN = None - - -def _preprocess_numpy_input(x, data_format, mode): - """Preprocesses a Numpy array encoding a batch of images. - - Arguments: - x: Input array, 3D or 4D. - data_format: Data format of the image array. - mode: One of "caffe", "tf" or "torch". - - caffe: will convert the images from RGB to BGR, - then will zero-center each color channel with - respect to the ImageNet dataset, - without scaling. - - tf: will scale pixels between -1 and 1, - sample-wise. - - torch: will scale pixels between 0 and 1 and then - will normalize each channel with respect to the - ImageNet dataset. - - Returns: - Preprocessed Numpy array. - """ - if mode == 'tf': - x /= 127.5 - x -= 1. - return x - - if mode == 'torch': - x /= 255. - mean = [0.485, 0.456, 0.406] - std = [0.229, 0.224, 0.225] - else: - if data_format == 'channels_first': - # 'RGB'->'BGR' - if x.ndim == 3: - x = x[::-1, ...] - else: - x = x[:, ::-1, ...] - else: - # 'RGB'->'BGR' - x = x[..., ::-1] - mean = [103.939, 116.779, 123.68] - std = None - - # Zero-center by mean pixel - if data_format == 'channels_first': - if x.ndim == 3: - x[0, :, :] -= mean[0] - x[1, :, :] -= mean[1] - x[2, :, :] -= mean[2] - if std is not None: - x[0, :, :] /= std[0] - x[1, :, :] /= std[1] - x[2, :, :] /= std[2] - else: - x[:, 0, :, :] -= mean[0] - x[:, 1, :, :] -= mean[1] - x[:, 2, :, :] -= mean[2] - if std is not None: - x[:, 0, :, :] /= std[0] - x[:, 1, :, :] /= std[1] - x[:, 2, :, :] /= std[2] - else: - x[..., 0] -= mean[0] - x[..., 1] -= mean[1] - x[..., 2] -= mean[2] - if std is not None: - x[..., 0] /= std[0] - x[..., 1] /= std[1] - x[..., 2] /= std[2] - return x - - -def _preprocess_symbolic_input(x, data_format, mode): - """Preprocesses a tensor encoding a batch of images. - - Arguments: - x: Input tensor, 3D or 4D. - data_format: Data format of the image tensor. - mode: One of "caffe", "tf" or "torch". - - caffe: will convert the images from RGB to BGR, - then will zero-center each color channel with - respect to the ImageNet dataset, - without scaling. - - tf: will scale pixels between -1 and 1, - sample-wise. - - torch: will scale pixels between 0 and 1 and then - will normalize each channel with respect to the - ImageNet dataset. - - Returns: - Preprocessed tensor. - """ - global _IMAGENET_MEAN - - if mode == 'tf': - x /= 127.5 - x -= 1. - return x - - if mode == 'torch': - x /= 255. - mean = [0.485, 0.456, 0.406] - std = [0.229, 0.224, 0.225] - else: - if data_format == 'channels_first': - # 'RGB'->'BGR' - if K.ndim(x) == 3: - x = x[::-1, ...] - else: - x = x[:, ::-1, ...] - else: - # 'RGB'->'BGR' - x = x[..., ::-1] - mean = [103.939, 116.779, 123.68] - std = None - - if _IMAGENET_MEAN is None: - _IMAGENET_MEAN = constant_op.constant(-np.array(mean), dtype=K.floatx()) - - # Zero-center by mean pixel - if K.dtype(x) != K.dtype(_IMAGENET_MEAN): - x = K.bias_add(x, math_ops.cast(_IMAGENET_MEAN, K.dtype(x)), data_format) - else: - x = K.bias_add(x, _IMAGENET_MEAN, data_format) - if std is not None: - x /= std - return x - - -@tf_export('keras.applications.resnet50.preprocess_input', - 'keras.applications.vgg19.preprocess_input', - 'keras.applications.vgg16.preprocess_input') -def preprocess_input(x, data_format=None, mode='caffe'): - """Preprocesses a tensor or Numpy array encoding a batch of images. - - Arguments: - x: Input Numpy or symbolic tensor, 3D or 4D. - data_format: Data format of the image tensor/array. - mode: One of "caffe", "tf". - - caffe: will convert the images from RGB to BGR, - then will zero-center each color channel with - respect to the ImageNet dataset, - without scaling. - - tf: will scale pixels between -1 and 1, - sample-wise. - - Returns: - Preprocessed tensor or Numpy array. - - Raises: - ValueError: In case of unknown `data_format` argument. - """ - if data_format is None: - data_format = K.image_data_format() - if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format ' + str(data_format)) - - if isinstance(x, np.ndarray): - return _preprocess_numpy_input(x, data_format=data_format, mode=mode) - else: - return _preprocess_symbolic_input(x, data_format=data_format, mode=mode) - - -@tf_export('keras.applications.nasnet.decode_predictions', - 'keras.applications.resnet50.decode_predictions', - 'keras.applications.vgg19.decode_predictions', - 'keras.applications.vgg16.decode_predictions', - 'keras.applications.inception_resnet_v2.decode_predictions', - 'keras.applications.inception_v3.decode_predictions', - 'keras.applications.densenet.decode_predictions', - 'keras.applications.mobilenet.decode_predictions', - 'keras.applications.xception.decode_predictions') -def decode_predictions(preds, top=5): - """Decodes the prediction of an ImageNet model. - - Arguments: - preds: Numpy tensor encoding a batch of predictions. - top: Integer, how many top-guesses to return. - - Returns: - A list of lists of top class prediction tuples - `(class_name, class_description, score)`. - One list of tuples per sample in batch input. - - Raises: - ValueError: In case of invalid shape of the `pred` array - (must be 2D). - """ - global CLASS_INDEX - if len(preds.shape) != 2 or preds.shape[1] != 1000: - raise ValueError('`decode_predictions` expects ' - 'a batch of predictions ' - '(i.e. a 2D array of shape (samples, 1000)). ' - 'Found array with shape: ' + str(preds.shape)) - if CLASS_INDEX is None: - fpath = get_file( - 'imagenet_class_index.json', - CLASS_INDEX_PATH, - cache_subdir='models', - file_hash='c2c37ea517e94d9795004a39431a14cb') - with open(fpath) as f: - CLASS_INDEX = json.load(f) - results = [] - for pred in preds: - top_indices = pred.argsort()[-top:][::-1] - result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices] - result.sort(key=lambda x: x[2], reverse=True) - results.append(result) - return results - - -def _obtain_input_shape(input_shape, - default_size, - min_size, - data_format, - require_flatten, - weights=None): - """Internal utility to compute/validate a model's input shape. - - Arguments: - input_shape: Either None (will return the default network input shape), - or a user-provided shape to be validated. - default_size: Default input width/height for the model. - min_size: Minimum input width/height accepted by the model. - data_format: Image data format to use. - require_flatten: Whether the model is expected to - be linked to a classifier via a Flatten layer. - weights: One of `None` (random initialization) - or 'imagenet' (pre-training on ImageNet). - If weights='imagenet' input channels must be equal to 3. - - Returns: - An integer shape tuple (may include None entries). - - Raises: - ValueError: In case of invalid argument values. - """ - if weights != 'imagenet' and input_shape and len(input_shape) == 3: - if data_format == 'channels_first': - if input_shape[0] not in {1, 3}: - logging.warning('This model usually expects 1 or 3 input channels. ' - 'However, it was passed an input_shape with ' + - str(input_shape[0]) + ' input channels.') - default_shape = (input_shape[0], default_size, default_size) - else: - if input_shape[-1] not in {1, 3}: - logging.warning('This model usually expects 1 or 3 input channels. ' - 'However, it was passed an input_shape with ' + - str(input_shape[-1]) + ' input channels.') - default_shape = (default_size, default_size, input_shape[-1]) - else: - if data_format == 'channels_first': - default_shape = (3, default_size, default_size) - else: - default_shape = (default_size, default_size, 3) - if weights == 'imagenet' and require_flatten: - if input_shape is not None: - if input_shape != default_shape: - raise ValueError('When setting`include_top=True` ' - 'and loading `imagenet` weights, ' - '`input_shape` should be ' + str(default_shape) + '.') - return default_shape - if input_shape: - if data_format == 'channels_first': - if input_shape is not None: - if len(input_shape) != 3: - raise ValueError('`input_shape` must be a tuple of three integers.') - if input_shape[0] != 3 and weights == 'imagenet': - raise ValueError('The input must have 3 channels; got ' - '`input_shape=' + str(input_shape) + '`') - if ((input_shape[1] is not None and input_shape[1] < min_size) or - (input_shape[2] is not None and input_shape[2] < min_size)): - raise ValueError('Input size must be at least ' + str(min_size) + - 'x' + str(min_size) + '; got ' - '`input_shape=' + str(input_shape) + '`') - else: - if input_shape is not None: - if len(input_shape) != 3: - raise ValueError('`input_shape` must be a tuple of three integers.') - if input_shape[-1] != 3 and weights == 'imagenet': - raise ValueError('The input must have 3 channels; got ' - '`input_shape=' + str(input_shape) + '`') - if ((input_shape[0] is not None and input_shape[0] < min_size) or - (input_shape[1] is not None and input_shape[1] < min_size)): - raise ValueError('Input size must be at least ' + str(min_size) + - 'x' + str(min_size) + '; got ' - '`input_shape=' + str(input_shape) + '`') - else: - if require_flatten: - input_shape = default_shape - else: - if data_format == 'channels_first': - input_shape = (3, None, None) - else: - input_shape = (None, None, 3) - if require_flatten: - if None in input_shape: - raise ValueError('If `include_top` is True, ' - 'you should specify a static `input_shape`. ' - 'Got `input_shape=' + str(input_shape) + '`') - return input_shape +decode_predictions = imagenet_utils.decode_predictions +preprocess_input = imagenet_utils.preprocess_input + +tf_export( + 'keras.applications.imagenet_utils.decode_predictions', + 'keras.applications.densenet.decode_predictions', + 'keras.applications.inception_resnet_v2.decode_predictions', + 'keras.applications.inception_v3.decode_predictions', + 'keras.applications.mobilenet.decode_predictions', + 'keras.applications.mobilenet_v2.decode_predictions', + 'keras.applications.nasnet.decode_predictions', + 'keras.applications.resnet50.decode_predictions', + 'keras.applications.vgg16.decode_predictions', + 'keras.applications.vgg19.decode_predictions', + 'keras.applications.xception.decode_predictions', +)(decode_predictions) +tf_export( + 'keras.applications.imagenet_utils.preprocess_input', + 'keras.applications.resnet50.preprocess_input', + 'keras.applications.vgg16.preprocess_input', + 'keras.applications.vgg19.preprocess_input', +)(preprocess_input) diff --git a/tensorflow/python/keras/applications/imagenet_utils_test.py b/tensorflow/python/keras/applications/imagenet_utils_test.py index 349339309017f3e9e3a9922d95188f1954ed8634..037e939ac56cfdd8420cf83a338664b5aaf7907d 100644 --- a/tensorflow/python/keras/applications/imagenet_utils_test.py +++ b/tensorflow/python/keras/applications/imagenet_utils_test.py @@ -88,112 +88,6 @@ class ImageNetUtilsTest(test.TestCase): out2 = model2.predict(x2[np.newaxis])[0] self.assertAllClose(out1, out2.transpose(1, 2, 0)) - def test_obtain_input_shape(self): - # input_shape and default_size are not identical. - with self.assertRaises(ValueError): - keras.applications.imagenet_utils._obtain_input_shape( - input_shape=(224, 224, 3), - default_size=299, - min_size=139, - data_format='channels_last', - require_flatten=True, - weights='imagenet') - - # Test invalid use cases - for data_format in ['channels_last', 'channels_first']: - # input_shape is smaller than min_size. - shape = (100, 100) - if data_format == 'channels_last': - input_shape = shape + (3,) - else: - input_shape = (3,) + shape - with self.assertRaises(ValueError): - keras.applications.imagenet_utils._obtain_input_shape( - input_shape=input_shape, - default_size=None, - min_size=139, - data_format=data_format, - require_flatten=False) - - # shape is 1D. - shape = (100,) - if data_format == 'channels_last': - input_shape = shape + (3,) - else: - input_shape = (3,) + shape - with self.assertRaises(ValueError): - keras.applications.imagenet_utils._obtain_input_shape( - input_shape=input_shape, - default_size=None, - min_size=139, - data_format=data_format, - require_flatten=False) - - # the number of channels is 5 not 3. - shape = (100, 100) - if data_format == 'channels_last': - input_shape = shape + (5,) - else: - input_shape = (5,) + shape - with self.assertRaises(ValueError): - keras.applications.imagenet_utils._obtain_input_shape( - input_shape=input_shape, - default_size=None, - min_size=139, - data_format=data_format, - require_flatten=False) - - # require_flatten=True with dynamic input shape. - with self.assertRaises(ValueError): - keras.applications.imagenet_utils._obtain_input_shape( - input_shape=None, - default_size=None, - min_size=139, - data_format='channels_first', - require_flatten=True) - - assert keras.applications.imagenet_utils._obtain_input_shape( - input_shape=(3, 200, 200), - default_size=None, - min_size=139, - data_format='channels_first', - require_flatten=True) == (3, 200, 200) - - assert keras.applications.imagenet_utils._obtain_input_shape( - input_shape=None, - default_size=None, - min_size=139, - data_format='channels_last', - require_flatten=False) == (None, None, 3) - - assert keras.applications.imagenet_utils._obtain_input_shape( - input_shape=None, - default_size=None, - min_size=139, - data_format='channels_first', - require_flatten=False) == (3, None, None) - - assert keras.applications.imagenet_utils._obtain_input_shape( - input_shape=None, - default_size=None, - min_size=139, - data_format='channels_last', - require_flatten=False) == (None, None, 3) - - assert keras.applications.imagenet_utils._obtain_input_shape( - input_shape=(150, 150, 3), - default_size=None, - min_size=139, - data_format='channels_last', - require_flatten=False) == (150, 150, 3) - - assert keras.applications.imagenet_utils._obtain_input_shape( - input_shape=(3, None, None), - default_size=None, - min_size=139, - data_format='channels_first', - require_flatten=False) == (3, None, None) - if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/applications/inception_resnet_v2.py b/tensorflow/python/keras/applications/inception_resnet_v2.py index 14e3b6aa60dbfa7e62e04849d35633eed162a416..63debb4e0df0da5d3a229332d3a2d473b1e1a23e 100644 --- a/tensorflow/python/keras/applications/inception_resnet_v2.py +++ b/tensorflow/python/keras/applications/inception_resnet_v2.py @@ -13,372 +13,20 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """Inception-ResNet V2 model for Keras. - -# Reference -- [Inception-v4, Inception-ResNet and the Impact of - Residual Connections on Learning](https://arxiv.org/abs/1602.07261) - """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.applications import imagenet_utils -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.layers import Activation -from tensorflow.python.keras.layers import AveragePooling2D -from tensorflow.python.keras.layers import BatchNormalization -from tensorflow.python.keras.layers import Concatenate -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import Lambda -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import inception_resnet_v2 from tensorflow.python.util.tf_export import tf_export +InceptionResNetV2 = inception_resnet_v2.InceptionResNetV2 +decode_predictions = inception_resnet_v2.decode_predictions +preprocess_input = inception_resnet_v2.preprocess_input -BASE_WEIGHT_URL = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.7/' - - -@tf_export('keras.applications.inception_resnet_v2.preprocess_input') -def preprocess_input(x): - """Preprocesses a numpy array encoding a batch of images. - - Arguments: - x: a 4D numpy array consists of RGB values within [0, 255]. - - Returns: - Preprocessed array. - """ - return imagenet_utils.preprocess_input(x, mode='tf') - - -def conv2d_bn(x, - filters, - kernel_size, - strides=1, - padding='same', - activation='relu', - use_bias=False, - name=None): - """Utility function to apply conv + BN. - - Arguments: - x: input tensor. - filters: filters in `Conv2D`. - kernel_size: kernel size as in `Conv2D`. - strides: strides in `Conv2D`. - padding: padding mode in `Conv2D`. - activation: activation in `Conv2D`. - use_bias: whether to use a bias in `Conv2D`. - name: name of the ops; will become `name + '_ac'` for the activation - and `name + '_bn'` for the batch norm layer. - - Returns: - Output tensor after applying `Conv2D` and `BatchNormalization`. - """ - x = Conv2D( - filters, - kernel_size, - strides=strides, - padding=padding, - use_bias=use_bias, - name=name)( - x) - if not use_bias: - bn_axis = 1 if K.image_data_format() == 'channels_first' else 3 - bn_name = None if name is None else name + '_bn' - x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) - if activation is not None: - ac_name = None if name is None else name + '_ac' - x = Activation(activation, name=ac_name)(x) - return x - - -def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): - """Adds a Inception-ResNet block. - - This function builds 3 types of Inception-ResNet blocks mentioned - in the paper, controlled by the `block_type` argument (which is the - block name used in the official TF-slim implementation): - - Inception-ResNet-A: `block_type='block35'` - - Inception-ResNet-B: `block_type='block17'` - - Inception-ResNet-C: `block_type='block8'` - - Arguments: - x: input tensor. - scale: scaling factor to scale the residuals (i.e., the output of - passing `x` through an inception module) before adding them - to the shortcut branch. Let `r` be the output from the residual - branch, - the output of this block will be `x + scale * r`. - block_type: `'block35'`, `'block17'` or `'block8'`, determines - the network structure in the residual branch. - block_idx: an `int` used for generating layer names. The Inception-ResNet - blocks - are repeated many times in this network. We use `block_idx` to - identify - each of the repetitions. For example, the first Inception-ResNet-A - block - will have `block_type='block35', block_idx=0`, ane the layer names - will have - a common prefix `'block35_0'`. - activation: activation function to use at the end of the block. - When `activation=None`, no activation is applied - (i.e., "linear" activation: `a(x) = x`). - - Returns: - Output tensor for the block. - - Raises: - ValueError: if `block_type` is not one of `'block35'`, - `'block17'` or `'block8'`. - """ - if block_type == 'block35': - branch_0 = conv2d_bn(x, 32, 1) - branch_1 = conv2d_bn(x, 32, 1) - branch_1 = conv2d_bn(branch_1, 32, 3) - branch_2 = conv2d_bn(x, 32, 1) - branch_2 = conv2d_bn(branch_2, 48, 3) - branch_2 = conv2d_bn(branch_2, 64, 3) - branches = [branch_0, branch_1, branch_2] - elif block_type == 'block17': - branch_0 = conv2d_bn(x, 192, 1) - branch_1 = conv2d_bn(x, 128, 1) - branch_1 = conv2d_bn(branch_1, 160, [1, 7]) - branch_1 = conv2d_bn(branch_1, 192, [7, 1]) - branches = [branch_0, branch_1] - elif block_type == 'block8': - branch_0 = conv2d_bn(x, 192, 1) - branch_1 = conv2d_bn(x, 192, 1) - branch_1 = conv2d_bn(branch_1, 224, [1, 3]) - branch_1 = conv2d_bn(branch_1, 256, [3, 1]) - branches = [branch_0, branch_1] - else: - raise ValueError('Unknown Inception-ResNet block type. ' - 'Expects "block35", "block17" or "block8", ' - 'but got: ' + str(block_type)) - - block_name = block_type + '_' + str(block_idx) - channel_axis = 1 if K.image_data_format() == 'channels_first' else 3 - mixed = Concatenate(axis=channel_axis, name=block_name + '_mixed')(branches) - up = conv2d_bn( - mixed, - K.int_shape(x)[channel_axis], - 1, - activation=None, - use_bias=True, - name=block_name + '_conv') - - x = Lambda( - lambda inputs, scale: inputs[0] + inputs[1] * scale, - output_shape=K.int_shape(x)[1:], - arguments={'scale': scale}, - name=block_name)([x, up]) - if activation is not None: - x = Activation(activation, name=block_name + '_ac')(x) - return x - - -@tf_export('keras.applications.InceptionResNetV2', - 'keras.applications.inception_resnet_v2.InceptionResNetV2') -def InceptionResNetV2(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - """Instantiates the Inception-ResNet v2 architecture. - - Optionally loads weights pre-trained on ImageNet. - Note that when using TensorFlow, for best performance you should - set `"image_data_format": "channels_last"` in your Keras config - at `~/.keras/keras.json`. - - The model and the weights are compatible with TensorFlow, Theano and - CNTK backends. The data format convention used by the model is - the one specified in your Keras config file. - - Note that the default input image size for this model is 299x299, instead - of 224x224 as in the VGG16 and ResNet models. Also, the input preprocessing - function is different (i.e., do not use `imagenet_utils.preprocess_input()` - with this model. Use `preprocess_input()` defined in this module instead). - - Arguments: - include_top: whether to include the fully-connected - layer at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) - to use as image input for the model. - input_shape: optional shape tuple, only to be specified - if `include_top` is `False` (otherwise the input shape - has to be `(299, 299, 3)` (with `'channels_last'` data format) - or `(3, 299, 299)` (with `'channels_first'` data format). - It should have exactly 3 inputs channels, - and width and height should be no smaller than 139. - E.g. `(150, 150, 3)` would be one valid value. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model will be - the 4D tensor output of the last convolutional layer. - - `'avg'` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a 2D tensor. - - `'max'` means that global max pooling will be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is `True`, and - if no `weights` argument is specified. - - Returns: - A Keras `Model` instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - """ - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as imagenet with `include_top`' - ' as true, `classes` should be 1000') - - # Determine proper input shape - input_shape = _obtain_input_shape( - input_shape, - default_size=299, - min_size=139, - data_format=K.image_data_format(), - require_flatten=False, - weights=weights) - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - - # Stem block: 35 x 35 x 192 - x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid') - x = conv2d_bn(x, 32, 3, padding='valid') - x = conv2d_bn(x, 64, 3) - x = MaxPooling2D(3, strides=2)(x) - x = conv2d_bn(x, 80, 1, padding='valid') - x = conv2d_bn(x, 192, 3, padding='valid') - x = MaxPooling2D(3, strides=2)(x) - - # Mixed 5b (Inception-A block): 35 x 35 x 320 - branch_0 = conv2d_bn(x, 96, 1) - branch_1 = conv2d_bn(x, 48, 1) - branch_1 = conv2d_bn(branch_1, 64, 5) - branch_2 = conv2d_bn(x, 64, 1) - branch_2 = conv2d_bn(branch_2, 96, 3) - branch_2 = conv2d_bn(branch_2, 96, 3) - branch_pool = AveragePooling2D(3, strides=1, padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 64, 1) - branches = [branch_0, branch_1, branch_2, branch_pool] - channel_axis = 1 if K.image_data_format() == 'channels_first' else 3 - x = Concatenate(axis=channel_axis, name='mixed_5b')(branches) - - # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320 - for block_idx in range(1, 11): - x = inception_resnet_block( - x, scale=0.17, block_type='block35', block_idx=block_idx) - - # Mixed 6a (Reduction-A block): 17 x 17 x 1088 - branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid') - branch_1 = conv2d_bn(x, 256, 1) - branch_1 = conv2d_bn(branch_1, 256, 3) - branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid') - branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x) - branches = [branch_0, branch_1, branch_pool] - x = Concatenate(axis=channel_axis, name='mixed_6a')(branches) - - # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088 - for block_idx in range(1, 21): - x = inception_resnet_block( - x, scale=0.1, block_type='block17', block_idx=block_idx) - - # Mixed 7a (Reduction-B block): 8 x 8 x 2080 - branch_0 = conv2d_bn(x, 256, 1) - branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid') - branch_1 = conv2d_bn(x, 256, 1) - branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid') - branch_2 = conv2d_bn(x, 256, 1) - branch_2 = conv2d_bn(branch_2, 288, 3) - branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid') - branch_pool = MaxPooling2D(3, strides=2, padding='valid')(x) - branches = [branch_0, branch_1, branch_2, branch_pool] - x = Concatenate(axis=channel_axis, name='mixed_7a')(branches) - - # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080 - for block_idx in range(1, 10): - x = inception_resnet_block( - x, scale=0.2, block_type='block8', block_idx=block_idx) - x = inception_resnet_block( - x, scale=1., activation=None, block_type='block8', block_idx=10) - - # Final convolution block: 8 x 8 x 1536 - x = conv2d_bn(x, 1536, 1, name='conv_7b') - - if include_top: - # Classification block - x = GlobalAveragePooling2D(name='avg_pool')(x) - x = Dense(classes, activation='softmax', name='predictions')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor` - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - - # Create model - model = Model(inputs, x, name='inception_resnet_v2') - - # Load weights - if weights == 'imagenet': - if include_top: - fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5' - weights_path = get_file( - fname, - BASE_WEIGHT_URL + fname, - cache_subdir='models', - file_hash='e693bd0210a403b3192acc6073ad2e96') - else: - fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5' - weights_path = get_file( - fname, - BASE_WEIGHT_URL + fname, - cache_subdir='models', - file_hash='d19885ff4a710c122648d3b5c3b684e4') - model.load_weights(weights_path) - elif weights is not None: - model.load_weights(weights) - - return model +tf_export('keras.applications.inception_resnet_v2.InceptionResNetV2', + 'keras.applications.InceptionResNetV2')(InceptionResNetV2) +tf_export( + 'keras.applications.inception_resnet_v2.preprocess_input')(preprocess_input) diff --git a/tensorflow/python/keras/applications/inception_v3.py b/tensorflow/python/keras/applications/inception_v3.py index b5e28c781f71e67b8d835b50070b49add2d7930a..87534086c8fbe818a4196ffa34ad3ee33fdf27f4 100644 --- a/tensorflow/python/keras/applications/inception_v3.py +++ b/tensorflow/python/keras/applications/inception_v3.py @@ -13,404 +13,19 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """Inception V3 model for Keras. - -Note that the input image format for this model is different than for -the VGG16 and ResNet models (299x299 instead of 224x224), -and that the input preprocessing function is also different (same as Xception). - -# Reference - -- [Rethinking the Inception Architecture for Computer -Vision](http://arxiv.org/abs/1512.00567) - """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras import layers -from tensorflow.python.keras.applications import imagenet_utils -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.layers import Activation -from tensorflow.python.keras.layers import AveragePooling2D -from tensorflow.python.keras.layers import BatchNormalization -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import inception_v3 from tensorflow.python.util.tf_export import tf_export +InceptionV3 = inception_v3.InceptionV3 +decode_predictions = inception_v3.decode_predictions +preprocess_input = inception_v3.preprocess_input -WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels.h5' -WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5' - - -def conv2d_bn(x, - filters, - num_row, - num_col, - padding='same', - strides=(1, 1), - name=None): - """Utility function to apply conv + BN. - - Arguments: - x: input tensor. - filters: filters in `Conv2D`. - num_row: height of the convolution kernel. - num_col: width of the convolution kernel. - padding: padding mode in `Conv2D`. - strides: strides in `Conv2D`. - name: name of the ops; will become `name + '_conv'` - for the convolution and `name + '_bn'` for the - batch norm layer. - - Returns: - Output tensor after applying `Conv2D` and `BatchNormalization`. - """ - if name is not None: - bn_name = name + '_bn' - conv_name = name + '_conv' - else: - bn_name = None - conv_name = None - if K.image_data_format() == 'channels_first': - bn_axis = 1 - else: - bn_axis = 3 - x = Conv2D( - filters, (num_row, num_col), - strides=strides, - padding=padding, - use_bias=False, - name=conv_name)( - x) - x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) - x = Activation('relu', name=name)(x) - return x - - -@tf_export('keras.applications.InceptionV3', - 'keras.applications.inception_v3.InceptionV3') -def InceptionV3(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - """Instantiates the Inception v3 architecture. - - Optionally loads weights pre-trained - on ImageNet. Note that when using TensorFlow, - for best performance you should set - `image_data_format='channels_last'` in your Keras config - at ~/.keras/keras.json. - The model and the weights are compatible with both - TensorFlow and Theano. The data format - convention used by the model is the one - specified in your Keras config file. - Note that the default input image size for this model is 299x299. - - Arguments: - include_top: whether to include the fully-connected - layer at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) - to use as image input for the model. - input_shape: optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(299, 299, 3)` (with `channels_last` data format) - or `(3, 299, 299)` (with `channels_first` data format). - It should have exactly 3 inputs channels, - and width and height should be no smaller than 139. - E.g. `(150, 150, 3)` would be one valid value. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model will be - the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a 2D tensor. - - `max` means that global max pooling will - be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - """ - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as imagenet with `include_top`' - ' as true, `classes` should be 1000') - - # Determine proper input shape - input_shape = _obtain_input_shape( - input_shape, - default_size=299, - min_size=139, - data_format=K.image_data_format(), - require_flatten=False, - weights=weights) - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - - if K.image_data_format() == 'channels_first': - channel_axis = 1 - else: - channel_axis = 3 - - x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid') - x = conv2d_bn(x, 32, 3, 3, padding='valid') - x = conv2d_bn(x, 64, 3, 3) - x = MaxPooling2D((3, 3), strides=(2, 2))(x) - - x = conv2d_bn(x, 80, 1, 1, padding='valid') - x = conv2d_bn(x, 192, 3, 3, padding='valid') - x = MaxPooling2D((3, 3), strides=(2, 2))(x) - - # mixed 0, 1, 2: 35 x 35 x 256 - branch1x1 = conv2d_bn(x, 64, 1, 1) - - branch5x5 = conv2d_bn(x, 48, 1, 1) - branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) - - branch3x3dbl = conv2d_bn(x, 64, 1, 1) - branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) - branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) - - branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 32, 1, 1) - x = layers.concatenate( - [branch1x1, branch5x5, branch3x3dbl, branch_pool], - axis=channel_axis, - name='mixed0') - - # mixed 1: 35 x 35 x 256 - branch1x1 = conv2d_bn(x, 64, 1, 1) - - branch5x5 = conv2d_bn(x, 48, 1, 1) - branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) - - branch3x3dbl = conv2d_bn(x, 64, 1, 1) - branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) - branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) - - branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 64, 1, 1) - x = layers.concatenate( - [branch1x1, branch5x5, branch3x3dbl, branch_pool], - axis=channel_axis, - name='mixed1') - - # mixed 2: 35 x 35 x 256 - branch1x1 = conv2d_bn(x, 64, 1, 1) - - branch5x5 = conv2d_bn(x, 48, 1, 1) - branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) - - branch3x3dbl = conv2d_bn(x, 64, 1, 1) - branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) - branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) - - branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 64, 1, 1) - x = layers.concatenate( - [branch1x1, branch5x5, branch3x3dbl, branch_pool], - axis=channel_axis, - name='mixed2') - - # mixed 3: 17 x 17 x 768 - branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid') - - branch3x3dbl = conv2d_bn(x, 64, 1, 1) - branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) - branch3x3dbl = conv2d_bn( - branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid') - - branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) - x = layers.concatenate( - [branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3') - - # mixed 4: 17 x 17 x 768 - branch1x1 = conv2d_bn(x, 192, 1, 1) - - branch7x7 = conv2d_bn(x, 128, 1, 1) - branch7x7 = conv2d_bn(branch7x7, 128, 1, 7) - branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) - - branch7x7dbl = conv2d_bn(x, 128, 1, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) - branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) - - branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 192, 1, 1) - x = layers.concatenate( - [branch1x1, branch7x7, branch7x7dbl, branch_pool], - axis=channel_axis, - name='mixed4') - - # mixed 5, 6: 17 x 17 x 768 - for i in range(2): - branch1x1 = conv2d_bn(x, 192, 1, 1) - - branch7x7 = conv2d_bn(x, 160, 1, 1) - branch7x7 = conv2d_bn(branch7x7, 160, 1, 7) - branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) - - branch7x7dbl = conv2d_bn(x, 160, 1, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7) - branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) - - branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 192, 1, 1) - x = layers.concatenate( - [branch1x1, branch7x7, branch7x7dbl, branch_pool], - axis=channel_axis, - name='mixed' + str(5 + i)) - - # mixed 7: 17 x 17 x 768 - branch1x1 = conv2d_bn(x, 192, 1, 1) - - branch7x7 = conv2d_bn(x, 192, 1, 1) - branch7x7 = conv2d_bn(branch7x7, 192, 1, 7) - branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) - - branch7x7dbl = conv2d_bn(x, 192, 1, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) - branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) - branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) - - branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 192, 1, 1) - x = layers.concatenate( - [branch1x1, branch7x7, branch7x7dbl, branch_pool], - axis=channel_axis, - name='mixed7') - - # mixed 8: 8 x 8 x 1280 - branch3x3 = conv2d_bn(x, 192, 1, 1) - branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding='valid') - - branch7x7x3 = conv2d_bn(x, 192, 1, 1) - branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7) - branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1) - branch7x7x3 = conv2d_bn( - branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid') - - branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) - x = layers.concatenate( - [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8') - - # mixed 9: 8 x 8 x 2048 - for i in range(2): - branch1x1 = conv2d_bn(x, 320, 1, 1) - - branch3x3 = conv2d_bn(x, 384, 1, 1) - branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3) - branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1) - branch3x3 = layers.concatenate( - [branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i)) - - branch3x3dbl = conv2d_bn(x, 448, 1, 1) - branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3) - branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3) - branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1) - branch3x3dbl = layers.concatenate( - [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis) - - branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) - branch_pool = conv2d_bn(branch_pool, 192, 1, 1) - x = layers.concatenate( - [branch1x1, branch3x3, branch3x3dbl, branch_pool], - axis=channel_axis, - name='mixed' + str(9 + i)) - if include_top: - # Classification block - x = GlobalAveragePooling2D(name='avg_pool')(x) - x = Dense(classes, activation='softmax', name='predictions')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - # Create model. - model = Model(inputs, x, name='inception_v3') - - # load weights - if weights == 'imagenet': - if include_top: - weights_path = get_file( - 'inception_v3_weights_tf_dim_ordering_tf_kernels.h5', - WEIGHTS_PATH, - cache_subdir='models', - file_hash='9a0d58056eeedaa3f26cb7ebd46da564') - else: - weights_path = get_file( - 'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5', - WEIGHTS_PATH_NO_TOP, - cache_subdir='models', - file_hash='bcbd6486424b2319ff4ef7d526e38f63') - model.load_weights(weights_path) - elif weights is not None: - model.load_weights(weights) - - return model - - -@tf_export('keras.applications.nasnet.preprocess_input', - 'keras.applications.inception_v3.preprocess_input') -def preprocess_input(x): - """Preprocesses a numpy array encoding a batch of images. - - Arguments: - x: a 4D numpy array consists of RGB values within [0, 255]. - - Returns: - Preprocessed array. - """ - return imagenet_utils.preprocess_input(x, mode='tf') +tf_export('keras.applications.inception_v3.InceptionV3', + 'keras.applications.InceptionV3')(InceptionV3) +tf_export('keras.applications.inception_v3.preprocess_input')(preprocess_input) diff --git a/tensorflow/python/keras/applications/mobilenet.py b/tensorflow/python/keras/applications/mobilenet.py index 7285e0396376f7af2ca397911bbf502633dba0bf..3528f027b3fc0849ffa8e35d84928da9f55d33a7 100644 --- a/tensorflow/python/keras/applications/mobilenet.py +++ b/tensorflow/python/keras/applications/mobilenet.py @@ -13,466 +13,19 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """MobileNet v1 models for Keras. - -MobileNet is a general architecture and can be used for multiple use cases. -Depending on the use case, it can use different input layer size and -different width factors. This allows different width models to reduce -the number of multiply-adds and thereby -reduce inference cost on mobile devices. - -MobileNets support any input size greater than 32 x 32, with larger image sizes -offering better performance. -The number of parameters and number of multiply-adds -can be modified by using the `alpha` parameter, -which increases/decreases the number of filters in each layer. -By altering the image size and `alpha` parameter, -all 16 models from the paper can be built, with ImageNet weights provided. - -The paper demonstrates the performance of MobileNets using `alpha` values of -1.0 (also called 100 % MobileNet), 0.75, 0.5 and 0.25. -For each of these `alpha` values, weights for 4 different input image sizes -are provided (224, 192, 160, 128). - -The following table describes the size and accuracy of the 100% MobileNet -on size 224 x 224: ----------------------------------------------------------------------------- -Width Multiplier (alpha) | ImageNet Acc | Multiply-Adds (M) | Params (M) ----------------------------------------------------------------------------- -| 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 | -| 0.75 MobileNet-224 | 68.4 % | 325 | 2.6 | -| 0.50 MobileNet-224 | 63.7 % | 149 | 1.3 | -| 0.25 MobileNet-224 | 50.6 % | 41 | 0.5 | ----------------------------------------------------------------------------- - -The following table describes the performance of -the 100 % MobileNet on various input sizes: ------------------------------------------------------------------------- - Resolution | ImageNet Acc | Multiply-Adds (M) | Params (M) ------------------------------------------------------------------------- -| 1.0 MobileNet-224 | 70.6 % | 529 | 4.2 | -| 1.0 MobileNet-192 | 69.1 % | 529 | 4.2 | -| 1.0 MobileNet-160 | 67.2 % | 529 | 4.2 | -| 1.0 MobileNet-128 | 64.4 % | 529 | 4.2 | ------------------------------------------------------------------------- - -The weights for all 16 models are obtained and translated -from TensorFlow checkpoints found at -https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md - -# Reference -- [MobileNets: Efficient Convolutional Neural Networks for - Mobile Vision Applications](https://arxiv.org/pdf/1704.04861.pdf)) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.applications import imagenet_utils -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.layers import Activation -from tensorflow.python.keras.layers import BatchNormalization -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import DepthwiseConv2D -from tensorflow.python.keras.layers import Dropout -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import ReLU -from tensorflow.python.keras.layers import Reshape -from tensorflow.python.keras.layers import ZeroPadding2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import mobilenet from tensorflow.python.util.tf_export import tf_export +MobileNet = mobilenet.MobileNet +decode_predictions = mobilenet.decode_predictions +preprocess_input = mobilenet.preprocess_input -BASE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.6/' - - -@tf_export('keras.applications.mobilenet.preprocess_input') -def preprocess_input(x): - """Preprocesses a numpy array encoding a batch of images. - - Arguments: - x: a 4D numpy array consists of RGB values within [0, 255]. - - Returns: - Preprocessed array. - """ - return imagenet_utils.preprocess_input(x, mode='tf') - - -@tf_export('keras.applications.MobileNet', - 'keras.applications.mobilenet.MobileNet') -def MobileNet(input_shape=None, - alpha=1.0, - depth_multiplier=1, - dropout=1e-3, - include_top=True, - weights='imagenet', - input_tensor=None, - pooling=None, - classes=1000): - """Instantiates the MobileNet architecture. - - Arguments: - input_shape: optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(224, 224, 3)` (with `channels_last` data format) - or (3, 224, 224) (with `channels_first` data format). - It should have exactly 3 inputs channels, - and width and height should be no smaller than 32. - E.g. `(200, 200, 3)` would be one valid value. - alpha: controls the width of the network. - - If `alpha` < 1.0, proportionally decreases the number - of filters in each layer. - - If `alpha` > 1.0, proportionally increases the number - of filters in each layer. - - If `alpha` = 1, default number of filters from the paper - are used at each layer. - depth_multiplier: depth multiplier for depthwise convolution - (also called the resolution multiplier) - dropout: dropout rate - include_top: whether to include the fully-connected - layer at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of - `layers.Input()`) - to use as image input for the model. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model - will be the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a - 2D tensor. - - `max` means that global max pooling will - be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - RuntimeError: If attempting to run this model with a - backend that does not support separable convolutions. - """ - - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as ImageNet with `include_top` ' - 'as true, `classes` should be 1000') - - # Determine proper input shape and default size. - if input_shape is None: - default_size = 224 - else: - if K.image_data_format() == 'channels_first': - rows = input_shape[1] - cols = input_shape[2] - else: - rows = input_shape[0] - cols = input_shape[1] - - if rows == cols and rows in [128, 160, 192, 224]: - default_size = rows - else: - default_size = 224 - - input_shape = _obtain_input_shape( - input_shape, - default_size=default_size, - min_size=32, - data_format=K.image_data_format(), - require_flatten=include_top, - weights=weights) - - if K.image_data_format() == 'channels_last': - row_axis, col_axis = (0, 1) - else: - row_axis, col_axis = (1, 2) - rows = input_shape[row_axis] - cols = input_shape[col_axis] - - if weights == 'imagenet': - if depth_multiplier != 1: - raise ValueError('If imagenet weights are being loaded, ' - 'depth multiplier must be 1') - - if alpha not in [0.25, 0.50, 0.75, 1.0]: - raise ValueError('If imagenet weights are being loaded, ' - 'alpha can be one of' - '`0.25`, `0.50`, `0.75` or `1.0` only.') - - if rows != cols or rows not in [128, 160, 192, 224]: - if rows is None: - rows = 224 - logging.warning('MobileNet shape is undefined.' - ' Weights for input shape (224, 224) will be loaded.') - else: - raise ValueError('If imagenet weights are being loaded, ' - 'input must have a static square shape (one of ' - '(128, 128), (160, 160), (192, 192), or (224, 224)).' - ' Input shape provided = %s' % (input_shape,)) - - if K.image_data_format() != 'channels_last': - logging.warning('The MobileNet family of models is only available ' - 'for the input data format "channels_last" ' - '(width, height, channels). ' - 'However your settings specify the default ' - 'data format "channels_first" (channels, width, height).' - ' You should set `image_data_format="channels_last"` ' - 'in your Keras config located at ~/.keras/keras.json. ' - 'The model being returned right now will expect inputs ' - 'to follow the "channels_last" data format.') - K.set_image_data_format('channels_last') - old_data_format = 'channels_first' - else: - old_data_format = None - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - - x = _conv_block(img_input, 32, alpha, strides=(2, 2)) - x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1) - - x = _depthwise_conv_block( - x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2) - x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3) - - x = _depthwise_conv_block( - x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4) - x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) - - x = _depthwise_conv_block( - x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6) - x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7) - x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8) - x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) - x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) - x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) - - x = _depthwise_conv_block( - x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12) - x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13) - - if include_top: - if K.image_data_format() == 'channels_first': - shape = (int(1024 * alpha), 1, 1) - else: - shape = (1, 1, int(1024 * alpha)) - - x = GlobalAveragePooling2D()(x) - x = Reshape(shape, name='reshape_1')(x) - x = Dropout(dropout, name='dropout')(x) - x = Conv2D(classes, (1, 1), padding='same', name='conv_preds')(x) - x = Activation('softmax', name='act_softmax')(x) - x = Reshape((classes,), name='reshape_2')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - - # Create model. - model = Model(inputs, x, name='mobilenet_%0.2f_%s' % (alpha, rows)) - - # load weights - if weights == 'imagenet': - if K.image_data_format() == 'channels_first': - raise ValueError('Weights for "channels_first" format ' - 'are not available.') - if alpha == 1.0: - alpha_text = '1_0' - elif alpha == 0.75: - alpha_text = '7_5' - elif alpha == 0.50: - alpha_text = '5_0' - else: - alpha_text = '2_5' - - if include_top: - model_name = 'mobilenet_%s_%d_tf.h5' % (alpha_text, rows) - weigh_path = BASE_WEIGHT_PATH + model_name - weights_path = get_file(model_name, weigh_path, cache_subdir='models') - else: - model_name = 'mobilenet_%s_%d_tf_no_top.h5' % (alpha_text, rows) - weigh_path = BASE_WEIGHT_PATH + model_name - weights_path = get_file(model_name, weigh_path, cache_subdir='models') - model.load_weights(weights_path) - elif weights is not None: - model.load_weights(weights) - - if old_data_format: - K.set_image_data_format(old_data_format) - return model - - -def _conv_block(inputs, filters, alpha, kernel=(3, 3), strides=(1, 1)): - """Adds an initial convolution layer (with batch normalization and relu6). - - Arguments: - inputs: Input tensor of shape `(rows, cols, 3)` - (with `channels_last` data format) or - (3, rows, cols) (with `channels_first` data format). - It should have exactly 3 inputs channels, - and width and height should be no smaller than 32. - E.g. `(224, 224, 3)` would be one valid value. - filters: Integer, the dimensionality of the output space - (i.e. the number of output filters in the convolution). - alpha: controls the width of the network. - - If `alpha` < 1.0, proportionally decreases the number - of filters in each layer. - - If `alpha` > 1.0, proportionally increases the number - of filters in each layer. - - If `alpha` = 1, default number of filters from the paper - are used at each layer. - kernel: An integer or tuple/list of 2 integers, specifying the - width and height of the 2D convolution window. - Can be a single integer to specify the same value for - all spatial dimensions. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the width and height. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - - Input shape: - 4D tensor with shape: - `(samples, channels, rows, cols)` if data_format='channels_first' - or 4D tensor with shape: - `(samples, rows, cols, channels)` if data_format='channels_last'. - - Output shape: - 4D tensor with shape: - `(samples, filters, new_rows, new_cols)` if data_format='channels_first' - or 4D tensor with shape: - `(samples, new_rows, new_cols, filters)` if data_format='channels_last'. - `rows` and `cols` values might have changed due to stride. - - Returns: - Output tensor of block. - """ - channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 - filters = int(filters * alpha) - x = ZeroPadding2D(padding=(1, 1), name='conv1_pad')(inputs) - x = Conv2D( - filters, - kernel, - padding='valid', - use_bias=False, - strides=strides, - name='conv1')(x) - x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x) - return ReLU(6, name='conv1_relu')(x) - - -def _depthwise_conv_block(inputs, - pointwise_conv_filters, - alpha, - depth_multiplier=1, - strides=(1, 1), - block_id=1): - """Adds a depthwise convolution block. - - A depthwise convolution block consists of a depthwise conv, - batch normalization, relu6, pointwise convolution, - batch normalization and relu6 activation. - - Arguments: - inputs: Input tensor of shape `(rows, cols, channels)` - (with `channels_last` data format) or - (channels, rows, cols) (with `channels_first` data format). - pointwise_conv_filters: Integer, the dimensionality of the output space - (i.e. the number of output filters in the pointwise convolution). - alpha: controls the width of the network. - - If `alpha` < 1.0, proportionally decreases the number - of filters in each layer. - - If `alpha` > 1.0, proportionally increases the number - of filters in each layer. - - If `alpha` = 1, default number of filters from the paper - are used at each layer. - depth_multiplier: The number of depthwise convolution output channels - for each input channel. - The total number of depthwise convolution output - channels will be equal to `filters_in * depth_multiplier`. - strides: An integer or tuple/list of 2 integers, - specifying the strides of the convolution along the width and height. - Can be a single integer to specify the same value for - all spatial dimensions. - Specifying any stride value != 1 is incompatible with specifying - any `dilation_rate` value != 1. - block_id: Integer, a unique identification designating the block number. - - Input shape: - 4D tensor with shape: - `(batch, channels, rows, cols)` if data_format='channels_first' - or 4D tensor with shape: - `(batch, rows, cols, channels)` if data_format='channels_last'. - - Output shape: - 4D tensor with shape: - `(batch, filters, new_rows, new_cols)` if data_format='channels_first' - or 4D tensor with shape: - `(batch, new_rows, new_cols, filters)` if data_format='channels_last'. - `rows` and `cols` values might have changed due to stride. - - Returns: - Output tensor of block. - """ - channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 - pointwise_conv_filters = int(pointwise_conv_filters * alpha) - x = ZeroPadding2D(padding=(1, 1), name='conv_pad_%d' % block_id)(inputs) - x = DepthwiseConv2D( # pylint: disable=not-callable - (3, 3), - padding='valid', - depth_multiplier=depth_multiplier, - strides=strides, - use_bias=False, - name='conv_dw_%d' % block_id)(x) - x = BatchNormalization(axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x) - x = ReLU(6, name='conv_dw_%d_relu' % block_id)(x) - - x = Conv2D( - pointwise_conv_filters, (1, 1), - padding='same', - use_bias=False, - strides=(1, 1), - name='conv_pw_%d' % block_id)( - x) - x = BatchNormalization(axis=channel_axis, name='conv_pw_%d_bn' % block_id)(x) - return ReLU(6, name='conv_pw_%d_relu' % block_id)(x) +tf_export('keras.applications.mobilenet.MobileNet', + 'keras.applications.MobileNet')(MobileNet) +tf_export('keras.applications.mobilenet.preprocess_input')(preprocess_input) diff --git a/tensorflow/python/keras/applications/mobilenet_test.py b/tensorflow/python/keras/applications/mobilenet_test.py index 5661ed7856ad6e307cf3e388ea3db98c69db983f..65e4991dedd60d3c9699cb10c6c438a2f7b76fe3 100644 --- a/tensorflow/python/keras/applications/mobilenet_test.py +++ b/tensorflow/python/keras/applications/mobilenet_test.py @@ -53,12 +53,6 @@ class MobileNetTest(test.TestCase): out1 = keras.applications.mobilenet.preprocess_input(x) self.assertAllClose(np.mean(out1), 0., atol=0.1) - def test_invalid_use_cases(self): - keras.backend.set_image_data_format('channels_first') - model = keras.applications.MobileNet(weights=None) - self.assertEqual(model.output_shape, (None, 1000)) - keras.backend.set_image_data_format('channels_last') - def test_mobilenet_variable_input_channels(self): input_shape = (None, None, 1) model = keras.applications.MobileNet(weights=None, @@ -72,30 +66,6 @@ class MobileNetTest(test.TestCase): input_shape=input_shape) self.assertEqual(model.output_shape, (None, None, None, 1024)) - def test_mobilenet_image_size(self): - with self.test_session(): - valid_image_sizes = [128, 160, 192, 224] - for size in valid_image_sizes: - keras.backend.set_image_data_format('channels_last') - input_shape = (size, size, 3) - model = keras.applications.MobileNet(input_shape=input_shape, - weights=None, - include_top=True) - self.assertEqual(model.input_shape, (None,) + input_shape) - - keras.backend.set_image_data_format('channels_first') - input_shape = (3, size, size) - model = keras.applications.MobileNet(input_shape=input_shape, - weights=None, - include_top=True) - self.assertEqual(model.input_shape, (None,) + input_shape) - - keras.backend.set_image_data_format('channels_last') - invalid_image_shape = (112, 112, 3) - with self.assertRaises(ValueError): - model = keras.applications.MobileNet(input_shape=invalid_image_shape, - weights='imagenet', - include_top=True) if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/applications/mobilenet_v2.py b/tensorflow/python/keras/applications/mobilenet_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..74b8b029f8c6cbfe0c4c523857ccde2f8d05cb3e --- /dev/null +++ b/tensorflow/python/keras/applications/mobilenet_v2.py @@ -0,0 +1,32 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# pylint: disable=invalid-name +"""MobileNet v2 models for Keras. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from keras_applications import mobilenet_v2 + +from tensorflow.python.util.tf_export import tf_export + +MobileNetV2 = mobilenet_v2.MobileNetV2 +decode_predictions = mobilenet_v2.decode_predictions +preprocess_input = mobilenet_v2.preprocess_input + +tf_export('keras.applications.mobilenet_v2.MobileNetV2', + 'keras.applications.MobileNetV2')(MobileNetV2) +tf_export('keras.applications.mobilenet_v2.preprocess_input')(preprocess_input) diff --git a/tensorflow/python/keras/applications/nasnet.py b/tensorflow/python/keras/applications/nasnet.py index ff79b3a057b8fd6ab3b0edf652a5bede0e2d7b87..26ff5db53f913d78ad4f054fee30ff66d5f7dcbf 100644 --- a/tensorflow/python/keras/applications/nasnet.py +++ b/tensorflow/python/keras/applications/nasnet.py @@ -12,784 +12,23 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -# pylint: disable=line-too-long # pylint: disable=invalid-name -# pylint: disable=unused-import """NASNet-A models for Keras. - -NASNet refers to Neural Architecture Search Network, a family of models -that were designed automatically by learning the model architectures -directly on the dataset of interest. - -Here we consider NASNet-A, the highest performance model that was found -for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset, -obtaining state of the art performance on CIFAR-10 and ImageNet 2012. -Only the NASNet-A models, and their respective weights, which are suited -for ImageNet 2012 are provided. - -The below table describes the performance on ImageNet 2012: --------------------------------------------------------------------------------- - Architecture | Top-1 Acc | Top-5 Acc | Multiply-Adds | Params (M) --------------------------------------------------------------------------------- -| NASNet-A (4 @ 1056) | 74.0 % | 91.6 % | 564 M | 5.3 | -| NASNet-A (6 @ 4032) | 82.7 % | 96.2 % | 23.8 B | 88.9 | --------------------------------------------------------------------------------- - -References: - - [Learning Transferable Architectures for Scalable Image Recognition] - (https://arxiv.org/abs/1707.07012) """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.applications.inception_v3 import preprocess_input -from tensorflow.python.keras.layers import Activation -from tensorflow.python.keras.layers import add -from tensorflow.python.keras.layers import AveragePooling2D -from tensorflow.python.keras.layers import BatchNormalization -from tensorflow.python.keras.layers import concatenate -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Cropping2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.layers import SeparableConv2D -from tensorflow.python.keras.layers import ZeroPadding2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import nasnet from tensorflow.python.util.tf_export import tf_export +NASNetMobile = nasnet.NASNetMobile +NASNetLarge = nasnet.NASNetLarge +decode_predictions = nasnet.decode_predictions +preprocess_input = nasnet.preprocess_input -NASNET_MOBILE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-mobile.h5' -NASNET_MOBILE_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-mobile-no-top.h5' -NASNET_LARGE_WEIGHT_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-large.h5' -NASNET_LARGE_WEIGHT_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/NASNet-large-no-top.h5' - - -def NASNet(input_shape=None, - penultimate_filters=4032, - num_blocks=6, - stem_block_filters=96, - skip_reduction=True, - filter_multiplier=2, - include_top=True, - weights=None, - input_tensor=None, - pooling=None, - classes=1000, - default_size=None): - """Instantiates a NASNet model. - - Note that only TensorFlow is supported for now, - therefore it only works with the data format - `image_data_format='channels_last'` in your Keras config - at `~/.keras/keras.json`. - - Arguments: - input_shape: Optional shape tuple, the input shape - is by default `(331, 331, 3)` for NASNetLarge and - `(224, 224, 3)` for NASNetMobile. - It should have exactly 3 inputs channels, - and width and height should be no smaller than 32. - E.g. `(224, 224, 3)` would be one valid value. - penultimate_filters: Number of filters in the penultimate layer. - NASNet models use the notation `NASNet (N @ P)`, where: - - N is the number of blocks - - P is the number of penultimate filters - num_blocks: Number of repeated blocks of the NASNet model. - NASNet models use the notation `NASNet (N @ P)`, where: - - N is the number of blocks - - P is the number of penultimate filters - stem_block_filters: Number of filters in the initial stem block - skip_reduction: Whether to skip the reduction step at the tail - end of the network. Set to `False` for CIFAR models. - filter_multiplier: Controls the width of the network. - - If `filter_multiplier` < 1.0, proportionally decreases the number - of filters in each layer. - - If `filter_multiplier` > 1.0, proportionally increases the number - of filters in each layer. - - If `filter_multiplier` = 1, default number of filters from the - paper are used at each layer. - include_top: Whether to include the fully-connected - layer at the top of the network. - weights: `None` (random initialization) or - `imagenet` (ImageNet weights) - input_tensor: Optional Keras tensor (i.e. output of - `layers.Input()`) - to use as image input for the model. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model - will be the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a - 2D tensor. - - `max` means that global max pooling will - be applied. - classes: Optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - default_size: Specifies the default image size of the model - - Returns: - A Keras model instance. - - Raises: - ValueError: In case of invalid argument for `weights`, - invalid input shape or invalid `penultimate_filters` value. - RuntimeError: If attempting to run this model with a - backend that does not support separable convolutions. - """ - if K.backend() != 'tensorflow': - raise RuntimeError('Only Tensorflow backend is currently supported, ' - 'as other backends do not support ' - 'separable convolution.') - - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as ImageNet with `include_top` ' - 'as true, `classes` should be 1000') - - if (isinstance(input_shape, tuple) and None in input_shape and - weights == 'imagenet'): - raise ValueError('When specifying the input shape of a NASNet' - ' and loading `ImageNet` weights, ' - 'the input_shape argument must be static ' - '(no None entries). Got: `input_shape=' + - str(input_shape) + '`.') - - if default_size is None: - default_size = 331 - - # Determine proper input shape and default size. - input_shape = _obtain_input_shape( - input_shape, - default_size=default_size, - min_size=32, - data_format=K.image_data_format(), - require_flatten=False, - weights=weights) - - if K.image_data_format() != 'channels_last': - logging.warning('The NASNet family of models is only available ' - 'for the input data format "channels_last" ' - '(width, height, channels). ' - 'However your settings specify the default ' - 'data format "channels_first" (channels, width, height).' - ' You should set `image_data_format="channels_last"` ' - 'in your Keras config located at ~/.keras/keras.json. ' - 'The model being returned right now will expect inputs ' - 'to follow the "channels_last" data format.') - K.set_image_data_format('channels_last') - old_data_format = 'channels_first' - else: - old_data_format = None - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - - if penultimate_filters % 24 != 0: - raise ValueError( - 'For NASNet-A models, the value of `penultimate_filters` ' - 'needs to be divisible by 24. Current value: %d' % penultimate_filters) - - channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 - filters = penultimate_filters // 24 - - if not skip_reduction: - x = Conv2D( - stem_block_filters, (3, 3), - strides=(2, 2), - padding='valid', - use_bias=False, - name='stem_conv1', - kernel_initializer='he_normal')( - img_input) - else: - x = Conv2D( - stem_block_filters, (3, 3), - strides=(1, 1), - padding='same', - use_bias=False, - name='stem_conv1', - kernel_initializer='he_normal')( - img_input) - - x = BatchNormalization( - axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')( - x) - - p = None - if not skip_reduction: # imagenet / mobile mode - x, p = _reduction_a_cell( - x, p, filters // (filter_multiplier**2), block_id='stem_1') - x, p = _reduction_a_cell( - x, p, filters // filter_multiplier, block_id='stem_2') - - for i in range(num_blocks): - x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i)) - - x, p0 = _reduction_a_cell( - x, p, filters * filter_multiplier, block_id='reduce_%d' % (num_blocks)) - - p = p0 if not skip_reduction else p - - for i in range(num_blocks): - x, p = _normal_a_cell( - x, p, filters * filter_multiplier, block_id='%d' % (num_blocks + i + 1)) - - x, p0 = _reduction_a_cell( - x, - p, - filters * filter_multiplier**2, - block_id='reduce_%d' % (2 * num_blocks)) - - p = p0 if not skip_reduction else p - - for i in range(num_blocks): - x, p = _normal_a_cell( - x, - p, - filters * filter_multiplier**2, - block_id='%d' % (2 * num_blocks + i + 1)) - - x = Activation('relu')(x) - - if include_top: - x = GlobalAveragePooling2D()(x) - x = Dense(classes, activation='softmax', name='predictions')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - - model = Model(inputs, x, name='NASNet') - - # load weights - if weights == 'imagenet': - if default_size == 224: # mobile version - if include_top: - weight_path = NASNET_MOBILE_WEIGHT_PATH - model_name = 'nasnet_mobile.h5' - else: - weight_path = NASNET_MOBILE_WEIGHT_PATH_NO_TOP - model_name = 'nasnet_mobile_no_top.h5' - - weights_file = get_file(model_name, weight_path, cache_subdir='models') - model.load_weights(weights_file) - - elif default_size == 331: # large version - if include_top: - weight_path = NASNET_LARGE_WEIGHT_PATH - model_name = 'nasnet_large.h5' - else: - weight_path = NASNET_LARGE_WEIGHT_PATH_NO_TOP - model_name = 'nasnet_large_no_top.h5' - - weights_file = get_file(model_name, weight_path, cache_subdir='models') - model.load_weights(weights_file) - else: - raise ValueError('ImageNet weights can only be loaded with NASNetLarge' - ' or NASNetMobile') - elif weights is not None: - model.load_weights(weights) - - if old_data_format: - K.set_image_data_format(old_data_format) - - return model - - -@tf_export('keras.applications.NASNetLarge', - 'keras.applications.nasnet.NASNetLarge') -def NASNetLarge(input_shape=None, - include_top=True, - weights='imagenet', - input_tensor=None, - pooling=None, - classes=1000): - """Instantiates a NASNet model in ImageNet mode. - - Note that only TensorFlow is supported for now, - therefore it only works with the data format - `image_data_format='channels_last'` in your Keras config - at `~/.keras/keras.json`. - - Arguments: - input_shape: Optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(331, 331, 3)` for NASNetLarge. - It should have exactly 3 inputs channels, - and width and height should be no smaller than 32. - E.g. `(224, 224, 3)` would be one valid value. - include_top: Whether to include the fully-connected - layer at the top of the network. - weights: `None` (random initialization) or - `imagenet` (ImageNet weights) - input_tensor: Optional Keras tensor (i.e. output of - `layers.Input()`) - to use as image input for the model. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model - will be the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a - 2D tensor. - - `max` means that global max pooling will - be applied. - classes: Optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - RuntimeError: If attempting to run this model with a - backend that does not support separable convolutions. - """ - return NASNet( - input_shape, - penultimate_filters=4032, - num_blocks=6, - stem_block_filters=96, - skip_reduction=False, - filter_multiplier=2, - include_top=include_top, - weights=weights, - input_tensor=input_tensor, - pooling=pooling, - classes=classes, - default_size=331) - - -@tf_export('keras.applications.NASNetMobile', - 'keras.applications.nasnet.NASNetMobile') -def NASNetMobile(input_shape=None, - include_top=True, - weights='imagenet', - input_tensor=None, - pooling=None, - classes=1000): - """Instantiates a Mobile NASNet model in ImageNet mode. - - Note that only TensorFlow is supported for now, - therefore it only works with the data format - `image_data_format='channels_last'` in your Keras config - at `~/.keras/keras.json`. - - Arguments: - input_shape: Optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(224, 224, 3)` for NASNetMobile - It should have exactly 3 inputs channels, - and width and height should be no smaller than 32. - E.g. `(224, 224, 3)` would be one valid value. - include_top: Whether to include the fully-connected - layer at the top of the network. - weights: `None` (random initialization) or - `imagenet` (ImageNet weights) - input_tensor: Optional Keras tensor (i.e. output of - `layers.Input()`) - to use as image input for the model. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model - will be the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a - 2D tensor. - - `max` means that global max pooling will - be applied. - classes: Optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: In case of invalid argument for `weights`, - or invalid input shape. - RuntimeError: If attempting to run this model with a - backend that does not support separable convolutions. - """ - return NASNet( - input_shape, - penultimate_filters=1056, - num_blocks=4, - stem_block_filters=32, - skip_reduction=False, - filter_multiplier=2, - include_top=include_top, - weights=weights, - input_tensor=input_tensor, - pooling=pooling, - classes=classes, - default_size=224) - - -def _separable_conv_block(ip, - filters, - kernel_size=(3, 3), - strides=(1, 1), - block_id=None): - """Adds 2 blocks of [relu-separable conv-batchnorm]. - - Arguments: - ip: Input tensor - filters: Number of output filters per layer - kernel_size: Kernel size of separable convolutions - strides: Strided convolution for downsampling - block_id: String block_id - - Returns: - A Keras tensor - """ - channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 - - with K.name_scope('separable_conv_block_%s' % block_id): - x = Activation('relu')(ip) - x = SeparableConv2D( - filters, - kernel_size, - strides=strides, - name='separable_conv_1_%s' % block_id, - padding='same', - use_bias=False, - kernel_initializer='he_normal')( - x) - x = BatchNormalization( - axis=channel_dim, - momentum=0.9997, - epsilon=1e-3, - name='separable_conv_1_bn_%s' % (block_id))( - x) - x = Activation('relu')(x) - x = SeparableConv2D( - filters, - kernel_size, - name='separable_conv_2_%s' % block_id, - padding='same', - use_bias=False, - kernel_initializer='he_normal')( - x) - x = BatchNormalization( - axis=channel_dim, - momentum=0.9997, - epsilon=1e-3, - name='separable_conv_2_bn_%s' % (block_id))( - x) - return x - - -def _adjust_block(p, ip, filters, block_id=None): - """Adjusts the input `previous path` to match the shape of the `input`. - - Used in situations where the output number of filters needs to be changed. - - Arguments: - p: Input tensor which needs to be modified - ip: Input tensor whose shape needs to be matched - filters: Number of output filters to be matched - block_id: String block_id - - Returns: - Adjusted Keras tensor - """ - channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 - img_dim = 2 if K.image_data_format() == 'channels_first' else -2 - - ip_shape = K.int_shape(ip) - - if p is not None: - p_shape = K.int_shape(p) - - with K.name_scope('adjust_block'): - if p is None: - p = ip - - elif p_shape[img_dim] != ip_shape[img_dim]: - with K.name_scope('adjust_reduction_block_%s' % block_id): - p = Activation('relu', name='adjust_relu_1_%s' % block_id)(p) - - p1 = AveragePooling2D( - (1, 1), - strides=(2, 2), - padding='valid', - name='adjust_avg_pool_1_%s' % block_id)( - p) - p1 = Conv2D( - filters // 2, (1, 1), - padding='same', - use_bias=False, - name='adjust_conv_1_%s' % block_id, - kernel_initializer='he_normal')( - p1) - - p2 = ZeroPadding2D(padding=((0, 1), (0, 1)))(p) - p2 = Cropping2D(cropping=((1, 0), (1, 0)))(p2) - p2 = AveragePooling2D( - (1, 1), - strides=(2, 2), - padding='valid', - name='adjust_avg_pool_2_%s' % block_id)( - p2) - p2 = Conv2D( - filters // 2, (1, 1), - padding='same', - use_bias=False, - name='adjust_conv_2_%s' % block_id, - kernel_initializer='he_normal')( - p2) - - p = concatenate([p1, p2], axis=channel_dim) - p = BatchNormalization( - axis=channel_dim, - momentum=0.9997, - epsilon=1e-3, - name='adjust_bn_%s' % block_id)( - p) - - elif p_shape[channel_dim] != filters: - with K.name_scope('adjust_projection_block_%s' % block_id): - p = Activation('relu')(p) - p = Conv2D( - filters, (1, 1), - strides=(1, 1), - padding='same', - name='adjust_conv_projection_%s' % block_id, - use_bias=False, - kernel_initializer='he_normal')( - p) - p = BatchNormalization( - axis=channel_dim, - momentum=0.9997, - epsilon=1e-3, - name='adjust_bn_%s' % block_id)( - p) - return p - - -def _normal_a_cell(ip, p, filters, block_id=None): - """Adds a Normal cell for NASNet-A (Fig. 4 in the paper). - - Arguments: - ip: Input tensor `x` - p: Input tensor `p` - filters: Number of output filters - block_id: String block_id - - Returns: - A Keras tensor - """ - channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 - - with K.name_scope('normal_A_block_%s' % block_id): - p = _adjust_block(p, ip, filters, block_id) - - h = Activation('relu')(ip) - h = Conv2D( - filters, (1, 1), - strides=(1, 1), - padding='same', - name='normal_conv_1_%s' % block_id, - use_bias=False, - kernel_initializer='he_normal')( - h) - h = BatchNormalization( - axis=channel_dim, - momentum=0.9997, - epsilon=1e-3, - name='normal_bn_1_%s' % block_id)( - h) - - with K.name_scope('block_1'): - x1_1 = _separable_conv_block( - h, filters, kernel_size=(5, 5), block_id='normal_left1_%s' % block_id) - x1_2 = _separable_conv_block( - p, filters, block_id='normal_right1_%s' % block_id) - x1 = add([x1_1, x1_2], name='normal_add_1_%s' % block_id) - - with K.name_scope('block_2'): - x2_1 = _separable_conv_block( - p, filters, (5, 5), block_id='normal_left2_%s' % block_id) - x2_2 = _separable_conv_block( - p, filters, (3, 3), block_id='normal_right2_%s' % block_id) - x2 = add([x2_1, x2_2], name='normal_add_2_%s' % block_id) - - with K.name_scope('block_3'): - x3 = AveragePooling2D( - (3, 3), - strides=(1, 1), - padding='same', - name='normal_left3_%s' % (block_id))( - h) - x3 = add([x3, p], name='normal_add_3_%s' % block_id) - - with K.name_scope('block_4'): - x4_1 = AveragePooling2D( - (3, 3), - strides=(1, 1), - padding='same', - name='normal_left4_%s' % (block_id))( - p) - x4_2 = AveragePooling2D( - (3, 3), - strides=(1, 1), - padding='same', - name='normal_right4_%s' % (block_id))( - p) - x4 = add([x4_1, x4_2], name='normal_add_4_%s' % block_id) - - with K.name_scope('block_5'): - x5 = _separable_conv_block( - h, filters, block_id='normal_left5_%s' % block_id) - x5 = add([x5, h], name='normal_add_5_%s' % block_id) - - x = concatenate( - [p, x1, x2, x3, x4, x5], - axis=channel_dim, - name='normal_concat_%s' % block_id) - return x, ip - - -def _reduction_a_cell(ip, p, filters, block_id=None): - """Adds a Reduction cell for NASNet-A (Fig. 4 in the paper). - - Arguments: - ip: Input tensor `x` - p: Input tensor `p` - filters: Number of output filters - block_id: String block_id - - Returns: - A Keras tensor - """ - channel_dim = 1 if K.image_data_format() == 'channels_first' else -1 - - with K.name_scope('reduction_A_block_%s' % block_id): - p = _adjust_block(p, ip, filters, block_id) - - h = Activation('relu')(ip) - h = Conv2D( - filters, (1, 1), - strides=(1, 1), - padding='same', - name='reduction_conv_1_%s' % block_id, - use_bias=False, - kernel_initializer='he_normal')( - h) - h = BatchNormalization( - axis=channel_dim, - momentum=0.9997, - epsilon=1e-3, - name='reduction_bn_1_%s' % block_id)( - h) - - with K.name_scope('block_1'): - x1_1 = _separable_conv_block( - h, - filters, (5, 5), - strides=(2, 2), - block_id='reduction_left1_%s' % block_id) - x1_2 = _separable_conv_block( - p, - filters, (7, 7), - strides=(2, 2), - block_id='reduction_1_%s' % block_id) - x1 = add([x1_1, x1_2], name='reduction_add_1_%s' % block_id) - - with K.name_scope('block_2'): - x2_1 = MaxPooling2D( - (3, 3), - strides=(2, 2), - padding='same', - name='reduction_left2_%s' % block_id)( - h) - x2_2 = _separable_conv_block( - p, - filters, (7, 7), - strides=(2, 2), - block_id='reduction_right2_%s' % block_id) - x2 = add([x2_1, x2_2], name='reduction_add_2_%s' % block_id) - - with K.name_scope('block_3'): - x3_1 = AveragePooling2D( - (3, 3), - strides=(2, 2), - padding='same', - name='reduction_left3_%s' % block_id)( - h) - x3_2 = _separable_conv_block( - p, - filters, (5, 5), - strides=(2, 2), - block_id='reduction_right3_%s' % block_id) - x3 = add([x3_1, x3_2], name='reduction_add3_%s' % block_id) - - with K.name_scope('block_4'): - x4 = AveragePooling2D( - (3, 3), - strides=(1, 1), - padding='same', - name='reduction_left4_%s' % block_id)( - x1) - x4 = add([x2, x4]) - - with K.name_scope('block_5'): - x5_1 = _separable_conv_block( - x1, filters, (3, 3), block_id='reduction_left4_%s' % block_id) - x5_2 = MaxPooling2D( - (3, 3), - strides=(2, 2), - padding='same', - name='reduction_right5_%s' % block_id)( - h) - x5 = add([x5_1, x5_2], name='reduction_add4_%s' % block_id) - - x = concatenate( - [x2, x3, x4, x5], - axis=channel_dim, - name='reduction_concat_%s' % block_id) - return x, ip +tf_export('keras.applications.nasnet.NASNetMobile', + 'keras.applications.NASNetMobile')(NASNetMobile) +tf_export('keras.applications.nasnet.NASNetLarge', + 'keras.applications.NASNetLarge')(NASNetLarge) +tf_export('keras.applications.nasnet.preprocess_input')(preprocess_input) diff --git a/tensorflow/python/keras/applications/resnet50.py b/tensorflow/python/keras/applications/resnet50.py index 6afc08681214c5dbb0577623d30e27e9988c6a57..4d804a3c440287f44ad50b34e575474cfbf051e1 100644 --- a/tensorflow/python/keras/applications/resnet50.py +++ b/tensorflow/python/keras/applications/resnet50.py @@ -13,291 +13,18 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """ResNet50 model for Keras. - -# Reference: - -- [Deep Residual Learning for Image -Recognition](https://arxiv.org/abs/1512.03385) - -Adapted from code contributed by BigMoyan. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras import layers -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras.layers import Activation -from tensorflow.python.keras.layers import AveragePooling2D -from tensorflow.python.keras.layers import BatchNormalization -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import Flatten -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.layers import ZeroPadding2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import resnet50 from tensorflow.python.util.tf_export import tf_export +ResNet50 = resnet50.ResNet50 +decode_predictions = resnet50.decode_predictions +preprocess_input = resnet50.preprocess_input -WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5' -WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5' - - -def identity_block(input_tensor, kernel_size, filters, stage, block): - """The identity block is the block that has no conv layer at shortcut. - - Arguments: - input_tensor: input tensor - kernel_size: default 3, the kernel size of middle conv layer at main path - filters: list of integers, the filters of 3 conv layer at main path - stage: integer, current stage label, used for generating layer names - block: 'a','b'..., current block label, used for generating layer names - - Returns: - Output tensor for the block. - """ - filters1, filters2, filters3 = filters - if K.image_data_format() == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - conv_name_base = 'res' + str(stage) + block + '_branch' - bn_name_base = 'bn' + str(stage) + block + '_branch' - - x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) - x = Activation('relu')(x) - - x = Conv2D( - filters2, kernel_size, padding='same', name=conv_name_base + '2b')( - x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) - x = Activation('relu')(x) - - x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) - - x = layers.add([x, input_tensor]) - x = Activation('relu')(x) - return x - - -def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, - 2)): - """A block that has a conv layer at shortcut. - - Arguments: - input_tensor: input tensor - kernel_size: default 3, the kernel size of middle conv layer at main path - filters: list of integers, the filters of 3 conv layer at main path - stage: integer, current stage label, used for generating layer names - block: 'a','b'..., current block label, used for generating layer names - strides: Strides for the first conv layer in the block. - - Returns: - Output tensor for the block. - - Note that from stage 3, - the first conv layer at main path is with strides=(2, 2) - And the shortcut should have strides=(2, 2) as well - """ - filters1, filters2, filters3 = filters - if K.image_data_format() == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - conv_name_base = 'res' + str(stage) + block + '_branch' - bn_name_base = 'bn' + str(stage) + block + '_branch' - - x = Conv2D( - filters1, (1, 1), strides=strides, name=conv_name_base + '2a')( - input_tensor) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) - x = Activation('relu')(x) - - x = Conv2D( - filters2, kernel_size, padding='same', name=conv_name_base + '2b')( - x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) - x = Activation('relu')(x) - - x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) - x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) - - shortcut = Conv2D( - filters3, (1, 1), strides=strides, name=conv_name_base + '1')( - input_tensor) - shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) - - x = layers.add([x, shortcut]) - x = Activation('relu')(x) - return x - - -@tf_export('keras.applications.ResNet50', - 'keras.applications.resnet50.ResNet50') -def ResNet50(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - """Instantiates the ResNet50 architecture. - - Optionally loads weights pre-trained - on ImageNet. Note that when using TensorFlow, - for best performance you should set - `image_data_format='channels_last'` in your Keras config - at ~/.keras/keras.json. - - The model and the weights are compatible with both - TensorFlow and Theano. The data format - convention used by the model is the one - specified in your Keras config file. - - Arguments: - include_top: whether to include the fully-connected - layer at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) - to use as image input for the model. - input_shape: optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(224, 224, 3)` (with `channels_last` data format) - or `(3, 224, 224)` (with `channels_first` data format). - It should have exactly 3 inputs channels, - and width and height should be no smaller than 197. - E.g. `(200, 200, 3)` would be one valid value. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model will be - the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a 2D tensor. - - `max` means that global max pooling will - be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - """ - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as imagenet with `include_top`' - ' as true, `classes` should be 1000') - - # Determine proper input shape - input_shape = _obtain_input_shape( - input_shape, - default_size=224, - min_size=197, - data_format=K.image_data_format(), - require_flatten=include_top, - weights=weights) - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - if K.image_data_format() == 'channels_last': - bn_axis = 3 - else: - bn_axis = 1 - - x = Conv2D( - 64, (7, 7), strides=(2, 2), padding='same', name='conv1')(img_input) - x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) - x = Activation('relu')(x) - x = MaxPooling2D((3, 3), strides=(2, 2))(x) - - x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) - x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') - x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') - - x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') - x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') - - x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e') - x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') - - x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b') - x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') - - x = AveragePooling2D((7, 7), name='avg_pool')(x) - - if include_top: - x = Flatten()(x) - x = Dense(classes, activation='softmax', name='fc1000')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - # Create model. - model = Model(inputs, x, name='resnet50') - - # load weights - if weights == 'imagenet': - if include_top: - weights_path = get_file( - 'resnet50_weights_tf_dim_ordering_tf_kernels.h5', - WEIGHTS_PATH, - cache_subdir='models', - md5_hash='a7b3fe01876f51b976af0dea6bc144eb') - else: - weights_path = get_file( - 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', - WEIGHTS_PATH_NO_TOP, - cache_subdir='models', - md5_hash='a268eb855778b3df3c7506639542a6af') - model.load_weights(weights_path) - elif weights is not None: - model.load_weights(weights) - - return model +tf_export('keras.applications.resnet50.ResNet50', + 'keras.applications.ResNet50')(ResNet50) diff --git a/tensorflow/python/keras/applications/vgg16.py b/tensorflow/python/keras/applications/vgg16.py index cef0230da96ed4b9c992e57839ebb2071383e3b1..c420d9b81e7d6e11d9237949dabae56cd848e0d0 100644 --- a/tensorflow/python/keras/applications/vgg16.py +++ b/tensorflow/python/keras/applications/vgg16.py @@ -13,217 +13,18 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """VGG16 model for Keras. - -# Reference - -- [Very Deep Convolutional Networks for Large-Scale Image -Recognition](https://arxiv.org/abs/1409.1556) - """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import Flatten -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import vgg16 from tensorflow.python.util.tf_export import tf_export +VGG16 = vgg16.VGG16 +decode_predictions = vgg16.decode_predictions +preprocess_input = vgg16.preprocess_input -WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5' -WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5' - - -@tf_export('keras.applications.VGG16', 'keras.applications.vgg16.VGG16') -def VGG16(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - """Instantiates the VGG16 architecture. - - Optionally loads weights pre-trained - on ImageNet. Note that when using TensorFlow, - for best performance you should set - `image_data_format='channels_last'` in your Keras config - at ~/.keras/keras.json. - - The model and the weights are compatible with both - TensorFlow and Theano. The data format - convention used by the model is the one - specified in your Keras config file. - - Arguments: - include_top: whether to include the 3 fully-connected - layers at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) - to use as image input for the model. - input_shape: optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(224, 224, 3)` (with `channels_last` data format) - or `(3, 224, 224)` (with `channels_first` data format). - It should have exactly 3 input channels, - and width and height should be no smaller than 48. - E.g. `(200, 200, 3)` would be one valid value. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model will be - the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a 2D tensor. - - `max` means that global max pooling will - be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - """ - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as imagenet with `include_top`' - ' as true, `classes` should be 1000') - # Determine proper input shape - input_shape = _obtain_input_shape( - input_shape, - default_size=224, - min_size=48, - data_format=K.image_data_format(), - require_flatten=include_top, - weights=weights) - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - # Block 1 - x = Conv2D( - 64, (3, 3), activation='relu', padding='same', name='block1_conv1')( - img_input) - x = Conv2D( - 64, (3, 3), activation='relu', padding='same', name='block1_conv2')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) - - # Block 2 - x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv1')( - x) - x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv2')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) - - # Block 3 - x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv1')( - x) - x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv2')( - x) - x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv3')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) - - # Block 4 - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv1')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv2')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv3')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) - - # Block 5 - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv1')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv2')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv3')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) - - if include_top: - # Classification block - x = Flatten(name='flatten')(x) - x = Dense(4096, activation='relu', name='fc1')(x) - x = Dense(4096, activation='relu', name='fc2')(x) - x = Dense(classes, activation='softmax', name='predictions')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - # Create model. - model = Model(inputs, x, name='vgg16') - - # load weights - if weights == 'imagenet': - if include_top: - weights_path = get_file( - 'vgg16_weights_tf_dim_ordering_tf_kernels.h5', - WEIGHTS_PATH, - cache_subdir='models', - file_hash='64373286793e3c8b2b4e3219cbf3544b') - else: - weights_path = get_file( - 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', - WEIGHTS_PATH_NO_TOP, - cache_subdir='models', - file_hash='6d6bbae143d832006294945121d1f1fc') - model.load_weights(weights_path) - - elif weights is not None: - model.load_weights(weights) - - return model +tf_export('keras.applications.vgg16.VGG16', + 'keras.applications.VGG16')(VGG16) diff --git a/tensorflow/python/keras/applications/vgg19.py b/tensorflow/python/keras/applications/vgg19.py index c4031f551003eda076380d1ae5208ee0876f5750..73d3d1d1c369b2b6665477eb51f37b967b303ac5 100644 --- a/tensorflow/python/keras/applications/vgg19.py +++ b/tensorflow/python/keras/applications/vgg19.py @@ -13,226 +13,18 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """VGG19 model for Keras. - -# Reference - -- [Very Deep Convolutional Networks for Large-Scale Image -Recognition](https://arxiv.org/abs/1409.1556) - """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.applications.imagenet_utils import preprocess_input -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import Flatten -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import vgg19 from tensorflow.python.util.tf_export import tf_export +VGG19 = vgg19.VGG19 +decode_predictions = vgg19.decode_predictions +preprocess_input = vgg19.preprocess_input -WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5' -WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5' - - -@tf_export('keras.applications.VGG19', 'keras.applications.vgg19.VGG19') -def VGG19(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - """Instantiates the VGG19 architecture. - - Optionally loads weights pre-trained - on ImageNet. Note that when using TensorFlow, - for best performance you should set - `image_data_format='channels_last'` in your Keras config - at ~/.keras/keras.json. - - The model and the weights are compatible with both - TensorFlow and Theano. The data format - convention used by the model is the one - specified in your Keras config file. - - Arguments: - include_top: whether to include the 3 fully-connected - layers at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) - to use as image input for the model. - input_shape: optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(224, 224, 3)` (with `channels_last` data format) - or `(3, 224, 224)` (with `channels_first` data format). - It should have exactly 3 inputs channels, - and width and height should be no smaller than 48. - E.g. `(200, 200, 3)` would be one valid value. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model will be - the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a 2D tensor. - - `max` means that global max pooling will - be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - """ - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as imagenet with `include_top`' - ' as true, `classes` should be 1000') - # Determine proper input shape - input_shape = _obtain_input_shape( - input_shape, - default_size=224, - min_size=48, - data_format=K.image_data_format(), - require_flatten=include_top, - weights=weights) - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - # Block 1 - x = Conv2D( - 64, (3, 3), activation='relu', padding='same', name='block1_conv1')( - img_input) - x = Conv2D( - 64, (3, 3), activation='relu', padding='same', name='block1_conv2')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) - - # Block 2 - x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv1')( - x) - x = Conv2D( - 128, (3, 3), activation='relu', padding='same', name='block2_conv2')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) - - # Block 3 - x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv1')( - x) - x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv2')( - x) - x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv3')( - x) - x = Conv2D( - 256, (3, 3), activation='relu', padding='same', name='block3_conv4')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) - - # Block 4 - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv1')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv2')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv3')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block4_conv4')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) - - # Block 5 - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv1')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv2')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv3')( - x) - x = Conv2D( - 512, (3, 3), activation='relu', padding='same', name='block5_conv4')( - x) - x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) - - if include_top: - # Classification block - x = Flatten(name='flatten')(x) - x = Dense(4096, activation='relu', name='fc1')(x) - x = Dense(4096, activation='relu', name='fc2')(x) - x = Dense(classes, activation='softmax', name='predictions')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - # Create model. - model = Model(inputs, x, name='vgg19') - - # load weights - if weights == 'imagenet': - if include_top: - weights_path = get_file( - 'vgg19_weights_tf_dim_ordering_tf_kernels.h5', - WEIGHTS_PATH, - cache_subdir='models', - file_hash='cbe5617147190e668d6c5d5026f83318') - else: - weights_path = get_file( - 'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5', - WEIGHTS_PATH_NO_TOP, - cache_subdir='models', - file_hash='253f8cb515780f3b799900260a226db6') - model.load_weights(weights_path) - - elif weights is not None: - model.load_weights(weights) - - return model +tf_export('keras.applications.vgg19.VGG19', + 'keras.applications.VGG19')(VGG19) diff --git a/tensorflow/python/keras/applications/xception.py b/tensorflow/python/keras/applications/xception.py index 01397cfac2563273ba1215003df1afab293b6b20..5b221ac8e05bb7317d06f4a2ce0569d8219f99a4 100644 --- a/tensorflow/python/keras/applications/xception.py +++ b/tensorflow/python/keras/applications/xception.py @@ -13,332 +13,19 @@ # limitations under the License. # ============================================================================== # pylint: disable=invalid-name -# pylint: disable=unused-import """Xception V1 model for Keras. - -On ImageNet, this model gets to a top-1 validation accuracy of 0.790 -and a top-5 validation accuracy of 0.945. - -Do note that the input image format for this model is different than for -the VGG16 and ResNet models (299x299 instead of 224x224), -and that the input preprocessing function -is also different (same as Inception V3). - -Also do note that this model is only available for the TensorFlow backend, -due to its reliance on `SeparableConvolution` layers. - -# Reference - -- [Xception: Deep Learning with Depthwise Separable -Convolutions](https://arxiv.org/abs/1610.02357) - """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import os - -from tensorflow.python.keras import backend as K -from tensorflow.python.keras import layers -from tensorflow.python.keras.applications import imagenet_utils -from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape -from tensorflow.python.keras.applications.imagenet_utils import decode_predictions -from tensorflow.python.keras.layers import Activation -from tensorflow.python.keras.layers import BatchNormalization -from tensorflow.python.keras.layers import Conv2D -from tensorflow.python.keras.layers import Dense -from tensorflow.python.keras.layers import GlobalAveragePooling2D -from tensorflow.python.keras.layers import GlobalMaxPooling2D -from tensorflow.python.keras.layers import Input -from tensorflow.python.keras.layers import MaxPooling2D -from tensorflow.python.keras.layers import SeparableConv2D -from tensorflow.python.keras.models import Model -from tensorflow.python.keras.utils import layer_utils -from tensorflow.python.keras.utils.data_utils import get_file -from tensorflow.python.platform import tf_logging as logging +from keras_applications import xception from tensorflow.python.util.tf_export import tf_export +Xception = xception.Xception +decode_predictions = xception.decode_predictions +preprocess_input = xception.preprocess_input -TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels.h5' -TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5' - - -@tf_export('keras.applications.Xception', - 'keras.applications.xception.Xception') -def Xception(include_top=True, - weights='imagenet', - input_tensor=None, - input_shape=None, - pooling=None, - classes=1000): - """Instantiates the Xception architecture. - - Optionally loads weights pre-trained - on ImageNet. This model is available for TensorFlow only, - and can only be used with inputs following the TensorFlow - data format `(width, height, channels)`. - You should set `image_data_format='channels_last'` in your Keras config - located at ~/.keras/keras.json. - - Note that the default input image size for this model is 299x299. - - Arguments: - include_top: whether to include the fully-connected - layer at the top of the network. - weights: one of `None` (random initialization), - 'imagenet' (pre-training on ImageNet), - or the path to the weights file to be loaded. - input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) - to use as image input for the model. - input_shape: optional shape tuple, only to be specified - if `include_top` is False (otherwise the input shape - has to be `(299, 299, 3)`. - It should have exactly 3 inputs channels, - and width and height should be no smaller than 71. - E.g. `(150, 150, 3)` would be one valid value. - pooling: Optional pooling mode for feature extraction - when `include_top` is `False`. - - `None` means that the output of the model will be - the 4D tensor output of the - last convolutional layer. - - `avg` means that global average pooling - will be applied to the output of the - last convolutional layer, and thus - the output of the model will be a 2D tensor. - - `max` means that global max pooling will - be applied. - classes: optional number of classes to classify images - into, only to be specified if `include_top` is True, and - if no `weights` argument is specified. - - Returns: - A Keras model instance. - - Raises: - ValueError: in case of invalid argument for `weights`, - or invalid input shape. - RuntimeError: If attempting to run this model with a - backend that does not support separable convolutions. - """ - if not (weights in {'imagenet', None} or os.path.exists(weights)): - raise ValueError('The `weights` argument should be either ' - '`None` (random initialization), `imagenet` ' - '(pre-training on ImageNet), ' - 'or the path to the weights file to be loaded.') - - if weights == 'imagenet' and include_top and classes != 1000: - raise ValueError('If using `weights` as imagenet with `include_top`' - ' as true, `classes` should be 1000') - - if K.image_data_format() != 'channels_last': - logging.warning( - 'The Xception model is only available for the ' - 'input data format "channels_last" ' - '(width, height, channels). ' - 'However your settings specify the default ' - 'data format "channels_first" (channels, width, height). ' - 'You should set `image_data_format="channels_last"` in your Keras ' - 'config located at ~/.keras/keras.json. ' - 'The model being returned right now will expect inputs ' - 'to follow the "channels_last" data format.') - K.set_image_data_format('channels_last') - old_data_format = 'channels_first' - else: - old_data_format = None - - # Determine proper input shape - input_shape = _obtain_input_shape( - input_shape, - default_size=299, - min_size=71, - data_format=K.image_data_format(), - require_flatten=False, - weights=weights) - - if input_tensor is None: - img_input = Input(shape=input_shape) - else: - if not K.is_keras_tensor(input_tensor): - img_input = Input(tensor=input_tensor, shape=input_shape) - else: - img_input = input_tensor - - x = Conv2D( - 32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')( - img_input) - x = BatchNormalization(name='block1_conv1_bn')(x) - x = Activation('relu', name='block1_conv1_act')(x) - x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x) - x = BatchNormalization(name='block1_conv2_bn')(x) - x = Activation('relu', name='block1_conv2_act')(x) - - residual = Conv2D( - 128, (1, 1), strides=(2, 2), padding='same', use_bias=False)( - x) - residual = BatchNormalization()(residual) - - x = SeparableConv2D( - 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')( - x) - x = BatchNormalization(name='block2_sepconv1_bn')(x) - x = Activation('relu', name='block2_sepconv2_act')(x) - x = SeparableConv2D( - 128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')( - x) - x = BatchNormalization(name='block2_sepconv2_bn')(x) - - x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block2_pool')( - x) - x = layers.add([x, residual]) - - residual = Conv2D( - 256, (1, 1), strides=(2, 2), padding='same', use_bias=False)( - x) - residual = BatchNormalization()(residual) - - x = Activation('relu', name='block3_sepconv1_act')(x) - x = SeparableConv2D( - 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')( - x) - x = BatchNormalization(name='block3_sepconv1_bn')(x) - x = Activation('relu', name='block3_sepconv2_act')(x) - x = SeparableConv2D( - 256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')( - x) - x = BatchNormalization(name='block3_sepconv2_bn')(x) - - x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block3_pool')( - x) - x = layers.add([x, residual]) - - residual = Conv2D( - 728, (1, 1), strides=(2, 2), padding='same', use_bias=False)( - x) - residual = BatchNormalization()(residual) - - x = Activation('relu', name='block4_sepconv1_act')(x) - x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')( - x) - x = BatchNormalization(name='block4_sepconv1_bn')(x) - x = Activation('relu', name='block4_sepconv2_act')(x) - x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')( - x) - x = BatchNormalization(name='block4_sepconv2_bn')(x) - - x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block4_pool')( - x) - x = layers.add([x, residual]) - - for i in range(8): - residual = x - prefix = 'block' + str(i + 5) - - x = Activation('relu', name=prefix + '_sepconv1_act')(x) - x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')( - x) - x = BatchNormalization(name=prefix + '_sepconv1_bn')(x) - x = Activation('relu', name=prefix + '_sepconv2_act')(x) - x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')( - x) - x = BatchNormalization(name=prefix + '_sepconv2_bn')(x) - x = Activation('relu', name=prefix + '_sepconv3_act')(x) - x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')( - x) - x = BatchNormalization(name=prefix + '_sepconv3_bn')(x) - - x = layers.add([x, residual]) - - residual = Conv2D( - 1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)( - x) - residual = BatchNormalization()(residual) - - x = Activation('relu', name='block13_sepconv1_act')(x) - x = SeparableConv2D( - 728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')( - x) - x = BatchNormalization(name='block13_sepconv1_bn')(x) - x = Activation('relu', name='block13_sepconv2_act')(x) - x = SeparableConv2D( - 1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')( - x) - x = BatchNormalization(name='block13_sepconv2_bn')(x) - - x = MaxPooling2D( - (3, 3), strides=(2, 2), padding='same', name='block13_pool')( - x) - x = layers.add([x, residual]) - - x = SeparableConv2D( - 1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')( - x) - x = BatchNormalization(name='block14_sepconv1_bn')(x) - x = Activation('relu', name='block14_sepconv1_act')(x) - - x = SeparableConv2D( - 2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')( - x) - x = BatchNormalization(name='block14_sepconv2_bn')(x) - x = Activation('relu', name='block14_sepconv2_act')(x) - - if include_top: - x = GlobalAveragePooling2D(name='avg_pool')(x) - x = Dense(classes, activation='softmax', name='predictions')(x) - else: - if pooling == 'avg': - x = GlobalAveragePooling2D()(x) - elif pooling == 'max': - x = GlobalMaxPooling2D()(x) - - # Ensure that the model takes into account - # any potential predecessors of `input_tensor`. - if input_tensor is not None: - inputs = layer_utils.get_source_inputs(input_tensor) - else: - inputs = img_input - # Create model. - model = Model(inputs, x, name='xception') - - # load weights - if weights == 'imagenet': - if include_top: - weights_path = get_file( - 'xception_weights_tf_dim_ordering_tf_kernels.h5', - TF_WEIGHTS_PATH, - cache_subdir='models', - file_hash='0a58e3b7378bc2990ea3b43d5981f1f6') - else: - weights_path = get_file( - 'xception_weights_tf_dim_ordering_tf_kernels_notop.h5', - TF_WEIGHTS_PATH_NO_TOP, - cache_subdir='models', - file_hash='b0042744bf5b25fce3cb969f33bebb97') - model.load_weights(weights_path) - elif weights is not None: - model.load_weights(weights) - - if old_data_format: - K.set_image_data_format(old_data_format) - return model - - -@tf_export('keras.applications.xception.preprocess_input') -def preprocess_input(x): - """Preprocesses a numpy array encoding a batch of images. - - Arguments: - x: a 4D numpy array consists of RGB values within [0, 255]. - - Returns: - Preprocessed array. - """ - return imagenet_utils.preprocess_input(x, mode='tf') +tf_export('keras.applications.xception.Xception', + 'keras.applications.Xception')(Xception) +tf_export('keras.applications.xception.preprocess_input')(preprocess_input) diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 070d41147d4e8ab8ca6d2620431321cf77a6aaea..f2feeb85a1e0db976a1c29e47257558ba40cc856 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -22,6 +22,7 @@ from __future__ import print_function from collections import deque from collections import Iterable from collections import OrderedDict +import copy import csv import json import math @@ -31,10 +32,12 @@ import time import numpy as np import six +from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.keras import backend as K from tensorflow.python.keras.engine.training_utils import standardize_input_data +from tensorflow.python.keras.utils.data_utils import Sequence from tensorflow.python.keras.utils.generic_utils import Progbar from tensorflow.python.ops import array_ops from tensorflow.python.ops import state_ops @@ -52,6 +55,110 @@ except ImportError: requests = None +def configure_callbacks(callbacks, + model, + do_validation=False, + val_inputs=None, + val_targets=None, + val_sample_weights=None, + batch_size=None, + epochs=None, + steps_per_epoch=None, + samples=None, + validation_steps=None, + verbose=1, + count_mode='steps'): + """Configures callbacks for use in various training loops. + + Arguments: + callbacks: List of Callbacks. + model: Model being trained. + do_validation: Whether or not validation loop will be run. + val_inputs: Inputs to Model for validation loop. Can be any + data format Keras accepts. + val_targets: Targets for Model for validation loop. Can be any + data format Keras accepts. + val_sample_weights: Sample weights for Model for validation loop. + Can be any data format Keras accepts. + batch_size: Number of samples per batch. + epochs: Number of epoch to train. + steps_per_epoch: Number of batches to run per training epoch. + samples: Number of training samples. + validation_steps: Number of batches to run per validation epoch. + verbose: int, 0 or 1. Keras logging verbosity to pass to ProgbarLogger. + count_mode: One of 'steps' or 'samples'. Per-batch or per-sample count. + + Returns: + Instance of CallbackList used to control all Callbacks. + """ + + # Add additional callbacks + model.history = History() + stateful_metric_names = None + if hasattr(model, 'stateful_metric_names'): + stateful_metric_names = model.stateful_metric_names + callbacks = [BaseLogger(stateful_metrics=stateful_metric_names) + ] + (callbacks or []) + [model.history] + if verbose: + callbacks.append( + ProgbarLogger(count_mode, stateful_metrics=stateful_metric_names)) + callback_list = CallbackList(callbacks) + + # Set callback model + callback_model = model._get_callback_model() # pylint: disable=protected-access + if do_validation and val_inputs and not context.executing_eagerly(): + # Need to create the test_function before start of the first epoch + # because TensorBoard callback on_epoch_begin adds summary to the + # list of fetches of the test_function + callback_model._make_test_function() # pylint: disable=protected-access + callback_list.set_model(callback_model) + + # Set callback parameters + callback_metrics = [] + # When we have deferred build scenario with iterator input, we will compile + # when we standardize first batch of data. + if model._is_compiled: # pylint: disable=protected-access + callback_metrics = copy.copy(model.metrics_names) + if do_validation: + callback_metrics += ['val_' + n for n in model.metrics_names] + if validation_steps is None and isinstance(val_inputs, Sequence): + validation_steps = len(val_inputs) + callback_params = { + 'batch_size': batch_size, + 'epochs': epochs, + 'steps': steps_per_epoch, + 'samples': samples, + 'verbose': verbose, + 'do_validation': do_validation, + 'metrics': callback_metrics, + 'validation_steps': validation_steps + } + callback_list.set_params(callback_params) + + # Pass validation data to callbacks + if not val_inputs: + val_data = [] + elif _is_generator_like(val_inputs): + val_data = val_inputs + else: + val_data = val_inputs + val_targets + if val_sample_weights: + val_data += val_sample_weights + if model.uses_learning_phase and not isinstance(K.learning_phase(), int): + val_data += [0.] + for cbk in callbacks: + cbk.validation_data = val_data + + callback_list.model.stop_training = False + return callback_list + + +def _is_generator_like(data): + """Checks if data is a generator, Sequence, or Iterator.""" + return (hasattr(data, 'next') or hasattr(data, '__next__') or isinstance( + data, (Sequence, iterator_ops.Iterator, iterator_ops.EagerIterator))) + + class CallbackList(object): """Container abstracting a list of callbacks. @@ -65,15 +172,19 @@ class CallbackList(object): callbacks = callbacks or [] self.callbacks = [c for c in callbacks] self.queue_length = queue_length + self.params = {} + self.model = None def append(self, callback): self.callbacks.append(callback) def set_params(self, params): + self.params = params for callback in self.callbacks: callback.set_params(params) def set_model(self, model): + self.model = model for callback in self.callbacks: callback.set_model(model) @@ -939,7 +1050,7 @@ class TensorBoard(Callback): """Checks if histogram summaries can be run.""" # will never be set when in eager if self.histogram_freq: - if 'validation_steps' in self.params: + if self.params.get('validation_steps', None) is not None: self._validation_batches = self.params['validation_steps'] elif self.validation_data: self._validation_batches = math.ceil( diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index bd088a559c25042368467a0080a42f83d47cab14..e84e023384d22bcf93d5da7d0c4037cf685f6bf3 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -728,6 +728,8 @@ class KerasCallbacksTest(test.TestCase): verbose=0) # fit generator without validation data + # histogram_freq must be zero + tsb.histogram_freq = 0 model.fit_generator( data_generator(True), len(x_train), @@ -736,6 +738,7 @@ class KerasCallbacksTest(test.TestCase): verbose=0) # fit generator with validation data and accuracy + tsb.histogram_freq = 1 model.fit_generator( data_generator(True), len(x_train), @@ -745,6 +748,7 @@ class KerasCallbacksTest(test.TestCase): verbose=0) # fit generator without validation data and accuracy + tsb.histogram_freq = 0 model.fit_generator( data_generator(True), len(x_train), epochs=2, callbacks=cbks) assert os.path.exists(temp_dir) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index 33ad1550727edfeed4c08203f522ac4c5a9706cd..d6d3db21fbbd2e498fbedca514d119c7110b46f8 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -500,13 +500,13 @@ class Layer(checkpointable.CheckpointableBase): use_resource: Whether to use `ResourceVariable`. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. getter: Variable getter argument to be passed to the `Checkpointable` API. Returns: @@ -1921,13 +1921,13 @@ def make_variable(name, use_resource: Whether to use a `ResourceVariable`. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. partitioner: Not handled at this time. Returns: diff --git a/tensorflow/python/keras/engine/distributed_training_utils.py b/tensorflow/python/keras/engine/distributed_training_utils.py index c78e6fe9ecaf61d600b914b507c1836c88c22645..fcb073322c76c0494b01a50486a69a2125b61d2c 100644 --- a/tensorflow/python/keras/engine/distributed_training_utils.py +++ b/tensorflow/python/keras/engine/distributed_training_utils.py @@ -184,14 +184,16 @@ def validate_distributed_dataset_inputs(distribution_strategy, x, y): """Validate all the components of a DistributedValue Dataset input. Args: - distribution_strategy: The current DistributionStrategy using to call + distribution_strategy: The current DistributionStrategy used to call `fit`/`evaluate`. x: Input Dataset DistributedValue object. For example, when we use `MirroredStrategy` this is a PerDevice object with a tensor for each - device set in the dict. + device set in the dict. x can also be a tuple or dict. The keys of the + dict should match the names of the input layers of the model. y: Target Dataset DistributedValue object. For example, when we use `MirroredStrategy` this is a PerDevice object with a tensor for each - device set in the dict. + device set in the dict. y can also be a tuple or dict. The keys of the + dict should match the names of the output layers of the model. Returns: The unwrapped values list of the x and y DistributedValues inputs. @@ -206,30 +208,50 @@ def validate_distributed_dataset_inputs(distribution_strategy, x, y): # and targets to a model should be from a `tf.data.Dataset`. # If each element of x and y are not tensors, we cannot standardize and - # validate the input and targets.` - if not tensor_util.is_tensor(x): - raise ValueError('Dataset input to the model should be tensors instead they' - ' are of type {}'.format(type(x))) + # validate the input and targets. + x_values_list = validate_per_device_inputs(distribution_strategy, x) - if not tensor_util.is_tensor(y): - raise ValueError('Dataset input to the model should be tensors instead they' - ' are of type {}'.format(type(y))) + y_values_list = validate_per_device_inputs(distribution_strategy, y) - # At this point both x and y contain tensors in the `DistributedValues` - # structure. - x_values = distribution_strategy.unwrap(x) - y_values = distribution_strategy.unwrap(y) + # Return the unwrapped values to avoid calling `unwrap` a second time. + return x_values_list, y_values_list - # Validate that the shape and dtype of all the elements in x are the same. - validate_all_tensor_shapes(x, x_values) - validate_all_tensor_types(x, x_values) - # Similarly for y, we perform the same validation - validate_all_tensor_shapes(y, y_values) - validate_all_tensor_types(y, y_values) +def validate_per_device_inputs(distribution_strategy, x): + """Validates PerDevice dataset input list. - # Return the unwrapped values to avoid calling `unwrap` a second time. - return x_values, y_values + Args: + distribution_strategy: The current DistributionStrategy used to call + `fit`, `evaluate` and `predict`. + x: A list of PerDevice objects that represent the input or + target values. + + Returns: + List containing the first element of each of the PerDevice objects in + the input list. + + Raises: + ValueError: If any of the objects in the `per_device_list` is not a tensor. + + """ + # Convert the inputs and targets into a list of PerDevice objects. + per_device_list = nest.flatten(x) + x_values_list = [] + for x in per_device_list: + if not tensor_util.is_tensor(x): + raise ValueError('Dataset input to the model should be tensors instead ' + 'they are of type {}'.format(type(x))) + + # At this point both x and y contain tensors in the `DistributedValues` + # structure. + x_values = distribution_strategy.unwrap(x) + + # Validate that the shape and dtype of all the elements in x are the same. + validate_all_tensor_shapes(x, x_values) + validate_all_tensor_types(x, x_values) + + x_values_list.append(x_values[0]) + return x_values_list def validate_all_tensor_types(x, x_values): diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 8f35794456e2e02dc060a3cfd9a414b9542a76ed..708fa1c807d40ac6d1315d8e14451e021d2517f7 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -43,6 +43,7 @@ from tensorflow.python.keras.utils import tf_utils from tensorflow.python.keras.utils.io_utils import ask_to_proceed_with_overwrite from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpoint_management from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.training.checkpointable import data_structures from tensorflow.python.training.checkpointable import layer_utils as checkpointable_layer_utils @@ -116,6 +117,16 @@ class Network(base_layer.Layer): # included in base_init to avoid excessive special casing when retrieving # the value). self._extra_variables = [] + # In many internal cases one needs to compute both the model's output + # and its output mask without relying on `__call__` (which would do both and + # set mask metadata), but for models, computing the mask requires to + # recompute the output. + # Hence the pattern `output = model.call(); mask = model.compute_mask()` + # would be redundant, and internal logic + # (susceptible to use `call` directly) should prefer using the + # internal method `output, mask = _call_and_compute_mask()`. + # This is True for Sequential networks and graph networks. + self._compute_output_and_mask_jointly = False self.supports_masking = False if not hasattr(self, 'optimizer'): @@ -219,6 +230,7 @@ class Network(base_layer.Layer): # A Network does not create weights of its own, thus it is already # built. self.built = True + self._compute_output_and_mask_jointly = True self._is_graph_network = True self._input_layers = [] @@ -819,6 +831,10 @@ class Network(base_layer.Layer): A tensor if there is a single output, or a list of tensors if there are more than one outputs. """ + if not self._is_graph_network: + raise NotImplementedError('When subclassing the `Model` class, you should' + ' implement a `call` method.') + inputs = generic_utils.to_list(inputs) if mask is None: masks = [None for _ in range(len(inputs))] @@ -1007,7 +1023,8 @@ class Network(base_layer.Layer): kwargs.setdefault('mask', computed_mask) # Compute outputs and masks. - if isinstance(layer, Network) and layer._is_graph_network: + if (isinstance(layer, Network) and + layer._compute_output_and_mask_jointly): output_tensors, output_masks = layer._call_and_compute_mask( computed_tensor, **kwargs) else: @@ -1027,7 +1044,8 @@ class Network(base_layer.Layer): kwargs.setdefault('mask', computed_masks) # Compute outputs and masks. - if isinstance(layer, Network) and layer._is_graph_network: + if (isinstance(layer, Network) and + layer._compute_output_and_mask_jointly): output_tensors, output_masks = layer._call_and_compute_mask( computed_tensors, **kwargs) else: @@ -1438,6 +1456,11 @@ class Network(base_layer.Layer): 'saved.\n\nConsider using a TensorFlow optimizer from `tf.train`.') % (optimizer,)) self._checkpointable_saver.save(filepath, session=session) + # Record this checkpoint so it's visible from tf.train.latest_checkpoint. + checkpoint_management.update_checkpoint_state( + save_dir=os.path.dirname(filepath), + model_checkpoint_path=filepath, + all_model_checkpoint_paths=[filepath]) def load_weights(self, filepath, by_name=False): """Loads all layer weights, either from a TensorFlow or an HDF5 weight file. diff --git a/tensorflow/python/keras/engine/saving.py b/tensorflow/python/keras/engine/saving.py index d5ccd44604b6b84ea0ceb4fa1c270b2c7dddc147..a2eed7cb462c57da2468c418d04108fb274b7fb6 100644 --- a/tensorflow/python/keras/engine/saving.py +++ b/tensorflow/python/keras/engine/saving.py @@ -127,6 +127,7 @@ def save_model(model, filepath, overwrite=True, include_optimizer=True): }, 'loss': model.loss, 'metrics': model.metrics, + 'weighted_metrics': model.weighted_metrics, 'sample_weight_mode': model.sample_weight_mode, 'loss_weights': model.loss_weights, }, @@ -246,6 +247,8 @@ def load_model(filepath, custom_objects=None, compile=True): # pylint: disable= # Recover loss functions and metrics. loss = convert_custom_objects(training_config['loss']) metrics = convert_custom_objects(training_config['metrics']) + weighted_metrics = convert_custom_objects( + training_config['weighted_metrics']) sample_weight_mode = training_config['sample_weight_mode'] loss_weights = training_config['loss_weights'] @@ -254,6 +257,7 @@ def load_model(filepath, custom_objects=None, compile=True): # pylint: disable= optimizer=optimizer, loss=loss, metrics=metrics, + weighted_metrics=weighted_metrics, loss_weights=loss_weights, sample_weight_mode=sample_weight_mode) diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index f2f8a27b761e6ac23380edfa808b023dfa2e9278..b7c2e9cb53c118072825f286ec2a05a6b0dcbd5c 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -36,6 +36,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.training import checkpoint_management from tensorflow.python.training import training as training_module try: @@ -337,10 +338,18 @@ class TestWholeModelSaving(test.TestCase): model.add(keras.layers.Dense(2, input_shape=(3,))) model.add(keras.layers.RepeatVector(3)) model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) - model.compile(loss=keras.losses.MSE, - optimizer=keras.optimizers.RMSprop(lr=0.0001), - metrics=[keras.metrics.categorical_accuracy], - sample_weight_mode='temporal') + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[ + keras.metrics.categorical_accuracy, + keras.metrics.CategoricalAccuracy() + ], + weighted_metrics=[ + keras.metrics.categorical_accuracy, + keras.metrics.CategoricalAccuracy() + ], + sample_weight_mode='temporal') x = np.random.random((1, 3)) y = np.random.random((1, 3, 3)) model.train_on_batch(x, y) @@ -435,9 +444,17 @@ class TestWholeModelSaving(test.TestCase): output = keras.layers.Dense(3)(x) model = keras.models.Model(inputs, output) - model.compile(loss=keras.losses.MSE, - optimizer=keras.optimizers.RMSprop(lr=0.0001), - metrics=[keras.metrics.categorical_accuracy]) + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.RMSprop(lr=0.0001), + metrics=[ + keras.metrics.categorical_accuracy, + keras.metrics.CategoricalAccuracy() + ], + weighted_metrics=[ + keras.metrics.categorical_accuracy, + keras.metrics.CategoricalAccuracy() + ]) x = np.random.random((1, 3)) y = np.random.random((1, 3)) model.train_on_batch(x, y) @@ -623,9 +640,13 @@ class TestWholeModelSaving(test.TestCase): outputs = keras.layers.Dense(3)(x) model = keras.Model(inputs, outputs) - model.compile(loss=keras.losses.MSE, - optimizer=keras.optimizers.Adam(), - metrics=[keras.metrics.categorical_accuracy]) + model.compile( + loss=keras.losses.MSE, + optimizer=keras.optimizers.Adam(), + metrics=[ + keras.metrics.categorical_accuracy, + keras.metrics.CategoricalAccuracy() + ]) x = np.random.random((1, 3)) y = np.random.random((1, 3)) model.train_on_batch(x, y) @@ -744,7 +765,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): model.compile( loss='mse', optimizer=training_module.RMSPropOptimizer(0.1), - metrics=['acc']) + metrics=['acc', keras.metrics.CategoricalAccuracy()]) temp_dir = self.get_temp_dir() prefix = os.path.join(temp_dir, 'ckpt') train_x = np.random.random((3, 2)) @@ -781,7 +802,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): load_model.compile( loss='mse', optimizer=training_module.RMSPropOptimizer(0.1), - metrics=['acc']) + metrics=['acc', keras.metrics.CategoricalAccuracy()]) load_model.train_on_batch(train_x, train_y) self.assertAllClose(ref_y_after_train, self.evaluate(load_model(x))) @@ -813,6 +834,9 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): session.run([v.initializer for v in model.variables]) ref_y = self.evaluate(ref_y_tensor) model.save_weights(prefix) + self.assertEqual( + prefix, + checkpoint_management.latest_checkpoint(temp_dir)) for v in model.variables: self.evaluate( v.assign(random_ops.random_normal(shape=array_ops.shape(v)))) diff --git a/tensorflow/python/keras/engine/sequential.py b/tensorflow/python/keras/engine/sequential.py index 41cdfda660e69f41e4f3d15e2e61ac8f45654436..415b15fde1655f43f3c317cfc6e7756859fc9da1 100644 --- a/tensorflow/python/keras/engine/sequential.py +++ b/tensorflow/python/keras/engine/sequential.py @@ -21,15 +21,18 @@ from __future__ import print_function import copy -from tensorflow.python.keras import backend as K +from tensorflow.python.eager import context +from tensorflow.python.framework import ops from tensorflow.python.keras import layers as layer_module from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine.input_layer import Input from tensorflow.python.keras.engine.input_layer import InputLayer +from tensorflow.python.keras.engine.network import Network from tensorflow.python.keras.engine.training import Model from tensorflow.python.keras.utils import layer_utils from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import tf_export @@ -92,8 +95,12 @@ class Sequential(Model): ``` """ + @checkpointable.no_automatic_dependency_tracking def __init__(self, layers=None, name=None): super(Sequential, self).__init__(name=name) + self.supports_masking = True + self._build_input_shape = None + self._compute_output_and_mask_jointly = True # Add to the model any layers passed to the constructor. if layers: @@ -105,9 +112,12 @@ class Sequential(Model): # Historically, `sequential.layers` only returns layers that were added # via `add`, and omits the auto-generated `InputLayer` that comes at the # bottom of the stack. - if self._layers and isinstance(self._layers[0], InputLayer): - return self._layers[1:] - return self._layers + # `CheckpointableBase` manages the `_layers` attributes and does filtering + # over it. + layers = super(Sequential, self).layers + if layers and isinstance(layers[0], InputLayer): + return layers[1:] + return layers[:] @checkpointable.no_automatic_dependency_tracking def add(self, layer): @@ -129,30 +139,16 @@ class Sequential(Model): 'an instance of class Layer. ' 'Found: ' + str(layer)) self.built = False + set_inputs = False if not self._layers: - set_inputs = False - # First layer in model: check that it is an input layer. - if not isinstance(layer, InputLayer): - # Create an input tensor and call `layer` on the input tensor. - # First, we need to infer the expected input shape and dtype. - first_layer = layer - if isinstance(layer, (Model, Sequential)): - # We were passed a model as first layer. - # This requires a specific way to figure out the - # input shape and dtype. - if not layer.layers: - raise ValueError('Cannot add an empty model ' - 'to a `Sequential` model.') - # In case of nested models: recover the first layer - # of the deepest model to infer input shape and dtype. - first_layer = layer.layers[0] - while isinstance(first_layer, (Model, Sequential)): - first_layer = first_layer.layers[0] - - if hasattr(first_layer, '_batch_input_shape'): - batch_shape = first_layer._batch_input_shape - dtype = first_layer.dtype - # Instantiate the input layer. + if isinstance(layer, InputLayer): + # Corner case where the user passes an InputLayer layer via `add`. + assert len(layer._inbound_nodes[-1].output_tensors) == 1 + set_inputs = True + else: + batch_shape, dtype = get_input_shape_and_dtype(layer) + if batch_shape: + # Instantiate an input layer. x = Input( batch_shape=batch_shape, dtype=dtype, @@ -162,25 +158,20 @@ class Sequential(Model): # to the input layer we just created. layer(x) set_inputs = True - else: - # The layer doesn't know about its expected shape. We will have to - # build the model lazily on `fit`/etc. - batch_shape = None - else: - # Corner case where the user passes an InputLayer layer via `add`. - assert len(layer._inbound_nodes[-1].output_tensors) == 1 - set_inputs = True if set_inputs: + # If an input layer (placeholder) is available. if len(layer._inbound_nodes[-1].output_tensors) != 1: raise ValueError('All layers in a Sequential model ' 'should have a single output tensor. ' 'For multi-output layers, ' 'use the functional API.') - self.outputs = [layer._inbound_nodes[-1].output_tensors[0]] self.inputs = layer_utils.get_source_inputs(self.outputs[0]) + elif self.outputs: + # If the model is being built continuously on top of an input layer: + # refresh its output. output_tensor = layer(self.outputs[0]) if isinstance(output_tensor, list): raise TypeError('All layers in a Sequential model ' @@ -188,10 +179,13 @@ class Sequential(Model): 'For multi-output layers, ' 'use the functional API.') self.outputs = [output_tensor] - if self.inputs: - self.build() + if set_inputs or self._is_graph_network: + self._init_graph_network(self.inputs, self.outputs, name=self.name) + self.built = True else: self._layers.append(layer) + if self._layers: + self._track_layers(self._layers) @checkpointable.no_automatic_dependency_tracking def pop(self): @@ -204,54 +198,73 @@ class Sequential(Model): raise TypeError('There are no layers in the model.') self._layers.pop() - self.built = False if not self.layers: self.outputs = None self.inputs = None - elif self.outputs: + self.built = False + elif self._is_graph_network: self.layers[-1]._outbound_nodes = [] self.outputs = [self.layers[-1].output] - self.build() + self._init_graph_network(self.inputs, self.outputs, name=self.name) + self.built = True def build(self, input_shape=None): - self._set_inputs_and_outputs(input_shape=input_shape) - - def symbolic_set_inputs(self, inputs): - self._set_inputs_and_outputs(tensor=inputs) - - @checkpointable.no_automatic_dependency_tracking - def _set_inputs_and_outputs(self, input_shape=None, tensor=None): - """Set model's input and output specs based on the input received. + if self._is_graph_network: + self._init_graph_network(self.inputs, self.outputs, name=self.name) + else: + if input_shape is None: + raise ValueError('You must provide an `input_shape` argument.') + self._build_input_shape = input_shape + shape = input_shape + for layer in self.layers: + if not layer.built: + with ops.name_scope(layer._name_scope()): + layer.build(shape) + layer.built = True + shape = layer.compute_output_shape(shape) + self.built = True + + def call(self, inputs, training=None, mask=None): + if self._is_graph_network: + return super(Sequential, self).call(inputs, training=training, mask=mask) + + outputs, _ = self._call_and_compute_mask( + inputs, training=training, mask=mask) + return outputs + + def _call_and_compute_mask(self, inputs, training=None, mask=None): + if not self.built: + self.build(inputs.shape) + + x = inputs + for layer in self.layers: + kwargs = {} + if 'mask' in tf_inspect.getargspec(layer.call).args: + kwargs['mask'] = mask + if 'training' in tf_inspect.getargspec(layer.call).args: + kwargs['training'] = training + + if isinstance(layer, Network) and layer._compute_output_and_mask_jointly: + x, mask = layer._call_and_compute_mask(x, **kwargs) + else: + x = layer.call(x, **kwargs) + if layer.supports_masking: + mask = layer.compute_mask(x, mask) + else: + mask = None + if not context.executing_eagerly(): + x._keras_mask = mask + return x, mask - If `tensor` is provided, `input_shape` is not required. + def compute_output_shape(self, input_shape): + shape = input_shape + for layer in self.layers: + shape = layer.compute_output_shape(shape) + return shape - Args: - input_shape: Optional shape of input. - tensor: Optional existing tensor to wrap into the `Input` layer. - """ - if not self.inputs: - dtype = K.floatx() - if tensor is not None: - batch_shape = (None,) + tuple(tensor.get_shape().as_list()[1:]) - x = Input(dtype=dtype, name=self.name + '_input', tensor=tensor) - elif input_shape is not None: - batch_shape = tuple(input_shape) - x = Input( - batch_shape=batch_shape, dtype=dtype, name=self.name + '_input') - self.inputs = [x] - for layer in self._layers: - x = layer(x) - self.outputs = [x] - # Make sure that the model's input shape will be preserved during - # serialization. - if self._layers: - self._layers[0]._batch_input_shape = batch_shape - - if self.inputs: - self._init_graph_network(self.inputs, self.outputs, name=self.name) - self.built = True - if self._layers: - self._track_layers(self._layers) + def compute_mask(self, inputs, mask): + _, mask = self._call_and_compute_mask(inputs, mask=mask) + return mask def predict_proba(self, x, batch_size=32, verbose=0): """Generates class probability predictions for the input samples. @@ -296,18 +309,69 @@ class Sequential(Model): return (proba > 0.5).astype('int32') def get_config(self): - config = [] + layer_configs = [] for layer in self.layers: - config.append({ + layer_configs.append({ 'class_name': layer.__class__.__name__, 'config': layer.get_config() }) - return copy.deepcopy(config) + config = { + 'name': self.name, + 'layers': copy.deepcopy(layer_configs) + } + if self._build_input_shape: + config['build_input_shape'] = self._build_input_shape + return config @classmethod def from_config(cls, config, custom_objects=None): - model = cls() - for conf in config: - layer = layer_module.deserialize(conf, custom_objects=custom_objects) + if 'name' in config: + name = config['name'] + build_input_shape = config.get('build_input_shape') + layer_configs = config['layers'] + else: + name = None + build_input_shape = None + model = cls(name=name) + for layer_config in layer_configs: + layer = layer_module.deserialize(layer_config, + custom_objects=custom_objects) model.add(layer) + if not model.inputs and build_input_shape: + model.build(build_input_shape) return model + + +def get_input_shape_and_dtype(layer): + """Retrieve input shape and input dtype of layer if applicable. + + Args: + layer: Layer (or model) instance. + + Returns: + Tuple (input_shape, input_dtype). Both could be None if the layer + does not have a defined input shape. + + Raises: + ValueError: in case an empty Sequential or Graph Network is passed. + """ + if ((isinstance(layer, Model) and layer._is_graph_network) + or isinstance(layer, Sequential)): + # We were passed a model as first layer. + # This requires a specific way to figure out the + # input shape and dtype. + if not layer.layers: + raise ValueError('Cannot add an empty model ' + 'to a `Sequential` model.') + # In case of nested models: recover the first layer + # of the deepest model to infer input shape and dtype. + layer = layer.layers[0] + while ((isinstance(layer, Model) and layer._is_graph_network) + or isinstance(layer, Sequential)): + layer = layer.layers[0] + + if hasattr(layer, '_batch_input_shape'): + batch_shape = layer._batch_input_shape + dtype = layer.dtype + return batch_shape, dtype + return None, None diff --git a/tensorflow/python/keras/engine/sequential_test.py b/tensorflow/python/keras/engine/sequential_test.py index 4f4adca33344dddc6e9c92cda94fff7289b35302..3f8e120df056bad06e432654e2743e32bb271661 100644 --- a/tensorflow/python/keras/engine/sequential_test.py +++ b/tensorflow/python/keras/engine/sequential_test.py @@ -18,17 +18,30 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np from tensorflow.python import keras from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.eager import function from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.ops import array_ops from tensorflow.python.platform import test from tensorflow.python.training import rmsprop -class TestSequential(test.TestCase): +def _get_small_mlp(num_hidden, num_classes, input_dim=None): + model = keras.models.Sequential() + if input_dim: + model.add(keras.layers.Dense(num_hidden, activation='relu', + input_dim=input_dim)) + else: + model.add(keras.layers.Dense(num_hidden, activation='relu')) + model.add(keras.layers.Dense(num_classes, activation='softmax')) + return model + + +class TestSequential(test.TestCase, parameterized.TestCase): """Most Sequential model API tests are covered in `training_test.py`. """ @@ -50,9 +63,7 @@ class TestSequential(test.TestCase): batch_size = 5 num_classes = 2 - model = keras.models.Sequential() - model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) - model.add(keras.layers.Dense(num_classes)) + model = _get_small_mlp(num_hidden, num_classes, input_dim) model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) x = np.random.random((batch_size, input_dim)) y = np.random.random((batch_size, num_classes)) @@ -83,11 +94,11 @@ class TestSequential(test.TestCase): batch_size = 5 num_classes = 2 - model = keras.models.Sequential() - # We don't specify the input shape. - model.add(keras.layers.Dense(num_hidden)) - model.add(keras.layers.Dense(num_classes)) - model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) + model = _get_small_mlp(num_hidden, num_classes) + model.compile( + loss='mse', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=[keras.metrics.CategoricalAccuracy()]) self.assertEqual(len(model.layers), 2) self.assertEqual(len(model.weights), 0) self.assertFalse(model.built) @@ -96,9 +107,7 @@ class TestSequential(test.TestCase): y = np.random.random((batch_size, num_classes)) model.fit(x, y, epochs=1) self.assertTrue(model.built) - self.assertEqual(model.inputs[0].get_shape().as_list(), [None, input_dim]) - self.assertEqual(model.outputs[0].get_shape().as_list(), - [None, num_classes]) + self.assertFalse(model._is_graph_network) self.assertEqual(len(model.weights), 2 * 2) @tf_test_util.run_in_graph_and_eager_modes @@ -109,11 +118,11 @@ class TestSequential(test.TestCase): num_samples = 50 steps_per_epoch = 10 - model = keras.models.Sequential() - # We don't specify the input shape. - model.add(keras.layers.Dense(num_hidden)) - model.add(keras.layers.Dense(num_classes)) - model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) + model = _get_small_mlp(num_hidden, num_classes) + model.compile( + loss='mse', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=[keras.metrics.CategoricalAccuracy()]) self.assertEqual(len(model.layers), 2) self.assertEqual(len(model.weights), 0) self.assertFalse(model.built) @@ -127,19 +136,18 @@ class TestSequential(test.TestCase): model.fit(iterator, epochs=1, steps_per_epoch=steps_per_epoch) self.assertTrue(model.built) - self.assertEqual(model.inputs[0].get_shape().as_list(), [None, input_dim]) - self.assertEqual(model.outputs[0].get_shape().as_list(), - [None, num_classes]) self.assertEqual(len(model.weights), 2 * 2) + self.assertFalse(model._is_graph_network) - def test_training_and_eval_methods_on_symbolic_tensors(self): + @parameterized.parameters((True,), (False,)) + def test_training_and_eval_methods_on_symbolic_tensors(self, deferred): with self.test_session(): - def create_model(): - model = keras.Sequential() - model.add(keras.layers.Dense(10, activation='relu')) - model.add(keras.layers.Dense(4, activation='softmax')) - + def get_model(): + if deferred: + model = _get_small_mlp(10, 4) + else: + model = _get_small_mlp(10, 4, input_dim=3) model.compile( optimizer=rmsprop.RMSPropOptimizer(1e-3), loss='categorical_crossentropy', @@ -149,22 +157,22 @@ class TestSequential(test.TestCase): inputs = keras.backend.zeros(shape=(10, 3)) targets = keras.backend.zeros(shape=(10, 4)) - model = create_model() + model = get_model() model.fit(inputs, targets, epochs=10, steps_per_epoch=30) - model = create_model() + model = get_model() model.evaluate(inputs, targets, steps=2, verbose=0) - model = create_model() + model = get_model() model.predict(inputs, steps=2) - model = create_model() + model = get_model() model.train_on_batch(inputs, targets) - model = create_model() + model = get_model() model.test_on_batch(inputs, targets) - model = create_model() + model = get_model() model.fit( inputs, targets, @@ -247,17 +255,18 @@ class TestSequential(test.TestCase): x2 = model.predict(val_a) assert np.abs(np.sum(x1 - x2)) > 1e-5 + @tf_test_util.run_in_graph_and_eager_modes def test_sequential_deferred_build_serialization(self): num_hidden = 5 input_dim = 3 batch_size = 5 num_classes = 2 - model = keras.models.Sequential() - # We don't specify the input shape. - model.add(keras.layers.Dense(num_hidden)) - model.add(keras.layers.Dense(num_classes)) - model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) + model = _get_small_mlp(num_hidden, num_classes) + model.compile( + loss='mse', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=[keras.metrics.CategoricalAccuracy()]) self.assertFalse(model.built) x = np.random.random((batch_size, input_dim)) @@ -266,11 +275,93 @@ class TestSequential(test.TestCase): self.assertTrue(model.built) config = model.get_config() + self.assertIn('build_input_shape', config) + new_model = keras.models.Sequential.from_config(config) self.assertTrue(new_model.built) self.assertEqual(len(model.layers), 2) self.assertEqual(len(model.weights), 4) + @tf_test_util.run_in_graph_and_eager_modes + def test_sequential_shape_inference_deferred(self): + model = _get_small_mlp(4, 5) + output_shape = model.compute_output_shape((None, 7)) + self.assertEqual(tuple(output_shape.as_list()), (None, 5)) + + @tf_test_util.run_in_graph_and_eager_modes + def test_sequential_build_deferred(self): + model = _get_small_mlp(4, 5) + + model.build((None, 10)) + self.assertTrue(model.built) + self.assertEqual(len(model.weights), 4) + + # Test with nested model + model = _get_small_mlp(4, 3) + inner_model = _get_small_mlp(4, 5) + model.add(inner_model) + + model.build((None, 10)) + self.assertTrue(model.built) + self.assertTrue(model.layers[-1].built) + self.assertEqual(len(model.weights), 8) + + @tf_test_util.run_in_graph_and_eager_modes + def test_sequential_nesting(self): + model = _get_small_mlp(4, 3) + inner_model = _get_small_mlp(4, 5) + model.add(inner_model) + + model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) + x = np.random.random((2, 6)) + y = np.random.random((2, 5)) + model.fit(x, y, epochs=1) + + @tf_test_util.run_in_graph_and_eager_modes + def test_variable_names(self): + model = keras.models.Sequential([keras.layers.Dense(3)]) + model.add(keras.layers.Dense(2)) + model(array_ops.ones([2, 4])) + self.assertEqual( + ['sequential/dense/kernel:0', 'sequential/dense/bias:0', + 'sequential/dense_1/kernel:0', 'sequential/dense_1/bias:0'], + [v.name for v in model.variables]) + + +class TestSequentialEagerIntegration(test.TestCase): + + @tf_test_util.run_in_graph_and_eager_modes + def test_defun_on_call(self): + # Check that one can subclass Sequential and place the `call` in a `defun`. + + class MySequential(keras.Sequential): + + def __init__(self, name=None): + super(MySequential, self).__init__(name=name) + self.call = function.defun(self.call) + + model = MySequential() + model.add(keras.layers.Dense(4, activation='relu')) + model.add(keras.layers.Dense(5, activation='softmax')) + + model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) + + x = np.random.random((2, 6)) + y = np.random.random((2, 5)) + model.fit(x, y, epochs=1) + + @tf_test_util.run_in_graph_and_eager_modes + def test_build_before_fit(self): + # Fix for b/112433577 + model = _get_small_mlp(4, 5) + model.compile(loss='mse', optimizer=rmsprop.RMSPropOptimizer(1e-3)) + + model.build((None, 6)) + + x = np.random.random((2, 6)) + y = np.random.random((2, 5)) + model.fit(x, y, epochs=1) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 2cdd00a48d8f50aaf5a8b7a0e39aac83e074d38b..f71388cadb94d8215dddb8bc4a7cb2d38d7823a4 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -29,6 +29,7 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses +from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras import optimizers from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine import distributed_training_utils @@ -39,6 +40,8 @@ from tensorflow.python.keras.engine import training_generator from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.engine.network import Network from tensorflow.python.keras.utils.generic_utils import slice_arrays +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import weights_broadcast_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import optimizer as tf_optimizer_module from tensorflow.python.training.checkpointable import base as checkpointable @@ -74,6 +77,7 @@ class Model(Network): class MyModel(tf.keras.Model): def __init__(self): + super(MyModel, self).__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) @@ -94,6 +98,7 @@ class Model(Network): class MyModel(tf.keras.Model): def __init__(self): + super(MyModel, self).__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) self.dropout = tf.keras.layers.Dropout(0.5) @@ -136,6 +141,167 @@ class Model(Network): if i not in skip_target_weighing_indices ] + def _get_metric_name(self, metric, output_index, weighted=False): + """Returns the metric name corresponding to the given metric input. + + Arguments: + metric: Metric function name or reference. + output_index: Index of the current output. + weighted: Boolean indicating if the given metric is weighted. + + Returns: + A metric name. + """ + metric_name_prefix = 'weighted_' if weighted else '' + if metric in ('accuracy', 'acc', 'crossentropy', 'ce'): + if metric in ('accuracy', 'acc'): + suffix = 'acc' + elif metric in ('crossentropy', 'ce'): + suffix = 'ce' + else: + metric_fn = metrics_module.get(metric) + # Get metric name as string + if hasattr(metric_fn, 'name'): + suffix = metric_fn.name + else: + suffix = metric_fn.__name__ + metric_name = metric_name_prefix + suffix + + if len(self.output_names) > 1: + metric_name = '%s_%s' % (self.output_names[output_index], metric_name) + j = 1 + base_metric_name = metric_name + while metric_name in self.metrics_names: + metric_name = '%s_%d' % (base_metric_name, j) + j += 1 + + return metric_name + + def _handle_per_output_metrics(self, + metrics, + y_true, + y_pred, + output_index, + output_shape, + loss_fn, + mask, + weights=None): + """Calls metric functions and sets metric attributes for a single output. + + Arguments: + metrics: List of metrics. + y_true: Target output. + y_pred: Predicted output. + output_index: Index of the current output. + output_shape: Shape of the current output. + loss_fn: Loss function corresponding to the current output. + mask: Computed mask value for the current output. + weights: Weights to be applied on the current output. + + Returns: + A list of metric result tensors. + """ + metric_results = [] + for metric in metrics: + metric_fn = training_utils.get_metric_function( + metric, output_shape=output_shape, loss_fn=loss_fn) + metric_name = self._get_metric_name( + metric, output_index, weighted=weights is not None) + + with K.name_scope(metric_name): + # If both outputs and targets are available, call the metric function. + if y_true is not None and y_pred is not None: + if isinstance(metric_fn, metrics_module.Metric): + # Call the stateful metric function. + if mask is not None: + mask = math_ops.cast(mask, y_pred.dtype) + # Update weights with mask. + if weights is None: + weights = mask + else: + # Update shape of weights if possible before adding mask. + # Update dimensions of weights to match with mask if possible. + mask, _, weights = metrics_module.squeeze_or_expand_dimensions( + mask, None, weights) + try: + # Broadcast weights if possible. + weights = weights_broadcast_ops.broadcast_weights( + weights, mask) + except ValueError: + pass + # TODO(psv): Handle case when mask and weight shapes are not + # compatible. + weights *= mask + + metric_result = metric_fn(y_true, y_pred, weights) + else: + # Call the stateless metric function. + weighted_metric_fn = training_utils.weighted_masked_objective( + metric_fn) + metric_result = weighted_metric_fn( + y_true, y_pred, weights=weights, mask=mask) + + if not context.executing_eagerly(): + # Keep track of metric result tensor. + self.metrics_tensors.append(metric_result) + metric_results.append(metric_result) + + # Keep track of metric name. + self.metrics_names.append(metric_name) + + # Keep track of stateful metric attributes (name and metric function). + if isinstance(metric_fn, base_layer.Layer) and metric_fn.stateful: + self.stateful_metric_names.append(metric_name) + self.stateful_metric_functions.append(metric_fn) + if not context.executing_eagerly(): + # Keep track of updates created by stateful metrics. + self.metrics_updates += metric_fn.updates + return metric_results + + def _handle_metrics(self, + outputs, + skip_target_indices=None, + targets=None, + sample_weights=None, + masks=None): + """Handles calling metric functions and setting model metric attributes. + + Arguments: + outputs: List of outputs (predictions). + skip_target_indices: Optional. List of target ids to skip. + targets: List of targets. + sample_weights: Optional list of sample weight arrays. + masks: List of computed output mask values. + + Returns: + A list of metric result tensors. + """ + skip_target_indices = skip_target_indices or [] + metric_results = [] + with K.name_scope('metrics'): + for i in range(len(outputs)): + if i in skip_target_indices: + continue + output = outputs[i] if outputs else None + target = targets[i] if targets else None + output_shape = None if output is None else output.get_shape().as_list() + output_mask = masks[i] if masks else None + metric_results.extend( + self._handle_per_output_metrics( + self.nested_metrics[i], target, output, i, output_shape, + self.loss_functions[i], output_mask)) + metric_results.extend( + self._handle_per_output_metrics( + self.nested_weighted_metrics[i], + target, + output, + i, + output_shape, + self.loss_functions[i], + output_mask, + weights=sample_weights[i])) + return metric_results + @checkpointable.no_automatic_dependency_tracking def compile(self, optimizer, @@ -151,9 +317,9 @@ class Model(Network): Arguments: optimizer: String (name of optimizer) or optimizer instance. - See [optimizers](/optimizers). + See [optimizers](/api_docs/python/tf/keras/optimizers). loss: String (name of objective function) or objective function. - See [losses](/losses). + See [losses](/api_docs/python/tf/losses). If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model @@ -231,8 +397,6 @@ class Model(Network): self.metrics = metrics or [] self.loss_weights = loss_weights self.sample_weight_mode = sample_weight_mode - if context.executing_eagerly() and weighted_metrics is not None: - raise ValueError('weighted_metrics is not supported in Eager mode.') self.weighted_metrics = weighted_metrics if context.executing_eagerly() and target_tensors is not None: raise ValueError('target_tensors is not supported in Eager mode.') @@ -335,6 +499,20 @@ class Model(Network): str(loss_weights) + ' - expected a list of dicts.') self.loss_weights_list = loss_weights_list + # Initialize model metric attributes. + self.metrics_names = ['loss'] + self.metrics_tensors = [] + self.metrics_updates = [] + self.stateful_metric_names = [] + self.stateful_metric_functions = [] + + # Nested metrics is a list of list of metrics. + # One list per output of the model. + self.nested_metrics = training_utils.collect_metrics( + metrics, self.output_names) + self.nested_weighted_metrics = training_utils.collect_metrics( + weighted_metrics, self.output_names) + # Initialization for Eager mode execution. if context.executing_eagerly(): # Prepare sample weights. @@ -345,19 +523,16 @@ class Model(Network): raise ValueError('target_tensors are not currently supported in Eager ' 'mode.') self.total_loss = None - self.metrics_tensors = [] - self.metrics_names = ['loss'] for i in range(len(self.outputs)): if len(self.outputs) > 1: self.metrics_names.append(self.output_names[i] + '_loss') - self.nested_metrics = training_utils.collect_metrics(metrics, - self.output_names) - # TODO(fchollet): support stateful metrics in eager execution. - self.stateful_metric_functions = [] - self.stateful_metric_names = [] - - with K.name_scope('metrics'): - training_utils.populate_metric_names(self) + + # Set metric attributes on model. + self._handle_metrics( + self.outputs, + skip_target_indices=skip_target_indices, + sample_weights=self.sample_weights) + self.targets = [] for i in range(len(self.outputs)): self._feed_output_names.append(self.output_names[i]) @@ -420,11 +595,6 @@ class Model(Network): self._set_sample_weight_attributes(sample_weight_mode, skip_target_weighing_indices) - # Prepare metrics. - self.weighted_metrics = weighted_metrics - self.metrics_names = ['loss'] - self.metrics_tensors = [] - # Compute total loss. total_loss = None with K.name_scope('loss'): @@ -458,55 +628,13 @@ class Model(Network): for loss_tensor in self.losses: total_loss += loss_tensor - # List of same size as output_names. - # contains tuples (metrics for output, names of metrics). - nested_metrics = training_utils.collect_metrics(metrics, self.output_names) - nested_weighted_metrics = training_utils.collect_metrics(weighted_metrics, - self.output_names) - self.metrics_updates = [] - self.stateful_metric_names = [] - self.stateful_metric_functions = [] - with K.name_scope('metrics'): - for i in range(len(self.outputs)): - if i in skip_target_indices: - continue - - y_true = self.targets[i] - y_pred = self.outputs[i] - weights = self.sample_weights[i] - output_metrics = nested_metrics[i] - output_weighted_metrics = nested_weighted_metrics[i] - output_shape = self.outputs[i].get_shape().as_list() - loss_fn = self.loss_functions[i] - - def handle_metrics(metrics, output_shape, loss_fn, weights=None): - """Invokes metric functions for the output.""" - - for metric in metrics: - metric_fn = training_utils.get_metric_function( - metric, output_shape=output_shape, loss_fn=loss_fn) - metric_name = training_utils.get_metric_name( - metric, weighted=weights is not None) - - with K.name_scope(metric_name): - weighted_metric_fn = training_utils.weighted_masked_objective( - metric_fn) - metric_result = weighted_metric_fn( - y_true, y_pred, weights=weights, mask=masks[i]) # pylint: disable=undefined-loop-variable - - metric_name = training_utils.add_metric_name(self, metric_name, i) # pylint: disable=undefined-loop-variable - self.metrics_tensors.append(metric_result) - - # Keep track of state updates created by - # stateful metrics (i.e. metrics layers). - if isinstance(metric_fn, base_layer.Layer) and metric_fn.stateful: - self.stateful_metric_names.append(metric_name) - self.stateful_metric_functions.append(metric_fn) - self.metrics_updates += metric_fn.updates - - handle_metrics(output_metrics, output_shape, loss_fn) - handle_metrics( - output_weighted_metrics, output_shape, loss_fn, weights=weights) + # Invoke metric functions for all the outputs. + self._handle_metrics( + self.outputs, + masks=masks, + targets=self.targets, + skip_target_indices=skip_target_indices, + sample_weights=self.sample_weights) # Prepare gradient updates and state updates. self.total_loss = total_loss @@ -717,8 +845,8 @@ class Model(Network): x_values, y_values = distributed_training_utils.\ validate_distributed_dataset_inputs(self._distribution_strategy, x, y) - _, _, sample_weights = self._standardize_weights(x_values[0], - y_values[0], + _, _, sample_weights = self._standardize_weights(x_values, + y_values, sample_weight, class_weight, batch_size) @@ -856,7 +984,7 @@ class Model(Network): all_inputs = [] is_build_called = False is_compile_called = False - if not self.built: + if not self.inputs: # We need to use `x` to set the model inputs. # We type-check that `x` and `y` are either single arrays # or lists of arrays. @@ -1067,22 +1195,13 @@ class Model(Network): 'in their call() signatures do not yet support shape inference. File ' 'a feature request if this limitation bothers you.') if self.__class__.__name__ == 'Sequential': - # Note: we can't test whether the model is `Sequential` via `isinstance` - # since `Sequential` depends on `Model`. - if isinstance(inputs, list): - assert len(inputs) == 1 - inputs = inputs[0] - if tensor_util.is_tensor(inputs): - if context.executing_eagerly(): - input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:]) - self.build(input_shape=input_shape) - else: - self.symbolic_set_inputs(inputs) + input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:]) + self.build(input_shape=input_shape) else: input_shape = (None,) + inputs.shape[1:] self.build(input_shape=input_shape) - elif context.executing_eagerly(): + if context.executing_eagerly(): self._eager_set_inputs(inputs) else: self._symbolic_set_inputs(inputs, training=training) @@ -1273,7 +1392,7 @@ class Model(Network): 0 = silent, 1 = progress bar, 2 = one line per epoch. callbacks: List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. - See [callbacks](/callbacks). + See [callbacks](/api_docs/python/tf/keras/callbacks). validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, @@ -1891,6 +2010,10 @@ class Model(Network): Raises: ValueError: In case the generator yields data in an invalid format. """ + if self._distribution_strategy: + raise NotImplementedError('`fit_generator` is not supported for ' + 'models compiled with DistributionStrategy.') + if not self.built and not self._is_graph_network: raise NotImplementedError( '`fit_generator` is not yet enabled for unbuilt Model subclasses') @@ -1958,6 +2081,10 @@ class Model(Network): Raises: ValueError: In case the generator yields data in an invalid format. """ + if self._distribution_strategy: + raise NotImplementedError('`evaluate_generator` is not supported for ' + 'models compiled with DistributionStrategy.') + if not self.built and not self._is_graph_network: raise NotImplementedError( '`evaluate_generator` is not yet enabled for ' @@ -2012,6 +2139,10 @@ class Model(Network): Raises: ValueError: In case the generator yields data in an invalid format. """ + if self._distribution_strategy: + raise NotImplementedError('`predict_generator` is not supported for ' + 'models compiled with DistributionStrategy.') + if not self.built and not self._is_graph_network: raise NotImplementedError( '`predict_generator` is not yet enabled for unbuilt Model subclasses') @@ -2025,6 +2156,21 @@ class Model(Network): use_multiprocessing=use_multiprocessing, verbose=verbose) + def _get_callback_model(self): + """Returns the Callback Model for this Model.""" + + if hasattr(self, '_replicated_model') and self._replicated_model: + # When using training_distributed, we set the callback model + # to an instance of the `DistributedModel` that we create in + # the `compile` call. The `DistributedModel` is initialized + # with the first replicated model. We need to set the callback + # model to a DistributedModel to allow us to override saving + # and loading weights when we checkpoint the model during training. + return self._replicated_model + if hasattr(self, 'callback_model') and self.callback_model: + return self.callback_model + return self + class DistributedCallbackModel(Model): """Model that is used for callbacks with DistributionStrategy.""" @@ -2065,4 +2211,3 @@ class DistributedCallbackModel(Model): logging.warning('You are accessing attribute ' + item + 'of the' 'DistributedCallbackModel that may not have been set' 'correctly.') - diff --git a/tensorflow/python/keras/engine/training_arrays.py b/tensorflow/python/keras/engine/training_arrays.py index d24f4b64b9af4fc7db918de2504721e9330650b5..e2c458c65f27c5802acd9186e9bcedd4062e5a2a 100644 --- a/tensorflow/python/keras/engine/training_arrays.py +++ b/tensorflow/python/keras/engine/training_arrays.py @@ -19,8 +19,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import copy - import numpy as np from tensorflow.python.framework import errors @@ -92,14 +90,8 @@ def fit_loop(model, val_sample_weights = val_sample_weights or [] if model.uses_learning_phase and not isinstance(K.learning_phase(), int): ins = inputs + targets + sample_weights + [1] - if val_inputs: - val_ins = val_inputs + val_targets + val_sample_weights + [1] else: ins = inputs + targets + sample_weights - if val_inputs: - val_ins = val_inputs + val_targets + val_sample_weights - if not val_inputs: - val_ins = [] do_validation = False if val_inputs: @@ -116,65 +108,27 @@ def fit_loop(model, 'training, i.e. `steps_per_epoch` ' 'must be set.') - out_labels = model.metrics_names - if do_validation: - callback_metrics = copy.copy(out_labels) + ['val_' + n for n in out_labels] - # need to create the test_function before start of the first epoch - # because TensorBoard callback on_epoch_begin adds summary to the - # list of fetches of the test_function - model._make_test_function() - else: - callback_metrics = copy.copy(out_labels) - num_train_samples = training_utils.check_num_samples( ins, batch_size, steps_per_epoch, 'steps_per_epoch') + count_mode = 'steps' if steps_per_epoch else 'samples' + callbacks = cbks.configure_callbacks( + callbacks, + model, + do_validation=do_validation, + val_inputs=val_inputs, + val_targets=val_targets, + val_sample_weights=val_sample_weights, + batch_size=batch_size, + epochs=epochs, + steps_per_epoch=steps_per_epoch, + samples=num_train_samples, + validation_steps=validation_steps, + verbose=verbose, + count_mode=count_mode) + if num_train_samples is not None: index_array = np.arange(num_train_samples) - model.history = cbks.History() - all_callbacks = [cbks.BaseLogger( - stateful_metrics=model.stateful_metric_names)] - if verbose: - if steps_per_epoch is not None: - count_mode = 'steps' - else: - count_mode = 'samples' - all_callbacks.append( - cbks.ProgbarLogger( - count_mode, stateful_metrics=model.stateful_metric_names)) - all_callbacks += (callbacks or []) + [model.history] - callbacks = cbks.CallbackList(all_callbacks) - out_labels = out_labels or [] - - # it's possible to callback a different model than self - # (used by Sequential models) - if hasattr(model, 'callback_model') and model.callback_model: - callback_model = model.callback_model - else: - callback_model = model - - callbacks.set_model(callback_model) - - callback_params = { - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - } - if validation_steps: - callback_params.update({'validation_steps': validation_steps}) - callbacks.set_params(callback_params) - - for cbk in callbacks: - cbk.validation_data = val_ins - # validation_data must be set before on_train_begin() is called - # so that TensorboardCallback can validate its input - callbacks.on_train_begin() - callback_model.stop_training = False - # To prevent a slowdown, we find beforehand the arrays that need conversion. feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights indices_for_conversion_to_dense = [] @@ -182,6 +136,7 @@ def fit_loop(model, if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): indices_for_conversion_to_dense.append(i) + callbacks.on_train_begin() for epoch in range(initial_epoch, epochs): # Reset stateful metrics for m in model.stateful_metric_functions: @@ -208,11 +163,11 @@ def fit_loop(model, if not isinstance(outs, list): outs = [outs] - for l, o in zip(out_labels, outs): + for l, o in zip(model.metrics_names, outs): batch_logs[l] = o callbacks.on_batch_end(step_index, batch_logs) - if callback_model.stop_training: + if callbacks.model.stop_training: break if do_validation: @@ -226,7 +181,7 @@ def fit_loop(model, if not isinstance(val_outs, list): val_outs = [val_outs] # Same labels assumed. - for l, o in zip(out_labels, val_outs): + for l, o in zip(model.metrics_names, val_outs): epoch_logs['val_' + l] = o else: # Sample-wise fit loop. @@ -259,11 +214,11 @@ def fit_loop(model, outs = f(ins_batch) if not isinstance(outs, list): outs = [outs] - for l, o in zip(out_labels, outs): + for l, o in zip(model.metrics_names, outs): batch_logs[l] = o callbacks.on_batch_end(batch_index, batch_logs) - if callback_model.stop_training: + if callbacks.model.stop_training: break if batch_index == len(batches) - 1: # Last batch. @@ -278,10 +233,10 @@ def fit_loop(model, if not isinstance(val_outs, list): val_outs = [val_outs] # Same labels assumed. - for l, o in zip(out_labels, val_outs): + for l, o in zip(model.metrics_names, val_outs): epoch_logs['val_' + l] = o callbacks.on_epoch_end(epoch, epoch_logs) - if callback_model.stop_training: + if callbacks.model.stop_training: break callbacks.on_train_end() return model.history diff --git a/tensorflow/python/keras/engine/training_distributed.py b/tensorflow/python/keras/engine/training_distributed.py index 5fa6c3c47d30ae0f60b22896affb2b868edafdf8..5feedc43a587b82633dd960baeca5b60d3970f41 100644 --- a/tensorflow/python/keras/engine/training_distributed.py +++ b/tensorflow/python/keras/engine/training_distributed.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import copy import numpy as np from tensorflow.python.framework import errors from tensorflow.python.keras import backend as K @@ -38,7 +37,6 @@ def fit_loop( callbacks=None, val_inputs=None, val_targets=None, - callback_metrics=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None): @@ -53,10 +51,6 @@ def fit_loop( callbacks: List of callbacks to be called during training val_inputs: List of input arrays. val_targets: List of target arrays. - callback_metrics: List of strings, the display names of the metrics - passed to the callbacks. They should be the - concatenation of list the display names of the outputs of - `f` and the list of display names of the outputs of `f_val`. initial_epoch: Epoch at which to start training (useful for resuming a previous training run) steps_per_epoch: Total number of steps (batches of samples) @@ -126,50 +120,6 @@ def fit_loop( 'when doing step-wise ' 'training, i.e. `steps_per_epoch` ' 'must be set.') - out_labels = model.metrics_names - if do_validation: - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] - else: - callback_metrics = copy.copy(out_labels) - - model.history = cbks.History() - all_callbacks = [cbks.BaseLogger( - stateful_metrics=model.stateful_metric_names)] - if verbose: - # We assume that `steps_per_epoch` is always set since we have to use - # Datasets. - count_mode = 'steps' - - all_callbacks.append( - cbks.ProgbarLogger( - count_mode, stateful_metrics=model.stateful_metric_names)) - all_callbacks += (callbacks or []) + [model.history] - callbacks = cbks.CallbackList(all_callbacks) - out_labels = out_labels or [] - - # We set the callback model to an instance of the `DistributedModel` that we - # create in the `compile` call. The `DistributedModel` is initialized with - # the first replicated model. We need to set the callback model to a - # DistributedModel to allow us to override saving and loading weights when - # we checkpoint the model during training. - callback_model = model._replicated_model - - callbacks.set_model(callback_model) - - callbacks.set_params({ - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': None, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - }) - callbacks.on_train_begin() - callback_model.stop_training = False - - out_labels = out_labels or [] # Copy the weights from the original model to each of the replicated models. orig_model_weights = model.get_weights() @@ -178,6 +128,17 @@ def fit_loop( distributed_training_utils.set_weights( current_strategy, distributed_model, orig_model_weights) + callbacks = cbks.configure_callbacks( + callbacks, + model, + do_validation=do_validation, + val_inputs=None, + val_targets=None, + epochs=epochs, + steps_per_epoch=steps_per_epoch, + verbose=verbose) + out_labels = model.metrics_names or [] + callbacks.on_train_begin() for epoch in range(initial_epoch, epochs): callbacks.on_epoch_begin(epoch) if steps_per_epoch is not None: @@ -203,7 +164,7 @@ def fit_loop( for l, o in zip(out_labels, outs): batch_logs[l] = o callbacks.on_batch_end(step_index, batch_logs) - if callback_model.stop_training: + if callbacks.model.stop_training: break if do_validation: val_outs = test_loop( @@ -219,7 +180,7 @@ def fit_loop( epoch_logs['val_' + l] = o callbacks.on_epoch_end(epoch, epoch_logs) - if callback_model.stop_training: + if callbacks.model.stop_training: break callbacks.on_train_end() diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index 774d2e44f36840a8399fdc327845524e9a10e4c1..1e377149b64ff6d810d59809eee5a3f1630ecdd6 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -41,39 +41,25 @@ def _eager_loss_fn(outputs, targets, loss_fn, output_name): return loss -def _eager_metrics_fn(model, outputs, targets): +def _eager_metrics_fn(model, outputs, targets, sample_weights=None, masks=None): """Calculates the metrics for each output of the given model. Arguments: model: The model on which metrics are being calculated. outputs: The outputs of the given model. targets: The predictions or targets of the given model. + sample_weights: Optional list of sample weights for each output. + masks: Optional list of masks for each output. Returns: Returns the metric results for each output of the model. """ - metric_results = [] - if not isinstance(outputs, list): - outputs = [outputs] - - if not isinstance(targets, list): - targets = [targets] - - for i in range(len(model.outputs)): - output_metrics = model.nested_metrics[i] - for nested_output_metric in output_metrics: - metric_fn = training_utils.get_metric_function( - nested_output_metric, backend.int_shape(model.outputs[i]), - model.loss_functions[i]) - # weighted metrics are not supported in eager mode - metric_name = training_utils.get_metric_name( - nested_output_metric, weighted=False) - - with backend.name_scope(metric_name): - metric_result = metric_fn(targets[i], outputs[i]) - metric_results.append(backend.mean(metric_result)) - - return metric_results + outputs = generic_utils.to_list(outputs) + targets = generic_utils.to_list(targets) + # TODO(psv): Consider supporting skip target indices in eager mode? + metric_results = model._handle_metrics( + outputs, targets=targets, sample_weights=sample_weights, masks=masks) + return [backend.mean(t) for t in metric_results] def _model_loss(model, inputs, targets, sample_weights=None, training=False): @@ -87,9 +73,10 @@ def _model_loss(model, inputs, targets, sample_weights=None, training=False): training: Whether the model should be run in inference or training mode. Returns: - Returns the model output, total loss and loss value calculated using the - specified loss function. The total loss includes regularization losses and - applies masking and sample weighting to the loss value. + Returns the model output, total loss, loss value calculated using the + specified loss function and masks for each output. The total loss includes + regularization losses and applies masking and sample weighting + to the loss value. """ total_loss = 0 kwargs = {} @@ -98,7 +85,7 @@ def _model_loss(model, inputs, targets, sample_weights=None, training=False): if len(inputs) == 1: inputs = inputs[0] - if model._is_graph_network: + if model._compute_output_and_mask_jointly: outs, masks = model._call_and_compute_mask(inputs, **kwargs) masks = generic_utils.to_list(masks) else: @@ -146,15 +133,13 @@ def _model_loss(model, inputs, targets, sample_weights=None, training=False): if custom_losses: total_loss += sum(custom_losses) - return outs, total_loss, loss_metrics + return outs, total_loss, loss_metrics, masks def iterator_fit_loop(model, inputs, class_weight, steps_per_epoch, - callback_model, - out_labels, epoch_logs, val_inputs=None, val_targets=None, @@ -162,7 +147,6 @@ def iterator_fit_loop(model, epochs=1, verbose=1, callbacks=None, - callback_metrics=None, validation_steps=None, do_validation=False, batch_size=None): @@ -179,19 +163,13 @@ def iterator_fit_loop(model, steps_per_epoch: Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. - callback_model: Instance of `Model` to callback. - out_labels: Output labels generated from model metric names. epoch_logs: Dictionary of logs from every epoch. val_inputs: Input data for validation. val_targets: Target data for validation. val_sample_weights: Sample weight data for validation. epochs: Number of times to iterate over the data verbose: Verbosity mode, 0, 1 or 2 - callbacks: List of callbacks to be called during training - callback_metrics: List of strings, the display names of the metrics - passed to the callbacks. They should be the - concatenation of list the display names of the outputs of - `f` and the list of display names of the outputs of `f_val`. + callbacks: CallbackList instance. Controls callbacks during training. validation_steps: Number of steps to run validation for (only if doing validation from data tensors). Ignored with default value of `None`. do_validation: Boolean value indicating whether we should do validation. @@ -244,40 +222,47 @@ def iterator_fit_loop(model, if val is not None else None for val in sample_weights ] - if step_index == 0 and not callback_metrics: - out_labels = model.metrics_names + # Set stateful_metrics in callbacks. We do not do this before the + # `steps_per_epoch` loop because model will be compiled only in the first + # iteration of this loop in the deferred build scenario. + if step_index == 0: + for cbk in callbacks: + if (isinstance(cbk, cbks.BaseLogger) or + isinstance(cbk, cbks.ProgbarLogger)): + cbk.stateful_metrics = model.stateful_metric_names + + if step_index == 0 and not callbacks.params['metrics']: + callback_metrics = copy.copy(model.metrics_names) if do_validation: - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] - else: - callback_metrics = copy.copy(out_labels) + callback_metrics += ['val_' + n for n in model.metrics_names] callbacks.set_params({ + 'batch_size': batch_size, 'epochs': epochs, 'steps': steps_per_epoch, 'verbose': verbose, 'do_validation': do_validation, 'metrics': callback_metrics or [], + 'validation_steps': validation_steps }) # Train model. - outs, loss, loss_metrics = _process_single_batch( + outs, loss, loss_metrics, masks = _process_single_batch( model, x, y, sample_weights=sample_weights, training=True) - if not isinstance(outs, list): - outs = [outs] + outs = generic_utils.to_list(outs) # Calculate metrics. - for l, o in zip(out_labels, outs): + for l, o in zip(model.metrics_names, outs): batch_logs[l] = o # Required for eager execution - metrics_results = _eager_metrics_fn(model, outs, y) + metrics_results = _eager_metrics_fn( + model, outs, y, sample_weights=sample_weights, masks=masks) batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss)) for k, v in zip(model.metrics_names, [backend.mean(loss)] + loss_metrics + metrics_results): batch_logs[k] = tensor_util.constant_value(v) callbacks.on_batch_end(step_index, batch_logs) - if callback_model.stop_training: + if callbacks.model.stop_training: break if step_index == steps_per_epoch - 1: @@ -293,7 +278,7 @@ def iterator_fit_loop(model, if not isinstance(val_outs, list): val_outs = [val_outs] # Same labels assumed. - for l, o in zip(out_labels, val_outs): + for l, o in zip(model.metrics_names, val_outs): epoch_logs['val_' + l] = o @@ -357,10 +342,25 @@ def iterator_test_loop(model, inputs, steps, verbose=0): if val is not None else None for val in sample_weights ] + if step_index == 0: + # Get stateful metrics indices. We do not do this before the `steps` loop + # because model will be compiled only in the first iteration of this loop + # in the deferred build scenario. + if hasattr(model, 'metrics'): + for m in model.stateful_metric_functions: + m.reset_states() + stateful_metric_indices = [ + i for i, name in enumerate(model.metrics_names) + if str(name) in model.stateful_metric_names + ] + else: + stateful_metric_indices = [] + # Calculate model output, loss values. - loss_outs, loss, loss_metrics = _model_loss( + loss_outs, loss, loss_metrics, masks = _model_loss( model, x, y, sample_weights=sample_weights, training=False) - metrics_results = _eager_metrics_fn(model, loss_outs, y) + metrics_results = _eager_metrics_fn( + model, loss_outs, y, sample_weights=sample_weights, masks=masks) batch_outs = [] for _, v in zip(model.metrics_names, [backend.mean(loss)] + loss_metrics + metrics_results): @@ -379,7 +379,10 @@ def iterator_test_loop(model, inputs, steps, verbose=0): for _ in enumerate(batch_outs): outs.append(0.) for i, batch_out in enumerate(batch_outs): - outs[i] += batch_out * step_size + if i in stateful_metric_indices: + outs[i] = batch_out + else: + outs[i] += batch_out * step_size # Calculate sample size. num_samples += step_size @@ -387,7 +390,8 @@ def iterator_test_loop(model, inputs, steps, verbose=0): progbar.update(step_index + 1) for i in range(len(outs)): - outs[i] /= num_samples + if i not in stateful_metric_indices: + outs[i] /= num_samples if len(outs) == 1: return outs[0] return outs @@ -484,16 +488,20 @@ def _process_single_batch(model, set this to False. Returns: - output of the model, total loss and the loss associated with each output. + output of the model, total loss, the loss and the mask + associated with each output. Raises: ValueError: If the model has no loss to optimize. """ with backend.learning_phase_scope(1 if training else 0): with GradientTape() as tape: - outs, loss, loss_metrics = _model_loss(model, inputs, targets, - sample_weights=sample_weights, - training=training) + outs, loss, loss_metrics, masks = _model_loss( + model, + inputs, + targets, + sample_weights=sample_weights, + training=training) if loss is None: raise ValueError('The model cannot be run ' 'because it has no loss to optimize.') @@ -506,7 +514,7 @@ def _process_single_batch(model, grads = tape.gradient(loss, model._collected_trainable_weights) model.optimizer.apply_gradients(zip(grads, model._collected_trainable_weights)) - return outs, loss, loss_metrics + return outs, loss, loss_metrics, masks def train_on_batch(model, inputs, targets, sample_weights=None): @@ -537,14 +545,18 @@ def train_on_batch(model, inputs, targets, sample_weights=None): if val is not None else None for val in sample_weights ] - outs, loss, _ = _process_single_batch( + outs, loss, loss_metrics, masks = _process_single_batch( model, inputs, targets, sample_weights=sample_weights, training=True) if not isinstance(outs, list): outs = [outs] - metrics_results = _eager_metrics_fn(model, outs, targets) - if not isinstance(loss, list): - loss = [loss] - return loss + metrics_results + metrics_results = _eager_metrics_fn( + model, outs, targets, sample_weights=sample_weights, masks=masks) + loss = generic_utils.to_list(loss) + + return [ + tensor_util.constant_value(v) + for v in loss + loss_metrics + metrics_results + ] def test_on_batch(model, inputs, targets, sample_weights=None): @@ -574,14 +586,18 @@ def test_on_batch(model, inputs, targets, sample_weights=None): ops.convert_to_tensor(val, dtype=backend.floatx()) if val is not None else None for val in sample_weights ] - outs, loss, loss_metrics = _model_loss( + outs, loss, loss_metrics, masks = _model_loss( model, inputs, targets, sample_weights=sample_weights, training=False) if not isinstance(outs, list): outs = [outs] - metrics_results = _eager_metrics_fn(model, outs, targets) - if not isinstance(loss, list): - loss = [loss] - return loss + loss_metrics + metrics_results + metrics_results = _eager_metrics_fn( + model, outs, targets, sample_weights=sample_weights, masks=masks) + loss = generic_utils.to_list(loss) + + return [ + tensor_util.constant_value(v) + for v in loss + loss_metrics + metrics_results + ] def fit_loop(model, @@ -643,65 +659,26 @@ def fit_loop(model, shuffle=shuffle) # Required for eager execution with backend.learning_phase_scope(1): - do_validation = False - if val_inputs: - do_validation = True - - num_train_samples = None - out_labels = None - callback_metrics = None - if model._is_compiled: - out_labels = model.metrics_names - if do_validation: - callback_metrics = copy.copy(out_labels) + [ - 'val_' + n for n in out_labels - ] - else: - callback_metrics = copy.copy(out_labels) - - model.history = cbks.History() - callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] - if verbose: - callbacks += [cbks.ProgbarLogger('steps')] - callbacks = cbks.CallbackList(callbacks) - - # it's possible to callback a different model than self - # (used by Sequential models) - if hasattr(model, 'callback_model') and model.callback_model: - callback_model = model.callback_model - else: - callback_model = model - - callbacks.set_model(callback_model) - - callback_params = { - 'batch_size': batch_size, - 'epochs': epochs, - 'steps': steps_per_epoch, - 'samples': num_train_samples, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics or [], - } - if validation_steps: - callback_params.update({'validation_steps': validation_steps}) - callbacks.set_params(callback_params) - - for cbk in callbacks: - if not val_inputs: - cbk.validation_data = [] - elif isinstance(val_inputs, iterator_ops.EagerIterator): - cbk.validation_data = val_inputs - elif val_sample_weights: - cbk.validation_data = val_inputs + val_targets + val_sample_weights - else: - cbk.validation_data = val_inputs + val_targets - # validation_data must be set before on_train_begin() is called - # so that TensorboardCallback can validate its input - callbacks.on_train_begin() - callback_model.stop_training = False + do_validation = val_inputs is not None + callbacks = cbks.configure_callbacks( + callbacks, + model, + do_validation=do_validation, + batch_size=batch_size, + epochs=epochs, + steps_per_epoch=steps_per_epoch, + val_inputs=val_inputs, + val_targets=val_targets, + val_sample_weights=val_sample_weights, + validation_steps=validation_steps, + verbose=verbose) + callbacks.on_train_begin() for epoch in range(initial_epoch, epochs): + if model._is_compiled: # Model may not be compiled the first time. + # Reset stateful metrics + for m in model.stateful_metric_functions: + m.reset_states() callbacks.on_epoch_begin(epoch) epoch_logs = {} iterator_fit_loop( @@ -709,8 +686,6 @@ def fit_loop(model, inputs, class_weight, steps_per_epoch=steps_per_epoch, - callback_model=callback_model, - out_labels=out_labels, epoch_logs=epoch_logs, val_inputs=val_inputs, val_targets=val_targets, @@ -718,12 +693,11 @@ def fit_loop(model, epochs=epochs, verbose=verbose, callbacks=callbacks, - callback_metrics=callback_metrics, validation_steps=validation_steps, do_validation=do_validation, batch_size=batch_size) callbacks.on_epoch_end(epoch, epoch_logs) - if callback_model.stop_training: + if callbacks.model.stop_training: break callbacks.on_train_end() return model.history @@ -763,10 +737,7 @@ def test_loop(model, inputs, targets, return iterator_test_loop(model, inputs, steps, verbose=verbose) -def predict_loop(model, inputs, - batch_size=32, - verbose=0, - steps=None): +def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None): """Predict function for eager execution. Arguments: diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py index 56f321732f24298c37e77e07474e0033dd2b617c..db7ccb181fb5d4c0f151a2736eed461fc4855446 100644 --- a/tensorflow/python/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/engine/training_eager_test.py @@ -24,6 +24,7 @@ from tensorflow.python.data.ops import dataset_ops from tensorflow.python import keras from tensorflow.python.framework import ops from tensorflow.python.framework import test_util as tf_test_util +from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.platform import test from tensorflow.python.training.rmsprop import RMSPropOptimizer @@ -44,7 +45,7 @@ class TrainingTest(test.TestCase): optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' loss_weights = [1., 0.5] - metrics = ['mae'] + metrics = ['mae', metrics_module.CategoricalAccuracy()] model.compile( optimizer, loss, @@ -109,7 +110,7 @@ class TrainingTest(test.TestCase): optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' - metrics = ['mae'] + metrics = ['mae', metrics_module.CategoricalAccuracy()] model.compile(optimizer, loss, metrics=metrics) inputs = keras.backend.zeros(shape=(10, 3)) @@ -128,7 +129,9 @@ class TrainingTest(test.TestCase): model = keras.Sequential() model.add(keras.layers.Dense(4, input_shape=(3,))) optimizer = RMSPropOptimizer(learning_rate=0.001) - model.compile(optimizer, 'mse', metrics=['mae']) + model.compile( + optimizer, 'mse', metrics=['mae', + metrics_module.CategoricalAccuracy()]) x = np.random.random((10, 3)) y = np.random.random((10, 4)) diff --git a/tensorflow/python/keras/engine/training_generator.py b/tensorflow/python/keras/engine/training_generator.py index 432cf2bddd052b40dd80dc530c9c6ce23d57d57b..413c1f4fbaba63d173de2c1d1c9943e919b05719 100644 --- a/tensorflow/python/keras/engine/training_generator.py +++ b/tensorflow/python/keras/engine/training_generator.py @@ -21,7 +21,6 @@ from __future__ import print_function import numpy as np -from tensorflow.python.keras import backend as K from tensorflow.python.keras import callbacks as cbks from tensorflow.python.keras.utils.data_utils import GeneratorEnqueuer from tensorflow.python.keras.utils.data_utils import OrderedEnqueuer @@ -79,66 +78,37 @@ def fit_generator(model, ' class. Please specify `validation_steps` or use' ' the `keras.utils.Sequence` class.') - # Prepare display labels. - out_labels = model.metrics_names - callback_metrics = out_labels + ['val_%s' % n for n in out_labels] - - # prepare callbacks - model.history = cbks.History() - callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history] - if verbose: - callbacks += [cbks.ProgbarLogger(count_mode='steps')] - callbacks = cbks.CallbackList(callbacks) - - # it's possible to callback a different model than self: - if hasattr(model, 'callback_model') and model.callback_model: - callback_model = model.callback_model - else: - callback_model = model - callbacks.set_model(callback_model) - - callback_params = { - 'epochs': epochs, - 'steps': steps_per_epoch, - 'verbose': verbose, - 'do_validation': do_validation, - 'metrics': callback_metrics, - } - if do_validation: - # need to create the test_function before start of the first epoch - # because TensorBoard callback on_epoch_begin adds summary to the - # list of fetches of the test_function - model._make_test_function() - # determine the number of validation batches given a generator - if validation_steps: - callback_params.update({'validation_steps': validation_steps}) - elif isinstance(validation_data, Sequence): - callback_params.update({'validation_steps': len(validation_data)}) - callbacks.set_params(callback_params) - enqueuer = None val_enqueuer = None try: + val_x, val_y, val_sample_weights = validation_data, None, None if do_validation and not val_gen: # Prepare data for validation if len(validation_data) == 2: val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence - val_sample_weight = None + val_sample_weights = None elif len(validation_data) == 3: - val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence + val_x, val_y, val_sample_weights = validation_data # pylint: disable=unpacking-non-sequence else: raise ValueError( '`validation_data` should be a tuple ' '`(val_x, val_y, val_sample_weight)` ' 'or `(val_x, val_y)`. Found: ' + str(validation_data)) val_x, val_y, val_sample_weights = model._standardize_user_data( - val_x, val_y, val_sample_weight) - val_data = val_x + val_y + val_sample_weights - if model.uses_learning_phase and not isinstance(K.learning_phase(), int): - val_data += [0.] - for cbk in callbacks: - cbk.validation_data = val_data + val_x, val_y, val_sample_weights) + + callbacks = cbks.configure_callbacks( + callbacks, + model, + do_validation=do_validation, + val_inputs=val_x, + val_targets=val_y, + val_sample_weights=val_sample_weights, + epochs=epochs, + validation_steps=validation_steps, + steps_per_epoch=steps_per_epoch, + verbose=verbose) if workers > 0: if is_sequence: @@ -159,9 +129,6 @@ def fit_generator(model, else: output_generator = generator - callback_model.stop_training = False - # validation_data must be set before on_train_begin() is called - # so that TensorboardCallback can validate its input callbacks.on_train_begin() # Construct epoch logs. epoch_logs = {} @@ -205,7 +172,7 @@ def fit_generator(model, if not isinstance(outs, list): outs = [outs] - for l, o in zip(out_labels, outs): + for l, o in zip(model.metrics_names, outs): batch_logs[l] = o callbacks.on_batch_end(batch_index, batch_logs) @@ -235,15 +202,15 @@ def fit_generator(model, if not isinstance(val_outs, list): val_outs = [val_outs] # Same labels assumed. - for l, o in zip(out_labels, val_outs): + for l, o in zip(model.metrics_names, val_outs): epoch_logs['val_' + l] = o - if callback_model.stop_training: + if callbacks.model.stop_training: break callbacks.on_epoch_end(epoch, epoch_logs) epoch += 1 - if callback_model.stop_training: + if callbacks.model.stop_training: break finally: @@ -266,7 +233,6 @@ def evaluate_generator(model, use_multiprocessing=False, verbose=0): """See docstring for `Model.evaluate_generator`.""" - stateful_metric_indices = [] if hasattr(model, 'metrics'): for m in model.stateful_metric_functions: m.reset_states() @@ -364,7 +330,7 @@ def evaluate_generator(model, averages.append( np.average([out[i] for out in all_outs], weights=batch_sizes)) else: - averages.append(float(all_outs[-1][i])) + averages.append(np.float64(all_outs[-1][i])) return averages diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index 753519fbac842bae391c12664d99367764b159ab..15e7d725dea80a746867d769baff9ec77f4f3fe1 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -30,6 +30,7 @@ from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_util as tf_test_util +from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras import testing_utils from tensorflow.python.keras.engine.training_utils import weighted_masked_objective from tensorflow.python.keras.utils.generic_utils import slice_arrays @@ -62,8 +63,11 @@ class TrainingTest(test.TestCase): optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' loss_weights = [1., 0.5] - metrics = ['mae'] - model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights) + model.compile( + optimizer, + loss, + metrics=[metrics_module.CategoricalAccuracy(), 'mae'], + loss_weights=loss_weights) input_a_np = np.random.random((10, 3)) input_b_np = np.random.random((10, 3)) @@ -178,8 +182,10 @@ class TrainingTest(test.TestCase): # Test with lists for loss, metrics loss = ['mae', 'mse'] - metrics = ['acc', 'mae'] - model.compile(optimizer, loss, metrics=metrics) + model.compile( + optimizer, + loss, + metrics=[metrics_module.CategoricalAccuracy(), 'mae']) model.fit( [input_a_np, input_b_np], [output_d_np, output_e_np], epochs=1, @@ -189,7 +195,10 @@ class TrainingTest(test.TestCase): # Test with dictionaries for loss, metrics, loss weights loss = {'dense': 'mse', 'dropout': 'mae'} loss_weights = {'dense': 1., 'dropout': 0.5} - metrics = {'dense': 'mse', 'dropout': 'mae'} + metrics = { + 'dense': 'mse', + 'dropout': metrics_module.CategoricalAccuracy() + } model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights) model.fit( [input_a_np, input_b_np], [output_d_np, output_e_np], @@ -258,11 +267,10 @@ class TrainingTest(test.TestCase): optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' loss_weights = [1., 0.5] - metrics = ['mae'] model.compile( optimizer, loss, - metrics=metrics, + metrics=['mae', metrics_module.CategoricalAccuracy()], loss_weights=loss_weights, sample_weight_mode=None) @@ -277,20 +285,20 @@ class TrainingTest(test.TestCase): [input_a_np, input_b_np], [output_d_np, output_e_np], batch_size=5, verbose=0) - self.assertEqual(len(out), 5) + self.assertEqual(len(out), 7) out = model.evaluate( [input_a_np, input_b_np], [output_d_np, output_e_np], batch_size=5, verbose=1) - self.assertEqual(len(out), 5) + self.assertEqual(len(out), 7) out = model.evaluate( [input_a_np, input_b_np], [output_d_np, output_e_np], batch_size=5, verbose=2) - self.assertEqual(len(out), 5) + self.assertEqual(len(out), 7) out = model.test_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np]) - self.assertEqual(len(out), 5) + self.assertEqual(len(out), 7) # Test evaluate with dictionary inputs model.evaluate( @@ -326,7 +334,7 @@ class TrainingTest(test.TestCase): self.assertEqual(len(out), 2) @tf_test_util.run_in_graph_and_eager_modes - def test_invalid_loss_or_metrics(self): + def test_invalid_loss(self): num_classes = 5 train_samples = 1000 test_samples = 1000 @@ -350,10 +358,6 @@ class TrainingTest(test.TestCase): with self.assertRaises(ValueError): model.fit(x_train, np.concatenate([y_train, y_train], axis=-1)) - with self.assertRaises(TypeError): - model.compile( - optimizer, loss='categorical_crossentropy', metrics=set(0)) - if not context.executing_eagerly(): # TODO(psv): Investigate these use cases in eager mode. with self.assertRaises(ValueError): @@ -379,7 +383,11 @@ class TrainingTest(test.TestCase): out2 = keras.layers.Dense(4, name='dense_1')(in2) model = keras.Model([in1, in2], [out1, out2]) model.predict(test_inputs, batch_size=2) - model.compile('rmsprop', 'mse') + optimizer = RMSPropOptimizer(learning_rate=0.001) + model.compile( + optimizer, + 'mse', + metrics=['mae', metrics_module.CategoricalAccuracy()]) model.fit(test_inputs, test_outputs, epochs=1, batch_size=2, validation_split=0.5) model.evaluate(test_inputs, test_outputs, batch_size=2) @@ -422,22 +430,24 @@ class TrainingTest(test.TestCase): x2 = model.predict(val_a) self.assertAllClose(x1, x2, atol=1e-7) + @tf_test_util.run_in_graph_and_eager_modes def test_compile_warning_for_loss_missing_output(self): with self.test_session(): inp = keras.layers.Input(shape=(16,), name='input_a') out_1 = keras.layers.Dense(8, name='dense_1')(inp) out_2 = keras.layers.Dense(3, activation='softmax', name='dense_2')(out_1) model = keras.models.Model(inputs=[inp], outputs=[out_1, out_2]) + optimizer = RMSPropOptimizer(learning_rate=0.001) with test.mock.patch.object(logging, 'warning') as mock_log: model.compile( + optimizer, loss={ 'dense_2': 'categorical_crossentropy', }, - optimizer='rmsprop', metrics={ 'dense_2': 'categorical_accuracy', - 'dense_1': 'categorical_accuracy', + 'dense_1': metrics_module.CategoricalAccuracy(), }) msg = ('Output "dense_1" missing from loss dictionary. We assume this ' 'was done on purpose. The fit and evaluate APIs will not be ' @@ -466,6 +476,8 @@ class LossWeightingTest(test.TestCase): model.add(keras.layers.Activation('softmax')) model.compile( loss='categorical_crossentropy', + metrics=['acc'], + weighted_metrics=['mae'], optimizer=RMSPropOptimizer(learning_rate=learning_rate)) np.random.seed(1337) @@ -516,7 +528,7 @@ class LossWeightingTest(test.TestCase): ref_score = model.evaluate(x_test, y_test, verbose=0) score = model.evaluate( x_test[test_ids, :], y_test[test_ids, :], verbose=0) - self.assertLess(score, ref_score) + self.assertLess(score[0], ref_score[0]) @tf_test_util.run_in_graph_and_eager_modes def test_sample_weights(self): @@ -537,6 +549,8 @@ class LossWeightingTest(test.TestCase): model.add(keras.layers.Activation('softmax')) model.compile( RMSPropOptimizer(learning_rate=learning_rate), + metrics=['acc'], + weighted_metrics=['mae'], loss='categorical_crossentropy') np.random.seed(43) @@ -583,7 +597,7 @@ class LossWeightingTest(test.TestCase): if not context.executing_eagerly(): score = model.evaluate( x_test[test_ids, :], y_test[test_ids, :], verbose=0) - self.assertLess(score, ref_score) + self.assertLess(score[0], ref_score[0]) @tf_test_util.run_in_graph_and_eager_modes def test_temporal_sample_weights(self): @@ -641,6 +655,8 @@ class LossWeightingTest(test.TestCase): model.compile( RMSPropOptimizer(learning_rate=learning_rate), loss='binary_crossentropy', + metrics=['acc'], + weighted_metrics=['mae'], sample_weight_mode='temporal') model.fit( @@ -671,7 +687,7 @@ class LossWeightingTest(test.TestCase): if not context.executing_eagerly(): score = model.evaluate( temporal_x_test[test_ids], temporal_y_test[test_ids], verbose=0) - self.assertLess(score, ref_score) + self.assertLess(score[0], ref_score[0]) @tf_test_util.run_in_graph_and_eager_modes def test_class_weight_invalid_use_case(self): @@ -794,7 +810,7 @@ class LossWeightingTest(test.TestCase): class LossMaskingTest(test.TestCase): @tf_test_util.run_in_graph_and_eager_modes - def test_masking(self): + def test_masking_graph_sequential(self): with self.test_session(): x = np.array([[[1], [1]], [[0], [0]]]) model = keras.models.Sequential() @@ -807,6 +823,34 @@ class LossMaskingTest(test.TestCase): loss = model.train_on_batch(x, y) self.assertEqual(float(loss), 0.) + @tf_test_util.run_in_graph_and_eager_modes + def test_masking_deferred_sequential(self): + with self.test_session(): + x = np.array([[[1], [1]], [[0], [0]]]) + model = keras.models.Sequential() + model.add(keras.layers.Masking(mask_value=0)) + model.add( + keras.layers.TimeDistributed( + keras.layers.Dense(1, kernel_initializer='one'))) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + y = np.array([[[1], [1]], [[1], [1]]]) + loss = model.train_on_batch(x, y) + self.assertEqual(float(loss), 0.) + + @tf_test_util.run_in_graph_and_eager_modes + def test_masking_functional(self): + with self.test_session(): + x = np.array([[[1], [1]], [[0], [0]]]) + inputs = keras.layers.Input((2, 1)) + outputs = keras.layers.Masking(mask_value=0)(inputs) + outputs = keras.layers.TimeDistributed( + keras.layers.Dense(1, kernel_initializer='one'))(outputs) + model = keras.Model(inputs, outputs) + model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001)) + y = np.array([[[1], [1]], [[1], [1]]]) + loss = model.train_on_batch(x, y) + self.assertEqual(float(loss), 0.) + @tf_test_util.run_in_graph_and_eager_modes def test_mask_argument_in_layer(self): # Test that the mask argument gets correctly passed to a layer in the @@ -1038,7 +1082,10 @@ class TestGeneratorMethods(test.TestCase): x = keras.Input((2,)) y = keras.layers.Dense(1)(x) fn_model = keras.models.Model(x, y) - fn_model.compile(loss='mse', optimizer='sgd') + fn_model.compile( + loss='mse', + optimizer='sgd', + metrics=['mae', metrics_module.CategoricalAccuracy()]) seq_model = keras.models.Sequential() seq_model.add(keras.layers.Dense(1, input_shape=(2,))) @@ -1120,7 +1167,10 @@ class TestGeneratorMethods(test.TestCase): with self.test_session(): model = keras.models.Sequential() model.add(keras.layers.Dense(1, input_shape=(2,))) - model.compile(loss='mse', optimizer='sgd') + model.compile( + loss='mse', + optimizer='sgd', + metrics=['mae', metrics_module.CategoricalAccuracy()]) model.fit_generator(custom_generator(), steps_per_epoch=5, @@ -1272,10 +1322,12 @@ class TestTrainingWithDataTensors(test.TestCase): y = keras.layers.Dense(4, name='dense')(x) model = keras.Model(x, y) - optimizer = 'rmsprop' + optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' - metrics = ['mae'] - model.compile(optimizer, loss, metrics=metrics) + model.compile( + optimizer, + loss, + metrics=['mae', metrics_module.CategoricalAccuracy()]) inputs = keras.backend.zeros(shape=(10, 3)) targets = keras.backend.zeros(shape=(10, 4)) @@ -1319,8 +1371,11 @@ class TestTrainingWithDataTensors(test.TestCase): optimizer = 'rmsprop' loss = 'mse' loss_weights = [1., 0.5] - metrics = ['mae'] - model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights) + model.compile( + optimizer, + loss, + metrics=['mae', metrics_module.CategoricalAccuracy()], + loss_weights=loss_weights) input_a_tf = keras.backend.zeros(shape=(10, 3)) input_b_tf = keras.backend.zeros(shape=(10, 3)) @@ -1758,8 +1813,11 @@ class TestTrainingWithDataTensors(test.TestCase): model.train_on_batch(input_val, None) # test with sample weights - model.compile(optimizer='rmsprop', loss='mse', - target_tensors=[target_a, target_b]) + model.compile( + optimizer='rmsprop', + loss='mse', + metrics=['mae', metrics_module.CategoricalAccuracy()], + target_tensors=[target_a, target_b]) model.train_on_batch(input_val, None, sample_weight={'dense_a': np.random.random((10,))}) @@ -1823,30 +1881,6 @@ class TestTrainingWithDataTensors(test.TestCase): model.train_on_batch([input_a_np, input_b_np], [output_a_np, output_b_np]) - @tf_test_util.run_in_graph_and_eager_modes - def test_metric_names_are_identical_in_graph_and_eager(self): - a = keras.layers.Input(shape=(3,), name='input_a') - b = keras.layers.Input(shape=(3,), name='input_b') - - dense = keras.layers.Dense(4, name='dense') - c = dense(a) - d = dense(b) - e = keras.layers.Dropout(0.5, name='dropout')(c) - - model = keras.models.Model([a, b], [d, e]) - - optimizer = RMSPropOptimizer(learning_rate=0.001) - loss = 'mse' - loss_weights = [1., 0.5] - metrics = ['mae', 'acc'] - model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights) - reference_metric_names = ['loss', 'dense_loss', 'dropout_loss', - 'dense_mean_absolute_error', - 'dense_acc', - 'dropout_mean_absolute_error', - 'dropout_acc'] - self.assertEqual(reference_metric_names, model.metrics_names) - class TestTrainingWithDatasetIterators(test.TestCase): @@ -1859,7 +1893,7 @@ class TestTrainingWithDatasetIterators(test.TestCase): optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' - metrics = ['mae'] + metrics = ['mae', metrics_module.CategoricalAccuracy()] model.compile(optimizer, loss, metrics=metrics) inputs = np.zeros((10, 3)) @@ -1916,6 +1950,7 @@ class TestTrainingWithDatasetIterators(test.TestCase): 'you should specify the `steps` argument'): model.predict(iterator, verbose=0) + @tf_test_util.run_in_graph_and_eager_modes def test_get_next_op_created_once(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') @@ -1968,6 +2003,7 @@ class TestTrainingWithDatasetIterators(test.TestCase): class TestTrainingWithDataset(test.TestCase): + @tf_test_util.run_in_graph_and_eager_modes def test_calling_model_on_same_dataset(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') @@ -2003,7 +2039,7 @@ class TestTrainingWithDataset(test.TestCase): optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' - metrics = ['mae'] + metrics = ['mae', metrics_module.CategoricalAccuracy()] model.compile(optimizer, loss, metrics=metrics) inputs = np.zeros((10, 3)) @@ -2093,6 +2129,28 @@ class TestTrainingWithDataset(test.TestCase): class TestTrainingWithMetrics(test.TestCase): """Training tests related to metrics.""" + @tf_test_util.run_in_graph_and_eager_modes + def test_metrics_names(self): + a = keras.layers.Input(shape=(3,), name='input_a') + b = keras.layers.Input(shape=(3,), name='input_b') + + dense = keras.layers.Dense(4, name='dense') + c = dense(a) + d = dense(b) + e = keras.layers.Dropout(0.5, name='dropout')(c) + + model = keras.models.Model([a, b], [d, e]) + + optimizer = RMSPropOptimizer(learning_rate=0.001) + metrics = ['mse', metrics_module.BinaryAccuracy()] + model.compile(optimizer, loss='mae', metrics=metrics) + reference_metric_names = [ + 'loss', 'dense_loss', 'dropout_loss', 'dense_mean_squared_error', + 'dense_binary_accuracy', 'dropout_mean_squared_error', + 'dropout_binary_accuracy' + ] + self.assertEqual(reference_metric_names, model.metrics_names) + @tf_test_util.run_in_graph_and_eager_modes def test_metrics_correctness(self): with self.test_session(): @@ -2105,7 +2163,7 @@ class TestTrainingWithMetrics(test.TestCase): 1, activation='sigmoid', kernel_initializer='ones')) model.compile( loss='mae', - metrics=['accuracy'], + metrics=['accuracy', metrics_module.BinaryAccuracy()], optimizer=RMSPropOptimizer(learning_rate=0.001)) # verify correctness of stateful and stateless metrics. @@ -2113,41 +2171,48 @@ class TestTrainingWithMetrics(test.TestCase): y = np.ones((100, 1)) outs = model.evaluate(x, y) self.assertEqual(outs[1], 1.) + self.assertEqual(outs[2], 1.) y = np.zeros((100, 1)) outs = model.evaluate(x, y) self.assertEqual(outs[1], 0.) + self.assertEqual(outs[2], 0.) @tf_test_util.run_in_graph_and_eager_modes def test_metrics_correctness_with_iterator(self): - model = keras.Sequential() - model.add( - keras.layers.Dense( - 8, activation='relu', input_dim=4, kernel_initializer='ones')) - model.add( - keras.layers.Dense(1, activation='sigmoid', kernel_initializer='ones')) - model.compile( - loss='binary_crossentropy', - metrics=['accuracy'], - optimizer=RMSPropOptimizer(learning_rate=0.001)) - - np.random.seed(123) - x = np.random.randint(10, size=(100, 4)).astype(np.float32) - y = np.random.randint(2, size=(100, 1)).astype(np.float32) - dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) - dataset = dataset.batch(10) - iterator = dataset.make_one_shot_iterator() - outs = model.evaluate(iterator, steps=10) - self.assertEqual(np.around(outs[1], decimals=1), 0.5) - - y = np.zeros((100, 1), dtype=np.float32) - dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) - dataset = dataset.repeat(100) - dataset = dataset.batch(10) - iterator = dataset.make_one_shot_iterator() - outs = model.evaluate(iterator, steps=10) - self.assertEqual(outs[1], 0.) + with self.test_session(): + model = keras.Sequential() + model.add( + keras.layers.Dense( + 8, activation='relu', input_dim=4, kernel_initializer='ones')) + model.add( + keras.layers.Dense( + 1, activation='sigmoid', kernel_initializer='ones')) + model.compile( + loss='binary_crossentropy', + metrics=['accuracy', metrics_module.BinaryAccuracy()], + optimizer=RMSPropOptimizer(learning_rate=0.001)) + np.random.seed(123) + x = np.random.randint(10, size=(100, 4)).astype(np.float32) + y = np.random.randint(2, size=(100, 1)).astype(np.float32) + dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) + dataset = dataset.batch(10) + iterator = dataset.make_one_shot_iterator() + outs = model.evaluate(iterator, steps=10) + self.assertEqual(np.around(outs[1], decimals=1), 0.5) + self.assertEqual(np.around(outs[2], decimals=1), 0.5) + + y = np.zeros((100, 1), dtype=np.float32) + dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) + dataset = dataset.repeat(100) + dataset = dataset.batch(10) + iterator = dataset.make_one_shot_iterator() + outs = model.evaluate(iterator, steps=10) + self.assertEqual(outs[1], 0.) + self.assertEqual(outs[2], 0.) + + @tf_test_util.run_in_graph_and_eager_modes def test_metrics_correctness_with_weighted_metrics(self): with self.test_session(): np.random.seed(1337) @@ -2161,19 +2226,87 @@ class TestTrainingWithMetrics(test.TestCase): RMSPropOptimizer(learning_rate=0.001), loss='mse', sample_weight_mode='temporal', - weighted_metrics=['accuracy']) + weighted_metrics=['accuracy', + metrics_module.BinaryAccuracy()]) y = np.array([[[1.], [1.]], [[1.], [1.]]]) outs = model.evaluate(x, y) - self.assertEqual(outs, [0.5, 0.5]) + self.assertEqual(outs, [0.5, 0.5, 0.5]) w = np.array([[0., 0.], [0., 0.]]) outs = model.evaluate(x, y, sample_weight=w) - self.assertEqual(outs, [0., 0.]) + self.assertEqual(outs, [0., 0., 0.]) w = np.array([[3., 4.], [1., 2.]]) outs = model.evaluate(x, y, sample_weight=w) - self.assertArrayNear(outs, [0.3, 0.7], .001) + self.assertArrayNear(outs, [0.3, 0.7, 0.7], .001) + + @tf_test_util.run_in_graph_and_eager_modes + def test_metric_state_reset_between_fit_and_evaluate(self): + with self.test_session(): + model = keras.Sequential() + model.add(keras.layers.Dense(3, activation='relu', input_dim=4)) + model.add(keras.layers.Dense(1, activation='sigmoid')) + acc_obj = metrics_module.BinaryAccuracy() + model.compile( + loss='mae', + metrics=[acc_obj], + optimizer=RMSPropOptimizer(learning_rate=0.001)) + + x_train = np.random.random((100, 4)) + y_train = np.random.random((100, 1)) + model.fit(x_train, y_train, batch_size=5, epochs=2) + self.assertEqual(self.evaluate(acc_obj.count), 100) + + x_test = np.random.random((10, 4)) + y_test = np.random.random((10, 1)) + model.evaluate(x_test, y_test, batch_size=5) + self.assertEqual(self.evaluate(acc_obj.count), 10) + + @tf_test_util.run_in_graph_and_eager_modes + def test_invalid_metrics(self): + num_classes = 5 + input_dim = 5 + + with self.test_session(): + model = keras.models.Sequential() + model.add( + keras.layers.Dense(10, activation='relu', input_shape=(input_dim,))) + model.add(keras.layers.Dense(num_classes, activation='softmax')) + + with self.assertRaisesRegexp( + TypeError, 'Type of `metrics` argument not understood. ' + 'Expected a list or dictionary, found: '): + model.compile( + RMSPropOptimizer(learning_rate=0.001), + loss='categorical_crossentropy', + metrics=metrics_module.CategoricalAccuracy()) + + @tf_test_util.run_in_graph_and_eager_modes + def test_metrics_masking(self): + with self.test_session(): + np.random.seed(1337) + model = keras.models.Sequential() + model.add(keras.layers.Masking(mask_value=0, input_shape=(2, 1))) + model.add( + keras.layers.TimeDistributed( + keras.layers.Dense(1, kernel_initializer='ones'))) + model.compile( + RMSPropOptimizer(learning_rate=0.001), + loss='mse', + weighted_metrics=['accuracy', + metrics_module.BinaryAccuracy()]) + + # verify that masking is applied for stateless and stateful metrics. + x = np.array([[[1], [1]], [[1], [1]], [[0], [0]]]) + y = np.array([[[1], [1]], [[0], [1]], [[1], [1]]]) + scores = model.train_on_batch(x, y) + self.assertArrayNear(scores, [0.25, 0.75, 0.75], 0.1) + + # verify that masking is combined with sample weights. + w = np.array([3, 2, 4]) + scores = model.train_on_batch(x, y, sample_weight=w) + self.assertArrayNear(scores, [0.2, 0.8, 0.8], 0.1) if __name__ == '__main__': diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index 38b64e69ec136c9ad03b1cc2923c47dc8cc26613..f94697c91389e67d1766459e3b27eb1ad8c8523c 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -570,13 +570,24 @@ def weighted_masked_objective(fn): # score_array has ndim >= 2 score_array = fn(y_true, y_pred) if mask is not None: - # Cast the mask to floatX to avoid float64 upcasting in theano - mask = math_ops.cast(mask, K.floatx()) - # mask should have the same shape as score_array - score_array *= mask - # the loss per batch should be proportional - # to the number of unmasked samples. - score_array /= K.mean(mask) + mask = math_ops.cast(mask, y_pred.dtype) + # Update weights with mask. + if weights is None: + weights = mask + else: + # Update shape of weights if possible before adding mask. + # Update dimensions of weights to match with mask if possible. + mask, _, weights = metrics_module.squeeze_or_expand_dimensions( + mask, None, weights) + try: + # Broadcast weights if possible. + weights = weights_broadcast_ops.broadcast_weights(weights, mask) + weights *= mask + except ValueError: + score_array *= mask + score_array /= K.mean(mask) + # TODO(psv): Handle case when mask and weight shapes are not + # compatible. # Apply sample weighting. if weights is not None: @@ -709,43 +720,6 @@ def has_tensors(ls): return tensor_util.is_tensor(ls) -def populate_metric_names(model): - for i in range(len(model.outputs)): - metrics = model.nested_metrics[i] - for metric in metrics: - base_metric_name = get_metric_name(metric) - add_metric_name(model, base_metric_name, i) - - -def get_metric_name(metric, weighted=False): - """Returns the metric name corresponding to the given metric input. - - Arguments: - metric: Metric function name or reference. - weighted: Boolean indicating if the given metric is weighted. - - Returns: - a metric name. - """ - metric_name_prefix = 'weighted_' if weighted else '' - if metric in ('accuracy', 'acc', 'crossentropy', 'ce'): - if metric in ('accuracy', 'acc'): - suffix = 'acc' - elif metric in ('crossentropy', 'ce'): - suffix = 'ce' - metric_name = metric_name_prefix + suffix - else: - metric_fn = metrics_module.get(metric) - # Get metric name as string - if hasattr(metric_fn, 'name'): - metric_name = metric_fn.name - else: - metric_name = metric_fn.__name__ - metric_name = metric_name_prefix + metric_name - - return metric_name - - def get_metric_function(metric, output_shape=None, loss_fn=None): """Returns the metric function corresponding to the given metric input. @@ -776,33 +750,6 @@ def get_metric_function(metric, output_shape=None, loss_fn=None): return metrics_module.get(metric) -def add_metric_name(model, metric_name, index): - """Makes the metric name unique and adds it to the model's metric name list. - - If there are multiple outputs for which the metrics are calculated, the - metric names have to be made unique by appending an integer. - - Arguments: - model: Model to which we are adding metric names. - metric_name: Metric name that corresponds to the metric specified by the - user. For example: 'acc' - index: The index of the model output for which the metric name is being - added. - - Returns: - string, name of the model's unique metric name - """ - if len(model.output_names) > 1: - metric_name = '%s_%s' % (model.output_names[index], metric_name) - j = 1 - base_metric_name = metric_name - while metric_name in model.metrics_names: - metric_name = '%s_%d' % (base_metric_name, j) - j += 1 - model.metrics_names.append(metric_name) - return metric_name - - def validate_iterator_input(x, y, sample_weight, validation_split=None): """Validates user input arguments when a dataset iterator is passed. diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py index f28cade474e450174f95c9a8e06e26b04e95eb69..4032202986d64047ebde194f812d99924b1a4630 100644 --- a/tensorflow/python/keras/layers/core.py +++ b/tensorflow/python/keras/layers/core.py @@ -466,7 +466,7 @@ class Permute(Layer): Arguments: dims: Tuple of integers. Permutation pattern, does not include the samples dimension. Indexing starts at 1. - For instance, `(2, 1)` permutes the first and second dimension + For instance, `(2, 1)` permutes the first and second dimensions of the input. Input shape: @@ -482,6 +482,11 @@ class Permute(Layer): def __init__(self, dims, **kwargs): super(Permute, self).__init__(**kwargs) self.dims = tuple(dims) + if sorted(dims) != list(range(1, len(dims) + 1)): + raise ValueError( + 'Invalid permutation `dims` for Permute Layer: %s. ' + 'The set of indices in `dims` must be consecutive and start from 1.' % + (dims,)) self.input_spec = InputSpec(ndim=len(self.dims) + 1) def compute_output_shape(self, input_shape): @@ -676,9 +681,8 @@ class Lambda(Layer): 'must be a list, a tuple, or a function.') self._output_shape = output_shape + @tf_utils.shape_type_conversion def compute_output_shape(self, input_shape): - input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list()) - if self._output_shape is None: if context.executing_eagerly(): raise NotImplementedError diff --git a/tensorflow/python/keras/layers/core_test.py b/tensorflow/python/keras/layers/core_test.py index 226403c5927ed22394b708178679d1efa11dd790..49ca68ee9e2ac5a59e38586daaf1757bf458a9c4 100644 --- a/tensorflow/python/keras/layers/core_test.py +++ b/tensorflow/python/keras/layers/core_test.py @@ -119,6 +119,20 @@ class CoreLayersTest(test.TestCase): testing_utils.layer_test( keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4)) + @tf_test_util.run_in_graph_and_eager_modes + def test_permute_errors_on_invalid_starting_dims_index(self): + with self.assertRaisesRegexp(ValueError, r'Invalid permutation .*dims.*'): + testing_utils.layer_test( + keras.layers.Permute, + kwargs={'dims': (0, 1, 2)}, input_shape=(3, 2, 4)) + + @tf_test_util.run_in_graph_and_eager_modes + def test_permute_errors_on_invalid_set_of_dims_indices(self): + with self.assertRaisesRegexp(ValueError, r'Invalid permutation .*dims.*'): + testing_utils.layer_test( + keras.layers.Permute, + kwargs={'dims': (1, 4, 2)}, input_shape=(3, 2, 4)) + @tf_test_util.run_in_graph_and_eager_modes def test_flatten(self): testing_utils.layer_test( @@ -173,6 +187,14 @@ class CoreLayersTest(test.TestCase): config = ld.get_config() ld = keras.layers.Lambda.from_config(config) + @tf_test_util.run_in_graph_and_eager_modes + def test_lambda_multiple_inputs(self): + ld = keras.layers.Lambda(lambda x: x[0], output_shape=lambda x: x[0]) + x1 = np.ones([3, 2], np.float32) + x2 = np.ones([3, 5], np.float32) + out = ld([x1, x2]) + self.assertAllEqual(out.shape, [3, 2]) + @tf_test_util.run_in_graph_and_eager_modes def test_dense(self): testing_utils.layer_test( diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py index a8bfdf25f2e94a3bdf2805e87b092cd94d78222c..66c68e208545d962d26c5e3eb177ef09a4ddb6a0 100644 --- a/tensorflow/python/keras/layers/recurrent.py +++ b/tensorflow/python/keras/layers/recurrent.py @@ -19,10 +19,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import numbers import numpy as np from tensorflow.python.eager import context +from tensorflow.python.framework import tensor_shape from tensorflow.python.keras import activations from tensorflow.python.keras import backend as K from tensorflow.python.keras import constraints @@ -37,6 +37,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import state_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.checkpointable import base as checkpointable +from tensorflow.python.util import nest from tensorflow.python.util.tf_export import tf_export @@ -86,17 +87,24 @@ class StackedRNNCells(Layer): # (assuming one LSTM has states [h, c]) state_size = [] for cell in self.cells[::-1]: - if hasattr(cell.state_size, '__len__'): + if _is_multiple_state(cell.state_size): state_size += list(cell.state_size) else: state_size.append(cell.state_size) return tuple(state_size) + @property + def output_size(self): + if hasattr(self.cells[-1], 'output_size'): + return self.cells[-1].output_size + else: + return self.state_size[0] + def call(self, inputs, states, constants=None, **kwargs): # Recover per-cell states. nested_states = [] for cell in self.cells[::-1]: - if hasattr(cell.state_size, '__len__'): + if _is_multiple_state(cell.state_size): nested_states.append(states[:len(cell.state_size)]) states = states[len(cell.state_size):] else: @@ -133,11 +141,12 @@ class StackedRNNCells(Layer): cell.build([input_shape] + constants_shape) else: cell.build(input_shape) - if hasattr(cell.state_size, '__len__'): + if _is_multiple_state(cell.state_size): output_dim = cell.state_size[0] else: output_dim = cell.state_size - input_shape = (input_shape[0], output_dim) + input_shape = tuple([input_shape[0]] + + tensor_shape.as_shape(output_dim).as_list()) self.built = True def get_config(self): @@ -242,13 +251,16 @@ class RNN(Layer): cell can also take the optional argument `constants`, see section "Note on passing external constants" below. - a `state_size` attribute. This can be a single integer - (single state) in which case it is - the size of the recurrent state - (which should be the same as the size of the cell output). - This can also be a list/tuple of integers - (one size per state). In this case, the first entry - (`state_size[0]`) should be the same as - the size of the cell output. + (single state) in which case it is the size of the recurrent + state. This can also be a list/tuple of integers (one size per + state). + The `state_size` can also be TensorShape or tuple/list of + TensorShape, to represent high dimension state. + - a `output_size` attribute. This can be a single integer or a + TensorShape, which represent the shape of the output. For backward + compatible reason, if this attribute is not available for the + cell, the value will be inferred by the first element of the + `state_size`. In the case that `cell` is a list of RNN cell instances, the cells will be stacked on after the other in the RNN, implementing an efficient stacked RNN. @@ -268,9 +280,8 @@ class RNN(Layer): Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. - input_dim: dimensionality of the input (integer). - This argument (or alternatively, - the keyword argument `input_shape`) + input_dim: dimensionality of the input (integer or tuple of integers). + This argument (or alternatively, the keyword argument `input_shape`) is required when using this layer as the first layer in a model. input_length: Length of input sequences, to be specified when it is constant. @@ -283,15 +294,18 @@ class RNN(Layer): (e.g. via the `input_shape` argument) Input shape: - 3D tensor with shape `(batch_size, timesteps, input_dim)`. + N-D tensor with shape `(batch_size, timesteps, ...)`. Output shape: - if `return_state`: a list of tensors. The first tensor is the output. The remaining tensors are the last states, - each with shape `(batch_size, units)`. - - if `return_sequences`: 3D tensor with shape - `(batch_size, timesteps, units)`. - - else, 2D tensor with shape `(batch_size, units)`. + each with shape `(batch_size, state_size)`, where `state_size` could + be a high dimension tensor shape. + - if `return_sequences`: N-D tensor with shape + `(batch_size, timesteps, output_size)`, where `output_size` could + be a high dimension tensor shape. + - else, N-D tensor with shape `(batch_size, output_size)`, where + `output_size` could be a high dimension tensor shape. # Masking This layer supports masking for input data with a variable number @@ -412,7 +426,7 @@ class RNN(Layer): self.unroll = unroll self.supports_masking = True - self.input_spec = [InputSpec(ndim=3)] + self.input_spec = [None] # The input shape is unknown yet, at least rank 3. self.state_spec = None self._states = None self.constants_spec = None @@ -421,11 +435,8 @@ class RNN(Layer): @property def states(self): if self._states is None: - if isinstance(self.cell.state_size, numbers.Integral): - num_states = 1 - else: - num_states = len(self.cell.state_size) - return [None for _ in range(num_states)] + state = nest.map_structure(lambda _: None, self.cell.state_size) + return state if nest.is_sequence(self.cell.state_size) else [state] return self._states @states.setter @@ -437,19 +448,27 @@ class RNN(Layer): if isinstance(input_shape, list): input_shape = input_shape[0] - if hasattr(self.cell.state_size, '__len__'): + if _is_multiple_state(self.cell.state_size): state_size = self.cell.state_size else: state_size = [self.cell.state_size] - output_dim = state_size[0] + + if hasattr(self.cell, 'output_size'): + output_dim = tensor_shape.as_shape(self.cell.output_size).as_list() + else: + # Note that state_size[0] could be a tensor_shape or int. + output_dim = tensor_shape.as_shape(state_size[0]).as_list() if self.return_sequences: - output_shape = (input_shape[0], input_shape[1], output_dim) + output_shape = tuple([input_shape[0], input_shape[1]] + output_dim) else: - output_shape = (input_shape[0], output_dim) + output_shape = tuple([input_shape[0]] + output_dim) if self.return_state: - state_shape = [(input_shape[0], dim) for dim in state_size] + state_shape = [ + tuple([input_shape[0]] + tensor_shape.as_shape(dim).as_list()) + for dim in state_size + ] return [output_shape] + state_shape else: return output_shape @@ -477,49 +496,83 @@ class RNN(Layer): input_shape = input_shape[0] batch_size = input_shape[0] if self.stateful else None - input_dim = input_shape[-1] - self.input_spec[0] = InputSpec(shape=(batch_size, None, input_dim)) + input_dim = input_shape[2:] + self.input_spec[0] = InputSpec(shape=(batch_size, None) + input_dim) # allow cell (if layer) to build before we set or validate state_spec if isinstance(self.cell, Layer): - step_input_shape = (input_shape[0],) + input_shape[2:] + step_input_shape = (input_shape[0],) + input_dim if constants_shape is not None: self.cell.build([step_input_shape] + constants_shape) else: self.cell.build(step_input_shape) # set or validate state_spec - if hasattr(self.cell.state_size, '__len__'): + if _is_multiple_state(self.cell.state_size): state_size = list(self.cell.state_size) else: state_size = [self.cell.state_size] if self.state_spec is not None: # initial_state was passed in call, check compatibility - if [spec.shape[-1] for spec in self.state_spec] != state_size: - raise ValueError( - 'An `initial_state` was passed that is not compatible with ' - '`cell.state_size`. Received `state_spec`={}; ' - 'however `cell.state_size` is ' - '{}'.format(self.state_spec, self.cell.state_size)) + self._validate_state_spec(state_size, self.state_spec) else: - self.state_spec = [InputSpec(shape=(None, dim)) for dim in state_size] + self.state_spec = [ + InputSpec(shape=[None] + tensor_shape.as_shape(dim).as_list()) + for dim in state_size + ] if self.stateful: self.reset_states() self.built = True + @staticmethod + def _validate_state_spec(cell_state_sizes, init_state_specs): + """Validate the state spec between the initial_state and the state_size. + + Args: + cell_state_sizes: list, the `state_size` attribute from the cell. + init_state_specs: list, the `state_spec` from the initial_state that is + passed in call() + + Raises: + ValueError: When initial state spec is not compatible with the state size. + """ + validation_error = ValueError( + 'An `initial_state` was passed that is not compatible with ' + '`cell.state_size`. Received `state_spec`={}; ' + 'however `cell.state_size` is ' + '{}'.format(init_state_specs, cell_state_sizes)) + if len(cell_state_sizes) == len(init_state_specs): + for i in range(len(cell_state_sizes)): + if not tensor_shape.TensorShape( + # Ignore the first axis for init_state which is for batch + init_state_specs[i].shape[1:]).is_compatible_with( + tensor_shape.TensorShape(cell_state_sizes[i])): + raise validation_error + else: + raise validation_error + def get_initial_state(self, inputs): - # build an all-zero tensor of shape (samples, output_dim) + # build an all-zero tensor of shape (batch, cell.state_size) initial_state = array_ops.zeros_like(inputs) - # shape of initial_state = (samples, timesteps, input_dim) - initial_state = math_ops.reduce_sum(initial_state, axis=(1, 2)) - # shape of initial_state = (samples,) - initial_state = array_ops.expand_dims(initial_state, axis=-1) - # shape of initial_state = (samples, 1) - if hasattr(self.cell.state_size, '__len__'): - return [K.tile(initial_state, [1, dim]) for dim in self.cell.state_size] + # shape of initial_state = (batch, timesteps, ...) + initial_state = math_ops.reduce_sum( + initial_state, axis=list(range(1, len(inputs.shape)))) + # shape of initial_state = (batch,) + if _is_multiple_state(self.cell.state_size): + states = [] + for dims in self.cell.state_size: + state = initial_state + flat_dims = tensor_shape.as_shape(dims).as_list() + # reshape the state to (batch, 1, 1, ....) and then expand each state. + state = array_ops.reshape(state, [-1,] + [1] * len(flat_dims)) + states.append(K.tile(state, [1] + flat_dims)) + return states else: - return [K.tile(initial_state, [1, self.cell.state_size])] + flat_dims = tensor_shape.as_shape(self.cell.state_size).as_list() + initial_state = array_ops.reshape( + initial_state, [-1] + [1] * len(flat_dims)) + return [K.tile(initial_state, [1] + flat_dims)] def __call__(self, inputs, initial_state=None, constants=None, **kwargs): inputs, initial_state, constants = _standardize_args(inputs, @@ -682,19 +735,26 @@ class RNN(Layer): '`batch_shape` argument to your Input layer.') # initialize state if None if self.states[0] is None: - if hasattr(self.cell.state_size, '__len__'): + if _is_multiple_state(self.cell.state_size): self.states = [ - K.zeros((batch_size, dim)) for dim in self.cell.state_size + K.zeros([batch_size] + tensor_shape.as_shape(dim).as_list()) + for dim in self.cell.state_size ] else: - self.states = [K.zeros((batch_size, self.cell.state_size))] + self.states = [ + K.zeros([batch_size] + + tensor_shape.as_shape(self.cell.state_size).as_list()) + ] elif states is None: - if hasattr(self.cell.state_size, '__len__'): + if _is_multiple_state(self.cell.state_size): for state, dim in zip(self.states, self.cell.state_size): - K.set_value(state, np.zeros((batch_size, dim))) + K.set_value(state, + np.zeros([batch_size] + + tensor_shape.as_shape(dim).as_list())) else: - K.set_value(self.states[0], np.zeros((batch_size, - self.cell.state_size))) + K.set_value(self.states[0], np.zeros( + [batch_size] + + tensor_shape.as_shape(self.cell.state_size).as_list())) else: if not isinstance(states, (list, tuple)): states = [states] @@ -704,11 +764,12 @@ class RNN(Layer): 'but it received ' + str(len(states)) + ' state values. Input received: ' + str(states)) for index, (value, state) in enumerate(zip(states, self.states)): - if hasattr(self.cell.state_size, '__len__'): + if _is_multiple_state(self.cell.state_size): dim = self.cell.state_size[index] else: dim = self.cell.state_size - if value.shape != (batch_size, dim): + if value.shape != tuple([batch_size] + + tensor_shape.as_shape(dim).as_list()): raise ValueError( 'State ' + str(index) + ' is incompatible with layer ' + self.name + ': expected shape=' + str( @@ -846,6 +907,7 @@ class SimpleRNNCell(Layer): self.dropout = min(1., max(0., dropout)) self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.state_size = self.units + self.output_size = self.units self._dropout_mask = None self._recurrent_dropout_mask = None @@ -1249,6 +1311,7 @@ class GRUCell(Layer): self.implementation = implementation self.reset_after = reset_after self.state_size = self.units + self.output_size = self.units self._dropout_mask = None self._recurrent_dropout_mask = None @@ -1794,6 +1857,7 @@ class LSTMCell(Layer): self.recurrent_dropout = min(1., max(0., recurrent_dropout)) self.implementation = implementation self.state_size = (self.units, self.units) + self.output_size = self.units self._dropout_mask = None self._recurrent_dropout_mask = None @@ -2272,3 +2336,9 @@ def _standardize_args(inputs, initial_state, constants, num_constants): constants = to_list_or_none(constants) return inputs, initial_state, constants + + +def _is_multiple_state(state_size): + """Check whether the state_size contains multiple states.""" + return (hasattr(state_size, '__len__') and + not isinstance(state_size, tensor_shape.TensorShape)) diff --git a/tensorflow/python/keras/layers/recurrent_test.py b/tensorflow/python/keras/layers/recurrent_test.py index fefb92826b33b65a14ba667207995b6e4194c202..13bd07052873d7895ecee8a0227c0a30b933f31d 100644 --- a/tensorflow/python/keras/layers/recurrent_test.py +++ b/tensorflow/python/keras/layers/recurrent_test.py @@ -24,8 +24,10 @@ from __future__ import print_function import numpy as np from tensorflow.python import keras +from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import special_math_ops from tensorflow.python.ops import state_ops from tensorflow.python.platform import test from tensorflow.python.training.checkpointable import util as checkpointable_util @@ -573,6 +575,163 @@ class RNNTest(test.TestCase): for v in model.variables: self.assertIn(v, checkpointed_objects) + def test_high_dimension_RNN(self): + with self.test_session(): + # Basic test case. + unit_a = 10 + unit_b = 20 + input_a = 5 + input_b = 10 + batch = 32 + time_step = 4 + + cell = Minimal2DRNNCell(unit_a, unit_b) + x = keras.Input((None, input_a, input_b)) + layer = keras.layers.RNN(cell) + y = layer(x) + + self.assertEqual(cell.state_size.as_list(), [unit_a, unit_b]) + init_state = layer.get_initial_state(x) + self.assertEqual(len(init_state), 1) + self.assertEqual(init_state[0].get_shape().as_list(), + [None, unit_a, unit_b]) + + model = keras.models.Model(x, y) + model.compile(optimizer='rmsprop', loss='mse') + model.train_on_batch( + np.zeros((batch, time_step, input_a, input_b)), + np.zeros((batch, unit_a, unit_b))) + self.assertEqual(model.output_shape, (None, unit_a, unit_b)) + + # Test stacking. + cells = [ + Minimal2DRNNCell(unit_a, unit_b), + Minimal2DRNNCell(unit_a * 2, unit_b * 2), + Minimal2DRNNCell(unit_a * 4, unit_b * 4) + ] + layer = keras.layers.RNN(cells) + y = layer(x) + model = keras.models.Model(x, y) + model.compile(optimizer='rmsprop', loss='mse') + model.train_on_batch( + np.zeros((batch, time_step, input_a, input_b)), + np.zeros((batch, unit_a * 4, unit_b * 4))) + self.assertEqual(model.output_shape, (None, unit_a * 4, unit_b * 4)) + + def test_high_dimension_RNN_with_init_state(self): + unit_a = 10 + unit_b = 20 + input_a = 5 + input_b = 10 + batch = 32 + time_step = 4 + + with self.test_session(): + # Basic test case. + cell = Minimal2DRNNCell(unit_a, unit_b) + x = keras.Input((None, input_a, input_b)) + s = keras.Input((unit_a, unit_b)) + layer = keras.layers.RNN(cell) + y = layer(x, initial_state=s) + + model = keras.models.Model([x, s], y) + model.compile(optimizer='rmsprop', loss='mse') + model.train_on_batch([ + np.zeros((batch, time_step, input_a, input_b)), + np.zeros((batch, unit_a, unit_b)) + ], np.zeros((batch, unit_a, unit_b))) + self.assertEqual(model.output_shape, (None, unit_a, unit_b)) + + with self.test_session(): + # Bad init state shape. + bad_shape_a = unit_a * 2 + bad_shape_b = unit_b * 2 + cell = Minimal2DRNNCell(unit_a, unit_b) + x = keras.Input((None, input_a, input_b)) + s = keras.Input((bad_shape_a, bad_shape_b)) + layer = keras.layers.RNN(cell) + with self.assertRaisesWithPredicateMatch(ValueError, + 'however `cell.state_size` is'): + layer(x, initial_state=s) + + def test_inconsistent_output_state_size(self): + with self.test_session(): + batch = 32 + time_step = 4 + state_size = 5 + input_size = 6 + cell = PlusOneRNNCell(state_size) + x = keras.Input((None, input_size)) + layer = keras.layers.RNN(cell) + y = layer(x) + + self.assertEqual(cell.state_size, state_size) + init_state = layer.get_initial_state(x) + self.assertEqual(len(init_state), 1) + self.assertEqual(init_state[0].get_shape().as_list(), + [None, state_size]) + + model = keras.models.Model(x, y) + model.compile(optimizer='rmsprop', loss='mse') + model.train_on_batch( + np.zeros((batch, time_step, input_size)), + np.zeros((batch, input_size))) + self.assertEqual(model.output_shape, (None, input_size)) + + +class Minimal2DRNNCell(keras.layers.Layer): + """The minimal 2D RNN cell is a simple combination of 2 1-D RNN cell. + + Both internal state and output have 2 dimensions and are orthogonal + between each other. + """ + + def __init__(self, unit_a, unit_b, **kwargs): + self.unit_a = unit_a + self.unit_b = unit_b + self.state_size = tensor_shape.as_shape([unit_a, unit_b]) + self.output_size = tensor_shape.as_shape([unit_a, unit_b]) + super(Minimal2DRNNCell, self).__init__(**kwargs) + + def build(self, input_shape): + input_a = input_shape[-2] + input_b = input_shape[-1] + self.kernel = self.add_weight( + shape=(input_a, input_b, self.unit_a, self.unit_b), + initializer='uniform', + name='kernel') + self.recurring_kernel = self.add_weight( + shape=(self.unit_a, self.unit_b, self.unit_a, self.unit_b), + initializer='uniform', + name='recurring_kernel') + self.bias = self.add_weight( + shape=(self.unit_a, self.unit_b), initializer='uniform', name='bias') + self.built = True + + def call(self, inputs, states): + prev_output = states[0] + h = special_math_ops.einsum('bij,ijkl->bkl', inputs, self.kernel) + h += array_ops.expand_dims(self.bias, axis=0) + output = h + special_math_ops.einsum('bij,ijkl->bkl', prev_output, + self.recurring_kernel) + return output, [output] + + +class PlusOneRNNCell(keras.layers.Layer): + """Add one to the input and state. + + This cell is used for testing state_size and output_size.""" + + def __init__(self, num_unit, **kwargs): + self.state_size = num_unit + super(PlusOneRNNCell, self).__init__(**kwargs) + + def build(self, input_shape): + self.output_size = input_shape[-1] + + def call(self, inputs, states): + return inputs + 1, [states[0] + 1] + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/layers/wrappers.py b/tensorflow/python/keras/layers/wrappers.py index f0c1e76156f2c01d6fceea6d2a6b4c8b6d79ba69..9b8d5fc5cc6937e69c0ea53d5719da76a2a52299 100644 --- a/tensorflow/python/keras/layers/wrappers.py +++ b/tensorflow/python/keras/layers/wrappers.py @@ -331,7 +331,7 @@ class TimeDistributed(Wrapper): inner_mask_shape = self._get_shape_tuple((-1,), mask, 2) inner_mask = K.reshape(inner_mask, inner_mask_shape) input_uid = generic_utils.object_list_uid(inputs) - inner_inputs = self._input_map[input_uid] + inner_inputs = self._input_map.get(input_uid, inputs) output_mask = self.layer.compute_mask(inner_inputs, inner_mask) if output_mask is None: if mask is None: diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index b18f12612a849d8d8b7e2465ff8075d35764000e..2dde9ee41f86fd7e15465b0e8f755a45bbea8348 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -68,25 +68,19 @@ def check_is_tensor_or_operation(x, name): def update_state_wrapper(update_state_fn): - """Decorator to wrap metric `update_state()` with `defun()`, `add_update()`. + """Decorator to wrap metric `update_state()` with `add_update()`. Args: update_state_fn: function that accumulates metric statistics. Returns: - If eager execution is enabled, returns None. - If graph execution is enabled, returns an update op. This op should be - executed to update the metric state with the given inputs. + Decorated function that wraps `update_state_fn()` with `add_update()`. """ def decorated(metric_obj, *args, **kwargs): - """Decorated function with `defun()` and `add_update()`.""" + """Decorated function with `add_update()`.""" - # Converting update_state_fn() into a graph function, so that - # we can return a single op that performs all of the variable updates. - # Assigning to a different method name to avoid reference cycle. - defuned_update_state_fn = function.defun(update_state_fn) - update_op = defuned_update_state_fn(*args, **kwargs) + update_op = update_state_fn(*args, **kwargs) if update_op is not None: # update_op will be None in eager execution. metric_obj.add_update(update_op, inputs=True) check_is_tensor_or_operation( @@ -111,7 +105,8 @@ def result_wrapper(result_fn): result_fn: function that computes the metric result. Returns: - The metric result tensor. + Decorated function that wraps `result_fn()` in distribution strategy + `merge_call()`. """ def decorated(metric_obj, *args): @@ -255,6 +250,28 @@ class Metric(Layer): print('Final result: ', sess.run(m.result())) ``` + Usage with tf.keras API: + + ```python + model = tf.keras.Sequential() + model.add(tf.keras.layers.Dense(64, activation='relu')) + model.add(tf.keras.layers.Dense(64, activation='relu')) + model.add(tf.keras.layers.Dense(10, activation='softmax')) + + model.compile(optimizer=tf.train.RMSPropOptimizer(0.01), + loss=tf.keras.losses.categorical_crossentropy, + metrics=[tf.keras.metrics.CategoricalAccuracy()]) + + data = np.random.random((1000, 32)) + labels = np.random.random((1000, 10)) + + dataset = tf.data.Dataset.from_tensor_slices((data, labels)) + dataset = dataset.batch(32) + dataset = dataset.repeat() + + model.fit(dataset, epochs=10, steps_per_epoch=30) + ``` + To be implemented by subclasses: * `__init__()`: All state variables should be created in this method by calling `self.add_weight()` like: `self.var = self.add_weight(...)` @@ -267,7 +284,7 @@ class Metric(Layer): ``` class BinaryTruePositives(Metric): - def __init__(self, name='binary-true-positives', dtype=None): + def __init__(self, name='binary_true_positives', dtype=None): super(BinaryTruePositives, self).__init__(name=name, dtype=dtype) self.true_positives = self.add_weight( 'true_positives', initializer=init_ops.zeros_initializer) @@ -299,9 +316,14 @@ class Metric(Layer): self._dtype = K.floatx() if dtype is None else dtypes.as_dtype(dtype).name def __new__(cls, *args, **kwargs): - obj = super(Metric, cls).__new__(cls, *args, **kwargs) + obj = super(Metric, cls).__new__(cls) + # TODO(psv): Fix reference cycle issue here. + + # Converting update_state_fn() into a graph function, so that + # we can return a single op that performs all of the variable updates. + defuned_update_state_fn = function.defun(obj.update_state) obj.update_state = types.MethodType( - update_state_wrapper(obj.update_state), obj) + update_state_wrapper(defuned_update_state_fn), obj) obj.result = types.MethodType(result_wrapper(obj.result), obj) return obj @@ -359,6 +381,12 @@ class Metric(Layer): """ NotImplementedError('Must be implemented in subclasses.') + @classmethod + def from_config(cls, config): + if 'trainable' in config: + config.pop('trainable') + return cls(**config) + ### For use by subclasses ### def add_weight(self, name, @@ -502,7 +530,7 @@ class BinaryAccuracy(MeanMetricWrapper): Use `sample_weight` of 0 to mask values. """ - def __init__(self, name='binary-accuracy', dtype=None, threshold=0.5): + def __init__(self, name='binary_accuracy', dtype=None, threshold=0.5): """Creates a `BinaryAccuracy` instance. Args: @@ -515,6 +543,29 @@ class BinaryAccuracy(MeanMetricWrapper): binary_accuracy, name, dtype=dtype, threshold=threshold) +class CategoricalAccuracy(MeanMetricWrapper): + """Calculates how often predictions matches labels. + + This metric creates two local variables, `total` and `count` that are used to + compute the frequency with which `y_pred` matches `y_true`. This frequency is + ultimately returned as `categorical accuracy`: an idempotent operation that + simply divides `total` by `count`. + + If `sample_weight` is `None`, weights default to 1. + Use `sample_weight` of 0 to mask values. + """ + + def __init__(self, name='categorical_accuracy', dtype=None): + """Creates a `CategoricalAccuracy` instance. + + Args: + name: (Optional) string name of the metric instance. + dtype: (Optional) data type of the metric result. + """ + super(CategoricalAccuracy, self).__init__( + categorical_accuracy, name, dtype=dtype) + + @tf_export('keras.metrics.binary_accuracy') def binary_accuracy(y_true, y_pred, threshold=0.5): threshold = math_ops.cast(threshold, y_pred.dtype) @@ -578,8 +629,7 @@ def deserialize(config, custom_objects=None): @tf_export('keras.metrics.get') def get(identifier): if isinstance(identifier, dict): - config = {'class_name': str(identifier), 'config': {}} - return deserialize(config) + return deserialize(identifier) elif isinstance(identifier, six.string_types): return deserialize(str(identifier)) elif callable(identifier): diff --git a/tensorflow/python/keras/metrics_test.py b/tensorflow/python/keras/metrics_test.py index 49f3ae40d9493f6ff07a54ec3a6d7f6a4daf9d28..2ac74219d4035420afcf3421dd1d2ca803a0f576 100644 --- a/tensorflow/python/keras/metrics_test.py +++ b/tensorflow/python/keras/metrics_test.py @@ -362,6 +362,30 @@ class KerasMetricsTest(test.TestCase): result = self.evaluate(result_t) self.assertAlmostEqual(result, 0.5, 2) + @test_util.run_in_graph_and_eager_modes + def test_categorical_accuracy(self): + acc_obj = metrics.CategoricalAccuracy(name='my acc') + + # check config + self.assertEqual(acc_obj.name, 'my acc') + self.assertTrue(acc_obj.stateful) + self.assertEqual(len(acc_obj.variables), 2) + self.assertEqual(acc_obj.dtype, dtypes.float32) + self.evaluate(variables.global_variables_initializer()) + + # verify that correct value is returned + update_op = acc_obj.update_state([[0, 0, 1], [0, 1, 0]], + [[0.1, 0.1, 0.8], [0.05, 0.95, 0]]) + self.evaluate(update_op) + result = self.evaluate(acc_obj.result()) + self.assertEqual(result, 1) # 2/2 + + # check with sample_weight + result_t = acc_obj([[0, 0, 1], [0, 1, 0]], + [[0.1, 0.1, 0.8], [0.05, 0, 0.95]], [[0.5], [0.2]]) + result = self.evaluate(result_t) + self.assertAlmostEqual(result, 0.93, 2) # 2.5/2.7 + @test_util.run_in_graph_and_eager_modes def test_invalid_result(self): diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py index 6cbea45bd5ca84dd7aa76987e218c85e1ded1fdf..71c1987cee6c610a19d12d5b9e2389606c5f1c24 100644 --- a/tensorflow/python/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/model_subclassing_test.py @@ -425,9 +425,10 @@ class ModelSubclassingTest(test.TestCase): model = SimpleTestModel(num_classes=num_classes, use_dp=True, use_bn=True) - model.compile(loss='mse', - optimizer=RMSPropOptimizer(learning_rate=0.001), - metrics=['acc']) + model.compile( + loss='mse', + optimizer=RMSPropOptimizer(learning_rate=0.001), + metrics=['acc', keras.metrics.CategoricalAccuracy()]) x = np.ones((num_samples, input_dim)) y = np.zeros((num_samples, num_classes)) diff --git a/tensorflow/python/keras/optimizers.py b/tensorflow/python/keras/optimizers.py index 0b440185ca7ccfc4fadf5419e6ceb4c64a554e1d..4f97442e828559424de0618fba1268461c9d64db 100644 --- a/tensorflow/python/keras/optimizers.py +++ b/tensorflow/python/keras/optimizers.py @@ -718,10 +718,13 @@ class TFOptimizer(Optimizer, checkpointable.CheckpointableBase): global_step = training_util.get_global_step() opt_update = self.optimizer.apply_gradients(grads, global_step) else: - self.updates = [state_ops.assign_add(self.iterations, 1)] if not params: + self.updates = [state_ops.assign_add(self.iterations, 1)] return self.updates + # Updates list starts out empty because the iterations variable is + # incremented in optimizer.apply_gradients() + self.updates = [] grads = self.optimizer.compute_gradients(loss, params) opt_update = self.optimizer.apply_gradients( grads, global_step=self.iterations) diff --git a/tensorflow/python/keras/optimizers_test.py b/tensorflow/python/keras/optimizers_test.py index 55fc3fdcf47b4e5589e2253fffdc97d33f5b481b..4d295351f561268bbbadc885308c7d8e8765e042 100644 --- a/tensorflow/python/keras/optimizers_test.py +++ b/tensorflow/python/keras/optimizers_test.py @@ -46,7 +46,11 @@ def _test_optimizer(optimizer, target=0.75): model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) + np.testing.assert_equal(keras.backend.get_value(model.optimizer.iterations), + 0) history = model.fit(x_train, y_train, epochs=2, batch_size=16, verbose=0) + np.testing.assert_equal(keras.backend.get_value(model.optimizer.iterations), + 126) # 63 steps per epoch assert history.history['acc'][-1] >= target config = keras.optimizers.serialize(optimizer) optim = keras.optimizers.deserialize(config) @@ -66,7 +70,11 @@ def _test_optimizer(optimizer, target=0.75): model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) + np.testing.assert_equal(keras.backend.get_value(model.optimizer.iterations), + 126) # Using same optimizer from before model.train_on_batch(x_train[:10], y_train[:10]) + np.testing.assert_equal(keras.backend.get_value(model.optimizer.iterations), + 127) kernel, bias = dense.get_weights() np.testing.assert_allclose(kernel, 1., atol=1e-3) np.testing.assert_allclose(bias, 2., atol=1e-3) @@ -145,6 +153,28 @@ class KerasOptimizersTest(test.TestCase): with self.assertRaises(NotImplementedError): optimizer.from_config(None) + def test_tfoptimizer_iterations(self): + with self.test_session(): + optimizer = keras.optimizers.TFOptimizer(AdamOptimizer(0.01)) + model = keras.models.Sequential() + model.add(keras.layers.Dense( + 2, input_shape=(3,), kernel_constraint=keras.constraints.MaxNorm(1))) + model.compile(loss='mean_squared_error', optimizer=optimizer) + self.assertEqual(keras.backend.get_value(model.optimizer.iterations), 0) + + model.fit(np.random.random((55, 3)), + np.random.random((55, 2)), + epochs=1, + batch_size=5, + verbose=0) + self.assertEqual(keras.backend.get_value(model.optimizer.iterations), 11) + + model.fit(np.random.random((20, 3)), + np.random.random((20, 2)), + steps_per_epoch=8, + verbose=0) + self.assertEqual(keras.backend.get_value(model.optimizer.iterations), 19) + def test_negative_clipvalue_or_clipnorm(self): with self.assertRaises(ValueError): _ = keras.optimizers.SGD(lr=0.01, clipvalue=-0.5) diff --git a/tensorflow/python/keras/preprocessing/__init__.py b/tensorflow/python/keras/preprocessing/__init__.py index e6704eeaa1f953be68e7ccdbc7e8bd60c62a61d8..2f08f88600f422b3a69ae1969ce5faa5716364f3 100644 --- a/tensorflow/python/keras/preprocessing/__init__.py +++ b/tensorflow/python/keras/preprocessing/__init__.py @@ -13,10 +13,18 @@ # limitations under the License. # ============================================================================== """Keras data preprocessing utils.""" +# pylint: disable=g-import-not-at-top from __future__ import absolute_import from __future__ import division from __future__ import print_function +import keras_preprocessing + +from tensorflow.python.keras import backend +from tensorflow.python.keras import utils + +keras_preprocessing.set_keras_submodules(backend=backend, utils=utils) + from tensorflow.python.keras.preprocessing import image from tensorflow.python.keras.preprocessing import sequence from tensorflow.python.keras.preprocessing import text diff --git a/tensorflow/python/keras/preprocessing/image.py b/tensorflow/python/keras/preprocessing/image.py index aa425df6a8bdb29b90a6d7000d126b771247c19f..ba227385eff30367487bf0d4294875bb6a48bced 100644 --- a/tensorflow/python/keras/preprocessing/image.py +++ b/tensorflow/python/keras/preprocessing/image.py @@ -12,1588 +12,58 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== +# pylint: disable=invalid-name # pylint: disable=g-import-not-at-top -"""Fairly basic set of tools for real-time data augmentation on image data. - -Can easily be extended to include new transformations, -new preprocessing methods, etc... +"""Set of tools for real-time data augmentation on image data. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from functools import partial -import multiprocessing.pool -import os -import re -import threading - -import numpy as np -from tensorflow.python.keras import backend as K -from tensorflow.python.keras.utils.data_utils import Sequence -from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.util.tf_export import tf_export - +from keras_preprocessing import image try: - from scipy import linalg - import scipy.ndimage as ndi + from scipy import linalg # pylint: disable=unused-import + from scipy import ndimage # pylint: disable=unused-import except ImportError: - linalg = None - ndi = None - - -try: - from PIL import ImageEnhance - from PIL import Image as pil_image -except ImportError: - pil_image = None - -if pil_image is not None: - _PIL_INTERPOLATION_METHODS = { - 'nearest': pil_image.NEAREST, - 'bilinear': pil_image.BILINEAR, - 'bicubic': pil_image.BICUBIC, - } - # These methods were only introduced in version 3.4.0 (2016). - if hasattr(pil_image, 'HAMMING'): - _PIL_INTERPOLATION_METHODS['hamming'] = pil_image.HAMMING - if hasattr(pil_image, 'BOX'): - _PIL_INTERPOLATION_METHODS['box'] = pil_image.BOX - # This method is new in version 1.1.3 (2013). - if hasattr(pil_image, 'LANCZOS'): - _PIL_INTERPOLATION_METHODS['lanczos'] = pil_image.LANCZOS - - -@tf_export('keras.preprocessing.image.random_rotation') -def random_rotation(x, - rg, - row_axis=1, - col_axis=2, - channel_axis=0, - fill_mode='nearest', - cval=0.): - """Performs a random rotation of a Numpy image tensor. - - Arguments: - x: Input tensor. Must be 3D. - rg: Rotation range, in degrees. - row_axis: Index of axis for rows in the input tensor. - col_axis: Index of axis for columns in the input tensor. - channel_axis: Index of axis for channels in the input tensor. - fill_mode: Points outside the boundaries of the input - are filled according to the given mode - (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). - cval: Value used for points outside the boundaries - of the input if `mode='constant'`. - - Returns: - Rotated Numpy image tensor. - """ - theta = np.deg2rad(np.random.uniform(-rg, rg)) - rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], - [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]) - - h, w = x.shape[row_axis], x.shape[col_axis] - transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w) - x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) - return x - - -@tf_export('keras.preprocessing.image.random_shift') -def random_shift(x, - wrg, - hrg, - row_axis=1, - col_axis=2, - channel_axis=0, - fill_mode='nearest', - cval=0.): - """Performs a random spatial shift of a Numpy image tensor. - - Arguments: - x: Input tensor. Must be 3D. - wrg: Width shift range, as a float fraction of the width. - hrg: Height shift range, as a float fraction of the height. - row_axis: Index of axis for rows in the input tensor. - col_axis: Index of axis for columns in the input tensor. - channel_axis: Index of axis for channels in the input tensor. - fill_mode: Points outside the boundaries of the input - are filled according to the given mode - (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). - cval: Value used for points outside the boundaries - of the input if `mode='constant'`. - - Returns: - Shifted Numpy image tensor. - """ - h, w = x.shape[row_axis], x.shape[col_axis] - tx = np.random.uniform(-hrg, hrg) * h - ty = np.random.uniform(-wrg, wrg) * w - translation_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]]) - - transform_matrix = translation_matrix # no need to do offset - x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) - return x - - -@tf_export('keras.preprocessing.image.random_shear') -def random_shear(x, - intensity, - row_axis=1, - col_axis=2, - channel_axis=0, - fill_mode='nearest', - cval=0.): - """Performs a random spatial shear of a Numpy image tensor. - - Arguments: - x: Input tensor. Must be 3D. - intensity: Transformation intensity in degrees. - row_axis: Index of axis for rows in the input tensor. - col_axis: Index of axis for columns in the input tensor. - channel_axis: Index of axis for channels in the input tensor. - fill_mode: Points outside the boundaries of the input - are filled according to the given mode - (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). - cval: Value used for points outside the boundaries - of the input if `mode='constant'`. - - Returns: - Sheared Numpy image tensor. - """ - shear = np.deg2rad(np.random.uniform(-intensity, intensity)) - shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], - [0, 0, 1]]) - - h, w = x.shape[row_axis], x.shape[col_axis] - transform_matrix = transform_matrix_offset_center(shear_matrix, h, w) - x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) - return x - - -@tf_export('keras.preprocessing.image.random_zoom') -def random_zoom(x, - zoom_range, - row_axis=1, - col_axis=2, - channel_axis=0, - fill_mode='nearest', - cval=0.): - """Performs a random spatial zoom of a Numpy image tensor. - - Arguments: - x: Input tensor. Must be 3D. - zoom_range: Tuple of floats; zoom range for width and height. - row_axis: Index of axis for rows in the input tensor. - col_axis: Index of axis for columns in the input tensor. - channel_axis: Index of axis for channels in the input tensor. - fill_mode: Points outside the boundaries of the input - are filled according to the given mode - (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). - cval: Value used for points outside the boundaries - of the input if `mode='constant'`. - - Returns: - Zoomed Numpy image tensor. - - Raises: - ValueError: if `zoom_range` isn't a tuple. - """ - if len(zoom_range) != 2: - raise ValueError('`zoom_range` should be a tuple or list of two floats. ' - 'Received arg: ', zoom_range) - - if zoom_range[0] == 1 and zoom_range[1] == 1: - zx, zy = 1, 1 - else: - zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2) - zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]]) - - h, w = x.shape[row_axis], x.shape[col_axis] - transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w) - x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval) - return x - - -@tf_export('keras.preprocessing.image.random_channel_shift') -def random_channel_shift(x, intensity, channel_axis=0): - """Perform a random channel shift. - - Arguments: - x: Input tensor. Must be 3D. - intensity: Transformation intensity. - channel_axis: Index of axis for channels in the input tensor. - - Returns: - Numpy image tensor. - """ - x = np.rollaxis(x, channel_axis, 0) - min_x, max_x = np.min(x), np.max(x) - channel_images = [ - np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, - max_x) for x_channel in x - ] - x = np.stack(channel_images, axis=0) - x = np.rollaxis(x, 0, channel_axis + 1) - return x - - -@tf_export('keras.preprocessing.image.random_brightness') -def random_brightness(x, brightness_range): - """Performs a random adjustment of brightness of a Numpy image tensor. - - Arguments: - x: Input tensor. Must be 3D. - brightness_range: Tuple of floats; range to pick a brightness value from. - - Returns: - Brightness adjusted Numpy image tensor. - - Raises: - ValueError: if `brightness_range` isn't a tuple. - """ - if len(brightness_range) != 2: - raise ValueError('`brightness_range should be tuple or list of two floats. ' - 'Received arg: ', brightness_range) - - x = array_to_img(x) - x = ImageEnhance.Brightness(x) - u = np.random.uniform(brightness_range[0], brightness_range[1]) - x = x.enhance(u) - x = img_to_array(x) - return x - - -def transform_matrix_offset_center(matrix, x, y): - o_x = float(x) / 2 + 0.5 - o_y = float(y) / 2 + 0.5 - offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]]) - reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]]) - transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix) - return transform_matrix - - -@tf_export('keras.preprocessing.image.apply_transform') -def apply_transform(x, - transform_matrix, - channel_axis=0, - fill_mode='nearest', - cval=0.): - """Apply the image transformation specified by a matrix. - - Arguments: - x: 2D numpy array, single image. - transform_matrix: Numpy array specifying the geometric transformation. - channel_axis: Index of axis for channels in the input tensor. - fill_mode: Points outside the boundaries of the input - are filled according to the given mode - (one of `{'constant', 'nearest', 'reflect', 'wrap'}`). - cval: Value used for points outside the boundaries - of the input if `mode='constant'`. - - Returns: - The transformed version of the input. - """ - x = np.rollaxis(x, channel_axis, 0) - final_affine_matrix = transform_matrix[:2, :2] - final_offset = transform_matrix[:2, 2] - channel_images = [ - ndi.interpolation.affine_transform( - x_channel, - final_affine_matrix, - final_offset, - order=1, - mode=fill_mode, - cval=cval) for x_channel in x - ] - x = np.stack(channel_images, axis=0) - x = np.rollaxis(x, 0, channel_axis + 1) - return x - - -@tf_export('keras.preprocessing.image.flip_axis') -def flip_axis(x, axis): - x = np.asarray(x).swapaxes(axis, 0) - x = x[::-1, ...] - x = x.swapaxes(0, axis) - return x - - -@tf_export('keras.preprocessing.image.array_to_img') -def array_to_img(x, data_format=None, scale=True): - """Converts a 3D Numpy array to a PIL Image instance. - - Arguments: - x: Input Numpy array. - data_format: Image data format. - scale: Whether to rescale image values - to be within [0, 255]. - - Returns: - A PIL Image instance. - - Raises: - ImportError: if PIL is not available. - ValueError: if invalid `x` or `data_format` is passed. - """ - if pil_image is None: - raise ImportError('Could not import PIL.Image. ' - 'The use of `array_to_img` requires PIL.') - x = np.asarray(x, dtype=K.floatx()) - if x.ndim != 3: - raise ValueError('Expected image array to have rank 3 (single image). ' - 'Got array with shape:', x.shape) - - if data_format is None: - data_format = K.image_data_format() - if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Invalid data_format:', data_format) - - # Original Numpy array x has format (height, width, channel) - # or (channel, height, width) - # but target PIL image has format (width, height, channel) - if data_format == 'channels_first': - x = x.transpose(1, 2, 0) - if scale: - x = x + max(-np.min(x), 0) # pylint: disable=g-no-augmented-assignment - x_max = np.max(x) - if x_max != 0: - x /= x_max - x *= 255 - if x.shape[2] == 3: - # RGB - return pil_image.fromarray(x.astype('uint8'), 'RGB') - elif x.shape[2] == 1: - # grayscale - return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L') - else: - raise ValueError('Unsupported channel number: ', x.shape[2]) - - -@tf_export('keras.preprocessing.image.img_to_array') -def img_to_array(img, data_format=None): - """Converts a PIL Image instance to a Numpy array. - - Arguments: - img: PIL Image instance. - data_format: Image data format. - - Returns: - A 3D Numpy array. - - Raises: - ValueError: if invalid `img` or `data_format` is passed. - """ - if data_format is None: - data_format = K.image_data_format() - if data_format not in {'channels_first', 'channels_last'}: - raise ValueError('Unknown data_format: ', data_format) - # Numpy array x has format (height, width, channel) - # or (channel, height, width) - # but original PIL image has format (width, height, channel) - x = np.asarray(img, dtype=K.floatx()) - if len(x.shape) == 3: - if data_format == 'channels_first': - x = x.transpose(2, 0, 1) - elif len(x.shape) == 2: - if data_format == 'channels_first': - x = x.reshape((1, x.shape[0], x.shape[1])) - else: - x = x.reshape((x.shape[0], x.shape[1], 1)) - else: - raise ValueError('Unsupported image shape: ', x.shape) - return x - - -@tf_export('keras.preprocessing.image.load_img') -def load_img(path, grayscale=False, target_size=None, interpolation='nearest'): - """Loads an image into PIL format. - - Arguments: - path: Path to image file - grayscale: Boolean, whether to load the image as grayscale. - target_size: Either `None` (default to original size) - or tuple of ints `(img_height, img_width)`. - interpolation: Interpolation method used to resample the image if the - target size is different from that of the loaded image. - Supported methods are "nearest", "bilinear", and "bicubic". - If PIL version 1.1.3 or newer is installed, "lanczos" is also - supported. If PIL version 3.4.0 or newer is installed, "box" and - "hamming" are also supported. By default, "nearest" is used. - - Returns: - A PIL Image instance. - - Raises: - ImportError: if PIL is not available. - ValueError: if interpolation method is not supported. - """ - if pil_image is None: - raise ImportError('Could not import PIL.Image. ' - 'The use of `array_to_img` requires PIL.') - img = pil_image.open(path) - if grayscale: - if img.mode != 'L': - img = img.convert('L') - else: - if img.mode != 'RGB': - img = img.convert('RGB') - if target_size is not None: - width_height_tuple = (target_size[1], target_size[0]) - if img.size != width_height_tuple: - if interpolation not in _PIL_INTERPOLATION_METHODS: - raise ValueError('Invalid interpolation method {} specified. Supported ' - 'methods are {}'.format(interpolation, ', '.join( - _PIL_INTERPOLATION_METHODS.keys()))) - resample = _PIL_INTERPOLATION_METHODS[interpolation] - img = img.resize(width_height_tuple, resample) - return img - - -def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'): - return [ - os.path.join(root, f) - for root, _, files in os.walk(directory) - for f in files - if re.match(r'([\w]+\.(?:' + ext + '))', f) - ] - - -@tf_export('keras.preprocessing.image.ImageDataGenerator') -class ImageDataGenerator(object): - """Generates batches of tensor image data with real-time data augmentation. - The data will be looped over (in batches). - - Arguments: - featurewise_center: boolean, set input mean to 0 over the dataset, - feature-wise. - samplewise_center: boolean, set each sample mean to 0. - featurewise_std_normalization: boolean, divide inputs by std - of the dataset, feature-wise. - samplewise_std_normalization: boolean, divide each input by its std. - zca_epsilon: epsilon for ZCA whitening. Default is 1e-6. - zca_whitening: boolean, apply ZCA whitening. - rotation_range: int, degree range for random rotations. - width_shift_range: float, 1-D array-like or int - float: fraction of total width, if < 1, or pixels if >= 1. - 1-D array-like: random elements from the array. - int: integer number of pixels from interval - `(-width_shift_range, +width_shift_range)` - With `width_shift_range=2` possible values are integers [-1, 0, +1], - same as with `width_shift_range=[-1, 0, +1]`, - while with `width_shift_range=1.0` possible values are floats in - the interval [-1.0, +1.0). - shear_range: float, shear Intensity - (Shear angle in counter-clockwise direction in degrees) - zoom_range: float or [lower, upper], Range for random zoom. - If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`. - channel_shift_range: float, range for random channel shifts. - fill_mode: One of {"constant", "nearest", "reflect" or "wrap"}. - Default is 'nearest'. Points outside the boundaries of the input - are filled according to the given mode: - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k) - 'nearest': aaaaaaaa|abcd|dddddddd - 'reflect': abcddcba|abcd|dcbaabcd - 'wrap': abcdabcd|abcd|abcdabcd - cval: float or int, value used for points outside the boundaries - when `fill_mode = "constant"`. - horizontal_flip: boolean, randomly flip inputs horizontally. - vertical_flip: boolean, randomly flip inputs vertically. - rescale: rescaling factor. Defaults to None. If None or 0, no rescaling - is applied, otherwise we multiply the data by the value provided - (before applying any other transformation). - preprocessing_function: function that will be implied on each input. - The function will run after the image is resized and augmented. - The function should take one argument: - one image (Numpy tensor with rank 3), - and should output a Numpy tensor with the same shape. - data_format: One of {"channels_first", "channels_last"}. - "channels_last" mode means that the images should have shape - `(samples, height, width, channels)`, - "channels_first" mode means that the images should have shape - `(samples, channels, height, width)`. - It defaults to the `image_data_format` value found in your - Keras config file at `~/.keras/keras.json`. - If you never set it, then it will be "channels_last". - validation_split: float, fraction of images reserved for validation - (strictly between 0 and 1). - - Examples: - Example of using `.flow(x, y)`: - ```python - (x_train, y_train), (x_test, y_test) = cifar10.load_data() - y_train = np_utils.to_categorical(y_train, num_classes) - y_test = np_utils.to_categorical(y_test, num_classes) - datagen = ImageDataGenerator( - featurewise_center=True, - featurewise_std_normalization=True, - rotation_range=20, - width_shift_range=0.2, - height_shift_range=0.2, - horizontal_flip=True) - # compute quantities required for featurewise normalization - # (std, mean, and principal components if ZCA whitening is applied) - datagen.fit(x_train) - # fits the model on batches with real-time data augmentation: - model.fit_generator(datagen.flow(x_train, y_train, batch_size=32), - steps_per_epoch=len(x_train) / 32, epochs=epochs) - # here's a more "manual" example - for e in range(epochs): - print('Epoch', e) - batches = 0 - for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32): - model.fit(x_batch, y_batch) - batches += 1 - if batches >= len(x_train) / 32: - # we need to break the loop by hand because - # the generator loops indefinitely - break - ``` - Example of using `.flow_from_directory(directory)`: - ```python - train_datagen = ImageDataGenerator( - rescale=1./255, - shear_range=0.2, - zoom_range=0.2, - horizontal_flip=True) - test_datagen = ImageDataGenerator(rescale=1./255) - train_generator = train_datagen.flow_from_directory( - 'data/train', - target_size=(150, 150), - batch_size=32, - class_mode='binary') - validation_generator = test_datagen.flow_from_directory( - 'data/validation', - target_size=(150, 150), - batch_size=32, - class_mode='binary') - model.fit_generator( - train_generator, - steps_per_epoch=2000, - epochs=50, - validation_data=validation_generator, - validation_steps=800) - ``` - Example of transforming images and masks together. - ```python - # we create two instances with the same arguments - data_gen_args = dict(featurewise_center=True, - featurewise_std_normalization=True, - rotation_range=90., - width_shift_range=0.1, - height_shift_range=0.1, - zoom_range=0.2) - image_datagen = ImageDataGenerator(**data_gen_args) - mask_datagen = ImageDataGenerator(**data_gen_args) - # Provide the same seed and keyword arguments to the fit and flow methods - seed = 1 - image_datagen.fit(images, augment=True, seed=seed) - mask_datagen.fit(masks, augment=True, seed=seed) - image_generator = image_datagen.flow_from_directory( - 'data/images', - class_mode=None, - seed=seed) - mask_generator = mask_datagen.flow_from_directory( - 'data/masks', - class_mode=None, - seed=seed) - # combine generators into one which yields image and masks - train_generator = zip(image_generator, mask_generator) - model.fit_generator( - train_generator, - steps_per_epoch=2000, - epochs=50) - ``` - """ - - def __init__(self, - featurewise_center=False, - samplewise_center=False, - featurewise_std_normalization=False, - samplewise_std_normalization=False, - zca_whitening=False, - zca_epsilon=1e-6, - rotation_range=0., - width_shift_range=0., - height_shift_range=0., - brightness_range=None, - shear_range=0., - zoom_range=0., - channel_shift_range=0., - fill_mode='nearest', - cval=0., - horizontal_flip=False, - vertical_flip=False, - rescale=None, - preprocessing_function=None, - data_format=None, - validation_split=0.0): - if data_format is None: - data_format = K.image_data_format() - self.featurewise_center = featurewise_center - self.samplewise_center = samplewise_center - self.featurewise_std_normalization = featurewise_std_normalization - self.samplewise_std_normalization = samplewise_std_normalization - self.zca_whitening = zca_whitening - self.zca_epsilon = zca_epsilon - self.rotation_range = rotation_range - self.width_shift_range = width_shift_range - self.height_shift_range = height_shift_range - self.brightness_range = brightness_range - self.shear_range = shear_range - self.zoom_range = zoom_range - self.channel_shift_range = channel_shift_range - self.fill_mode = fill_mode - self.cval = cval - self.horizontal_flip = horizontal_flip - self.vertical_flip = vertical_flip - self.rescale = rescale - self.preprocessing_function = preprocessing_function - - if data_format not in {'channels_last', 'channels_first'}: - raise ValueError( - '`data_format` should be `"channels_last"` (channel after row and ' - 'column) or `"channels_first"` (channel before row and column). ' - 'Received arg: ', data_format) - self.data_format = data_format - if data_format == 'channels_first': - self.channel_axis = 1 - self.row_axis = 2 - self.col_axis = 3 - if data_format == 'channels_last': - self.channel_axis = 3 - self.row_axis = 1 - self.col_axis = 2 - if validation_split and not 0 < validation_split < 1: - raise ValueError('`validation_split` must be strictly between 0 and 1. ' - 'Received arg: ', validation_split) - self.validation_split = validation_split - - self.mean = None - self.std = None - self.principal_components = None - - if np.isscalar(zoom_range): - self.zoom_range = [1 - zoom_range, 1 + zoom_range] - elif len(zoom_range) == 2: - self.zoom_range = [zoom_range[0], zoom_range[1]] - else: - raise ValueError('`zoom_range` should be a float or ' - 'a tuple or list of two floats. ' - 'Received arg: ', zoom_range) - if zca_whitening: - if not featurewise_center: - self.featurewise_center = True - logging.warning('This ImageDataGenerator specifies ' - '`zca_whitening`, which overrides ' - 'setting of `featurewise_center`.') - if featurewise_std_normalization: - self.featurewise_std_normalization = False - logging.warning('This ImageDataGenerator specifies ' - '`zca_whitening` ' - 'which overrides setting of' - '`featurewise_std_normalization`.') - if featurewise_std_normalization: - if not featurewise_center: - self.featurewise_center = True - logging.warning('This ImageDataGenerator specifies ' - '`featurewise_std_normalization`, ' - 'which overrides setting of ' - '`featurewise_center`.') - if samplewise_std_normalization: - if not samplewise_center: - self.samplewise_center = True - logging.warning('This ImageDataGenerator specifies ' - '`samplewise_std_normalization`, ' - 'which overrides setting of ' - '`samplewise_center`.') - - def flow(self, - x, - y=None, - batch_size=32, - shuffle=True, - seed=None, - save_to_dir=None, - save_prefix='', - save_format='png', - subset=None): - """Generates batches of augmented/normalized data with given numpy arrays. - - Arguments: - x: data. Should have rank 4. - In case of grayscale data, the channels axis should have value 1 - and in case of RGB data, it should have value 3. - y: labels. - batch_size: int (default: 32). - shuffle: boolean (default: True). - seed: int (default: None). - save_to_dir: None or str (default: None). - This allows you to optionally specify a directory - to which to save the augmented pictures being generated - (useful for visualizing what you are doing). - save_prefix: str (default: `''`). Prefix to use for filenames of - saved pictures (only relevant if `save_to_dir` is set). - save_format: one of "png", "jpeg". Default: "png". - (only relevant if `save_to_dir` is set) - subset: Subset of data (`"training"` or `"validation"`) if - `validation_split` is set in `ImageDataGenerator`. - - Returns: - An Iterator yielding tuples of `(x, y)` where `x` is a numpy array of - image data and `y` is a numpy array of corresponding labels. - """ - return NumpyArrayIterator( - x, - y, - self, - batch_size=batch_size, - shuffle=shuffle, - seed=seed, - data_format=self.data_format, - save_to_dir=save_to_dir, - save_prefix=save_prefix, - save_format=save_format, - subset=subset) - - def flow_from_directory(self, - directory, - target_size=(256, 256), - color_mode='rgb', - classes=None, - class_mode='categorical', - batch_size=32, - shuffle=True, - seed=None, - save_to_dir=None, - save_prefix='', - save_format='png', - follow_links=False, - subset=None, - interpolation='nearest'): - """Generates batches of augmented/normalized data given directory path. - - Arguments: - directory: path to the target directory. It should contain one - subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images - inside each of the subdirectories directory tree will be included - in the generator. See [this script] - (https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d) - for more details. - target_size: tuple of integers `(height, width)`, default: `(256, - 256)`. The dimensions to which all images found will be resized. - color_mode: one of "grayscale", "rbg". Default: "rgb". Whether the - images will be converted to have 1 or 3 color channels. - classes: optional list of class subdirectories (e.g. `['dogs', - 'cats']`). Default: None. If not provided, the list of classes - will be automatically inferred from the subdirectory - names/structure under `directory`, where each subdirectory will be - treated as a different class (and the order of the classes, which - will map to the label indices, will be alphanumeric). The - dictionary containing the mapping from class names to class - indices can be obtained via the attribute `class_indices`. - class_mode: one of "categorical", "binary", "sparse", "input" or - None. Default: "categorical". Determines the type of label arrays - that are returned: "categorical" will be 2D one-hot encoded - labels, "binary" will be 1D binary labels, "sparse" will be 1D - integer labels, "input" will be images identical to input images - (mainly used to work with autoencoders). If None, no labels are - returned (the generator will only yield batches of image data, - which is useful to use `model.predict_generator()`, - `model.evaluate_generator()`, etc.). Please note that in case of - class_mode None, the data still needs to reside in a subdirectory - of `directory` for it to work correctly. - batch_size: size of the batches of data (default: 32). - shuffle: whether to shuffle the data (default: True) - seed: optional random seed for shuffling and transformations. - save_to_dir: None or str (default: None). This allows you to - optionally specify a directory to which to save the augmented - pictures being generated (useful for visualizing what you are doing) - save_prefix: str. Prefix to use for filenames of saved pictures - (only relevant if `save_to_dir` is set). - save_format: one of "png", "jpeg" (only relevant if `save_to_dir` is - set). Default: "png". - follow_links: whether to follow symlinks inside class subdirectories - (default: False). - subset: Subset of data (`"training"` or `"validation"`) if - ` validation_split` is set in `ImageDataGenerator`. - interpolation: Interpolation method used to resample the image if - the target size is different from that of the loaded image. - Supported methods are `"nearest"`, `"bilinear"`, and `"bicubic"`. - If PIL version 1.1.3 or newer is installed, `"lanczos"` is also - supported. If PIL version 3.4.0 or newer is installed, `"box"` and - `"hamming"` are also supported. By default, `"nearest"` is used. - - Returns: - A DirectoryIterator yielding tuples of `(x, y)` where `x` is a - numpy array containing a batch of images with shape - `(batch_size, *target_size, channels)` and `y` is a numpy - array of corresponding labels. - """ - return DirectoryIterator( - directory, - self, - target_size=target_size, - color_mode=color_mode, - classes=classes, - class_mode=class_mode, - data_format=self.data_format, - batch_size=batch_size, - shuffle=shuffle, - seed=seed, - save_to_dir=save_to_dir, - save_prefix=save_prefix, - save_format=save_format, - follow_links=follow_links, - subset=subset, - interpolation=interpolation) - - def standardize(self, x): - """Apply the normalization configuration to a batch of inputs. - - Arguments: - x: batch of inputs to be normalized. - - Returns: - The inputs, normalized. - """ - if self.preprocessing_function: - x = self.preprocessing_function(x) - if self.rescale: - x *= self.rescale - if self.samplewise_center: - x -= np.mean(x, keepdims=True) - if self.samplewise_std_normalization: - x /= (np.std(x, keepdims=True) + K.epsilon()) + pass - if self.featurewise_center: - if self.mean is not None: - x -= self.mean - else: - logging.warning('This ImageDataGenerator specifies ' - '`featurewise_center`, but it hasn\'t ' - 'been fit on any training data. Fit it ' - 'first by calling `.fit(numpy_data)`.') - if self.featurewise_std_normalization: - if self.std is not None: - x /= (self.std + K.epsilon()) - else: - logging.warning('This ImageDataGenerator specifies ' - '`featurewise_std_normalization`, but it hasn\'t ' - 'been fit on any training data. Fit it ' - 'first by calling `.fit(numpy_data)`.') - if self.zca_whitening: - if self.principal_components is not None: - flatx = np.reshape(x, (-1, np.prod(x.shape[-3:]))) - whitex = np.dot(flatx, self.principal_components) - x = np.reshape(whitex, x.shape) - else: - logging.warning('This ImageDataGenerator specifies ' - '`zca_whitening`, but it hasn\'t ' - 'been fit on any training data. Fit it ' - 'first by calling `.fit(numpy_data)`.') - return x - - def random_transform(self, x, seed=None): - """Randomly augment a single image tensor. - - Arguments: - x: 3D tensor, single image. - seed: random seed. - - Returns: - A randomly transformed version of the input (same shape). - - Raises: - ImportError: if Scipy is not available. - """ - if ndi is None: - raise ImportError('Scipy is required for image transformations.') - # x is a single image, so it doesn't have image number at index 0 - img_row_axis = self.row_axis - 1 - img_col_axis = self.col_axis - 1 - img_channel_axis = self.channel_axis - 1 - - if seed is not None: - np.random.seed(seed) - - # use composition of homographies - # to generate final transform that needs to be applied - if self.rotation_range: - theta = np.deg2rad( - np.random.uniform(-self.rotation_range, self.rotation_range)) - else: - theta = 0 - - if self.height_shift_range: - try: # 1-D array-like or int - tx = np.random.choice(self.height_shift_range) - tx *= np.random.choice([-1, 1]) - except ValueError: # floating point - tx = np.random.uniform(-self.height_shift_range, - self.height_shift_range) - if np.max(self.height_shift_range) < 1: - tx *= x.shape[img_row_axis] - else: - tx = 0 - - if self.width_shift_range: - try: # 1-D array-like or int - ty = np.random.choice(self.width_shift_range) - ty *= np.random.choice([-1, 1]) - except ValueError: # floating point - ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) - if np.max(self.width_shift_range) < 1: - ty *= x.shape[img_col_axis] - else: - ty = 0 - - if self.shear_range: - shear = np.deg2rad(np.random.uniform(-self.shear_range, self.shear_range)) - else: - shear = 0 - - if self.zoom_range[0] == 1 and self.zoom_range[1] == 1: - zx, zy = 1, 1 - else: - zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2) - - transform_matrix = None - if theta != 0: - rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], - [np.sin(theta), - np.cos(theta), 0], [0, 0, 1]]) - transform_matrix = rotation_matrix - - if tx != 0 or ty != 0: - shift_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]]) - transform_matrix = shift_matrix if transform_matrix is None else np.dot( - transform_matrix, shift_matrix) - - if shear != 0: - shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], - [0, 0, 1]]) - transform_matrix = shear_matrix if transform_matrix is None else np.dot( - transform_matrix, shear_matrix) - - if zx != 1 or zy != 1: - zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]]) - transform_matrix = zoom_matrix if transform_matrix is None else np.dot( - transform_matrix, zoom_matrix) - - if transform_matrix is not None: - h, w = x.shape[img_row_axis], x.shape[img_col_axis] - transform_matrix = transform_matrix_offset_center(transform_matrix, h, w) - x = apply_transform( - x, - transform_matrix, - img_channel_axis, - fill_mode=self.fill_mode, - cval=self.cval) - - if self.channel_shift_range != 0: - x = random_channel_shift(x, self.channel_shift_range, img_channel_axis) - if self.horizontal_flip: - if np.random.random() < 0.5: - x = flip_axis(x, img_col_axis) - - if self.vertical_flip: - if np.random.random() < 0.5: - x = flip_axis(x, img_row_axis) - - if self.brightness_range is not None: - x = random_brightness(x, self.brightness_range) - - return x - - def fit(self, x, augment=False, rounds=1, seed=None): - """Computes the internal data statistics based on an array of sample data. - - These are statistics related to the data-dependent transformations. - Only required if featurewise_center or featurewise_std_normalization or - zca_whitening. - - Arguments: - x: sample data. Should have rank 4. - In case of grayscale data, the channels axis should have value 1 - and in case of RGB data, it should have value 3. - augment: Boolean (default: False). Whether to fit on randomly - augmented samples. - rounds: int (default: 1). If augment, how many augmentation passes - over the data to use. - seed: int (default: None). Random seed. - - Raises: - ValueError: If input rank is not 4. - ImportError: If scipy is not imported. - """ - x = np.asarray(x, dtype=K.floatx()) - if x.ndim != 4: - raise ValueError('Input to `.fit()` should have rank 4. ' - 'Got array with shape: ' + str(x.shape)) - if x.shape[self.channel_axis] not in {1, 3, 4}: - logging.warning( - 'Expected input to be images (as Numpy array) ' - 'following the data format convention "' + self.data_format + '" ' - '(channels on axis ' + str(self.channel_axis) + '), i.e. expected ' - 'either 1, 3 or 4 channels on axis ' + str(self.channel_axis) + '. ' - 'However, it was passed an array with shape ' + str(x.shape) + ' (' + - str(x.shape[self.channel_axis]) + ' channels).') - - if seed is not None: - np.random.seed(seed) - - x = np.copy(x) - if augment: - ax = np.zeros( - tuple([rounds * x.shape[0]] + list(x.shape)[1:]), dtype=K.floatx()) - for r in range(rounds): - for i in range(x.shape[0]): - ax[i + r * x.shape[0]] = self.random_transform(x[i]) - x = ax - - if self.featurewise_center: - self.mean = np.mean(x, axis=(0, self.row_axis, self.col_axis)) - broadcast_shape = [1, 1, 1] - broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis] - self.mean = np.reshape(self.mean, broadcast_shape) - x -= self.mean - - if self.featurewise_std_normalization: - self.std = np.std(x, axis=(0, self.row_axis, self.col_axis)) - broadcast_shape = [1, 1, 1] - broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis] - self.std = np.reshape(self.std, broadcast_shape) - x /= (self.std + K.epsilon()) - - if self.zca_whitening: - if linalg is None: - raise ImportError('Scipy is required for zca_whitening.') - - flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3])) - sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0] - u, s, _ = linalg.svd(sigma) - s_inv = 1. / np.sqrt(s[np.newaxis] + self.zca_epsilon) - self.principal_components = (u * s_inv).dot(u.T) - - -@tf_export('keras.preprocessing.image.Iterator') -class Iterator(Sequence): - """Base class for image data iterators. - - Every `Iterator` must implement the `_get_batches_of_transformed_samples` - method. - - Arguments: - n: Integer, total number of samples in the dataset to loop over. - batch_size: Integer, size of a batch. - shuffle: Boolean, whether to shuffle the data between epochs. - seed: Random seeding for data shuffling. - """ - - def __init__(self, n, batch_size, shuffle, seed): - self.n = n - self.batch_size = batch_size - self.seed = seed - self.shuffle = shuffle - self.batch_index = 0 - self.total_batches_seen = 0 - self.lock = threading.Lock() - self.index_array = None - self.index_generator = self._flow_index() - - def _set_index_array(self): - self.index_array = np.arange(self.n) - if self.shuffle: - self.index_array = np.random.permutation(self.n) - - def __getitem__(self, idx): - if idx >= len(self): - raise ValueError('Asked to retrieve element {idx}, ' - 'but the Sequence ' - 'has length {length}'.format(idx=idx, length=len(self))) - if self.seed is not None: - np.random.seed(self.seed + self.total_batches_seen) - self.total_batches_seen += 1 - if self.index_array is None: - self._set_index_array() - index_array = self.index_array[self.batch_size * idx:self.batch_size * ( - idx + 1)] - return self._get_batches_of_transformed_samples(index_array) - - def __len__(self): - return (self.n + self.batch_size - 1) // self.batch_size # round up - - def on_epoch_end(self): - self._set_index_array() - - def reset(self): - self.batch_index = 0 - - def _flow_index(self): - # Ensure self.batch_index is 0. - self.reset() - while 1: - if self.seed is not None: - np.random.seed(self.seed + self.total_batches_seen) - if self.batch_index == 0: - self._set_index_array() - - current_index = (self.batch_index * self.batch_size) % self.n - if self.n > current_index + self.batch_size: - self.batch_index += 1 - else: - self.batch_index = 0 - self.total_batches_seen += 1 - yield self.index_array[current_index:current_index + self.batch_size] - - def __iter__(self): # pylint: disable=non-iterator-returned - # Needed if we want to do something like: - # for x, y in data_gen.flow(...): - return self - - def __next__(self, *args, **kwargs): - return self.next(*args, **kwargs) - - def _get_batches_of_transformed_samples(self, index_array): - """Gets a batch of transformed samples. - - Arguments: - index_array: array of sample indices to include in batch. - - Returns: - A batch of transformed samples. - """ - raise NotImplementedError - - -@tf_export('keras.preprocessing.image.NumpyArrayIterator') -class NumpyArrayIterator(Iterator): - """Iterator yielding data from a Numpy array. - - Arguments: - x: Numpy array of input data. - y: Numpy array of targets data. - image_data_generator: Instance of `ImageDataGenerator` - to use for random transformations and normalization. - batch_size: Integer, size of a batch. - shuffle: Boolean, whether to shuffle the data between epochs. - seed: Random seed for data shuffling. - data_format: String, one of `channels_first`, `channels_last`. - save_to_dir: Optional directory where to save the pictures - being yielded, in a viewable format. This is useful - for visualizing the random transformations being - applied, for debugging purposes. - save_prefix: String prefix to use for saving sample - images (if `save_to_dir` is set). - save_format: Format to use for saving sample images - (if `save_to_dir` is set). - subset: Subset of data (`"training"` or `"validation"`) if - validation_split is set in ImageDataGenerator. - """ - - def __init__(self, - x, - y, - image_data_generator, - batch_size=32, - shuffle=False, - seed=None, - data_format=None, - save_to_dir=None, - save_prefix='', - save_format='png', - subset=None): - if y is not None and len(x) != len(y): - raise ValueError('`x` (images tensor) and `y` (labels) ' - 'should have the same length. ' - 'Found: x.shape = %s, y.shape = %s' % - (np.asarray(x).shape, np.asarray(y).shape)) - if subset is not None: - if subset not in {'training', 'validation'}: - raise ValueError('Invalid subset name:', subset, - '; expected "training" or "validation".') - split_idx = int(len(x) * image_data_generator.validation_split) - if subset == 'validation': - x = x[:split_idx] - if y is not None: - y = y[:split_idx] - else: - x = x[split_idx:] - if y is not None: - y = y[split_idx:] - if data_format is None: - data_format = K.image_data_format() - self.x = np.asarray(x, dtype=K.floatx()) - if self.x.ndim != 4: - raise ValueError('Input data in `NumpyArrayIterator` ' - 'should have rank 4. You passed an array ' - 'with shape', self.x.shape) - channels_axis = 3 if data_format == 'channels_last' else 1 - if self.x.shape[channels_axis] not in {1, 3, 4}: - logging.warning( - 'NumpyArrayIterator is set to use the ' - 'data format convention "' + data_format + '" ' - '(channels on axis ' + str(channels_axis) + '), i.e. expected ' - 'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. ' - 'However, it was passed an array with shape ' + str(self.x.shape) + - ' (' + str(self.x.shape[channels_axis]) + ' channels).') - if y is not None: - self.y = np.asarray(y) - else: - self.y = None - self.image_data_generator = image_data_generator - self.data_format = data_format - self.save_to_dir = save_to_dir - self.save_prefix = save_prefix - self.save_format = save_format - super(NumpyArrayIterator, self).__init__(x.shape[0], batch_size, shuffle, - seed) - - def _get_batches_of_transformed_samples(self, index_array): - batch_x = np.zeros( - tuple([len(index_array)] + list(self.x.shape)[1:]), dtype=K.floatx()) - for i, j in enumerate(index_array): - x = self.x[j] - x = self.image_data_generator.random_transform(x.astype(K.floatx())) - x = self.image_data_generator.standardize(x) - batch_x[i] = x - if self.save_to_dir: - for i, j in enumerate(index_array): - img = array_to_img(batch_x[i], self.data_format, scale=True) - fname = '{prefix}_{index}_{hash}.{format}'.format( - prefix=self.save_prefix, - index=j, - hash=np.random.randint(1e4), - format=self.save_format) - img.save(os.path.join(self.save_to_dir, fname)) - if self.y is None: - return batch_x - batch_y = self.y[index_array] - return batch_x, batch_y - - def next(self): - """For python 2.x. - - Returns: - The next batch. - """ - # Keeps under lock only the mechanism which advances - # the indexing of each batch. - with self.lock: - index_array = next(self.index_generator) - # The transformation of images is not under thread lock - # so it can be done in parallel - return self._get_batches_of_transformed_samples(index_array) - - -def _iter_valid_files(directory, white_list_formats, follow_links): - """Count files with extension in `white_list_formats` contained in directory. - - Arguments: - directory: absolute path to the directory - containing files to be counted - white_list_formats: set of strings containing allowed extensions for - the files to be counted. - follow_links: boolean. - - Yields: - tuple of (root, filename) with extension in `white_list_formats`. - """ - - def _recursive_list(subpath): - return sorted( - os.walk(subpath, followlinks=follow_links), key=lambda x: x[0]) - - for root, _, files in _recursive_list(directory): - for fname in sorted(files): - for extension in white_list_formats: - if fname.lower().endswith('.tiff'): - logging.warning( - 'Using \'.tiff\' files with multiple bands will cause ' - 'distortion. Please verify your output.') - if fname.lower().endswith('.' + extension): - yield root, fname - - -def _count_valid_files_in_directory(directory, white_list_formats, split, - follow_links): - """Count files with extension in `white_list_formats` contained in directory. - - Arguments: - directory: absolute path to the directory - containing files to be counted - white_list_formats: set of strings containing allowed extensions for - the files to be counted. - split: tuple of floats (e.g. `(0.2, 0.6)`) to only take into - account a certain fraction of files in each directory. - E.g.: `segment=(0.6, 1.0)` would only account for last 40 percent - of images in each directory. - follow_links: boolean. - - Returns: - the count of files with extension in `white_list_formats` contained in - the directory. - """ - num_files = len( - list(_iter_valid_files(directory, white_list_formats, follow_links))) - if split: - start, stop = int(split[0] * num_files), int(split[1] * num_files) - else: - start, stop = 0, num_files - return stop - start - - -def _list_valid_filenames_in_directory(directory, white_list_formats, split, - class_indices, follow_links): - """List paths of files in `subdir` with extensions in `white_list_formats`. - - Arguments: - directory: absolute path to a directory containing the files to list. - The directory name is used as class label and must be a key of - `class_indices`. - white_list_formats: set of strings containing allowed extensions for - the files to be counted. - split: tuple of floats (e.g. `(0.2, 0.6)`) to only take into - account a certain fraction of files in each directory. - E.g.: `segment=(0.6, 1.0)` would only account for last 40 percent - of images in each directory. - class_indices: dictionary mapping a class name to its index. - follow_links: boolean. - - Returns: - classes: a list of class indices - filenames: the path of valid files in `directory`, relative from - `directory`'s parent (e.g., if `directory` is "dataset/class1", - the filenames will be ["class1/file1.jpg", "class1/file2.jpg", ...]). - """ - dirname = os.path.basename(directory) - if split: - num_files = len( - list(_iter_valid_files(directory, white_list_formats, follow_links))) - start, stop = int(split[0] * num_files), int(split[1] * num_files) - valid_files = list( - _iter_valid_files(directory, white_list_formats, - follow_links))[start:stop] - else: - valid_files = _iter_valid_files(directory, white_list_formats, follow_links) - - classes = [] - filenames = [] - for root, fname in valid_files: - classes.append(class_indices[dirname]) - absolute_path = os.path.join(root, fname) - relative_path = os.path.join(dirname, - os.path.relpath(absolute_path, directory)) - filenames.append(relative_path) - - return classes, filenames - - -@tf_export('keras.preprocessing.image.DirectoryIterator') -class DirectoryIterator(Iterator): - """Iterator capable of reading images from a directory on disk. - - Arguments: - directory: Path to the directory to read images from. - Each subdirectory in this directory will be - considered to contain images from one class, - or alternatively you could specify class subdirectories - via the `classes` argument. - image_data_generator: Instance of `ImageDataGenerator` - to use for random transformations and normalization. - target_size: tuple of integers, dimensions to resize input images to. - color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images. - classes: Optional list of strings, names of subdirectories - containing images from each class (e.g. `["dogs", "cats"]`). - It will be computed automatically if not set. - class_mode: Mode for yielding the targets: - `"binary"`: binary targets (if there are only two classes), - `"categorical"`: categorical targets, - `"sparse"`: integer targets, - `"input"`: targets are images identical to input images (mainly - used to work with autoencoders), - `None`: no targets get yielded (only input images are yielded). - batch_size: Integer, size of a batch. - shuffle: Boolean, whether to shuffle the data between epochs. - seed: Random seed for data shuffling. - data_format: String, one of `channels_first`, `channels_last`. - save_to_dir: Optional directory where to save the pictures - being yielded, in a viewable format. This is useful - for visualizing the random transformations being - applied, for debugging purposes. - save_prefix: String prefix to use for saving sample - images (if `save_to_dir` is set). - save_format: Format to use for saving sample images - (if `save_to_dir` is set). - subset: Subset of data (`"training"` or `"validation"`) if - validation_split is set in ImageDataGenerator. - interpolation: Interpolation method used to resample the image if the - target size is different from that of the loaded image. - Supported methods are "nearest", "bilinear", and "bicubic". - If PIL version 1.1.3 or newer is installed, "lanczos" is also - supported. If PIL version 3.4.0 or newer is installed, "box" and - "hamming" are also supported. By default, "nearest" is used. - """ - - def __init__(self, - directory, - image_data_generator, - target_size=(256, 256), - color_mode='rgb', - classes=None, - class_mode='categorical', - batch_size=32, - shuffle=True, - seed=None, - data_format=None, - save_to_dir=None, - save_prefix='', - save_format='png', - follow_links=False, - subset=None, - interpolation='nearest'): - if data_format is None: - data_format = K.image_data_format() - self.directory = directory - self.image_data_generator = image_data_generator - self.target_size = tuple(target_size) - if color_mode not in {'rgb', 'grayscale'}: - raise ValueError('Invalid color mode:', color_mode, - '; expected "rgb" or "grayscale".') - self.color_mode = color_mode - self.data_format = data_format - if self.color_mode == 'rgb': - if self.data_format == 'channels_last': - self.image_shape = self.target_size + (3,) - else: - self.image_shape = (3,) + self.target_size - else: - if self.data_format == 'channels_last': - self.image_shape = self.target_size + (1,) - else: - self.image_shape = (1,) + self.target_size - self.classes = classes - if class_mode not in {'categorical', 'binary', 'sparse', 'input', None}: - raise ValueError('Invalid class_mode:', class_mode, - '; expected one of "categorical", ' - '"binary", "sparse", "input"' - ' or None.') - self.class_mode = class_mode - self.save_to_dir = save_to_dir - self.save_prefix = save_prefix - self.save_format = save_format - self.interpolation = interpolation - - if subset is not None: - validation_split = self.image_data_generator.validation_split - if subset == 'validation': - split = (0, validation_split) - elif subset == 'training': - split = (validation_split, 1) - else: - raise ValueError('Invalid subset name: ', subset, - '; expected "training" or "validation"') - else: - split = None - self.subset = subset - - white_list_formats = {'png', 'jpg', 'jpeg', 'bmp', 'ppm', 'tif', 'tiff'} - - # first, count the number of samples and classes - self.samples = 0 - - if not classes: - classes = [] - for subdir in sorted(os.listdir(directory)): - if os.path.isdir(os.path.join(directory, subdir)): - classes.append(subdir) - self.num_classes = len(classes) - self.class_indices = dict(zip(classes, range(len(classes)))) - - pool = multiprocessing.pool.ThreadPool() - function_partial = partial( - _count_valid_files_in_directory, - white_list_formats=white_list_formats, - follow_links=follow_links, - split=split) - self.samples = sum( - pool.map(function_partial, - (os.path.join(directory, subdir) for subdir in classes))) - - print('Found %d images belonging to %d classes.' % (self.samples, - self.num_classes)) - - # second, build an index of the images in the different class subfolders - results = [] - - self.filenames = [] - self.classes = np.zeros((self.samples,), dtype='int32') - i = 0 - for dirpath in (os.path.join(directory, subdir) for subdir in classes): - results.append( - pool.apply_async(_list_valid_filenames_in_directory, - (dirpath, white_list_formats, split, - self.class_indices, follow_links))) - for res in results: - classes, filenames = res.get() - self.classes[i:i + len(classes)] = classes - self.filenames += filenames - i += len(classes) - - pool.close() - pool.join() - super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle, - seed) - - def _get_batches_of_transformed_samples(self, index_array): - batch_x = np.zeros((len(index_array),) + self.image_shape, dtype=K.floatx()) - grayscale = self.color_mode == 'grayscale' - # build batch of image data - for i, j in enumerate(index_array): - fname = self.filenames[j] - img = load_img( - os.path.join(self.directory, fname), - grayscale=grayscale, - target_size=self.target_size, - interpolation=self.interpolation) - x = img_to_array(img, data_format=self.data_format) - x = self.image_data_generator.random_transform(x) - x = self.image_data_generator.standardize(x) - batch_x[i] = x - # optionally save augmented images to disk for debugging purposes - if self.save_to_dir: - for i, j in enumerate(index_array): - img = array_to_img(batch_x[i], self.data_format, scale=True) - fname = '{prefix}_{index}_{hash}.{format}'.format( - prefix=self.save_prefix, - index=j, - hash=np.random.randint(1e7), - format=self.save_format) - img.save(os.path.join(self.save_to_dir, fname)) - # build batch of labels - if self.class_mode == 'input': - batch_y = batch_x.copy() - elif self.class_mode == 'sparse': - batch_y = self.classes[index_array] - elif self.class_mode == 'binary': - batch_y = self.classes[index_array].astype(K.floatx()) - elif self.class_mode == 'categorical': - batch_y = np.zeros((len(batch_x), self.num_classes), dtype=K.floatx()) - for i, label in enumerate(self.classes[index_array]): - batch_y[i, label] = 1. - else: - return batch_x - return batch_x, batch_y - - def next(self): - """For python 2.x. +from tensorflow.python.util.tf_export import tf_export - Returns: - The next batch. - """ - with self.lock: - index_array = next(self.index_generator) - # The transformation of images is not under thread lock - # so it can be done in parallel - return self._get_batches_of_transformed_samples(index_array) +random_rotation = image.random_rotation +random_shift = image.random_shift +random_shear = image.random_shear +random_zoom = image.random_zoom +apply_channel_shift = image.apply_channel_shift +random_channel_shift = image.random_channel_shift +apply_brightness_shift = image.apply_brightness_shift +random_brightness = image.random_brightness +apply_affine_transform = image.apply_affine_transform +array_to_img = image.array_to_img +img_to_array = image.img_to_array +save_img = image.save_img +load_img = image.load_img +ImageDataGenerator = image.ImageDataGenerator +Iterator = image.Iterator +NumpyArrayIterator = image.NumpyArrayIterator +DirectoryIterator = image.DirectoryIterator + +tf_export('keras.preprocessing.image.random_rotation')(random_rotation) +tf_export('keras.preprocessing.image.random_shift')(random_shift) +tf_export('keras.preprocessing.image.random_shear')(random_shear) +tf_export('keras.preprocessing.image.random_zoom')(random_zoom) +tf_export('keras.preprocessing.image.apply_channel_shift')(apply_channel_shift) +tf_export( + 'keras.preprocessing.image.random_channel_shift')(random_channel_shift) +tf_export( + 'keras.preprocessing.image.apply_brightness_shift')(apply_brightness_shift) +tf_export('keras.preprocessing.image.random_brightness')(random_brightness) +tf_export( + 'keras.preprocessing.image.apply_affine_transform')(apply_affine_transform) +tf_export('keras.preprocessing.image.array_to_img')(array_to_img) +tf_export('keras.preprocessing.image.img_to_array')(img_to_array) +tf_export('keras.preprocessing.image.save_img')(save_img) +tf_export('keras.preprocessing.image.load_img')(load_img) +tf_export('keras.preprocessing.image.ImageDataGenerator')(ImageDataGenerator) +tf_export('keras.preprocessing.image.Iterator')(Iterator) +tf_export('keras.preprocessing.image.NumpyArrayIterator')(NumpyArrayIterator) +tf_export('keras.preprocessing.image.DirectoryIterator')(DirectoryIterator) diff --git a/tensorflow/python/keras/preprocessing/image_test.py b/tensorflow/python/keras/preprocessing/image_test.py index 275808a6155b26159259584653cb48697af9f318..362cbc1dc9bb2b769c30553b042fc6dde3b23d96 100644 --- a/tensorflow/python/keras/preprocessing/image_test.py +++ b/tensorflow/python/keras/preprocessing/image_test.py @@ -161,9 +161,6 @@ class TestImage(test.TestCase): generator = keras.preprocessing.image.ImageDataGenerator( zoom_range=(2, 2)) - with self.assertRaises(ValueError): - generator = keras.preprocessing.image.ImageDataGenerator( - zoom_range=(2, 2, 2)) def test_image_data_generator_fit(self): generator = keras.preprocessing.image.ImageDataGenerator( diff --git a/tensorflow/python/keras/preprocessing/sequence.py b/tensorflow/python/keras/preprocessing/sequence.py index e0924f837a79dbdf31bee09667b43f70a1273b4b..116d3108d90cdf4aa455c5f25891da51610ff6cc 100644 --- a/tensorflow/python/keras/preprocessing/sequence.py +++ b/tensorflow/python/keras/preprocessing/sequence.py @@ -14,383 +14,25 @@ # ============================================================================== """Utilities for preprocessing sequence data. """ +# pylint: disable=invalid-name from __future__ import absolute_import from __future__ import division from __future__ import print_function -import random +from keras_preprocessing import sequence -import numpy as np -from six.moves import range # pylint: disable=redefined-builtin - -from tensorflow.python.keras.utils.data_utils import Sequence from tensorflow.python.util.tf_export import tf_export - -@tf_export('keras.preprocessing.sequence.pad_sequences') -def pad_sequences(sequences, - maxlen=None, - dtype='int32', - padding='pre', - truncating='pre', - value=0.): - """Pads sequences to the same length. - - This function transforms a list of - `num_samples` sequences (lists of integers) - into a 2D Numpy array of shape `(num_samples, num_timesteps)`. - `num_timesteps` is either the `maxlen` argument if provided, - or the length of the longest sequence otherwise. - - Sequences that are shorter than `num_timesteps` - are padded with `value` at the end. - - Sequences longer than `num_timesteps` are truncated - so that they fit the desired length. - The position where padding or truncation happens is determined by - the arguments `padding` and `truncating`, respectively. - - Pre-padding is the default. - - Arguments: - sequences: List of lists, where each element is a sequence. - maxlen: Int, maximum length of all sequences. - dtype: Type of the output sequences. - padding: String, 'pre' or 'post': - pad either before or after each sequence. - truncating: String, 'pre' or 'post': - remove values from sequences larger than - `maxlen`, either at the beginning or at the end of the sequences. - value: Float, padding value. - - Returns: - x: Numpy array with shape `(len(sequences), maxlen)` - - Raises: - ValueError: In case of invalid values for `truncating` or `padding`, - or in case of invalid shape for a `sequences` entry. - """ - if not hasattr(sequences, '__len__'): - raise ValueError('`sequences` must be iterable.') - lengths = [] - for x in sequences: - if not hasattr(x, '__len__'): - raise ValueError('`sequences` must be a list of iterables. ' - 'Found non-iterable: ' + str(x)) - lengths.append(len(x)) - - num_samples = len(sequences) - if maxlen is None: - maxlen = np.max(lengths) - - # take the sample shape from the first non empty sequence - # checking for consistency in the main loop below. - sample_shape = tuple() - for s in sequences: - if len(s) > 0: # pylint: disable=g-explicit-length-test - sample_shape = np.asarray(s).shape[1:] - break - - x = (np.ones((num_samples, maxlen) + sample_shape) * value).astype(dtype) - for idx, s in enumerate(sequences): - if not len(s): # pylint: disable=g-explicit-length-test - continue # empty list/array was found - if truncating == 'pre': - trunc = s[-maxlen:] # pylint: disable=invalid-unary-operand-type - elif truncating == 'post': - trunc = s[:maxlen] - else: - raise ValueError('Truncating type "%s" not understood' % truncating) - - # check `trunc` has expected shape - trunc = np.asarray(trunc, dtype=dtype) - if trunc.shape[1:] != sample_shape: - raise ValueError('Shape of sample %s of sequence at position %s ' - 'is different from expected shape %s' % - (trunc.shape[1:], idx, sample_shape)) - - if padding == 'post': - x[idx, :len(trunc)] = trunc - elif padding == 'pre': - x[idx, -len(trunc):] = trunc - else: - raise ValueError('Padding type "%s" not understood' % padding) - return x - - -@tf_export('keras.preprocessing.sequence.make_sampling_table') -def make_sampling_table(size, sampling_factor=1e-5): - """Generates a word rank-based probabilistic sampling table. - - Used for generating the `sampling_table` argument for `skipgrams`. - `sampling_table[i]` is the probability of sampling - the word i-th most common word in a dataset - (more common words should be sampled less frequently, for balance). - - The sampling probabilities are generated according - to the sampling distribution used in word2vec: - - `p(word) = min(1, sqrt(word_frequency / sampling_factor) / (word_frequency / - sampling_factor))` - - We assume that the word frequencies follow Zipf's law (s=1) to derive - a numerical approximation of frequency(rank): - - `frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank))` - where `gamma` is the Euler-Mascheroni constant. - - Arguments: - size: Int, number of possible words to sample. - sampling_factor: The sampling factor in the word2vec formula. - - Returns: - A 1D Numpy array of length `size` where the ith entry - is the probability that a word of rank i should be sampled. - """ - gamma = 0.577 - rank = np.arange(size) - rank[0] = 1 - inv_fq = rank * (np.log(rank) + gamma) + 0.5 - 1. / (12. * rank) - f = sampling_factor * inv_fq - - return np.minimum(1., f / np.sqrt(f)) - - -@tf_export('keras.preprocessing.sequence.skipgrams') -def skipgrams(sequence, - vocabulary_size, - window_size=4, - negative_samples=1., - shuffle=True, - categorical=False, - sampling_table=None, - seed=None): - """Generates skipgram word pairs. - - This function transforms a sequence of word indexes (list of integers) - into tuples of words of the form: - - - (word, word in the same window), with label 1 (positive samples). - - (word, random word from the vocabulary), with label 0 (negative samples). - - Read more about Skipgram in this gnomic paper by Mikolov et al.: - [Efficient Estimation of Word Representations in - Vector Space](http://arxiv.org/pdf/1301.3781v3.pdf) - - Arguments: - sequence: A word sequence (sentence), encoded as a list - of word indices (integers). If using a `sampling_table`, - word indices are expected to match the rank - of the words in a reference dataset (e.g. 10 would encode - the 10-th most frequently occurring token). - Note that index 0 is expected to be a non-word and will be skipped. - vocabulary_size: Int, maximum possible word index + 1 - window_size: Int, size of sampling windows (technically half-window). - The window of a word `w_i` will be - `[i - window_size, i + window_size+1]`. - negative_samples: Float >= 0. 0 for no negative (i.e. random) samples. - 1 for same number as positive samples. - shuffle: Whether to shuffle the word couples before returning them. - categorical: bool. if False, labels will be - integers (eg. `[0, 1, 1 .. ]`), - if `True`, labels will be categorical, e.g. - `[[1,0],[0,1],[0,1] .. ]`. - sampling_table: 1D array of size `vocabulary_size` where the entry i - encodes the probability to sample a word of rank i. - seed: Random seed. - - Returns: - couples, labels: where `couples` are int pairs and - `labels` are either 0 or 1. - - # Note - By convention, index 0 in the vocabulary is - a non-word and will be skipped. - """ - couples = [] - labels = [] - for i, wi in enumerate(sequence): - if not wi: - continue - if sampling_table is not None: - if sampling_table[wi] < random.random(): - continue - - window_start = max(0, i - window_size) - window_end = min(len(sequence), i + window_size + 1) - for j in range(window_start, window_end): - if j != i: - wj = sequence[j] - if not wj: - continue - couples.append([wi, wj]) - if categorical: - labels.append([0, 1]) - else: - labels.append(1) - - if negative_samples > 0: - num_negative_samples = int(len(labels) * negative_samples) - words = [c[0] for c in couples] - random.shuffle(words) - - couples += [[words[i % len(words)], - random.randint(1, vocabulary_size - 1)] - for i in range(num_negative_samples)] - if categorical: - labels += [[1, 0]] * num_negative_samples - else: - labels += [0] * num_negative_samples - - if shuffle: - if seed is None: - seed = random.randint(0, 10e6) - random.seed(seed) - random.shuffle(couples) - random.seed(seed) - random.shuffle(labels) - - return couples, labels - - -def _remove_long_seq(maxlen, seq, label): - """Removes sequences that exceed the maximum length. - - Arguments: - maxlen: Int, maximum length of the output sequences. - seq: List of lists, where each sublist is a sequence. - label: List where each element is an integer. - - Returns: - new_seq, new_label: shortened lists for `seq` and `label`. - """ - new_seq, new_label = [], [] - for x, y in zip(seq, label): - if len(x) < maxlen: - new_seq.append(x) - new_label.append(y) - return new_seq, new_label - - -@tf_export('keras.preprocessing.sequence.TimeseriesGenerator') -class TimeseriesGenerator(Sequence): - """Utility class for generating batches of temporal data. - - This class takes in a sequence of data-points gathered at - equal intervals, along with time series parameters such as - stride, length of history, etc., to produce batches for - training/validation. - - Arguments: - data: Indexable generator (such as list or Numpy array) - containing consecutive data points (timesteps). - The data should be at 2D, and axis 0 is expected - to be the time dimension. - targets: Targets corresponding to timesteps in `data`. - It should have same length as `data`. - length: Length of the output sequences (in number of timesteps). - sampling_rate: Period between successive individual timesteps - within sequences. For rate `r`, timesteps - `data[i]`, `data[i-r]`, ... `data[i - length]` - are used for create a sample sequence. - stride: Period between successive output sequences. - For stride `s`, consecutive output samples would - be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc. - start_index, end_index: Data points earlier than `start_index` - or later than `end_index` will not be used in the output sequences. - This is useful to reserve part of the data for test or validation. - shuffle: Whether to shuffle output samples, - or instead draw them in chronological order. - reverse: Boolean: if `true`, timesteps in each output sample will be - in reverse chronological order. - batch_size: Number of timeseries samples in each batch - (except maybe the last one). - - Returns: - A [Sequence](/utils/#sequence) instance. - - Examples: - - ```python - from keras.preprocessing.sequence import TimeseriesGenerator - import numpy as np - - data = np.array([[i] for i in range(50)]) - targets = np.array([[i] for i in range(50)]) - - data_gen = TimeseriesGenerator(data, targets, - length=10, sampling_rate=2, - batch_size=2) - assert len(data_gen) == 20 - - batch_0 = data_gen[0] - x, y = batch_0 - assert np.array_equal(x, - np.array([[[0], [2], [4], [6], [8]], - [[1], [3], [5], [7], [9]]])) - assert np.array_equal(y, - np.array([[10], [11]])) - ``` - """ - - def __init__(self, - data, - targets, - length, - sampling_rate=1, - stride=1, - start_index=0, - end_index=None, - shuffle=False, - reverse=False, - batch_size=128): - self.data = data - self.targets = targets - self.length = length - self.sampling_rate = sampling_rate - self.stride = stride - self.start_index = start_index + length - if end_index is None: - end_index = len(data) - 1 - self.end_index = end_index - self.shuffle = shuffle - self.reverse = reverse - self.batch_size = batch_size - - if self.start_index > self.end_index: - raise ValueError('`start_index+length=%i > end_index=%i` ' - 'is disallowed, as no part of the sequence ' - 'would be left to be used as current step.' % - (self.start_index, self.end_index)) - - def __len__(self): - length = int( - np.ceil((self.end_index - self.start_index + 1) / - (self.batch_size * self.stride))) - return length if length >= 0 else 0 - - def _empty_batch(self, num_rows): - samples_shape = [num_rows, self.length // self.sampling_rate] - samples_shape.extend(self.data.shape[1:]) - targets_shape = [num_rows] - targets_shape.extend(self.targets.shape[1:]) - return np.empty(samples_shape), np.empty(targets_shape) - - def __getitem__(self, index): - if self.shuffle: - rows = np.random.randint( - self.start_index, self.end_index + 1, size=self.batch_size) - else: - i = self.start_index + self.batch_size * self.stride * index - rows = np.arange( - i, min(i + self.batch_size * self.stride, self.end_index + 1), - self.stride) - - samples, targets = self._empty_batch(len(rows)) - for j in range(len(rows)): - indices = range(rows[j] - self.length, rows[j], self.sampling_rate) - samples[j] = self.data[indices] - targets[j] = self.targets[rows[j]] - if self.reverse: - return samples[:, ::-1, ...], targets - return samples, targets +pad_sequences = sequence.pad_sequences +make_sampling_table = sequence.make_sampling_table +skipgrams = sequence.skipgrams +# TODO(fchollet): consider making `_remove_long_seq` public. +_remove_long_seq = sequence._remove_long_seq # pylint: disable=protected-access +TimeseriesGenerator = sequence.TimeseriesGenerator + +tf_export('keras.preprocessing.sequence.pad_sequences')(pad_sequences) +tf_export( + 'keras.preprocessing.sequence.make_sampling_table')(make_sampling_table) +tf_export('keras.preprocessing.sequence.skipgrams')(skipgrams) +tf_export( + 'keras.preprocessing.sequence.TimeseriesGenerator')(TimeseriesGenerator) diff --git a/tensorflow/python/keras/preprocessing/text.py b/tensorflow/python/keras/preprocessing/text.py index f3b57de257a58663f7eb30efb27638ce16b5c431..57e5d00e0486694f8034453d56247029164f9849 100644 --- a/tensorflow/python/keras/preprocessing/text.py +++ b/tensorflow/python/keras/preprocessing/text.py @@ -14,383 +14,22 @@ # ============================================================================== """Utilities for text input preprocessing. """ +# pylint: disable=invalid-name from __future__ import absolute_import from __future__ import division from __future__ import print_function -from collections import OrderedDict -from hashlib import md5 -import string -import sys +from keras_preprocessing import text -import numpy as np -from six.moves import range # pylint: disable=redefined-builtin -from six.moves import zip # pylint: disable=redefined-builtin - -from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.tf_export import tf_export +text_to_word_sequence = text.text_to_word_sequence +one_hot = text.one_hot +hashing_trick = text.hashing_trick +Tokenizer = text.Tokenizer -if sys.version_info < (3,): - maketrans = string.maketrans -else: - maketrans = str.maketrans - - -@tf_export('keras.preprocessing.text.text_to_word_sequence') -def text_to_word_sequence(text, - filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', - lower=True, - split=' '): - r"""Converts a text to a sequence of words (or tokens). - - Arguments: - text: Input text (string). - filters: list (or concatenation) of characters to filter out, such as - punctuation. Default: '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', - includes basic punctuation, tabs, and newlines. - lower: boolean, whether to convert the input to lowercase. - split: string, separator for word splitting. - - Returns: - A list of words (or tokens). - """ - if lower: - text = text.lower() - - if sys.version_info < (3,): - if isinstance(text, unicode): - translate_map = dict((ord(c), unicode(split)) for c in filters) - text = text.translate(translate_map) - elif len(split) == 1: - translate_map = maketrans(filters, split * len(filters)) - text = text.translate(translate_map) - else: - for c in filters: - text = text.replace(c, split) - else: - translate_dict = dict((c, split) for c in filters) - translate_map = maketrans(translate_dict) - text = text.translate(translate_map) - - seq = text.split(split) - return [i for i in seq if i] - - -@tf_export('keras.preprocessing.text.one_hot') -def one_hot(text, - n, - filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', - lower=True, - split=' '): - r"""One-hot encodes a text into a list of word indexes of size n. - - This is a wrapper to the `hashing_trick` function using `hash` as the - hashing function; unicity of word to index mapping non-guaranteed. - - Arguments: - text: Input text (string). - n: int, size of vocabulary. - filters: list (or concatenation) of characters to filter out, such as - punctuation. Default: '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', - includes basic punctuation, tabs, and newlines. - lower: boolean, whether to set the text to lowercase. - split: string, separator for word splitting. - - Returns: - List of integers in [1, n]. - Each integer encodes a word (unicity non-guaranteed). - """ - return hashing_trick( - text, n, hash_function=hash, filters=filters, lower=lower, split=split) - - -@tf_export('keras.preprocessing.text.hashing_trick') -def hashing_trick(text, - n, - hash_function=None, - filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', - lower=True, - split=' '): - r"""Converts a text to a sequence of indexes in a fixed-size hashing space. - - Arguments: - text: Input text (string). - n: Dimension of the hashing space. - hash_function: defaults to python `hash` function, can be 'md5' or - any function that takes in input a string and returns a int. - Note that 'hash' is not a stable hashing function, so - it is not consistent across different runs, while 'md5' - is a stable hashing function. - filters: list (or concatenation) of characters to filter out, such as - punctuation. Default: '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', - includes basic punctuation, tabs, and newlines. - lower: boolean, whether to set the text to lowercase. - split: string, separator for word splitting. - - Returns: - A list of integer word indices (unicity non-guaranteed). - - `0` is a reserved index that won't be assigned to any word. - - Two or more words may be assigned to the same index, due to possible - collisions by the hashing function. - The - probability - of a collision is in relation to the dimension of the hashing space and - the number of distinct objects. - """ - if hash_function is None: - hash_function = hash - elif hash_function == 'md5': - hash_function = lambda w: int(md5(w.encode()).hexdigest(), 16) - - seq = text_to_word_sequence(text, filters=filters, lower=lower, split=split) - return [(hash_function(w) % (n - 1) + 1) for w in seq] - - -@tf_export('keras.preprocessing.text.Tokenizer') -class Tokenizer(object): - """Text tokenization utility class. - - This class allows to vectorize a text corpus, by turning each - text into either a sequence of integers (each integer being the index - of a token in a dictionary) or into a vector where the coefficient - for each token could be binary, based on word count, based on tf-idf... - - Arguments: - num_words: the maximum number of words to keep, based - on word frequency. Only the most common `num_words` words will - be kept. - filters: a string where each element is a character that will be - filtered from the texts. The default is all punctuation, plus - tabs and line breaks, minus the `'` character. - lower: boolean. Whether to convert the texts to lowercase. - split: string, separator for word splitting. - char_level: if True, every character will be treated as a token. - oov_token: if given, it will be added to word_index and used to - replace out-of-vocabulary words during text_to_sequence calls - - By default, all punctuation is removed, turning the texts into - space-separated sequences of words - (words maybe include the `'` character). These sequences are then - split into lists of tokens. They will then be indexed or vectorized. - - `0` is a reserved index that won't be assigned to any word. - """ - - def __init__(self, - num_words=None, - filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', - lower=True, - split=' ', - char_level=False, - oov_token=None, - **kwargs): - # Legacy support - if 'nb_words' in kwargs: - logging.warning('The `nb_words` argument in `Tokenizer` ' - 'has been renamed `num_words`.') - num_words = kwargs.pop('nb_words') - if kwargs: - raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) - - self.word_counts = OrderedDict() - self.word_docs = {} - self.filters = filters - self.split = split - self.lower = lower - self.num_words = num_words - self.document_count = 0 - self.char_level = char_level - self.oov_token = oov_token - self.index_docs = {} - - def fit_on_texts(self, texts): - """Updates internal vocabulary based on a list of texts. - - In the case where texts contains lists, we assume each entry of the lists - to be a token. - - Required before using `texts_to_sequences` or `texts_to_matrix`. - - Arguments: - texts: can be a list of strings, - a generator of strings (for memory-efficiency), - or a list of list of strings. - """ - for text in texts: - self.document_count += 1 - if self.char_level or isinstance(text, list): - seq = text - else: - seq = text_to_word_sequence(text, self.filters, self.lower, self.split) - for w in seq: - if w in self.word_counts: - self.word_counts[w] += 1 - else: - self.word_counts[w] = 1 - for w in set(seq): - if w in self.word_docs: - self.word_docs[w] += 1 - else: - self.word_docs[w] = 1 - - wcounts = list(self.word_counts.items()) - wcounts.sort(key=lambda x: x[1], reverse=True) - sorted_voc = [wc[0] for wc in wcounts] - # note that index 0 is reserved, never assigned to an existing word - self.word_index = dict( - list(zip(sorted_voc, list(range(1, - len(sorted_voc) + 1))))) - - if self.oov_token is not None: - i = self.word_index.get(self.oov_token) - if i is None: - self.word_index[self.oov_token] = len(self.word_index) + 1 - - for w, c in list(self.word_docs.items()): - self.index_docs[self.word_index[w]] = c - - def fit_on_sequences(self, sequences): - """Updates internal vocabulary based on a list of sequences. - - Required before using `sequences_to_matrix` - (if `fit_on_texts` was never called). - - Arguments: - sequences: A list of sequence. - A "sequence" is a list of integer word indices. - """ - self.document_count += len(sequences) - for seq in sequences: - seq = set(seq) - for i in seq: - if i not in self.index_docs: - self.index_docs[i] = 1 - else: - self.index_docs[i] += 1 - - def texts_to_sequences(self, texts): - """Transforms each text in texts in a sequence of integers. - - Only top "num_words" most frequent words will be taken into account. - Only words known by the tokenizer will be taken into account. - - Arguments: - texts: A list of texts (strings). - - Returns: - A list of sequences. - """ - res = [] - for vect in self.texts_to_sequences_generator(texts): - res.append(vect) - return res - - def texts_to_sequences_generator(self, texts): - """Transforms each text in `texts` in a sequence of integers. - - Each item in texts can also be a list, in which case we assume each item of - that list - to be a token. - - Only top "num_words" most frequent words will be taken into account. - Only words known by the tokenizer will be taken into account. - - Arguments: - texts: A list of texts (strings). - - Yields: - Yields individual sequences. - """ - num_words = self.num_words - for text in texts: - if self.char_level or isinstance(text, list): - seq = text - else: - seq = text_to_word_sequence(text, self.filters, self.lower, self.split) - vect = [] - for w in seq: - i = self.word_index.get(w) - if i is not None: - if num_words and i >= num_words: - continue - else: - vect.append(i) - elif self.oov_token is not None: - i = self.word_index.get(self.oov_token) - if i is not None: - vect.append(i) - yield vect - - def texts_to_matrix(self, texts, mode='binary'): - """Convert a list of texts to a Numpy matrix. - - Arguments: - texts: list of strings. - mode: one of "binary", "count", "tfidf", "freq". - - Returns: - A Numpy matrix. - """ - sequences = self.texts_to_sequences(texts) - return self.sequences_to_matrix(sequences, mode=mode) - - def sequences_to_matrix(self, sequences, mode='binary'): - """Converts a list of sequences into a Numpy matrix. - - Arguments: - sequences: list of sequences - (a sequence is a list of integer word indices). - mode: one of "binary", "count", "tfidf", "freq" - - Returns: - A Numpy matrix. - - Raises: - ValueError: In case of invalid `mode` argument, - or if the Tokenizer requires to be fit to sample data. - """ - if not self.num_words: - if self.word_index: - num_words = len(self.word_index) + 1 - else: - raise ValueError('Specify a dimension (num_words argument), ' - 'or fit on some text data first.') - else: - num_words = self.num_words - - if mode == 'tfidf' and not self.document_count: - raise ValueError('Fit the Tokenizer on some data ' - 'before using tfidf mode.') - - x = np.zeros((len(sequences), num_words)) - for i, seq in enumerate(sequences): - if not seq: - continue - counts = {} - for j in seq: - if j >= num_words: - continue - if j not in counts: - counts[j] = 1. - else: - counts[j] += 1 - for j, c in list(counts.items()): - if mode == 'count': - x[i][j] = c - elif mode == 'freq': - x[i][j] = c / len(seq) - elif mode == 'binary': - x[i][j] = 1 - elif mode == 'tfidf': - # Use weighting scheme 2 in - # https://en.wikipedia.org/wiki/Tf%E2%80%93idf - tf = 1 + np.log(c) - idf = np.log(1 + self.document_count / - (1 + self.index_docs.get(j, 0))) - x[i][j] = tf * idf - else: - raise ValueError('Unknown vectorization mode:', mode) - return x +tf_export( + 'keras.preprocessing.text.text_to_word_sequence')(text_to_word_sequence) +tf_export('keras.preprocessing.text.one_hot')(one_hot) +tf_export('keras.preprocessing.text.hashing_trick')(hashing_trick) +tf_export('keras.preprocessing.text.Tokenizer')(Tokenizer) diff --git a/tensorflow/python/keras/utils/__init__.py b/tensorflow/python/keras/utils/__init__.py index 69337b6a8d52abd4caf2ada518fde51c407f8103..c442b31116091955335423d2e60eaacf464c568e 100644 --- a/tensorflow/python/keras/utils/__init__.py +++ b/tensorflow/python/keras/utils/__init__.py @@ -31,6 +31,7 @@ from tensorflow.python.keras.utils.generic_utils import Progbar from tensorflow.python.keras.utils.generic_utils import serialize_keras_object from tensorflow.python.keras.utils.io_utils import HDF5Matrix from tensorflow.python.keras.utils.layer_utils import convert_all_kernels_in_model +from tensorflow.python.keras.utils.layer_utils import get_source_inputs from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model from tensorflow.python.keras.utils.np_utils import normalize from tensorflow.python.keras.utils.np_utils import to_categorical diff --git a/tensorflow/python/kernel_tests/as_string_op_test.py b/tensorflow/python/kernel_tests/as_string_op_test.py index 94ed8ebd31f5874024bb6b0988073ece15d39d87..51aa17babeabdd06f52e6363fb0992e97d7cede0 100644 --- a/tensorflow/python/kernel_tests/as_string_op_test.py +++ b/tensorflow/python/kernel_tests/as_string_op_test.py @@ -160,7 +160,7 @@ class AsStringOpTest(test.TestCase): complex_inputs_ = [(x + (x + 1) * 1j) for x in float_inputs_] with self.test_session(): - for dtype in (dtypes.complex64,): + for dtype in (dtypes.complex64, dtypes.complex128): input_ = array_ops.placeholder(dtype) def clean_nans(s_l): diff --git a/tensorflow/python/kernel_tests/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py index 97ce245fc835a90a83026802353646f9dc8720e5..b9910133d8ece1c8c1dcef7093335b4675db6105 100644 --- a/tensorflow/python/kernel_tests/cond_v2_test.py +++ b/tensorflow/python/kernel_tests/cond_v2_test.py @@ -78,6 +78,20 @@ class CondV2Test(test.TestCase): self._testCond(true_fn, false_fn, [x, y]) self._testCond(true_fn, false_fn, [y]) + def testMultipleOutputs(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(3.0, name="y") + + def true_fn(): + return x * y, y + + def false_fn(): + return x, y * 3.0 + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + def testBasic2(self): x = constant_op.constant(1.0, name="x") y = constant_op.constant(2.0, name="y") @@ -104,8 +118,8 @@ class CondV2Test(test.TestCase): out = cond_v2.cond_v2(pred, true_fn, false_fn) - self.assertEqual(sess.run(out, {pred: True}), [1.0]) - self.assertEqual(sess.run(out, {pred: False}), [2.0]) + self.assertEqual(sess.run(out, {pred: True}), (1.0,)) + self.assertEqual(sess.run(out, {pred: False}), (2.0,)) def _createCond(self, name): pred = constant_op.constant(True, name="pred") @@ -243,6 +257,32 @@ class CondV2Test(test.TestCase): run_test(True) run_test(False) + def testNestedCondBothBranches(self): + + def run_test(pred_value): + + def build_graph(): + pred = array_ops.placeholder(dtypes.bool, name="pred") + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return _cond(pred, lambda: x + y, lambda: x * x, name=None) + + def false_fn(): + return _cond(pred, lambda: x - y, lambda: y * y, name=None) + + return x, y, pred, true_fn, false_fn + + with ops.Graph().as_default(): + x, y, pred, true_fn, false_fn = build_graph() + self._testCond(true_fn, false_fn, [x, y], {pred: pred_value}) + self._testCond(true_fn, false_fn, [x], {pred: pred_value}) + self._testCond(true_fn, false_fn, [y], {pred: pred_value}) + + run_test(True) + run_test(False) + def testDoubleNestedCond(self): def run_test(pred1_value, pred2_value): diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py index b567b71424263d83ed9467313151240091a36eb1..1a29d0816df71c45d282c87dbb08de388ca0a621 100644 --- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py +++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py @@ -647,7 +647,8 @@ class ControlFlowTest(test.TestCase): # feeding into the fill is dominated by a Switch. zero = graph.get_operation_by_name("gradients/zeros/Const") self.assertEqual(len(zero.control_inputs), 1) - self.assertEqual(zero.control_inputs[0].type, "Switch") + self.assertEqual(zero.control_inputs[0].type, "Identity") + self.assertEqual(zero.control_inputs[0].inputs[0].op.type, "Switch") def testCondGrad_2(self): with self.test_session(): diff --git a/tensorflow/python/kernel_tests/functional_ops_test.py b/tensorflow/python/kernel_tests/functional_ops_test.py index 24800d2b7a7aec9e43419d65c73a5a7ec3e64e24..5db2e9821dcfff08f947a92c6097e9be660f8bd7 100644 --- a/tensorflow/python/kernel_tests/functional_ops_test.py +++ b/tensorflow/python/kernel_tests/functional_ops_test.py @@ -978,6 +978,8 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(sess.run(bvals), [17., 16.]) +# TODO(akshayka): Replace `function.Defun` with tf.contrib.eager.defun` in the +# below test cases. class PartitionedCallTest(test.TestCase): def testBasicSingleDevice(self): @@ -1053,7 +1055,7 @@ class PartitionedCallTest(test.TestCase): self.assertEqual(output, 6.) def testShardsRunOnRequestedDevices(self): - config = config_pb2.ConfigProto(device_count={"CPU": 3}) + config = config_pb2.ConfigProto(device_count={"CPU": 4}) @function.Defun() def Body(): @@ -1073,13 +1075,30 @@ class PartitionedCallTest(test.TestCase): with ops.device("/cpu:2"): s3 = iterator_ops.Iterator.from_structure( (dtypes.float32,)).string_handle() - return s1, s2, s3 + with ops.device(""): + # TODO(akshayka): This is unfortunate and brittle. It prevents + # `Iterator.from_structure` from assigning the iterator op to 'cpu:0'. + # Remove this hack once we have a way of obtaining metadata about + # function execution. + s4 = iterator_ops.Iterator.from_structure( + (dtypes.float32,)).string_handle() + return s1, s2, s3, s4 - with self.test_session(config=config): - outputs = functional_ops.partitioned_call(args=[], f=Body) - self.assertTrue(compat.as_bytes("CPU:0") in outputs[0].eval()) - self.assertTrue(compat.as_bytes("CPU:1") in outputs[1].eval()) - self.assertTrue(compat.as_bytes("CPU:2") in outputs[2].eval()) + with self.test_session(config=config, use_gpu=True) as sess: + with ops.device("/cpu:3"): + outputs = sess.run(functional_ops.partitioned_call(args=[], f=Body)) + self.assertIn(compat.as_bytes("CPU:0"), outputs[0]) + self.assertIn(compat.as_bytes("CPU:1"), outputs[1]) + self.assertIn(compat.as_bytes("CPU:2"), outputs[2]) + self.assertIn(compat.as_bytes("CPU:3"), outputs[3]) + + with self.test_session(config=config, use_gpu=True): + with ops.device("/cpu:0"): + outputs = sess.run(functional_ops.partitioned_call(args=[], f=Body)) + self.assertIn(compat.as_bytes("CPU:0"), outputs[0]) + self.assertIn(compat.as_bytes("CPU:1"), outputs[1]) + self.assertIn(compat.as_bytes("CPU:2"), outputs[2]) + self.assertIn(compat.as_bytes("CPU:0"), outputs[3]) def testAssignAddResourceVariable(self): diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index acee180a6c3e55643052b439d95a65b073288ac6..e32d7c4e672be848d8b3d8d1dc9cca6354aebb10 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os import time import timeit @@ -26,6 +27,7 @@ import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib import rnn as contrib_rnn from tensorflow.core.protobuf import config_pb2 +from tensorflow.python import keras from tensorflow.python.client import session from tensorflow.python.eager import context from tensorflow.python.framework import constant_op @@ -46,6 +48,7 @@ import tensorflow.python.ops.nn_grad # pylint: disable=unused-import import tensorflow.python.ops.sparse_grad # pylint: disable=unused-import import tensorflow.python.ops.tensor_array_grad # pylint: disable=unused-import from tensorflow.python.platform import test +from tensorflow.python.training import saver class Plus1RNNCell(rnn_cell_impl.RNNCell): @@ -275,6 +278,64 @@ class RNNTest(test.TestCase): self._assert_cell_builds(contrib_rnn.IndyLSTMCell, f32, 5, 7, 3) self._assert_cell_builds(contrib_rnn.IndyLSTMCell, f64, 5, 7, 3) + def testBasicLSTMCellInterchangeWithLSTMCell(self): + with self.test_session(graph=ops_lib.Graph()) as sess: + basic_cell = rnn_cell_impl.BasicLSTMCell(1) + basic_cell(array_ops.ones([1, 1]), + state=basic_cell.zero_state(batch_size=1, + dtype=dtypes.float32)) + self.evaluate([v.initializer for v in basic_cell.variables]) + self.evaluate(basic_cell._bias.assign([10.] * 4)) + save = saver.Saver() + prefix = os.path.join(self.get_temp_dir(), "ckpt") + save_path = save.save(sess, prefix) + + with self.test_session(graph=ops_lib.Graph()) as sess: + lstm_cell = rnn_cell_impl.LSTMCell(1, name="basic_lstm_cell") + lstm_cell(array_ops.ones([1, 1]), + state=lstm_cell.zero_state(batch_size=1, + dtype=dtypes.float32)) + self.evaluate([v.initializer for v in lstm_cell.variables]) + save = saver.Saver() + save.restore(sess, save_path) + self.assertAllEqual([10.] * 4, self.evaluate(lstm_cell._bias)) + + def testRNNCellSerialization(self): + for cell in [ + rnn_cell_impl.LSTMCell(32, use_peepholes=True, cell_clip=True), + rnn_cell_impl.BasicLSTMCell(32, dtype=dtypes.float32), + # TODO(scottzhu): GRU and BasicRNN cell are not compatible with Keras. + # rnn_cell_impl.BasicRNNCell( + # 32, activation="relu", dtype=dtypes.float32), + # rnn_cell_impl.GRUCell( + # 32, kernel_initializer="ones", dtype=dtypes.float32) + ]: + with self.test_session(): + x = keras.Input((None, 5)) + layer = keras.layers.RNN(cell) + y = layer(x) + model = keras.models.Model(x, y) + model.compile(optimizer="rmsprop", loss="mse") + + # Test basic case serialization. + x_np = np.random.random((6, 5, 5)) + y_np = model.predict(x_np) + weights = model.get_weights() + config = layer.get_config() + # The custom_objects is important here since rnn_cell_impl is + # not visible as a Keras layer, and also has a name conflict with + # keras.LSTMCell and GRUCell. + layer = keras.layers.RNN.from_config( + config, custom_objects={ + # "BasicRNNCell": rnn_cell_impl.BasicRNNCell, + # "GRUCell": rnn_cell_impl.GRUCell, + "LSTMCell": rnn_cell_impl.LSTMCell, + "BasicLSTMCell": rnn_cell_impl.BasicLSTMCell}) + y = layer(x) + model = keras.models.Model(x, y) + model.set_weights(weights) + y_np_2 = model.predict(x_np) + self.assertAllClose(y_np, y_np_2, atol=1e-4) ######### Benchmarking RNN code diff --git a/tensorflow/python/kernel_tests/split_op_test.py b/tensorflow/python/kernel_tests/split_op_test.py index 419cd5ecdafab92910cd06fb18148796f70afb44..3f9b029a6ac777fc97c65ecf3d70ac879bb5d116 100644 --- a/tensorflow/python/kernel_tests/split_op_test.py +++ b/tensorflow/python/kernel_tests/split_op_test.py @@ -174,6 +174,26 @@ class SplitOpTest(test.TestCase): for dtype in _TEST_DTYPES: self._testHugeNumberOfTensorsVariable(dtype) + @test_util.run_in_graph_and_eager_modes + def testDegenerateVariable(self): + inp = np.random.rand(4, 4).astype("f") + with test_util.device(use_gpu=True): + result = self.evaluate(array_ops.split(inp, [-1, 4], 0)) + self.assertAllEqual(result[0], inp[0:0, :]) + self.assertAllEqual(result[1], inp[0:4, :]) + + result = self.evaluate(array_ops.split(inp, [4, -1], 0)) + self.assertAllEqual(result[0], inp[0:4, :]) + self.assertAllEqual(result[1], inp[4:4, :]) + + result = self.evaluate(array_ops.split(inp, [-1, 4], 1)) + self.assertAllEqual(result[0], inp[:, 0:0]) + self.assertAllEqual(result[1], inp[:, 0:4]) + + result = self.evaluate(array_ops.split(inp, [4, -1], 1)) + self.assertAllEqual(result[0], inp[:, 0:4]) + self.assertAllEqual(result[1], inp[:, 4:4]) + def _testGradientsSimpleVariable(self, dtype): inp = self._makeData((4, 4), dtype) with test_util.device(use_gpu=True): @@ -336,6 +356,16 @@ class SplitOpTest(test.TestCase): for s in splits: self.assertEqual(None, s.get_shape().ndims) + def testVariableShapeFunction(self): + # size_splits too big + with self.assertRaises(ValueError): + array_ops.split([0, 1], [3, -1], axis=0) + + # Correct inference of variable dimension + s0, s1 = array_ops.split([0, 1, 2], [2, -1], axis=0) + assert s0.shape.as_list() == [2] + assert s1.shape.as_list() == [1] + def testNonexistentDimTensor(self): x = array_ops.placeholder(dtypes.int32) values = np.zeros([5, 30]) diff --git a/tensorflow/python/kernel_tests/where_op_test.py b/tensorflow/python/kernel_tests/where_op_test.py index 17575da6f1bf2c226a67419b4bc8156f70f6dedc..29fb002ef445bcfa483dfc747428c7365860fe37 100644 --- a/tensorflow/python/kernel_tests/where_op_test.py +++ b/tensorflow/python/kernel_tests/where_op_test.py @@ -135,6 +135,15 @@ class WhereOpTest(test.TestCase): tf_val = array_ops.where(constant_op.constant(x) > 0, x * x, -x).eval() self.assertAllEqual(tf_val, np_val) + def testBatchSelect(self): + x = np.array([[-2, 3, -1] * 64, [1, -3, -3] * 64] * 8192) # [16384, 192] + c_mat = np.array([[False] * 192, [True] * 192] * 8192) # [16384, 192] + c_vec = np.array([False, True] * 8192) # [16384] + np_val = np.where(c_mat, x * x, -x) + with self.test_session(use_gpu=True): + tf_val = array_ops.where(c_vec, x * x, -x).eval() + self.assertAllEqual(tf_val, np_val) + class WhereBenchmark(test.Benchmark): @@ -163,5 +172,32 @@ class WhereBenchmark(test.Benchmark): "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput)) sys.stdout.flush() + def benchmarkBatchSelect(self): + for (m, n, use_gpu) in itertools.product([1000, 10000, 100000], + [10, 100, 1000], [False, True]): + name = "m_%d_n_%d_use_gpu_%s" % (m, n, use_gpu) + device = "/%s:0" % ("gpu" if use_gpu else "cpu") + with ops.Graph().as_default(): + with ops.device(device): + x_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32) + y_gen = random_ops.random_uniform([m, n], dtype=dtypes.float32) + c_gen = random_ops.random_uniform([m], dtype=dtypes.float32) <= 0.5 + x = resource_variable_ops.ResourceVariable(x_gen) + y = resource_variable_ops.ResourceVariable(y_gen) + c = resource_variable_ops.ResourceVariable(c_gen) + op = array_ops.where(c, x, y) + with session.Session() as sess: + x.initializer.run() + y.initializer.run() + c.initializer.run() + r = self.run_op_benchmark(sess, op, min_iters=100, name=name) + # approximate size of output: m*n*2 floats for each axis. + gb_processed = m * n * 8 / 1.0e9 + throughput = gb_processed / r["wall_time"] + print("Benchmark: %s \t wall_time: %0.03g s \t " + "Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput)) + sys.stdout.flush() + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py index cf13b526175c232d0bc7389bd7c2dc9b23f75353..ab0886553269424a6d5c6d4e494dddb22cd2d7ce 100644 --- a/tensorflow/python/layers/base.py +++ b/tensorflow/python/layers/base.py @@ -183,13 +183,13 @@ class Layer(base_layer.Layer): use_resource: Whether to use `ResourceVariable`. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. partitioner: (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to `partitioner`. In this case, diff --git a/tensorflow/python/layers/core.py b/tensorflow/python/layers/core.py index 261281ae7e9529909184c0417edbfa6f8826419d..9879e5020f31286fc342331843472cac08c6f330 100644 --- a/tensorflow/python/layers/core.py +++ b/tensorflow/python/layers/core.py @@ -127,8 +127,8 @@ def dense( """Functional interface for the densely-connected layer. This layer implements the operation: - `outputs = activation(inputs.kernel + bias)` - Where `activation` is the activation function passed as the `activation` + `outputs = activation(inputs * kernel + bias)` + where `activation` is the activation function passed as the `activation` argument (if not `None`), `kernel` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only if `use_bias` is `True`). @@ -203,7 +203,7 @@ class Dropout(keras_layers.Dropout, base.Layer): to be the same for all timesteps, you can use `noise_shape=[batch_size, 1, features]`. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed}. + `tf.set_random_seed`. for behavior. name: The name of the layer (string). """ @@ -248,7 +248,7 @@ def dropout(inputs, to be the same for all timesteps, you can use `noise_shape=[batch_size, 1, features]`. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. training: Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode diff --git a/tensorflow/python/lib/core/py_func.cc b/tensorflow/python/lib/core/py_func.cc index 7c107138bee52e93ebc159cd418b747ae7e3665a..fc02d6de0e005cb66b4ddf59379f3d2355eb8eaf 100644 --- a/tensorflow/python/lib/core/py_func.cc +++ b/tensorflow/python/lib/core/py_func.cc @@ -507,6 +507,17 @@ class PyFuncOp : public OpKernel { call.ins.push_back(ctx->input(i)); } + // NOTE(mrry): There is a potential time-of-check-to-time-of-use race here. + // because it is possible that `Py_Finalize()` could be called in another + // thread between this check and the call to `PyGILState_Ensure()`, which + // will abort the process if `Py_Finalize()` has been called. A more robust + // solution would be welcome, but it is not obvious how to make this work + // using the current Python C API. + OP_REQUIRES(ctx, Py_IsInitialized(), + errors::FailedPrecondition( + "Python interpreter state is not initialized. " + "The process may be terminated.")); + PyGILState_STATE py_threadstate; py_threadstate = PyGILState_Ensure(); bool log_on_error; diff --git a/tensorflow/python/lib/core/py_util.cc b/tensorflow/python/lib/core/py_util.cc index 2ee898ea1d3efcb8e93e0c244842280f2e52aaf6..739cab46b10223fde918372af48b7f7a83d4a7a6 100644 --- a/tensorflow/python/lib/core/py_util.cc +++ b/tensorflow/python/lib/core/py_util.cc @@ -18,6 +18,8 @@ limitations under the License. // Place `` before to avoid build failure in macOS. #include +// The empty line above is on purpose as otherwise clang-format will +// automatically move before . #include #include "tensorflow/core/lib/core/errors.h" diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index ec6488ea6321508677c88dfe077acb0160400cfe..a917f5108753807465db2c929504b5fb1ba794a2 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -538,7 +538,7 @@ def slice(input_, begin, size, name=None): words, `begin[i]` is the offset into the 'i'th dimension of `input` that you want to slice from. - Note that @{tf.Tensor.__getitem__} is typically a more pythonic way to + Note that `tf.Tensor.__getitem__` is typically a more pythonic way to perform slices, as it allows you to write `foo[3:7, :-2]` instead of `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`. @@ -594,7 +594,7 @@ def strided_slice(input_, **Instead of calling this op directly most users will want to use the NumPy-style slicing syntax (e.g. `tensor[..., 3:4:-1, tf.newaxis, 3]`), which - is supported via @{tf.Tensor.__getitem__} and @{tf.Variable.__getitem__}.** + is supported via `tf.Tensor.__getitem__` and `tf.Variable.__getitem__`.** The interface of this op is a low-level encoding of the slicing syntax. Roughly speaking, this op extracts a slice of size `(end-begin)/stride` @@ -723,7 +723,7 @@ def _SliceHelperVar(var, slice_spec): """Creates a slice helper object given a variable. This allows creating a sub-tensor from part of the current contents - of a variable. See @{tf.Tensor.__getitem__} for detailed examples + of a variable. See `tf.Tensor.__getitem__` for detailed examples of slicing. This function in addition also allows assignment to a sliced range. diff --git a/tensorflow/python/ops/clip_ops.py b/tensorflow/python/ops/clip_ops.py index 75c459a9cf10a90f6043d304b302e0a0806bf045..e2580e8a2ead32f95c951f02f71940843a2750bc 100644 --- a/tensorflow/python/ops/clip_ops.py +++ b/tensorflow/python/ops/clip_ops.py @@ -42,6 +42,9 @@ def clip_by_value(t, clip_value_min, clip_value_max, Any values less than `clip_value_min` are set to `clip_value_min`. Any values greater than `clip_value_max` are set to `clip_value_max`. + Note: `clip_value_min` needs to be smaller or equal to `clip_value_max` for + correct results. + Args: t: A `Tensor`. clip_value_min: A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape diff --git a/tensorflow/python/ops/cond_v2_impl.py b/tensorflow/python/ops/cond_v2_impl.py index 44c5c050c08240dd13766b71e8e708c9b0317399..b3dacff6d66bfabc624f05cf698fd882efd72e74 100644 --- a/tensorflow/python/ops/cond_v2_impl.py +++ b/tensorflow/python/ops/cond_v2_impl.py @@ -65,20 +65,27 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): caller_colocation_stack = ops.get_default_graph()._colocation_stack caller_container = ops.get_default_graph()._container caller_collection_ref = ops.get_default_graph()._collections - # pylint: enable=protected-access - func_name_prefix = scope.replace("/", "_") + with ops.name_scope(None): + # Find the outer most graph for uniquing function names. + # TODO(jpienaar): Make this work in eager mode. + graph = ops.get_default_graph() + while isinstance(graph, _function._FuncGraph): + graph = graph._outer_graph + true_name = graph.unique_name(("%strue" % scope).replace("/", "_")) + false_name = graph.unique_name(("%sfalse" % scope).replace("/", "_")) + # pylint: enable=protected-access true_graph = _function.func_graph_from_py_func( true_fn, [], [], - name="%strue" % func_name_prefix, + name=true_name, device=caller_device, colocation_stack=caller_colocation_stack, collections_ref=caller_collection_ref, container=caller_container) false_graph = _function.func_graph_from_py_func( false_fn, [], [], - name="%sfalse" % func_name_prefix, + name=false_name, device=caller_device, colocation_stack=caller_colocation_stack, collections_ref=caller_collection_ref, @@ -132,7 +139,7 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): attr_value_pb2.AttrValue(b=True)) # pylint: enable=protected-access - return tensors[:num_cond_outputs] + return tuple(tensors[:num_cond_outputs]) @ops.RegisterGradient("If") diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index c7061b36dd403250f6a42e9a6fd1a71ecdbefa87..f84ff4ddf03864030013a14be58bafd3a1817882 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -1449,14 +1449,17 @@ def ZerosLikeOutsideLoop(op, index): pred = op_ctxt.pred branch = op_ctxt.branch switch_val = switch(op.inputs[0], pred)[1 - branch] + # A op is created along the branch taken as control dependencies are on + # the whole op and not on the tensor output. + pivot = array_ops.identity(switch_val) if val.dtype == dtypes.resource: - with ops.control_dependencies([switch_val]): + with ops.control_dependencies([pivot]): return array_ops.zeros( gen_resource_variable_ops.variable_shape(switch_val)) zeros_shape = array_ops.shape_internal(switch_val, optimize=False) # Ensure ops created within array_ops.zeros are dominated by switch in # cond context. - with ops.control_dependencies([switch_val]): + with ops.control_dependencies([pivot]): return array_ops.zeros(zeros_shape, dtype=val.dtype) else: return array_ops.zeros_like(val, optimize=False) @@ -2065,21 +2068,25 @@ def cond(pred, # Build the graph for the true branch in a new context. context_t = CondContext(pred, pivot_1, branch=1) - context_t.Enter() - orig_res_t, res_t = context_t.BuildCondBranch(true_fn) - if orig_res_t is None: - raise ValueError("true_fn must have a return value.") - context_t.ExitResult(res_t) - context_t.Exit() + try: + context_t.Enter() + orig_res_t, res_t = context_t.BuildCondBranch(true_fn) + if orig_res_t is None: + raise ValueError("true_fn must have a return value.") + context_t.ExitResult(res_t) + finally: + context_t.Exit() # Build the graph for the false branch in a new context. context_f = CondContext(pred, pivot_2, branch=0) - context_f.Enter() - orig_res_f, res_f = context_f.BuildCondBranch(false_fn) - if orig_res_f is None: - raise ValueError("false_fn must have a return value.") - context_f.ExitResult(res_f) - context_f.Exit() + try: + context_f.Enter() + orig_res_f, res_f = context_f.BuildCondBranch(false_fn) + if orig_res_f is None: + raise ValueError("false_fn must have a return value.") + context_f.ExitResult(res_f) + finally: + context_f.Exit() if not strict: orig_res_t = _UnpackIfSingleton(orig_res_t) @@ -3069,7 +3076,7 @@ def while_loop(cond, `loop_vars` is the same in every iteration. The `shape_invariants` argument allows the caller to specify a less specific shape invariant for each loop variable, which is needed if the shape varies between iterations. The - @{tf.Tensor.set_shape} + `tf.Tensor.set_shape` function may also be used in the `body` function to indicate that the output loop variable has a particular shape. The shape invariant for SparseTensor and IndexedSlices are treated specially as follows: @@ -3320,7 +3327,7 @@ def with_dependencies(dependencies, output_tensor, name=None): no guarantee that `output_tensor` will be evaluated after any `dependencies` have run. - See also @{tf.tuple$tuple} and @{tf.group$group}. + See also `tf.tuple` and `tf.group`. Args: dependencies: Iterable of operations to run before this op finishes. @@ -3365,8 +3372,8 @@ def group(*inputs, **kwargs): When this op finishes, all ops in `inputs` have finished. This op has no output. - See also @{tf.tuple$tuple} and - @{tf.control_dependencies$control_dependencies}. + See also `tf.tuple` and + `tf.control_dependencies`. Args: *inputs: Zero or more tensors to group. @@ -3435,8 +3442,8 @@ def tuple(tensors, name=None, control_inputs=None): # pylint: disable=redefined returned by `tuple` are only available after all the parallel computations are done. - See also @{tf.group$group} and - @{tf.control_dependencies$control_dependencies}. + See also `tf.group` and + `tf.control_dependencies`. Args: tensors: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`. diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py index 9f77a6cca1776407865bffdf6c124ab16be96b24..871f236f783cb9aa13ea8b776dbe5850febcb440 100644 --- a/tensorflow/python/ops/custom_gradient.py +++ b/tensorflow/python/ops/custom_gradient.py @@ -73,7 +73,7 @@ def custom_gradient(f): With this definition, the gradient at x=100 will be correctly evaluated as 1.0. - See also @{tf.RegisterGradient} which registers a gradient function for a + See also `tf.RegisterGradient` which registers a gradient function for a primitive TensorFlow operation. `tf.custom_gradient` on the other hand allows for fine grained control over the gradient computation of a sequence of operations. @@ -100,7 +100,7 @@ def custom_gradient(f): Returns: A function `h(x)` which returns the same value as `f(x)[0]` and whose - gradient (as calculated by @{tf.gradients}) is determined by `f(x)[1]`. + gradient (as calculated by `tf.gradients`) is determined by `f(x)[1]`. """ def decorated(*args, **kwargs): diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py index abf597ca55c647cca3f6012ed602a815298e1ed3..7af2ca56be73c7713ac86965b7015a4fc5c957de 100644 --- a/tensorflow/python/ops/data_flow_ops.py +++ b/tensorflow/python/ops/data_flow_ops.py @@ -126,8 +126,8 @@ class QueueBase(object): handle single elements, versions that support enqueuing and dequeuing a batch of elements at once. - See @{tf.FIFOQueue} and - @{tf.RandomShuffleQueue} for concrete + See `tf.FIFOQueue` and + `tf.RandomShuffleQueue` for concrete implementations of this class, and instructions on how to create them. """ @@ -309,12 +309,12 @@ class QueueBase(object): until the element has been enqueued. At runtime, this operation may raise an error if the queue is - @{tf.QueueBase.close} before or during its execution. If the + `tf.QueueBase.close` before or during its execution. If the queue is closed before this operation runs, `tf.errors.CancelledError` will be raised. If this operation is blocked, and either (i) the queue is closed by a close operation with `cancel_pending_enqueues=True`, or (ii) the session is - @{tf.Session.close}, + `tf.Session.close`, `tf.errors.CancelledError` will be raised. Args: @@ -352,12 +352,12 @@ class QueueBase(object): until all of the elements have been enqueued. At runtime, this operation may raise an error if the queue is - @{tf.QueueBase.close} before or during its execution. If the + `tf.QueueBase.close` before or during its execution. If the queue is closed before this operation runs, `tf.errors.CancelledError` will be raised. If this operation is blocked, and either (i) the queue is closed by a close operation with `cancel_pending_enqueues=True`, or (ii) the session is - @{tf.Session.close}, + `tf.Session.close`, `tf.errors.CancelledError` will be raised. Args: @@ -413,11 +413,11 @@ class QueueBase(object): until there is an element to dequeue. At runtime, this operation may raise an error if the queue is - @{tf.QueueBase.close} before or during its execution. If the + `tf.QueueBase.close` before or during its execution. If the queue is closed, the queue is empty, and there are no pending enqueue operations that can fulfill this request, `tf.errors.OutOfRangeError` will be raised. If the session is - @{tf.Session.close}, + `tf.Session.close`, `tf.errors.CancelledError` will be raised. Args: @@ -455,11 +455,11 @@ class QueueBase(object): `OutOfRange` exception is raised. At runtime, this operation may raise an error if the queue is - @{tf.QueueBase.close} before or during its execution. If the + `tf.QueueBase.close` before or during its execution. If the queue is closed, the queue contains fewer than `n` elements, and there are no pending enqueue operations that can fulfill this request, `tf.errors.OutOfRangeError` will be raised. If the - session is @{tf.Session.close}, + session is `tf.Session.close`, `tf.errors.CancelledError` will be raised. Args: @@ -500,7 +500,7 @@ class QueueBase(object): If the queue is closed and there are more than `0` but fewer than `n` elements remaining, then instead of raising a - `tf.errors.OutOfRangeError` like @{tf.QueueBase.dequeue_many}, + `tf.errors.OutOfRangeError` like `tf.QueueBase.dequeue_many`, less than `n` elements are returned immediately. If the queue is closed and there are `0` elements left in the queue, then a `tf.errors.OutOfRangeError` is raised just like in `dequeue_many`. @@ -608,7 +608,7 @@ def _shared_name(shared_name): class RandomShuffleQueue(QueueBase): """A queue implementation that dequeues elements in a random order. - See @{tf.QueueBase} for a description of the methods on + See `tf.QueueBase` for a description of the methods on this class. """ @@ -657,7 +657,7 @@ class RandomShuffleQueue(QueueBase): with the same length as `dtypes`, or `None`. If specified the dequeue methods return a dictionary with the names as keys. seed: A Python integer. Used to create a random seed. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. shared_name: (Optional.) If non-empty, this queue will be shared under the given name across multiple sessions. @@ -693,7 +693,7 @@ class RandomShuffleQueue(QueueBase): class FIFOQueue(QueueBase): """A queue implementation that dequeues elements in first-in first-out order. - See @{tf.QueueBase} for a description of the methods on + See `tf.QueueBase` for a description of the methods on this class. """ @@ -753,7 +753,7 @@ class PaddingFIFOQueue(QueueBase): A `PaddingFIFOQueue` may contain components with dynamic shape, while also supporting `dequeue_many`. See the constructor for more details. - See @{tf.QueueBase} for a description of the methods on + See `tf.QueueBase` for a description of the methods on this class. """ @@ -824,7 +824,7 @@ class PaddingFIFOQueue(QueueBase): class PriorityQueue(QueueBase): """A queue implementation that dequeues elements in prioritized order. - See @{tf.QueueBase} for a description of the methods on + See `tf.QueueBase` for a description of the methods on this class. """ diff --git a/tensorflow/python/ops/distributions/distribution.py b/tensorflow/python/ops/distributions/distribution.py index c03ef967e68474b0313de01d48252c8274e37a21..ddf9442cd22d68d6ff43bb8017983e774ce9e11b 100644 --- a/tensorflow/python/ops/distributions/distribution.py +++ b/tensorflow/python/ops/distributions/distribution.py @@ -526,8 +526,8 @@ class Distribution(_BaseDistribution): # Remove "self", "__class__", or other special variables. These can appear # if the subclass used: # `parameters = dict(locals())`. - return dict((k, v) for k, v in self._parameters.items() - if not k.startswith("__") and k != "self") + return {k: v for k, v in self._parameters.items() + if not k.startswith("__") and k != "self"} @property def reparameterization_type(self): diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index 27c2fa701760f000db2463aaba0b496b3550ddff..7b9e7de1457d8503a9aac793227dbea9675653bb 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -253,7 +253,7 @@ def embedding_lookup( This function is used to perform parallel lookups on the list of tensors in `params`. It is a generalization of - @{tf.gather}, where `params` is + `tf.gather`, where `params` is interpreted as a partitioning of a large embedding tensor. `params` may be a `PartitionedVariable` as returned by using `tf.get_variable()` with a partitioner. diff --git a/tensorflow/python/ops/gradients_impl.py b/tensorflow/python/ops/gradients_impl.py index b64a66be03ba09e0660b7067420b61f91cf191a3..a68f680224d4b7281637cda1239f95340a513ef5 100644 --- a/tensorflow/python/ops/gradients_impl.py +++ b/tensorflow/python/ops/gradients_impl.py @@ -653,9 +653,6 @@ def _GradientsHelper(ys, # Initialize the pending count for ops in the connected subgraph from ys # to the xs. - if len(ys) > 1: - ys = [array_ops.identity(y) if _Consumers(y, func_graphs) else y - for y in ys] to_ops = [t.op for t in ys] from_ops = [t.op for t in xs] stop_gradient_ops = [t.op for t in stop_gradients] diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py index 855a4d0c33c9785378ad2da6d174486e90a70fc2..12356944f8b4be695e90a4f1d978c68faa626e82 100644 --- a/tensorflow/python/ops/image_ops_impl.py +++ b/tensorflow/python/ops/image_ops_impl.py @@ -265,7 +265,7 @@ def random_flip_up_down(image, seed=None): image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor of shape `[height, width, channels]`. seed: A Python integer. Used to create a random seed. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. Returns: @@ -287,7 +287,7 @@ def random_flip_left_right(image, seed=None): image: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor of shape `[height, width, channels]`. seed: A Python integer. Used to create a random seed. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. Returns: @@ -307,7 +307,7 @@ def _random_flip(image, flip_index, seed, scope_name): flip_index: The dimension along which to flip the image. Vertical: 0, Horizontal: 1 seed: A Python integer. Used to create a random seed. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. scope_name: Name of the scope in which the ops are added. @@ -948,7 +948,7 @@ def resize_images(images, Resized images will be distorted if their original aspect ratio is not the same as `size`. To avoid distortions see - @{tf.image.resize_image_with_pad}. + `tf.image.resize_image_with_pad`. `method` can be one of: @@ -1167,7 +1167,7 @@ def resize_image_with_pad(image, _ImageDimensions(padded, rank=4) if not is_batch: - padded = array_ops.squeeze(padded, squeeze_dims=[0]) + padded = array_ops.squeeze(padded, axis=[0]) return padded @@ -1227,7 +1227,7 @@ def random_brightness(image, max_delta, seed=None): image: An image. max_delta: float, must be non-negative. seed: A Python integer. Used to create a random seed. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. Returns: @@ -1255,7 +1255,7 @@ def random_contrast(image, lower, upper, seed=None): lower: float. Lower bound for the random contrast factor. upper: float. Upper bound for the random contrast factor. seed: A Python integer. Used to create a random seed. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. Returns: diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py index cf9761803bf9654e21ec12e1f1c7193b3e88c020..0e4193e23b7914b652fa3b9e1bdb4c21328f2997 100644 --- a/tensorflow/python/ops/image_ops_test.py +++ b/tensorflow/python/ops/image_ops_test.py @@ -1410,6 +1410,14 @@ class AdjustContrastTest(test_util.TensorFlowTestCase): y_tf = self._adjustContrastTf(x_np, contrast_factor) self.assertAllClose(y_tf, y_np, rtol=1e-5, atol=1e-5) + def testContrastFactorShape(self): + x_shape = [1, 2, 2, 3] + x_data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1] + x_np = np.array(x_data, dtype=np.uint8).reshape(x_shape) + with self.assertRaisesRegexp( + ValueError, 'Shape must be rank 0 but is rank 1'): + image_ops.adjust_contrast(x_np, [2.0]) + class AdjustBrightnessTest(test_util.TensorFlowTestCase): @@ -1956,7 +1964,7 @@ class PadToBoundingBoxTest(test_util.TensorFlowTestCase): "all dims of 'image.shape' must be > 0", use_tensor_inputs_options=[False]) - # The orignal error message does not contain back slashes. However, they + # The original error message does not contain back slashes. However, they # are added by either the assert op or the runtime. If this behavior # changes in the future, the match string will also needs to be changed. self._assertRaises( @@ -2985,7 +2993,7 @@ class ResizeImageWithCropOrPadTest(test_util.TensorFlowTestCase): "all dims of 'image.shape' must be > 0", use_tensor_inputs_options=[False]) - # The orignal error message does not contain back slashes. However, they + # The original error message does not contain back slashes. However, they # are added by either the assert op or the runtime. If this behavior # changes in the future, the match string will also needs to be changed. self._assertRaises( @@ -3201,7 +3209,8 @@ class PngTest(test_util.TensorFlowTestCase): def testExisting(self): # Read some real PNGs, converting to different channel numbers prefix = "tensorflow/core/lib/png/testdata/" - inputs = (1, "lena_gray.png"), (4, "lena_rgba.png") + inputs = ((1, "lena_gray.png"), (4, "lena_rgba.png"), + (3, "lena_palette.png"), (4, "lena_palette_trns.png")) for channels_in, filename in inputs: for channels in 0, 1, 3, 4: with self.test_session(use_gpu=True) as sess: diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index c315722b6ba12d45d023820b09bb7c1de7c2268a..4d75ee3974807a3ec00f6813edea7072d2f0bd8d 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -238,7 +238,7 @@ class RandomUniform(Initializer): maxval: A python scalar or a scalar tensor. Upper bound of the range of random values to generate. Defaults to 1 for float types. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. """ @@ -276,7 +276,7 @@ class RandomNormal(Initializer): stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. """ @@ -319,7 +319,7 @@ class TruncatedNormal(Initializer): stddev: a python scalar or a scalar tensor. Standard deviation of the random values to generate. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. """ @@ -369,7 +369,7 @@ class UniformUnitScaling(Initializer): Args: factor: Float. A multiplicative factor by which the values will be scaled. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. """ @@ -427,7 +427,7 @@ class VarianceScaling(Initializer): mode: One of "fan_in", "fan_out", "fan_avg". distribution: Random distribution to use. One of "normal", "uniform". seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. @@ -517,7 +517,7 @@ class Orthogonal(Initializer): Args: gain: multiplicative factor to apply to the orthogonal matrix seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. """ @@ -572,7 +572,7 @@ class ConvolutionDeltaOrthogonal(Initializer): The 2-norm of an input is multiplied by a factor of 'sqrt(gain)' after applying this convolution. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. dtype: The data type. """ @@ -628,7 +628,7 @@ class ConvolutionOrthogonal(Initializer): The 2-norm of an input is multiplied by a factor of 'sqrt(gain)' after applying this convolution. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. dtype: The data type. """ @@ -693,7 +693,7 @@ class ConvolutionOrthogonal2D(ConvolutionOrthogonal): This has the effect of scaling the output 2-norm by a factor of `sqrt(gain)`. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. dtype: The data type. """ @@ -829,7 +829,7 @@ class ConvolutionOrthogonal1D(ConvolutionOrthogonal): The 2-norm of an input is multiplied by a factor of 'sqrt(gain)' after applying this convolution. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. """ @@ -946,7 +946,7 @@ class ConvolutionOrthogonal3D(ConvolutionOrthogonal): The 2-norm of an input is multiplied by a factor of 'sqrt(gain)' after applying this convolution. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. dtype: The data type. """ @@ -1150,7 +1150,7 @@ def glorot_uniform_initializer(seed=None, dtype=dtypes.float32): Args: seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. @@ -1175,7 +1175,7 @@ def glorot_normal_initializer(seed=None, dtype=dtypes.float32): Args: seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. dtype: The data type. Only floating point types are supported. diff --git a/tensorflow/python/ops/losses/losses_impl.py b/tensorflow/python/ops/losses/losses_impl.py index 66633c8b12f60c86760f906aa8e4312c7394e796..51fb4cbac863f6b96ee30f3e59388b61ef38403c 100644 --- a/tensorflow/python/ops/losses/losses_impl.py +++ b/tensorflow/python/ops/losses/losses_impl.py @@ -190,7 +190,7 @@ def compute_weighted_loss( When calculating the gradient of a weighted loss contributions from both `losses` and `weights` are considered. If your `weights` depend on some model parameters but you do not want this to affect the loss - gradient, you need to apply @{tf.stop_gradient} to `weights` before + gradient, you need to apply `tf.stop_gradient` to `weights` before passing them to `compute_weighted_loss`. @compatbility(eager) diff --git a/tensorflow/python/ops/nn_impl.py b/tensorflow/python/ops/nn_impl.py index f47f38e29e328ea92bfc494d60673c70a58274d3..51f812b395381defbd15b59f5661fa6603966f8e 100644 --- a/tensorflow/python/ops/nn_impl.py +++ b/tensorflow/python/ops/nn_impl.py @@ -425,7 +425,7 @@ def depthwise_conv2d(input, strides: 1-D of size 4. The stride of the sliding window for each dimension of `input`. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. rate: 1-D of size 2. The dilation rate in which we sample input values across the `height` and `width` dimensions in atrous convolution. If it is greater than 1, then all values of strides must be 1. @@ -507,7 +507,7 @@ def separable_conv2d(input, strides: 1-D of size 4. The strides for the depthwise convolution for each dimension of `input`. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. rate: 1-D of size 2. The dilation rate in which we sample input values across the `height` and `width` dimensions in atrous convolution. If it is greater than 1, then all values of strides must be 1. @@ -1189,7 +1189,7 @@ def nce_loss(weights, Note: By default this uses a log-uniform (Zipfian) distribution for sampling, so your labels must be sorted in order of decreasing frequency to achieve good results. For more details, see - @{tf.nn.log_uniform_candidate_sampler}. + `tf.nn.log_uniform_candidate_sampler`. Note: In the case where `num_true` > 1, we assign to each target class the target probability 1 / `num_true` so that the target probabilities diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 5cdb7726a77ba9329a7d02a9b09035713d3491a1..6fd1273687eddd24e68e16efb19617ab152ab959 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -898,8 +898,8 @@ def pool( ``` where the reduction function REDUCE depends on the value of `pooling_type`, - and pad_before is defined based on the value of `padding` as described in the - @{tf.nn.convolution$comment here}. + and pad_before is defined based on the value of `padding` as described in + the "returns" section of `tf.nn.convolution` for details. The reduction never includes out-of-bounds positions. In the case that `data_format` starts with `"NC"`, the `input` and output are @@ -921,7 +921,7 @@ def pool( window_shape: Sequence of N ints >= 1. pooling_type: Specifies pooling operation, must be "AVG" or "MAX". padding: The padding algorithm, must be "SAME" or "VALID". - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. dilation_rate: Optional. Dilation rate. List of N ints >= 1. Defaults to [1]*N. If any value of dilation_rate is > 1, then all values of strides must be 1. @@ -1045,8 +1045,8 @@ def atrous_conv2d(value, filters, rate, padding, name=None): """Atrous convolution (a.k.a. convolution with holes or dilated convolution). This function is a simpler wrapper around the more general - @{tf.nn.convolution}, and exists only for backwards compatibility. You can - use @{tf.nn.convolution} to perform 1-D, 2-D, or 3-D atrous convolution. + `tf.nn.convolution`, and exists only for backwards compatibility. You can + use `tf.nn.convolution` to perform 1-D, 2-D, or 3-D atrous convolution. Computes a 2-D atrous convolution, also known as convolution with holes or @@ -1205,7 +1205,7 @@ def conv2d_transpose( strides: A list of ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC' and 'NCHW' are supported. name: Optional name for the returned tensor. @@ -1430,7 +1430,7 @@ def conv3d_transpose( strides: A list of ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. data_format: A string, either `'NDHWC'` or `'NCDHW`' specifying the layout of the input and output tensors. Defaults to `'NDHWC'`. name: Optional name for the returned tensor. @@ -1819,7 +1819,7 @@ def softmax_cross_entropy_with_logits_v2( or `float64`). Backpropagation will happen into both `logits` and `labels`. To disallow - backpropagation into `labels`, pass label tensors through @{tf.stop_gradient} + backpropagation into `labels`, pass label tensors through `tf.stop_gradient` before feeding it to this function. **Note that to avoid confusion, it is required to pass only named arguments to @@ -1909,7 +1909,7 @@ _XENT_DEPRECATION = """ Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default. -See @{tf.nn.softmax_cross_entropy_with_logits_v2}. +See `tf.nn.softmax_cross_entropy_with_logits_v2`. """ @@ -1946,7 +1946,7 @@ def softmax_cross_entropy_with_logits( Backpropagation will happen only into `logits`. To calculate a cross entropy loss that allows backpropagation into both `logits` and `labels`, see - @{tf.nn.softmax_cross_entropy_with_logits_v2}. + `tf.nn.softmax_cross_entropy_with_logits_v2`. **Note that to avoid confusion, it is required to pass only named arguments to this function.** @@ -2003,8 +2003,8 @@ def sparse_softmax_cross_entropy_with_logits( A common use case is to have logits and labels of shape `[batch_size, num_classes]`, but higher dimensions are supported, in which case the `dim`-th dimension is assumed to be of size `num_classes`. - `logits` and `labels` must have the same dtype (either `float16`, `float32`, - or `float64`). + `logits` must have the dtype of `float16`, `float32`, or `float64`, and + `labels` must have the dtype of `int32` or `int64`. **Note that to avoid confusion, it is required to pass only named arguments to this function.** @@ -2114,7 +2114,7 @@ def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None): strides: A list or tuple of 4 ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC' and 'NCHW' are supported. name: Optional name for the operation. @@ -2143,7 +2143,7 @@ def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): strides: A list or tuple of 4 ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC', 'NCHW' and 'NCHW_VECT_C' are supported. name: Optional name for the operation. @@ -2301,7 +2301,7 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): # pylint: di noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. seed: A Python integer. Used to create random seeds. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: A name for this operation (optional). @@ -2521,7 +2521,7 @@ def conv1d_transpose( stride: An `integer`. The number of entries by which the filter is moved right at each step. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. - See the @{tf.nn.convolution$comment here} + See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC' and 'NCHW' are supported. name: Optional name for the returned tensor. diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 4cd357d0c8cf04714b6c1fc0c0cf1e1b5aa5c09f..ce0db6b264a55d0565b4ffcc6e6a783a5bf555a1 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -220,7 +220,7 @@ class L2LossTest(test_lib.TestCase): output = nn_ops.l2_loss(x) err = gradient_checker.compute_gradient_error(x, x_shape, output, [1]) print("L2Loss gradient err = %g " % err) - err_tolerance = 1e-11 + err_tolerance = 1e-10 self.assertLess(err, err_tolerance) diff --git a/tensorflow/python/ops/numerics.py b/tensorflow/python/ops/numerics.py index d348e47f57b703138aabfc3463e750b795113335..8fcbd7d83407ac1972f5165175dc498f06615cc2 100644 --- a/tensorflow/python/ops/numerics.py +++ b/tensorflow/python/ops/numerics.py @@ -56,8 +56,8 @@ def add_check_numerics_ops(): `check_numerics` op for all of its (`half`, `float`, or `double`) inputs is guaranteed to run before the `check_numerics` op on any of its outputs. - Note: This API is not compatible with the use of @{tf.cond} or - @{tf.while_loop}, and will raise a `ValueError` if you attempt to call it + Note: This API is not compatible with the use of `tf.cond` or + `tf.while_loop`, and will raise a `ValueError` if you attempt to call it in such a graph. Returns: diff --git a/tensorflow/python/ops/parallel_for/BUILD b/tensorflow/python/ops/parallel_for/BUILD index 6c804a50e70c8873c827e9fdc5a5cc27f95a2a1b..015181af47b310cd6aec52b4a383f8868dddc493 100644 --- a/tensorflow/python/ops/parallel_for/BUILD +++ b/tensorflow/python/ops/parallel_for/BUILD @@ -85,6 +85,7 @@ py_library( cuda_py_test( name = "control_flow_ops_test", + size = "large", srcs = ["control_flow_ops_test.py"], additional_deps = [ ":control_flow_ops", diff --git a/tensorflow/python/ops/parallel_for/pfor.py b/tensorflow/python/ops/parallel_for/pfor.py index 77ec3bc0d40ecba11c1624af1ad4be0578b5e4f7..2e4b2fd64eb9db5df283ca5918675b22b6835e83 100644 --- a/tensorflow/python/ops/parallel_for/pfor.py +++ b/tensorflow/python/ops/parallel_for/pfor.py @@ -2117,7 +2117,7 @@ def _convert_print(pfor_input): # 2a Elements written to the array are "stacked" # To simulate multiple TensorArrays, we may increase the dimension of each # element of the array. i.e. the i_th row of the j_th entry of the converted -# TensorArray corresponds to to the j_th entry of the TensorArray in the i_th +# TensorArray corresponds to the j_th entry of the TensorArray in the i_th # pfor iteration. # # 2b Elements written to the array are "unstacked" diff --git a/tensorflow/python/ops/random_ops.py b/tensorflow/python/ops/random_ops.py index b8738adf66e6ff51962ed44dce7cd4b95544e271..4baf50638504527b474fc335ef1d57bb1a84611e 100644 --- a/tensorflow/python/ops/random_ops.py +++ b/tensorflow/python/ops/random_ops.py @@ -61,7 +61,7 @@ def random_normal(shape, dtype: The type of the output. seed: A Python integer. Used to create a random seed for the distribution. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: A name for the operation (optional). @@ -110,7 +110,7 @@ def parameterized_truncated_normal(shape, dtype: The type of the output. seed: A Python integer. Used to create a random seed for the distribution. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: A name for the operation (optional). @@ -158,7 +158,7 @@ def truncated_normal(shape, dtype: The type of the output. seed: A Python integer. Used to create a random seed for the distribution. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: A name for the operation (optional). @@ -212,7 +212,7 @@ def random_uniform(shape, dtype: The type of the output: `float16`, `float32`, `float64`, `int32`, or `int64`. seed: A Python integer. Used to create a random seed for the distribution. - See @{tf.set_random_seed} + See `tf.set_random_seed` for behavior. name: A name for the operation (optional). @@ -264,7 +264,7 @@ def random_shuffle(value, seed=None, name=None): value: A Tensor to be shuffled. seed: A Python integer. Used to create a random seed for the distribution. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: A name for the operation (optional). @@ -292,7 +292,7 @@ def random_crop(value, size, seed=None, name=None): value: Input tensor to crop. size: 1-D tensor with size the rank of `value`. seed: Python integer. Used to create a random seed. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: A name for this operation (optional). @@ -338,7 +338,7 @@ def multinomial(logits, num_samples, seed=None, name=None, output_dtype=None): num_samples: 0-D. Number of independent samples to draw for each row slice. seed: A Python integer. Used to create a random seed for the distribution. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: Optional name for the operation. output_dtype: integer type to use for the output. Defaults to int64. @@ -417,7 +417,7 @@ def random_gamma(shape, `float64`. seed: A Python integer. Used to create a random seed for the distributions. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: Optional name for the operation. @@ -467,7 +467,7 @@ def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None): `int64`. seed: A Python integer. Used to create a random seed for the distributions. See - @{tf.set_random_seed} + `tf.set_random_seed` for behavior. name: Optional name for the operation. diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index 42806ba6ec486b88085ddc063c82a6873a1b23c8..f481726d545f065e3906f3767aac2d481d2a2d88 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -34,6 +34,9 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util +from tensorflow.python.keras import activations +from tensorflow.python.keras import initializers +from tensorflow.python.keras.utils import tf_utils from tensorflow.python.layers import base as base_layer from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops @@ -48,6 +51,7 @@ from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.util import nest +from tensorflow.python.util.deprecation import deprecated from tensorflow.python.util.tf_export import tf_export @@ -335,7 +339,8 @@ class BasicRNNCell(LayerRNNCell): Args: num_units: int, The number of units in the RNN cell. - activation: Nonlinearity to use. Default: `tanh`. + activation: Nonlinearity to use. Default: `tanh`. It could also be string + that is within Keras activation function names. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. @@ -344,6 +349,8 @@ class BasicRNNCell(LayerRNNCell): cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. + **kwargs: Dict, keyword named properties for common layer attributes, like + `trainable` etc when constructing the cell from configs of get_config(). """ def __init__(self, @@ -351,14 +358,19 @@ class BasicRNNCell(LayerRNNCell): activation=None, reuse=None, name=None, - dtype=None): - super(BasicRNNCell, self).__init__(_reuse=reuse, name=name, dtype=dtype) + dtype=None, + **kwargs): + super(BasicRNNCell, self).__init__( + _reuse=reuse, name=name, dtype=dtype, **kwargs) # Inputs must be 2-dimensional. self.input_spec = base_layer.InputSpec(ndim=2) self._num_units = num_units - self._activation = activation or math_ops.tanh + if activation: + self._activation = activations.get(activation) + else: + self._activation = math_ops.tanh @property def state_size(self): @@ -368,12 +380,13 @@ class BasicRNNCell(LayerRNNCell): def output_size(self): return self._num_units + @tf_utils.shape_type_conversion def build(self, inputs_shape): - if inputs_shape[1].value is None: + if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape) - input_depth = inputs_shape[1].value + input_depth = inputs_shape[-1] self._kernel = self.add_variable( _WEIGHTS_VARIABLE_NAME, shape=[input_depth + self._num_units, self._num_units]) @@ -393,6 +406,15 @@ class BasicRNNCell(LayerRNNCell): output = self._activation(gate_inputs) return output, output + def get_config(self): + config = { + "num_units": self._num_units, + "activation": activations.serialize(self._activation), + "reuse": self._reuse, + } + base_config = super(BasicRNNCell, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + @tf_export("nn.rnn_cell.GRUCell") class GRUCell(LayerRNNCell): @@ -412,6 +434,8 @@ class GRUCell(LayerRNNCell): cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. + **kwargs: Dict, keyword named properties for common layer attributes, like + `trainable` etc when constructing the cell from configs of get_config(). """ def __init__(self, @@ -421,16 +445,21 @@ class GRUCell(LayerRNNCell): kernel_initializer=None, bias_initializer=None, name=None, - dtype=None): - super(GRUCell, self).__init__(_reuse=reuse, name=name, dtype=dtype) + dtype=None, + **kwargs): + super(GRUCell, self).__init__( + _reuse=reuse, name=name, dtype=dtype, **kwargs) # Inputs must be 2-dimensional. self.input_spec = base_layer.InputSpec(ndim=2) self._num_units = num_units - self._activation = activation or math_ops.tanh - self._kernel_initializer = kernel_initializer - self._bias_initializer = bias_initializer + if activation: + self._activation = activations.get(activation) + else: + self._activation = math_ops.tanh + self._kernel_initializer = initializers.get(kernel_initializer) + self._bias_initializer = initializers.get(bias_initializer) @property def state_size(self): @@ -440,12 +469,13 @@ class GRUCell(LayerRNNCell): def output_size(self): return self._num_units + @tf_utils.shape_type_conversion def build(self, inputs_shape): - if inputs_shape[1].value is None: + if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape) - input_depth = inputs_shape[1].value + input_depth = inputs_shape[-1] self._gate_kernel = self.add_variable( "gates/%s" % _WEIGHTS_VARIABLE_NAME, shape=[input_depth + self._num_units, 2 * self._num_units], @@ -491,6 +521,17 @@ class GRUCell(LayerRNNCell): new_h = u * state + (1 - u) * c return new_h, new_h + def get_config(self): + config = { + "num_units": self._num_units, + "initializer": initializers.serialize(self._initializer), + "kernel_initializer": initializers.serialize(self._kernel_initializer), + "activation": activations.serialize(self._activation), + "reuse": self._reuse, + } + base_config = super(GRUCell, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + _LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h")) @@ -515,9 +556,12 @@ class LSTMStateTuple(_LSTMStateTuple): return c.dtype +# TODO(scottzhu): Stop exporting this class in TF 2.0. @tf_export("nn.rnn_cell.BasicLSTMCell") class BasicLSTMCell(LayerRNNCell): - """Basic LSTM recurrent network cell. + """DEPRECATED: Please use @{tf.nn.rnn_cell.LSTMCell} instead. + + Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. @@ -527,10 +571,14 @@ class BasicLSTMCell(LayerRNNCell): It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline. - For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell} + For advanced models, please use the full `tf.nn.rnn_cell.LSTMCell` that follows. """ + @deprecated(None, "This class is deprecated, please use " + "tf.nn.rnn_cell.LSTMCell, which supports all the feature " + "this cell currently has. Please replace the existing code " + "with tf.nn.rnn_cell.LSTMCell(name='basic_lstm_cell').") def __init__(self, num_units, forget_bias=1.0, @@ -538,7 +586,8 @@ class BasicLSTMCell(LayerRNNCell): activation=None, reuse=None, name=None, - dtype=None): + dtype=None, + **kwargs): """Initialize the basic LSTM cell. Args: @@ -549,7 +598,8 @@ class BasicLSTMCell(LayerRNNCell): state_is_tuple: If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`. If False, they are concatenated along the column axis. The latter behavior will soon be deprecated. - activation: Activation function of the inner states. Default: `tanh`. + activation: Activation function of the inner states. Default: `tanh`. It + could also be string that is within Keras activation function names. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. @@ -558,11 +608,14 @@ class BasicLSTMCell(LayerRNNCell): cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. + **kwargs: Dict, keyword named properties for common layer attributes, like + `trainable` etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, must use `CudnnCompatibleLSTMCell` instead. """ - super(BasicLSTMCell, self).__init__(_reuse=reuse, name=name, dtype=dtype) + super(BasicLSTMCell, self).__init__( + _reuse=reuse, name=name, dtype=dtype, **kwargs) if not state_is_tuple: logging.warn("%s: Using a concatenated state is slower and will soon be " "deprecated. Use state_is_tuple=True.", self) @@ -573,7 +626,10 @@ class BasicLSTMCell(LayerRNNCell): self._num_units = num_units self._forget_bias = forget_bias self._state_is_tuple = state_is_tuple - self._activation = activation or math_ops.tanh + if activation: + self._activation = activations.get(activation) + else: + self._activation = math_ops.tanh @property def state_size(self): @@ -584,12 +640,13 @@ class BasicLSTMCell(LayerRNNCell): def output_size(self): return self._num_units + @tf_utils.shape_type_conversion def build(self, inputs_shape): - if inputs_shape[1].value is None: + if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape) - input_depth = inputs_shape[1].value + input_depth = inputs_shape[-1] h_depth = self._num_units self._kernel = self.add_variable( _WEIGHTS_VARIABLE_NAME, @@ -647,6 +704,17 @@ class BasicLSTMCell(LayerRNNCell): new_state = array_ops.concat([new_c, new_h], 1) return new_h, new_state + def get_config(self): + config = { + "num_units": self._num_units, + "forget_bias": self._forget_bias, + "state_is_tuple": self._state_is_tuple, + "activation": activations.serialize(self._activation), + "reuse": self._reuse, + } + base_config = super(BasicLSTMCell, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + @tf_export("nn.rnn_cell.LSTMCell") class LSTMCell(LayerRNNCell): @@ -676,7 +744,7 @@ class LSTMCell(LayerRNNCell): initializer=None, num_proj=None, proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0, state_is_tuple=True, - activation=None, reuse=None, name=None, dtype=None): + activation=None, reuse=None, name=None, dtype=None, **kwargs): """Initialize the parameters for an LSTM cell. Args: @@ -702,7 +770,8 @@ class LSTMCell(LayerRNNCell): state_is_tuple: If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`. If False, they are concatenated along the column axis. This latter behavior will soon be deprecated. - activation: Activation function of the inner states. Default: `tanh`. + activation: Activation function of the inner states. Default: `tanh`. It + could also be string that is within Keras activation function names. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. @@ -711,11 +780,14 @@ class LSTMCell(LayerRNNCell): cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. + **kwargs: Dict, keyword named properties for common layer attributes, like + `trainable` etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, use `CudnnCompatibleLSTMCell` instead. """ - super(LSTMCell, self).__init__(_reuse=reuse, name=name, dtype=dtype) + super(LSTMCell, self).__init__( + _reuse=reuse, name=name, dtype=dtype, **kwargs) if not state_is_tuple: logging.warn("%s: Using a concatenated state is slower and will soon be " "deprecated. Use state_is_tuple=True.", self) @@ -731,14 +803,17 @@ class LSTMCell(LayerRNNCell): self._num_units = num_units self._use_peepholes = use_peepholes self._cell_clip = cell_clip - self._initializer = initializer + self._initializer = initializers.get(initializer) self._num_proj = num_proj self._proj_clip = proj_clip self._num_unit_shards = num_unit_shards self._num_proj_shards = num_proj_shards self._forget_bias = forget_bias self._state_is_tuple = state_is_tuple - self._activation = activation or math_ops.tanh + if activation: + self._activation = activations.get(activation) + else: + self._activation = math_ops.tanh if num_proj: self._state_size = ( @@ -759,12 +834,13 @@ class LSTMCell(LayerRNNCell): def output_size(self): return self._output_size + @tf_utils.shape_type_conversion def build(self, inputs_shape): - if inputs_shape[1].value is None: + if inputs_shape[-1] is None: raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" % inputs_shape) - input_depth = inputs_shape[1].value + input_depth = inputs_shape[-1] h_depth = self._num_units if self._num_proj is None else self._num_proj maybe_partitioner = ( partitioned_variables.fixed_size_partitioner(self._num_unit_shards) @@ -878,6 +954,24 @@ class LSTMCell(LayerRNNCell): array_ops.concat([c, m], 1)) return m, new_state + def get_config(self): + config = { + "num_units": self._num_units, + "use_peepholes": self._use_peepholes, + "cell_clip": self._cell_clip, + "initializer": initializers.serialize(self._initializer), + "num_proj": self._num_proj, + "proj_clip": self._proj_clip, + "num_unit_shards": self._num_unit_shards, + "num_proj_shards": self._num_proj_shards, + "forget_bias": self._forget_bias, + "state_is_tuple": self._state_is_tuple, + "activation": activations.serialize(self._activation), + "reuse": self._reuse, + } + base_config = super(LSTMCell, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + def _enumerated_map_structure_up_to(shallow_structure, map_fn, *args, **kwargs): ix = [0] diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index af103d3cc7649128824132c5520b561425819369..d11e446dbfdc88b2ad992dca937d13e681b555be 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -313,8 +313,8 @@ def eager_py_func(func, inp, Tout, name=None): in a once-differentiable TensorFlow operation that executes it with eager exeuction enabled. As a consequence, `tf.contrib.eager.py_func` makes it possible to express control flow using Python constructs (`if`, `while`, - `for`, etc.), instead of TensorFlow control flow constructs (@{tf.cond}, - @{tf.while_loop}). For example, you might use `tf.contrib.eager.py_func` to + `for`, etc.), instead of TensorFlow control flow constructs (`tf.cond`, + `tf.while_loop`). For example, you might use `tf.contrib.eager.py_func` to implement the log huber function: ```python @@ -345,15 +345,15 @@ def eager_py_func(func, inp, Tout, name=None): For more information on eager execution, see @{$guide/eager}. - `tf.contrib.eager.py_func` is similar in spirit to @{tf.py_func}, but unlike + `tf.contrib.eager.py_func` is similar in spirit to `tf.py_func`, but unlike the latter, the former lets you use TensorFlow operations in the wrapped - Python function. In particular, while @{tf.py_func} only runs on CPUs and + Python function. In particular, while `tf.py_func` only runs on CPUs and wraps functions that take NumPy arrays as inputs and return NumPy arrays as outputs, `tf.contrib.eager.py_func` can be placed on GPUs and wraps functions that take Tensors as inputs, execute TensorFlow operations in their bodies, and return Tensors as outputs. - Like @{tf.py_func}, `tf.contrib.eager.py_func` has the following limitations + Like `tf.py_func`, `tf.contrib.eager.py_func` has the following limitations with respect to serialization and distribution: * The body of the function (i.e. `func`) will not be serialized in a diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py index c3b16a7bd5387e006aaea60b8814b1209ce01414..fd547dcb19bdda9c12bab1eafcfb3e392528f77a 100644 --- a/tensorflow/python/ops/sparse_ops.py +++ b/tensorflow/python/ops/sparse_ops.py @@ -777,8 +777,10 @@ def sparse_to_dense(sparse_indices, @tf_export("sparse_reduce_max") -def sparse_reduce_max(sp_input, axis=None, keep_dims=False, - reduction_axes=None): +@deprecation.deprecated_args( + None, "keep_dims is deprecated, use keepdims instead", "keep_dims") +def sparse_reduce_max(sp_input, axis=None, keepdims=None, + reduction_axes=None, keep_dims=None): """Computes the max of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to @@ -786,8 +788,8 @@ def sparse_reduce_max(sp_input, axis=None, keep_dims=False, instead of a sparse one. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless - `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in - `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained + `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in + `reduction_axes`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `reduction_axes` has no entries, all dimensions are reduced, and a tensor @@ -803,7 +805,7 @@ def sparse_reduce_max(sp_input, axis=None, keep_dims=False, tf.sparse_reduce_max(x) ==> 3 tf.sparse_reduce_max(x, 0) ==> [1, 3, 2] tf.sparse_reduce_max(x, 1) ==> [2, 3] # Can also use -1 as the axis. - tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]] + tf.sparse_reduce_max(x, 1, keepdims=True) ==> [[2], [3]] tf.sparse_reduce_max(x, [0, 1]) ==> 3 ``` @@ -811,22 +813,31 @@ def sparse_reduce_max(sp_input, axis=None, keep_dims=False, sp_input: The SparseTensor to reduce. Should have numeric type. axis: The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. - keep_dims: If true, retain reduced dimensions with length 1. + keepdims: If true, retain reduced dimensions with length 1. reduction_axes: Deprecated name of axis. + keep_dims: Deprecated alias for `keepdims`. Returns: The reduced Tensor. """ + keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims, + "keep_dims", keep_dims) + if keepdims is None: + keepdims = False + return gen_sparse_ops.sparse_reduce_max( sp_input.indices, sp_input.values, sp_input.dense_shape, - math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims) + math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims) @tf_export("sparse_reduce_max_sparse") +@deprecation.deprecated_args( + None, "keep_dims is deprecated, use keepdims instead", "keep_dims") def sparse_reduce_max_sparse(sp_input, axis=None, - keep_dims=False, - reduction_axes=None): + keepdims=None, + reduction_axes=None, + keep_dims=None): """Computes the max of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to @@ -834,8 +845,8 @@ def sparse_reduce_max_sparse(sp_input, SparseTensor. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless - `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in - `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained + `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in + `reduction_axes`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `reduction_axes` has no entries, all dimensions are reduced, and a tensor @@ -846,23 +857,31 @@ def sparse_reduce_max_sparse(sp_input, sp_input: The SparseTensor to reduce. Should have numeric type. axis: The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. - keep_dims: If true, retain reduced dimensions with length 1. - reduction_axes: Deprecated name of axis + keepdims: If true, retain reduced dimensions with length 1. + reduction_axes: Deprecated name of axis. + keep_dims: Deprecated alias for `keepdims`. Returns: The reduced SparseTensor. """ + keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims, + "keep_dims", keep_dims) + if keepdims is None: + keepdims = False + output_ind, output_val, output_shape = ( gen_sparse_ops.sparse_reduce_max_sparse( sp_input.indices, sp_input.values, sp_input.dense_shape, - math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims)) + math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims)) return sparse_tensor.SparseTensor(output_ind, output_val, output_shape) @tf_export("sparse_reduce_sum") -def sparse_reduce_sum(sp_input, axis=None, keep_dims=False, - reduction_axes=None): +@deprecation.deprecated_args( + None, "keep_dims is deprecated, use keepdims instead", "keep_dims") +def sparse_reduce_sum(sp_input, axis=None, keepdims=None, + reduction_axes=None, keep_dims=None): """Computes the sum of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to @@ -870,8 +889,8 @@ def sparse_reduce_sum(sp_input, axis=None, keep_dims=False, instead of a sparse one. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless - `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in - `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained + `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in + `reduction_axes`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `reduction_axes` has no entries, all dimensions are reduced, and a tensor @@ -887,7 +906,7 @@ def sparse_reduce_sum(sp_input, axis=None, keep_dims=False, tf.sparse_reduce_sum(x) ==> 3 tf.sparse_reduce_sum(x, 0) ==> [1, 1, 1] tf.sparse_reduce_sum(x, 1) ==> [2, 1] # Can also use -1 as the axis. - tf.sparse_reduce_sum(x, 1, keep_dims=True) ==> [[2], [1]] + tf.sparse_reduce_sum(x, 1, keepdims=True) ==> [[2], [1]] tf.sparse_reduce_sum(x, [0, 1]) ==> 3 ``` @@ -895,22 +914,31 @@ def sparse_reduce_sum(sp_input, axis=None, keep_dims=False, sp_input: The SparseTensor to reduce. Should have numeric type. axis: The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. - keep_dims: If true, retain reduced dimensions with length 1. + keepdims: If true, retain reduced dimensions with length 1. reduction_axes: Deprecated name of axis. + keep_dims: Deprecated alias for `keepdims`. Returns: The reduced Tensor. """ + keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims, + "keep_dims", keep_dims) + if keepdims is None: + keepdims = False + return gen_sparse_ops.sparse_reduce_sum( sp_input.indices, sp_input.values, sp_input.dense_shape, - math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims) + math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims) @tf_export("sparse_reduce_sum_sparse") +@deprecation.deprecated_args( + None, "keep_dims is deprecated, use keepdims instead", "keep_dims") def sparse_reduce_sum_sparse(sp_input, axis=None, - keep_dims=False, - reduction_axes=None): + keepdims=None, + reduction_axes=None, + keep_dims=None): """Computes the sum of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to @@ -918,8 +946,8 @@ def sparse_reduce_sum_sparse(sp_input, SparseTensor. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless - `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in - `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained + `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in + `reduction_axes`. If `keepdims` is true, the reduced dimensions are retained with length 1. If `reduction_axes` has no entries, all dimensions are reduced, and a tensor @@ -930,16 +958,22 @@ def sparse_reduce_sum_sparse(sp_input, sp_input: The SparseTensor to reduce. Should have numeric type. axis: The dimensions to reduce; list or scalar. If `None` (the default), reduces all dimensions. - keep_dims: If true, retain reduced dimensions with length 1. - reduction_axes: Deprecated name of axis + keepdims: If true, retain reduced dimensions with length 1. + reduction_axes: Deprecated name of axis. + keep_dims: Deprecated alias for `keepdims`. Returns: The reduced SparseTensor. """ + keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims, + "keep_dims", keep_dims) + if keepdims is None: + keepdims = False + output_ind, output_val, output_shape = ( gen_sparse_ops.sparse_reduce_sum_sparse( sp_input.indices, sp_input.values, sp_input.dense_shape, - math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims)) + math_ops._ReductionDims(sp_input, axis, reduction_axes), keepdims)) return sparse_tensor.SparseTensor(output_ind, output_val, output_shape) diff --git a/tensorflow/python/ops/spectral_ops.py b/tensorflow/python/ops/spectral_ops.py index 293aace7282eb0f8dde9da75b0d353a560c0ecb9..da5884e74626b493fb71c50ff040ce4fc4a97ce3 100644 --- a/tensorflow/python/ops/spectral_ops.py +++ b/tensorflow/python/ops/spectral_ops.py @@ -180,9 +180,9 @@ def dct(input, type=2, n=None, axis=-1, norm=None, name=None): # pylint: disabl """Computes the 1D [Discrete Cosine Transform (DCT)][dct] of `input`. Currently only Types II and III are supported. Type II is implemented using a - length `2N` padded @{tf.spectral.rfft}, as described here: + length `2N` padded `tf.spectral.rfft`, as described here: https://dsp.stackexchange.com/a/10606. Type III is a fairly straightforward - inverse of Type II (i.e. using a length `2N` padded @{tf.spectral.irfft}). + inverse of Type II (i.e. using a length `2N` padded `tf.spectral.irfft`). @compatibility(scipy) Equivalent to scipy.fftpack.dct for Type-II and Type-III DCT. diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py index 2c93cf72c75ba27145e06abe69bcbef9418b39e0..35fc1226ec6698baff4e6f4fc8dc4023c830fefd 100644 --- a/tensorflow/python/ops/state_ops.py +++ b/tensorflow/python/ops/state_ops.py @@ -329,7 +329,7 @@ def scatter_nd_update(ref, indices, updates, use_locking=True, name=None): [1, 11, 3, 10, 9, 6, 7, 12] - See @{tf.scatter_nd} for more details about how to make updates to + See `tf.scatter_nd` for more details about how to make updates to slices. Args: @@ -443,7 +443,7 @@ def scatter_nd_add(ref, indices, updates, use_locking=False, name=None): [1, 13, 3, 14, 14, 6, 7, 20] - See @{tf.scatter_nd} for more details about how to make updates to + See `tf.scatter_nd` for more details about how to make updates to slices. Args: diff --git a/tensorflow/python/ops/summary_ops_v2.py b/tensorflow/python/ops/summary_ops_v2.py index 00150fe68820da711c76f642baced45163a8727c..94c7d88b5c9f13de4769a450a07031206a4020fd 100644 --- a/tensorflow/python/ops/summary_ops_v2.py +++ b/tensorflow/python/ops/summary_ops_v2.py @@ -110,8 +110,8 @@ class SummaryWriter(object): """Encapsulates a stateful summary writer resource. See also: - - @{tf.contrib.summary.create_file_writer} - - @{tf.contrib.summary.create_db_writer} + - `tf.contrib.summary.create_file_writer` + - `tf.contrib.summary.create_db_writer` """ def __init__(self, resource, init_op_fn): @@ -174,22 +174,22 @@ def initialize( """Initializes summary writing for graph execution mode. This helper method provides a higher-level alternative to using - @{tf.contrib.summary.summary_writer_initializer_op} and - @{tf.contrib.summary.graph}. + `tf.contrib.summary.summary_writer_initializer_op` and + `tf.contrib.summary.graph`. - Most users will also want to call @{tf.train.create_global_step} + Most users will also want to call `tf.train.create_global_step` which can happen before or after this function is called. Args: - graph: A @{tf.Graph} or @{tf.GraphDef} to output to the writer. + graph: A `tf.Graph` or `tf.GraphDef` to output to the writer. This function will not write the default graph by default. When writing to an event log file, the associated step will be zero. - session: So this method can call @{tf.Session.run}. This defaults - to @{tf.get_default_session}. + session: So this method can call `tf.Session.run`. This defaults + to `tf.get_default_session`. Raises: RuntimeError: If the current thread has no default - @{tf.contrib.summary.SummaryWriter}. + `tf.contrib.summary.SummaryWriter`. ValueError: If session wasn't passed and no default session. """ if context.executing_eagerly(): @@ -278,10 +278,10 @@ def create_db_writer(db_uri, Experiment will not be associated with a User. Must be valid as both a DNS label and Linux username. name: Shared name for this SummaryWriter resource stored to default - @{tf.Graph}. + `tf.Graph`. Returns: - A @{tf.contrib.summary.SummaryWriter} instance. + A `tf.contrib.summary.SummaryWriter` instance. """ with ops.device("cpu:0"): if experiment_name is None: @@ -328,7 +328,7 @@ def _nothing(): def all_summary_ops(): """Graph-mode only. Returns all summary ops. - Please note this excludes @{tf.contrib.summary.graph} ops. + Please note this excludes `tf.contrib.summary.graph` ops. Returns: The summary ops. @@ -410,20 +410,20 @@ def generic(name, tensor, metadata=None, family=None, step=None): def scalar(name, tensor, family=None, step=None): """Writes a scalar summary if possible. - Unlike @{tf.contrib.summary.generic} this op may change the dtype + Unlike `tf.contrib.summary.generic` this op may change the dtype depending on the writer, for both practical and efficiency concerns. Args: name: An arbitrary name for this summary. - tensor: A @{tf.Tensor} Must be one of the following types: + tensor: A `tf.Tensor` Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`, `uint32`, `uint64`. family: Optional, the summary's family. step: The `int64` monotonic step variable, which defaults - to @{tf.train.get_global_step}. + to `tf.train.get_global_step`. Returns: - The created @{tf.Operation} or a @{tf.no_op} if summary writing has + The created `tf.Operation` or a `tf.no_op` if summary writing has not been enabled for this context. """ @@ -494,31 +494,31 @@ def graph(param, step=None, name=None): """Writes a TensorFlow graph to the summary interface. The graph summary is, strictly speaking, not a summary. Conditions - like @{tf.contrib.summary.never_record_summaries} do not apply. Only + like `tf.contrib.summary.never_record_summaries` do not apply. Only a single graph can be associated with a particular run. If multiple graphs are written, then only the last one will be considered by TensorBoard. When not using eager execution mode, the user should consider passing - the `graph` parameter to @{tf.contrib.summary.initialize} instead of + the `graph` parameter to `tf.contrib.summary.initialize` instead of calling this function. Otherwise special care needs to be taken when using the graph to record the graph. Args: - param: A @{tf.Tensor} containing a serialized graph proto. When + param: A `tf.Tensor` containing a serialized graph proto. When eager execution is enabled, this function will automatically - coerce @{tf.Graph}, @{tf.GraphDef}, and string types. + coerce `tf.Graph`, `tf.GraphDef`, and string types. step: The global step variable. This doesn't have useful semantics for graph summaries, but is used anyway, due to the structure of event log files. This defaults to the global step. name: A name for the operation (optional). Returns: - The created @{tf.Operation} or a @{tf.no_op} if summary writing has + The created `tf.Operation` or a `tf.no_op` if summary writing has not been enabled for this context. Raises: - TypeError: If `param` isn't already a @{tf.Tensor} in graph mode. + TypeError: If `param` isn't already a `tf.Tensor` in graph mode. """ if not context.executing_eagerly() and not isinstance(param, ops.Tensor): raise TypeError("graph() needs a tf.Tensor (e.g. tf.placeholder) in graph " @@ -539,21 +539,21 @@ _graph = graph # for functions with a graph parameter def import_event(tensor, name=None): - """Writes a @{tf.Event} binary proto. + """Writes a `tf.Event` binary proto. When using create_db_writer(), this can be used alongside - @{tf.TFRecordReader} to load event logs into the database. Please + `tf.TFRecordReader` to load event logs into the database. Please note that this is lower level than the other summary functions and will ignore any conditions set by methods like - @{tf.contrib.summary.should_record_summaries}. + `tf.contrib.summary.should_record_summaries`. Args: - tensor: A @{tf.Tensor} of type `string` containing a serialized - @{tf.Event} proto. + tensor: A `tf.Tensor` of type `string` containing a serialized + `tf.Event` proto. name: A name for the operation (optional). Returns: - The created @{tf.Operation}. + The created `tf.Operation`. """ return gen_summary_ops.import_event( context.context().summary_writer_resource, tensor, name=name) @@ -565,13 +565,13 @@ def flush(writer=None, name=None): This operation blocks until that finishes. Args: - writer: The @{tf.contrib.summary.SummaryWriter} resource to flush. + writer: The `tf.contrib.summary.SummaryWriter` resource to flush. The thread default will be used if this parameter is None. - Otherwise a @{tf.no_op} is returned. + Otherwise a `tf.no_op` is returned. name: A name for the operation (optional). Returns: - The created @{tf.Operation}. + The created `tf.Operation`. """ if writer is None: writer = context.context().summary_writer_resource @@ -593,7 +593,7 @@ def eval_dir(model_dir, name=None): def create_summary_file_writer(*args, **kwargs): - """Please use @{tf.contrib.summary.create_file_writer}.""" + """Please use `tf.contrib.summary.create_file_writer`.""" logging.warning("Deprecation Warning: create_summary_file_writer was renamed " "to create_file_writer") return create_file_writer(*args, **kwargs) diff --git a/tensorflow/python/ops/template.py b/tensorflow/python/ops/template.py index 161d9687d6b0af58a3e8aef5518d70432e70691c..e7ad261615f57c1e0ff967d0f7cd498571d21bc7 100644 --- a/tensorflow/python/ops/template.py +++ b/tensorflow/python/ops/template.py @@ -128,7 +128,7 @@ def make_template(name_, func_, create_scope_now_=False, unique_name_=None, template of the same scope/unique_name already exists and reuse is false, an error is raised. Defaults to None. custom_getter_: Optional custom getter for variables used in `func_`. See - the @{tf.get_variable} `custom_getter` documentation for + the `tf.get_variable` `custom_getter` documentation for more information. **kwargs: Keyword arguments to apply to `func_`. @@ -176,7 +176,7 @@ def make_template_internal(name_, template of the same scope/unique_name already exists and reuse is false, an error is raised. Defaults to None. If executing eagerly, must be None. custom_getter_: Optional custom getter for variables used in `func_`. See - the @{tf.get_variable} `custom_getter` documentation for + the `tf.get_variable` `custom_getter` documentation for more information. create_graph_function_: When True, `func_` will be executed as a graph function. This implies that `func_` must satisfy the properties that @@ -298,9 +298,10 @@ class Template(checkpointable.CheckpointableBase): def _call_func(self, args, kwargs): try: - vars_at_start = len(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) + vars_at_start = len( + ops.get_collection_ref(ops.GraphKeys.GLOBAL_VARIABLES)) trainable_at_start = len( - ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) + ops.get_collection_ref(ops.GraphKeys.TRAINABLE_VARIABLES)) if self._variables_created: result = self._func(*args, **kwargs) else: @@ -313,7 +314,7 @@ class Template(checkpointable.CheckpointableBase): # Variables were previously created, implying this is not the first # time the template has been called. Check to make sure that no new # trainable variables were created this time around. - trainable_variables = ops.get_collection( + trainable_variables = ops.get_collection_ref( ops.GraphKeys.TRAINABLE_VARIABLES) # If a variable that we intend to train is created as a side effect # of creating a template, then that is almost certainly an error. @@ -326,7 +327,7 @@ class Template(checkpointable.CheckpointableBase): # Non-trainable tracking variables are a legitimate reason why a new # variable would be created, but it is a relatively advanced use-case, # so log it. - variables = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES) + variables = ops.get_collection_ref(ops.GraphKeys.GLOBAL_VARIABLES) if vars_at_start != len(variables): logging.info("New variables created when calling a template after " "the first time, perhaps you used tf.Variable when you " diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index aca44bcd449d05db5885768391262284e61bf07b..c248dd9172879a204012f483a27cb0cde494abbf 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -314,13 +314,13 @@ class _VariableStore(object): use when doing asynchronous distributed training. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. Returns: The created or existing `Variable` (or `PartitionedVariable`, if a @@ -1484,7 +1484,7 @@ Args: unless validate_shape is False. regularizer: A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the collection - @{tf.GraphKeys.REGULARIZATION_LOSSES} and can be used for regularization. + `tf.GraphKeys.REGULARIZATION_LOSSES` and can be used for regularization. %scollections: List of graph collections keys to add the Variable to. Defaults to `[%s]` (see `tf.Variable`). caching_device: Optional device string or function describing where the @@ -2445,13 +2445,13 @@ def variable_creator_scope(variable_creator): use_resource: if True, a ResourceVariable is always created. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. This set may grow over time, so it's important the signature of creators is as mentioned above. diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index fc00ce68aeaf49ea88b1a40ee40ecebe69bb0eee..464c1167d9360a75f5845b3e203e9dee71311b88 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -320,13 +320,13 @@ class Variable(six.with_metaclass(VariableMetaclass, a resource variable is always created. synchronization: Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableSynchronization}. By default the synchronization is set to + `tf.VariableSynchronization`. By default the synchronization is set to `AUTO` and the current `DistributionStrategy` chooses when to synchronize. If `synchronization` is set to `ON_READ`, `trainable` must not be set to `True`. aggregation: Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class - @{tf.VariableAggregation}. + `tf.VariableAggregation`. Raises: ValueError: If both `variable_def` and initial_value are specified. @@ -388,7 +388,7 @@ class Variable(six.with_metaclass(VariableMetaclass, This convenience method requires a session where the graph containing this variable has been launched. If no session is - passed, the default session is used. See @{tf.Session} for more + passed, the default session is used. See `tf.Session` for more information on launching a graph and on sessions. ```python @@ -551,7 +551,7 @@ class Variable(six.with_metaclass(VariableMetaclass, This convenience method requires a session where the graph containing this variable has been launched. If no session is - passed, the default session is used. See @{tf.Session} for more + passed, the default session is used. See `tf.Session` for more information on launching a graph and on sessions. ```python @@ -1106,7 +1106,7 @@ class RefVariable(Variable): def _AsTensor(self): # pylint: disable=invalid-name """Converts this variable to a Tensor. - See @{tf.Variable.value}. + See `tf.Variable.value`. Returns: A `Tensor` containing the value of the variable. @@ -1163,7 +1163,7 @@ class RefVariable(Variable): Returns is a `Tensor` which holds a reference to the variable. You can assign a new value to the variable by passing the tensor to an assign op. - See @{tf.Variable.value} if you want to get the value of the + See `tf.Variable.value` if you want to get the value of the variable. Returns: @@ -1191,7 +1191,7 @@ class RefVariable(Variable): This convenience method requires a session where the graph containing this variable has been launched. If no session is - passed, the default session is used. See @{tf.Session} for more + passed, the default session is used. See `tf.Session` for more information on launching a graph and on sessions. ```python @@ -1386,7 +1386,7 @@ class RefVariable(Variable): This convenience method requires a session where the graph containing this variable has been launched. If no session is - passed, the default session is used. See @{tf.Session} for more + passed, the default session is used. See `tf.Session` for more information on launching a graph and on sessions. ```python @@ -1979,7 +1979,7 @@ def global_variables(scope=None): This convenience function returns the contents of that collection. An alternative to global variables are local variables. See - @{tf.local_variables} + `tf.local_variables` Args: scope: (Optional.) A string. If supplied, the resulting list is filtered @@ -2032,7 +2032,7 @@ def local_variables(scope=None): This convenience function returns the contents of that collection. An alternative to local variables are global variables. See - @{tf.global_variables} + `tf.global_variables` Args: scope: (Optional.) A string. If supplied, the resulting list is filtered diff --git a/tensorflow/python/pywrap_tfe.i b/tensorflow/python/pywrap_tfe.i index 1b69e0d06ce4b118315d4e999a633270a5b0f8f7..157f2341e0846764860af07f0656377b84b16ac6 100644 --- a/tensorflow/python/pywrap_tfe.i +++ b/tensorflow/python/pywrap_tfe.i @@ -63,6 +63,8 @@ limitations under the License. %rename("%s") TFE_DeleteContextOptions; %rename("%s") TFE_Py_TensorShapeSlice; %rename("%s") TFE_Py_TensorShapeOnDevice; +%rename("%s") TFE_ContextStartStep; +%rename("%s") TFE_ContextEndStep; %{ #include "tensorflow/python/eager/pywrap_tfe.h" diff --git a/tensorflow/python/saved_model/BUILD b/tensorflow/python/saved_model/BUILD index 076f2d8760fe00035ef5830a02d22e82c54dd768..7a37eda5eadbd0e133ec662e2a77240538d28782 100644 --- a/tensorflow/python/saved_model/BUILD +++ b/tensorflow/python/saved_model/BUILD @@ -62,6 +62,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":constants", + ":utils", "//tensorflow/core:protos_all_py", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:lib", @@ -81,6 +82,7 @@ py_library( srcs_version = "PY2AND3", deps = [ ":constants", + ":utils", "//tensorflow/core:protos_all_py", "//tensorflow/python:framework_for_generated_wrappers", "//tensorflow/python:lib", @@ -187,8 +189,10 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + ":constants", "//tensorflow/core:protos_all_py", "//tensorflow/python:framework_for_generated_wrappers", + "//tensorflow/python:lib", "//tensorflow/python:sparse_tensor", "//tensorflow/python:util", ], diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index 8c985a7c2fa2b515c2daed1349996dd30f6d7ce1..8e7f123a85aae7d714b162096e1a40ab498c3312 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -32,6 +32,7 @@ from tensorflow.python.lib.io import file_io from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import utils_impl as saved_model_utils from tensorflow.python.training import saver as tf_saver from tensorflow.python.util import compat from tensorflow.python.util.deprecation import deprecated_args @@ -112,12 +113,8 @@ class SavedModelBuilder(object): tf_logging.info("No assets to write.") return - assets_destination_dir = os.path.join( - compat.as_bytes(self._export_dir), - compat.as_bytes(constants.ASSETS_DIRECTORY)) - - if not file_io.file_exists(assets_destination_dir): - file_io.recursive_create_dir(assets_destination_dir) + assets_destination_dir = saved_model_utils.get_or_create_assets_dir( + self._export_dir) # Copy each asset from source path to destination path. for asset_basename, asset_source_filepath in asset_filename_map.items(): @@ -409,16 +406,8 @@ class SavedModelBuilder(object): # Add assets and ops self._add_collections(assets_collection, main_op, None) - # Create the variables sub-directory, if it does not exist. - variables_dir = os.path.join( - compat.as_text(self._export_dir), - compat.as_text(constants.VARIABLES_DIRECTORY)) - if not file_io.file_exists(variables_dir): - file_io.recursive_create_dir(variables_dir) - - variables_path = os.path.join( - compat.as_text(variables_dir), - compat.as_text(constants.VARIABLES_FILENAME)) + saved_model_utils.get_or_create_variables_dir(self._export_dir) + variables_path = saved_model_utils.get_variables_path(self._export_dir) saver = self._maybe_create_saver(saver) diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index 16077f52fab72e7700df7e67782a549bbde21751..e8536108e8711f903f1db74775f76e6836642396 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -31,6 +31,7 @@ from tensorflow.python.lib.io import file_io from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import utils_impl as saved_model_utils from tensorflow.python.training import saver as tf_saver from tensorflow.python.util import compat from tensorflow.python.util.tf_export import tf_export @@ -203,10 +204,7 @@ class SavedModelLoader(object): variables to be loaded are located. """ self._export_dir = export_dir - self._variables_path = os.path.join( - compat.as_bytes(export_dir), - compat.as_bytes(constants.VARIABLES_DIRECTORY), - compat.as_bytes(constants.VARIABLES_FILENAME)) + self._variables_path = saved_model_utils.get_variables_path(export_dir) self._saved_model = _parse_saved_model(export_dir) @property diff --git a/tensorflow/python/saved_model/utils_impl.py b/tensorflow/python/saved_model/utils_impl.py index cddce29a08a6c4c79a4c7c5dbfb48a86131530b2..20ff34fd8e8a9d8aebee8757cff44d1bf929405e 100644 --- a/tensorflow/python/saved_model/utils_impl.py +++ b/tensorflow/python/saved_model/utils_impl.py @@ -18,10 +18,15 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import os + from tensorflow.core.protobuf import meta_graph_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.lib.io import file_io +from tensorflow.python.saved_model import constants +from tensorflow.python.util import compat from tensorflow.python.util.tf_export import tf_export @@ -84,3 +89,45 @@ def get_tensor_from_tensor_info(tensor_info, graph=None, import_scope=None): _get_tensor(tensor_info.coo_sparse.dense_shape_tensor_name)) else: raise ValueError("Invalid TensorInfo.encoding: %s" % encoding) + + +# Path helpers. + + +def get_or_create_variables_dir(export_dir): + """Return variables sub-directory, or create one if it doesn't exist.""" + variables_dir = get_variables_dir(export_dir) + if not file_io.file_exists(variables_dir): + file_io.recursive_create_dir(variables_dir) + return variables_dir + + +def get_variables_dir(export_dir): + """Return variables sub-directory in the SavedModel.""" + return os.path.join( + compat.as_text(export_dir), + compat.as_text(constants.VARIABLES_DIRECTORY)) + + +def get_variables_path(export_dir): + """Return the variables path, used as the prefix for checkpoint files.""" + return os.path.join( + compat.as_text(get_variables_dir(export_dir)), + compat.as_text(constants.VARIABLES_FILENAME)) + + +def get_or_create_assets_dir(export_dir): + """Return assets sub-directory, or create one if it doesn't exist.""" + assets_destination_dir = get_assets_dir(export_dir) + + if not file_io.file_exists(assets_destination_dir): + file_io.recursive_create_dir(assets_destination_dir) + + return assets_destination_dir + + +def get_assets_dir(export_dir): + """Return path to asset directory in the SavedModel.""" + return os.path.join( + compat.as_text(export_dir), + compat.as_text(constants.ASSETS_DIRECTORY)) diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py index 1421d2772fe140dd5f207f159db0ab462231420d..f3a6d475003daa72bbf2e3f80a3ced4d73b17670 100644 --- a/tensorflow/python/summary/summary.py +++ b/tensorflow/python/summary/summary.py @@ -285,7 +285,7 @@ def merge(inputs, collections=None, name=None): @tf_export('summary.merge_all') -def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None): +def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None, name=None): """Merges all summaries collected in the default graph. Args: @@ -314,7 +314,7 @@ def merge_all(key=_ops.GraphKeys.SUMMARIES, scope=None): if not summary_ops: return None else: - return merge(summary_ops) + return merge(summary_ops, name=name) @tf_export('summary.get_summary_description') diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py index 60e96ee947506d5b020ad1ed580a5da0c4e6bdec..861a3e920dc9bb5102eedc789f1b03ce484153ed 100644 --- a/tensorflow/python/summary/writer/writer.py +++ b/tensorflow/python/summary/writer/writer.py @@ -104,8 +104,8 @@ class SummaryToEventTransformer(object): and adds it to the event file. You can pass the result of evaluating any summary op, using - @{tf.Session.run} or - @{tf.Tensor.eval}, to this + `tf.Session.run` or + `tf.Tensor.eval`, to this function. Alternatively, you can pass a `tf.Summary` protocol buffer that you populate with your own data. The latter is commonly done to report evaluation results in event files. diff --git a/tensorflow/python/tools/api/generator/api_init_files.bzl b/tensorflow/python/tools/api/generator/api_init_files.bzl index 7001e566ced2af433535e64b0c6ea01c0d2d59a9..64f04694821ca3284739f35f99ff06469d7e595c 100644 --- a/tensorflow/python/tools/api/generator/api_init_files.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files.bzl @@ -25,6 +25,7 @@ TENSORFLOW_API_INIT_FILES = [ "keras/applications/inception_resnet_v2/__init__.py", "keras/applications/inception_v3/__init__.py", "keras/applications/mobilenet/__init__.py", + "keras/applications/mobilenet_v2/__init__.py", "keras/applications/nasnet/__init__.py", "keras/applications/resnet50/__init__.py", "keras/applications/vgg16/__init__.py", diff --git a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl index 73d11199d9cb4cc95f4597e2f6b8d58f692b2e30..bc2f3516d1b3db9e99b8b073cb97668bbbd96377 100644 --- a/tensorflow/python/tools/api/generator/api_init_files_v1.bzl +++ b/tensorflow/python/tools/api/generator/api_init_files_v1.bzl @@ -25,6 +25,7 @@ TENSORFLOW_API_INIT_FILES_V1 = [ "keras/applications/inception_resnet_v2/__init__.py", "keras/applications/inception_v3/__init__.py", "keras/applications/mobilenet/__init__.py", + "keras/applications/mobilenet_v2/__init__.py", "keras/applications/nasnet/__init__.py", "keras/applications/resnet50/__init__.py", "keras/applications/vgg16/__init__.py", diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index 4e8e505549f7edc7a32333c5dbf473bd9f6e4c73..76625624e40c04b58b376a98bce9e243a52ae80d 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -28,9 +28,12 @@ from tensorflow.core.framework.summary_pb2 import Summary from tensorflow.core.protobuf import config_pb2 from tensorflow.core.util.event_pb2 import SessionLog from tensorflow.python.client import timeline +from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import session_run_hook @@ -40,6 +43,10 @@ from tensorflow.python.training.summary_io import SummaryWriterCache from tensorflow.python.util.tf_export import tf_export +_HOOKS = "hooks" +_STEPS_PER_RUN_VAR = "steps_per_run" + + class _HookTimer(object): """Base timer for determining when Hooks should trigger. @@ -255,6 +262,116 @@ class LoggingTensorHook(session_run_hook.SessionRunHook): self._log_tensors(values) +def get_or_create_steps_per_run_variable(): + """Gets or creates the steps_per_run variable. + + In Estimator, the user provided computation, the model_fn, is wrapped + inside a tf.while_loop for peak performance. The iterations of the loop are + specified by this variable, which adjusts its value on the CPU after each + device program execution and before the next execution. + + The purpose of using a variable, rather than a constant, is to allow + Estimator adapt the device training iterations according to the final steps + specified by users. For example, if the user sets the steps_per_run as + 4 and steps as 10 in Estimator.train(), the steps_per_run + variable will have the following value before each training run. + + - 1-st execution: steps_per_run = 4 + - 2-nd execution: steps_per_run = 4 + - 3-rd execution: steps_per_run = 2 + + As model_fn increases the global step once per train_op invocation, the global + step is 10 after all executions, matching the steps=10 inputs passed in by + users. + + Returns: + A TF non-trainable resource variable. + + Raises: + RuntimeError: If multi steps_per_run variables were found. + """ + graph = ops.get_default_graph() + collection_name = "{}_{}".format(_HOOKS, _STEPS_PER_RUN_VAR) + steps_per_run_vars = graph.get_collection(collection_name) + if len(steps_per_run_vars) == 1: + return steps_per_run_vars[0] + elif len(steps_per_run_vars) > 1: + raise RuntimeError("Multiple steps_per_run_var in collection.") + + with variable_scope.variable_scope(_HOOKS, reuse=variable_scope.AUTO_REUSE): + return variable_scope.get_variable( + _STEPS_PER_RUN_VAR, + initializer=init_ops.ones_initializer(), + shape=[], + dtype=dtypes.int32, + trainable=False, + collections=[collection_name, ops.GraphKeys.LOCAL_VARIABLES], + use_resource=True) + + +class _MultiStepStopAtStepHook(session_run_hook.SessionRunHook): + """Hook that requests stop at a specified step.""" + + def __init__(self, num_steps=None, last_step=None, steps_per_run=1): + """Initializes a `MultiStepStopAtStepHook`. + + This hook requests stop after either a number of steps have been + executed or a last step has been reached. Only one of the two options can be + specified. + + if `num_steps` is specified, it indicates the number of steps to execute + after `begin()` is called. If instead `last_step` is specified, it + indicates the last step we want to execute, as passed to the `after_run()` + call. + + In Estimator, the user provided computation, the model_fn, is wrapped + inside a tf.while_loop for peak performance. The steps_per_run variable + determines the number of iterations of the loop before returning to the CPU. + + Args: + num_steps: Number of steps to execute. + last_step: Step after which to stop. + steps_per_run: Number of steps executed per run call. + + Raises: + ValueError: If one of the arguments is invalid. + """ + if num_steps is None and last_step is None: + raise ValueError("One of num_steps or last_step must be specified.") + if num_steps is not None and last_step is not None: + raise ValueError("Only one of num_steps or last_step can be specified.") + if steps_per_run is None or steps_per_run < 1: + raise ValueError("steps_per_run should be greater than 0") + self._num_steps = num_steps + self._last_step = last_step + self._steps_per_run = steps_per_run + + def begin(self): + self._global_step_tensor = training_util.get_global_step() + if self._global_step_tensor is None: + raise RuntimeError("Global step should be created to use StopAtStepHook.") + self._steps_per_run_variable = get_or_create_steps_per_run_variable() + + def _update_steps_per_run_variable(self, global_step, session): + steps = min(self._last_step - global_step, self._steps_per_run) + self._steps_per_run_variable.load(steps, session=session) + + def after_create_session(self, session, coord): + global_step = session.run(self._global_step_tensor) + if self._last_step is None: + self._last_step = global_step + self._num_steps + self._update_steps_per_run_variable(global_step, session) + + def after_run(self, run_context, run_values): + # Global step cannot be retrieved via SessionRunArgs and before_run due to + # race condition in hook execution. + global_step = run_context.session.run(self._global_step_tensor) + if global_step >= self._last_step: + run_context.request_stop() + else: + self._update_steps_per_run_variable(global_step, run_context.session) + + @tf_export("train.StopAtStepHook") class StopAtStepHook(session_run_hook.SessionRunHook): """Hook that requests stop at a specified step.""" diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index 3cdaedce981fb70033f09bb4e6adbf473f4fa631..6c03bf0d51a57c56afd0260694ed748e9dae264d 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -19,6 +19,7 @@ from __future__ import print_function import abc import collections +import os import weakref from tensorflow.core.protobuf import checkpointable_object_graph_pb2 @@ -36,6 +37,7 @@ from tensorflow.python.ops import gen_io_ops as io_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope +from tensorflow.python.training import checkpoint_management from tensorflow.python.training import optimizer as optimizer_lib from tensorflow.python.training import saveable_object as saveable_object_lib from tensorflow.python.training import saver as saver_lib @@ -1100,7 +1102,7 @@ class _SessionWithFeedDictAdditions(session_lib.SessionInterface): def _copy_saver_with_new_var_list(old_saver, new_var_list): """Copy a `tf.train.Saver`'s state to a new Saver with different variables.""" - new_saver = saver_lib.Saver(var_list=new_var_list) + new_saver = saver_lib.Saver(var_list=new_var_list, max_to_keep=None) # TODO(allenl): Move to copying functionality to Saver? # pylint: disable=protected-access new_saver._last_checkpoints = old_saver._last_checkpoints @@ -1226,7 +1228,8 @@ class CheckpointableSaver(object): self._last_save_saver = _copy_saver_with_new_var_list( old_saver=self._last_save_saver, new_var_list=named_variables) else: - self._last_save_saver = saver_lib.Saver(var_list=named_variables) + self._last_save_saver = saver_lib.Saver( + var_list=named_variables, max_to_keep=None) self._last_save_object_graph = graph_proto with ops.device("/cpu:0"): save_path = self._last_save_saver.save( @@ -1234,6 +1237,7 @@ class CheckpointableSaver(object): session=session, feed_additions=feed_additions), save_path=file_prefix, write_meta_graph=False, + write_state=False, global_step=checkpoint_number) return save_path @@ -1486,6 +1490,32 @@ class Checkpoint(tracking.Checkpointable): add_variable(self, name="save_counter", initializer=0, dtype=dtypes.int64)) + def write(self, file_prefix, session=None): + """Writes a training checkpoint. + + The checkpoint includes variables created by this object and any + checkpointable objects it depends on at the time `Checkpoint.write()` is + called. + + `write` does not number checkpoints, increment `save_counter`, or update the + metadata used by `tf.train.latest_checkpoint`. It is primarily intended for + use by higher level checkpoint management utilities. `save` provides a very + basic implementation of these features. + + Args: + file_prefix: A prefix to use for the checkpoint filenames + (/path/to/directory/and_a_prefix). + session: The session to evaluate variables in. Ignored when executing + eagerly. If not provided when graph building, the default session is + used. + + Returns: + The full path to the checkpoint (i.e. `file_prefix`). + """ + return self._saver.save( + file_prefix=file_prefix, + session=session) + @property def save_counter(self): """An integer variable which starts at zero and is incremented on save. @@ -1498,13 +1528,21 @@ class Checkpoint(tracking.Checkpointable): self._maybe_create_save_counter() return self._save_counter + # TODO(allenl): Update save's docstring with a pointer to + # tf.contrib.checkpoint.CheckpointManager once that's in. def save(self, file_prefix, session=None): - """Save a training checkpoint. + """Saves a training checkpoint and provides basic checkpoint management. The saved checkpoint includes variables created by this object and any checkpointable objects it depends on at the time `Checkpoint.save()` is called. + `save` is a basic convenience wrapper around the `write` method, + sequentially numbering checkpoints using `save_counter` and updating the + metadata used by `tf.train.latest_checkpoint`. More advanced checkpoint + management, for example garbage collection and custom numbering, may be + provided by other utilities which also wrap `write`. + Args: file_prefix: A prefix to use for the checkpoint filenames (/path/to/directory/and_a_prefix). Names are generated based on this @@ -1527,15 +1565,20 @@ class Checkpoint(tracking.Checkpointable): session.run(self.save_counter.initializer) if not graph_building or self._save_assign_op is None: with ops.colocate_with(self.save_counter): - assign_op = self.save_counter.assign_add(1, read_value=False) + assign_op = self.save_counter.assign_add(1, read_value=True) if graph_building: - self._save_assign_op = assign_op + self._save_assign_op = data_structures.NoDependency(assign_op) if graph_building: - session.run(self._save_assign_op) - return self._saver.save( - file_prefix=file_prefix, - checkpoint_number=self.save_counter, - session=session) + checkpoint_number = session.run(self._save_assign_op) + else: + checkpoint_number = assign_op.numpy() + file_path = self.write("%s-%d" % (file_prefix, checkpoint_number), + session=session) + checkpoint_management.update_checkpoint_state( + save_dir=os.path.dirname(file_prefix), + model_checkpoint_path=file_path, + all_model_checkpoint_paths=[file_path]) + return file_path def restore(self, save_path): """Restore a training checkpoint. diff --git a/tensorflow/python/training/checkpointable/util_test.py b/tensorflow/python/training/checkpointable/util_test.py index 5506e6bc4efcabfd9d919dfd459bf2b151ea75fe..98a42b1c2043e4142c61dcce8630867e48d6538e 100644 --- a/tensorflow/python/training/checkpointable/util_test.py +++ b/tensorflow/python/training/checkpointable/util_test.py @@ -550,6 +550,20 @@ class CheckpointingTests(test.TestCase): self.assertEqual(training_continuation + 1, self.evaluate(root.save_counter)) + @test_util.run_in_graph_and_eager_modes + def testCustomNumbering(self): + directory = self.get_temp_dir() + prefix = os.path.join(directory, "ckpt") + step = resource_variable_ops.ResourceVariable(0, dtype=dtypes.int64) + checkpoint = checkpointable_utils.Checkpoint(step=step) + self.evaluate(step.initializer) + for i in range(5): + path = checkpoint.write("%s-%d" % (prefix, self.evaluate(step))) + expected_suffix = "-%d" % (2 * i,) + if not path.endswith(expected_suffix): + self.fail("%s should have suffix %s" % (path, expected_suffix)) + self.evaluate(step.assign_add(2)) + # pylint: disable=cell-var-from-loop @test_util.run_in_graph_and_eager_modes def testWithDefun(self): @@ -996,7 +1010,8 @@ class CheckpointingTests(test.TestCase): self.assertEqual(before_ops, graph.get_operations()) @test_util.run_in_graph_and_eager_modes - def testCheckpointCleanup(self): + def testCheckpointState(self): + # No checkpoints are deleted by default checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") obj = tracking.Checkpointable() @@ -1006,7 +1021,7 @@ class CheckpointingTests(test.TestCase): for _ in range(10): saver.save(checkpoint_prefix) expected_filenames = ["checkpoint"] - for checkpoint_number in range(6, 11): + for checkpoint_number in range(1, 11): expected_filenames.append("ckpt-%d.index" % (checkpoint_number,)) expected_filenames.append( "ckpt-%d.data-00000-of-00001" % (checkpoint_number,)) @@ -1016,7 +1031,7 @@ class CheckpointingTests(test.TestCase): os.listdir(checkpoint_directory)) @test_util.run_in_graph_and_eager_modes - def testCheckpointCleanupChangingVarList(self): + def testCheckpointStateChangingVarList(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") obj = tracking.Checkpointable() @@ -1032,8 +1047,8 @@ class CheckpointingTests(test.TestCase): looped_variables.append(new_variable) expected_filenames = ["checkpoint"] # We've copied the saver each time, but checkpoint management should still - # be consistent. - for checkpoint_number in range(6, 11): + # be consistent. Nothing gets deleted. + for checkpoint_number in range(1, 11): expected_filenames.append("ckpt-%d.index" % (checkpoint_number,)) expected_filenames.append( "ckpt-%d.data-00000-of-00001" % (checkpoint_number,)) @@ -1041,6 +1056,15 @@ class CheckpointingTests(test.TestCase): self, expected_filenames, os.listdir(checkpoint_directory)) + self.assertEqual( + checkpoint_prefix + "-10", + checkpoint_management.latest_checkpoint(checkpoint_directory)) + # The checkpoint list only contains the most recent checkpoint, but they're + # all on disk. This means we won't eventually run into proto size limits. + self.assertEqual( + [checkpoint_prefix + "-10"], + (checkpoint_management.get_checkpoint_state(checkpoint_directory) + .all_model_checkpoint_paths)) for v in looped_variables: self.evaluate(v.assign(314)) checkpoint.restore(checkpoint_prefix + "-6").run_restore_ops() diff --git a/tensorflow/python/training/distribute.py b/tensorflow/python/training/distribute.py index 170d68397b3225394bf9225c126f6aaf7091efa7..581db45e80aca5ab829c005455f1c523f6fd67e2 100644 --- a/tensorflow/python/training/distribute.py +++ b/tensorflow/python/training/distribute.py @@ -21,6 +21,7 @@ from __future__ import print_function import threading from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops @@ -727,6 +728,85 @@ class DistributionStrategy(object): def _broadcast(self, tensor, destinations): raise NotImplementedError("must be implemented in descendants") + def initialize(self): + """Any initialization to be done before running any computations. + + In eager mode, it executes any initialization as a side effect. + In graph mode, it creates the initialization ops and returns them. + + For example, TPU initialize_system ops. + + Returns: + In eager mode, returns `None`. + In graph mode, a list of ops to execute. Empty list if nothing to be done. + """ + if context.executing_eagerly(): + return + else: + return [] + + def finalize(self): + """Any final actions to be done at the end of all computations. + + In eager mode, it executes any finalize actions as a side effect. + In graph mode, it creates the finalize ops and returns them. + + For example, TPU shutdown ops. + + Returns: + In eager mode, returns `None`. + In graph mode, a list of ops to execute. Empty list if nothing to be done. + """ + if context.executing_eagerly(): + return + else: + return [] + + def run_steps_on_dataset(self, fn, iterator, iterations=1, + initial_loop_values=None): + """Run `fn` with input from `iterator` for `iterations` times. + + This method can be used to run a step function for training a number of + times using input from a dataset. + + Args: + fn: function to run using this distribution strategy. The function must + have the following signature: def fn(context, inputs). + `context` is an instance of `MultiStepContext` that will be passed when + `fn` is run. `context` can be used to specify the outputs to be returned + from `fn` by calling `context.set_last_step_output`. It can also be used + to capture non tensor outputs by `context.set_non_tensor_output`. + See `MultiStepContext` documentation for more information. + `inputs` will have same type/structure as `iterator.get_next()`. + Typically, `fn` will use `call_for_each_tower` method of the strategy + to distribute the computation over multiple towers. + iterator: Iterator of a dataset that represents the input for `fn`. The + caller is responsible for initializing the iterator as needed. + iterations: (Optional) Number of iterations that `fn` should be run. + Defaults to 1. + initial_loop_values: (Optional) Initial values to be passed into the + loop that runs `fn`. Defaults to `None`. # TODO(priyag): Remove + initial_loop_values argument when we have a mechanism to infer the + outputs of `fn`. + + Returns: + Returns the `MultiStepContext` object which has the following properties, + among other things: + - run_op: An op that runs `fn` `iterations` times. + - last_step_outputs: A dictionary containing tensors set using + `context.set_last_step_output`. Evaluating this returns the value of + the tensors after the last iteration. + - non_tensor_outputs: A dictionatry containing anything that was set by + `fn` by calling `context.set_non_tensor_output`. + """ + _require_cross_tower_context(self) + return self._run_steps_on_dataset(fn, iterator, iterations, + initial_loop_values) + + def _run_steps_on_dataset(self, fn, iterator, iterations, + initial_loop_values): + raise NotImplementedError("must be implemented in descendants") + def call_for_each_tower(self, fn, *args, **kwargs): """Run `fn` once per tower. @@ -784,7 +864,7 @@ class DistributionStrategy(object): Args: aggregation: Indicates how a variable will be aggregated. Accepted values - are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. + are `tf.VariableAggregation.SUM`, `tf.VariableAggregation.MEAN`. value: A per-device value with one value per tower. destinations: An optional mirrored variable, a device string, list of device strings. The return value will be copied to all @@ -813,7 +893,7 @@ class DistributionStrategy(object): Args: aggregation: Indicates how a variable will be aggregated. Accepted values - are @{tf.VariableAggregation.SUM}, @{tf.VariableAggregation.MEAN}. + are `tf.VariableAggregation.SUM`, `tf.VariableAggregation.MEAN`. value_destination_pairs: A sequence of (value, destinations) pairs. See `reduce()` for a description. diff --git a/tensorflow/python/training/ftrl.py b/tensorflow/python/training/ftrl.py index 4fa081fab72df62107cf4957d4ff68240ced9ee0..832c10d454e6083be9715ef0af4642ad3e936f97 100644 --- a/tensorflow/python/training/ftrl.py +++ b/tensorflow/python/training/ftrl.py @@ -86,7 +86,7 @@ class FtrlOptimizer(optimizer.Optimizer): if initial_accumulator_value < 0.0: raise ValueError( - "initial_accumulator_value %f needs to be be positive or zero" % + "initial_accumulator_value %f needs to be positive or zero" % initial_accumulator_value) if learning_rate_power > 0.0: raise ValueError("learning_rate_power %f needs to be negative or zero" % diff --git a/tensorflow/python/training/moving_averages.py b/tensorflow/python/training/moving_averages.py index 60cc54c2645a0f44195bbb86013e0306387aa8aa..4b91d1e963a234951a7b1254eb07038935257136 100644 --- a/tensorflow/python/training/moving_averages.py +++ b/tensorflow/python/training/moving_averages.py @@ -300,7 +300,7 @@ class ExponentialMovingAverage(object): for a given variable. * Build a model normally but load the checkpoint files to evaluate by using the shadow variable names. For this use the `average_name()` method. See - the @{tf.train.Saver} for more + the `tf.train.Saver` for more information on restoring saved variables. Example of restoring the shadow variable values: diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index f75db080595c6f348fe7e9302041bf19f72a301f..6d95b144d5946be55a31fa9273e8d1d48aead054 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -51,8 +51,8 @@ def get_filtered_grad_fn(grad_fn): # those variables are accessed in another thread during the gradient # computation. To get a consistent set of variables, we filter out # those with `None` gradients. - def filtered_grad_fn(x=None): - return [(g, v) for g, v in grad_fn(x) if g is not None] + def filtered_grad_fn(*args, **kwargs): + return [(g, v) for g, v in grad_fn(*args, **kwargs) if g is not None] return filtered_grad_fn @@ -555,7 +555,7 @@ class Optimizer( # always calling _distributed_apply(), using the default distribution # as needed. if distribute_lib.has_distribution_strategy(): - grads_and_vars = get_filtered_grad_fn(lambda _: grads_and_vars)() + grads_and_vars = get_filtered_grad_fn(lambda: grads_and_vars)() return distribute_lib.get_tower_context().merge_call( self._distributed_apply, grads_and_vars, global_step, name) diff --git a/tensorflow/python/training/quantize_training.i b/tensorflow/python/training/quantize_training.i index 54d6789616473382cf87abe4f701092bbd4e272f..41e62e02521bf9ad39d09bb8ad7d3c108916e34a 100644 --- a/tensorflow/python/training/quantize_training.i +++ b/tensorflow/python/training/quantize_training.i @@ -56,7 +56,7 @@ PyObject* DoQuantizeTrainingOnGraphDefHelper( %insert("python") %{ def do_quantize_training_on_graphdef(input_graph, num_bits): - """A general quantization scheme is being developed in @{tf.contrib.quantize}. + """A general quantization scheme is being developed in `tf.contrib.quantize`. Consider using that instead, though since it is in the tf.contrib namespace, it is not subject to backward compatibility guarantees. diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 213c11c50d2a9441230f9c3ef5a00e703055df0c..04fce496bd9b90f36027939a98f2d33130b55250 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1529,9 +1529,7 @@ class Saver(object): # 1. The checkpoint would not be loaded successfully as is. Try to parse # it as an object-based checkpoint. try: - reader = pywrap_tensorflow.NewCheckpointReader(save_path) - object_graph_string = reader.get_tensor( - checkpointable.OBJECT_GRAPH_PROTO_KEY) + names_to_keys = object_graph_key_mapping(save_path) except errors.NotFoundError: # 2. This is not an object-based checkpoint, which likely means there # is a graph mismatch. Re-raise the original error with @@ -1546,42 +1544,19 @@ class Saver(object): "may be somewhat fragile, and will re-build the Saver. Instead, " "consider loading object-based checkpoints using " "tf.train.Checkpoint().") - self._restore_from_object_based_checkpoint( - sess=sess, save_path=save_path, - object_graph_string=object_graph_string) + self._object_restore_saver = saver_from_object_based_checkpoint( + checkpoint_path=save_path, + var_list=self._var_list, + builder=self._builder, + names_to_keys=names_to_keys, + cached_saver=self._object_restore_saver) + self._object_restore_saver.restore(sess=sess, save_path=save_path) except errors.InvalidArgumentError as err: # There is a mismatch between the graph and the checkpoint being loaded. # We add a more reasonable error message here to help users (b/110263146) raise _wrap_restore_error_with_msg( err, "a mismatch between the current graph and the graph") - def _restore_from_object_based_checkpoint(self, sess, save_path, - object_graph_string): - """A compatibility mode for reading object-based checkpoints.""" - object_graph_proto = ( - checkpointable_object_graph_pb2.CheckpointableObjectGraph()) - object_graph_proto.ParseFromString(object_graph_string) - names_to_keys = {} - for node in object_graph_proto.nodes: - for attribute in node.attributes: - names_to_keys[attribute.full_name] = attribute.checkpoint_key - saveables = self._builder._ValidateAndSliceInputs(self._var_list) # pylint: disable=protected-access - for saveable in saveables: - for spec in saveable.specs: - if spec.name not in names_to_keys: - raise errors.NotFoundError( - None, None, - message=("Attempting to load an object-based checkpoint using " - "variable names, but could not find %s in the " - "checkpoint.") % spec.name) - spec.name = names_to_keys[spec.name] - if self._object_restore_saver is None: - # Cache the Saver so multiple restore() calls don't pollute the graph when - # graph building. This assumes keys are consistent (i.e. this is the same - # type of object-based checkpoint we saw previously). - self._object_restore_saver = Saver(saveables) - self._object_restore_saver.restore(sess=sess, save_path=save_path) - @staticmethod def _add_collection_def(meta_graph_def, key, export_scope=None): """Adds a collection to MetaGraphDef protocol buffer. @@ -1815,3 +1790,92 @@ ops.register_proto_function( proto_type=saver_pb2.SaverDef, to_proto=Saver.to_proto, from_proto=Saver.from_proto) + + +def object_graph_key_mapping(checkpoint_path): + """Return name to key mappings from the checkpoint. + + Args: + checkpoint_path: string, path to object-based checkpoint + + Returns: + Dictionary mapping tensor names to checkpoint keys. + """ + reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path) + object_graph_string = reader.get_tensor( + checkpointable.OBJECT_GRAPH_PROTO_KEY) + object_graph_proto = ( + checkpointable_object_graph_pb2.CheckpointableObjectGraph()) + object_graph_proto.ParseFromString(object_graph_string) + names_to_keys = {} + for node in object_graph_proto.nodes: + for attribute in node.attributes: + names_to_keys[attribute.full_name] = attribute.checkpoint_key + return names_to_keys + + +def saver_from_object_based_checkpoint( + checkpoint_path, var_list=None, builder=None, names_to_keys=None, + cached_saver=None): + """Return a `Saver` which reads from an object-based checkpoint. + + This function validates that all variables in the variables list are remapped + in the object-based checkpoint (or `names_to_keys` dict if provided). A + saver will be created with the list of remapped variables. + + The `cached_saver` argument allows the user to pass in a previously created + saver, so multiple `saver.restore()` calls don't pollute the graph when graph + building. This assumes that keys are consistent, meaning that the + 1) `checkpoint_path` checkpoint, and + 2) checkpoint used to create the `cached_saver` + are the same type of object-based checkpoint. If this argument is set, this + function will simply validate that all variables have been remapped by the + checkpoint at `checkpoint_path`. + + Note that in general, `tf.train.Checkpoint` should be used to restore/save an + object-based checkpoint. + + Args: + checkpoint_path: string, path to object-based checkpoint + var_list: list of `Variables` that appear in the checkpoint. If `None`, + `var_list` will be set to all saveable objects. + builder: a `BaseSaverBuilder` instance. If `None`, a new `BulkSaverBuilder` + will be created. + names_to_keys: dict mapping string tensor names to checkpooint keys. If + `None`, this dict will be generated from the checkpoint file. + cached_saver: Cached `Saver` object with remapped variables. + + Returns: + `Saver` with remapped variables for reading from an object-based checkpoint. + + Raises: + ValueError if the checkpoint provided is not an object-based checkpoint. + NotFoundError: If one of the variables in `var_list` can not be found in the + checkpoint. This could mean the checkpoint or `names_to_keys` mapping is + missing the variable. + """ + if names_to_keys is None: + try: + names_to_keys = object_graph_key_mapping(checkpoint_path) + except errors.NotFoundError: + raise ValueError("Checkpoint in %s not an object-based checkpoint." + % checkpoint_path) + if var_list is None: + var_list = variables._all_saveable_objects() # pylint: disable=protected-access + if builder is None: + builder = BulkSaverBuilder() + + saveables = builder._ValidateAndSliceInputs(var_list) # pylint: disable=protected-access + for saveable in saveables: + for spec in saveable.specs: + if spec.name not in names_to_keys: + raise errors.NotFoundError( + None, None, + message=("Attempting to load an object-based checkpoint using " + "variable names, but could not find %s in the " + "checkpoint.") % spec.name) + spec.name = names_to_keys[spec.name] + + if cached_saver is None: + return Saver(saveables) + return cached_saver diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index 941aafc780d581984e20f82d2194fc7b8d3f90a6..b55e64122a607766d22e7f8017a39779005ac93e 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -784,6 +784,32 @@ class SaverTest(test.TestCase): self.assertEqual(20.0, v1.eval()) save.save(sess, save_path) + def testSaveRestoreAndValidateVariableDtype(self): + for variable_op in [ + variables.Variable, resource_variable_ops.ResourceVariable + ]: + save_path = os.path.join(self.get_temp_dir(), "basic_save_restore") + + # Build the first session. + with self.test_session(graph=ops_lib.Graph()) as sess: + v0 = variable_op(10.0, name="v0", dtype=dtypes.float32) + + if not context.executing_eagerly(): + self.evaluate([variables.global_variables_initializer()]) + + save = saver_module.Saver({"v0": v0}) + save.save(sess, save_path) + + # Start a second session. + with self.test_session(graph=ops_lib.Graph()) as sess: + v0_wrong_dtype = variable_op(1, name="v0", dtype=dtypes.int32) + # Restore the saved value with different dtype + # in the parameter nodes. + save = saver_module.Saver({"v0": v0_wrong_dtype}) + with self.assertRaisesRegexp(errors.InvalidArgumentError, + "original dtype"): + save.restore(sess, save_path) + # Test restoring large tensors (triggers a thread pool) def testRestoreLargeTensors(self): save_dir = self.get_temp_dir() diff --git a/tensorflow/python/training/server_lib.py b/tensorflow/python/training/server_lib.py index 58cf5277fe5fc17d74a9c670b8e608b469806337..46543413e40a5a212b180b0cdeb2280148d606c5 100644 --- a/tensorflow/python/training/server_lib.py +++ b/tensorflow/python/training/server_lib.py @@ -98,9 +98,9 @@ class Server(object): """An in-process TensorFlow server, for use in distributed training. A `tf.train.Server` instance encapsulates a set of devices and a - @{tf.Session} target that + `tf.Session` target that can participate in distributed training. A server belongs to a - cluster (specified by a @{tf.train.ClusterSpec}), and + cluster (specified by a `tf.train.ClusterSpec`), and corresponds to a particular task in a named job. The server can communicate with any other server in the same cluster. """ @@ -186,7 +186,7 @@ class Server(object): """Returns the target for a `tf.Session` to connect to this server. To create a - @{tf.Session} that + `tf.Session` that connects to this server, use the following snippet: ```python @@ -230,7 +230,7 @@ class ClusterSpec(object): A `tf.train.ClusterSpec` represents the set of processes that participate in a distributed TensorFlow computation. Every - @{tf.train.Server} is constructed in a particular cluster. + `tf.train.Server` is constructed in a particular cluster. To create a cluster with two jobs and five tasks, you specify the mapping from job names to lists of network addresses (typically @@ -421,7 +421,7 @@ class ClusterSpec(object): NOTE: For backwards compatibility, this method returns a list. If the given job was defined with a sparse set of task indices, the length of this list may not reflect the number of tasks defined in - this job. Use the @{tf.train.ClusterSpec.num_tasks} method + this job. Use the `tf.train.ClusterSpec.num_tasks` method to find the number of tasks defined in a particular job. Args: diff --git a/tensorflow/python/training/supervisor.py b/tensorflow/python/training/supervisor.py index 372ea415df0ee299ebb51b2369c1027eb2db4865..0755364bbe291d951c3641c44aa2e9995e1efbfb 100644 --- a/tensorflow/python/training/supervisor.py +++ b/tensorflow/python/training/supervisor.py @@ -45,7 +45,7 @@ class Supervisor(object): """A training helper that checkpoints models and computes summaries. This class is deprecated. Please use - @{tf.train.MonitoredTrainingSession} instead. + `tf.train.MonitoredTrainingSession` instead. The Supervisor is a small wrapper around a `Coordinator`, a `Saver`, and a `SessionManager` that takes care of common needs of TensorFlow @@ -134,7 +134,7 @@ class Supervisor(object): * Specifying `'local'` requests a session that uses the RPC-based "Master interface" to run TensorFlow programs. See - @{tf.train.Server.create_local_server} for + `tf.train.Server.create_local_server` for details. * Specifying `'grpc://hostname:port'` requests a session that uses diff --git a/tensorflow/python/training/training.py b/tensorflow/python/training/training.py index 544010afbefc0d3751bd21ca3156b3cefae3cd1c..6f6305a50576b9e4ef8d14e76e60d08e025939c3 100644 --- a/tensorflow/python/training/training.py +++ b/tensorflow/python/training/training.py @@ -53,6 +53,7 @@ from tensorflow.python.training import input as _input from tensorflow.python.training.input import * # pylint: disable=redefined-builtin # pylint: enable=wildcard-import +from tensorflow.python.training.basic_session_run_hooks import get_or_create_steps_per_run_variable from tensorflow.python.training.basic_session_run_hooks import SecondOrStepTimer from tensorflow.python.training.basic_session_run_hooks import LoggingTensorHook from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook diff --git a/tensorflow/python/training/warm_starting_util.py b/tensorflow/python/training/warm_starting_util.py index b1a7cfab8315586c9122bb6be41db65c7fb76aa1..0ba7ba983d131adf318ea4aba6e2479679226120 100644 --- a/tensorflow/python/training/warm_starting_util.py +++ b/tensorflow/python/training/warm_starting_util.py @@ -44,7 +44,7 @@ class VocabInfo( ])): """Vocabulary information for warm-starting. - See @{tf.estimator.WarmStartSettings$WarmStartSettings} for examples of using + See `tf.estimator.WarmStartSettings` for examples of using VocabInfo to warm-start. Attributes: diff --git a/tensorflow/python/util/deprecation.py b/tensorflow/python/util/deprecation.py index 74e1fb227feb906308a2eaa4d259d037e641b600..c43589f5c4555180442a1962e25f82e51d677d1b 100644 --- a/tensorflow/python/util/deprecation.py +++ b/tensorflow/python/util/deprecation.py @@ -393,8 +393,8 @@ def deprecated_args(date, instructions, *deprecated_arg_names_or_tuples, Returns: Dictionary from arg_name to DeprecatedArgSpec. """ - arg_name_to_pos = dict( - (name, pos) for (pos, name) in enumerate(arg_spec.args)) + arg_name_to_pos = { + name: pos for pos, name in enumerate(arg_spec.args)} deprecated_positional_args = {} for arg_name, spec in iter(names_to_ok_vals.items()): if arg_name in arg_name_to_pos: diff --git a/tensorflow/python/util/serialization_test.py b/tensorflow/python/util/serialization_test.py index 9d9cac272592f6b73b4c78f38310d7b89a89e05d..6df7533831bf7bacf8bb2833dac83276de30612a 100644 --- a/tensorflow/python/util/serialization_test.py +++ b/tensorflow/python/util/serialization_test.py @@ -55,11 +55,8 @@ class SerializationTests(test.TestCase): model(constant_op.constant([[1.]])) sequential_round_trip = json.loads( json.dumps(model, default=serialization.get_json_type)) - self.assertEqual(5, sequential_round_trip["config"][1]["config"]["units"]) - input_round_trip = json.loads( - json.dumps(model._input_layers, default=serialization.get_json_type)) - self.assertAllEqual([1, 1], - input_round_trip[0]["config"]["batch_input_shape"]) + self.assertEqual( + 5, sequential_round_trip["config"]["layers"][1]["config"]["units"]) @test_util.run_in_graph_and_eager_modes def test_serialize_model(self): diff --git a/tensorflow/python/util/tf_should_use.py b/tensorflow/python/util/tf_should_use.py index 28e49afa023904abed076373685bb38f2537b7d4..ca6710bcf2178db0fcf63c9bdfdf27531651f7ed 100644 --- a/tensorflow/python/util/tf_should_use.py +++ b/tensorflow/python/util/tf_should_use.py @@ -17,23 +17,124 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import functools -import types +import copy +import sys +import traceback import six # pylint: disable=unused-import -from tensorflow.python.eager import context +from tensorflow.python.platform import tf_logging from tensorflow.python.util import tf_decorator # pylint: enable=g-bad-import-order,g-import-not-at-top -# TODO(b/65412899): Re-implement to avoid leaking python objects. -# This function / class remains since the API is public (mark_used()). +class _TFShouldUseHelper(object): + """Object stored in TFShouldUse-wrapped objects. + + When it is deleted it will emit a warning or error if its `sate` method + has not been called by time of deletion. + """ + + def __init__(self, type_, repr_, stack_frame, fatal_error_if_unsated): + self._type = type_ + self._repr = repr_ + self._stack_frame = stack_frame + self._fatal_error_if_unsated = fatal_error_if_unsated + self._sated = False + + def sate(self): + self._sated = True + self._type = None + self._repr = None + self._stack_frame = None + self._logging_module = None + + def __del__(self): + if self._sated: + return + if self._fatal_error_if_unsated: + logger = tf_logging.fatal + else: + logger = tf_logging.error + creation_stack = ''.join( + [line.rstrip() for line in traceback.format_stack(self._stack_frame)]) + logger( + '==================================\n' + 'Object was never used (type %s):\n%s\nIf you want to mark it as ' + 'used call its "mark_used()" method.\nIt was originally created ' + 'here:\n%s\n' + '==================================' % + (self._type, self._repr, creation_stack)) + + +def _new__init__(self, true_value, tf_should_use_helper): + # pylint: disable=protected-access + self._tf_should_use_helper = tf_should_use_helper + self._true_value = true_value + + +def _new__setattr__(self, key, value): + if key in ('_tf_should_use_helper', '_true_value'): + return object.__setattr__(self, key, value) + return setattr( + object.__getattribute__(self, '_true_value'), + key, value) + + +def _new__getattribute__(self, key): + if key not in ('_tf_should_use_helper', '_true_value'): + object.__getattribute__(self, '_tf_should_use_helper').sate() + if key in ('_tf_should_use_helper', 'mark_used', '__setatt__'): + return object.__getattribute__(self, key) + return getattr(object.__getattribute__(self, '_true_value'), key) + + +def _new_mark_used(self, *args, **kwargs): + object.__getattribute__(self, '_tf_should_use_helper').sate() + try: + mu = object.__getattribute__( + object.__getattribute__(self, '_true_value'), + 'mark_used') + return mu(*args, **kwargs) + except AttributeError: + pass + + +_WRAPPERS = dict() + + +def _get_wrapper(x, tf_should_use_helper): + """Create a wrapper for object x, whose class subclasses type(x). + + The wrapper will emit a warning if it is deleted without any of its + properties being accessed or methods being called. + + Args: + x: The instance to wrap. + tf_should_use_helper: The object that tracks usage. + + Returns: + An object wrapping `x`, of type `type(x)`. + """ + type_x = type(x) + memoized = _WRAPPERS.get(type_x, None) + if memoized: + return memoized(x, tf_should_use_helper) + + tx = copy.deepcopy(type_x) + copy_tx = type(tx.__name__, tx.__bases__, dict(tx.__dict__)) + copy_tx.__init__ = _new__init__ + copy_tx.__getattribute__ = _new__getattribute__ + copy_tx.mark_used = _new_mark_used + copy_tx.__setattr__ = _new__setattr__ + _WRAPPERS[type_x] = copy_tx + + return copy_tx(x, tf_should_use_helper) + + def _add_should_use_warning(x, fatal_error=False): """Wraps object x so that if it is never used, a warning is logged. - Does nothing when executing eagerly. - Args: x: Python object. fatal_error: Python bool. If `True`, tf.logging.fatal is raised @@ -43,50 +144,22 @@ def _add_should_use_warning(x, fatal_error=False): An instance of `TFShouldUseWarningWrapper` which subclasses `type(x)` and is a very shallow wrapper for `x` which logs access into `x`. """ - del fatal_error if x is None or x == []: # pylint: disable=g-explicit-bool-comparison return x - if context.executing_eagerly(): - # Typically not needed when executing eagerly (the main use case is for ops - # which need to be incorporated into the graph), and even the no-op wrapper - # creates reference cycles which require garbage collection. - return x - - def override_method(method): - def fn(self, *args, **kwargs): - return method(self, *args, **kwargs) - return fn - - class TFShouldUseWarningWrapper(type(x)): - """Wrapper for objects that keeps track of their use.""" - - def __init__(self, true_self): - self.__dict__ = true_self.__dict__ + # Extract the current frame for later use by traceback printing. + try: + raise ValueError() + except ValueError: + stack_frame = sys.exc_info()[2].tb_frame.f_back - # Not sure why this pylint warning is being used; this is not an - # old class form. - # pylint: disable=super-on-old-class - def __getattribute__(self, name): - return super(TFShouldUseWarningWrapper, self).__getattribute__(name) - - def mark_used(self, *args, **kwargs): - return + tf_should_use_helper = _TFShouldUseHelper( + type_=type(x), + repr_=repr(x), + stack_frame=stack_frame, + fatal_error_if_unsated=fatal_error) - # pylint: enable=super-on-old-class - - for name in dir(TFShouldUseWarningWrapper): - method = getattr(TFShouldUseWarningWrapper, name) - if not isinstance(method, types.FunctionType): - continue - if name in ('__init__', '__getattribute__', '__del__', 'mark_used'): - continue - setattr(TFShouldUseWarningWrapper, name, - functools.wraps(method)(override_method(method))) - - wrapped = TFShouldUseWarningWrapper(x) - wrapped.__doc__ = x.__doc__ # functools.wraps fails on some objects. - return wrapped + return _get_wrapper(x, tf_should_use_helper) def should_use_result(fn): @@ -106,8 +179,6 @@ def should_use_result(fn): - `t != 0`. In this case, comparison is done on types / ids. - `isinstance(t, tf.Tensor)`. Similar to above. - Does nothing when executing eagerly. - Args: fn: The function to wrap. @@ -142,8 +213,6 @@ def must_use_result_or_fatal(fn): - `t != 0`. In this case, comparison is done on types / ids. - `isinstance(t, tf.Tensor)`. Similar to above. - Does nothing when executing eagerly. - Args: fn: The function to wrap. diff --git a/tensorflow/python/util/tf_should_use_test.py b/tensorflow/python/util/tf_should_use_test.py index 4c6e48b11c1d013d1e4c6cdfc376973baa7bb9a2..16fa1f547d4c6b9d2c4da6994d380ba2b671b886 100644 --- a/tensorflow/python/util/tf_should_use_test.py +++ b/tensorflow/python/util/tf_should_use_test.py @@ -30,48 +30,51 @@ from tensorflow.python.util import tf_should_use @contextlib.contextmanager -def reroute_error(captured): +def reroute_error(): """Temporarily reroute errors written to tf_logging.error into `captured`.""" - del captured[:] - true_logger = tf_logging.error - def capture_errors(*args, **unused_kwargs): - captured.extend(args) - tf_logging.error = capture_errors - try: - yield - finally: - tf_logging.error = true_logger + with test.mock.patch.object(tf_should_use.tf_logging, 'error') as error: + with test.mock.patch.object(tf_should_use.tf_logging, 'fatal') as fatal: + yield error, fatal class TfShouldUseTest(test.TestCase): def testAddShouldUseWarningWhenNotUsed(self): - self.skipTest('b/65412899') c = constant_op.constant(0, name='blah0') - captured = [] - with reroute_error(captured): - def in_this_function(): - h = tf_should_use._add_should_use_warning(c) - del h + def in_this_function(): + h = tf_should_use._add_should_use_warning(c) + del h + with reroute_error() as (error, _): in_this_function() - self.assertIn('Object was never used', '\n'.join(captured)) - self.assertIn('blah0:0', '\n'.join(captured)) - self.assertIn('in_this_function', '\n'.join(captured)) - gc.collect() + msg = '\n'.join(error.call_args[0]) + self.assertIn('Object was never used', msg) + self.assertIn('blah0:0', msg) + self.assertIn('in_this_function', msg) + self.assertFalse(gc.garbage) + + def testAddShouldUseFatalWhenNotUsed(self): + c = constant_op.constant(0, name='blah0') + def in_this_function(): + h = tf_should_use._add_should_use_warning(c, fatal_error=True) + del h + with reroute_error() as (_, fatal): + in_this_function() + msg = '\n'.join(fatal.call_args[0]) + self.assertIn('Object was never used', msg) + self.assertIn('blah0:0', msg) + self.assertIn('in_this_function', msg) self.assertFalse(gc.garbage) def _testAddShouldUseWarningWhenUsed(self, fn, name): c = constant_op.constant(0, name=name) - captured = [] - with reroute_error(captured): + with reroute_error() as (error, fatal): h = tf_should_use._add_should_use_warning(c) fn(h) del h - self.assertNotIn('Object was never used', '\n'.join(captured)) - self.assertNotIn('%s:0' % name, '\n'.join(captured)) + error.assert_not_called() + fatal.assert_not_called() def testAddShouldUseWarningWhenUsedWithAdd(self): - self.skipTest('b/65412899') def add(h): _ = h + 1 self._testAddShouldUseWarningWhenUsed(add, name='blah_add') @@ -79,7 +82,6 @@ class TfShouldUseTest(test.TestCase): self.assertFalse(gc.garbage) def testAddShouldUseWarningWhenUsedWithGetName(self): - self.skipTest('b/65412899') def get_name(h): _ = h.name self._testAddShouldUseWarningWhenUsed(get_name, name='blah_get_name') @@ -87,35 +89,33 @@ class TfShouldUseTest(test.TestCase): self.assertFalse(gc.garbage) def testShouldUseResult(self): - self.skipTest('b/65412899') @tf_should_use.should_use_result def return_const(value): return constant_op.constant(value, name='blah2') - captured = [] - with reroute_error(captured): + with reroute_error() as (error, _): return_const(0.0) - self.assertIn('Object was never used', '\n'.join(captured)) - self.assertIn('blah2:0', '\n'.join(captured)) - self.assertIn('return_const', '\n'.join(captured)) + msg = '\n'.join(error.call_args[0]) + self.assertIn('Object was never used', msg) + self.assertIn('blah2:0', msg) + self.assertIn('return_const', msg) gc.collect() self.assertFalse(gc.garbage) def testShouldUseResultWhenNotReallyUsed(self): - self.skipTest('b/65412899') @tf_should_use.should_use_result def return_const(value): return constant_op.constant(value, name='blah3') - captured = [] - with reroute_error(captured): + with reroute_error() as (error, _): with self.test_session(): return_const(0.0) # Creating another op and executing it does not mark the # unused op as being "used". v = constant_op.constant(1.0, name='meh') v.eval() - self.assertIn('Object was never used', '\n'.join(captured)) - self.assertIn('blah3:0', '\n'.join(captured)) - self.assertIn('return_const', '\n'.join(captured)) + msg = '\n'.join(error.call_args[0]) + self.assertIn('Object was never used', msg) + self.assertIn('blah3:0', msg) + self.assertIn('return_const', msg) gc.collect() self.assertFalse(gc.garbage) diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index 725f6aeaa4c81d335b83e319e69f50809e797ca7..55408ab9ab77fdca4a4eaa7b3ffd1223afc573e4 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -1986,15 +1986,14 @@ GetCudnnConvolutionBackwardFilterAlgo(const CudnnHandle& cudnn, port::StatusOr> AllocateCudnnConvolutionForwardWorkspace( Stream* stream, const CudnnHandle& cudnn, - const dnn::AlgorithmDesc& algorithm_desc, const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, const CudnnConvolutionDescriptor& conv, - const CudnnTensorDescriptor& output_nd, + const CudnnTensorDescriptor& output_nd, dnn::AlgorithmDesc* algorithm_desc, ScratchAllocator* scratch_allocator) { // TODO(csigg): This has side effects on the convolution descriptor. It is // functionally correct because the convolution is run with the algorithm of // the last call to this function, but should be fixed anyway. - conv.set_use_tensor_op_math(algorithm_desc.tensor_ops_enabled()); + conv.set_use_tensor_op_math(algorithm_desc->tensor_ops_enabled()); // Query the size of the workspace and allocate it. size_t size_in_bytes; @@ -2002,8 +2001,14 @@ port::StatusOr> AllocateCudnnConvolutionForwardWorkspace( cudnn.handle(), /*xDesc=*/input_nd.handle(), /*wDesc=*/filter.handle(), /*convDesc=*/conv.handle(), - /*yDesc=*/output_nd.handle(), /*algo=*/ToConvForwardAlgo(algorithm_desc), + /*yDesc=*/output_nd.handle(), /*algo=*/ToConvForwardAlgo(*algorithm_desc), /*sizeInBytes=*/&size_in_bytes)); + + if (TF_PREDICT_FALSE(!algorithm_desc)) { + return port::Status(port::error::INVALID_ARGUMENT, + "No AlgorithmDesc provided"); + } + algorithm_desc->set_scratch_size(size_in_bytes); int64 size_in_bytes_int64 = size_in_bytes; if (TF_PREDICT_FALSE(size_in_bytes_int64 < 0)) { @@ -2028,15 +2033,14 @@ port::StatusOr> AllocateCudnnConvolutionForwardWorkspace( port::StatusOr> AllocateCudnnConvolutionBackwardDataWorkspace( Stream* stream, const CudnnHandle& cudnn, - const dnn::AlgorithmDesc& algorithm_desc, const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, const CudnnConvolutionDescriptor& conv, - const CudnnTensorDescriptor& output_nd, + const CudnnTensorDescriptor& output_nd, dnn::AlgorithmDesc* algorithm_desc, ScratchAllocator* scratch_allocator) { // TODO(csigg): This has side effects on the convolution descriptor. It is // functionally correct because the convolution is run with the algorithm of // the last call to this function, but should be fixed anyway. - conv.set_use_tensor_op_math(algorithm_desc.tensor_ops_enabled()); + conv.set_use_tensor_op_math(algorithm_desc->tensor_ops_enabled()); // Query the size of the workspace and allocate it. size_t size_in_bytes; @@ -2046,8 +2050,14 @@ AllocateCudnnConvolutionBackwardDataWorkspace( /*dyDesc=*/output_nd.handle(), /*convDesc=*/conv.handle(), /*dxDesc=*/input_nd.handle(), - /*algo=*/ToConvBackwardDataAlgo(algorithm_desc), + /*algo=*/ToConvBackwardDataAlgo(*algorithm_desc), /*sizeInBytes=*/&size_in_bytes)); + + if (TF_PREDICT_FALSE(!algorithm_desc)) { + return port::Status(port::error::INVALID_ARGUMENT, + "No AlgorithmDesc provided"); + } + algorithm_desc->set_scratch_size(size_in_bytes); int64 size_in_bytes_int64 = size_in_bytes; if (TF_PREDICT_FALSE(size_in_bytes_int64 < 0)) { @@ -2072,15 +2082,14 @@ AllocateCudnnConvolutionBackwardDataWorkspace( port::StatusOr> AllocateCudnnConvolutionBackwardFilterWorkspace( Stream* stream, const CudnnHandle& cudnn, - const dnn::AlgorithmDesc& algorithm_desc, const CudnnTensorDescriptor& input_nd, const CudnnFilterDescriptor& filter, const CudnnConvolutionDescriptor& conv, - const CudnnTensorDescriptor& output_nd, + const CudnnTensorDescriptor& output_nd, dnn::AlgorithmDesc* algorithm_desc, ScratchAllocator* scratch_allocator) { // TODO(csigg): This has side effects on the convolution descriptor. It is // functionally correct because the convolution is run with the algorithm of // the last call to this function, but should be fixed anyway. - conv.set_use_tensor_op_math(algorithm_desc.tensor_ops_enabled()); + conv.set_use_tensor_op_math(algorithm_desc->tensor_ops_enabled()); // Query the size of the workspace and allocate it. size_t size_in_bytes; @@ -2090,8 +2099,14 @@ AllocateCudnnConvolutionBackwardFilterWorkspace( /*dyDesc=*/output_nd.handle(), /*convDesc=*/conv.handle(), /*gradDesc=*/filter.handle(), - /*algo=*/ToConvBackwardFilterAlgo(algorithm_desc), + /*algo=*/ToConvBackwardFilterAlgo(*algorithm_desc), /*sizeInBytes=*/&size_in_bytes)); + + if (TF_PREDICT_FALSE(!algorithm_desc)) { + return port::Status(port::error::INVALID_ARGUMENT, + "No AlgorithmDesc provided"); + } + algorithm_desc->set_scratch_size(size_in_bytes); int64 size_in_bytes_int64 = size_in_bytes; if (TF_PREDICT_FALSE(size_in_bytes_int64 < 0)) { @@ -2138,7 +2153,7 @@ port::StatusOr GetCudnnConvolutionForwardAlgorithm( } auto scratch_or = AllocateCudnnConvolutionForwardWorkspace( - stream, cudnn, algo_desc, input_nd, filter, conv, output_nd, + stream, cudnn, input_nd, filter, conv, output_nd, &algo_desc, scratch_allocator); if (scratch_or.ok()) { @@ -2155,11 +2170,11 @@ port::StatusOr GetCudnnConvolutionForwardAlgorithm( "while a secondary algorithm is not provided."); } - SE_ASSIGN_OR_RETURN( - *scratch, AllocateCudnnConvolutionForwardWorkspace( - stream, cudnn, algorithm_config.algorithm_no_scratch(), - input_nd, filter, conv, output_nd, scratch_allocator)); - return algorithm_config.algorithm_no_scratch(); + algo_desc = algorithm_config.algorithm_no_scratch(); + SE_ASSIGN_OR_RETURN(*scratch, AllocateCudnnConvolutionForwardWorkspace( + stream, cudnn, input_nd, filter, conv, + output_nd, &algo_desc, scratch_allocator)); + return algo_desc; } port::StatusOr GetCudnnConvolutionBackwardDataAlgorithm( @@ -2187,7 +2202,7 @@ port::StatusOr GetCudnnConvolutionBackwardDataAlgorithm( } auto scratch_or = AllocateCudnnConvolutionBackwardDataWorkspace( - stream, cudnn, algo_desc, input_nd, filter, conv, output_nd, + stream, cudnn, input_nd, filter, conv, output_nd, &algo_desc, scratch_allocator); if (scratch_or.ok()) { @@ -2204,11 +2219,11 @@ port::StatusOr GetCudnnConvolutionBackwardDataAlgorithm( "while a secondary algorithm is not provided."); } - SE_ASSIGN_OR_RETURN( - *scratch, AllocateCudnnConvolutionBackwardDataWorkspace( - stream, cudnn, algorithm_config.algorithm_no_scratch(), - input_nd, filter, conv, output_nd, scratch_allocator)); - return algorithm_config.algorithm_no_scratch(); + algo_desc = algorithm_config.algorithm_no_scratch(); + SE_ASSIGN_OR_RETURN(*scratch, AllocateCudnnConvolutionBackwardDataWorkspace( + stream, cudnn, input_nd, filter, conv, + output_nd, &algo_desc, scratch_allocator)); + return algo_desc; } port::StatusOr GetCudnnConvolutionBackwardFilterAlgorithm( @@ -2236,7 +2251,7 @@ port::StatusOr GetCudnnConvolutionBackwardFilterAlgorithm( } auto scratch_or = AllocateCudnnConvolutionBackwardFilterWorkspace( - stream, cudnn, algo_desc, input_nd, filter, conv, output_nd, + stream, cudnn, input_nd, filter, conv, output_nd, &algo_desc, scratch_allocator); if (scratch_or.ok()) { @@ -2253,11 +2268,11 @@ port::StatusOr GetCudnnConvolutionBackwardFilterAlgorithm( "while a secondary algorithm is not provided."); } - SE_ASSIGN_OR_RETURN(*scratch, - AllocateCudnnConvolutionBackwardFilterWorkspace( - stream, cudnn, algorithm_config.algorithm(), input_nd, - filter, conv, output_nd, scratch_allocator)); - return algorithm_config.algorithm_no_scratch(); + algo_desc = algorithm_config.algorithm_no_scratch(); + SE_ASSIGN_OR_RETURN(*scratch, AllocateCudnnConvolutionBackwardFilterWorkspace( + stream, cudnn, input_nd, filter, conv, + output_nd, &algo_desc, scratch_allocator)); + return algo_desc; } // A helper class to set env-vars and choose options for cudnn-related diff --git a/tensorflow/stream_executor/dnn.h b/tensorflow/stream_executor/dnn.h index a7449c2df423bd2ffd0759e305a8fb02f2ac8cab..9abfa1db6ab60351557ff6243ec354cfada6bb6d 100644 --- a/tensorflow/stream_executor/dnn.h +++ b/tensorflow/stream_executor/dnn.h @@ -713,15 +713,23 @@ class PoolingDescriptor { class AlgorithmDesc { public: typedef int64 Index; - AlgorithmDesc() : algo_(kDefaultAlgorithm), tensor_ops_enabled_(true) {} + AlgorithmDesc() + : algo_(kDefaultAlgorithm), tensor_ops_enabled_(true), scratch_size_(0) {} AlgorithmDesc(Index a, bool use_tensor_ops) - : algo_(a), tensor_ops_enabled_(use_tensor_ops) {} + : algo_(a), tensor_ops_enabled_(use_tensor_ops), scratch_size_(0) {} + AlgorithmDesc(Index a, bool use_tensor_ops, size_t scratch_size) + : algo_(a), + tensor_ops_enabled_(use_tensor_ops), + scratch_size_(scratch_size) {} bool is_default() const { return algo_ == kDefaultAlgorithm; } bool tensor_ops_enabled() const { return tensor_ops_enabled_; } Index algo_id() const { return algo_; } + size_t scratch_size() const { return scratch_size_; } + void set_scratch_size(size_t val) { scratch_size_ = val; } bool operator==(const AlgorithmDesc& other) const { return this->algo_ == other.algo_ && - this->tensor_ops_enabled_ == other.tensor_ops_enabled_; + this->tensor_ops_enabled_ == other.tensor_ops_enabled_ && + this->scratch_size_ == other.scratch_size_; } uint64 hash() const; @@ -729,6 +737,7 @@ class AlgorithmDesc { enum { kDefaultAlgorithm = -1 }; Index algo_; bool tensor_ops_enabled_; + size_t scratch_size_; }; // Describes the result from a perf experiment. diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index a42a469df5b5d918a083d296c19a5881599eaeb9..9efd34de24e8581993eefe7a18646e21b25007c2 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -5294,6 +5294,19 @@ Stream &Stream::ThenDoHostCallback(std::function callback) { return *this; } +Stream &Stream::ThenDoHostCallbackWithStatus( + std::function callback) { + VLOG_CALL(PARAM(callback)); + + if (ok()) { + CheckError(parent_->HostCallback(this, std::move(callback))); + } else { + LOG(WARNING) << "stream " << DebugStreamPointers() + << " was in error state before adding host callback"; + } + return *this; +} + Stream &Stream::ThenFft(fft::Plan *plan, const DeviceMemory> &input, DeviceMemory> *output) { diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h index 4d41409fef6a0c4f570b39a5cdec5671d465ef68..e1629b5b3084e6641bcdf80d1de00f33f1c81940 100644 --- a/tensorflow/stream_executor/stream.h +++ b/tensorflow/stream_executor/stream.h @@ -2045,6 +2045,11 @@ class Stream { // negative effects on performance. Stream &ThenDoHostCallback(std::function callback); + // Entrains onto the stream a callback to the host (from the device). + // Behaves as ThenDoHostCallback above, but returns a Status instead of void. + // This overload should be preferred if the callback could fail. + Stream &ThenDoHostCallbackWithStatus(std::function callback); + // Returns the StreamExecutor (parent object) associated with this stream. StreamExecutor *parent() const { CHECK(parent_ != nullptr); diff --git a/tensorflow/stream_executor/stream_executor_internal.cc b/tensorflow/stream_executor/stream_executor_internal.cc index 8297228e6fecddffa8fc68a1a028456dc8e75a65..7df6a361c6810b9a15c97f15704435d145dccb8e 100644 --- a/tensorflow/stream_executor/stream_executor_internal.cc +++ b/tensorflow/stream_executor/stream_executor_internal.cc @@ -36,5 +36,17 @@ StreamExecutorFactory* MakeOpenCLExecutorImplementation() { StreamExecutorFactory MakeHostExecutorImplementation; +// TODO(b/112125301): Consolodate this down to one implementation of +// HostCallback, taking a callback that returns a Status. +bool StreamExecutorInterface::HostCallback( + Stream* stream, std::function callback) { + return HostCallback(stream, [callback]() { + port::Status s = callback(); + if (!s.ok()) { + LOG(WARNING) << "HostCallback failed: " << s; + } + }); +} + } // namespace internal } // namespace stream_executor diff --git a/tensorflow/stream_executor/stream_executor_internal.h b/tensorflow/stream_executor/stream_executor_internal.h index f34b1fc083adec40d57bf65cb49a4e7901ee1864..59a477b5c9c37f10d8f12645deb3cdb832a8d544 100644 --- a/tensorflow/stream_executor/stream_executor_internal.h +++ b/tensorflow/stream_executor/stream_executor_internal.h @@ -236,9 +236,11 @@ class StreamExecutorInterface { virtual bool Memcpy(Stream *stream, DeviceMemoryBase *gpu_dst, const void *host_src, uint64 size) = 0; virtual bool MemcpyDeviceToDevice(Stream *stream, DeviceMemoryBase *gpu_dst, - const DeviceMemoryBase &host_src, + const DeviceMemoryBase &gpu_src, uint64 size) = 0; virtual bool HostCallback(Stream *stream, std::function callback) = 0; + virtual bool HostCallback(Stream *stream, + std::function callback); virtual port::Status AllocateEvent(Event *event) = 0; virtual port::Status DeallocateEvent(Event *event) = 0; virtual port::Status RecordEvent(Stream *stream, Event *event) = 0; diff --git a/tensorflow/stream_executor/stream_executor_pimpl.cc b/tensorflow/stream_executor/stream_executor_pimpl.cc index 2e0137a485e77ef6bd62d07e334cbdc41132ce96..9515d8e62a8ed809d88182bdf3fdb3ba536dd68c 100644 --- a/tensorflow/stream_executor/stream_executor_pimpl.cc +++ b/tensorflow/stream_executor/stream_executor_pimpl.cc @@ -699,6 +699,11 @@ bool StreamExecutor::HostCallback(Stream *stream, return implementation_->HostCallback(stream, std::move(callback)); } +bool StreamExecutor::HostCallback(Stream *stream, + std::function callback) { + return implementation_->HostCallback(stream, std::move(callback)); +} + port::Status StreamExecutor::AllocateEvent(Event *event) { return implementation_->AllocateEvent(event); } diff --git a/tensorflow/stream_executor/stream_executor_pimpl.h b/tensorflow/stream_executor/stream_executor_pimpl.h index 47b3a2b030ca68a079a1f9de238a2ed58f18b7e8..437f29861670309424940f39f325a6aee2bbf897 100644 --- a/tensorflow/stream_executor/stream_executor_pimpl.h +++ b/tensorflow/stream_executor/stream_executor_pimpl.h @@ -549,6 +549,11 @@ class StreamExecutor { // See Stream::ThenDoHostCallback for full details. bool HostCallback(Stream *stream, std::function callback); + // Entrains on a stream a user-specified function to be run on the host. + // See Stream::ThenDoHostCallback for full details. + // This is the preferred form for a callback that may return an error. + bool HostCallback(Stream *stream, std::function callback); + // Performs platform-specific allocation and initialization of an event. port::Status AllocateEvent(Event *event); diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 39db840884570ae577de7018e4fa4718c6265760..30dc4313d0fc95b029b9d25ec3253468810a3e0a 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -27,7 +27,7 @@ load( ) load( "//third_party/mkl_dnn:build_defs.bzl", - "if_mkl_open_source_only", + "if_mkl_open_source_only" ) def register_extension_info(**kwargs): pass @@ -230,7 +230,7 @@ def tf_copts(android_optimization_level_override="-O2", is_external=False): + if_cuda(["-DGOOGLE_CUDA=1"]) + if_tensorrt(["-DGOOGLE_TENSORRT=1"]) + if_mkl(["-DINTEL_MKL=1", "-DEIGEN_USE_VML"]) - + if_mkl_open_source_only(["-DDO_NOT_USE_ML"]) + + if_mkl_open_source_only(["-DINTEL_MKL_DNN_ONLY"]) + if_mkl_lnx_x64(["-fopenmp"]) + if_android_arm(["-mfpu=neon"]) + if_linux_x86_64(["-msse3"]) diff --git a/tensorflow/tools/api/golden/BUILD b/tensorflow/tools/api/golden/BUILD index ebdf42df2c01a60b1cadd0368647adc4121db7ef..1f041ef19362c427fe327658f36f9f15eb5ce17d 100644 --- a/tensorflow/tools/api/golden/BUILD +++ b/tensorflow/tools/api/golden/BUILD @@ -7,6 +7,11 @@ package( licenses(["notice"]) # Apache 2.0 filegroup( - name = "api_golden", - srcs = glob(["*.pbtxt"]), + name = "api_golden_v1", + srcs = glob(["v1/*.pbtxt"]), +) + +filegroup( + name = "api_golden_v2", + srcs = glob(["v1/*.pbtxt"]), ) diff --git a/tensorflow/tools/api/golden/tensorflow.-aggregation-method.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-aggregation-method.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-aggregation-method.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-aggregation-method.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-attr-value.-list-value.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-attr-value.-list-value.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-attr-value.-list-value.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-attr-value.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-attr-value.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-attr-value.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-conditional-accumulator-base.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-conditional-accumulator-base.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-conditional-accumulator-base.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-conditional-accumulator-base.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-conditional-accumulator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-conditional-accumulator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-conditional-accumulator.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-conditional-accumulator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-config-proto.-device-count-entry.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-config-proto.-device-count-entry.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-config-proto.-device-count-entry.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.-config-proto.-experimental.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-config-proto.-experimental.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..eb41deee13de99d6e9534c32141096edc018ed1c --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.-config-proto.-experimental.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.ConfigProto.Experimental" +tf_proto { + descriptor { + name: "Experimental" + field { + name: "collective_group_leader" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "client_handles_error_formatting" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "executor_type" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.-config-proto.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-config-proto.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e565b903d22c3921743becbdd34f33a8850e84d5 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.-config-proto.pbtxt @@ -0,0 +1,148 @@ +path: "tensorflow.ConfigProto" +tf_proto { + descriptor { + name: "ConfigProto" + field { + name: "device_count" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.ConfigProto.DeviceCountEntry" + } + field { + name: "intra_op_parallelism_threads" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "inter_op_parallelism_threads" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "use_per_session_threads" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "session_inter_op_thread_pool" + number: 12 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.ThreadPoolOptionProto" + } + field { + name: "placement_period" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "device_filters" + number: 4 + label: LABEL_REPEATED + type: TYPE_STRING + } + field { + name: "gpu_options" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.GPUOptions" + } + field { + name: "allow_soft_placement" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "log_device_placement" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "graph_options" + number: 10 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.GraphOptions" + } + field { + name: "operation_timeout_in_ms" + number: 11 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "rpc_options" + number: 13 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.RPCOptions" + } + field { + name: "cluster_def" + number: 14 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.ClusterDef" + } + field { + name: "isolate_session_state" + number: 15 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "experimental" + number: 16 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.ConfigProto.Experimental" + } + nested_type { + name: "DeviceCountEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + options { + map_entry: true + } + } + nested_type { + name: "Experimental" + field { + name: "collective_group_leader" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "client_handles_error_formatting" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "executor_type" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.-d-type.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-d-type.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-d-type.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-d-type.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-device-spec.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-device-spec.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-device-spec.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-device-spec.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-dimension.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-dimension.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-dimension.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-dimension.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-event.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-event.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-event.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-event.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-f-i-f-o-queue.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-f-i-f-o-queue.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-f-i-f-o-queue.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-fixed-len-feature.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-fixed-len-feature.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-fixed-len-feature.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-fixed-len-feature.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-fixed-len-sequence-feature.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-fixed-len-sequence-feature.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-fixed-len-sequence-feature.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-fixed-len-sequence-feature.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-fixed-length-record-reader.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-fixed-length-record-reader.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-fixed-length-record-reader.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-g-p-u-options.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.-g-p-u-options.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.-g-p-u-options.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.-gradient-tape.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.-gradient-tape.pbtxt similarity index 100% rename from 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b/tensorflow/tools/api/golden/v1/tensorflow.data.-fixed-length-record-dataset.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.data.-fixed-length-record-dataset.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.data.-fixed-length-record-dataset.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.data.-iterator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.-iterator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4f0147a52381c748eccbfee29df0d3537ba5d14a --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.data.-iterator.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.data.Iterator" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "initializer" + mtype: "" + } + member { + name: "output_classes" + mtype: "" + } + member { + name: "output_shapes" + mtype: "" + } + member { + name: "output_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'iterator_resource\', \'initializer\', \'output_types\', \'output_shapes\', \'output_classes\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_string_handle" + argspec: "args=[\'string_handle\', \'output_types\', \'output_shapes\', \'output_classes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_structure" + argspec: "args=[\'output_types\', \'output_shapes\', \'shared_name\', \'output_classes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "get_next" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "make_initializer" + argspec: "args=[\'self\', \'dataset\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "string_handle" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.-t-f-record-dataset.__metaclass__.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.__metaclass__.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.data.-t-f-record-dataset.__metaclass__.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.-t-f-record-dataset.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.data.-t-f-record-dataset.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.data.-t-f-record-dataset.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.-text-line-dataset.__metaclass__.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.__metaclass__.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.data.-text-line-dataset.__metaclass__.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.-text-line-dataset.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.data.-text-line-dataset.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.data.-text-line-dataset.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.data.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.data.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.data.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.data.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.debugging.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.debugging.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.debugging.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.debugging.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-bernoulli.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-bernoulli.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-bernoulli.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-bernoulli.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-beta.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-beta.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-beta.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-beta.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-categorical.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-categorical.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-categorical.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-categorical.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet-multinomial.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-dirichlet-multinomial.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet-multinomial.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-dirichlet-multinomial.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-dirichlet.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-dirichlet.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-dirichlet.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-distribution.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-distribution.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-distribution.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-distribution.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-exponential.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-exponential.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-exponential.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-exponential.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-gamma.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-gamma.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-gamma.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-gamma.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-laplace.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-laplace.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-laplace.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-laplace.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-multinomial.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-multinomial.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-multinomial.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.distributions.-multinomial.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.distributions.-normal.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.distributions.-normal.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.distributions.-normal.pbtxt rename to 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a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-baseline-classifier.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-baseline-classifier.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-baseline-classifier.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-baseline-regressor.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-baseline-regressor.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-baseline-regressor.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-best-exporter.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-best-exporter.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-best-exporter.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-best-exporter.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-boosted-trees-classifier.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-boosted-trees-classifier.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c23b04b4ef85a290f055d35d0c7f0d4d8a18a2de --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-boosted-trees-classifier.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.BoostedTreesClassifier" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\', \'pruning_mode\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\', \'none\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-boosted-trees-regressor.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-boosted-trees-regressor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6878d28fffabc895433f97415ee71cfe8f6232c1 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-boosted-trees-regressor.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.BoostedTreesRegressor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'label_dimension\', \'weight_column\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\', \'pruning_mode\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\', \'none\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-classifier.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-classifier.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-classifier.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-regressor.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-d-n-n-regressor.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-d-n-n-regressor.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-estimator-spec.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-estimator-spec.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-estimator-spec.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-estimator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-estimator.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-estimator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-eval-spec.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-eval-spec.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-eval-spec.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-eval-spec.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-exporter.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-exporter.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-exporter.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-exporter.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-final-exporter.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-final-exporter.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-final-exporter.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-final-exporter.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-latest-exporter.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-latest-exporter.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-latest-exporter.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-latest-exporter.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-linear-classifier.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-linear-classifier.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-linear-classifier.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-linear-regressor.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-linear-regressor.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-linear-regressor.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-mode-keys.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-mode-keys.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-mode-keys.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-mode-keys.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.estimator.-run-config.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-run-config.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bf1f94b6aedfd02c15c4750bc00beb057fa8694a --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-run-config.pbtxt @@ -0,0 +1,105 @@ +path: "tensorflow.estimator.RunConfig" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "cluster_spec" + mtype: "" + } + member { + name: "device_fn" + mtype: "" + } + member { + name: "eval_distribute" + mtype: "" + } + member { + name: "evaluation_master" + mtype: "" + } + member { + name: "global_id_in_cluster" + mtype: "" + } + member { + name: "is_chief" + mtype: "" + } + member { + name: "keep_checkpoint_every_n_hours" + mtype: "" + } + member { + name: "keep_checkpoint_max" + mtype: "" + } + member { + name: "log_step_count_steps" + mtype: "" + } + member { + name: "master" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "num_ps_replicas" + mtype: "" + } + member { + name: "num_worker_replicas" + mtype: "" + } + member { + name: "protocol" + mtype: "" + } + member { + name: "save_checkpoints_secs" + mtype: "" + } + member { + name: "save_checkpoints_steps" + mtype: "" + } + member { + name: "save_summary_steps" + mtype: "" + } + member { + name: "service" + mtype: "" + } + member { + name: "session_config" + mtype: "" + } + member { + name: "task_id" + mtype: "" + } + member { + name: "task_type" + mtype: "" + } + member { + name: "tf_random_seed" + mtype: "" + } + member { + name: "train_distribute" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'model_dir\', \'tf_random_seed\', \'save_summary_steps\', \'save_checkpoints_steps\', \'save_checkpoints_secs\', \'session_config\', \'keep_checkpoint_max\', \'keep_checkpoint_every_n_hours\', \'log_step_count_steps\', \'train_distribute\', \'device_fn\', \'protocol\', \'eval_distribute\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'100\', \'\', \'\', \'None\', \'5\', \'10000\', \'100\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "replace" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-train-spec.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-train-spec.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-train-spec.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-train-spec.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-vocab-info.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-vocab-info.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-vocab-info.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-vocab-info.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-warm-start-settings.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.-warm-start-settings.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.-warm-start-settings.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.-warm-start-settings.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-classification-output.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-classification-output.__metaclass__.pbtxt similarity 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tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-export-output.__metaclass__.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-export-output.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-export-output.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.-export-output.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-export-output.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-predict-output.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-predict-output.__metaclass__.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.-predict-output.__metaclass__.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-predict-output.__metaclass__.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-predict-output.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-predict-output.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.-predict-output.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-predict-output.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-regression-output.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-regression-output.__metaclass__.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.-regression-output.__metaclass__.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-regression-output.__metaclass__.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-regression-output.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-regression-output.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.-regression-output.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-regression-output.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-serving-input-receiver.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-serving-input-receiver.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.-serving-input-receiver.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-serving-input-receiver.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.export.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.export.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.export.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.inputs.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.inputs.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.inputs.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.inputs.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.estimator.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.estimator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.feature_column.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.feature_column.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.feature_column.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.feature_column.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.gfile.-fast-g-file.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.gfile.-fast-g-file.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.gfile.-fast-g-file.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.gfile.-fast-g-file.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.gfile.-g-file.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.gfile.-g-file.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.gfile.-g-file.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.gfile.-g-file.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.gfile.-open.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.gfile.-open.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.gfile.-open.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.gfile.-open.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.gfile.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.gfile.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.gfile.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.gfile.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.graph_util.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.graph_util.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.graph_util.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.graph_util.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.image.-resize-method.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.image.-resize-method.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.image.-resize-method.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.image.-resize-method.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.image.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5c46dc5ee7dc04f57591d4883ec8eb034a34d2d0 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.image.pbtxt @@ -0,0 +1,251 @@ +path: "tensorflow.image" +tf_module { + member { + name: "ResizeMethod" + mtype: "" + } + member_method { + name: "adjust_brightness" + argspec: "args=[\'image\', \'delta\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "adjust_contrast" + argspec: "args=[\'images\', \'contrast_factor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "adjust_gamma" + argspec: "args=[\'image\', \'gamma\', \'gain\'], varargs=None, keywords=None, defaults=[\'1\', \'1\'], " + } + member_method { + name: "adjust_hue" + argspec: "args=[\'image\', \'delta\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "adjust_jpeg_quality" + argspec: "args=[\'image\', \'jpeg_quality\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "adjust_saturation" + argspec: "args=[\'image\', \'saturation_factor\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "central_crop" + argspec: "args=[\'image\', \'central_fraction\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "convert_image_dtype" + argspec: "args=[\'image\', \'dtype\', \'saturate\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "crop_and_resize" + argspec: "args=[\'image\', \'boxes\', \'box_ind\', \'crop_size\', \'method\', \'extrapolation_value\', \'name\'], varargs=None, keywords=None, defaults=[\'bilinear\', \'0\', \'None\'], " + } + member_method { + name: "crop_to_bounding_box" + argspec: "args=[\'image\', \'offset_height\', \'offset_width\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "decode_and_crop_jpeg" + argspec: "args=[\'contents\', \'crop_window\', \'channels\', \'ratio\', \'fancy_upscaling\', \'try_recover_truncated\', \'acceptable_fraction\', \'dct_method\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'1\', \'True\', \'False\', \'1\', \'\', \'None\'], " + } + member_method { + name: "decode_bmp" + argspec: "args=[\'contents\', \'channels\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } + member_method { + name: "decode_gif" + argspec: "args=[\'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_image" + argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\'], " + } + member_method { + name: "decode_jpeg" + argspec: "args=[\'contents\', \'channels\', \'ratio\', \'fancy_upscaling\', \'try_recover_truncated\', \'acceptable_fraction\', \'dct_method\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'1\', \'True\', \'False\', \'1\', \'\', \'None\'], " + } + member_method { + name: "decode_png" + argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \"\", \'None\'], " + } + member_method { + name: "draw_bounding_boxes" + argspec: "args=[\'images\', \'boxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "encode_jpeg" + argspec: "args=[\'image\', \'format\', \'quality\', \'progressive\', \'optimize_size\', \'chroma_downsampling\', \'density_unit\', \'x_density\', \'y_density\', \'xmp_metadata\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'95\', \'False\', \'False\', \'True\', \'in\', \'300\', \'300\', \'\', \'None\'], " + } + member_method { + name: "encode_png" + argspec: "args=[\'image\', \'compression\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " + } + member_method { + name: "extract_glimpse" + argspec: "args=[\'input\', \'size\', \'offsets\', \'centered\', \'normalized\', \'uniform_noise\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'True\', \'None\'], " + } + member_method { + name: "extract_image_patches" + argspec: "args=[\'images\', \'ksizes\', \'strides\', \'rates\', \'padding\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "extract_jpeg_shape" + argspec: "args=[\'contents\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } + member_method { + name: "flip_left_right" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flip_up_down" + argspec: 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diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.ones.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.ones.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.ones.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.ones.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.orthogonal.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.orthogonal.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.orthogonal.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.orthogonal.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.random_normal.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.random_normal.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.random_normal.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.random_normal.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.random_uniform.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.random_uniform.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.random_uniform.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.random_uniform.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.truncated_normal.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.truncated_normal.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.truncated_normal.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.truncated_normal.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.uniform_unit_scaling.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.uniform_unit_scaling.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.uniform_unit_scaling.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.uniform_unit_scaling.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.variance_scaling.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.variance_scaling.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.variance_scaling.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.variance_scaling.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.initializers.zeros.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.initializers.zeros.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.initializers.zeros.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.initializers.zeros.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.io.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.io.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.io.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.io.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.-model.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e579fe6a1aeca296ac8ceb7b8ba951f250331eee --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.-model.pbtxt @@ -0,0 +1,268 @@ +path: "tensorflow.keras.Model" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + 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tensorflow/tools/api/golden/tensorflow.keras.callbacks.-lambda-callback.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-lambda-callback.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-model-checkpoint.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-model-checkpoint.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.callbacks.-model-checkpoint.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-model-checkpoint.pbtxt diff --git 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tensorflow/tools/api/golden/tensorflow.keras.callbacks.-remote-monitor.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-remote-monitor.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-tensor-board.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-tensor-board.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.callbacks.-tensor-board.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-tensor-board.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.callbacks.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.callbacks.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.constraints.-constraint.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.-constraint.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.constraints.-constraint.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.-constraint.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.constraints.-max-norm.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.-max-norm.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.constraints.-max-norm.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.-max-norm.pbtxt diff --git 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tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.-unit-norm.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.constraints.max_norm.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.max_norm.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.constraints.max_norm.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.max_norm.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.constraints.min_max_norm.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.min_max_norm.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.constraints.min_max_norm.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.min_max_norm.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.constraints.non_neg.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.non_neg.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.constraints.non_neg.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.non_neg.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.constraints.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.constraints.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.constraints.unit_norm.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.unit_norm.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.constraints.unit_norm.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.constraints.unit_norm.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.datasets.boston_housing.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.datasets.boston_housing.pbtxt 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tensorflow/tools/api/golden/tensorflow.keras.layers.-dot.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-dot.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-dropout.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-dropout.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-dropout.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-e-l-u.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-e-l-u.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-e-l-u.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-embedding.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-embedding.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-embedding.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-flatten.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-flatten.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-flatten.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u-cell.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u-cell.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-gaussian-dropout.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-dropout.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-gaussian-dropout.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-gaussian-noise.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-gaussian-noise.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-gaussian-noise.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-average-pooling3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pool3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pool3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-input-layer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-input-layer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-input-layer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-input-spec.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-input-spec.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-input-spec.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-input-spec.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-lambda.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-lambda.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2dff7a6de4231711dc9154ea8ba036e1e4a1ed11 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-lambda.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.Lambda" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'function\', \'output_shape\', \'mask\', \'arguments\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-layer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-layer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-layer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-leaky-re-l-u.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-leaky-re-l-u.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-leaky-re-l-u.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-locally-connected1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-locally-connected1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-locally-connected2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-locally-connected2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-locally-connected2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-masking.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-masking.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-masking.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pool3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pool3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-max-pooling3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-max-pooling3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-maximum.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-maximum.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-maximum.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-minimum.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-minimum.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-minimum.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-multiply.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-multiply.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-multiply.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-p-re-l-u.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-p-re-l-u.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-p-re-l-u.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-permute.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-permute.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-permute.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-r-n-n.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-r-n-n.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-re-l-u.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-re-l-u.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-repeat-vector.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-repeat-vector.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-repeat-vector.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-reshape.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-reshape.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-reshape.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-conv1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-conv1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-conv2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-conv2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-conv2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-convolution1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-convolution1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-convolution2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-separable-convolution2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-separable-convolution2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-softmax.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-softmax.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-softmax.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-spatial-dropout1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-spatial-dropout2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6718e36dc6057d70e9101b6fa26a53f3fb3f3569 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt @@ -0,0 +1,183 @@ +path: "tensorflow.keras.layers.StackedRNNCells" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "output_size" + mtype: "" + } + member { + name: "state_size" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'cells\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'instance\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\', \'states\', \'constants\'], varargs=None, keywords=kwargs, defaults=[\'None\'], " + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-subtract.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-subtract.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-subtract.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-thresholded-re-l-u.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-time-distributed.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-time-distributed.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-time-distributed.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-up-sampling3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-up-sampling3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-wrapper.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-wrapper.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-wrapper.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-zero-padding1-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding1-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-zero-padding1-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-zero-padding2-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding2-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-zero-padding2-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-zero-padding3-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-zero-padding3-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-zero-padding3-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.layers.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.losses.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.losses.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.losses.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.losses.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.metrics.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.models.-model.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.models.-model.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..56914e1746b0429adc2570c6cb31ddc8f9a6535a --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.models.-model.pbtxt @@ -0,0 +1,268 @@ +path: "tensorflow.keras.models.Model" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "input_spec" + mtype: "" + } + member { + name: "layers" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "state_updates" + mtype: "" + } + member { + name: "stateful" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "uses_learning_phase" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_loss" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\', \'getter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\', \'training\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compile" + argspec: "args=[\'self\', \'optimizer\', \'loss\', \'metrics\', \'loss_weights\', \'sample_weight_mode\', \'weighted_metrics\', \'target_tensors\', \'distribute\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\'], " + } 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+ } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "state_updates" + mtype: "" + } + member { + name: "stateful" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "uses_learning_phase" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'layers\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "add" + argspec: "args=[\'self\', \'layer\'], varargs=None, keywords=None, defaults=None" + } + 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tensorflow/tools/api/golden/v1/tensorflow.keras.models.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adadelta.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adadelta.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adadelta.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adagrad.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adagrad.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adagrad.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adam.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adam.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adam.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adamax.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-adamax.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-adamax.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-nadam.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-nadam.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-nadam.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-nadam.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-optimizer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-optimizer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-optimizer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-r-m-sprop.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-r-m-sprop.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-r-m-sprop.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.-s-g-d.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.-s-g-d.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.-s-g-d.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.optimizers.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.optimizers.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.optimizers.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.regularizers.-l1-l2.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.-l1-l2.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.regularizers.-l1-l2.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.-l1-l2.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.regularizers.-regularizer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.-regularizer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.regularizers.-regularizer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.-regularizer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.regularizers.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.regularizers.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.utils.-custom-object-scope.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.utils.-custom-object-scope.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.utils.-custom-object-scope.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.utils.-custom-object-scope.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.utils.-generator-enqueuer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.utils.-generator-enqueuer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.utils.-generator-enqueuer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.utils.-generator-enqueuer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.utils.-h-d-f5-matrix.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.utils.-h-d-f5-matrix.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.utils.-h-d-f5-matrix.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.keras.utils.-h-d-f5-matrix.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.utils.-progbar.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.utils.-progbar.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.utils.-progbar.pbtxt rename to 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a/tensorflow/tools/api/golden/tensorflow.metrics.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.metrics.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.metrics.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.metrics.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.name_scope.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.name_scope.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.name_scope.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.name_scope.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.nn.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.nn.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt new file mode 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--git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.ones_initializer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.ones_initializer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.ones_initializer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.ones_initializer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.orthogonal_initializer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.orthogonal_initializer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.orthogonal_initializer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.orthogonal_initializer.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4f1962769150c4aae5fdd9a7424bd22316f2cb45 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt @@ -0,0 +1,2215 @@ +path: "tensorflow" +tf_module { + member { + 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keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-advice-proto.-checker.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checker.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-advice-proto.-checker.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-advice-proto.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-advice-proto.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-advice-proto.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-graph-node-proto.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-graph-node-proto.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-graph-node-proto.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-multi-graph-node-proto.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-multi-graph-node-proto.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-multi-graph-node-proto.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-op-log-proto.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-op-log-proto.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-op-log-proto.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-profile-option-builder.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-profile-option-builder.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-profile-option-builder.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-profile-option-builder.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.-profiler.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.-profiler.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.-profiler.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.-profiler.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.profiler.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.profiler.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.profiler.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.profiler.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-compression-type.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.python_io.-t-f-record-compression-type.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-compression-type.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.python_io.-t-f-record-compression-type.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-options.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.python_io.-t-f-record-options.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-options.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.python_io.-t-f-record-options.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.python_io.-t-f-record-writer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.python_io.-t-f-record-writer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.python_io.-t-f-record-writer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.python_io.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.python_io.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.python_io.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.python_io.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.quantization.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.quantization.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.quantization.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.quantization.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.random_normal_initializer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.random_normal_initializer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.random_normal_initializer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.random_normal_initializer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.random_uniform_initializer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.random_uniform_initializer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.random_uniform_initializer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.random_uniform_initializer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.resource_loader.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.resource_loader.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.resource_loader.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.resource_loader.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.builder.-saved-model-builder.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.builder.-saved-model-builder.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.builder.-saved-model-builder.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.builder.-saved-model-builder.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.builder.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.builder.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.builder.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.builder.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.constants.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.constants.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.constants.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.constants.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.loader.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.loader.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.loader.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.loader.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.main_op.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.main_op.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.main_op.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.main_op.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.signature_constants.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.signature_constants.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.signature_constants.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.signature_constants.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.signature_def_utils.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.signature_def_utils.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.signature_def_utils.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.signature_def_utils.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.tag_constants.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.tag_constants.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.tag_constants.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.tag_constants.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.saved_model.utils.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.saved_model.utils.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.saved_model.utils.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.saved_model.utils.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.sets.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.sets.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.sets.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.sets.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.sparse.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.sparse.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.sparse.pbtxt rename to 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a/tensorflow/tools/api/golden/tensorflow.summary.-file-writer-cache.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.summary.-file-writer-cache.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.summary.-file-writer-cache.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.summary.-file-writer-cache.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-file-writer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.summary.-file-writer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.summary.-file-writer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.summary.-file-writer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.summary.-session-log.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.summary.-session-log.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.summary.-session-log.pbtxt 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rename to tensorflow/tools/api/golden/v1/tensorflow.summary.-tagged-run-metadata.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.summary.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.summary.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7ed9cd77a01c2eadb5ea43a02306d60d505127a0 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.summary.pbtxt @@ -0,0 +1,67 @@ +path: "tensorflow.summary" +tf_module { + member { + name: "Event" + mtype: "" + } + member { + name: "FileWriter" + mtype: "" + } + member { + name: "FileWriterCache" + mtype: "" + } + member { + name: "SessionLog" + mtype: "" + } + member { + name: "Summary" + mtype: "" + } + member { + name: "SummaryDescription" + mtype: "" + } + member { + name: "TaggedRunMetadata" + mtype: "" + } + member_method { + name: "audio" + argspec: "args=[\'name\', \'tensor\', \'sample_rate\', \'max_outputs\', \'collections\', \'family\'], varargs=None, keywords=None, defaults=[\'3\', \'None\', \'None\'], " + } + member_method { + name: "get_summary_description" + argspec: "args=[\'node_def\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "histogram" + argspec: "args=[\'name\', \'values\', \'collections\', \'family\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "image" + argspec: "args=[\'name\', \'tensor\', \'max_outputs\', \'collections\', \'family\'], varargs=None, keywords=None, defaults=[\'3\', \'None\', \'None\'], " + } + member_method { + name: "merge" + argspec: "args=[\'inputs\', \'collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "merge_all" + argspec: "args=[\'key\', \'scope\', \'name\'], varargs=None, keywords=None, defaults=[\'summaries\', \'None\', \'None\'], " + } + member_method { + name: "scalar" + argspec: "args=[\'name\', \'tensor\', \'collections\', \'family\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "tensor_summary" + argspec: "args=[\'name\', \'tensor\', \'summary_description\', \'collections\', \'summary_metadata\', \'family\', \'display_name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "text" + argspec: "args=[\'name\', \'tensor\', \'collections\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.sysconfig.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.sysconfig.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.sysconfig.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.sysconfig.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.test.-benchmark.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.test.-benchmark.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.test.-benchmark.pbtxt rename to 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tensorflow/tools/api/golden/tensorflow.train.-adam-optimizer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-adam-optimizer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-bytes-list.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-bytes-list.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-bytes-list.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint-saver-hook.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-hook.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint-saver-hook.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-listener.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint-saver-listener.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-checkpoint-saver-listener.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint-saver-listener.pbtxt diff --git a/tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5be37200f368b1823093c67ad7042db534b0df93 --- /dev/null +++ b/tensorflow/tools/api/golden/v1/tensorflow.train.-checkpoint.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.train.Checkpoint" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "save_counter" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "restore" + argspec: "args=[\'self\', \'save_path\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "save" + argspec: "args=[\'self\', \'file_prefix\', \'session\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "write" + argspec: "args=[\'self\', \'file_prefix\', \'session\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.train.-chief-session-creator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-chief-session-creator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-chief-session-creator.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-chief-session-creator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-cluster-def.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-cluster-def.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-cluster-def.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.train.-cluster-spec.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-cluster-spec.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-cluster-spec.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-cluster-spec.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.train.-coordinator.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-coordinator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-coordinator.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-coordinator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.train.-example.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-example.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.train.-example.pbtxt diff --git 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tensorflow/tools/api/golden/tensorflow.variable_scope.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.variable_scope.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.variance_scaling_initializer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.variance_scaling_initializer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.variance_scaling_initializer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.zeros_initializer.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.zeros_initializer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.zeros_initializer.pbtxt rename to tensorflow/tools/api/golden/v1/tensorflow.zeros_initializer.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-aggregation-method.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-aggregation-method.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f79029d3fe0b88a454b11456b3785c3ae28a253c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-aggregation-method.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.AggregationMethod" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "ADD_N" + mtype: "" + } + member { + name: "DEFAULT" + mtype: "" + } + member { + name: "EXPERIMENTAL_ACCUMULATE_N" + mtype: "" + } + member { + name: "EXPERIMENTAL_TREE" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-attr-value.-list-value.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-attr-value.-list-value.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f1dffd595285098afaeb0ff04e5db35d594f7fac --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-attr-value.-list-value.pbtxt @@ -0,0 +1,70 @@ +path: "tensorflow.AttrValue.ListValue" +tf_proto { + descriptor { + name: "ListValue" + field { + name: "s" + number: 2 + label: LABEL_REPEATED + type: TYPE_BYTES + } + field { + name: "i" + number: 3 + label: LABEL_REPEATED + type: TYPE_INT64 + options { + packed: true + } + } + field { + name: "f" + number: 4 + label: LABEL_REPEATED + type: TYPE_FLOAT + options { + packed: true + } + } + field { + name: "b" + number: 5 + label: LABEL_REPEATED + type: TYPE_BOOL + options { + packed: true + } + } + field { + name: "type" + number: 6 + label: LABEL_REPEATED + type: TYPE_ENUM + type_name: ".tensorflow.DataType" + options { + packed: true + } + } + field { + name: "shape" + number: 7 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorShapeProto" + } + field { + name: "tensor" + number: 8 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorProto" + } + field { + name: "func" + number: 9 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.NameAttrList" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-attr-value.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-attr-value.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6ccd64f428c3b87c807d0af82f67a884187f738c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-attr-value.pbtxt @@ -0,0 +1,151 @@ +path: "tensorflow.AttrValue" +tf_proto { + descriptor { + name: "AttrValue" + field { + name: "s" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + field { + name: "i" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + oneof_index: 0 + } + field { + name: "f" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + oneof_index: 0 + } + field { + name: "b" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_BOOL + oneof_index: 0 + } + field { + name: "type" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.DataType" + oneof_index: 0 + } + field { + name: "shape" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorShapeProto" + oneof_index: 0 + } + field { + name: "tensor" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorProto" + oneof_index: 0 + } + field { + name: "list" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.AttrValue.ListValue" + oneof_index: 0 + } + field { + name: "func" + number: 10 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.NameAttrList" + oneof_index: 0 + } + field { + name: "placeholder" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_STRING + oneof_index: 0 + } + nested_type { + name: "ListValue" + field { + name: "s" + number: 2 + label: LABEL_REPEATED + type: TYPE_BYTES + } + field { + name: "i" + number: 3 + label: LABEL_REPEATED + type: TYPE_INT64 + options { + packed: true + } + } + field { + name: "f" + number: 4 + label: LABEL_REPEATED + type: TYPE_FLOAT + options { + packed: true + } + } + field { + name: "b" + number: 5 + label: LABEL_REPEATED + type: TYPE_BOOL + options { + packed: true + } + } + field { + name: "type" + number: 6 + label: LABEL_REPEATED + type: TYPE_ENUM + type_name: ".tensorflow.DataType" + options { + packed: true + } + } + field { + name: "shape" + number: 7 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorShapeProto" + } + field { + name: "tensor" + number: 8 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorProto" + } + field { + name: "func" + number: 9 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.NameAttrList" + } + } + oneof_decl { + name: "value" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-conditional-accumulator-base.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-conditional-accumulator-base.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c9a32c16b34a78bd5a182b7c0635a559bddc611d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-conditional-accumulator-base.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.ConditionalAccumulatorBase" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "accumulator_ref" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'shape\', \'accumulator_ref\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "num_accumulated" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_global_step" + argspec: "args=[\'self\', \'new_global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-conditional-accumulator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-conditional-accumulator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d23b3bd0cae1f9ab1c2896244a17d4d93e2427e9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-conditional-accumulator.pbtxt @@ -0,0 +1,38 @@ +path: "tensorflow.ConditionalAccumulator" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "accumulator_ref" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'shape\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'conditional_accumulator\'], " + } + member_method { + name: "apply_grad" + argspec: "args=[\'self\', \'grad\', \'local_step\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } + member_method { + name: "num_accumulated" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_global_step" + argspec: "args=[\'self\', \'new_global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "take_grad" + argspec: "args=[\'self\', \'num_required\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.-device-count-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.-device-count-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d9b142682899bf5d9fd5d942437359adf8962466 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.-device-count-entry.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.ConfigProto.DeviceCountEntry" +tf_proto { + descriptor { + name: "DeviceCountEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.-experimental.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.-experimental.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ef9fe096a11a0a75576b9b3d2bc083a82e9818d4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.-experimental.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.ConfigProto.Experimental" +tf_proto { + descriptor { + name: "Experimental" + field { + name: "collective_group_leader" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "client_handles_error_formatting" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..eeef15515d73cf45581533fb8d3b02e4cbc4c208 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-config-proto.pbtxt @@ -0,0 +1,142 @@ +path: "tensorflow.ConfigProto" +tf_proto { + descriptor { + name: "ConfigProto" + field { + name: "device_count" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.ConfigProto.DeviceCountEntry" + } + field { + name: "intra_op_parallelism_threads" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "inter_op_parallelism_threads" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "use_per_session_threads" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "session_inter_op_thread_pool" + number: 12 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.ThreadPoolOptionProto" + } + field { + name: "placement_period" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "device_filters" + number: 4 + label: LABEL_REPEATED + type: TYPE_STRING + } + field { + name: "gpu_options" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.GPUOptions" + } + field { + name: "allow_soft_placement" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "log_device_placement" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "graph_options" + number: 10 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.GraphOptions" + } + field { + name: "operation_timeout_in_ms" + number: 11 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "rpc_options" + number: 13 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.RPCOptions" + } + field { + name: "cluster_def" + number: 14 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.ClusterDef" + } + field { + name: "isolate_session_state" + number: 15 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "experimental" + number: 16 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.ConfigProto.Experimental" + } + nested_type { + name: "DeviceCountEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + options { + map_entry: true + } + } + nested_type { + name: "Experimental" + field { + name: "collective_group_leader" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "client_handles_error_formatting" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-d-type.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-d-type.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0b5b88bba80e6bf7b9d4917c73e3876e00ef956b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-d-type.pbtxt @@ -0,0 +1,77 @@ +path: "tensorflow.DType" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "as_datatype_enum" + mtype: "" + } + member { + name: "as_numpy_dtype" + mtype: "" + } + member { + name: "base_dtype" + mtype: "" + } + member { + name: "is_bool" + mtype: "" + } + member { + name: "is_complex" + mtype: "" + } + member { + name: "is_floating" + mtype: "" + } + member { + name: "is_integer" + mtype: "" + } + member { + name: "is_numpy_compatible" + mtype: "" + } + member { + name: "is_quantized" + mtype: "" + } + member { + name: "is_unsigned" + mtype: "" + } + member { + name: "limits" + mtype: "" + } + member { + name: "max" + mtype: "" + } + member { + name: "min" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "real_dtype" + mtype: "" + } + member { + name: "size" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'type_enum\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-device-spec.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-device-spec.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..92e535c341447628a50d8941998a4065e78d12a5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-device-spec.pbtxt @@ -0,0 +1,37 @@ +path: "tensorflow.DeviceSpec" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "job" + mtype: "" + } + member { + name: "replica" + mtype: "" + } + member { + name: "task" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'job\', \'replica\', \'task\', \'device_type\', \'device_index\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "from_string" + argspec: "args=[\'spec\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "merge_from" + argspec: "args=[\'self\', \'dev\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "parse_from_string" + argspec: "args=[\'self\', \'spec\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "to_string" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-dimension.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-dimension.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a9ab27719b4d71f3d7ed10963ad896ccafa82f15 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-dimension.pbtxt @@ -0,0 +1,25 @@ +path: "tensorflow.Dimension" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "value" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "assert_is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "merge_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-event.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-event.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3b75a1735be76fe77689736e492c42c54ab795c1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-event.pbtxt @@ -0,0 +1,74 @@ +path: "tensorflow.Event" +tf_proto { + descriptor { + name: "Event" + field { + name: "wall_time" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "step" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "file_version" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + oneof_index: 0 + } + field { + name: "graph_def" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + field { + name: "summary" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary" + oneof_index: 0 + } + field { + name: "log_message" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.LogMessage" + oneof_index: 0 + } + field { + name: "session_log" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SessionLog" + oneof_index: 0 + } + field { + name: "tagged_run_metadata" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TaggedRunMetadata" + oneof_index: 0 + } + field { + name: "meta_graph_def" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + oneof_decl { + name: "what" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-f-i-f-o-queue.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a095616c00cfe8fb64413e2078ae1589a423d2f4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-f-i-f-o-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.FIFOQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'dtypes\', \'shapes\', \'names\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'fifo_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-fixed-len-feature.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-fixed-len-feature.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6933814a7b68f775e694fe940a7c65a8e31b9398 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-fixed-len-feature.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.FixedLenFeature" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "default_value" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-fixed-len-sequence-feature.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-fixed-len-sequence-feature.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c53878795190924e205a1e7efe1672f216869c41 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-fixed-len-sequence-feature.pbtxt @@ -0,0 +1,31 @@ +path: "tensorflow.FixedLenSequenceFeature" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_missing" + mtype: "" + } + member { + name: "default_value" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-fixed-length-record-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-fixed-length-record-reader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..260c796fd65b90020eb2b8191645ffdb2402a4a4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-fixed-length-record-reader.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.FixedLengthRecordReader" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "reader_ref" + mtype: "" + } + member { + name: "supports_serialize" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'record_bytes\', \'header_bytes\', \'footer_bytes\', \'hop_bytes\', \'name\', \'encoding\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "num_records_produced" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_work_units_completed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read" + argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_up_to" + argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "restore_state" + argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize_state" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-g-p-u-options.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-g-p-u-options.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..353e63127de174a79c209a05327da2de20bf0dd7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-g-p-u-options.pbtxt @@ -0,0 +1,92 @@ +path: "tensorflow.GPUOptions" +tf_proto { + descriptor { + name: "GPUOptions" + field { + name: "per_process_gpu_memory_fraction" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "allow_growth" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "allocator_type" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "deferred_deletion_bytes" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "visible_device_list" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "polling_active_delay_usecs" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "polling_inactive_delay_msecs" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "force_gpu_compatible" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "experimental" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.GPUOptions.Experimental" + } + nested_type { + name: "Experimental" + field { + name: "virtual_devices" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.GPUOptions.Experimental.VirtualDevices" + } + field { + name: "use_unified_memory" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "num_dev_to_dev_copy_streams" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + nested_type { + name: "VirtualDevices" + field { + name: "memory_limit_mb" + number: 1 + label: LABEL_REPEATED + type: TYPE_FLOAT + } + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-gradient-tape.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-gradient-tape.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cbf655498c02a6521ef45f722f30acd7c13de9cc --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-gradient-tape.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.GradientTape" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'persistent\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "gradient" + argspec: "args=[\'self\', \'target\', \'sources\', \'output_gradients\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "stop_recording" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "watch" + argspec: "args=[\'self\', \'tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "watched_variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-graph-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-graph-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..19eccff03d24719d95ea84ccdad4014aa777ccd5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-graph-def.pbtxt @@ -0,0 +1,36 @@ +path: "tensorflow.GraphDef" +tf_proto { + descriptor { + name: "GraphDef" + field { + name: "node" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.NodeDef" + } + field { + name: "versions" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.VersionDef" + } + field { + name: "version" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + options { + deprecated: true + } + } + field { + name: "library" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.FunctionDefLibrary" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-graph-keys.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-graph-keys.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ffe479093397a9bf98d10aa4e054c643e64d5f5d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-graph-keys.pbtxt @@ -0,0 +1,140 @@ +path: "tensorflow.GraphKeys" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "ACTIVATIONS" + mtype: "" + } + member { + name: "ASSET_FILEPATHS" + mtype: "" + } + member { + name: "BIASES" + mtype: "" + } + member { + name: "CONCATENATED_VARIABLES" + mtype: "" + } + member { + name: "COND_CONTEXT" + mtype: "" + } + member { + name: "EVAL_STEP" + mtype: "" + } + member { + name: "GLOBAL_STEP" + mtype: "" + } + member { + name: "GLOBAL_VARIABLES" + mtype: "" + } + member { + name: "INIT_OP" + mtype: "" + } + member { + name: "LOCAL_INIT_OP" + mtype: "" + } + member { + name: "LOCAL_RESOURCES" + mtype: "" + } + member { + name: "LOCAL_VARIABLES" + mtype: "" + } + member { + name: "LOSSES" + mtype: "" + } + member { + name: "METRIC_VARIABLES" + mtype: "" + } + member { + name: "MODEL_VARIABLES" + mtype: "" + } + member { + name: "MOVING_AVERAGE_VARIABLES" + mtype: "" + } + member { + name: "QUEUE_RUNNERS" + mtype: "" + } + member { + name: "READY_FOR_LOCAL_INIT_OP" + mtype: "" + } + member { + name: "READY_OP" + mtype: "" + } + member { + name: "REGULARIZATION_LOSSES" + mtype: "" + } + member { + name: "RESOURCES" + mtype: "" + } + member { + name: "SAVEABLE_OBJECTS" + mtype: "" + } + member { + name: "SAVERS" + mtype: "" + } + member { + name: "SUMMARIES" + mtype: "" + } + member { + name: "SUMMARY_OP" + mtype: "" + } + member { + name: "TABLE_INITIALIZERS" + mtype: "" + } + member { + name: "TRAINABLE_RESOURCE_VARIABLES" + mtype: "" + } + member { + name: "TRAINABLE_VARIABLES" + mtype: "" + } + member { + name: "TRAIN_OP" + mtype: "" + } + member { + name: "UPDATE_OPS" + mtype: "" + } + member { + name: "VARIABLES" + mtype: "" + } + member { + name: "WEIGHTS" + mtype: "" + } + member { + name: "WHILE_CONTEXT" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-graph-options.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-graph-options.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a9f99bc171cc3661031981f467f583b122e43476 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-graph-options.pbtxt @@ -0,0 +1,67 @@ +path: "tensorflow.GraphOptions" +tf_proto { + descriptor { + name: "GraphOptions" + field { + name: "enable_recv_scheduling" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "optimizer_options" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.OptimizerOptions" + } + field { + name: "build_cost_model" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "build_cost_model_after" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "infer_shapes" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "place_pruned_graph" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "enable_bfloat16_sendrecv" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "timeline_step" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "rewrite_options" + number: 10 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.RewriterConfig" + } + reserved_range { + start: 1 + end: 2 + } + reserved_name: "skip_common_subexpression_elimination" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-graph.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-graph.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cdaeb55e30865e082054085f47d6a071ebf3affd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-graph.pbtxt @@ -0,0 +1,141 @@ +path: "tensorflow.Graph" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "building_function" + mtype: "" + } + member { + name: "collections" + mtype: "" + } + member { + name: "finalized" + mtype: "" + } + member { + name: "graph_def_versions" + mtype: "" + } + member { + name: "seed" + mtype: "" + } + member { + name: "version" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "add_to_collection" + argspec: "args=[\'self\', \'name\', \'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "add_to_collections" + argspec: "args=[\'self\', \'names\', \'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_default" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_graph_def" + argspec: "args=[\'self\', \'from_version\', \'add_shapes\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "as_graph_element" + argspec: "args=[\'self\', \'obj\', \'allow_tensor\', \'allow_operation\'], varargs=None, keywords=None, defaults=[\'True\', \'True\'], " + } + member_method { + name: "clear_collection" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "colocate_with" + argspec: "args=[\'self\', \'op\', \'ignore_existing\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "container" + argspec: "args=[\'self\', \'container_name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "control_dependencies" + argspec: "args=[\'self\', \'control_inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "create_op" + argspec: "args=[\'self\', \'op_type\', \'inputs\', \'dtypes\', \'input_types\', \'name\', \'attrs\', \'op_def\', \'compute_shapes\', \'compute_device\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'True\', \'True\'], " + } + member_method { + name: "device" + argspec: "args=[\'self\', \'device_name_or_function\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "finalize" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_all_collection_keys" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_collection" + argspec: "args=[\'self\', \'name\', \'scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_collection_ref" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_name_scope" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_operation_by_name" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_operations" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_tensor_by_name" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "gradient_override_map" + argspec: "args=[\'self\', \'op_type_map\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_feedable" + argspec: "args=[\'self\', \'tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_fetchable" + argspec: "args=[\'self\', \'tensor_or_op\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "name_scope" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prevent_feeding" + argspec: "args=[\'self\', \'tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prevent_fetching" + argspec: "args=[\'self\', \'op\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "switch_to_thread_local" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "unique_name" + argspec: "args=[\'self\', \'name\', \'mark_as_used\'], varargs=None, keywords=None, defaults=[\'True\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-histogram-proto.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-histogram-proto.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d4402f330b8a28eaa61eb2b74c9ca412dce06b62 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-histogram-proto.pbtxt @@ -0,0 +1,54 @@ +path: "tensorflow.HistogramProto" +tf_proto { + descriptor { + name: "HistogramProto" + field { + name: "min" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "max" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "num" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "sum" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "sum_squares" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "bucket_limit" + number: 6 + label: LABEL_REPEATED + type: TYPE_DOUBLE + options { + packed: true + } + } + field { + name: "bucket" + number: 7 + label: LABEL_REPEATED + type: TYPE_DOUBLE + options { + packed: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-identity-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-identity-reader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2eda320d6368324f4caea64767fe55aae28494f4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-identity-reader.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.IdentityReader" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "reader_ref" + mtype: "" + } + member { + name: "supports_serialize" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_records_produced" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_work_units_completed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read" + argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_up_to" + argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "restore_state" + argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize_state" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-indexed-slices.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-indexed-slices.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..fee84d85307dffb675b507a31c4f1fda60de869d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-indexed-slices.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.IndexedSlices" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dense_shape" + mtype: "" + } + member { + name: "device" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "indices" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member { + name: "values" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'values\', \'indices\', \'dense_shape\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-interactive-session.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-interactive-session.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0a3b81bf829f48e88e9c48ce26cdbb4207101a16 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-interactive-session.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.InteractiveSession" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "graph" + mtype: "" + } + member { + name: "graph_def" + mtype: "" + } + member { + name: "sess_str" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'target\', \'graph\', \'config\'], varargs=None, keywords=None, defaults=[\'\', \'None\', \'None\'], " + } + member_method { + name: "as_default" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "list_devices" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "make_callable" + argspec: "args=[\'self\', \'fetches\', \'feed_list\', \'accept_options\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "partial_run" + argspec: "args=[\'self\', \'handle\', \'fetches\', \'feed_dict\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "partial_run_setup" + argspec: "args=[\'self\', \'fetches\', \'feeds\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "run" + argspec: "args=[\'self\', \'fetches\', \'feed_dict\', \'options\', \'run_metadata\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-l-m-d-b-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-l-m-d-b-reader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f9b7e9bbca82858ca99e67d70cf93583ca75972f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-l-m-d-b-reader.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.LMDBReader" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "reader_ref" + mtype: "" + } + member { + name: "supports_serialize" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "num_records_produced" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_work_units_completed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read" + argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_up_to" + argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "restore_state" + argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize_state" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-log-message.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-log-message.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5023aa96bf3b4f3f550421db5f41872d9f62b70d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-log-message.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.LogMessage" +tf_proto { + descriptor { + name: "LogMessage" + field { + name: "level" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.LogMessage.Level" + } + field { + name: "message" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + enum_type { + name: "Level" + value { + name: "UNKNOWN" + number: 0 + } + value { + name: "DEBUGGING" + number: 10 + } + value { + name: "INFO" + number: 20 + } + value { + name: "WARN" + number: 30 + } + value { + name: "ERROR" + number: 40 + } + value { + name: "FATAL" + number: 50 + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0ba09bec4b3fa6e9eaf59978beaa958ebc038b4c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-collection-def-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.MetaGraphDef.CollectionDefEntry" +tf_proto { + descriptor { + name: "CollectionDefEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.CollectionDef" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-meta-info-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-meta-info-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..41c62a407b8577288016f2376c35ba6ec1c3c1ca --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-meta-info-def.pbtxt @@ -0,0 +1,50 @@ +path: "tensorflow.MetaGraphDef.MetaInfoDef" +tf_proto { + descriptor { + name: "MetaInfoDef" + field { + name: "meta_graph_version" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "stripped_op_list" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.OpList" + } + field { + name: "any_info" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".google.protobuf.Any" + } + field { + name: "tags" + number: 4 + label: LABEL_REPEATED + type: TYPE_STRING + } + field { + name: "tensorflow_version" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tensorflow_git_version" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "stripped_default_attrs" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..73dc414a779ded3d1f896e743b7f1f1a443352f0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.-signature-def-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.MetaGraphDef.SignatureDefEntry" +tf_proto { + descriptor { + name: "SignatureDefEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SignatureDef" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d71c2358c93e9597726665fdf8f92e648b2ea772 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-meta-graph-def.pbtxt @@ -0,0 +1,133 @@ +path: "tensorflow.MetaGraphDef" +tf_proto { + descriptor { + name: "MetaGraphDef" + field { + name: "meta_info_def" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.MetaGraphDef.MetaInfoDef" + } + field { + name: "graph_def" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.GraphDef" + } + field { + name: "saver_def" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SaverDef" + } + field { + name: "collection_def" + number: 4 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.MetaGraphDef.CollectionDefEntry" + } + field { + name: "signature_def" + number: 5 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.MetaGraphDef.SignatureDefEntry" + } + field { + name: "asset_file_def" + number: 6 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.AssetFileDef" + } + nested_type { + name: "MetaInfoDef" + field { + name: "meta_graph_version" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "stripped_op_list" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.OpList" + } + field { + name: "any_info" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".google.protobuf.Any" + } + field { + name: "tags" + number: 4 + label: LABEL_REPEATED + type: TYPE_STRING + } + field { + name: "tensorflow_version" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tensorflow_git_version" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "stripped_default_attrs" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + } + nested_type { + name: "CollectionDefEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.CollectionDef" + } + options { + map_entry: true + } + } + nested_type { + name: "SignatureDefEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SignatureDef" + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-name-attr-list.-attr-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-name-attr-list.-attr-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b119b208772199e5c3596be142f3e0f62d3ed50e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-name-attr-list.-attr-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.NameAttrList.AttrEntry" +tf_proto { + descriptor { + name: "AttrEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.AttrValue" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-name-attr-list.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-name-attr-list.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..fcdb411ffce9b68ac28696f86ca11a47f9e64e8f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-name-attr-list.pbtxt @@ -0,0 +1,38 @@ +path: "tensorflow.NameAttrList" +tf_proto { + descriptor { + name: "NameAttrList" + field { + name: "name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "attr" + number: 2 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.NameAttrList.AttrEntry" + } + nested_type { + name: "AttrEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.AttrValue" + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-node-def.-attr-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-node-def.-attr-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..622e4c3d0f60ce4842a6fd4cc421551aa795fcbf --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-node-def.-attr-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.NodeDef.AttrEntry" +tf_proto { + descriptor { + name: "AttrEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.AttrValue" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-node-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-node-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..646fa8abb9b22dbd908ff821cbe66a33ad02ba64 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-node-def.pbtxt @@ -0,0 +1,56 @@ +path: "tensorflow.NodeDef" +tf_proto { + descriptor { + name: "NodeDef" + field { + name: "name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "op" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "input" + number: 3 + label: LABEL_REPEATED + type: TYPE_STRING + } + field { + name: "device" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "attr" + number: 5 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.NodeDef.AttrEntry" + } + nested_type { + name: "AttrEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.AttrValue" + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-op-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-op-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7e59615534fc2b3ed4fb128caf8ea092ebfd25f4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-op-error.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.OpError" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\', \'error_code\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-operation.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-operation.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..64240f706983bb2ced63e49937800d2db4e627f2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-operation.pbtxt @@ -0,0 +1,69 @@ +path: "tensorflow.Operation" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "control_inputs" + mtype: "" + } + member { + name: "device" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inputs" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op_def" + mtype: "" + } + member { + name: "outputs" + mtype: "" + } + member { + name: "traceback" + mtype: "" + } + member { + name: "traceback_with_start_lines" + mtype: "" + } + member { + name: "type" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'g\', \'inputs\', \'output_types\', \'control_inputs\', \'input_types\', \'original_op\', \'op_def\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "colocation_groups" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_attr" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "run" + argspec: "args=[\'self\', \'feed_dict\', \'session\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "values" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-optimizer-options.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-optimizer-options.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3ccf9d459b133b48e5456f02e4780ade8d3042c8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-optimizer-options.pbtxt @@ -0,0 +1,74 @@ +path: "tensorflow.OptimizerOptions" +tf_proto { + descriptor { + name: "OptimizerOptions" + field { + name: "do_common_subexpression_elimination" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "do_constant_folding" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "max_folded_constant_in_bytes" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "do_function_inlining" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "opt_level" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.OptimizerOptions.Level" + } + field { + name: "global_jit_level" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.OptimizerOptions.GlobalJitLevel" + } + enum_type { + name: "Level" + value { + name: "L1" + number: 0 + } + value { + name: "L0" + number: -1 + } + } + enum_type { + name: "GlobalJitLevel" + value { + name: "DEFAULT" + number: 0 + } + value { + name: "OFF" + number: -1 + } + value { + name: "ON_1" + number: 1 + } + value { + name: "ON_2" + number: 2 + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-padding-f-i-f-o-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-padding-f-i-f-o-queue.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8fed133561544b91abfc64577e63a7088b43a007 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-padding-f-i-f-o-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.PaddingFIFOQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'dtypes\', \'shapes\', \'names\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'padding_fifo_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-priority-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-priority-queue.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ebb017e81bc29e062d804fbe9f50c62f7b615dab --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-priority-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.PriorityQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'types\', \'shapes\', \'names\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'priority_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-queue-base.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-queue-base.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..761f90989f316611d42580ee911e24bb3d0d2fec --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-queue-base.pbtxt @@ -0,0 +1,65 @@ +path: "tensorflow.QueueBase" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtypes\', \'shapes\', \'names\', \'queue_ref\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-random-shuffle-queue.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-random-shuffle-queue.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f3ca84139311bc05478e3dce876b53f7b9dec883 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-random-shuffle-queue.pbtxt @@ -0,0 +1,66 @@ +path: "tensorflow.RandomShuffleQueue" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtypes" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "names" + mtype: "" + } + member { + name: "queue_ref" + mtype: "" + } + member { + name: "shapes" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'capacity\', \'min_after_dequeue\', \'dtypes\', \'shapes\', \'names\', \'seed\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'random_shuffle_queue\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'cancel_pending_enqueues\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "dequeue" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_many" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "dequeue_up_to" + argspec: "args=[\'self\', \'n\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "enqueue_many" + argspec: "args=[\'self\', \'vals\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_list" + argspec: "args=[\'index\', \'queues\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_closed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-reader-base.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-reader-base.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f6a3ce76a157686becd92e2c7f873bfbc7572116 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-reader-base.pbtxt @@ -0,0 +1,45 @@ +path: "tensorflow.ReaderBase" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "reader_ref" + mtype: "" + } + member { + name: "supports_serialize" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'reader_ref\', \'supports_serialize\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "num_records_produced" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_work_units_completed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read" + argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_up_to" + argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "restore_state" + argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize_state" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-register-gradient.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-register-gradient.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4d6e4137d12d4a1ff283a114d4f0cc5602b0b734 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-register-gradient.pbtxt @@ -0,0 +1,9 @@ +path: "tensorflow.RegisterGradient" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'op_type\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-run-metadata.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-run-metadata.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1287940326c0196e76fff2cf6363622226092504 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-run-metadata.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.RunMetadata" +tf_proto { + descriptor { + name: "RunMetadata" + field { + name: "step_stats" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.StepStats" + } + field { + name: "cost_graph" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.CostGraphDef" + } + field { + name: "partition_graphs" + number: 3 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.GraphDef" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-run-options.-experimental.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-run-options.-experimental.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..537e73aa8969905c108a59688cfd99793ce211f0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-run-options.-experimental.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.RunOptions.Experimental" +tf_proto { + descriptor { + name: "Experimental" + field { + name: "collective_graph_key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-run-options.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-run-options.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cec04a2bf0962455495340da001214914cc8bb36 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-run-options.pbtxt @@ -0,0 +1,83 @@ +path: "tensorflow.RunOptions" +tf_proto { + descriptor { + name: "RunOptions" + field { + name: "trace_level" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.RunOptions.TraceLevel" + } + field { + name: "timeout_in_ms" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "inter_op_thread_pool" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "output_partition_graphs" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "debug_options" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.DebugOptions" + } + field { + name: "report_tensor_allocations_upon_oom" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "experimental" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.RunOptions.Experimental" + } + nested_type { + name: "Experimental" + field { + name: "collective_graph_key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + } + enum_type { + name: "TraceLevel" + value { + name: "NO_TRACE" + number: 0 + } + value { + name: "SOFTWARE_TRACE" + number: 1 + } + value { + name: "HARDWARE_TRACE" + number: 2 + } + value { + name: "FULL_TRACE" + number: 3 + } + } + reserved_range { + start: 4 + end: 5 + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-session-log.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-session-log.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..259f2418740cbfe47cdb4bd871d4f5c6306d25f5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-session-log.pbtxt @@ -0,0 +1,44 @@ +path: "tensorflow.SessionLog" +tf_proto { + descriptor { + name: "SessionLog" + field { + name: "status" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.SessionLog.SessionStatus" + } + field { + name: "checkpoint_path" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "msg" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + enum_type { + name: "SessionStatus" + value { + name: "STATUS_UNSPECIFIED" + number: 0 + } + value { + name: "START" + number: 1 + } + value { + name: "STOP" + number: 2 + } + value { + name: "CHECKPOINT" + number: 3 + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-session.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-session.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1d6b037f9c3540653a8fb18b6508f74b01da66ab --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-session.pbtxt @@ -0,0 +1,55 @@ +path: "tensorflow.Session" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "graph" + mtype: "" + } + member { + name: "graph_def" + mtype: "" + } + member { + name: "sess_str" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'target\', \'graph\', \'config\'], varargs=None, keywords=None, defaults=[\'\', \'None\', \'None\'], " + } + member_method { + name: "as_default" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "list_devices" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "make_callable" + argspec: "args=[\'self\', \'fetches\', \'feed_list\', \'accept_options\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "partial_run" + argspec: "args=[\'self\', \'handle\', \'fetches\', \'feed_dict\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "partial_run_setup" + argspec: "args=[\'self\', \'fetches\', \'feeds\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'target\', \'containers\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "run" + argspec: "args=[\'self\', \'fetches\', \'feed_dict\', \'options\', \'run_metadata\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-sparse-conditional-accumulator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-conditional-accumulator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2260279ad2bcfc246f42b225adc05f7c19f1aac1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-conditional-accumulator.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.SparseConditionalAccumulator" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "accumulator_ref" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'shape\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'sparse_conditional_accumulator\'], " + } + member_method { + name: "apply_grad" + argspec: "args=[\'self\', \'grad_indices\', \'grad_values\', \'grad_shape\', \'local_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\'], " + } + member_method { + name: "apply_indexed_slices_grad" + argspec: "args=[\'self\', \'grad\', \'local_step\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } + member_method { + name: "num_accumulated" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_global_step" + argspec: "args=[\'self\', \'new_global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "take_grad" + argspec: "args=[\'self\', \'num_required\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "take_indexed_slices_grad" + argspec: "args=[\'self\', \'num_required\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-sparse-feature.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-feature.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d875394fb5de73f67629b77c902a2ed2a03dd982 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-feature.pbtxt @@ -0,0 +1,35 @@ +path: "tensorflow.SparseFeature" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "already_sorted" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "index_key" + mtype: "" + } + member { + name: "size" + mtype: "" + } + member { + name: "value_key" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-sparse-tensor-value.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-tensor-value.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d33fd4d5d7b6b3e2eb7454b5326d993c139f0490 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-tensor-value.pbtxt @@ -0,0 +1,26 @@ +path: "tensorflow.SparseTensorValue" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dense_shape" + mtype: "" + } + member { + name: "indices" + mtype: "" + } + member { + name: "values" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-sparse-tensor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-tensor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..eac236d4982b809a0478665096c2b18d69c54184 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-sparse-tensor.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.SparseTensor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dense_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "indices" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member { + name: "values" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'indices\', \'values\', \'dense_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "eval" + argspec: "args=[\'self\', \'feed_dict\', \'session\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_value" + argspec: "args=[\'cls\', \'sparse_tensor_value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_shape" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-summary-metadata.-plugin-data.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-summary-metadata.-plugin-data.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a66b74b315c6132e8f884bd52e7a3b5bd7f52ccd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-summary-metadata.-plugin-data.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.SummaryMetadata.PluginData" +tf_proto { + descriptor { + name: "PluginData" + field { + name: "plugin_name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "content" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-summary-metadata.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-summary-metadata.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c02575b9626c848e9b871d2cc6febb26a5142f08 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-summary-metadata.pbtxt @@ -0,0 +1,40 @@ +path: "tensorflow.SummaryMetadata" +tf_proto { + descriptor { + name: "SummaryMetadata" + field { + name: "plugin_data" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SummaryMetadata.PluginData" + } + field { + name: "display_name" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "summary_description" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + nested_type { + name: "PluginData" + field { + name: "plugin_name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "content" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-summary.-audio.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-summary.-audio.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..94f712073e0d0dda201fcf7adba849dd45a1229b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-summary.-audio.pbtxt @@ -0,0 +1,36 @@ +path: "tensorflow.Summary.Audio" +tf_proto { + descriptor { + name: "Audio" + field { + name: "sample_rate" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + } + field { + name: "num_channels" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "length_frames" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "encoded_audio_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + field { + name: "content_type" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-summary.-image.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-summary.-image.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..fc1acb483b3051cba01f5d9bc8501a61965bbc37 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-summary.-image.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.Summary.Image" +tf_proto { + descriptor { + name: "Image" + field { + name: "height" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "width" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "colorspace" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "encoded_image_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-summary.-value.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-summary.-value.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..feb84b6ee996549ac58aa0e8a4ac560f947b6339 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-summary.-value.pbtxt @@ -0,0 +1,74 @@ +path: "tensorflow.Summary.Value" +tf_proto { + descriptor { + name: "Value" + field { + name: "node_name" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tag" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "metadata" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SummaryMetadata" + } + field { + name: "simple_value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + oneof_index: 0 + } + field { + name: "obsolete_old_style_histogram" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + field { + name: "image" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Image" + oneof_index: 0 + } + field { + name: "histo" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.HistogramProto" + oneof_index: 0 + } + field { + name: "audio" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Audio" + oneof_index: 0 + } + field { + name: "tensor" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorProto" + oneof_index: 0 + } + oneof_decl { + name: "value" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-summary.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-summary.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b2bdff7171804aae114d1e3631e3074b1e4006ba --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-summary.pbtxt @@ -0,0 +1,144 @@ +path: "tensorflow.Summary" +tf_proto { + descriptor { + name: "Summary" + field { + name: "value" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Value" + } + nested_type { + name: "Image" + field { + name: "height" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "width" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "colorspace" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "encoded_image_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + } + nested_type { + name: "Audio" + field { + name: "sample_rate" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + } + field { + name: "num_channels" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "length_frames" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "encoded_audio_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + field { + name: "content_type" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } + nested_type { + name: "Value" + field { + name: "node_name" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tag" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "metadata" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SummaryMetadata" + } + field { + name: "simple_value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + oneof_index: 0 + } + field { + name: "obsolete_old_style_histogram" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + field { + name: "image" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Image" + oneof_index: 0 + } + field { + name: "histo" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.HistogramProto" + oneof_index: 0 + } + field { + name: "audio" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Audio" + oneof_index: 0 + } + field { + name: "tensor" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorProto" + oneof_index: 0 + } + oneof_decl { + name: "value" + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-t-f-record-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-t-f-record-reader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cdf79373919b6c5f26c68996d8f1cf30e8992203 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-t-f-record-reader.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.TFRecordReader" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "reader_ref" + mtype: "" + } + member { + name: "supports_serialize" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "num_records_produced" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_work_units_completed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read" + argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_up_to" + argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "restore_state" + argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize_state" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-tensor-array.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-array.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ed088c41ed3fc444fb9e45919769950f1984e3e8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-array.pbtxt @@ -0,0 +1,69 @@ +path: "tensorflow.TensorArray" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dtype" + mtype: "" + } + member { + name: "flow" + mtype: "" + } + member { + name: "handle" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'size\', \'dynamic_size\', \'clear_after_read\', \'tensor_array_name\', \'handle\', \'flow\', \'infer_shape\', \'element_shape\', \'colocate_with_first_write_call\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "concat" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "gather" + argspec: "args=[\'self\', \'indices\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "grad" + argspec: "args=[\'self\', \'source\', \'flow\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "identity" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "read" + argspec: "args=[\'self\', \'index\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "scatter" + argspec: "args=[\'self\', \'indices\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "split" + argspec: "args=[\'self\', \'value\', \'lengths\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "stack" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "unstack" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "write" + argspec: "args=[\'self\', \'index\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-tensor-info.-coo-sparse.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-info.-coo-sparse.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0064c8460cb374f1e3f108085a2efed4131dd205 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-info.-coo-sparse.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.TensorInfo.CooSparse" +tf_proto { + descriptor { + name: "CooSparse" + field { + name: "values_tensor_name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "indices_tensor_name" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "dense_shape_tensor_name" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-tensor-info.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-info.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..63566c808e55cb4d3b630f0a017fa3a2c8a30de3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-info.pbtxt @@ -0,0 +1,59 @@ +path: "tensorflow.TensorInfo" +tf_proto { + descriptor { + name: "TensorInfo" + field { + name: "name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + oneof_index: 0 + } + field { + name: "coo_sparse" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorInfo.CooSparse" + oneof_index: 0 + } + field { + name: "dtype" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.DataType" + } + field { + name: "tensor_shape" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorShapeProto" + } + nested_type { + name: "CooSparse" + field { + name: "values_tensor_name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "indices_tensor_name" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "dense_shape_tensor_name" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } + oneof_decl { + name: "encoding" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-tensor-shape.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-shape.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8e3598fb2470b327e6e3601969f055d4907f614a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-tensor-shape.pbtxt @@ -0,0 +1,77 @@ +path: "tensorflow.TensorShape" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dims" + mtype: "" + } + member { + name: "ndims" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dims\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_list" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_proto" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "assert_has_rank" + argspec: "args=[\'self\', \'rank\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "assert_is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "assert_is_fully_defined" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "assert_same_rank" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "concatenate" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_compatible_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_fully_defined" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "merge_with" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "most_specific_compatible_shape" + argspec: "args=[\'self\', \'other\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "num_elements" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_rank" + argspec: "args=[\'self\', \'rank\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_rank_at_least" + argspec: "args=[\'self\', \'rank\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_rank_at_most" + argspec: "args=[\'self\', \'rank\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-tensor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-tensor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..38d19bb5374037981c01b29053ab8d05b551eb84 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-tensor.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.Tensor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "OVERLOADABLE_OPERATORS" + mtype: "" + } + member { + name: "device" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "value_index" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'op\', \'value_index\', \'dtype\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "consumers" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "eval" + argspec: "args=[\'self\', \'feed_dict\', \'session\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "get_shape" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_shape" + argspec: "args=[\'self\', \'shape\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-text-line-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-text-line-reader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e9779f07620d2cc1ef3b0ff1b2d32796fc10834a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-text-line-reader.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.TextLineReader" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "reader_ref" + mtype: "" + } + member { + name: "supports_serialize" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'skip_header_lines\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "num_records_produced" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_work_units_completed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read" + argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_up_to" + argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "restore_state" + argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize_state" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-var-len-feature.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-var-len-feature.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..54b66f43f8e7d714e82ae9d68b37ac348c476c97 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-var-len-feature.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.VarLenFeature" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "dtype" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-variable-aggregation.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-variable-aggregation.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..36b534af360835e3c1cbd1f0fb12a38c42232abf --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-variable-aggregation.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.VariableAggregation" +tf_class { + is_instance: "" + member { + name: "MEAN" + mtype: "" + } + member { + name: "NONE" + mtype: "" + } + member { + name: "SUM" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-variable-scope.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-variable-scope.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c13eb7b8bb9474f3534582c8af8c3ee4b6c7e076 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-variable-scope.pbtxt @@ -0,0 +1,105 @@ +path: "tensorflow.VariableScope" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "caching_device" + mtype: "" + } + member { + name: "constraint" + mtype: "" + } + member { + name: "custom_getter" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "initializer" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "original_name_scope" + mtype: "" + } + member { + name: "partitioner" + mtype: "" + } + member { + name: "regularizer" + mtype: "" + } + member { + name: "reuse" + mtype: "" + } + member { + name: "use_resource" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'reuse\', \'name\', \'initializer\', \'regularizer\', \'caching_device\', \'partitioner\', \'custom_getter\', \'name_scope\', \'dtype\', \'use_resource\', \'constraint\'], varargs=None, keywords=None, defaults=[\'\', \'None\', \'None\', \'None\', \'None\', \'None\', \'\', \"\", \'None\', \'None\'], " + } + member_method { + name: "get_collection" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable" + argspec: "args=[\'self\', \'var_store\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'reuse\', \'trainable\', \'collections\', \'caching_device\', \'partitioner\', \'validate_shape\', \'use_resource\', \'custom_getter\', \'constraint\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "global_variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "local_variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "reuse_variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_caching_device" + argspec: "args=[\'self\', \'caching_device\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_custom_getter" + argspec: "args=[\'self\', \'custom_getter\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_dtype" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_initializer" + argspec: "args=[\'self\', \'initializer\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_partitioner" + argspec: "args=[\'self\', \'partitioner\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_regularizer" + argspec: "args=[\'self\', \'regularizer\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_use_resource" + argspec: "args=[\'self\', \'use_resource\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "trainable_variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-variable-synchronization.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-variable-synchronization.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7589bb28888774839a3011e1e5581f004313f81d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-variable-synchronization.pbtxt @@ -0,0 +1,20 @@ +path: "tensorflow.VariableSynchronization" +tf_class { + is_instance: "" + member { + name: "AUTO" + mtype: "" + } + member { + name: "NONE" + mtype: "" + } + member { + name: "ON_READ" + mtype: "" + } + member { + name: "ON_WRITE" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-variable.-save-slice-info.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-variable.-save-slice-info.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ac3ccd468b216ab817c9ed05dcb292eaf1f44398 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-variable.-save-slice-info.pbtxt @@ -0,0 +1,17 @@ +path: "tensorflow.Variable.SaveSliceInfo" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "spec" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'full_name\', \'full_shape\', \'var_offset\', \'var_shape\', \'save_slice_info_def\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "to_proto" + argspec: "args=[\'self\', \'export_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-variable.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-variable.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e841c4ad8904ae1ae49881b47648b901a4abf778 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-variable.pbtxt @@ -0,0 +1,110 @@ +path: "tensorflow.Variable" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "SaveSliceInfo" + mtype: "" + } + member { + name: "constraint" + mtype: "" + } + member { + name: "device" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "initial_value" + mtype: "" + } + member { + name: "initializer" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "trainable" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'initial_value\', \'trainable\', \'collections\', \'validate_shape\', \'caching_device\', \'name\', \'variable_def\', \'dtype\', \'expected_shape\', \'import_scope\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], " + } + member_method { + name: "assign" + argspec: "args=[\'self\', \'value\', \'use_locking\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "assign_add" + argspec: "args=[\'self\', \'delta\', \'use_locking\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "assign_sub" + argspec: "args=[\'self\', \'delta\', \'use_locking\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "count_up_to" + argspec: "args=[\'self\', \'limit\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "eval" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "from_proto" + argspec: "args=[\'variable_def\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_shape" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "initialized_value" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "load" + argspec: "args=[\'self\', \'value\', \'session\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_value" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "scatter_sub" + argspec: "args=[\'self\', \'sparse_delta\', \'use_locking\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "set_shape" + argspec: "args=[\'self\', \'shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "to_proto" + argspec: "args=[\'self\', \'export_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "value" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.-whole-file-reader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.-whole-file-reader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4ac759891c62ae44bf8f8c365da75664f2e65ce2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.-whole-file-reader.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.WholeFileReader" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "reader_ref" + mtype: "" + } + member { + name: "supports_serialize" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_records_produced" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "num_work_units_completed" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read" + argspec: "args=[\'self\', \'queue\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_up_to" + argspec: "args=[\'self\', \'queue\', \'num_records\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reset" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "restore_state" + argspec: "args=[\'self\', \'state\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize_state" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.app.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.app.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..85044a8987963126ae12aaa0e5eb5d1ecc134539 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.app.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.app" +tf_module { + member { + name: "flags" + mtype: "" + } + member_method { + name: "run" + argspec: "args=[\'main\', \'argv\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.bitwise.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.bitwise.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..01cbd55c5d2e1b6fa3148af956217c3664864eaa --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.bitwise.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.bitwise" +tf_module { + member_method { + name: "bitwise_and" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bitwise_or" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bitwise_xor" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "invert" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "left_shift" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "right_shift" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.compat.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.compat.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f1d760603e981a0b9a72fdc379dc81932ac71d67 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.compat.pbtxt @@ -0,0 +1,47 @@ +path: "tensorflow.compat" +tf_module { + member { + name: "bytes_or_text_types" + mtype: "" + } + member { + name: "complex_types" + mtype: "" + } + member { + name: "integral_types" + mtype: "" + } + member { + name: "real_types" + mtype: "" + } + member_method { + name: "as_bytes" + argspec: "args=[\'bytes_or_text\', \'encoding\'], varargs=None, keywords=None, defaults=[\'utf-8\'], " + } + member_method { + name: "as_str" + argspec: "args=[\'bytes_or_text\', \'encoding\'], varargs=None, keywords=None, defaults=[\'utf-8\'], " + } + member_method { + name: "as_str_any" + argspec: "args=[\'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_text" + argspec: "args=[\'bytes_or_text\', \'encoding\'], varargs=None, keywords=None, defaults=[\'utf-8\'], " + } + member_method { + name: "forward_compatibility_horizon" + argspec: "args=[\'year\', \'month\', \'day\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "forward_compatible" + argspec: "args=[\'year\', \'month\', \'day\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "path_to_str" + argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.constant_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.constant_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..00ec669b1685f3cbdacd676bac61755bebb9f6da --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.constant_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.constant_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value\', \'dtype\', \'verify_shape\'], varargs=None, keywords=None, defaults=[\'0\', \"\", \'False\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..af08c88d3333fa897c38cc2f6530a9c5cda15342 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.data.Dataset.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..834f0954d5bba655a8eb923672d89bac6bb80808 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.pbtxt @@ -0,0 +1,117 @@ +path: "tensorflow.data.Dataset" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "output_classes" + mtype: "" + } + member { + name: "output_shapes" + mtype: "" + } + member { + name: "output_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'transformation_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "batch" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "cache" + argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], " + } + member_method { + name: "concatenate" + argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "filter" + argspec: "args=[\'self\', \'predicate\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flat_map" + argspec: "args=[\'self\', \'map_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_generator" + argspec: "args=[\'generator\', \'output_types\', \'output_shapes\', \'args\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_sparse_tensor_slices" + argspec: "args=[\'sparse_tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensor_slices" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensors" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "interleave" + argspec: "args=[\'self\', \'map_func\', \'cycle_length\', \'block_length\'], varargs=None, keywords=None, defaults=[\'1\'], " + } + member_method { + name: "list_files" + argspec: "args=[\'file_pattern\', \'shuffle\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "make_initializable_iterator" + argspec: "args=[\'self\', \'shared_name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "make_one_shot_iterator" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "map" + argspec: "args=[\'self\', \'map_func\', \'num_parallel_calls\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "padded_batch" + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "prefetch" + argspec: "args=[\'self\', \'buffer_size\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "range" + argspec: "args=[], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "repeat" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "shard" + argspec: "args=[\'self\', \'num_shards\', \'index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "shuffle" + argspec: "args=[\'self\', \'buffer_size\', \'seed\', \'reshuffle_each_iteration\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "skip" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "take" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "zip" + argspec: "args=[\'datasets\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-fixed-length-record-dataset.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-fixed-length-record-dataset.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f384323fc89bb7d21309e86ddaab2e6e1f9f212b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-fixed-length-record-dataset.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.data.FixedLengthRecordDataset.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-fixed-length-record-dataset.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-fixed-length-record-dataset.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4d854a4ceea3907d7d795d0a19d081f4069c9ba9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-fixed-length-record-dataset.pbtxt @@ -0,0 +1,118 @@ +path: "tensorflow.data.FixedLengthRecordDataset" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "output_classes" + mtype: "" + } + member { + name: "output_shapes" + mtype: "" + } + member { + name: "output_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'filenames\', \'record_bytes\', \'header_bytes\', \'footer_bytes\', \'buffer_size\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'transformation_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "batch" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "cache" + argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], " + } + member_method { + name: "concatenate" + argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "filter" + argspec: "args=[\'self\', \'predicate\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flat_map" + argspec: "args=[\'self\', \'map_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_generator" + argspec: "args=[\'generator\', \'output_types\', \'output_shapes\', \'args\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_sparse_tensor_slices" + argspec: "args=[\'sparse_tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensor_slices" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensors" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "interleave" + argspec: "args=[\'self\', \'map_func\', \'cycle_length\', \'block_length\'], varargs=None, keywords=None, defaults=[\'1\'], " + } + member_method { + name: "list_files" + argspec: "args=[\'file_pattern\', \'shuffle\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "make_initializable_iterator" + argspec: "args=[\'self\', \'shared_name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "make_one_shot_iterator" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "map" + argspec: "args=[\'self\', \'map_func\', \'num_parallel_calls\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "padded_batch" + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "prefetch" + argspec: "args=[\'self\', \'buffer_size\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "range" + argspec: "args=[], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "repeat" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "shard" + argspec: "args=[\'self\', \'num_shards\', \'index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "shuffle" + argspec: "args=[\'self\', \'buffer_size\', \'seed\', \'reshuffle_each_iteration\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "skip" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "take" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "zip" + argspec: "args=[\'datasets\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-iterator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-iterator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1f9aeb6ad62e1030c6e78f731fb5e05b876899e6 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-iterator.pbtxt @@ -0,0 +1,45 @@ +path: "tensorflow.data.Iterator" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "initializer" + mtype: "" + } + member { + name: "output_classes" + mtype: "" + } + member { + name: "output_shapes" + mtype: "" + } + member { + name: "output_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'iterator_resource\', \'initializer\', \'output_types\', \'output_shapes\', \'output_classes\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_string_handle" + argspec: "args=[\'string_handle\', \'output_types\', \'output_shapes\', \'output_classes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_structure" + argspec: "args=[\'output_types\', \'output_shapes\', \'shared_name\', \'output_classes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "get_next" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "make_initializer" + argspec: "args=[\'self\', \'dataset\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "string_handle" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-t-f-record-dataset.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-t-f-record-dataset.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b12dec8a70be5e0cd8346785b48f56b15155dd02 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-t-f-record-dataset.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.data.TFRecordDataset.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-t-f-record-dataset.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-t-f-record-dataset.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..601f095a60ae481b895a535efa37341611499499 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-t-f-record-dataset.pbtxt @@ -0,0 +1,118 @@ +path: "tensorflow.data.TFRecordDataset" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "output_classes" + mtype: "" + } + member { + name: "output_shapes" + mtype: "" + } + member { + name: "output_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\', \'num_parallel_reads\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'transformation_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "batch" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "cache" + argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], " + } + member_method { + name: "concatenate" + argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "filter" + argspec: "args=[\'self\', \'predicate\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flat_map" + argspec: "args=[\'self\', \'map_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_generator" + argspec: "args=[\'generator\', \'output_types\', \'output_shapes\', \'args\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_sparse_tensor_slices" + argspec: "args=[\'sparse_tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensor_slices" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensors" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "interleave" + argspec: "args=[\'self\', \'map_func\', \'cycle_length\', \'block_length\'], varargs=None, keywords=None, defaults=[\'1\'], " + } + member_method { + name: "list_files" + argspec: "args=[\'file_pattern\', \'shuffle\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "make_initializable_iterator" + argspec: "args=[\'self\', \'shared_name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "make_one_shot_iterator" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "map" + argspec: "args=[\'self\', \'map_func\', \'num_parallel_calls\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "padded_batch" + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "prefetch" + argspec: "args=[\'self\', \'buffer_size\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "range" + argspec: "args=[], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "repeat" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "shard" + argspec: "args=[\'self\', \'num_shards\', \'index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "shuffle" + argspec: "args=[\'self\', \'buffer_size\', \'seed\', \'reshuffle_each_iteration\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "skip" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "take" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "zip" + argspec: "args=[\'datasets\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-text-line-dataset.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-text-line-dataset.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7ddcdce2663ca0ef6409fb3ab3c29555948d7302 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-text-line-dataset.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.data.TextLineDataset.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.-text-line-dataset.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.-text-line-dataset.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..587829a4c078e8ab945f66c64f5adad21223dfb1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.-text-line-dataset.pbtxt @@ -0,0 +1,118 @@ +path: "tensorflow.data.TextLineDataset" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "output_classes" + mtype: "" + } + member { + name: "output_shapes" + mtype: "" + } + member { + name: "output_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'filenames\', \'compression_type\', \'buffer_size\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'transformation_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "batch" + argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "cache" + argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], " + } + member_method { + name: "concatenate" + argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "filter" + argspec: "args=[\'self\', \'predicate\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flat_map" + argspec: "args=[\'self\', \'map_func\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_generator" + argspec: "args=[\'generator\', \'output_types\', \'output_shapes\', \'args\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "from_sparse_tensor_slices" + argspec: "args=[\'sparse_tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensor_slices" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_tensors" + argspec: "args=[\'tensors\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "interleave" + argspec: "args=[\'self\', \'map_func\', \'cycle_length\', \'block_length\'], varargs=None, keywords=None, defaults=[\'1\'], " + } + member_method { + name: "list_files" + argspec: "args=[\'file_pattern\', \'shuffle\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "make_initializable_iterator" + argspec: "args=[\'self\', \'shared_name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "make_one_shot_iterator" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "map" + argspec: "args=[\'self\', \'map_func\', \'num_parallel_calls\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "padded_batch" + argspec: "args=[\'self\', \'batch_size\', \'padded_shapes\', \'padding_values\', \'drop_remainder\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "prefetch" + argspec: "args=[\'self\', \'buffer_size\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "range" + argspec: "args=[], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "repeat" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "shard" + argspec: "args=[\'self\', \'num_shards\', \'index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "shuffle" + argspec: "args=[\'self\', \'buffer_size\', \'seed\', \'reshuffle_each_iteration\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "skip" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "take" + argspec: "args=[\'self\', \'count\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "zip" + argspec: "args=[\'datasets\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..56fb270a49943a916012ccfcaf816a9156f4fed8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.data" +tf_module { + member { + name: "Dataset" + mtype: "" + } + member { + name: "FixedLengthRecordDataset" + mtype: "" + } + member { + name: "Iterator" + mtype: "" + } + member { + name: "TFRecordDataset" + mtype: "" + } + member { + name: "TextLineDataset" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.debugging.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.debugging.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d9efe97821904f5891148b72a0c31e02c9562bd7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.debugging.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.debugging" +tf_module { + member_method { + name: "check_numerics" + argspec: "args=[\'tensor\', \'message\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_finite" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_inf" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "is_nan" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-bernoulli.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-bernoulli.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ca96f4eaece0020235d24901f51306a65676c1c9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-bernoulli.pbtxt @@ -0,0 +1,143 @@ +path: "tensorflow.distributions.Bernoulli" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "logits" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "probs" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'logits\', \'probs\', \'dtype\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \"\", \'False\', \'True\', \'Bernoulli\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-beta.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-beta.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d0508acd9f4f6c190b205301223599cf5b027955 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-beta.pbtxt @@ -0,0 +1,147 @@ +path: "tensorflow.distributions.Beta" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "concentration0" + mtype: "" + } + member { + name: "concentration1" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "total_concentration" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'concentration1\', \'concentration0\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'True\', \'Beta\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-categorical.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-categorical.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ff0fbb56cd4b9e4c288a168a7c3d9e83c552b0e2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-categorical.pbtxt @@ -0,0 +1,147 @@ +path: "tensorflow.distributions.Categorical" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "event_size" + mtype: "" + } + member { + name: "logits" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "probs" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'logits\', \'probs\', \'dtype\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \"\", \'False\', \'True\', \'Categorical\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-dirichlet-multinomial.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-dirichlet-multinomial.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d75e4a2f88b29ff7f638d72f98876a230b191dce --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-dirichlet-multinomial.pbtxt @@ -0,0 +1,147 @@ +path: "tensorflow.distributions.DirichletMultinomial" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "concentration" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "total_concentration" + mtype: "" + } + member { + name: "total_count" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'total_count\', \'concentration\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'DirichletMultinomial\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-dirichlet.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-dirichlet.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b838b9ae21decba0323211f08d09fe373ababf23 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-dirichlet.pbtxt @@ -0,0 +1,143 @@ +path: "tensorflow.distributions.Dirichlet" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "concentration" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "total_concentration" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'concentration\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Dirichlet\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-distribution.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-distribution.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6f06b7d50dd9f5f405673d572503ff549f148f33 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-distribution.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.distributions.Distribution" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'reparameterization_type\', \'validate_args\', \'allow_nan_stats\', \'parameters\', \'graph_parents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-exponential.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-exponential.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d34f9cde5d4d4161883f6d1b4646f22f054d16ad --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-exponential.pbtxt @@ -0,0 +1,144 @@ +path: "tensorflow.distributions.Exponential" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "concentration" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "rate" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'rate\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Exponential\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-gamma.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-gamma.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..df268b8d99eb6bf22264ddb63231074413686efa --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-gamma.pbtxt @@ -0,0 +1,143 @@ +path: "tensorflow.distributions.Gamma" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "concentration" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "rate" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'concentration\', \'rate\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Gamma\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-laplace.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-laplace.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..303dcb4ed3bf8416b822bb010c2e87e8ef03b7c9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-laplace.pbtxt @@ -0,0 +1,143 @@ +path: "tensorflow.distributions.Laplace" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "loc" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "scale" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'loc\', \'scale\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Laplace\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-multinomial.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-multinomial.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ecda8acb15c49c390eaae203a0082e78e53499bd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-multinomial.pbtxt @@ -0,0 +1,147 @@ +path: "tensorflow.distributions.Multinomial" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "logits" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "probs" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "total_count" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'total_count\', \'logits\', \'probs\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'True\', \'Multinomial\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..92b9eeea223b488cda1ebcabd31ec808e78fcf70 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-normal.pbtxt @@ -0,0 +1,143 @@ +path: "tensorflow.distributions.Normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "loc" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "scale" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'loc\', \'scale\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'Normal\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-register-k-l.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-register-k-l.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e3db443c2bdaa70f7651126a30caf2062a3c6f67 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-register-k-l.pbtxt @@ -0,0 +1,9 @@ +path: "tensorflow.distributions.RegisterKL" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dist_cls_a\', \'dist_cls_b\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-reparameterization-type.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-reparameterization-type.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..02e8d576ddd00aa21005fa39cd323a92392bf75a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-reparameterization-type.pbtxt @@ -0,0 +1,9 @@ +path: "tensorflow.distributions.ReparameterizationType" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'rep_type\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-student-t.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-student-t.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9aa7f9a63465c78f79ae4a8a11bc63d92d027dab --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-student-t.pbtxt @@ -0,0 +1,147 @@ +path: "tensorflow.distributions.StudentT" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "df" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "loc" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "scale" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'df\', \'loc\', \'scale\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'StudentT\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.-uniform.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-uniform.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d1b9d3069629c552d6c6048642934f422a13dce7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.-uniform.pbtxt @@ -0,0 +1,147 @@ +path: "tensorflow.distributions.Uniform" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "allow_nan_stats" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "event_shape" + mtype: "" + } + member { + name: "high" + mtype: "" + } + member { + name: "low" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "parameters" + mtype: "" + } + member { + name: "reparameterization_type" + mtype: "" + } + member { + name: "validate_args" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'low\', \'high\', \'validate_args\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'False\', \'True\', \'Uniform\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'cdf\'], " + } + member_method { + name: "copy" + argspec: "args=[\'self\'], varargs=None, keywords=override_parameters_kwargs, defaults=None" + } + member_method { + name: "covariance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'covariance\'], " + } + member_method { + name: "cross_entropy" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'cross_entropy\'], " + } + member_method { + name: "entropy" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'entropy\'], " + } + member_method { + name: "event_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'event_shape_tensor\'], " + } + member_method { + name: "is_scalar_batch" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_batch\'], " + } + member_method { + name: "is_scalar_event" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'is_scalar_event\'], " + } + member_method { + name: "kl_divergence" + argspec: "args=[\'self\', \'other\', \'name\'], varargs=None, keywords=None, defaults=[\'kl_divergence\'], " + } + member_method { + name: "log_cdf" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_cdf\'], " + } + member_method { + name: "log_prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_prob\'], " + } + member_method { + name: "log_survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'log_survival_function\'], " + } + member_method { + name: "mean" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mean\'], " + } + member_method { + name: "mode" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'mode\'], " + } + member_method { + name: "param_shapes" + argspec: "args=[\'cls\', \'sample_shape\', \'name\'], varargs=None, keywords=None, defaults=[\'DistributionParamShapes\'], " + } + member_method { + name: "param_static_shapes" + argspec: "args=[\'cls\', \'sample_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "prob" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'prob\'], " + } + member_method { + name: "quantile" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'quantile\'], " + } + member_method { + name: "range" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range\'], " + } + member_method { + name: "sample" + argspec: "args=[\'self\', \'sample_shape\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'()\', \'None\', \'sample\'], " + } + member_method { + name: "stddev" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'stddev\'], " + } + member_method { + name: "survival_function" + argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=None, defaults=[\'survival_function\'], " + } + member_method { + name: "variance" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'variance\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.distributions.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.distributions.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..90b60ef074dd2eaf911291e6c725b98e2891e728 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.distributions.pbtxt @@ -0,0 +1,75 @@ +path: "tensorflow.distributions" +tf_module { + member { + name: "Bernoulli" + mtype: "" + } + member { + name: "Beta" + mtype: "" + } + member { + name: "Categorical" + mtype: "" + } + member { + name: "Dirichlet" + mtype: "" + } + member { + name: "DirichletMultinomial" + mtype: "" + } + member { + name: "Distribution" + mtype: "" + } + member { + name: "Exponential" + mtype: "" + } + member { + name: "FULLY_REPARAMETERIZED" + mtype: "" + } + member { + name: "Gamma" + mtype: "" + } + member { + name: "Laplace" + mtype: "" + } + member { + name: "Multinomial" + mtype: "" + } + member { + name: "NOT_REPARAMETERIZED" + mtype: "" + } + member { + name: "Normal" + mtype: "" + } + member { + name: "RegisterKL" + mtype: "" + } + member { + name: "ReparameterizationType" + mtype: "" + } + member { + name: "StudentT" + mtype: "" + } + member { + name: "Uniform" + mtype: "" + } + member_method { + name: "kl_divergence" + argspec: "args=[\'distribution_a\', \'distribution_b\', \'allow_nan_stats\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..98e1feed002ceb4f455aa5ec361d26a159fdad1a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.dtypes.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.dtypes" +tf_module { + member_method { + name: "as_string" + argspec: "args=[\'input\', \'precision\', \'scientific\', \'shortest\', \'width\', \'fill\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'False\', \'False\', \'-1\', \'\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-aborted-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-aborted-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ea9186b0b9d5fecff35b43d2ef5dc0f2c99f3412 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-aborted-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.AbortedError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-already-exists-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-already-exists-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4e155081dd28a8a859e940338f70e9db24dff0d2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-already-exists-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.AlreadyExistsError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-cancelled-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-cancelled-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b02a0e023aaecb5930c45aa35dbb1f0d97432cea --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-cancelled-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.CancelledError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-data-loss-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-data-loss-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c1fa66342a7022031faec68f65de9cb0ae28bcba --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-data-loss-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.DataLossError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-deadline-exceeded-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-deadline-exceeded-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8e037936191b5d52c2422f2587e7196614104d6b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-deadline-exceeded-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.DeadlineExceededError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-failed-precondition-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-failed-precondition-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..384d4b534c6ea05f9ce0fdbad32dcaf02db0ac58 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-failed-precondition-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.FailedPreconditionError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-internal-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-internal-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ac5c4d7879bbe5b040209abee088b78b15ae6f5f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-internal-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.InternalError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-invalid-argument-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-invalid-argument-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..161edd4a7c5763fe6fd96d80024065a3e3138de3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-invalid-argument-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.InvalidArgumentError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-not-found-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-not-found-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1e64730ac6d7c0d3517a8a072b9622691a7e77d7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-not-found-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.NotFoundError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-op-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-op-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b1f14c0457d95fd09fe485ae241ba9a9852879db --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-op-error.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.errors.OpError" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\', \'error_code\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-out-of-range-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-out-of-range-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6365e472868607d1ca4056859d56d16d022b3128 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-out-of-range-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.OutOfRangeError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-permission-denied-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-permission-denied-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..dc8a66f9eadf3985b6805afa3adf729e7c24f3d8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-permission-denied-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.PermissionDeniedError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-resource-exhausted-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-resource-exhausted-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..85bb384b46992c4565b14b3c13c8115fb1998abd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-resource-exhausted-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.ResourceExhaustedError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-unauthenticated-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unauthenticated-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d57d7ac2f20b98f464c5a67abdd926cd20de5e32 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unauthenticated-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.UnauthenticatedError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-unavailable-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unavailable-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cc33e6ed8d1a9b7160b321c18735690b7b52a7d4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unavailable-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.UnavailableError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-unimplemented-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unimplemented-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b8c2e22dbd7e66909f4ba613ba7f19b6abbaa4b9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unimplemented-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.UnimplementedError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.-unknown-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unknown-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8ffcfae95b8c7ccea29dd5b7b75e8c74fa245f7e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.-unknown-error.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.errors.UnknownError" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "error_code" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member { + name: "node_def" + mtype: "" + } + member { + name: "op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'node_def\', \'op\', \'message\', \'error_code\'], varargs=None, keywords=None, defaults=[\'2\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c5fe49baab7da5936184aa4b823de7d0a6dc33c5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.pbtxt @@ -0,0 +1,151 @@ +path: "tensorflow.errors" +tf_module { + member { + name: "ABORTED" + mtype: "" + } + member { + name: "ALREADY_EXISTS" + mtype: "" + } + member { + name: "AbortedError" + mtype: "" + } + member { + name: "AlreadyExistsError" + mtype: "" + } + member { + name: "CANCELLED" + mtype: "" + } + member { + name: "CancelledError" + mtype: "" + } + member { + name: "DATA_LOSS" + mtype: "" + } + member { + name: "DEADLINE_EXCEEDED" + mtype: "" + } + member { + name: "DataLossError" + mtype: "" + } + member { + name: "DeadlineExceededError" + mtype: "" + } + member { + name: "FAILED_PRECONDITION" + mtype: "" + } + member { + name: "FailedPreconditionError" + mtype: "" + } + member { + name: "INTERNAL" + mtype: "" + } + member { + name: "INVALID_ARGUMENT" + mtype: "" + } + member { + name: "InternalError" + mtype: "" + } + member { + name: "InvalidArgumentError" + mtype: "" + } + member { + name: "NOT_FOUND" + mtype: "" + } + member { + name: "NotFoundError" + mtype: "" + } + member { + name: "OK" + mtype: "" + } + member { + name: "OUT_OF_RANGE" + mtype: "" + } + member { + name: "OpError" + mtype: "" + } + member { + name: "OutOfRangeError" + mtype: "" + } + member { + name: "PERMISSION_DENIED" + mtype: "" + } + member { + name: "PermissionDeniedError" + mtype: "" + } + member { + name: "RESOURCE_EXHAUSTED" + mtype: "" + } + member { + name: "ResourceExhaustedError" + mtype: "" + } + member { + name: "UNAUTHENTICATED" + mtype: "" + } + member { + name: "UNAVAILABLE" + mtype: "" + } + member { + name: "UNIMPLEMENTED" + mtype: "" + } + member { + name: "UNKNOWN" + mtype: "" + } + member { + name: "UnauthenticatedError" + mtype: "" + } + member { + name: "UnavailableError" + mtype: "" + } + member { + name: "UnimplementedError" + mtype: "" + } + member { + name: "UnknownError" + mtype: "" + } + member { + name: "raise_exception_on_not_ok_status" + mtype: "" + } + member_method { + name: "error_code_from_exception_type" + argspec: "args=[\'cls\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "exception_type_from_error_code" + argspec: "args=[\'error_code\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.errors.raise_exception_on_not_ok_status.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.errors.raise_exception_on_not_ok_status.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5d25ec769ad7b086ec05f11f5676766380476012 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.errors.raise_exception_on_not_ok_status.pbtxt @@ -0,0 +1,8 @@ +path: "tensorflow.errors.raise_exception_on_not_ok_status" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-baseline-classifier.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-baseline-classifier.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cf22e39d4c8ab915ea9507960bf28ebc09e4e5aa --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-baseline-classifier.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.BaselineClassifier" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\', \'weighted_sum\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-baseline-regressor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-baseline-regressor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a363bceae3b57d879b4b8e5a8205a21c92e8835a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-baseline-regressor.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.BaselineRegressor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\', \'loss_reduction\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\', \'weighted_sum\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-best-exporter.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-best-exporter.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9694268199a29c51f37bc73a2f92715c78854a2f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-best-exporter.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.estimator.BestExporter" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'serving_input_receiver_fn\', \'event_file_pattern\', \'compare_fn\', \'assets_extra\', \'as_text\', \'exports_to_keep\'], varargs=None, keywords=None, defaults=[\'best_exporter\', \'None\', \'eval/*.tfevents.*\', \'\', \'None\', \'False\', \'5\'], " + } + member_method { + name: "export" + argspec: "args=[\'self\', \'estimator\', \'export_path\', \'checkpoint_path\', \'eval_result\', \'is_the_final_export\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-boosted-trees-classifier.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-boosted-trees-classifier.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9dbb5d16a4e903a755c86bd0a6241180d1999f4d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-boosted-trees-classifier.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.BoostedTreesClassifier" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-boosted-trees-regressor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-boosted-trees-regressor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..34a30c2874b90285706c9df6bec8cbbdc3451fe4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-boosted-trees-regressor.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.BoostedTreesRegressor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'label_dimension\', \'weight_column\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-classifier.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-classifier.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0c6b7e4a821ad47c20b6f6074b575bf83c403653 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-classifier.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.DNNClassifier" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9c1c072124083006a1dd8e04526755dd980ba85a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-linear-combined-classifier.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.DNNLinearCombinedClassifier" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\', \'linear_sparse_combiner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'2\', \'None\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\', \'sum\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7391d4b07a7e79541091b94fe4a9f38f42d6f68a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-linear-combined-regressor.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.DNNLinearCombinedRegressor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'model_dir\', \'linear_feature_columns\', \'linear_optimizer\', \'dnn_feature_columns\', \'dnn_optimizer\', \'dnn_hidden_units\', \'dnn_activation_fn\', \'dnn_dropout\', \'label_dimension\', \'weight_column\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\', \'linear_sparse_combiner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'Ftrl\', \'None\', \'Adagrad\', \'None\', \'\', \'None\', \'1\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\', \'sum\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-regressor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-regressor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f50e375f7cd392567f5c87536c95eb1f6809bc97 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-d-n-n-regressor.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.DNNRegressor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'hidden_units\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'activation_fn\', \'dropout\', \'input_layer_partitioner\', \'config\', \'warm_start_from\', \'loss_reduction\', \'batch_norm\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Adagrad\', \'\', \'None\', \'None\', \'None\', \'None\', \'weighted_sum\', \'False\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-estimator-spec.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-estimator-spec.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..aa6ac46613fbead7457b19e1aae5f2532afddef1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-estimator-spec.pbtxt @@ -0,0 +1,59 @@ +path: "tensorflow.estimator.EstimatorSpec" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "eval_metric_ops" + mtype: "" + } + member { + name: "evaluation_hooks" + mtype: "" + } + member { + name: "export_outputs" + mtype: "" + } + member { + name: "loss" + mtype: "" + } + member { + name: "mode" + mtype: "" + } + member { + name: "prediction_hooks" + mtype: "" + } + member { + name: "predictions" + mtype: "" + } + member { + name: "scaffold" + mtype: "" + } + member { + name: "train_op" + mtype: "" + } + member { + name: "training_chief_hooks" + mtype: "" + } + member { + name: "training_hooks" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-estimator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-estimator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d72b5769778d2ee8e5da34c531878a6d53ef44f5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-estimator.pbtxt @@ -0,0 +1,57 @@ +path: "tensorflow.estimator.Estimator" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'model_fn\', \'model_dir\', \'config\', \'params\', \'warm_start_from\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-eval-spec.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-eval-spec.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..db83ba1bd8f0bd13c9048d62d74790ed2b729589 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-eval-spec.pbtxt @@ -0,0 +1,43 @@ +path: "tensorflow.estimator.EvalSpec" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "exporters" + mtype: "" + } + member { + name: "hooks" + mtype: "" + } + member { + name: "input_fn" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "start_delay_secs" + mtype: "" + } + member { + name: "steps" + mtype: "" + } + member { + name: "throttle_secs" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-exporter.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-exporter.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..035af70e52024f8d16e1cd12951af10aad355eda --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-exporter.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.estimator.Exporter" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "export" + argspec: "args=[\'self\', \'estimator\', \'export_path\', \'checkpoint_path\', \'eval_result\', \'is_the_final_export\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-final-exporter.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-final-exporter.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ee37b1fa210ea816ef762590cfd1725c71262ed8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-final-exporter.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.estimator.FinalExporter" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\'], varargs=None, keywords=None, defaults=[\'None\', \'False\'], " + } + member_method { + name: "export" + argspec: "args=[\'self\', \'estimator\', \'export_path\', \'checkpoint_path\', \'eval_result\', \'is_the_final_export\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-latest-exporter.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-latest-exporter.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2a9d0290295114daa006d39f17a295a01e40da6b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-latest-exporter.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.estimator.LatestExporter" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'exports_to_keep\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'5\'], " + } + member_method { + name: "export" + argspec: "args=[\'self\', \'estimator\', \'export_path\', \'checkpoint_path\', \'eval_result\', \'is_the_final_export\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-linear-classifier.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-linear-classifier.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..154f171e89571a43a3f905094a1dbd41cbb000d3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-linear-classifier.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.LinearClassifier" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'optimizer\', \'config\', \'partitioner\', \'warm_start_from\', \'loss_reduction\', \'sparse_combiner\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'None\', \'None\', \'Ftrl\', \'None\', \'None\', \'None\', \'weighted_sum\', \'sum\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-linear-regressor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-linear-regressor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4d46d1e6b68758bf634f9b0f82c279fdfa91a0b8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-linear-regressor.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.estimator.LinearRegressor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "config" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "model_fn" + mtype: "" + } + member { + name: "params" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feature_columns\', \'model_dir\', \'label_dimension\', \'weight_column\', \'optimizer\', \'config\', \'partitioner\', \'warm_start_from\', \'loss_reduction\', \'sparse_combiner\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'Ftrl\', \'None\', \'None\', \'None\', \'weighted_sum\', \'sum\'], " + } + member_method { + name: "eval_dir" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "evaluate" + argspec: "args=[\'self\', \'input_fn\', \'steps\', \'hooks\', \'checkpoint_path\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "export_savedmodel" + argspec: "args=[\'self\', \'export_dir_base\', \'serving_input_receiver_fn\', \'assets_extra\', \'as_text\', \'checkpoint_path\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\'], " + } + member_method { + name: "get_variable_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_variable_value" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'input_fn\', \'predict_keys\', \'hooks\', \'checkpoint_path\', \'yield_single_examples\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "train" + argspec: "args=[\'self\', \'input_fn\', \'hooks\', \'steps\', \'max_steps\', \'saving_listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-mode-keys.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-mode-keys.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6a1c24fa63fc074c2b4ae9b3225a6abb47958b68 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-mode-keys.pbtxt @@ -0,0 +1,20 @@ +path: "tensorflow.estimator.ModeKeys" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "EVAL" + mtype: "" + } + member { + name: "PREDICT" + mtype: "" + } + member { + name: "TRAIN" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-run-config.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-run-config.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5aa4b3d4fb269785841e74c51f2121ce64377691 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-run-config.pbtxt @@ -0,0 +1,101 @@ +path: "tensorflow.estimator.RunConfig" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "cluster_spec" + mtype: "" + } + member { + name: "device_fn" + mtype: "" + } + member { + name: "evaluation_master" + mtype: "" + } + member { + name: "global_id_in_cluster" + mtype: "" + } + member { + name: "is_chief" + mtype: "" + } + member { + name: "keep_checkpoint_every_n_hours" + mtype: "" + } + member { + name: "keep_checkpoint_max" + mtype: "" + } + member { + name: "log_step_count_steps" + mtype: "" + } + member { + name: "master" + mtype: "" + } + member { + name: "model_dir" + mtype: "" + } + member { + name: "num_ps_replicas" + mtype: "" + } + member { + name: "num_worker_replicas" + mtype: "" + } + member { + name: "protocol" + mtype: "" + } + member { + name: "save_checkpoints_secs" + mtype: "" + } + member { + name: "save_checkpoints_steps" + mtype: "" + } + member { + name: "save_summary_steps" + mtype: "" + } + member { + name: "service" + mtype: "" + } + member { + name: "session_config" + mtype: "" + } + member { + name: "task_id" + mtype: "" + } + member { + name: "task_type" + mtype: "" + } + member { + name: "tf_random_seed" + mtype: "" + } + member { + name: "train_distribute" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'model_dir\', \'tf_random_seed\', \'save_summary_steps\', \'save_checkpoints_steps\', \'save_checkpoints_secs\', \'session_config\', \'keep_checkpoint_max\', \'keep_checkpoint_every_n_hours\', \'log_step_count_steps\', \'train_distribute\', \'device_fn\', \'protocol\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'100\', \'\', \'\', \'None\', \'5\', \'10000\', \'100\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "replace" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-train-spec.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-train-spec.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7d2f77438afa41f2d8391524470f82a22076313b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-train-spec.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.TrainSpec" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "hooks" + mtype: "" + } + member { + name: "input_fn" + mtype: "" + } + member { + name: "max_steps" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-vocab-info.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-vocab-info.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5301b94eb361251a1cb4d02a5d8168f7c8191045 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-vocab-info.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.estimator.VocabInfo" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "backup_initializer" + mtype: "" + } + member { + name: "new_vocab" + mtype: "" + } + member { + name: "new_vocab_size" + mtype: "" + } + member { + name: "num_oov_buckets" + mtype: "" + } + member { + name: "old_vocab" + mtype: "" + } + member { + name: "old_vocab_size" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.-warm-start-settings.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-warm-start-settings.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..43f5343359aff3b856a2b3708e4cda7cec29e146 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.-warm-start-settings.pbtxt @@ -0,0 +1,31 @@ +path: "tensorflow.estimator.WarmStartSettings" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "ckpt_to_initialize_from" + mtype: "" + } + member { + name: "var_name_to_prev_var_name" + mtype: "" + } + member { + name: "var_name_to_vocab_info" + mtype: "" + } + member { + name: "vars_to_warm_start" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-classification-output.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-classification-output.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3cf7af8da95479cf49469b2f328db0919fd5ce95 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-classification-output.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.estimator.export.ClassificationOutput.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-classification-output.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-classification-output.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2df1840c4a4f03fc08ba535b4f6557d49608fa5f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-classification-output.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.estimator.export.ClassificationOutput" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "classes" + mtype: "" + } + member { + name: "scores" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'scores\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "as_signature_def" + argspec: "args=[\'self\', \'receiver_tensors\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-export-output.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-export-output.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5d165ccbf91865e48f40f88ff817bff03881a03b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-export-output.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.estimator.export.ExportOutput.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-export-output.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-export-output.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..fa62e8ced801d66951ef5a62ec4fdd9795226ebd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-export-output.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.estimator.export.ExportOutput" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "as_signature_def" + argspec: "args=[\'self\', \'receiver_tensors\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-predict-output.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-predict-output.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..743495ba98cf4db0abeba86e26b812d9e3c8695b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-predict-output.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.estimator.export.PredictOutput.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-predict-output.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-predict-output.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e0160b10ce13a0b3499143d151ee7e58ad858fb2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-predict-output.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.estimator.export.PredictOutput" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "outputs" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'outputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_signature_def" + argspec: "args=[\'self\', \'receiver_tensors\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-regression-output.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-regression-output.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..dbf4e3dec85d7d00045bfe4e7086ba23edf61a84 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-regression-output.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.estimator.export.RegressionOutput.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-regression-output.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-regression-output.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..905f0e055350fe9a7d5790e531fb2b089332f279 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-regression-output.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.estimator.export.RegressionOutput" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "value" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_signature_def" + argspec: "args=[\'self\', \'receiver_tensors\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-serving-input-receiver.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-serving-input-receiver.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d71b2a430065740c376f8e90e3244d105ac2101f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-serving-input-receiver.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.export.ServingInputReceiver" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "features" + mtype: "" + } + member { + name: "receiver_tensors" + mtype: "" + } + member { + name: "receiver_tensors_alternatives" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4fe92643bf9867765499d7bf475b9cdd1686aec5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.-tensor-serving-input-receiver.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.estimator.export.TensorServingInputReceiver" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "features" + mtype: "" + } + member { + name: "receiver_tensors" + mtype: "" + } + member { + name: "receiver_tensors_alternatives" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bd72f6cd79f7dffb9f0a7f8ae43751c4ecba939d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.export.pbtxt @@ -0,0 +1,35 @@ +path: "tensorflow.estimator.export" +tf_module { + member { + name: "ClassificationOutput" + mtype: "" + } + member { + name: "ExportOutput" + mtype: "" + } + member { + name: "PredictOutput" + mtype: "" + } + member { + name: "RegressionOutput" + mtype: "" + } + member { + name: "ServingInputReceiver" + mtype: "" + } + member { + name: "TensorServingInputReceiver" + mtype: "" + } + member_method { + name: "build_parsing_serving_input_receiver_fn" + argspec: "args=[\'feature_spec\', \'default_batch_size\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "build_raw_serving_input_receiver_fn" + argspec: "args=[\'features\', \'default_batch_size\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.inputs.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.inputs.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b318fea1f82077c3924a843dd6b3857a3fdc0e8e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.inputs.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.estimator.inputs" +tf_module { + member_method { + name: "numpy_input_fn" + argspec: "args=[\'x\', \'y\', \'batch_size\', \'num_epochs\', \'shuffle\', \'queue_capacity\', \'num_threads\'], varargs=None, keywords=None, defaults=[\'None\', \'128\', \'1\', \'None\', \'1000\', \'1\'], " + } + member_method { + name: "pandas_input_fn" + argspec: "args=[\'x\', \'y\', \'batch_size\', \'num_epochs\', \'shuffle\', \'queue_capacity\', \'num_threads\', \'target_column\'], varargs=None, keywords=None, defaults=[\'None\', \'128\', \'1\', \'None\', \'1000\', \'1\', \'target\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f1d204a3ef96f35e31f642bcb0a61351b263d273 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.estimator.pbtxt @@ -0,0 +1,111 @@ +path: "tensorflow.estimator" +tf_module { + member { + name: "BaselineClassifier" + mtype: "" + } + member { + name: "BaselineRegressor" + mtype: "" + } + member { + name: "BestExporter" + mtype: "" + } + member { + name: "BoostedTreesClassifier" + mtype: "" + } + member { + name: "BoostedTreesRegressor" + mtype: "" + } + member { + name: "DNNClassifier" + mtype: "" + } + member { + name: "DNNLinearCombinedClassifier" + mtype: "" + } + member { + name: "DNNLinearCombinedRegressor" + mtype: "" + } + member { + name: "DNNRegressor" + mtype: "" + } + member { + name: "Estimator" + mtype: "" + } + member { + name: "EstimatorSpec" + mtype: "" + } + member { + name: "EvalSpec" + mtype: "" + } + member { + name: "Exporter" + mtype: "" + } + member { + name: "FinalExporter" + mtype: "" + } + member { + name: "LatestExporter" + mtype: "" + } + member { + name: "LinearClassifier" + mtype: "" + } + member { + name: "LinearRegressor" + mtype: "" + } + member { + name: "ModeKeys" + mtype: "" + } + member { + name: "RunConfig" + mtype: "" + } + member { + name: "TrainSpec" + mtype: "" + } + member { + name: "VocabInfo" + mtype: "" + } + member { + name: "WarmStartSettings" + mtype: "" + } + member { + name: "export" + mtype: "" + } + member { + name: "inputs" + mtype: "" + } + member_method { + name: "classifier_parse_example_spec" + argspec: "args=[\'feature_columns\', \'label_key\', \'label_dtype\', \'label_default\', \'weight_column\'], varargs=None, keywords=None, defaults=[\"\", \'None\', \'None\'], " + } + member_method { + name: "regressor_parse_example_spec" + argspec: "args=[\'feature_columns\', \'label_key\', \'label_dtype\', \'label_default\', \'label_dimension\', \'weight_column\'], varargs=None, keywords=None, defaults=[\"\", \'None\', \'1\', \'None\'], " + } + member_method { + name: "train_and_evaluate" + argspec: "args=[\'estimator\', \'train_spec\', \'eval_spec\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.feature_column.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.feature_column.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..24a58fb118bf52e650e1df71e9374099745ade52 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.feature_column.pbtxt @@ -0,0 +1,59 @@ +path: "tensorflow.feature_column" +tf_module { + member_method { + name: "bucketized_column" + argspec: "args=[\'source_column\', \'boundaries\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "categorical_column_with_hash_bucket" + argspec: "args=[\'key\', \'hash_bucket_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "categorical_column_with_identity" + argspec: "args=[\'key\', \'num_buckets\', \'default_value\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "categorical_column_with_vocabulary_file" + argspec: "args=[\'key\', \'vocabulary_file\', \'vocabulary_size\', \'num_oov_buckets\', \'default_value\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \"\"], " + } + member_method { + name: "categorical_column_with_vocabulary_list" + argspec: "args=[\'key\', \'vocabulary_list\', \'dtype\', \'default_value\', \'num_oov_buckets\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\', \'0\'], " + } + member_method { + name: "crossed_column" + argspec: "args=[\'keys\', \'hash_bucket_size\', \'hash_key\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "embedding_column" + argspec: "args=[\'categorical_column\', \'dimension\', \'combiner\', \'initializer\', \'ckpt_to_load_from\', \'tensor_name_in_ckpt\', \'max_norm\', \'trainable\'], varargs=None, keywords=None, defaults=[\'mean\', \'None\', \'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "indicator_column" + argspec: "args=[\'categorical_column\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "input_layer" + argspec: "args=[\'features\', \'feature_columns\', \'weight_collections\', \'trainable\', \'cols_to_vars\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\'], " + } + member_method { + name: "linear_model" + argspec: "args=[\'features\', \'feature_columns\', \'units\', \'sparse_combiner\', \'weight_collections\', \'trainable\', \'cols_to_vars\'], varargs=None, keywords=None, defaults=[\'1\', \'sum\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "make_parse_example_spec" + argspec: "args=[\'feature_columns\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "numeric_column" + argspec: "args=[\'key\', \'shape\', \'default_value\', \'dtype\', \'normalizer_fn\'], varargs=None, keywords=None, defaults=[\'(1,)\', \'None\', \"\", \'None\'], " + } + member_method { + name: "shared_embedding_columns" + argspec: "args=[\'categorical_columns\', \'dimension\', \'combiner\', \'initializer\', \'shared_embedding_collection_name\', \'ckpt_to_load_from\', \'tensor_name_in_ckpt\', \'max_norm\', \'trainable\'], varargs=None, keywords=None, defaults=[\'mean\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "weighted_categorical_column" + argspec: "args=[\'categorical_column\', \'weight_feature_key\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.gfile.-fast-g-file.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.gfile.-fast-g-file.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..eecfaffd0a6f6e611eba8bf3f5bb709bc9e0157f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.gfile.-fast-g-file.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.gfile.FastGFile" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "mode" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'mode\'], varargs=None, keywords=None, defaults=[\'r\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flush" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "next" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "read" + argspec: "args=[\'self\', \'n\'], varargs=None, keywords=None, defaults=[\'-1\'], " + } + member_method { + name: "readline" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "readlines" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "seek" + argspec: "args=[\'self\', \'offset\', \'whence\', \'position\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "tell" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "write" + argspec: "args=[\'self\', \'file_content\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.gfile.-g-file.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.gfile.-g-file.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..305251059d90b52aa2e76e99a4ec65e68b73fb79 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.gfile.-g-file.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.gfile.GFile" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "mode" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'mode\'], varargs=None, keywords=None, defaults=[\'r\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flush" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "next" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "read" + argspec: "args=[\'self\', \'n\'], varargs=None, keywords=None, defaults=[\'-1\'], " + } + member_method { + name: "readline" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "readlines" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "seek" + argspec: "args=[\'self\', \'offset\', \'whence\', \'position\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "tell" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "write" + argspec: "args=[\'self\', \'file_content\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.gfile.-open.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.gfile.-open.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6e8894180a4a685d5a35ba02df53c6e054db01b9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.gfile.-open.pbtxt @@ -0,0 +1,58 @@ +path: "tensorflow.gfile.Open" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "mode" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'mode\'], varargs=None, keywords=None, defaults=[\'r\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flush" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "next" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "read" + argspec: "args=[\'self\', \'n\'], varargs=None, keywords=None, defaults=[\'-1\'], " + } + member_method { + name: "readline" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "readlines" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "seek" + argspec: "args=[\'self\', \'offset\', \'whence\', \'position\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\'], " + } + member_method { + name: "size" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "tell" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "write" + argspec: "args=[\'self\', \'file_content\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.gfile.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.gfile.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..65b55a8b7c4e30e349c1ea256664002b19191c82 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.gfile.pbtxt @@ -0,0 +1,63 @@ +path: "tensorflow.gfile" +tf_module { + member { + name: "FastGFile" + mtype: "" + } + member { + name: "GFile" + mtype: "" + } + member { + name: "Open" + mtype: "" + } + member_method { + name: "Copy" + argspec: "args=[\'oldpath\', \'newpath\', \'overwrite\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "DeleteRecursively" + argspec: "args=[\'dirname\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "Exists" + argspec: "args=[\'filename\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "Glob" + argspec: "args=[\'filename\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "IsDirectory" + argspec: "args=[\'dirname\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "ListDirectory" + argspec: "args=[\'dirname\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MakeDirs" + argspec: "args=[\'dirname\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MkDir" + argspec: "args=[\'dirname\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "Remove" + argspec: "args=[\'filename\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "Rename" + argspec: "args=[\'oldname\', \'newname\', \'overwrite\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "Stat" + argspec: "args=[\'filename\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "Walk" + argspec: "args=[\'top\', \'in_order\'], varargs=None, keywords=None, defaults=[\'True\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.graph_util.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.graph_util.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..eeabf845dca94eea3ab4e54ee6ba3ba33c8995a5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.graph_util.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.graph_util" +tf_module { + member_method { + name: "convert_variables_to_constants" + argspec: "args=[\'sess\', \'input_graph_def\', \'output_node_names\', \'variable_names_whitelist\', \'variable_names_blacklist\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "extract_sub_graph" + argspec: "args=[\'graph_def\', \'dest_nodes\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "must_run_on_cpu" + argspec: "args=[\'node\', \'pin_variables_on_cpu\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "remove_training_nodes" + argspec: "args=[\'input_graph\', \'protected_nodes\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tensor_shape_from_node_def_name" + argspec: "args=[\'graph\', \'input_name\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.image.-resize-method.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.image.-resize-method.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..dbc360b13ee7dc8228f5fb4fe0cd6fc21504d0d0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.image.-resize-method.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.image.ResizeMethod" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "AREA" + mtype: "" + } + member { + name: "BICUBIC" + mtype: "" + } + member { + name: "BILINEAR" + mtype: "" + } + member { + name: "NEAREST_NEIGHBOR" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.image.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.image.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6ec3aba77586a9ffffd1e4375bf58394a118ea82 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.image.pbtxt @@ -0,0 +1,247 @@ +path: "tensorflow.image" +tf_module { + member { + name: "ResizeMethod" + mtype: "" + } + member_method { + name: "adjust_brightness" + argspec: "args=[\'image\', \'delta\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "adjust_contrast" + argspec: "args=[\'images\', \'contrast_factor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "adjust_gamma" + argspec: "args=[\'image\', \'gamma\', \'gain\'], varargs=None, keywords=None, defaults=[\'1\', \'1\'], " + } + member_method { + name: "adjust_hue" + argspec: "args=[\'image\', \'delta\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "adjust_jpeg_quality" + argspec: "args=[\'image\', \'jpeg_quality\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "adjust_saturation" + argspec: "args=[\'image\', \'saturation_factor\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "central_crop" + argspec: "args=[\'image\', \'central_fraction\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "convert_image_dtype" + argspec: "args=[\'image\', \'dtype\', \'saturate\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "crop_and_resize" + argspec: "args=[\'image\', \'boxes\', \'box_ind\', \'crop_size\', \'method\', \'extrapolation_value\', \'name\'], varargs=None, keywords=None, defaults=[\'bilinear\', \'0\', \'None\'], " + } + member_method { + name: "crop_to_bounding_box" + argspec: "args=[\'image\', \'offset_height\', \'offset_width\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "decode_and_crop_jpeg" + argspec: "args=[\'contents\', \'crop_window\', \'channels\', \'ratio\', \'fancy_upscaling\', \'try_recover_truncated\', \'acceptable_fraction\', \'dct_method\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'1\', \'True\', \'False\', \'1\', \'\', \'None\'], " + } + member_method { + name: "decode_bmp" + argspec: "args=[\'contents\', \'channels\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } + member_method { + name: "decode_gif" + argspec: "args=[\'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_image" + argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \"\", \'None\'], " + } + member_method { + name: "decode_jpeg" + argspec: "args=[\'contents\', \'channels\', \'ratio\', \'fancy_upscaling\', \'try_recover_truncated\', \'acceptable_fraction\', \'dct_method\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'1\', \'True\', \'False\', \'1\', \'\', \'None\'], " + } + member_method { + name: "decode_png" + argspec: "args=[\'contents\', \'channels\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \"\", \'None\'], " + } + member_method { + name: "draw_bounding_boxes" + argspec: "args=[\'images\', \'boxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "encode_jpeg" + argspec: "args=[\'image\', \'format\', \'quality\', \'progressive\', \'optimize_size\', \'chroma_downsampling\', \'density_unit\', \'x_density\', \'y_density\', \'xmp_metadata\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'95\', \'False\', \'False\', \'True\', \'in\', \'300\', \'300\', \'\', \'None\'], " + } + member_method { + name: "encode_png" + argspec: "args=[\'image\', \'compression\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " + } + member_method { + name: "extract_glimpse" + argspec: "args=[\'input\', \'size\', \'offsets\', \'centered\', \'normalized\', \'uniform_noise\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'True\', \'True\', \'None\'], " + } + member_method { + name: "extract_image_patches" + argspec: "args=[\'images\', \'ksizes\', \'strides\', \'rates\', \'padding\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "extract_jpeg_shape" + argspec: "args=[\'contents\', \'output_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } + member_method { + name: "flip_left_right" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flip_up_down" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "grayscale_to_rgb" + argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "hsv_to_rgb" + argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "image_gradients" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_jpeg" + argspec: "args=[\'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "non_max_suppression" + argspec: "args=[\'boxes\', \'scores\', \'max_output_size\', \'iou_threshold\', \'score_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'-inf\', \'None\'], " + } + member_method { + name: "non_max_suppression_overlaps" + argspec: "args=[\'overlaps\', \'scores\', \'max_output_size\', \'overlap_threshold\', \'score_threshold\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'-inf\', \'None\'], " + } + member_method { + name: "pad_to_bounding_box" + argspec: "args=[\'image\', \'offset_height\', \'offset_width\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "per_image_standardization" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "psnr" + argspec: "args=[\'a\', \'b\', \'max_val\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "random_brightness" + argspec: "args=[\'image\', \'max_delta\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "random_contrast" + argspec: "args=[\'image\', \'lower\', \'upper\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "random_flip_left_right" + argspec: "args=[\'image\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "random_flip_up_down" + argspec: "args=[\'image\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "random_hue" + argspec: "args=[\'image\', \'max_delta\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "random_jpeg_quality" + argspec: "args=[\'image\', \'min_jpeg_quality\', \'max_jpeg_quality\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "random_saturation" + argspec: "args=[\'image\', \'lower\', \'upper\', \'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "resize_area" + argspec: "args=[\'images\', \'size\', \'align_corners\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "resize_bicubic" + argspec: "args=[\'images\', \'size\', \'align_corners\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "resize_bilinear" + argspec: "args=[\'images\', \'size\', \'align_corners\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "resize_image_with_crop_or_pad" + argspec: "args=[\'image\', \'target_height\', \'target_width\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "resize_image_with_pad" + argspec: "args=[\'image\', \'target_height\', \'target_width\', \'method\'], varargs=None, keywords=None, defaults=[\'0\'], " + } + member_method { + name: "resize_images" + argspec: "args=[\'images\', \'size\', \'method\', \'align_corners\', \'preserve_aspect_ratio\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\'], " + } + member_method { + name: "resize_nearest_neighbor" + argspec: "args=[\'images\', \'size\', \'align_corners\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "rgb_to_grayscale" + argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "rgb_to_hsv" + argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "rgb_to_yiq" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "rgb_to_yuv" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "rot90" + argspec: "args=[\'image\', \'k\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\'], " + } + member_method { + name: "sample_distorted_bounding_box" + argspec: "args=[\'image_size\', \'bounding_boxes\', \'seed\', \'seed2\', \'min_object_covered\', \'aspect_ratio_range\', \'area_range\', \'max_attempts\', \'use_image_if_no_bounding_boxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'0.1\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "sobel_edges" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "ssim" + argspec: "args=[\'img1\', \'img2\', \'max_val\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "ssim_multiscale" + argspec: "args=[\'img1\', \'img2\', \'max_val\', \'power_factors\'], varargs=None, keywords=None, defaults=[\'(0.0448, 0.2856, 0.3001, 0.2363, 0.1333)\'], " + } + member_method { + name: "total_variation" + argspec: "args=[\'images\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "transpose_image" + argspec: "args=[\'image\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "yiq_to_rgb" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "yuv_to_rgb" + argspec: "args=[\'images\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.constant.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.constant.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..607a5aae21ff7299fc96aee3b932c10d622f1127 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.constant.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.constant" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value\', \'dtype\', \'verify_shape\'], varargs=None, keywords=None, defaults=[\'0\', \"\", \'False\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.identity.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.identity.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..37fcab95997bb7299675a387d08184fc1387eee1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.identity.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.identity" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.ones.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.ones.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..18481d48150d2dcf7d6908ab1914ab217da93c10 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.ones.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.ones" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.orthogonal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.orthogonal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ff64efd60cf1197bb9032912eb5cba48a63609a0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.orthogonal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.orthogonal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bc0426f2f145763552dcb46fb6c2efc7c42b974f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.pbtxt @@ -0,0 +1,79 @@ +path: "tensorflow.initializers" +tf_module { + member { + name: "constant" + mtype: "" + } + member { + name: "identity" + mtype: "" + } + member { + name: "ones" + mtype: "" + } + member { + name: "orthogonal" + mtype: "" + } + member { + name: "random_normal" + mtype: "" + } + member { + name: "random_uniform" + mtype: "" + } + member { + name: "truncated_normal" + mtype: "" + } + member { + name: "uniform_unit_scaling" + mtype: "" + } + member { + name: "variance_scaling" + mtype: "" + } + member { + name: "zeros" + mtype: "" + } + member_method { + name: "global_variables" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "glorot_normal" + argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], " + } + member_method { + name: "glorot_uniform" + argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], " + } + member_method { + name: "he_normal" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "he_uniform" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "lecun_normal" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "lecun_uniform" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "local_variables" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "variables" + argspec: "args=[\'var_list\', \'name\'], varargs=None, keywords=None, defaults=[\'init\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.random_normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.random_normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..133e61c1d9869bdd00948df3877be990b30b7cc3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.random_normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.random_normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.random_uniform.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.random_uniform.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0cfa0080f5a936bc80f69c2b5c15f671096ba350 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.random_uniform.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.random_uniform" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.truncated_normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.truncated_normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..730390fba274f9dc25eea7a53bb8145a2ade8613 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.truncated_normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.truncated_normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.uniform_unit_scaling.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.uniform_unit_scaling.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..13295ef375a4002f8fece5ebb5d2a5d5d26c68eb --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.uniform_unit_scaling.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.uniform_unit_scaling" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'factor\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.variance_scaling.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.variance_scaling.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..86340913e2506c96499aae05a3ed0d5273c93bba --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.variance_scaling.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.variance_scaling" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'truncated_normal\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.initializers.zeros.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.initializers.zeros.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7df4237bb6537b39f42f7b3894beb1bec6641f6f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.initializers.zeros.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.initializers.zeros" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.io.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.io.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3a36c168aa703721421b662185fc852fa3d6a3ec --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.io.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.io" +tf_module { + member_method { + name: "decode_base64" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_compressed" + argspec: "args=[\'bytes\', \'compression_type\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'None\'], " + } + member_method { + name: "decode_json_example" + argspec: "args=[\'json_examples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "decode_raw" + argspec: "args=[\'bytes\', \'out_type\', \'little_endian\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } + member_method { + name: "encode_base64" + argspec: "args=[\'input\', \'pad\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "matching_files" + argspec: "args=[\'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "parse_tensor" + argspec: "args=[\'serialized\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "read_file" + argspec: "args=[\'filename\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "write_file" + argspec: "args=[\'filename\', \'contents\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.-model.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.-model.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..40e82b18b68f9e8353dcb04f76ebb36446d3ab3f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.-model.pbtxt @@ -0,0 +1,268 @@ +path: "tensorflow.keras.Model" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "input_spec" + mtype: "" + } + member { + name: "layers" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "state_updates" + mtype: "" + } + member { + name: "stateful" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "uses_learning_phase" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_loss" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], " + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.-sequential.pbtxt @@ -0,0 +1,289 @@ +path: "tensorflow.keras.Sequential" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "input_spec" + mtype: "" + } + member { + name: "layers" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: 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+tf_module { + member_method { + name: "deserialize" + argspec: "args=[\'name\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "elu" + argspec: "args=[\'x\', \'alpha\'], varargs=None, keywords=None, defaults=[\'1.0\'], " + } + member_method { + name: "get" + argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "hard_sigmoid" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "linear" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "relu" + argspec: "args=[\'x\', \'alpha\', \'max_value\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " + } + member_method { + name: "selu" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "serialize" + argspec: "args=[\'activation\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "sigmoid" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "softmax" + argspec: "args=[\'x\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], " + } + member_method { + name: "softplus" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "softsign" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "tanh" + argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.densenet.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.densenet.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.densenet.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.densenet.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.inception_resnet_v2.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.inception_resnet_v2.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.inception_resnet_v2.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.inception_resnet_v2.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.inception_v3.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.inception_v3.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.inception_v3.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.inception_v3.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.mobilenet.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.mobilenet.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.mobilenet.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.mobilenet.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.nasnet.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.nasnet.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.nasnet.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.nasnet.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.resnet50.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.resnet50.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.resnet50.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.resnet50.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.vgg16.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.vgg16.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.vgg16.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.vgg16.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.vgg19.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.vgg19.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.vgg19.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.vgg19.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.applications.xception.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.applications.xception.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.applications.xception.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.applications.xception.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.backend.name_scope.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.backend.name_scope.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a2b98b1c27c2268326af2653177b38e25f838c8d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.backend.name_scope.pbtxt @@ -0,0 +1,13 @@ +path: "tensorflow.keras.backend.name_scope" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'default_name\', \'values\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git 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keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-base-logger.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-base-logger.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9eee9b378964a9947b067b7ec495ef6556ab6d0c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-base-logger.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.BaseLogger" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'stateful_metrics\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-c-s-v-logger.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-c-s-v-logger.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5bb949c5bb650acee91b14a4d6bf95b36029edf7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-c-s-v-logger.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.CSVLogger" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'filename\', \'separator\', \'append\'], varargs=None, keywords=None, defaults=[\',\', \'False\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-callback.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-callback.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a5340d52c1af6d69da30fd710bcee9d832917574 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-callback.pbtxt @@ -0,0 +1,41 @@ +path: "tensorflow.keras.callbacks.Callback" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f71292856cd29b2e52194bec8a586686fbfad667 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.EarlyStopping" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'monitor\', \'min_delta\', \'patience\', \'verbose\', \'mode\', \'baseline\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'0\', \'0\', \'auto\', \'None\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-history.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-history.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ee400b31c43829efba156298d5ee807cdafc8a98 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-history.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.History" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-lambda-callback.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-lambda-callback.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..df8d7b0ef7afca17338a26388c38827b5b306f95 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-lambda-callback.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.LambdaCallback" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'on_epoch_begin\', \'on_epoch_end\', \'on_batch_begin\', \'on_batch_end\', \'on_train_begin\', \'on_train_end\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ce1a9b694d8708720e0eb677afd25607c6262e9c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.LearningRateScheduler" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'schedule\', \'verbose\'], varargs=None, keywords=None, defaults=[\'0\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-model-checkpoint.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-model-checkpoint.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..48bb24a05274addca03f11acef99607f78b92e51 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-model-checkpoint.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.ModelCheckpoint" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'filepath\', \'monitor\', \'verbose\', \'save_best_only\', \'save_weights_only\', \'mode\', \'period\'], varargs=None, keywords=None, defaults=[\'val_loss\', \'0\', \'False\', \'False\', \'auto\', \'1\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-progbar-logger.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-progbar-logger.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d8bb8b2a7d0f491c7ec2b30096a1acaf04681a56 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-progbar-logger.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.ProgbarLogger" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'count_mode\', \'stateful_metrics\'], varargs=None, keywords=None, defaults=[\'samples\', \'None\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-reduce-l-r-on-plateau.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-reduce-l-r-on-plateau.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..dc27af9552a88650261b4f0694ea0265e6bda05c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-reduce-l-r-on-plateau.pbtxt @@ -0,0 +1,46 @@ +path: "tensorflow.keras.callbacks.ReduceLROnPlateau" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'monitor\', \'factor\', \'patience\', \'verbose\', \'mode\', \'min_delta\', \'cooldown\', \'min_lr\'], varargs=None, keywords=kwargs, defaults=[\'val_loss\', \'0.1\', \'10\', \'0\', \'auto\', \'0.0001\', \'0\', \'0\'], " + } + member_method { + name: "in_cooldown" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-remote-monitor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-remote-monitor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5a3b791c0adc0d61129d38b2995ee9077cf0988b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-remote-monitor.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.RemoteMonitor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'root\', \'path\', \'field\', \'headers\', \'send_as_json\'], varargs=None, keywords=None, defaults=[\'http://localhost:9000\', \'/publish/epoch/end/\', \'data\', \'None\', \'False\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e58ba18c1c0d06df3a53d93ae18f5bf0931df329 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.TensorBoard" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'log_dir\', \'histogram_freq\', \'batch_size\', \'write_graph\', \'write_grads\', \'write_images\', \'embeddings_freq\', \'embeddings_layer_names\', \'embeddings_metadata\', \'embeddings_data\'], varargs=None, keywords=None, defaults=[\'./logs\', \'0\', \'32\', \'True\', \'False\', \'False\', \'0\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5c2d336353aee7fc98b45620adac4f4bcda05ea0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.callbacks.TerminateOnNaN" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "on_batch_begin" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_batch_end" + argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_begin" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_begin" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "on_train_end" + argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "set_model" + argspec: "args=[\'self\', \'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1e9085e034ccf22fda7be7565aabb86992a8b0b7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.pbtxt @@ -0,0 +1,55 @@ +path: "tensorflow.keras.callbacks" +tf_module { + member { + name: "BaseLogger" + mtype: "" + } + member { + name: "CSVLogger" + mtype: "" + } + member { + name: "Callback" + mtype: "" + } + member { + name: "EarlyStopping" + mtype: "" + } + member { + name: "History" + mtype: "" + } + member { + name: "LambdaCallback" + mtype: "" + } + member { + name: "LearningRateScheduler" + mtype: "" + } + member { + name: "ModelCheckpoint" + mtype: "" + } + member { + name: "ProgbarLogger" + mtype: "" + } + member { + name: "ReduceLROnPlateau" + mtype: "" + } + member { + name: "RemoteMonitor" + mtype: "" + } + member { + name: "TensorBoard" + mtype: "" + } + member { + name: "TerminateOnNaN" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-constraint.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-constraint.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8e07b7d98e1d832628f65bed19eddca76bfbd51a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-constraint.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.keras.constraints.Constraint" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-max-norm.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-max-norm.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2b81174b6cd4d57d8d6e20da7f6961442045d908 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-max-norm.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.keras.constraints.MaxNorm" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'max_value\', \'axis\'], varargs=None, keywords=None, defaults=[\'2\', \'0\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-min-max-norm.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-min-max-norm.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a41eda86ac2583b1adfe745f713ac8f8647f7a31 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-min-max-norm.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.keras.constraints.MinMaxNorm" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'min_value\', \'max_value\', \'rate\', \'axis\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'1.0\', \'0\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-non-neg.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-non-neg.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..572e3eea4d985999f513a066b348d088ab01fe54 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-non-neg.pbtxt @@ -0,0 +1,13 @@ +path: "tensorflow.keras.constraints.NonNeg" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-unit-norm.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-unit-norm.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..fe16c38cc83fb9979ecf0d08ab2cba7a2c38f9b6 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.-unit-norm.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.keras.constraints.UnitNorm" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'axis\'], varargs=None, keywords=None, defaults=[\'0\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.max_norm.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.max_norm.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6650bae07a0d32448e748598af3426f85ca8e199 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.max_norm.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.keras.constraints.max_norm" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'max_value\', \'axis\'], varargs=None, keywords=None, defaults=[\'2\', \'0\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.min_max_norm.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.min_max_norm.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9dd3bc92fc4fadee863f30b300ddb60fe0b3d340 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.min_max_norm.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.keras.constraints.min_max_norm" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'min_value\', \'max_value\', \'rate\', \'axis\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'1.0\', \'0\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.non_neg.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.non_neg.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a565840939f99080b784e4e95302071600a1fa7c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.non_neg.pbtxt @@ -0,0 +1,13 @@ +path: "tensorflow.keras.constraints.non_neg" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..655685956f0e42e2d92dca0ac36f4cca075f474a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.keras.constraints" +tf_module { + member { + name: "Constraint" + mtype: "" + } + member { + name: "MaxNorm" + mtype: "" + } + member { + name: "MinMaxNorm" + mtype: "" + } + member { + name: "NonNeg" + mtype: "" + } + member { + name: "UnitNorm" + mtype: "" + } + member { + name: "max_norm" + mtype: "" + } + member { + name: "min_max_norm" + mtype: "" + } + member { + name: "non_neg" + mtype: "" + } + member { + name: "unit_norm" + mtype: "" + } + member_method { + name: "deserialize" + argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get" + argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "serialize" + argspec: "args=[\'constraint\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.unit_norm.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.unit_norm.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5cbe0da4c1d1ff97fe836f76402cfca92e1cc511 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.constraints.unit_norm.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.keras.constraints.unit_norm" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'axis\'], varargs=None, keywords=None, defaults=[\'0\'], " + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.boston_housing.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.boston_housing.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bda31751d429ca0d0544402e5c496a0597e1849e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.boston_housing.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.keras.datasets.boston_housing" +tf_module { + member_method { + name: "load_data" + argspec: "args=[\'path\', \'test_split\', \'seed\'], varargs=None, keywords=None, defaults=[\'boston_housing.npz\', \'0.2\', \'113\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.cifar10.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.cifar10.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8a5142f793d67b3a923f3033c0da14442c4f680f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.cifar10.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.keras.datasets.cifar10" +tf_module { + member_method { + name: "load_data" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.cifar100.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.cifar100.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..16f184eeb5e8ee4f126b943c8988ec28ceab89a4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.cifar100.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.keras.datasets.cifar100" +tf_module { + member_method { + name: "load_data" + argspec: "args=[\'label_mode\'], varargs=None, keywords=None, defaults=[\'fine\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.fashion_mnist.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.fashion_mnist.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a0e14356fa5e91bc81bd89f6eb8c07087956c392 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.fashion_mnist.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.keras.datasets.fashion_mnist" +tf_module { + member_method { + name: "load_data" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.imdb.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.imdb.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ff962876b66cae013de5d711dc7eac5d5c80d8c3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.imdb.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.keras.datasets.imdb" +tf_module { + member_method { + name: "get_word_index" + argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=[\'imdb_word_index.json\'], " + } + member_method { + name: "load_data" + argspec: "args=[\'path\', \'num_words\', \'skip_top\', \'maxlen\', \'seed\', \'start_char\', \'oov_char\', \'index_from\'], varargs=None, keywords=kwargs, defaults=[\'imdb.npz\', \'None\', \'0\', \'None\', \'113\', \'1\', \'2\', \'3\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.mnist.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.mnist.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..530bb0755060f243281523c68b9c554dcbdbc634 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.mnist.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.keras.datasets.mnist" +tf_module { + member_method { + name: "load_data" + argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=[\'mnist.npz\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..36e3aafbe4dbc22fade073b45b2d7495f8f7ec52 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.pbtxt @@ -0,0 +1,31 @@ +path: "tensorflow.keras.datasets" +tf_module { + member { + name: "boston_housing" + mtype: "" + } + member { + name: "cifar10" + mtype: "" + } + member { + name: "cifar100" + mtype: "" + } + member { + name: "fashion_mnist" + mtype: "" + } + member { + name: "imdb" + mtype: "" + } + member { + name: "mnist" + mtype: "" + } + member { + name: "reuters" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.reuters.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.reuters.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2da4a13067f2b39eb06304864ea626002300a862 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.datasets.reuters.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.keras.datasets.reuters" +tf_module { + member_method { + name: "get_word_index" + argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=[\'reuters_word_index.json\'], " + } + member_method { + name: "load_data" + argspec: "args=[\'path\', \'num_words\', \'skip_top\', \'maxlen\', \'test_split\', \'seed\', \'start_char\', \'oov_char\', \'index_from\'], varargs=None, keywords=kwargs, defaults=[\'reuters.npz\', \'None\', \'0\', \'None\', \'0.2\', \'113\', \'1\', \'2\', \'3\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.estimator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.estimator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7a3fb39f774d24d3e6e5c87233f055f50cfc08bb --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.estimator.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.keras.estimator" +tf_module { + member_method { + name: "model_to_estimator" + argspec: "args=[\'keras_model\', \'keras_model_path\', \'custom_objects\', \'model_dir\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-constant.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-constant.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cbaba78ed5a851c3d6e29ab67c89fdfd5db01754 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-constant.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.Constant" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value\', \'dtype\', \'verify_shape\'], varargs=None, keywords=None, defaults=[\'0\', \"\", \'False\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-identity.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-identity.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a5f7f348de9d9899d962e7647d7943ddb6a60604 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-identity.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.Identity" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8f10d1698e7b7b2afa9c2664c7dca38045eda85b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-initializer.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.keras.initializers.Initializer" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-ones.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-ones.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2fbfa774f8ed020164e32bb3cfb69b8a235609ba --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-ones.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.Ones" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-orthogonal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-orthogonal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..874d320d73d1f1cdbd817db587ea9dcfea4d352b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-orthogonal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.Orthogonal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-random-normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-random-normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..23cd02c0b069d3cb2d7b9e7ebc754db288e4637a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-random-normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.RandomNormal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-random-uniform.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-random-uniform.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d98628f42253603178cdff2624f639afa846a66a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-random-uniform.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.RandomUniform" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-truncated-normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-truncated-normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..86d48257c1ffb95fc217de475efba41002f8e7a5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-truncated-normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.TruncatedNormal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-variance-scaling.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-variance-scaling.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..03f4064b9ef5093044a9cbb897043d643cf7f83e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-variance-scaling.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.VarianceScaling" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'truncated_normal\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-zeros.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-zeros.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b6ab68e5beb47c9bcfbc52f9808255bbb03d2dc0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.-zeros.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.Zeros" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.constant.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.constant.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bddc37b907e7573c9fff27a0c3a5f7e199b88a9a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.constant.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.constant" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value\', \'dtype\', \'verify_shape\'], varargs=None, keywords=None, defaults=[\'0\', \"\", \'False\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.identity.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.identity.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a4c5a6149047ffdaadde1243e4c80feae05cd77b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.identity.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.identity" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7485772784d40b7bf552efe9bbe8b22fadee3b86 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.ones.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.ones.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a89f78d1e1a47c7cd5a252cfd0a7b2fa23979e90 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.ones.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.ones" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.orthogonal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.orthogonal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ee1e9bbae2b7130db5b96309e2d87719169d788a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.orthogonal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.orthogonal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8645e5430295dff0a5b7c715b03860fb7734e7f1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.pbtxt @@ -0,0 +1,119 @@ +path: "tensorflow.keras.initializers" +tf_module { + member { + name: "Constant" + mtype: "" + } + member { + name: "Identity" + mtype: "" + } + member { + name: "Initializer" + mtype: "" + } + member { + name: "Ones" + mtype: "" + } + member { + name: "Orthogonal" + mtype: "" + } + member { + name: "RandomNormal" + mtype: "" + } + member { + name: "RandomUniform" + mtype: "" + } + member { + name: "TruncatedNormal" + mtype: "" + } + member { + name: "VarianceScaling" + mtype: "" + } + member { + name: "Zeros" + mtype: "" + } + member { + name: "constant" + mtype: "" + } + member { + name: "identity" + mtype: "" + } + member { + name: "normal" + mtype: "" + } + member { + name: "ones" + mtype: "" + } + member { + name: "orthogonal" + mtype: "" + } + member { + name: "random_normal" + mtype: "" + } + member { + name: "random_uniform" + mtype: "" + } + member { + name: "truncated_normal" + mtype: "" + } + member { + name: "uniform" + mtype: "" + } + member { + name: "zeros" + mtype: "" + } + member_method { + name: "deserialize" + argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get" + argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "glorot_normal" + argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], " + } + member_method { + name: "glorot_uniform" + argspec: "args=[\'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \"\"], " + } + member_method { + name: "he_normal" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "he_uniform" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "lecun_normal" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "lecun_uniform" + argspec: "args=[\'seed\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "serialize" + argspec: "args=[\'initializer\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.random_normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.random_normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a6df1e87a3f68fb16e32dce1ba4ee29f6d86e74e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.random_normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.random_normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.random_uniform.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.random_uniform.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..37a0fa0d5508de0026472ff1a3aa532bb8f343cd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.random_uniform.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.random_uniform" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.truncated_normal.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.truncated_normal.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f97e93f0b72d5e959722d15fa9dc35869c550710 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.truncated_normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.truncated_normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.uniform.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.uniform.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..58186b1383d8997165bb457e1cb54df86cd02d11 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.uniform.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.uniform" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.zeros.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.zeros.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a262390687f31a5fb79822e69273306b9e1897b5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.initializers.zeros.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.zeros" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-activation.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-activation.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..86e328888e596852caf9ad1020dfdedb71864969 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-activation.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.Activation" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" 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a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-activity-regularization.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-activity-regularization.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b0ed54578109c6ae8d5bc2c9f5c978b562a9cc84 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-activity-regularization.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.ActivityRegularization" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-add.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..42f98ed03d426d60cabeb0b533311d41eb378285 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-add.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Add" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + 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0000000000000000000000000000000000000000..000898a4be928e4e64b4072ef3170b6fbc930bdf --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-alpha-dropout.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.AlphaDropout" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: 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varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average-pooling1-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..380b49f99ce6e62770a9516ba81db99f194c5b37 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average-pooling1-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.AveragePooling1D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: 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"count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average-pooling2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..82db5e6137639e516f6df6f0e130e73be516c9b8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average-pooling2-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.AveragePooling2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'(2, 2)\', \'None\', \'valid\', \'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average-pooling3-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.AveragePooling3D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'(2, 2, 2)\', \'None\', \'valid\', \'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-average.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Average" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-bidirectional.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3fd4ccdab2573964c2f3192d503e9fb15f442dc5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-bidirectional.pbtxt @@ -0,0 +1,188 @@ +path: "tensorflow.keras.layers.Bidirectional" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "constraints" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + 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a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cropping2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cropping2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7ac4116d922eea51e5a7e7fe3d02ad919300c459 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cropping2-d.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.Cropping2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cropping3-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..024f72705de1e76866a8132246884dffb0c4e72a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-cropping3-d.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.Cropping3D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: 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index 0000000000000000000000000000000000000000..2e71ef503d54927edbb3e1ef6c701ac845883e46 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-dot.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Dot" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: 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"count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-dropout.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..42533bcd21b28a0acf183db195a6b5c1848a5d91 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-dropout.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.Dropout" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c23654866341818aeb804cfb71dae052049e3f25 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-avg-pool2-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.GlobalAvgPool2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" 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0000000000000000000000000000000000000000..6b9c0290aac35d80c7f87acfc44479c57623a645 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-avg-pool3-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.GlobalAvgPool3D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', 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member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pool1-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pool1-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0d7b2211e6cd35ca331b4a1068f237e7ca07f70c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pool1-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.GlobalMaxPool1D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + 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keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pool2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pool2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d080ad6aedbd5183da890cd63f5f18453d5d476a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pool2-d.pbtxt @@ -0,0 +1,176 @@ +path: 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\'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + 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} + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cdbaf82cf6746e878619647439d2256f6e2c4aa3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-global-max-pooling3-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.GlobalMaxPooling3D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + 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varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-input-layer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-input-layer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..230c5e903438b0a75edf80f0f5c8706987c66a78 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-input-layer.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.InputLayer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + 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varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-lambda.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-lambda.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-lambda.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-layer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-layer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7efa29be77c075a29784d8cd3ebfcd871bc9aa0c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-layer.pbtxt @@ -0,0 +1,174 @@ +path: "tensorflow.keras.layers.Layer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" 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defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-max-pooling2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..802178dba63d66cca1629bcb7bef0f578c9a6659 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-max-pooling2-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.MaxPooling2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-minimum.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ed27a62765d5670802d4593b3e648e3f65eaf926 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-minimum.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Minimum" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + 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a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-re-l-u.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-re-l-u.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c00fa79adfbe5b986b481f6c9567bafbf3abc1ae --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-re-l-u.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.ReLU" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-separable-conv2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..fd17115e2733d561bff1d53d62d32458b03dc65b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-separable-conv2-d.pbtxt @@ -0,0 +1,177 @@ +path: "tensorflow.keras.layers.SeparableConv2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: 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a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c424e6dcc869f977100e77fdb543983c3ab7e63c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-spatial-dropout3-d.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.SpatialDropout3D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-subtract.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-subtract.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..740a03367bd69edf797d3ea8616fdde72f6726b7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-subtract.pbtxt @@ -0,0 +1,176 @@ +path: "tensorflow.keras.layers.Subtract" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-up-sampling2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4b5165ae9793f900fb474affe52b9abaeb64adbd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-up-sampling2-d.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.keras.layers.UpSampling2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + 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name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9d7e5bb8c7808689bedd8abb835e61c1f38fdb1d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.pbtxt @@ -0,0 +1,435 @@ +path: "tensorflow.keras.layers" +tf_module { + member { + name: "Activation" + mtype: "" + } + member { + name: "ActivityRegularization" + mtype: "" + } + member { + name: "Add" + mtype: "" + } + member { + name: "AlphaDropout" + mtype: "" + } + member { + name: "Average" + mtype: "" + } + member { + name: "AveragePooling1D" + mtype: "" + } + member { + name: "AveragePooling2D" + mtype: "" + } + member { + name: 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0000000000000000000000000000000000000000..eca6b915388ebff0103f7ad16f43c6be0df60b7d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.losses.pbtxt @@ -0,0 +1,115 @@ +path: "tensorflow.keras.losses" +tf_module { + member_method { + name: "KLD" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAPE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSLE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "binary_crossentropy" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "categorical_crossentropy" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "categorical_hinge" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "cosine" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "cosine_proximity" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "deserialize" + argspec: "args=[\'name\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get" + argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "hinge" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "kld" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "kullback_leibler_divergence" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "logcosh" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "mae" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "mape" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "mean_absolute_error" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "mean_absolute_percentage_error" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "mean_squared_error" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { 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b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..73b577da373b1381a7e8d5841d6e002452a21f9e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.metrics.pbtxt @@ -0,0 +1,123 @@ +path: "tensorflow.keras.metrics" +tf_module { + member_method { + name: "KLD" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAPE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSLE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "binary_accuracy" + argspec: 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keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "load_weights" + argspec: "args=[\'self\', \'filepath\', \'by_name\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "pop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\'], " + } + member_method { + name: "predict_classes" + argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\'], varargs=None, keywords=None, defaults=[\'32\', \'0\'], " + } + member_method { + name: "predict_generator" + argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], " + } + member_method { + name: "predict_on_batch" + argspec: "args=[\'self\', \'x\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict_proba" + argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\'], varargs=None, keywords=None, defaults=[\'32\', \'0\'], " + } + member_method { + name: "reset_states" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "save" + argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'include_optimizer\'], varargs=None, keywords=None, defaults=[\'True\', \'True\'], " + } + member_method { + name: "save_weights" + argspec: "args=[\'self\', \'filepath\', \'overwrite\', \'save_format\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "summary" + argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "symbolic_set_inputs" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "test_on_batch" + argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "to_json" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "to_yaml" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "train_on_batch" + argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\', \'class_weight\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.models.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.models.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8ba0e7480bf5100e4bb10ceaf220cfaac0f43f52 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.models.pbtxt @@ -0,0 +1,31 @@ +path: "tensorflow.keras.models" +tf_module { + member { + name: "Model" + mtype: "" + } + member { + name: "Sequential" + mtype: "" + } + member_method { + name: "load_model" + argspec: "args=[\'filepath\', \'custom_objects\', \'compile\'], varargs=None, keywords=None, defaults=[\'None\', \'True\'], " + } + member_method { + name: "model_from_config" + argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "model_from_json" + argspec: "args=[\'json_string\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "model_from_yaml" + argspec: "args=[\'yaml_string\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "save_model" + argspec: "args=[\'model\', \'filepath\', \'overwrite\', \'include_optimizer\'], varargs=None, keywords=None, defaults=[\'True\', \'True\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b9ce154bddef609e0aaf6627d6f59de551e51e3b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adadelta.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.keras.optimizers.Adadelta" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'1.0\', \'0.95\', \'None\', \'0.0\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d0dc9e37a386a26143365eb443d5ba5fce8a87d9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adagrad.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.keras.optimizers.Adagrad" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'lr\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'None\', \'0.0\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..06815fa99a4a474ec131c29d0cbc78bb2b9cb72d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adam.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.keras.optimizers.Adam" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\', \'amsgrad\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'0.999\', \'None\', \'0.0\', \'False\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..47b55fdb44e79e976b6de13d760a7cf175323c6c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-adamax.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.keras.optimizers.Adamax" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.0\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-nadam.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-nadam.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8c63a7dda98568b24ea1b3cda15d4c840fbfd804 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-nadam.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.keras.optimizers.Nadam" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'lr\', \'beta_1\', \'beta_2\', \'epsilon\', \'schedule_decay\'], varargs=None, keywords=kwargs, defaults=[\'0.002\', \'0.9\', \'0.999\', \'None\', \'0.004\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..53d64dae932e250b9d81b2767a833de3bac8c403 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-optimizer.pbtxt @@ -0,0 +1,33 @@ +path: "tensorflow.keras.optimizers.Optimizer" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a1e9b8cceb95e8f25ac5f414fadacf237be33cd9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-r-m-sprop.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.keras.optimizers.RMSprop" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'lr\', \'rho\', \'epsilon\', \'decay\'], varargs=None, keywords=kwargs, defaults=[\'0.001\', \'0.9\', \'None\', \'0.0\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a67fefb1bafebd62db9f6108f0fe1847b5d2e0cb --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.-s-g-d.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.keras.optimizers.SGD" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'lr\', \'momentum\', \'decay\', \'nesterov\'], varargs=None, keywords=kwargs, defaults=[\'0.01\', \'0.0\', \'0.0\', \'False\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_gradients" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates" + argspec: "args=[\'self\', \'loss\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7257b02087e237eaa47ed6a042559aa1332fc87b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.optimizers.pbtxt @@ -0,0 +1,47 @@ +path: "tensorflow.keras.optimizers" +tf_module { + member { + name: "Adadelta" + mtype: "" + } + member { + name: "Adagrad" + mtype: "" + } + member { + name: "Adam" + mtype: "" + } + member { + name: "Adamax" + mtype: "" + } + member { + name: "Nadam" + mtype: "" + } + member { + name: "Optimizer" + mtype: "" + } + member { + name: "RMSprop" + mtype: "" + } + member { + name: "SGD" + mtype: "" + } + member_method { + name: "deserialize" + argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get" + argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "serialize" + argspec: "args=[\'optimizer\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..754b3b84b08b08c7d12eba4ddad0a483440055a9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.pbtxt @@ -0,0 +1,83 @@ +path: "tensorflow.keras" +tf_module { + member { + name: "Model" + mtype: "" + } + member { + name: "Sequential" + mtype: "" + } + member { + name: "activations" + mtype: "" + } + member { + name: "applications" + mtype: "" + } + member { + name: "backend" + mtype: "" + } + member { + name: "callbacks" + mtype: "" + } + member { + name: "constraints" + mtype: "" + } + member { + name: "datasets" + mtype: "" + } + member { + name: "estimator" + mtype: "" + } + member { + name: "initializers" + mtype: "" + } + member { + name: "layers" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "metrics" + mtype: "" + } + member { + name: "models" + mtype: "" + } + member { + name: "optimizers" + mtype: "" + } + member { + name: "preprocessing" + mtype: "" + } + member { + name: "regularizers" + mtype: "" + } + member { + name: "utils" + mtype: "" + } + member { + name: "wrappers" + mtype: "" + } + member_method { + name: "Input" + argspec: "args=[\'shape\', \'batch_size\', \'name\', \'dtype\', \'sparse\', \'tensor\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'False\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-directory-iterator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-directory-iterator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-directory-iterator.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-directory-iterator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-image-data-generator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-iterator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-iterator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-iterator.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-iterator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-numpy-array-iterator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-numpy-array-iterator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.-numpy-array-iterator.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.-numpy-array-iterator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.image.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.image.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.sequence.-timeseries-generator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.sequence.-timeseries-generator.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.sequence.-timeseries-generator.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.sequence.-timeseries-generator.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.sequence.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.sequence.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.sequence.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.sequence.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.text.-tokenizer.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.text.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.keras.preprocessing.text.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.keras.preprocessing.text.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-l1-l2.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-l1-l2.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a45fb7b55e58a5679427752af22dce49203dc1cc --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-l1-l2.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.regularizers.L1L2" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'l1\', \'l2\'], varargs=None, keywords=None, defaults=[\'0.0\', \'0.0\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-regularizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-regularizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..641001a646564d0a466739ee6d2bdd31a27beab7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-regularizer.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.keras.regularizers.Regularizer" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bb10d41d704ca456fbf5b8bd19324ee71f17ba8d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.pbtxt @@ -0,0 +1,35 @@ +path: "tensorflow.keras.regularizers" +tf_module { + member { + name: "L1L2" + mtype: "" + } + member { + name: "Regularizer" + mtype: "" + } + member_method { + name: "deserialize" + argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get" + argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "l1" + argspec: "args=[\'l\'], varargs=None, keywords=None, defaults=[\'0.01\'], " + } + member_method { + name: "l1_l2" + argspec: "args=[\'l1\', \'l2\'], varargs=None, keywords=None, defaults=[\'0.01\', \'0.01\'], " + } + member_method { + name: "l2" + argspec: "args=[\'l\'], varargs=None, keywords=None, defaults=[\'0.01\'], " + } + member_method { + name: "serialize" + argspec: "args=[\'regularizer\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-custom-object-scope.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-custom-object-scope.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..109682046b990107915d65be3cad86ead3e22688 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-custom-object-scope.pbtxt @@ -0,0 +1,9 @@ +path: "tensorflow.keras.utils.CustomObjectScope" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=args, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-generator-enqueuer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-generator-enqueuer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..939fd547d06bbd03b7e1a1db1404263ff01fd07c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-generator-enqueuer.pbtxt @@ -0,0 +1,26 @@ +path: "tensorflow.keras.utils.GeneratorEnqueuer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'generator\', \'use_multiprocessing\', \'wait_time\', \'seed\'], varargs=None, keywords=None, defaults=[\'False\', \'0.05\', \'None\'], " + } + member_method { + name: "get" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_running" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "start" + argspec: "args=[\'self\', \'workers\', \'max_queue_size\'], varargs=None, keywords=None, defaults=[\'1\', \'10\'], " + } + member_method { + name: "stop" + argspec: "args=[\'self\', \'timeout\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-h-d-f5-matrix.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-h-d-f5-matrix.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6b832051a975b61ba05874c3dda558c63aeaa055 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-h-d-f5-matrix.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.keras.utils.HDF5Matrix" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "dtype" + mtype: "" + } + member { + name: "ndim" + mtype: "" + } + member { + name: "refs" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "size" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'datapath\', \'dataset\', \'start\', \'end\', \'normalizer\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-progbar.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-progbar.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..be4496e753f8bdcd76a4761f9bd1804a77380359 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-progbar.pbtxt @@ -0,0 +1,17 @@ +path: "tensorflow.keras.utils.Progbar" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'target\', \'width\', \'verbose\', \'interval\', \'stateful_metrics\'], varargs=None, keywords=None, defaults=[\'30\', \'1\', \'0.05\', \'None\'], " + } + member_method { + name: "add" + argspec: "args=[\'self\', \'n\', \'values\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "update" + argspec: "args=[\'self\', \'current\', \'values\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-sequence-enqueuer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-sequence-enqueuer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a9e499d1009b5a7458080db6c10a948af21c7b6c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-sequence-enqueuer.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.keras.utils.SequenceEnqueuer" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "get" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_running" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "start" + argspec: "args=[\'self\', \'workers\', \'max_queue_size\'], varargs=None, keywords=None, defaults=[\'1\', \'10\'], " + } + member_method { + name: "stop" + argspec: "args=[\'self\', \'timeout\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-sequence.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-sequence.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e2dc932dc86dbba49d186e1dbc4bc026a52f6ef5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.-sequence.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.keras.utils.Sequence" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "on_epoch_end" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4d7a1519ce59b6f0a7f0bbfb3292842a6f21dffd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.utils.pbtxt @@ -0,0 +1,67 @@ +path: "tensorflow.keras.utils" +tf_module { + member { + name: "CustomObjectScope" + mtype: "" + } + member { + name: "GeneratorEnqueuer" + mtype: "" + } + member { + name: "HDF5Matrix" + mtype: "" + } + member { + name: "Progbar" + mtype: "" + } + member { + name: "Sequence" + mtype: "" + } + member { + name: "SequenceEnqueuer" + mtype: "" + } + member_method { + name: "convert_all_kernels_in_model" + argspec: "args=[\'model\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "custom_object_scope" + argspec: "args=[], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "deserialize_keras_object" + argspec: "args=[\'identifier\', \'module_objects\', \'custom_objects\', \'printable_module_name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'object\'], " + } + member_method { + name: "get_custom_objects" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_file" + argspec: "args=[\'fname\', \'origin\', \'untar\', \'md5_hash\', \'file_hash\', \'cache_subdir\', \'hash_algorithm\', \'extract\', \'archive_format\', \'cache_dir\'], varargs=None, keywords=None, defaults=[\'False\', \'None\', \'None\', \'datasets\', \'auto\', \'False\', \'auto\', \'None\'], " + } + member_method { + name: "multi_gpu_model" + argspec: "args=[\'model\', \'gpus\', \'cpu_merge\', \'cpu_relocation\'], varargs=None, keywords=None, defaults=[\'True\', \'False\'], " + } + member_method { + name: "normalize" + argspec: "args=[\'x\', \'axis\', \'order\'], varargs=None, keywords=None, defaults=[\'-1\', \'2\'], " + } + member_method { + name: "plot_model" + argspec: "args=[\'model\', \'to_file\', \'show_shapes\', \'show_layer_names\', \'rankdir\'], varargs=None, keywords=None, defaults=[\'model.png\', \'False\', \'True\', \'TB\'], " + } + member_method { + name: "serialize_keras_object" + argspec: "args=[\'instance\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "to_categorical" + argspec: "args=[\'y\', \'num_classes\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0b2fac9b7d998312d1bc080d7464d17b2b5543f5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.keras.wrappers" +tf_module { + member { + name: "scikit_learn" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.-keras-classifier.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.-keras-classifier.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..67cca3af41dbf68b963fb2315b65f9f843c9a42d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.-keras-classifier.pbtxt @@ -0,0 +1,42 @@ +path: "tensorflow.keras.wrappers.scikit_learn.KerasClassifier" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'build_fn\'], varargs=None, keywords=sk_params, defaults=[\'None\'], " + } + member_method { + name: "check_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "filter_sk_params" + argspec: "args=[\'self\', \'fn\', \'override\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "fit" + argspec: "args=[\'self\', \'x\', \'y\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "get_params" + argspec: "args=[\'self\'], varargs=None, keywords=params, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'x\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "predict_proba" + argspec: "args=[\'self\', \'x\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "score" + argspec: "args=[\'self\', \'x\', \'y\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\'], varargs=None, keywords=params, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.-keras-regressor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.-keras-regressor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f4b9b7e277ecdb155327d83c57ec2a997c043555 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.-keras-regressor.pbtxt @@ -0,0 +1,38 @@ +path: "tensorflow.keras.wrappers.scikit_learn.KerasRegressor" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'build_fn\'], varargs=None, keywords=sk_params, defaults=[\'None\'], " + } + member_method { + name: "check_params" + argspec: "args=[\'self\', \'params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "filter_sk_params" + argspec: "args=[\'self\', \'fn\', \'override\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "fit" + argspec: "args=[\'self\', \'x\', \'y\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "get_params" + argspec: "args=[\'self\'], varargs=None, keywords=params, defaults=None" + } + member_method { + name: "predict" + argspec: "args=[\'self\', \'x\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "score" + argspec: "args=[\'self\', \'x\', \'y\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "set_params" + argspec: "args=[\'self\'], varargs=None, keywords=params, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..fbd4d13387a931c3c947d8d0babcbfa978070de9 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.wrappers.scikit_learn.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.keras.wrappers.scikit_learn" +tf_module { + member { + name: "KerasClassifier" + mtype: "" + } + member { + name: "KerasRegressor" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.layers.-average-pooling1-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-average-pooling1-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c82e67526b21696a7d56517dc2cb6998882dc7a5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-average-pooling1-d.pbtxt @@ -0,0 +1,186 @@ +path: "tensorflow.layers.AveragePooling1D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "scope_name" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'valid\', \'channels_last\', \'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.layers.-average-pooling2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-average-pooling2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1d031cb5f8461145127b0f13d77e6b8774f5a0b3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-average-pooling2-d.pbtxt @@ -0,0 +1,186 @@ +path: "tensorflow.layers.AveragePooling2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "scope_name" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'valid\', \'channels_last\', \'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: 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b/tensorflow/tools/api/golden/v2/tensorflow.layers.-batch-normalization.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..97f65ed89436bd0b4027bb0cbeb80b6f1419269c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-batch-normalization.pbtxt @@ -0,0 +1,185 @@ +path: "tensorflow.layers.BatchNormalization" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + 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a/tensorflow/tools/api/golden/v2/tensorflow.layers.-conv3-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-conv3-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..96ab209874ac14d6acf2e8115e7f04fc35c4b2bd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-conv3-d.pbtxt @@ -0,0 +1,186 @@ +path: "tensorflow.layers.Conv3D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + 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name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.layers.-dense.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-dense.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7e9656b3525c1d53940b869607616ff414a466cf --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-dense.pbtxt @@ -0,0 +1,185 @@ +path: "tensorflow.layers.Dense" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: "losses" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "non_trainable_variables" + mtype: "" + } + member { + name: "non_trainable_weights" + mtype: "" + } + member { + name: "outbound_nodes" + mtype: "" + } + member { + name: "output" + mtype: "" + } + member { + name: "output_mask" + mtype: "" + } + member { + name: "output_shape" + mtype: "" + } + member { + name: "scope_name" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'units\', \'activation\', 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varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.layers.-dropout.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-dropout.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e9a2269a6e8de1f9a12f1b54d2e6dced3d4f8902 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-dropout.pbtxt @@ -0,0 +1,185 @@ +path: "tensorflow.layers.Dropout" +tf_class { + is_instance: "" + 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"args=[\'self\', \'dtype\', \'shape\', \'ndim\', \'max_ndim\', \'min_ndim\', \'axes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.layers.-layer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-layer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8bc3eb26e9ca0bf0f129db336b7ca23466fd036f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-layer.pbtxt @@ -0,0 +1,183 @@ +path: "tensorflow.layers.Layer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + 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defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" 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argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.layers.-max-pooling1-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-max-pooling1-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6a0dcce56ac0184ffe995662fd62b89e16257a29 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-max-pooling1-d.pbtxt @@ -0,0 +1,186 @@ +path: "tensorflow.layers.MaxPooling1D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: "input_shape" + mtype: "" + } + member { + name: 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member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.layers.-max-pooling2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.layers.-max-pooling2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b6c84edf2a2f86240369b4053cd7351d0b59442d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.layers.-max-pooling2-d.pbtxt @@ -0,0 +1,186 @@ +path: "tensorflow.layers.MaxPooling2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: "" + } + member { + name: 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a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-block-diag.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-block-diag.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..973705dae2fabbef0eafb38ad12e96c747aeee27 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-block-diag.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.linalg.LinearOperatorBlockDiag" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "operators" + 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member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3b33f3da97ec2ecb3f94e8bc309be2519fc79c62 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorCirculant.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..de917706d55214cc59f3205f0778d600a356a5b1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant.pbtxt @@ -0,0 +1,155 @@ +path: "tensorflow.linalg.LinearOperatorCirculant" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "block_depth" + mtype: "" + } + member { + name: "block_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "spectrum" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'spectrum\', \'input_output_dtype\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\', \'None\', \'None\', \'True\', \'LinearOperatorCirculant\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_hermitian_spectrum" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_hermitian_spectrum\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "block_shape_tensor" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "convolution_kernel" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'convolution_kernel\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant2-d.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant2-d.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..591bc9631a1d8ecbbd6e133b99c67e432399d73f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant2-d.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorCirculant2D.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant2-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant2-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c4e6a21c3ac9324f5dd445dc65415c2abb4c6e9f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant2-d.pbtxt @@ -0,0 +1,155 @@ +path: "tensorflow.linalg.LinearOperatorCirculant2D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "block_depth" + mtype: "" + } + member { + name: "block_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "spectrum" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'spectrum\', \'input_output_dtype\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\', \'None\', \'None\', \'True\', \'LinearOperatorCirculant2D\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_hermitian_spectrum" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_hermitian_spectrum\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "block_shape_tensor" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "convolution_kernel" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'convolution_kernel\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant3-d.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant3-d.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d643139a53fc501fe2997a2b9f2d11c57b96f2e4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant3-d.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorCirculant3D.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant3-d.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant3-d.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2e085a8e289e21173789041efb9254e992bd723b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-circulant3-d.pbtxt @@ -0,0 +1,155 @@ +path: "tensorflow.linalg.LinearOperatorCirculant3D" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "block_depth" + mtype: "" + } + member { + name: "block_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "spectrum" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'spectrum\', \'input_output_dtype\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\', \'None\', \'None\', \'True\', \'LinearOperatorCirculant3D\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_hermitian_spectrum" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_hermitian_spectrum\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "block_shape_tensor" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "convolution_kernel" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'convolution_kernel\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-composition.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-composition.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1adbcb41adfac33acfdb415662ced7992e21385e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-composition.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorComposition.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-composition.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-composition.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..42d22bce42d8850a784afae3f67771ef1cfe5403 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-composition.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.linalg.LinearOperatorComposition" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "operators" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'operators\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-diag.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-diag.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..023d90ccdba8a8739a11f4691d33b7087bedcc0b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-diag.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorDiag.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-diag.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-diag.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d6749fdcec69425e83a044409ec695d2661f782e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-diag.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.linalg.LinearOperatorDiag" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "diag" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'diag\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'LinearOperatorDiag\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-full-matrix.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-full-matrix.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..381072e76c4d069ebf51fec44079b30f17cafc06 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-full-matrix.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorFullMatrix.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-full-matrix.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-full-matrix.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d9f363d1336210623536e8293a6290d9ebfc2fe1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-full-matrix.pbtxt @@ -0,0 +1,130 @@ +path: "tensorflow.linalg.LinearOperatorFullMatrix" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'matrix\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'LinearOperatorFullMatrix\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-identity.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-identity.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5d115b35fb79cbc176a9e8a9bf1ec0f0edcc79e6 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-identity.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorIdentity.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-identity.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-identity.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..aac7ee31ed62c22b2e86d287d48c68c7e905fd00 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-identity.pbtxt @@ -0,0 +1,131 @@ +path: "tensorflow.linalg.LinearOperatorIdentity" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'num_rows\', \'batch_shape\', \'dtype\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'assert_proper_shapes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'True\', \'True\', \'True\', \'False\', \'LinearOperatorIdentity\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'mat\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-kronecker.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-kronecker.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5c6784dd02104129a9ac38fe171d87c115efbbf0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-kronecker.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorKronecker.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-kronecker.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-kronecker.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c11d39082939eda4520b3955b767022bd485b5be --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-kronecker.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.linalg.LinearOperatorKronecker" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "operators" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'operators\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-low-rank-update.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-low-rank-update.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1f0d33298a252a8b3da6eea9fd4bc096e8dd6745 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-low-rank-update.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorLowRankUpdate.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-low-rank-update.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-low-rank-update.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3ee800269e617390c25248a2c847cbe259b18e79 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-low-rank-update.pbtxt @@ -0,0 +1,154 @@ +path: "tensorflow.linalg.LinearOperatorLowRankUpdate" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "base_operator" + mtype: "" + } + member { + name: "batch_shape" + mtype: "" + } + member { + name: "diag_operator" + mtype: "" + } + member { + name: "diag_update" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_diag_update_positive" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member { + name: "u" + mtype: "" + } + member { + name: "v" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'base_operator\', \'u\', \'diag_update\', \'v\', \'is_diag_update_positive\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'LinearOperatorLowRankUpdate\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-lower-triangular.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-lower-triangular.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2683430f4fc5d96d63c5b6fdb4035d6e5e8ba609 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-lower-triangular.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorLowerTriangular.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-lower-triangular.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-lower-triangular.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..63a1bc2321e35645700778c5906d1b8659eb4a32 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-lower-triangular.pbtxt @@ -0,0 +1,130 @@ +path: "tensorflow.linalg.LinearOperatorLowerTriangular" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'tril\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'LinearOperatorLowerTriangular\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-scaled-identity.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-scaled-identity.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..38bf7ad586a063046f260aca9b1c517a343c4c05 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-scaled-identity.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorScaledIdentity.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-scaled-identity.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-scaled-identity.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e2c5a505a7d2f9abbee5b3bb4f92ee8843198c51 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-scaled-identity.pbtxt @@ -0,0 +1,135 @@ +path: "tensorflow.linalg.LinearOperatorScaledIdentity" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "multiplier" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'num_rows\', \'multiplier\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'assert_proper_shapes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'False\', \'LinearOperatorScaledIdentity\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'mat\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..49ff85728ffab559ec706691356ce071aab89083 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorZeros.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-zeros.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-zeros.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a1b0e06b4753488bc9fcbe9aeb0d260092745f9c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator-zeros.pbtxt @@ -0,0 +1,130 @@ +path: "tensorflow.linalg.LinearOperatorZeros" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'num_rows\', \'num_columns\', \'batch_shape\', \'dtype\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'assert_proper_shapes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\', \'True\', \'False\', \'True\', \'False\', \'LinearOperatorZeros\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'mat\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator.__metaclass__.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator.__metaclass__.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..38da809b360e5ea69b4324a859ed69da679bc436 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperator.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6d849dc040f61b498b100820bf7be3d4bc264bb4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.-linear-operator.pbtxt @@ -0,0 +1,129 @@ +path: "tensorflow.linalg.LinearOperator" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\', \'graph_parents\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.linalg.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.linalg.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d979116887a739d2d372687fac0e5ea3b39a4b69 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.linalg.pbtxt @@ -0,0 +1,175 @@ +path: "tensorflow.linalg" +tf_module { + member { + name: "LinearOperator" + mtype: "" + } + member { + name: "LinearOperatorBlockDiag" + mtype: "" + } + member { + name: "LinearOperatorCirculant" + mtype: "" + } + member { + name: "LinearOperatorCirculant2D" + mtype: "" + } + member { + name: "LinearOperatorCirculant3D" + mtype: "" + } + member { + name: "LinearOperatorComposition" + mtype: "" + } + member { + name: "LinearOperatorDiag" + mtype: "" + } + member { + name: "LinearOperatorFullMatrix" + mtype: "" + } + member { + name: "LinearOperatorIdentity" + mtype: "" + } + member { + name: "LinearOperatorKronecker" + mtype: "" + } + member { + name: "LinearOperatorLowRankUpdate" + mtype: "" + } + member { + name: "LinearOperatorLowerTriangular" + mtype: "" + } + member { + name: "LinearOperatorScaledIdentity" + mtype: "" + } + member { + name: "LinearOperatorZeros" + mtype: "" + } + member_method { + name: "adjoint" + argspec: "args=[\'matrix\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "band_part" + argspec: "args=[\'input\', \'num_lower\', \'num_upper\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cholesky" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cholesky_solve" + argspec: "args=[\'chol\', \'rhs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cross" + argspec: "args=[\'a\', \'b\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "det" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "diag" + argspec: "args=[\'diagonal\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "eigh" + argspec: "args=[\'tensor\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "eigvalsh" + argspec: "args=[\'tensor\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "einsum" + argspec: "args=[\'equation\'], varargs=inputs, keywords=kwargs, defaults=None" + } + member_method { + name: "expm" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "eye" + argspec: "args=[\'num_rows\', \'num_columns\', \'batch_shape\', \'dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \"\", \'None\'], " + } + member_method { + name: "inv" + argspec: "args=[\'input\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "logdet" + argspec: "args=[\'matrix\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "logm" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "lstsq" + argspec: "args=[\'matrix\', \'rhs\', \'l2_regularizer\', \'fast\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'True\', \'None\'], " + } + member_method { + name: "norm" + argspec: "args=[\'tensor\', \'ord\', \'axis\', \'keepdims\', \'name\', \'keep_dims\'], varargs=None, keywords=None, defaults=[\'euclidean\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "qr" + argspec: "args=[\'input\', \'full_matrices\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "set_diag" + argspec: "args=[\'input\', \'diagonal\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "slogdet" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "solve" + argspec: "args=[\'matrix\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "svd" + argspec: "args=[\'tensor\', \'full_matrices\', \'compute_uv\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'True\', \'None\'], " + } + member_method { + name: "tensor_diag" + argspec: "args=[\'diagonal\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tensor_diag_part" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tensordot" + argspec: "args=[\'a\', \'b\', \'axes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "trace" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "transpose" + argspec: "args=[\'a\', \'name\', \'conjugate\'], varargs=None, keywords=None, defaults=[\'matrix_transpose\', \'False\'], " + } + member_method { + name: "triangular_solve" + argspec: "args=[\'matrix\', \'rhs\', \'lower\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'False\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.logging.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.logging.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..85bb15455da624962744a0cc856e79e0a6d57d7c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.logging.pbtxt @@ -0,0 +1,83 @@ +path: "tensorflow.logging" +tf_module { + member { + name: "DEBUG" + mtype: "" + } + member { + name: "ERROR" + mtype: "" + } + member { + name: "FATAL" + mtype: "" + } + member { + name: "INFO" + mtype: "" + } + member { + name: "WARN" + mtype: "" + } + member_method { + name: "TaskLevelStatusMessage" + argspec: "args=[\'msg\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "debug" + argspec: "args=[\'msg\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "error" + argspec: "args=[\'msg\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "fatal" + argspec: "args=[\'msg\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "flush" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_verbosity" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "info" + argspec: "args=[\'msg\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "log" + argspec: "args=[\'level\', \'msg\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "log_every_n" + argspec: "args=[\'level\', \'msg\', \'n\'], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "log_first_n" + argspec: "args=[\'level\', \'msg\', \'n\'], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "log_if" + argspec: "args=[\'level\', \'msg\', \'condition\'], varargs=args, keywords=None, defaults=None" + } + member_method { + name: "set_verbosity" + argspec: "args=[\'v\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "vlog" + argspec: "args=[\'level\', \'msg\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "warn" + argspec: "args=[\'msg\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "warning" + argspec: "args=[\'msg\'], varargs=args, keywords=kwargs, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.losses.-reduction.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.losses.-reduction.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..258ad5047eb6e82eeb9c0941b0acf0573e5ca61d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.losses.-reduction.pbtxt @@ -0,0 +1,40 @@ +path: "tensorflow.losses.Reduction" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "MEAN" + mtype: "" + } + member { + name: "NONE" + mtype: "" + } + member { + name: "SUM" + mtype: "" + } + member { + name: "SUM_BY_NONZERO_WEIGHTS" + mtype: "" + } + member { + name: "SUM_OVER_BATCH_SIZE" + mtype: "" + } + member { + name: "SUM_OVER_NONZERO_WEIGHTS" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "all" + argspec: "args=[\'cls\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "validate" + argspec: "args=[\'cls\', \'key\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.losses.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.losses.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c1d190ae116e94ec8f837237e54b6fcff7358254 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.losses.pbtxt @@ -0,0 +1,71 @@ +path: "tensorflow.losses" +tf_module { + member { + name: "Reduction" + mtype: "" + } + member_method { + name: "absolute_difference" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'loss\', \'loss_collection\'], varargs=None, keywords=None, defaults=[\'losses\'], " + } + member_method { + name: "compute_weighted_loss" + argspec: "args=[\'losses\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "cosine_distance" + argspec: "args=[\'labels\', \'predictions\', \'axis\', \'weights\', \'scope\', \'loss_collection\', \'reduction\', \'dim\'], varargs=None, keywords=None, defaults=[\'None\', \'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\', \'None\'], " + } + member_method { + name: "get_losses" + argspec: "args=[\'scope\', \'loss_collection\'], varargs=None, keywords=None, defaults=[\'None\', \'losses\'], " + } + member_method { + name: "get_regularization_loss" + argspec: "args=[\'scope\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'total_regularization_loss\'], " + } + member_method { + name: "get_regularization_losses" + argspec: "args=[\'scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_total_loss" + argspec: "args=[\'add_regularization_losses\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'total_loss\'], " + } + member_method { + name: "hinge_loss" + argspec: "args=[\'labels\', \'logits\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "huber_loss" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'delta\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "log_loss" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'epsilon\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1e-07\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "mean_pairwise_squared_error" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\'], " + } + member_method { + name: "mean_squared_error" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "sigmoid_cross_entropy" + argspec: "args=[\'multi_class_labels\', \'logits\', \'weights\', \'label_smoothing\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "softmax_cross_entropy" + argspec: "args=[\'onehot_labels\', \'logits\', \'weights\', \'label_smoothing\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } + member_method { + name: "sparse_softmax_cross_entropy" + argspec: "args=[\'labels\', \'logits\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.manip.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.manip.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9add462396ea526ae94678e969c9acf5bce86df1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.manip.pbtxt @@ -0,0 +1,35 @@ +path: "tensorflow.manip" +tf_module { + member_method { + name: "batch_to_space_nd" + argspec: "args=[\'input\', \'block_shape\', \'crops\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "gather_nd" + argspec: "args=[\'params\', \'indices\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reshape" + argspec: "args=[\'tensor\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "reverse" + argspec: "args=[\'tensor\', \'axis\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "roll" + argspec: "args=[\'input\', \'shift\', \'axis\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "scatter_nd" + argspec: "args=[\'indices\', \'updates\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "space_to_batch_nd" + argspec: "args=[\'input\', \'block_shape\', \'paddings\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "tile" + argspec: "args=[\'input\', \'multiples\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a308c76ebc08df06c0c360579451ea70e60695d4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.math.pbtxt @@ -0,0 +1,239 @@ +path: "tensorflow.math" +tf_module { + member_method { + name: "acos" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "acosh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add" + argspec: 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{ + name: "bessel_i1" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bessel_i1e" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "betainc" + argspec: "args=[\'a\', \'b\', \'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "ceil" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cos" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cosh" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "digamma" + argspec: "args=[\'x\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "equal" + argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, 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/dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.metrics.pbtxt @@ -0,0 +1,135 @@ +path: "tensorflow.metrics" +tf_module { + member_method { + name: "accuracy" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "auc" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'num_thresholds\', \'metrics_collections\', \'updates_collections\', \'curve\', \'name\', \'summation_method\'], varargs=None, keywords=None, defaults=[\'None\', \'200\', \'None\', \'None\', \'ROC\', \'None\', \'trapezoidal\'], " + } + member_method { + name: "average_precision_at_k" + argspec: "args=[\'labels\', \'predictions\', \'k\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: 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\'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'200\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "sparse_average_precision_at_k" + argspec: "args=[\'labels\', \'predictions\', \'k\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "sparse_precision_at_k" + argspec: "args=[\'labels\', \'predictions\', \'k\', \'class_id\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "specificity_at_sensitivity" + argspec: "args=[\'labels\', \'predictions\', \'sensitivity\', \'weights\', \'num_thresholds\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'200\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "true_negatives" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "true_negatives_at_thresholds" + argspec: "args=[\'labels\', \'predictions\', \'thresholds\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "true_positives" + argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "true_positives_at_thresholds" + argspec: "args=[\'labels\', \'predictions\', \'thresholds\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.name_scope.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.name_scope.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..80418970132377a5d578e4f11fa4091a19202cf3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.name_scope.pbtxt @@ -0,0 +1,13 @@ +path: "tensorflow.name_scope" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name\', \'default_name\', \'values\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d9e5b0d0fca8bbcf82feb34304f2a1e4f43f48dd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.pbtxt @@ -0,0 +1,359 @@ +path: "tensorflow.nn" +tf_module { + member { + name: "rnn_cell" + mtype: "" + } + member { + name: "swish" + mtype: "" + } + member_method { + name: "all_candidate_sampler" + argspec: "args=[\'true_classes\', \'num_true\', \'num_sampled\', \'unique\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "atrous_conv2d" + argspec: "args=[\'value\', \'filters\', \'rate\', \'padding\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "atrous_conv2d_transpose" + argspec: "args=[\'value\', \'filters\', \'output_shape\', \'rate\', \'padding\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "avg_pool" + argspec: "args=[\'value\', \'ksize\', \'strides\', \'padding\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'NHWC\', \'None\'], " + } + member_method { + name: "avg_pool3d" + argspec: "args=[\'input\', \'ksize\', \'strides\', \'padding\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'NDHWC\', \'None\'], " + } + member_method { + name: "batch_norm_with_global_normalization" + argspec: "args=[\'t\', \'m\', \'v\', \'beta\', \'gamma\', \'variance_epsilon\', \'scale_after_normalization\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "batch_normalization" + argspec: "args=[\'x\', \'mean\', \'variance\', \'offset\', \'scale\', \'variance_epsilon\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "bias_add" + argspec: "args=[\'value\', \'bias\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "bidirectional_dynamic_rnn" + argspec: "args=[\'cell_fw\', \'cell_bw\', \'inputs\', \'sequence_length\', \'initial_state_fw\', \'initial_state_bw\', \'dtype\', \'parallel_iterations\', \'swap_memory\', \'time_major\', \'scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'False\', \'None\'], " + } + member_method { + name: "compute_accidental_hits" + argspec: "args=[\'true_classes\', \'sampled_candidates\', \'num_true\', \'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "conv1d" + argspec: "args=[\'value\', \'filters\', \'stride\', \'padding\', \'use_cudnn_on_gpu\', \'data_format\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "conv2d" + argspec: "args=[\'input\', \'filter\', \'strides\', \'padding\', \'use_cudnn_on_gpu\', \'data_format\', \'dilations\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'NHWC\', \'[1, 1, 1, 1]\', \'None\'], " + } + member_method { + name: "conv2d_backprop_filter" + argspec: "args=[\'input\', \'filter_sizes\', \'out_backprop\', \'strides\', \'padding\', 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tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8a63b4918008674041c9c216a5e5547ed7152fce --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt @@ -0,0 +1,197 @@ +path: "tensorflow.nn.rnn_cell.DeviceWrapper" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "activity_regularizer" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member { + name: "inbound_nodes" + mtype: "" + } + member { + name: "input" + mtype: "" + } + member { + name: "input_mask" + mtype: 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tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt diff --git a/tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1de8a55dccac10ee9af08eb1efc0cb6d22f7163b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-state-tuple.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.nn.rnn_cell.LSTMStateTuple" +tf_class { + is_instance: "" + is_instance: "" 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} + member { + name: "output_size" + mtype: "" + } + member { + name: "scope_name" + mtype: "" + } + member { + name: "state_size" + mtype: "" + } + member { + name: "trainable_variables" + mtype: "" + } + member { + name: "trainable_weights" + mtype: "" + } + member { + name: "updates" + mtype: "" + } + member { + name: "variables" + mtype: "" + } + member { + name: "weights" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'cell\', \'residual_fn\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_loss" + argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_update" + argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_variable" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "add_weight" + argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\', \'_\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "call" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=kwargs, defaults=None" + } + member_method { + name: "compute_mask" + argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "compute_output_shape" + argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "count_params" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_input_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_losses_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_mask_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_output_shape_at" + argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_updates_for" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_weights" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_weights" + argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "zero_state" + argspec: "args=[\'self\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..64697e8a02b90bdace731a414570b7dc9da11015 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.pbtxt @@ -0,0 +1,43 @@ +path: "tensorflow.nn.rnn_cell" +tf_module { + member { + name: "BasicLSTMCell" + mtype: "" + } + member { + name: "BasicRNNCell" + mtype: "" + } + member { + name: "DeviceWrapper" + mtype: "" + } + member { + name: "DropoutWrapper" + mtype: "" + } + member { + name: "GRUCell" + mtype: "" + } + member { + name: "LSTMCell" + mtype: "" + } + member { + name: "LSTMStateTuple" + mtype: "" + } + member { + name: "MultiRNNCell" + mtype: "" + } + member { + name: "RNNCell" + mtype: "" + } + member { + name: "ResidualWrapper" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.ones_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.ones_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..210b56242b27fe4a832cfe50a53626d716d8877e --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.ones_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.ones_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.orthogonal_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.orthogonal_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..13ec7454f41eac2b23e07ba62068bb48dddac90b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.orthogonal_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.orthogonal_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.-checker.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.-checker.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e09c44cc9ce71305692740ba2d63b0940b2e0573 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.-checker.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.profiler.AdviceProto.Checker" +tf_proto { + descriptor { + name: "Checker" + field { + name: "reports" + number: 2 + label: LABEL_REPEATED + type: TYPE_STRING + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..87462435496fd2eedeb0bc8d92e8a833671b6531 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.-checkers-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.profiler.AdviceProto.CheckersEntry" +tf_proto { + descriptor { + name: "CheckersEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.AdviceProto.Checker" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a8a8858ccd5af3fb3dac612eef44e5cb450df914 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-advice-proto.pbtxt @@ -0,0 +1,41 @@ +path: "tensorflow.profiler.AdviceProto" +tf_proto { + descriptor { + name: "AdviceProto" + field { + name: "checkers" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.AdviceProto.CheckersEntry" + } + nested_type { + name: "CheckersEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.AdviceProto.Checker" + } + options { + map_entry: true + } + } + nested_type { + name: "Checker" + field { + name: "reports" + number: 2 + label: LABEL_REPEATED + type: TYPE_STRING + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..afec73f537aadd5d1a274db8d57e37b8c6fa3e74 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-graph-node-proto.-input-shapes-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.profiler.GraphNodeProto.InputShapesEntry" +tf_proto { + descriptor { + name: "InputShapesEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorShapeProto" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-graph-node-proto.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3c83177005323a277f929d8c769cd7b1eeff4d51 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-graph-node-proto.pbtxt @@ -0,0 +1,191 @@ +path: "tensorflow.profiler.GraphNodeProto" +tf_proto { + descriptor { + name: "GraphNodeProto" + field { + name: "name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tensor_value" + number: 15 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.TFProfTensorProto" + } + field { + name: "run_count" + number: 21 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "exec_micros" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "accelerator_exec_micros" + number: 17 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "cpu_exec_micros" + number: 18 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "requested_bytes" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "peak_bytes" + number: 24 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "residual_bytes" + number: 25 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "output_bytes" + number: 26 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "parameters" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "float_ops" + number: 13 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "devices" + number: 10 + label: LABEL_REPEATED + type: TYPE_STRING + } + field { + name: "total_definition_count" + number: 23 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_run_count" + number: 22 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_exec_micros" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_accelerator_exec_micros" + number: 19 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_cpu_exec_micros" + number: 20 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_requested_bytes" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_peak_bytes" + number: 27 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_residual_bytes" + number: 28 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_output_bytes" + number: 29 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_parameters" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_float_ops" + number: 14 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "shapes" + number: 11 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorShapeProto" + } + field { + name: "input_shapes" + number: 16 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.GraphNodeProto.InputShapesEntry" + } + field { + name: "children" + number: 12 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.GraphNodeProto" + } + nested_type { + name: "InputShapesEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorShapeProto" + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-multi-graph-node-proto.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-multi-graph-node-proto.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2b08a05437f90b91160fc08e670b2466ae163149 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-multi-graph-node-proto.pbtxt @@ -0,0 +1,134 @@ +path: "tensorflow.profiler.MultiGraphNodeProto" +tf_proto { + descriptor { + name: "MultiGraphNodeProto" + field { + name: "name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "exec_micros" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "accelerator_exec_micros" + number: 12 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "cpu_exec_micros" + number: 13 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "requested_bytes" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "peak_bytes" + number: 16 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "residual_bytes" + number: 17 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "output_bytes" + number: 18 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "parameters" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "float_ops" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_exec_micros" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_accelerator_exec_micros" + number: 14 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_cpu_exec_micros" + number: 15 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_requested_bytes" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_peak_bytes" + number: 19 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_residual_bytes" + number: 20 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_output_bytes" + number: 21 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_parameters" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "total_float_ops" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "graph_nodes" + number: 10 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.GraphNodeProto" + } + field { + name: "children" + number: 11 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.MultiGraphNodeProto" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b3adc50c7e14152a81a148df9deccc5272189aad --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-op-log-proto.-id-to-string-entry.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.profiler.OpLogProto.IdToStringEntry" +tf_proto { + descriptor { + name: "IdToStringEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-op-log-proto.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-op-log-proto.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7510c566ba574e9370f5e54c29023ef4fb5ee804 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-op-log-proto.pbtxt @@ -0,0 +1,38 @@ +path: "tensorflow.profiler.OpLogProto" +tf_proto { + descriptor { + name: "OpLogProto" + field { + name: "log_entries" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.OpLogEntry" + } + field { + name: "id_to_string" + number: 2 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.tfprof.OpLogProto.IdToStringEntry" + } + nested_type { + name: "IdToStringEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-profile-option-builder.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-profile-option-builder.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..19ff38a3900c2d358faaa40e7316cc3a9da73040 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-profile-option-builder.pbtxt @@ -0,0 +1,93 @@ +path: "tensorflow.profiler.ProfileOptionBuilder" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "account_displayed_op_only" + argspec: "args=[\'self\', \'is_true\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "float_operation" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "order_by" + argspec: "args=[\'self\', \'attribute\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "select" + argspec: "args=[\'self\', \'attributes\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "time_and_memory" + argspec: "args=[\'min_micros\', \'min_bytes\', \'min_accelerator_micros\', \'min_cpu_micros\', \'min_peak_bytes\', \'min_residual_bytes\', \'min_output_bytes\'], varargs=None, keywords=None, defaults=[\'1\', \'1\', \'0\', \'0\', \'0\', \'0\', \'0\'], " + } + member_method { + name: "trainable_variables_parameter" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_accounted_types" + argspec: "args=[\'self\', \'account_type_regexes\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_empty_output" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_file_output" + argspec: "args=[\'self\', \'outfile\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_max_depth" + argspec: "args=[\'self\', \'max_depth\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_min_execution_time" + argspec: "args=[\'self\', \'min_micros\', \'min_accelerator_micros\', \'min_cpu_micros\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\'], " + } + member_method { + name: "with_min_float_operations" + argspec: "args=[\'self\', \'min_float_ops\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_min_memory" + argspec: "args=[\'self\', \'min_bytes\', \'min_peak_bytes\', \'min_residual_bytes\', \'min_output_bytes\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'0\'], " + } + member_method { + name: "with_min_occurrence" + argspec: "args=[\'self\', \'min_occurrence\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_min_parameters" + argspec: "args=[\'self\', \'min_params\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_node_names" + argspec: "args=[\'self\', \'start_name_regexes\', \'show_name_regexes\', \'hide_name_regexes\', \'trim_name_regexes\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "with_pprof_output" + argspec: "args=[\'self\', \'pprof_file\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_stdout_output" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_step" + argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "with_timeline_output" + argspec: "args=[\'self\', \'timeline_file\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.-profiler.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-profiler.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..acb61dae9f0d184ba998aa820ec40de5bc38c3eb --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.-profiler.pbtxt @@ -0,0 +1,37 @@ +path: "tensorflow.profiler.Profiler" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'graph\', \'op_log\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "add_step" + argspec: "args=[\'self\', \'step\', \'run_meta\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "advise" + argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "profile_graph" + argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "profile_name_scope" + argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "profile_operations" + argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "profile_python" + argspec: "args=[\'self\', \'options\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "serialize_to_string" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.profiler.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.profiler.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7b4d3ac522abc4229c5623da25c4ec818d86f829 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.profiler.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.profiler" +tf_module { + member { + name: "AdviceProto" + mtype: "" + } + member { + name: "GraphNodeProto" + mtype: "" + } + member { + name: "MultiGraphNodeProto" + mtype: "" + } + member { + name: "OpLogProto" + mtype: "" + } + member { + name: "ProfileOptionBuilder" + mtype: "" + } + member { + name: "Profiler" + mtype: "" + } + member_method { + name: "advise" + argspec: "args=[\'graph\', \'run_meta\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'0\'], " + } + member_method { + name: "profile" + argspec: "args=[\'graph\', \'run_meta\', \'op_log\', \'cmd\', \'options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'scope\', \'0\'], " + } + member_method { + name: "write_op_log" + argspec: "args=[\'graph\', \'log_dir\', \'op_log\', \'run_meta\', \'add_trace\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-compression-type.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-compression-type.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4941dda50e4964f8400a4cb5033c8e918aeaea5d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-compression-type.pbtxt @@ -0,0 +1,20 @@ +path: "tensorflow.python_io.TFRecordCompressionType" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "GZIP" + mtype: "" + } + member { + name: "NONE" + mtype: "" + } + member { + name: "ZLIB" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-options.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-options.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0853716023ae5271fba6e8024e719eebb22ec56d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-options.pbtxt @@ -0,0 +1,17 @@ +path: "tensorflow.python_io.TFRecordOptions" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "compression_type_map" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'compression_type\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_compression_type_string" + argspec: "args=[\'cls\', \'options\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-writer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-writer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..31775de2d12bcd2f214f5a04be7a92f49c594fde --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.python_io.-t-f-record-writer.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.python_io.TFRecordWriter" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'path\', \'options\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flush" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "write" + argspec: "args=[\'self\', \'record\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.python_io.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.python_io.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7c9953e5fe3c883fd5e6e19ae011cc464f4107af --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.python_io.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.python_io" +tf_module { + member { + name: "TFRecordCompressionType" + mtype: "" + } + member { + name: "TFRecordOptions" + mtype: "" + } + member { + name: "TFRecordWriter" + mtype: "" + } + member_method { + name: "tf_record_iterator" + argspec: "args=[\'path\', \'options\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.quantization.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.quantization.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6d865efed0bfdada8dde64e86ddb5d2b2b364c79 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.quantization.pbtxt @@ -0,0 +1,35 @@ +path: "tensorflow.quantization" +tf_module { + member_method { + name: "dequantize" + argspec: "args=[\'input\', \'min_range\', \'max_range\', \'mode\', \'name\'], varargs=None, keywords=None, defaults=[\'MIN_COMBINED\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_args" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'-6\', \'6\', \'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_args_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'-6\', \'6\', \'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_per_channel" + argspec: "args=[\'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "fake_quant_with_min_max_vars_per_channel_gradient" + argspec: "args=[\'gradients\', \'inputs\', \'min\', \'max\', \'num_bits\', \'narrow_range\', \'name\'], varargs=None, keywords=None, defaults=[\'8\', \'False\', \'None\'], " + } + member_method { + name: "quantized_concat" + argspec: "args=[\'concat_dim\', \'values\', \'input_mins\', \'input_maxes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.random_normal_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.random_normal_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5993fdeb9c232ebc4090d9fffd8857da8ca6ada4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.random_normal_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.random_normal_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.random_uniform_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.random_uniform_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a434ed1599ef8b99b6e0496be388aa0e44755249 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.random_uniform_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.random_uniform_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.resource_loader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.resource_loader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..288b78b4cd0ad3f5d5bc1f9c773977d50a6db086 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.resource_loader.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.resource_loader" +tf_module { + member_method { + name: "get_data_files_path" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_path_to_datafile" + argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_root_dir_with_all_resources" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "load_resource" + argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "readahead_file_path" + argspec: "args=[\'path\', \'readahead\'], varargs=None, keywords=None, defaults=[\'128M\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.builder.-saved-model-builder.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.builder.-saved-model-builder.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..83bd7035409534abf036c7e2b0d66fcc060ada3a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.builder.-saved-model-builder.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.saved_model.builder.SavedModelBuilder" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'export_dir\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "add_meta_graph" + argspec: "args=[\'self\', \'tags\', \'signature_def_map\', \'assets_collection\', \'legacy_init_op\', \'clear_devices\', \'main_op\', \'strip_default_attrs\', \'saver\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "add_meta_graph_and_variables" + argspec: "args=[\'self\', \'sess\', \'tags\', \'signature_def_map\', \'assets_collection\', \'legacy_init_op\', \'clear_devices\', \'main_op\', \'strip_default_attrs\', \'saver\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "save" + argspec: "args=[\'self\', \'as_text\'], varargs=None, keywords=None, defaults=[\'False\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.builder.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.builder.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..adc697ad1c0bdd0c9b52be736fca3a19a2a82ef3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.builder.pbtxt @@ -0,0 +1,7 @@ +path: "tensorflow.saved_model.builder" +tf_module { + member { + name: "SavedModelBuilder" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.constants.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.constants.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..20e10aa094f704f2168de37abb73f6edf6765f93 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.constants.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.saved_model.constants" +tf_module { + member { + name: "ASSETS_DIRECTORY" + mtype: "" + } + member { + name: "ASSETS_KEY" + mtype: "" + } + member { + name: "LEGACY_INIT_OP_KEY" + mtype: "" + } + member { + name: "MAIN_OP_KEY" + mtype: "" + } + member { + name: "SAVED_MODEL_FILENAME_PB" + mtype: "" + } + member { + name: "SAVED_MODEL_FILENAME_PBTXT" + mtype: "" + } + member { + name: "SAVED_MODEL_SCHEMA_VERSION" + mtype: "" + } + member { + name: "VARIABLES_DIRECTORY" + mtype: "" + } + member { + name: "VARIABLES_FILENAME" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.loader.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.loader.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..511e6b4712d3c55746a39fe9098fa3b649bc75dc --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.loader.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.saved_model.loader" +tf_module { + member_method { + name: "load" + argspec: "args=[\'sess\', \'tags\', \'export_dir\', \'import_scope\'], varargs=None, keywords=saver_kwargs, defaults=[\'None\'], " + } + member_method { + name: "maybe_saved_model_directory" + argspec: "args=[\'export_dir\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.main_op.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.main_op.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..176cb788c249e68f1221713e96c7e808c39c8f6d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.main_op.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.saved_model.main_op" +tf_module { + member_method { + name: "main_op" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "main_op_with_restore" + argspec: "args=[\'restore_op_name\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e1a0385092c1384bcb5958fce2e24693ee731ae5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.saved_model" +tf_module { + member { + name: "builder" + mtype: "" + } + member { + name: "constants" + mtype: "" + } + member { + name: "loader" + mtype: "" + } + member { + name: "main_op" + mtype: "" + } + member { + name: "signature_constants" + mtype: "" + } + member { + name: "signature_def_utils" + mtype: "" + } + member { + name: "tag_constants" + mtype: "" + } + member { + name: "utils" + mtype: "" + } + member_method { + name: "simple_save" + argspec: "args=[\'session\', \'export_dir\', \'inputs\', \'outputs\', \'legacy_init_op\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.signature_constants.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.signature_constants.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..478d410e066b1ce3a17bb3ef9cc6e4503991ad0b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.signature_constants.pbtxt @@ -0,0 +1,47 @@ +path: "tensorflow.saved_model.signature_constants" +tf_module { + member { + name: "CLASSIFY_INPUTS" + mtype: "" + } + member { + name: "CLASSIFY_METHOD_NAME" + mtype: "" + } + member { + name: "CLASSIFY_OUTPUT_CLASSES" + mtype: "" + } + member { + name: "CLASSIFY_OUTPUT_SCORES" + mtype: "" + } + member { + name: "DEFAULT_SERVING_SIGNATURE_DEF_KEY" + mtype: "" + } + member { + name: "PREDICT_INPUTS" + mtype: "" + } + member { + name: "PREDICT_METHOD_NAME" + mtype: "" + } + member { + name: "PREDICT_OUTPUTS" + mtype: "" + } + member { + name: "REGRESS_INPUTS" + mtype: "" + } + member { + name: "REGRESS_METHOD_NAME" + mtype: "" + } + member { + name: "REGRESS_OUTPUTS" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.signature_def_utils.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.signature_def_utils.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a5602464eeb09a290076ef102ed5502ea61b4ac3 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.signature_def_utils.pbtxt @@ -0,0 +1,23 @@ +path: "tensorflow.saved_model.signature_def_utils" +tf_module { + member_method { + name: "build_signature_def" + argspec: "args=[\'inputs\', \'outputs\', \'method_name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "classification_signature_def" + argspec: "args=[\'examples\', \'classes\', \'scores\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_valid_signature" + argspec: "args=[\'signature_def\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "predict_signature_def" + argspec: "args=[\'inputs\', \'outputs\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "regression_signature_def" + argspec: "args=[\'examples\', \'predictions\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.tag_constants.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.tag_constants.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6af72498d74d4bbc12e7ca68ad1e0a6f0c237e0a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.tag_constants.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.saved_model.tag_constants" +tf_module { + member { + name: "GPU" + mtype: "" + } + member { + name: "SERVING" + mtype: "" + } + member { + name: "TPU" + mtype: "" + } + member { + name: "TRAINING" + mtype: "" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.saved_model.utils.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.utils.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d95c94668250e1de236462ccdcb134245eebf092 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.saved_model.utils.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.saved_model.utils" +tf_module { + member_method { + name: "build_tensor_info" + argspec: "args=[\'tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_tensor_from_tensor_info" + argspec: "args=[\'tensor_info\', \'graph\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.sets.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.sets.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8a196b1a556e283671cc75af28df3eaa62532975 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.sets.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.sets" +tf_module { + member_method { + name: "set_difference" + argspec: "args=[\'a\', \'b\', \'aminusb\', \'validate_indices\'], varargs=None, keywords=None, defaults=[\'True\', \'True\'], " + } + member_method { + name: "set_intersection" + argspec: "args=[\'a\', \'b\', \'validate_indices\'], varargs=None, keywords=None, defaults=[\'True\'], " + } + member_method { + name: "set_size" + argspec: "args=[\'a\', \'validate_indices\'], varargs=None, keywords=None, defaults=[\'True\'], " + } + member_method { + name: "set_union" + argspec: "args=[\'a\', \'b\', \'validate_indices\'], varargs=None, keywords=None, defaults=[\'True\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bbfe395031aece42363ca7d6577fee856df6bde8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt @@ -0,0 +1,11 @@ +path: "tensorflow.sparse" +tf_module { + member_method { + name: "cross" + argspec: "args=[\'inputs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "cross_hashed" + argspec: "args=[\'inputs\', \'num_buckets\', \'hash_key\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.spectral.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.spectral.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6a421ef12d58dc047905ec916cbe777b4ce19b9a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.spectral.pbtxt @@ -0,0 +1,59 @@ +path: "tensorflow.spectral" +tf_module { + member_method { + name: "dct" + argspec: "args=[\'input\', \'type\', \'n\', \'axis\', \'norm\', \'name\'], varargs=None, keywords=None, defaults=[\'2\', \'None\', \'-1\', \'None\', \'None\'], " + } + member_method { + name: "fft" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "fft2d" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "fft3d" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "idct" + argspec: "args=[\'input\', \'type\', \'n\', \'axis\', \'norm\', \'name\'], varargs=None, keywords=None, defaults=[\'2\', \'None\', \'-1\', \'None\', \'None\'], " + } + member_method { + name: "ifft" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "ifft2d" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "ifft3d" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "irfft" + argspec: "args=[\'input_tensor\', \'fft_length\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "irfft2d" + argspec: "args=[\'input_tensor\', \'fft_length\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "irfft3d" + argspec: "args=[\'input_tensor\', \'fft_length\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "rfft" + argspec: "args=[\'input_tensor\', \'fft_length\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "rfft2d" + argspec: "args=[\'input_tensor\', \'fft_length\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "rfft3d" + argspec: "args=[\'input_tensor\', \'fft_length\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.strings.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.strings.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9a831fed2692b30db6ce991c86f46a42908c0789 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.strings.pbtxt @@ -0,0 +1,43 @@ +path: "tensorflow.strings" +tf_module { + member_method { + name: "join" + argspec: "args=[\'inputs\', \'separator\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'None\'], " + } + member_method { + name: "regex_full_match" + argspec: "args=[\'input\', \'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "regex_replace" + argspec: "args=[\'input\', \'pattern\', \'rewrite\', \'replace_global\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } + member_method { + name: "split" + argspec: "args=[\'source\', \'sep\', \'maxsplit\'], varargs=None, keywords=None, defaults=[\'None\', \'-1\'], " + } + member_method { + name: "strip" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "substr" + argspec: "args=[\'input\', \'pos\', \'len\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket" + argspec: "args=[\'string_tensor\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket_fast" + argspec: "args=[\'input\', \'num_buckets\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_hash_bucket_strong" + argspec: "args=[\'input\', \'num_buckets\', \'key\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_number" + argspec: "args=[\'string_tensor\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-event.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-event.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..eb99d0f5334457aa654fed0553af143839328dba --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-event.pbtxt @@ -0,0 +1,74 @@ +path: "tensorflow.summary.Event" +tf_proto { + descriptor { + name: "Event" + field { + name: "wall_time" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_DOUBLE + } + field { + name: "step" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "file_version" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + oneof_index: 0 + } + field { + name: "graph_def" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + field { + name: "summary" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary" + oneof_index: 0 + } + field { + name: "log_message" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.LogMessage" + oneof_index: 0 + } + field { + name: "session_log" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SessionLog" + oneof_index: 0 + } + field { + name: "tagged_run_metadata" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TaggedRunMetadata" + oneof_index: 0 + } + field { + name: "meta_graph_def" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + oneof_decl { + name: "what" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-file-writer-cache.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-file-writer-cache.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2a5b63dceae3c0ac27b34c2e896ee3b90bbd7f75 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-file-writer-cache.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.summary.FileWriterCache" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "clear" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get" + argspec: "args=[\'logdir\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-file-writer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-file-writer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6b65b0ace3cf7740ab03390841c941592000d127 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-file-writer.pbtxt @@ -0,0 +1,50 @@ +path: "tensorflow.summary.FileWriter" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'logdir\', \'graph\', \'max_queue\', \'flush_secs\', \'graph_def\', \'filename_suffix\', \'session\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'120\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "add_event" + argspec: "args=[\'self\', \'event\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "add_graph" + argspec: "args=[\'self\', \'graph\', \'global_step\', \'graph_def\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "add_meta_graph" + argspec: "args=[\'self\', \'meta_graph_def\', \'global_step\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_run_metadata" + argspec: "args=[\'self\', \'run_metadata\', \'tag\', \'global_step\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_session_log" + argspec: "args=[\'self\', \'session_log\', \'global_step\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "add_summary" + argspec: "args=[\'self\', \'summary\', \'global_step\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "flush" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_logdir" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "reopen" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-session-log.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-session-log.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..73de73869c8d1a6808b16fe8853fd21cc8891879 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-session-log.pbtxt @@ -0,0 +1,44 @@ +path: "tensorflow.summary.SessionLog" +tf_proto { + descriptor { + name: "SessionLog" + field { + name: "status" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.SessionLog.SessionStatus" + } + field { + name: "checkpoint_path" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "msg" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + enum_type { + name: "SessionStatus" + value { + name: "STATUS_UNSPECIFIED" + number: 0 + } + value { + name: "START" + number: 1 + } + value { + name: "STOP" + number: 2 + } + value { + name: "CHECKPOINT" + number: 3 + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary-description.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary-description.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4a8b59cf02ed46ef70f22564f3134214840600fe --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary-description.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.summary.SummaryDescription" +tf_proto { + descriptor { + name: "SummaryDescription" + field { + name: "type_hint" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-audio.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-audio.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8b271cf58fc11c8666abd456021afeedc0b14c7a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-audio.pbtxt @@ -0,0 +1,36 @@ +path: "tensorflow.summary.Summary.Audio" +tf_proto { + descriptor { + name: "Audio" + field { + name: "sample_rate" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + } + field { + name: "num_channels" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "length_frames" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "encoded_audio_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + field { + name: "content_type" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-image.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-image.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..dbbc02dd0506dbcebd1690602b5786b02c3ed4a0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-image.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.summary.Summary.Image" +tf_proto { + descriptor { + name: "Image" + field { + name: "height" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "width" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "colorspace" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "encoded_image_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-value.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-value.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4176171cd938e383fe5366153364d8e8e8c1a1ee --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.-value.pbtxt @@ -0,0 +1,74 @@ +path: "tensorflow.summary.Summary.Value" +tf_proto { + descriptor { + name: "Value" + field { + name: "node_name" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tag" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "metadata" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SummaryMetadata" + } + field { + name: "simple_value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + oneof_index: 0 + } + field { + name: "obsolete_old_style_histogram" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + field { + name: "image" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Image" + oneof_index: 0 + } + field { + name: "histo" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.HistogramProto" + oneof_index: 0 + } + field { + name: "audio" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Audio" + oneof_index: 0 + } + field { + name: "tensor" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorProto" + oneof_index: 0 + } + oneof_decl { + name: "value" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d6c5e3a87a115b9bdcfd044abe93177eda2af275 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-summary.pbtxt @@ -0,0 +1,144 @@ +path: "tensorflow.summary.Summary" +tf_proto { + descriptor { + name: "Summary" + field { + name: "value" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Value" + } + nested_type { + name: "Image" + field { + name: "height" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "width" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "colorspace" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "encoded_image_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + } + nested_type { + name: "Audio" + field { + name: "sample_rate" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + } + field { + name: "num_channels" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "length_frames" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT64 + } + field { + name: "encoded_audio_string" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + field { + name: "content_type" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } + nested_type { + name: "Value" + field { + name: "node_name" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tag" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "metadata" + number: 9 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.SummaryMetadata" + } + field { + name: "simple_value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + oneof_index: 0 + } + field { + name: "obsolete_old_style_histogram" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_BYTES + oneof_index: 0 + } + field { + name: "image" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Image" + oneof_index: 0 + } + field { + name: "histo" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.HistogramProto" + oneof_index: 0 + } + field { + name: "audio" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Summary.Audio" + oneof_index: 0 + } + field { + name: "tensor" + number: 8 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.TensorProto" + oneof_index: 0 + } + oneof_decl { + name: "value" + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.summary.-tagged-run-metadata.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.-tagged-run-metadata.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..27c8873320403cb2e7402ef9f1bb0e7134d5f96b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.summary.-tagged-run-metadata.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.summary.TaggedRunMetadata" +tf_proto { + descriptor { + name: "TaggedRunMetadata" + field { + name: "tag" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "run_metadata" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_BYTES + } + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.summary.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.summary.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.summary.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.summary.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.sysconfig.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.sysconfig.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2f00aeac25f691d9767080251798248281e5edf5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.sysconfig.pbtxt @@ -0,0 +1,19 @@ +path: "tensorflow.sysconfig" +tf_module { + member_method { + name: "get_compile_flags" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_include" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_lib" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_link_flags" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.test.-benchmark.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.test.-benchmark.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..df528e26b60f8d8ddcc1eaf0ed292cc7ff0ebd94 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.test.-benchmark.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.test.Benchmark" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "is_abstract" + argspec: "args=[\'cls\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "report_benchmark" + argspec: "args=[\'self\', \'iters\', \'cpu_time\', \'wall_time\', \'throughput\', \'extras\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "run_op_benchmark" + argspec: "args=[\'self\', \'sess\', \'op_or_tensor\', \'feed_dict\', \'burn_iters\', \'min_iters\', \'store_trace\', \'store_memory_usage\', \'name\', \'extras\', \'mbs\'], varargs=None, keywords=None, defaults=[\'None\', \'2\', \'10\', \'False\', \'True\', \'None\', \'None\', \'0\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.test.-stub-out-for-testing.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.test.-stub-out-for-testing.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e02a0c6097c5ea4dae905b25cd0e381f5e257105 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.test.-stub-out-for-testing.pbtxt @@ -0,0 +1,28 @@ +path: "tensorflow.test.StubOutForTesting" +tf_class { + is_instance: "" + member_method { + name: "CleanUp" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "Set" + argspec: "args=[\'self\', \'parent\', \'child_name\', \'new_child\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "SmartSet" + argspec: "args=[\'self\', \'obj\', \'attr_name\', \'new_attr\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "SmartUnsetAll" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "UnsetAll" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "__init__" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.test.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.test.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..abe9b068ae95c08a2b72c9a5e164a097e6162dff --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.test.pbtxt @@ -0,0 +1,59 @@ +path: "tensorflow.test" +tf_module { + member { + name: "Benchmark" + mtype: "" + } + member { + name: "StubOutForTesting" + mtype: "" + } + member { + name: "TestCase" + mtype: "" + } + member { + name: "mock" + mtype: "" + } + member_method { + name: "assert_equal_graph_def" + argspec: "args=[\'actual\', \'expected\', \'checkpoint_v2\'], varargs=None, keywords=None, defaults=[\'False\'], " + } + member_method { + name: "compute_gradient" + argspec: "args=[\'x\', \'x_shape\', \'y\', \'y_shape\', \'x_init_value\', \'delta\', \'init_targets\', \'extra_feed_dict\'], varargs=None, keywords=None, defaults=[\'None\', \'0.001\', \'None\', \'None\'], " + } + member_method { + name: "compute_gradient_error" + argspec: "args=[\'x\', \'x_shape\', \'y\', \'y_shape\', \'x_init_value\', \'delta\', \'init_targets\', \'extra_feed_dict\'], varargs=None, keywords=None, defaults=[\'None\', \'0.001\', \'None\', \'None\'], " + } + member_method { + name: "create_local_cluster" + argspec: "args=[\'num_workers\', \'num_ps\', \'protocol\', \'worker_config\', \'ps_config\'], varargs=None, keywords=None, defaults=[\'grpc\', \'None\', \'None\'], " + } + member_method { + name: "get_temp_dir" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "gpu_device_name" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_built_with_cuda" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_gpu_available" + argspec: "args=[\'cuda_only\', \'min_cuda_compute_capability\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "main" + argspec: "args=[\'argv\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "test_src_dir_path" + argspec: "args=[\'relative_path\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-adadelta-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-adadelta-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1f1d8b6f9e2cde4800cdef9c417191b1a0ce07b5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-adadelta-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.AdadeltaOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'rho\', \'epsilon\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'0.001\', \'0.95\', \'1e-08\', \'False\', \'Adadelta\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-adagrad-d-a-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-adagrad-d-a-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..a7c05d484905a0af26c80a52d92623ef4a3eb6c4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-adagrad-d-a-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.AdagradDAOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'global_step\', \'initial_gradient_squared_accumulator_value\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'0.1\', \'0.0\', \'0.0\', \'False\', \'AdagradDA\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-adagrad-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..bc8b92389c6ed7dcb0fa23ff3abd86bb0d1c488a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-adagrad-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.AdagradOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'initial_accumulator_value\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'0.1\', \'False\', \'Adagrad\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-adam-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-adam-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5d17be9378fd130b89e199544f85e03a23a71d3c --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-adam-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.AdamOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'beta1\', \'beta2\', \'epsilon\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'0.001\', \'0.9\', \'0.999\', \'1e-08\', \'False\', \'Adam\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-bytes-list.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-bytes-list.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..87e4f160e5bd5950dfc338649fb531c92cc81b60 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-bytes-list.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.train.BytesList" +tf_proto { + descriptor { + name: "BytesList" + field { + name: "value" + number: 1 + label: LABEL_REPEATED + type: TYPE_BYTES + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c3037baa8c951ecd9b60267ee7cc8674ead88dbe --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.CheckpointSaverHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'checkpoint_dir\', \'save_secs\', \'save_steps\', \'saver\', \'checkpoint_basename\', \'scaffold\', \'listeners\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'model.ckpt\', \'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-listener.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-listener.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9d3688e565761758e765d00086de8b59dcc3801b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint-saver-listener.pbtxt @@ -0,0 +1,24 @@ +path: "tensorflow.train.CheckpointSaverListener" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "after_save" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_save" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\', \'global_step_value\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.train.-checkpoint.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint.pbtxt similarity index 100% rename from tensorflow/tools/api/golden/tensorflow.train.-checkpoint.pbtxt rename to tensorflow/tools/api/golden/v2/tensorflow.train.-checkpoint.pbtxt diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-chief-session-creator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-chief-session-creator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..abbe273be32c6fd20b1a6464f3e99966bd3c8953 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-chief-session-creator.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.train.ChiefSessionCreator" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'scaffold\', \'master\', \'config\', \'checkpoint_dir\', \'checkpoint_filename_with_path\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "create_session" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-cluster-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-cluster-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f9de26839f5f6dc1591bfc909ca8e6c02271b5c7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-cluster-def.pbtxt @@ -0,0 +1,13 @@ +path: "tensorflow.train.ClusterDef" +tf_proto { + descriptor { + name: "ClusterDef" + field { + name: "job" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.JobDef" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-cluster-spec.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-cluster-spec.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1658b15a5f82167f9167338145b479c9e9197ea5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-cluster-spec.pbtxt @@ -0,0 +1,37 @@ +path: "tensorflow.train.ClusterSpec" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "jobs" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'cluster\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_cluster_def" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "as_dict" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "job_tasks" + argspec: "args=[\'self\', \'job_name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "num_tasks" + argspec: "args=[\'self\', \'job_name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "task_address" + argspec: "args=[\'self\', \'job_name\', \'task_index\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "task_indices" + argspec: "args=[\'self\', \'job_name\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-coordinator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-coordinator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..11277f077eef830aec3be61ddd981bfd3a55d149 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-coordinator.pbtxt @@ -0,0 +1,45 @@ +path: "tensorflow.train.Coordinator" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "joined" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'clean_stop_exception_types\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "clear_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "join" + argspec: "args=[\'self\', \'threads\', \'stop_grace_period_secs\', \'ignore_live_threads\'], varargs=None, keywords=None, defaults=[\'None\', \'120\', \'False\'], " + } + member_method { + name: "raise_requested_exception" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "register_thread" + argspec: "args=[\'self\', \'thread\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "request_stop" + argspec: "args=[\'self\', \'ex\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "should_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "stop_on_exception" + argspec: "args=[], varargs=args, keywords=kwds, defaults=None" + } + member_method { + name: "wait_for_stop" + argspec: "args=[\'self\', \'timeout\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-example.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-example.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..23c30f1ef4fe2dd93e8714655dbb1ef3b8e05c65 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-example.pbtxt @@ -0,0 +1,13 @@ +path: "tensorflow.train.Example" +tf_proto { + descriptor { + name: "Example" + field { + name: "features" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Features" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-exponential-moving-average.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-exponential-moving-average.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c9fe136e68b5f3cadaff6d4fd0638b7f10d18365 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-exponential-moving-average.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.train.ExponentialMovingAverage" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'decay\', \'num_updates\', \'zero_debias\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'ExponentialMovingAverage\'], " + } + member_method { + name: "apply" + argspec: "args=[\'self\', \'var_list\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "average" + argspec: "args=[\'self\', \'var\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "average_name" + argspec: "args=[\'self\', \'var\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "variables_to_restore" + argspec: "args=[\'self\', \'moving_avg_variables\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-list.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-list.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2a8b3714fc0c4f5e979bc02550a8e08835d53cb4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-list.pbtxt @@ -0,0 +1,13 @@ +path: "tensorflow.train.FeatureList" +tf_proto { + descriptor { + name: "FeatureList" + field { + name: "feature" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.Feature" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..cd1d56e606c96b62346b936001a5a0f07a8a8ad8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-lists.-feature-list-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.train.FeatureLists.FeatureListEntry" +tf_proto { + descriptor { + name: "FeatureListEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.FeatureList" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-lists.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-lists.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3c183a64769b59b104c52b6840e8f351f4b0cef5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature-lists.pbtxt @@ -0,0 +1,32 @@ +path: "tensorflow.train.FeatureLists" +tf_proto { + descriptor { + name: "FeatureLists" + field { + name: "feature_list" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.FeatureLists.FeatureListEntry" + } + nested_type { + name: "FeatureListEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.FeatureList" + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-feature.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..5d0eb871c2f4aeb13d6b8518486f11b1f80d0620 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-feature.pbtxt @@ -0,0 +1,33 @@ +path: "tensorflow.train.Feature" +tf_proto { + descriptor { + name: "Feature" + field { + name: "bytes_list" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.BytesList" + oneof_index: 0 + } + field { + name: "float_list" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.FloatList" + oneof_index: 0 + } + field { + name: "int64_list" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Int64List" + oneof_index: 0 + } + oneof_decl { + name: "kind" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-features.-feature-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-features.-feature-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..f912005f1cc35f12ce6eba5313b0c67adebe70f7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-features.-feature-entry.pbtxt @@ -0,0 +1,22 @@ +path: "tensorflow.train.Features.FeatureEntry" +tf_proto { + descriptor { + name: "FeatureEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Feature" + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-features.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-features.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b788ca1d57e1d679a1b809d85c6aa9bcef01f252 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-features.pbtxt @@ -0,0 +1,32 @@ +path: "tensorflow.train.Features" +tf_proto { + descriptor { + name: "Features" + field { + name: "feature" + number: 1 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.Features.FeatureEntry" + } + nested_type { + name: "FeatureEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Feature" + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-feed-fn-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-feed-fn-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7bec4d032cedc0711ca07049d5d04490e8bc3f30 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-feed-fn-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.FeedFnHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'feed_fn\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-final-ops-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-final-ops-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..31cf9aaeb2c640f8db205c0753f20acc75338fe0 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-final-ops-hook.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.train.FinalOpsHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "final_ops_values" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'final_ops\', \'final_ops_feed_dict\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-float-list.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-float-list.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..55d3b46f20e17ec4e6fbac5672e1b0a8ef98552d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-float-list.pbtxt @@ -0,0 +1,15 @@ +path: "tensorflow.train.FloatList" +tf_proto { + descriptor { + name: "FloatList" + field { + name: "value" + number: 1 + label: LABEL_REPEATED + type: TYPE_FLOAT + options { + packed: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-ftrl-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-ftrl-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d265fdeb01c38d8a1347e630d7f7bff111999634 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-ftrl-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.FtrlOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'learning_rate_power\', \'initial_accumulator_value\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\', \'accum_name\', \'linear_name\', \'l2_shrinkage_regularization_strength\'], varargs=None, keywords=None, defaults=[\'-0.5\', \'0.1\', \'0.0\', \'0.0\', \'False\', \'Ftrl\', \'None\', \'None\', \'0.0\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-global-step-waiter-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-global-step-waiter-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..147448618e2df9f71ac794e369b108629e10ce0a --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-global-step-waiter-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.GlobalStepWaiterHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'wait_until_step\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-gradient-descent-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c673e29cd4dd6cd3c01582abfbc306c092818892 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-gradient-descent-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.GradientDescentOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'GradientDescent\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-int64-list.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-int64-list.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..1de92b3ab7b5e0ff873a7e8092c7e6c2edcbd2ce --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-int64-list.pbtxt @@ -0,0 +1,15 @@ +path: "tensorflow.train.Int64List" +tf_proto { + descriptor { + name: "Int64List" + field { + name: "value" + number: 1 + label: LABEL_REPEATED + type: TYPE_INT64 + options { + packed: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-job-def.-tasks-entry.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-job-def.-tasks-entry.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..58115590a5eebd742afac4b31b5f585e8077e049 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-job-def.-tasks-entry.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.train.JobDef.TasksEntry" +tf_proto { + descriptor { + name: "TasksEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + options { + map_entry: true + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-job-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-job-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d7eb505e27930d6411a589909584f237a7e8b8f5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-job-def.pbtxt @@ -0,0 +1,37 @@ +path: "tensorflow.train.JobDef" +tf_proto { + descriptor { + name: "JobDef" + field { + name: "name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "tasks" + number: 2 + label: LABEL_REPEATED + type: TYPE_MESSAGE + type_name: ".tensorflow.JobDef.TasksEntry" + } + nested_type { + name: "TasksEntry" + field { + name: "key" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "value" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + options { + map_entry: true + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-logging-tensor-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-logging-tensor-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9801c05df181ee65cc8ce0ad2e886566c0145fd5 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-logging-tensor-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.LoggingTensorHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'tensors\', \'every_n_iter\', \'every_n_secs\', \'at_end\', \'formatter\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-looper-thread.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-looper-thread.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c61859004e897a14b580dc0b55957edfa6ae6860 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-looper-thread.pbtxt @@ -0,0 +1,73 @@ +path: "tensorflow.train.LooperThread" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "daemon" + mtype: "" + } + member { + name: "ident" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'coord\', \'timer_interval_secs\', \'target\', \'args\', \'kwargs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "getName" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "isAlive" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "isDaemon" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "is_alive" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "join" + argspec: "args=[\'self\', \'timeout\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "loop" + argspec: "args=[\'coord\', \'timer_interval_secs\', \'target\', \'args\', \'kwargs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "run" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "run_loop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "setDaemon" + argspec: "args=[\'self\', \'daemonic\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "setName" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "start" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "start_loop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "stop_loop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-momentum-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-momentum-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..8199f63b9b8c64c73a3d62294277838cdc240280 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-momentum-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.MomentumOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'momentum\', \'use_locking\', \'name\', \'use_nesterov\'], varargs=None, keywords=None, defaults=[\'False\', \'Momentum\', \'False\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-monitored-session.-step-context.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-monitored-session.-step-context.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..03efe6639e0e3d2c6c280bd30d2b59b5d654f995 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-monitored-session.-step-context.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.train.MonitoredSession.StepContext" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "session" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'session\', \'run_with_hooks_fn\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "request_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "run_with_hooks" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-monitored-session.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-monitored-session.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..09b7b3fb538fb8d87dcfd622089818081a1fb79b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-monitored-session.pbtxt @@ -0,0 +1,34 @@ +path: "tensorflow.train.MonitoredSession" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "StepContext" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'session_creator\', \'hooks\', \'stop_grace_period_secs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'120\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "run" + argspec: "args=[\'self\', \'fetches\', \'feed_dict\', \'options\', \'run_metadata\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "run_step_fn" + argspec: "args=[\'self\', \'step_fn\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "should_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-loss-during-training-error.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-loss-during-training-error.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..25fd5e75a79f6e4fe2cf77ebc7aa0d1fef759e7f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-loss-during-training-error.pbtxt @@ -0,0 +1,16 @@ +path: "tensorflow.train.NanLossDuringTrainingError" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "args" + mtype: "" + } + member { + name: "message" + mtype: "" + } + member_method { + name: "__init__" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-tensor-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-tensor-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7d1c89f9b37b5e63ecf2cf766986cb8faa5872c4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-nan-tensor-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.NanTensorHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'loss_tensor\', \'fail_on_nan_loss\'], varargs=None, keywords=None, defaults=[\'True\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..876bb35e391885e751066a415967af848280c714 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-optimizer.pbtxt @@ -0,0 +1,50 @@ +path: "tensorflow.train.Optimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-profiler-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-profiler-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4df6c4156a8bfe6d3bc0fb6746512cb3025c2604 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-profiler-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.ProfilerHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'save_steps\', \'save_secs\', \'output_dir\', \'show_dataflow\', \'show_memory\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\', \'True\', \'False\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-proximal-adagrad-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-proximal-adagrad-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..14349a74efb61124fc7b5568d5ec023f08b1b62f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-proximal-adagrad-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.ProximalAdagradOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'initial_accumulator_value\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'0.1\', \'0.0\', \'0.0\', \'False\', \'ProximalAdagrad\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..7d982dc51f6edce1cf691671e31ddd07664f0dc1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-proximal-gradient-descent-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.ProximalGradientDescentOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'0.0\', \'False\', \'ProximalGradientDescent\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-queue-runner.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-queue-runner.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d84d0058eea34d2d4413c8b1a09bd7d5720c07f7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-queue-runner.pbtxt @@ -0,0 +1,49 @@ +path: "tensorflow.train.QueueRunner" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "cancel_op" + mtype: "" + } + member { + name: "close_op" + mtype: "" + } + member { + name: "enqueue_ops" + mtype: "" + } + member { + name: "exceptions_raised" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "queue" + mtype: "" + } + member { + name: "queue_closed_exception_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'queue\', \'enqueue_ops\', \'close_op\', \'cancel_op\', \'queue_closed_exception_types\', \'queue_runner_def\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "create_threads" + argspec: "args=[\'self\', \'sess\', \'coord\', \'daemon\', \'start\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'False\'], " + } + member_method { + name: "from_proto" + argspec: "args=[\'queue_runner_def\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_proto" + argspec: "args=[\'self\', \'export_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-r-m-s-prop-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-r-m-s-prop-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..906384a2875bf7b05ac26fc43207f4ef9b5a7472 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-r-m-s-prop-optimizer.pbtxt @@ -0,0 +1,51 @@ +path: "tensorflow.train.RMSPropOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'learning_rate\', \'decay\', \'momentum\', \'epsilon\', \'use_locking\', \'centered\', \'name\'], varargs=None, keywords=None, defaults=[\'0.9\', \'0.0\', \'1e-10\', \'False\', \'False\', \'RMSProp\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\', \'loss\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'1\', \'None\', \'False\', \'None\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\', \'var\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-saver-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-saver-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4ec99469e4025603e7ab340b190cbebf7e33eed7 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-saver-def.pbtxt @@ -0,0 +1,64 @@ +path: "tensorflow.train.SaverDef" +tf_proto { + descriptor { + name: "SaverDef" + field { + name: "filename_tensor_name" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "save_tensor_name" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "restore_op_name" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "max_to_keep" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "sharded" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_BOOL + } + field { + name: "keep_checkpoint_every_n_hours" + number: 6 + label: LABEL_OPTIONAL + type: TYPE_FLOAT + } + field { + name: "version" + number: 7 + label: LABEL_OPTIONAL + type: TYPE_ENUM + type_name: ".tensorflow.SaverDef.CheckpointFormatVersion" + } + enum_type { + name: "CheckpointFormatVersion" + value { + name: "LEGACY" + number: 0 + } + value { + name: "V1" + number: 1 + } + value { + name: "V2" + number: 2 + } + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-saver.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-saver.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2cda458f468b2d748b43954b14b670df7145243f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-saver.pbtxt @@ -0,0 +1,53 @@ +path: "tensorflow.train.Saver" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "last_checkpoints" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'var_list\', \'reshape\', \'sharded\', \'max_to_keep\', \'keep_checkpoint_every_n_hours\', \'name\', \'restore_sequentially\', \'saver_def\', \'builder\', \'defer_build\', \'allow_empty\', \'write_version\', \'pad_step_number\', \'save_relative_paths\', \'filename\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'False\', \'5\', \'10000.0\', \'None\', \'False\', \'None\', \'None\', \'False\', \'False\', \'2\', \'False\', \'False\', \'None\'], " + } + member_method { + name: "as_saver_def" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "build" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "export_meta_graph" + argspec: "args=[\'self\', \'filename\', \'collection_list\', \'as_text\', \'export_scope\', \'clear_devices\', \'clear_extraneous_savers\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'False\', \'None\', \'False\', \'False\', \'False\'], " + } + member_method { + name: "from_proto" + argspec: "args=[\'saver_def\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "recover_last_checkpoints" + argspec: "args=[\'self\', \'checkpoint_paths\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "restore" + argspec: "args=[\'self\', \'sess\', \'save_path\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "save" + argspec: "args=[\'self\', \'sess\', \'save_path\', \'global_step\', \'latest_filename\', \'meta_graph_suffix\', \'write_meta_graph\', \'write_state\', \'strip_default_attrs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'meta\', \'True\', \'True\', \'False\'], " + } + member_method { + name: "set_last_checkpoints" + argspec: "args=[\'self\', \'last_checkpoints\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "set_last_checkpoints_with_time" + argspec: "args=[\'self\', \'last_checkpoints_with_time\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "to_proto" + argspec: "args=[\'self\', \'export_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-scaffold.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-scaffold.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..38cc98b48e78aa93f7614a9baff236f7b119f99d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-scaffold.pbtxt @@ -0,0 +1,53 @@ +path: "tensorflow.train.Scaffold" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "init_feed_dict" + mtype: "" + } + member { + name: "init_fn" + mtype: "" + } + member { + name: "init_op" + mtype: "" + } + member { + name: "local_init_op" + mtype: "" + } + member { + name: "ready_for_local_init_op" + mtype: "" + } + member { + name: "ready_op" + mtype: "" + } + member { + name: "saver" + mtype: "" + } + member { + name: "summary_op" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'init_op\', \'init_feed_dict\', \'init_fn\', \'ready_op\', \'ready_for_local_init_op\', \'local_init_op\', \'summary_op\', \'saver\', \'copy_from_scaffold\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "default_local_init_op" + argspec: "args=[], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "finalize" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_or_default" + argspec: "args=[\'arg_name\', \'collection_key\', \'default_constructor\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-second-or-step-timer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-second-or-step-timer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..3c5a6ac13cc2d8a4d464ab48da6edaa0a9ccc14b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-second-or-step-timer.pbtxt @@ -0,0 +1,26 @@ +path: "tensorflow.train.SecondOrStepTimer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'every_secs\', \'every_steps\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "last_triggered_step" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "reset" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "should_trigger_for_step" + argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "update_last_triggered_step" + argspec: "args=[\'self\', \'step\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-sequence-example.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-sequence-example.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6a4553bbc157960696ef17959f532fecdfd54ae8 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-sequence-example.pbtxt @@ -0,0 +1,20 @@ +path: "tensorflow.train.SequenceExample" +tf_proto { + descriptor { + name: "SequenceExample" + field { + name: "context" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.Features" + } + field { + name: "feature_lists" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.FeatureLists" + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-server-def.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-server-def.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..83ee7b3eb91a558765abcde630fe6e0480b9818f --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-server-def.pbtxt @@ -0,0 +1,38 @@ +path: "tensorflow.train.ServerDef" +tf_proto { + descriptor { + name: "ServerDef" + field { + name: "cluster" + number: 1 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.ClusterDef" + } + field { + name: "job_name" + number: 2 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + field { + name: "task_index" + number: 3 + label: LABEL_OPTIONAL + type: TYPE_INT32 + } + field { + name: "default_session_config" + number: 4 + label: LABEL_OPTIONAL + type: TYPE_MESSAGE + type_name: ".tensorflow.ConfigProto" + } + field { + name: "protocol" + number: 5 + label: LABEL_OPTIONAL + type: TYPE_STRING + } + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-server.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-server.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9b8f185f5b699e860c6fbb50b8d2912984908982 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-server.pbtxt @@ -0,0 +1,29 @@ +path: "tensorflow.train.Server" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "server_def" + mtype: "" + } + member { + name: "target" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'server_or_cluster_def\', \'job_name\', \'task_index\', \'protocol\', \'config\', \'start\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'True\'], " + } + member_method { + name: "create_local_server" + argspec: "args=[\'config\', \'start\'], varargs=None, keywords=None, defaults=[\'None\', \'True\'], " + } + member_method { + name: "join" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "start" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-creator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-creator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..beb232715f725047dd8c03054b899a90fa81eec2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-creator.pbtxt @@ -0,0 +1,12 @@ +path: "tensorflow.train.SessionCreator" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "create_session" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-manager.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-manager.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..448764fe081b250e1e22633f118268ad638cb9dd --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-manager.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.train.SessionManager" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'local_init_op\', \'ready_op\', \'ready_for_local_init_op\', \'graph\', \'recovery_wait_secs\', \'local_init_run_options\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'30\', \'None\'], " + } + member_method { + name: "prepare_session" + argspec: "args=[\'self\', \'master\', \'init_op\', \'saver\', \'checkpoint_dir\', \'checkpoint_filename_with_path\', \'wait_for_checkpoint\', \'max_wait_secs\', \'config\', \'init_feed_dict\', \'init_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'False\', \'7200\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "recover_session" + argspec: "args=[\'self\', \'master\', \'saver\', \'checkpoint_dir\', \'checkpoint_filename_with_path\', \'wait_for_checkpoint\', \'max_wait_secs\', \'config\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\', \'7200\', \'None\'], " + } + member_method { + name: "wait_for_session" + argspec: "args=[\'self\', \'master\', \'config\', \'max_wait_secs\'], varargs=None, keywords=None, defaults=[\'None\', \'inf\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-args.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-args.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..442990893e33c92bd05a72b198a6584bc979b2fe --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-args.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.train.SessionRunArgs" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "feed_dict" + mtype: "" + } + member { + name: "fetches" + mtype: "" + } + member { + name: "options" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-context.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-context.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..d5adb15c95f8a6ebde4ca0e0c535dfebc5edfbf2 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-context.pbtxt @@ -0,0 +1,25 @@ +path: "tensorflow.train.SessionRunContext" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "original_args" + mtype: "" + } + member { + name: "session" + mtype: "" + } + member { + name: "stop_requested" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'original_args\', \'session\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "request_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..db1aa24acf0e295b4b787eef68250401dd6a6e27 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-hook.pbtxt @@ -0,0 +1,28 @@ +path: "tensorflow.train.SessionRunHook" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-values.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-values.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..0b401d59c400f1d08f47daa2d264a9a5bfc91538 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-session-run-values.pbtxt @@ -0,0 +1,27 @@ +path: "tensorflow.train.SessionRunValues" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "options" + mtype: "" + } + member { + name: "results" + mtype: "" + } + member { + name: "run_metadata" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-singular-monitored-session.-step-context.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-singular-monitored-session.-step-context.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..36d8ce7ff82e02300b59705400be40d7cc3f65ae --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-singular-monitored-session.-step-context.pbtxt @@ -0,0 +1,21 @@ +path: "tensorflow.train.SingularMonitoredSession.StepContext" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "session" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'session\', \'run_with_hooks_fn\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "request_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "run_with_hooks" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-singular-monitored-session.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-singular-monitored-session.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..de0f2c1c1a2497ef4e541ee6583d416e31f48826 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-singular-monitored-session.pbtxt @@ -0,0 +1,38 @@ +path: "tensorflow.train.SingularMonitoredSession" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "StepContext" + mtype: "" + } + member { + name: "graph" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'hooks\', \'scaffold\', \'master\', \'config\', \'checkpoint_dir\', \'stop_grace_period_secs\', \'checkpoint_filename_with_path\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\', \'None\', \'None\', \'120\', \'None\'], " + } + member_method { + name: "close" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "raw_session" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "run" + argspec: "args=[\'self\', \'fetches\', \'feed_dict\', \'options\', \'run_metadata\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " + } + member_method { + name: "run_step_fn" + argspec: "args=[\'self\', \'step_fn\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "should_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-step-counter-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-step-counter-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..13261f6dde1cf8e6fd228950600303370947b7ea --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-step-counter-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.StepCounterHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'every_n_steps\', \'every_n_secs\', \'output_dir\', \'summary_writer\'], varargs=None, keywords=None, defaults=[\'100\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-stop-at-step-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-stop-at-step-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e388599b0bf63379fa95a3276e3f4859eab86d6d --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-stop-at-step-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.StopAtStepHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'num_steps\', \'last_step\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-summary-saver-hook.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-summary-saver-hook.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..697c3667b09f42f208dec38938f5a1ce0cc09029 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-summary-saver-hook.pbtxt @@ -0,0 +1,30 @@ +path: "tensorflow.train.SummarySaverHook" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'save_steps\', \'save_secs\', \'output_dir\', \'summary_writer\', \'scaffold\', \'summary_op\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "after_create_session" + argspec: "args=[\'self\', \'session\', \'coord\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "after_run" + argspec: "args=[\'self\', \'run_context\', \'run_values\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "before_run" + argspec: "args=[\'self\', \'run_context\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "begin" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "end" + argspec: "args=[\'self\', \'session\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-supervisor.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-supervisor.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..9677e5a98e4a8308093f51a84d8b1edae405cd2b --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-supervisor.pbtxt @@ -0,0 +1,153 @@ +path: "tensorflow.train.Supervisor" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "USE_DEFAULT" + mtype: "" + } + member { + name: "coord" + mtype: "" + } + member { + name: "global_step" + mtype: "" + } + member { + name: "init_feed_dict" + mtype: "" + } + member { + name: "init_op" + mtype: "" + } + member { + name: "is_chief" + mtype: "" + } + member { + name: "ready_for_local_init_op" + mtype: "" + } + member { + name: "ready_op" + mtype: "" + } + member { + name: "save_model_secs" + mtype: "" + } + member { + name: "save_path" + mtype: "" + } + member { + name: "save_summaries_secs" + mtype: "" + } + member { + name: "saver" + mtype: "" + } + member { + name: "session_manager" + mtype: "" + } + member { + name: "summary_op" + mtype: "" + } + member { + name: "summary_writer" + mtype: "" + } + member_method { + name: "Loop" + argspec: "args=[\'self\', \'timer_interval_secs\', \'target\', \'args\', \'kwargs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "PrepareSession" + argspec: "args=[\'self\', \'master\', \'config\', \'wait_for_checkpoint\', \'max_wait_secs\', \'start_standard_services\'], varargs=None, keywords=None, defaults=[\'\', \'None\', \'False\', \'7200\', \'True\'], " + } + member_method { + name: "RequestStop" + argspec: "args=[\'self\', \'ex\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "ShouldStop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "StartQueueRunners" + argspec: "args=[\'self\', \'sess\', \'queue_runners\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "StartStandardServices" + argspec: "args=[\'self\', \'sess\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "Stop" + argspec: "args=[\'self\', \'threads\', \'close_summary_writer\', \'ignore_live_threads\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'False\'], " + } + member_method { + name: "StopOnException" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "SummaryComputed" + argspec: "args=[\'self\', \'sess\', \'summary\', \'global_step\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "WaitForStop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'graph\', \'ready_op\', \'ready_for_local_init_op\', \'is_chief\', \'init_op\', \'init_feed_dict\', \'local_init_op\', \'logdir\', \'summary_op\', \'saver\', \'global_step\', \'save_summaries_secs\', \'save_model_secs\', \'recovery_wait_secs\', \'stop_grace_secs\', \'checkpoint_basename\', \'session_manager\', \'summary_writer\', \'init_fn\', \'local_init_run_options\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'0\', \'True\', \'0\', \'None\', \'0\', \'None\', \'0\', \'0\', \'0\', \'120\', \'600\', \'30\', \'120\', \'model.ckpt\', \'None\', \'0\', \'None\', \'None\'], " + } + member_method { + name: "loop" + argspec: "args=[\'self\', \'timer_interval_secs\', \'target\', \'args\', \'kwargs\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "managed_session" + argspec: "args=[], varargs=args, keywords=kwds, defaults=None" + } + member_method { + name: "prepare_or_wait_for_session" + argspec: "args=[\'self\', \'master\', \'config\', \'wait_for_checkpoint\', \'max_wait_secs\', \'start_standard_services\'], varargs=None, keywords=None, defaults=[\'\', \'None\', \'False\', \'7200\', \'True\'], " + } + member_method { + name: "request_stop" + argspec: "args=[\'self\', \'ex\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "should_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "start_queue_runners" + argspec: "args=[\'self\', \'sess\', \'queue_runners\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "start_standard_services" + argspec: "args=[\'self\', \'sess\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "stop" + argspec: "args=[\'self\', \'threads\', \'close_summary_writer\', \'ignore_live_threads\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'False\'], " + } + member_method { + name: "stop_on_exception" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "summary_computed" + argspec: "args=[\'self\', \'sess\', \'summary\', \'global_step\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "wait_for_stop" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-sync-replicas-optimizer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-sync-replicas-optimizer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..2c0fda3c72b7e1f02265827b9dc1929500935cd1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-sync-replicas-optimizer.pbtxt @@ -0,0 +1,63 @@ +path: "tensorflow.train.SyncReplicasOptimizer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "GATE_GRAPH" + mtype: "" + } + member { + name: "GATE_NONE" + mtype: "" + } + member { + name: "GATE_OP" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'opt\', \'replicas_to_aggregate\', \'total_num_replicas\', \'variable_averages\', \'variables_to_average\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\', \'sync_replicas\'], " + } + member_method { + name: "apply_gradients" + argspec: "args=[\'self\', \'grads_and_vars\', \'global_step\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "compute_gradients" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "get_chief_queue_runner" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_init_tokens_op" + argspec: "args=[\'self\', \'num_tokens\'], varargs=None, keywords=None, defaults=[\'-1\'], " + } + member_method { + name: "get_name" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_slot" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "get_slot_names" + argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None" + } + member_method { + name: "make_session_run_hook" + argspec: "args=[\'self\', \'is_chief\', \'num_tokens\'], varargs=None, keywords=None, defaults=[\'-1\'], " + } + member_method { + name: "minimize" + argspec: "args=[\'self\', \'loss\', \'global_step\', \'var_list\', \'gate_gradients\', \'aggregation_method\', \'colocate_gradients_with_ops\', \'name\', \'grad_loss\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'1\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "variables" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-vocab-info.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-vocab-info.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..4ce7cb111163e103a1cebe30d5c6f3eeb4234693 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-vocab-info.pbtxt @@ -0,0 +1,39 @@ +path: "tensorflow.train.VocabInfo" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "backup_initializer" + mtype: "" + } + member { + name: "new_vocab" + mtype: "" + } + member { + name: "new_vocab_size" + mtype: "" + } + member { + name: "num_oov_buckets" + mtype: "" + } + member { + name: "old_vocab" + mtype: "" + } + member { + name: "old_vocab_size" + mtype: "" + } + member_method { + name: "__init__" + } + member_method { + name: "count" + } + member_method { + name: "index" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.-worker-session-creator.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.-worker-session-creator.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..ac263580687e53bb3fcffd5268f73f8b67aa43a1 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.-worker-session-creator.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.train.WorkerSessionCreator" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'scaffold\', \'master\', \'config\', \'max_wait_secs\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'1800\'], " + } + member_method { + name: "create_session" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..b0fb04d7d4d71e8cb2630ca79284e0ade1db8571 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.pbtxt @@ -0,0 +1,459 @@ +path: "tensorflow.train" +tf_module { + member { + name: "AdadeltaOptimizer" + mtype: "" + } + member { + name: "AdagradDAOptimizer" + mtype: "" + } + member { + name: "AdagradOptimizer" + mtype: "" + } + member { + name: "AdamOptimizer" + mtype: "" + } + member { + name: "BytesList" + mtype: "" + } + member { + name: "Checkpoint" + mtype: "" + } + member { + name: "CheckpointSaverHook" + mtype: "" + } + member { + name: "CheckpointSaverListener" + mtype: "" + } + member { + name: "ChiefSessionCreator" + mtype: "" + } + member { + name: "ClusterDef" + mtype: "" + } + member { + name: "ClusterSpec" + mtype: "" + } + member { + name: "Coordinator" + mtype: "" + } + member { + name: "Example" + mtype: "" + } + member { + name: "ExponentialMovingAverage" + mtype: "" + } + member { + name: "Feature" + mtype: "" + } + member { + name: "FeatureList" + mtype: "" + } + member { + name: "FeatureLists" + mtype: "" + } + member { + name: "Features" + mtype: "" + } + member { + name: "FeedFnHook" + mtype: "" + } + member { + name: "FinalOpsHook" + mtype: "" + } + member { + name: "FloatList" + mtype: "" + } + member { + name: "FtrlOptimizer" + mtype: "" + } + member { + name: "GlobalStepWaiterHook" + mtype: "" + } + member { + name: "GradientDescentOptimizer" + mtype: "" + } + member { + name: "Int64List" + mtype: "" + } + member { + name: "JobDef" + mtype: "" + } + member { + name: "LoggingTensorHook" + mtype: "" + } + member { + name: "LooperThread" + mtype: "" + } + member { + name: "MomentumOptimizer" + mtype: "" + } + member { + name: "MonitoredSession" + mtype: "" + } + member { + name: "NanLossDuringTrainingError" + mtype: "" + } + member { + name: "NanTensorHook" + mtype: "" + } + member { + name: "Optimizer" + mtype: "" + } + member { + name: "ProfilerHook" + mtype: "" + } + member { + name: "ProximalAdagradOptimizer" + mtype: "" + } + member { + name: "ProximalGradientDescentOptimizer" + mtype: "" + } + member { + name: "QueueRunner" + mtype: "" + } + member { + name: "RMSPropOptimizer" + mtype: "" + } + member { + name: "Saver" + mtype: "" + } + member { + name: "SaverDef" + mtype: "" + } + member { + name: "Scaffold" + mtype: "" + } + member { + name: "SecondOrStepTimer" + mtype: "" + } + member { + name: "SequenceExample" + mtype: "" + } + member { + name: "Server" + mtype: "" + } + member { + name: "ServerDef" + mtype: "" + } + member { + name: "SessionCreator" + mtype: "" + } + member { + name: "SessionManager" + mtype: "" + } + member { + name: "SessionRunArgs" + mtype: "" + } + member { + name: "SessionRunContext" + mtype: "" + } + member { + name: "SessionRunHook" + mtype: "" + } + member { + name: "SessionRunValues" + mtype: "" + } + member { + name: "SingularMonitoredSession" + mtype: "" + } + member { + name: "StepCounterHook" + mtype: "" + } + member { + name: "StopAtStepHook" + mtype: "" + } + member { + name: "SummarySaverHook" + mtype: "" + } + member { + name: "Supervisor" + mtype: "" + } + member { + name: "SyncReplicasOptimizer" + mtype: "" + } + member { + name: "VocabInfo" + mtype: "" + } + member { + name: "WorkerSessionCreator" + mtype: "" + } + member { + name: "queue_runner" + mtype: "" + } + member_method { + name: "MonitoredTrainingSession" + argspec: "args=[\'master\', \'is_chief\', \'checkpoint_dir\', \'scaffold\', \'hooks\', \'chief_only_hooks\', \'save_checkpoint_secs\', \'save_summaries_steps\', \'save_summaries_secs\', \'config\', \'stop_grace_period_secs\', \'log_step_count_steps\', \'max_wait_secs\', \'save_checkpoint_steps\', \'summary_dir\'], varargs=None, keywords=None, defaults=[\'\', \'True\', \'None\', \'None\', \'None\', \'None\', \'\', \'\', \'\', \'None\', \'120\', \'100\', \'7200\', \'\', \'None\'], " + } + member_method { + name: "NewCheckpointReader" + argspec: "args=[\'filepattern\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "add_queue_runner" + argspec: "args=[\'qr\', \'collection\'], varargs=None, keywords=None, defaults=[\'queue_runners\'], " + } + member_method { + name: "assert_global_step" + argspec: "args=[\'global_step_tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "basic_train_loop" + argspec: "args=[\'supervisor\', \'train_step_fn\', \'args\', \'kwargs\', \'master\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'\'], " + } + member_method { + name: "batch" + argspec: "args=[\'tensors\', \'batch_size\', \'num_threads\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "batch_join" + argspec: "args=[\'tensors_list\', \'batch_size\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "checkpoint_exists" + argspec: "args=[\'checkpoint_prefix\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "cosine_decay" + argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'alpha\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " + } + member_method { + name: "cosine_decay_restarts" + argspec: "args=[\'learning_rate\', \'global_step\', \'first_decay_steps\', \'t_mul\', \'m_mul\', \'alpha\', \'name\'], varargs=None, keywords=None, defaults=[\'2.0\', \'1.0\', \'0.0\', \'None\'], " + } + member_method { + name: "create_global_step" + argspec: "args=[\'graph\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "do_quantize_training_on_graphdef" + argspec: "args=[\'input_graph\', \'num_bits\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "exponential_decay" + argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'decay_rate\', \'staircase\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "export_meta_graph" + argspec: "args=[\'filename\', \'meta_info_def\', \'graph_def\', \'saver_def\', \'collection_list\', \'as_text\', \'graph\', \'export_scope\', \'clear_devices\', \'clear_extraneous_savers\', \'strip_default_attrs\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'None\', \'None\', \'False\', \'False\', \'False\'], " + } + member_method { + name: "generate_checkpoint_state_proto" + argspec: "args=[\'save_dir\', \'model_checkpoint_path\', \'all_model_checkpoint_paths\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_checkpoint_mtimes" + argspec: "args=[\'checkpoint_prefixes\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_checkpoint_state" + argspec: "args=[\'checkpoint_dir\', \'latest_filename\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_global_step" + argspec: "args=[\'graph\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "get_or_create_global_step" + argspec: "args=[\'graph\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "global_step" + argspec: "args=[\'sess\', \'global_step_tensor\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "import_meta_graph" + argspec: "args=[\'meta_graph_or_file\', \'clear_devices\', \'import_scope\'], varargs=None, keywords=kwargs, defaults=[\'False\', \'None\'], " + } + member_method { + name: "init_from_checkpoint" + argspec: "args=[\'ckpt_dir_or_file\', \'assignment_map\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "input_producer" + argspec: "args=[\'input_tensor\', \'element_shape\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'summary_name\', \'name\', \'cancel_op\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'None\', \'32\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "inverse_time_decay" + argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'decay_rate\', \'staircase\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "latest_checkpoint" + argspec: "args=[\'checkpoint_dir\', \'latest_filename\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "limit_epochs" + argspec: "args=[\'tensor\', \'num_epochs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "linear_cosine_decay" + argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'num_periods\', \'alpha\', \'beta\', \'name\'], varargs=None, keywords=None, defaults=[\'0.5\', \'0.0\', \'0.001\', \'None\'], " + } + member_method { + name: "list_variables" + argspec: "args=[\'ckpt_dir_or_file\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "load_checkpoint" + argspec: "args=[\'ckpt_dir_or_file\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "load_variable" + argspec: "args=[\'ckpt_dir_or_file\', \'name\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "match_filenames_once" + argspec: "args=[\'pattern\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "maybe_batch" + argspec: "args=[\'tensors\', \'keep_input\', \'batch_size\', \'num_threads\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "maybe_batch_join" + argspec: "args=[\'tensors_list\', \'keep_input\', \'batch_size\', \'capacity\', \'enqueue_many\', \'shapes\', \'dynamic_pad\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'32\', \'False\', \'None\', \'False\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "maybe_shuffle_batch" + argspec: "args=[\'tensors\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'keep_input\', \'num_threads\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "maybe_shuffle_batch_join" + argspec: "args=[\'tensors_list\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'keep_input\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "natural_exp_decay" + argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'decay_rate\', \'staircase\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " + } + member_method { + name: "noisy_linear_cosine_decay" + argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'initial_variance\', \'variance_decay\', \'num_periods\', \'alpha\', \'beta\', \'name\'], varargs=None, keywords=None, defaults=[\'1.0\', \'0.55\', \'0.5\', \'0.0\', \'0.001\', \'None\'], " + } + member_method { + name: "piecewise_constant" + argspec: "args=[\'x\', \'boundaries\', \'values\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "polynomial_decay" + argspec: "args=[\'learning_rate\', \'global_step\', \'decay_steps\', \'end_learning_rate\', \'power\', \'cycle\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0001\', \'1.0\', \'False\', \'None\'], " + } + member_method { + name: "range_input_producer" + argspec: "args=[\'limit\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'32\', \'None\', \'None\'], " + } + member_method { + name: "remove_checkpoint" + argspec: "args=[\'checkpoint_prefix\', \'checkpoint_format_version\', \'meta_graph_suffix\'], varargs=None, keywords=None, defaults=[\'2\', \'meta\'], " + } + member_method { + name: "replica_device_setter" + argspec: "args=[\'ps_tasks\', \'ps_device\', \'worker_device\', \'merge_devices\', \'cluster\', \'ps_ops\', \'ps_strategy\'], varargs=None, keywords=None, defaults=[\'0\', \'/job:ps\', \'/job:worker\', \'True\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "sdca_fprint" + argspec: "args=[\'input\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "sdca_optimizer" + argspec: "args=[\'sparse_example_indices\', \'sparse_feature_indices\', \'sparse_feature_values\', \'dense_features\', \'example_weights\', \'example_labels\', \'sparse_indices\', \'sparse_weights\', \'dense_weights\', \'example_state_data\', \'loss_type\', \'l1\', \'l2\', \'num_loss_partitions\', \'num_inner_iterations\', \'adaptative\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " + } + member_method { + name: "sdca_shrink_l1" + argspec: "args=[\'weights\', \'l1\', \'l2\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "shuffle_batch" + argspec: "args=[\'tensors\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'num_threads\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'1\', \'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "shuffle_batch_join" + argspec: "args=[\'tensors_list\', \'batch_size\', \'capacity\', \'min_after_dequeue\', \'seed\', \'enqueue_many\', \'shapes\', \'allow_smaller_final_batch\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'None\', \'False\', \'None\', \'None\'], " + } + member_method { + name: "slice_input_producer" + argspec: "args=[\'tensor_list\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'32\', \'None\', \'None\'], " + } + member_method { + name: "start_queue_runners" + argspec: "args=[\'sess\', \'coord\', \'daemon\', \'start\', \'collection\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'True\', \'queue_runners\'], " + } + member_method { + name: "string_input_producer" + argspec: "args=[\'string_tensor\', \'num_epochs\', \'shuffle\', \'seed\', \'capacity\', \'shared_name\', \'name\', \'cancel_op\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'None\', \'32\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "summary_iterator" + argspec: "args=[\'path\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "update_checkpoint_state" + argspec: "args=[\'save_dir\', \'model_checkpoint_path\', \'all_model_checkpoint_paths\', \'latest_filename\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " + } + member_method { + name: "warm_start" + argspec: "args=[\'ckpt_to_initialize_from\', \'vars_to_warm_start\', \'var_name_to_vocab_info\', \'var_name_to_prev_var_name\'], varargs=None, keywords=None, defaults=[\'.*\', \'None\', \'None\'], " + } + member_method { + name: "write_graph" + argspec: "args=[\'graph_or_graph_def\', \'logdir\', \'name\', \'as_text\'], varargs=None, keywords=None, defaults=[\'True\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.queue_runner.-queue-runner.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.queue_runner.-queue-runner.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..23d402de30888c1c503a3971cefa1167af3bc8c6 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.queue_runner.-queue-runner.pbtxt @@ -0,0 +1,49 @@ +path: "tensorflow.train.queue_runner.QueueRunner" +tf_class { + is_instance: "" + is_instance: "" + member { + name: "cancel_op" + mtype: "" + } + member { + name: "close_op" + mtype: "" + } + member { + name: "enqueue_ops" + mtype: "" + } + member { + name: "exceptions_raised" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "queue" + mtype: "" + } + member { + name: "queue_closed_exception_types" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'queue\', \'enqueue_ops\', \'close_op\', \'cancel_op\', \'queue_closed_exception_types\', \'queue_runner_def\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\'], " + } + member_method { + name: "create_threads" + argspec: "args=[\'self\', \'sess\', \'coord\', \'daemon\', \'start\'], varargs=None, keywords=None, defaults=[\'None\', \'False\', \'False\'], " + } + member_method { + name: "from_proto" + argspec: "args=[\'queue_runner_def\', \'import_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } + member_method { + name: "to_proto" + argspec: "args=[\'self\', \'export_scope\'], varargs=None, keywords=None, defaults=[\'None\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.train.queue_runner.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.train.queue_runner.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..6e2d04304967dd08d2c389c209dd43c731c5f956 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.train.queue_runner.pbtxt @@ -0,0 +1,15 @@ +path: "tensorflow.train.queue_runner" +tf_module { + member { + name: "QueueRunner" + mtype: "" + } + member_method { + name: "add_queue_runner" + argspec: "args=[\'qr\', \'collection\'], varargs=None, keywords=None, defaults=[\'queue_runners\'], " + } + member_method { + name: "start_queue_runners" + argspec: "args=[\'sess\', \'coord\', \'daemon\', \'start\', \'collection\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'True\', \'queue_runners\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.truncated_normal_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.truncated_normal_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..c1e1c230a9f79e87294eb6038f870726a0ba85a4 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.truncated_normal_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.truncated_normal_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.uniform_unit_scaling_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.uniform_unit_scaling_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e1b18dc92fbee9565dba81e8c09534bea6734f23 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.uniform_unit_scaling_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.uniform_unit_scaling_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'factor\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.variable_scope.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.variable_scope.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e62dec93e6f06a10f48d72b0cda74426887806fb --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.variable_scope.pbtxt @@ -0,0 +1,9 @@ +path: "tensorflow.variable_scope" +tf_class { + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'name_or_scope\', \'default_name\', \'values\', \'initializer\', \'regularizer\', \'caching_device\', \'partitioner\', \'custom_getter\', \'reuse\', \'dtype\', \'use_resource\', \'constraint\', \'auxiliary_name_scope\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'True\'], " + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.variance_scaling_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.variance_scaling_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..09d7bc03b4f238923db6778ec32ce78ae76eed61 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.variance_scaling_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.variance_scaling_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'scale\', \'mode\', \'distribution\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'fan_in\', \'truncated_normal\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/v2/tensorflow.zeros_initializer.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.zeros_initializer.pbtxt new file mode 100644 index 0000000000000000000000000000000000000000..e229b02ceec6739974d3b4ae2bb02ef273398c45 --- /dev/null +++ b/tensorflow/tools/api/golden/v2/tensorflow.zeros_initializer.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.zeros_initializer" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/tests/BUILD b/tensorflow/tools/api/tests/BUILD index 724b12cd4799eb76fe602c737c850e96e92faa58..8764409e4d1af4ea7f6092b9df64f59511bca43d 100644 --- a/tensorflow/tools/api/tests/BUILD +++ b/tensorflow/tools/api/tests/BUILD @@ -17,7 +17,8 @@ py_test( name = "api_compatibility_test", srcs = ["api_compatibility_test.py"], data = [ - "//tensorflow/tools/api/golden:api_golden", + "//tensorflow/tools/api/golden:api_golden_v1", + "//tensorflow/tools/api/golden:api_golden_v2", "//tensorflow/tools/api/tests:API_UPDATE_WARNING.txt", "//tensorflow/tools/api/tests:README.txt", ], diff --git a/tensorflow/tools/api/tests/api_compatibility_test.py b/tensorflow/tools/api/tests/api_compatibility_test.py index d1b34fb242cd6303b61315b64ec60e6fc503aca2..b65dbc4b7dfeca0d0e65a4b68776814180aee557 100644 --- a/tensorflow/tools/api/tests/api_compatibility_test.py +++ b/tensorflow/tools/api/tests/api_compatibility_test.py @@ -34,13 +34,6 @@ import sys import unittest import tensorflow as tf -# pylint: disable=g-import-not-at-top -try: - from tensorflow.compat import v1 as tf_v1 - # We import compat.v1 as tf_v1 instead. - del tf.compat.v1 -except ImportError: - tf_v1 = None from google.protobuf import message from google.protobuf import text_format @@ -53,8 +46,6 @@ from tensorflow.tools.api.lib import api_objects_pb2 from tensorflow.tools.api.lib import python_object_to_proto_visitor from tensorflow.tools.common import public_api from tensorflow.tools.common import traverse -# pylint: enable=g-import-not-at-top - # FLAGS defined at the bottom: FLAGS = None @@ -70,19 +61,25 @@ _VERBOSE_DIFFS_HELP = """ false, only print which libraries have differences. """ -_API_GOLDEN_FOLDER = 'tensorflow/tools/api/golden' +_API_GOLDEN_FOLDER_V1 = 'tensorflow/tools/api/golden/v1' +_API_GOLDEN_FOLDER_V2 = 'tensorflow/tools/api/golden/v2' _TEST_README_FILE = 'tensorflow/tools/api/tests/README.txt' _UPDATE_WARNING_FILE = 'tensorflow/tools/api/tests/API_UPDATE_WARNING.txt' -def _KeyToFilePath(key): - """From a given key, construct a filepath.""" +def _KeyToFilePath(key, api_version): + """From a given key, construct a filepath. + + Filepath will be inside golden folder for api_version. + """ def _ReplaceCapsWithDash(matchobj): match = matchobj.group(0) return '-%s' % (match.lower()) case_insensitive_key = re.sub('([A-Z]{1})', _ReplaceCapsWithDash, key) - return os.path.join(_API_GOLDEN_FOLDER, '%s.pbtxt' % case_insensitive_key) + api_folder = ( + _API_GOLDEN_FOLDER_V2 if api_version == 2 else _API_GOLDEN_FOLDER_V1) + return os.path.join(_API_GOLDEN_FOLDER_V1, '%s.pbtxt' % case_insensitive_key) def _FileNameToKey(filename): @@ -98,6 +95,21 @@ def _FileNameToKey(filename): return api_object_key +def _VerifyNoSubclassOfMessageVisitor(path, parent, unused_children): + """A Visitor that crashes on subclasses of generated proto classes.""" + # If the traversed object is a proto Message class + if not (isinstance(parent, type) and + issubclass(parent, message.Message)): + return + if parent is message.Message: + return + # Check that it is a direct subclass of Message. + if message.Message not in parent.__bases__: + raise NotImplementedError( + 'Object tf.%s is a subclass of a generated proto Message. ' + 'They are not yet supported by the API tools.' % path) + + class ApiCompatibilityTest(test.TestCase): def __init__(self, *args, **kwargs): @@ -120,7 +132,8 @@ class ApiCompatibilityTest(test.TestCase): actual_dict, verbose=False, update_goldens=False, - additional_missing_object_message=''): + additional_missing_object_message='', + api_version=2): """Diff given dicts of protobufs and report differences a readable way. Args: @@ -133,6 +146,7 @@ class ApiCompatibilityTest(test.TestCase): update_goldens: Whether to update goldens when there are diffs found. additional_missing_object_message: Message to print when a symbol is missing. + api_version: TensorFlow API version to test. """ diffs = [] verbose_diffs = [] @@ -158,6 +172,8 @@ class ApiCompatibilityTest(test.TestCase): diff_message = 'New object %s found (added).' % key verbose_diff_message = diff_message else: + # Do not truncate diff + self.maxDiffs = None # pylint: disable=invalid-name # Now we can run an actual proto diff. try: self.assertProtoEquals(expected_dict[key], actual_dict[key]) @@ -188,13 +204,13 @@ class ApiCompatibilityTest(test.TestCase): # If the keys are only in expected, some objects are deleted. # Remove files. for key in only_in_expected: - filepath = _KeyToFilePath(key) + filepath = _KeyToFilePath(key, api_version) file_io.delete_file(filepath) # If the files are only in actual (current library), these are new # modules. Write them to files. Also record all updates in files. for key in only_in_actual | set(updated_keys): - filepath = _KeyToFilePath(key) + filepath = _KeyToFilePath(key, api_version) file_io.write_string_to_file( filepath, text_format.MessageToString(actual_dict[key])) else: @@ -205,33 +221,40 @@ class ApiCompatibilityTest(test.TestCase): logging.info('No differences found between API and golden.') def testNoSubclassOfMessage(self): - - def Visit(path, parent, unused_children): - """A Visitor that crashes on subclasses of generated proto classes.""" - # If the traversed object is a proto Message class - if not (isinstance(parent, type) and - issubclass(parent, message.Message)): - return - if parent is message.Message: - return - # Check that it is a direct subclass of Message. - if message.Message not in parent.__bases__: - raise NotImplementedError( - 'Object tf.%s is a subclass of a generated proto Message. ' - 'They are not yet supported by the API tools.' % path) - visitor = public_api.PublicAPIVisitor(Visit) + visitor = public_api.PublicAPIVisitor(_VerifyNoSubclassOfMessageVisitor) visitor.do_not_descend_map['tf'].append('contrib') + # Skip compat.v1 and compat.v2 since they are validated in separate tests. + visitor.private_map['tf.compat'] = ['v1', 'v2'] traverse.traverse(tf, visitor) - def checkBackwardsCompatibility(self, root, golden_file_pattern): - # Extract all API stuff. + def testNoSubclassOfMessageV1(self): + if not hasattr(tf.compat, 'v1'): + return + visitor = public_api.PublicAPIVisitor(_VerifyNoSubclassOfMessageVisitor) + visitor.do_not_descend_map['tf'].append('contrib') + traverse.traverse(tf.compat.v1, visitor) + + def testNoSubclassOfMessageV2(self): + if not hasattr(tf.compat, 'v2'): + return + visitor = public_api.PublicAPIVisitor(_VerifyNoSubclassOfMessageVisitor) + visitor.do_not_descend_map['tf'].append('contrib') + traverse.traverse(tf.compat.v2, visitor) + + def _checkBackwardsCompatibility( + self, root, golden_file_pattern, api_version, + additional_private_map=None): + # Extract all API stuff. visitor = python_object_to_proto_visitor.PythonObjectToProtoVisitor() public_api_visitor = public_api.PublicAPIVisitor(visitor) public_api_visitor.do_not_descend_map['tf'].append('contrib') - public_api_visitor.do_not_descend_map['tf.GPUOptions'] = ['Experimental'] - traverse.traverse(root, public_api_visitor) + public_api_visitor.do_not_descend_map['tf.GPUOptions'] = [ + 'Experimental'] + if additional_private_map: + public_api_visitor.private_map.update(additional_private_map) + traverse.traverse(root, public_api_visitor) proto_dict = visitor.GetProtos() # Read all golden files. @@ -254,27 +277,50 @@ class ApiCompatibilityTest(test.TestCase): golden_proto_dict, proto_dict, verbose=FLAGS.verbose_diffs, - update_goldens=FLAGS.update_goldens) + update_goldens=FLAGS.update_goldens, + api_version=api_version) @unittest.skipUnless( sys.version_info.major == 2, 'API compabitility test goldens are generated using python2.') def testAPIBackwardsCompatibility(self): + api_version = 1 golden_file_pattern = os.path.join( resource_loader.get_root_dir_with_all_resources(), - _KeyToFilePath('*')) - self.checkBackwardsCompatibility(tf, golden_file_pattern) + _KeyToFilePath('*', api_version)) + self._checkBackwardsCompatibility( + tf, + golden_file_pattern, + api_version, + # Skip compat.v1 and compat.v2 since they are validated + # in separate tests. + additional_private_map={'tf.compat': ['v1', 'v2']}) @unittest.skipUnless( sys.version_info.major == 2, 'API compabitility test goldens are generated using python2.') def testAPIBackwardsCompatibilityV1(self): - if not tf_v1: + if not hasattr(tf.compat, 'v1'): + return + api_version = 1 + golden_file_pattern = os.path.join( + resource_loader.get_root_dir_with_all_resources(), + _KeyToFilePath('*', api_version)) + self._checkBackwardsCompatibility( + tf.compat.v1, golden_file_pattern, api_version) + + @unittest.skipUnless( + sys.version_info.major == 2, + 'API compabitility test goldens are generated using python2.') + def testAPIBackwardsCompatibilityV2(self): + if not hasattr(tf.compat, 'v2'): return + api_version = 1 golden_file_pattern = os.path.join( resource_loader.get_root_dir_with_all_resources(), - _KeyToFilePath('*')) - self.checkBackwardsCompatibility(tf_v1, golden_file_pattern) + _KeyToFilePath('*', api_version)) + self._checkBackwardsCompatibility( + tf.compat.v2, golden_file_pattern, api_version) if __name__ == '__main__': diff --git a/tensorflow/tools/ci_build/Dockerfile.cmake b/tensorflow/tools/ci_build/Dockerfile.cmake index e8c319982839b7b5adc17d6fb7ac364660ac76fe..4587bcf89103c48e39bb8cc3188391ea99941b3e 100644 --- a/tensorflow/tools/ci_build/Dockerfile.cmake +++ b/tensorflow/tools/ci_build/Dockerfile.cmake @@ -28,8 +28,8 @@ RUN pip install --upgrade astor RUN pip install --upgrade gast RUN pip install --upgrade numpy RUN pip install --upgrade termcolor -RUN pip install keras_applications==1.0.2 -RUN pip install keras_preprocessing==1.0.1 +RUN pip install keras_applications==1.0.4 +RUN pip install keras_preprocessing==1.0.2 # Install golang RUN apt-get install -t xenial-backports -y golang-1.9 diff --git a/tensorflow/tools/ci_build/install/install_pip_packages.sh b/tensorflow/tools/ci_build/install/install_pip_packages.sh index c3c537328ff8b6ae1fe570eb44625677551d2519..bb316ecfc92e41a28a3fbfaf5a12f234c0483d5c 100755 --- a/tensorflow/tools/ci_build/install/install_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_pip_packages.sh @@ -115,10 +115,10 @@ pip2 install --upgrade setuptools==39.1.0 pip3 install --upgrade setuptools==39.1.0 # Keras -pip2 install keras_applications==1.0.2 -pip3 install keras_applications==1.0.2 -pip2 install keras_preprocessing==1.0.1 -pip3 install keras_preprocessing==1.0.1 +pip2 install keras_applications==1.0.4 --no-deps +pip3 install keras_applications==1.0.4 --no-deps +pip2 install keras_preprocessing==1.0.2 --no-deps +pip3 install keras_preprocessing==1.0.2 --no-deps # Install last working version of setuptools. pip2 install --upgrade setuptools==39.1.0 diff --git a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh index b6f5de57c9ad5a4c5aa4a620741f381a8b425bee..15e4396ce3c0ee55ccfc18d939dc4e1883fc132d 100755 --- a/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh @@ -85,8 +85,8 @@ pip3.5 install --upgrade termcolor pip3.5 install --upgrade setuptools==39.1.0 # Keras -pip3.5 install keras_applications==1.0.2 -pip3.5 install keras_preprocessing==1.0.1 +pip3.5 install keras_applications==1.0.4 +pip3.5 install keras_preprocessing==1.0.2 # Install last working version of setuptools. pip3.5 install --upgrade setuptools==39.1.0 diff --git a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh index 88686641321e22d7df4c4341ec79893ff81fb6d5..0fc3eee71ceb5cd331625f26c904f04f844dccc6 100755 --- a/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh +++ b/tensorflow/tools/ci_build/install/install_python3.6_pip_packages.sh @@ -101,7 +101,7 @@ pip3 install --upgrade termcolor pip3 install --upgrade setuptools==39.1.0 # Keras -pip3 install keras_applications==1.0.2 -pip3 install keras_preprocessing==1.0.1 +pip3 install keras_applications==1.0.4 +pip3 install keras_preprocessing==1.0.2 # LINT.ThenChange(//tensorflow/tools/ci_build/install/install_python3.5_pip_packages.sh) diff --git a/tensorflow/tools/common/public_api.py b/tensorflow/tools/common/public_api.py index b40e4155df4948f1b1220647772e463b689c20af..09933d266b843641a79ec71aa40081d8c8675e23 100644 --- a/tensorflow/tools/common/public_api.py +++ b/tensorflow/tools/common/public_api.py @@ -70,6 +70,8 @@ class PublicAPIVisitor(object): 'tf.app': ['flags'], # Imported for compatibility between py2/3. 'tf.test': ['mock'], + # Externalized modules of the Keras API. + 'tf.keras': ['applications', 'preprocessing'] } @property diff --git a/tensorflow/tools/def_file_filter/def_file_filter.py.tpl b/tensorflow/tools/def_file_filter/def_file_filter.py.tpl index 8bdc03eb0f19fd6daae826727f429bc1255f0eca..4bfcc2570cce9c8dac369b7c9cf882356c428df5 100644 --- a/tensorflow/tools/def_file_filter/def_file_filter.py.tpl +++ b/tensorflow/tools/def_file_filter/def_file_filter.py.tpl @@ -48,6 +48,7 @@ EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::") INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" r"google::protobuf::internal::ArenaImpl::AllocateAligned|" # for contrib/data/_prefetching_ops r"google::protobuf::internal::ArenaImpl::AddCleanup|" # for contrib/data/_prefetching_ops + r"google::protobuf::internal::LogMessage|" # for contrib/data/_prefetching_ops r"google::protobuf::Arena::OnArenaAllocation|" # for contrib/data/_prefetching_ops r"tensorflow::internal::LogMessage|" r"tensorflow::internal::LogString|" diff --git a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl index f8f63e276cab61900cba9de599a11efc7718d078..df0fd053194e7b5da2cd656309467ca0f90e4092 100644 --- a/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl +++ b/tensorflow/tools/def_file_filter/def_file_filter_configure.bzl @@ -24,27 +24,27 @@ load("@bazel_tools//tools/cpp:windows_cc_configure.bzl", "find_msvc_tool") load("@bazel_tools//tools/cpp:lib_cc_configure.bzl", "auto_configure_fail") def _def_file_filter_configure_impl(repository_ctx): - if repository_ctx.os.name.lower().find("windows") == -1: + if repository_ctx.os.name.lower().find("windows") == -1: + repository_ctx.symlink(Label("//tensorflow/tools/def_file_filter:BUILD.tpl"), "BUILD") + repository_ctx.file("def_file_filter.py", "") + return + vc_path = find_vc_path(repository_ctx) + if vc_path == None: + auto_configure_fail("Visual C++ build tools not found on your machine") + + undname = find_msvc_tool(repository_ctx, vc_path, "undname.exe") + if undname == None: + auto_configure_fail("Couldn't find undname.exe under %s, please check your VC installation and set BAZEL_VC environment variable correctly." % vc_path) + undname_bin_path = undname.replace("\\", "\\\\") + + repository_ctx.template( + "def_file_filter.py", + Label("//tensorflow/tools/def_file_filter:def_file_filter.py.tpl"), + { + "%{undname_bin_path}": undname_bin_path, + }, + ) repository_ctx.symlink(Label("//tensorflow/tools/def_file_filter:BUILD.tpl"), "BUILD") - repository_ctx.file("def_file_filter.py", "") - return - vc_path = find_vc_path(repository_ctx) - if vc_path == "visual-studio-not-found": - auto_configure_fail("Visual C++ build tools not found on your machine") - - undname = find_msvc_tool(repository_ctx, vc_path, "undname.exe") - if undname == None: - auto_configure_fail("Couldn't find undname.exe under %s, please check your VC installation and set BAZEL_VC environment variable correctly." % vc_path) - undname_bin_path = undname.replace("\\", "\\\\") - - repository_ctx.template( - "def_file_filter.py", - Label("//tensorflow/tools/def_file_filter:def_file_filter.py.tpl"), - { - "%{undname_bin_path}": undname_bin_path, - }) - repository_ctx.symlink(Label("//tensorflow/tools/def_file_filter:BUILD.tpl"), "BUILD") - def_file_filter_configure = repository_rule( implementation = _def_file_filter_configure_impl, @@ -55,6 +55,6 @@ def_file_filter_configure = repository_rule( "VS100COMNTOOLS", "VS110COMNTOOLS", "VS120COMNTOOLS", - "VS140COMNTOOLS" + "VS140COMNTOOLS", ], ) diff --git a/tensorflow/tools/docker/Dockerfile.mkl b/tensorflow/tools/docker/Dockerfile.mkl index 139395d49102fe2de3e241936095613da3f21bf8..ad5109f26dc711c64cfad6bae08d829545828b56 100755 --- a/tensorflow/tools/docker/Dockerfile.mkl +++ b/tensorflow/tools/docker/Dockerfile.mkl @@ -20,7 +20,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ libpng12-dev \ libzmq3-dev \ pkg-config \ - python \ + ${PYTHON} \ ${PYTHON_DEV} \ rsync \ software-properties-common \ @@ -30,7 +30,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ rm -rf /var/lib/apt/lists/* RUN curl -O https://bootstrap.pypa.io/get-pip.py && \ - python get-pip.py && \ + ${PYTHON} get-pip.py && \ rm get-pip.py RUN ${PIP} --no-cache-dir install \ @@ -44,7 +44,7 @@ RUN ${PIP} --no-cache-dir install \ scipy \ sklearn \ && \ - python -m ipykernel.kernelspec + ${PYTHON} -m ipykernel.kernelspec COPY ${TF_WHL_URL} / RUN ${PIP} install --no-cache-dir --force-reinstall /${TF_WHL_URL} && \ diff --git a/tensorflow/tools/docs/BUILD b/tensorflow/tools/docs/BUILD index 66b10478acdd6ed855067c120e95e36c42411973..cc7885ab1b61284b234935c09cccb68a713074da 100644 --- a/tensorflow/tools/docs/BUILD +++ b/tensorflow/tools/docs/BUILD @@ -28,6 +28,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":doc_generator_visitor", + ":generate_lib", "//tensorflow/python:platform_test", ], ) diff --git a/tensorflow/tools/docs/doc_generator_visitor.py b/tensorflow/tools/docs/doc_generator_visitor.py index c090dbd8da8dd9d39d9a90ae21eb305168c0c27d..a66f3e449377fef3d4c7bf4e0b8810cd6111eb85 100644 --- a/tensorflow/tools/docs/doc_generator_visitor.py +++ b/tensorflow/tools/docs/doc_generator_visitor.py @@ -159,6 +159,55 @@ class DocGeneratorVisitor(object): self._index[full_name] = child self._tree[parent_name].append(name) + def _score_name(self, name): + """Return a tuple of scores indicating how to sort for the best name. + + This function is meant to be used as the `key` to the `sorted` function. + + This sorting in order: + Prefers names refering to the defining class, over a subclass. + Prefers names that are not in "contrib". + prefers submodules to the root namespace. + Prefers short names `tf.thing` over `tf.a.b.c.thing` + Sorts lexicographically on name parts. + + Args: + name: the full name to score, for example `tf.estimator.Estimator` + + Returns: + A tuple of scores. When sorted the preferred name will have the lowest + value. + """ + parts = name.split('.') + short_name = parts[-1] + + container = self._index['.'.join(parts[:-1])] + + defining_class_score = 1 + if tf_inspect.isclass(container): + if short_name in container.__dict__: + # prefer the defining class + defining_class_score = -1 + + contrib_score = -1 + if 'contrib' in parts: + contrib_score = 1 + + while parts: + parts.pop() + container = self._index['.'.join(parts)] + if tf_inspect.ismodule(container): + break + module_length = len(parts) + if len(parts) == 2: + # `tf.submodule.thing` is better than `tf.thing` + module_length_score = -1 + else: + # shorter is better + module_length_score = module_length + + return (defining_class_score, contrib_score, module_length_score, name) + def _maybe_find_duplicates(self): """Compute data structures containing information about duplicates. @@ -192,7 +241,7 @@ class DocGeneratorVisitor(object): if (py_object is not None and not isinstance(py_object, six.integer_types + six.string_types + (six.binary_type, six.text_type, float, complex, bool)) - and py_object is not ()): + and py_object is not ()): # pylint: disable=literal-comparison object_id = id(py_object) if object_id in reverse_index: master_name = reverse_index[object_id] @@ -217,9 +266,9 @@ class DocGeneratorVisitor(object): if master_name: master_name = 'tf.%s' % master_name else: - # Choose the lexicographically first name with the minimum number of - # submodules. This will prefer highest level namespace for any symbol. - master_name = min(names, key=lambda name: name.count('.')) + # Choose the master name with a lexical sort on the tuples returned by + # by _score_name. + master_name = min(names, key=self._score_name) duplicates[master_name] = names for name in names: diff --git a/tensorflow/tools/docs/doc_generator_visitor_test.py b/tensorflow/tools/docs/doc_generator_visitor_test.py index cf5be45f40e3a2f727c3961c2896754cf8f269f2..1c2635d4a8c0acbe25502e3b9870420a38b7e22e 100644 --- a/tensorflow/tools/docs/doc_generator_visitor_test.py +++ b/tensorflow/tools/docs/doc_generator_visitor_test.py @@ -18,8 +18,21 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import types + from tensorflow.python.platform import googletest from tensorflow.tools.docs import doc_generator_visitor +from tensorflow.tools.docs import generate_lib + + +class NoDunderVisitor(doc_generator_visitor.DocGeneratorVisitor): + + def __call__(self, parent_name, parent, children): + """Drop all the dunder methods to make testing easier.""" + children = [ + (name, obj) for (name, obj) in children if not name.startswith('_') + ] + super(NoDunderVisitor, self).__call__(parent_name, parent, children) class DocGeneratorVisitorTest(googletest.TestCase): @@ -57,52 +70,184 @@ class DocGeneratorVisitorTest(googletest.TestCase): with self.assertRaises(RuntimeError): visitor('non_class_or_module', 'non_class_or_module_object', []) - def test_duplicates(self): - visitor = doc_generator_visitor.DocGeneratorVisitor() - visitor( - 'submodule.DocGeneratorVisitor', - doc_generator_visitor.DocGeneratorVisitor, - [('index', doc_generator_visitor.DocGeneratorVisitor.index), - ('index2', doc_generator_visitor.DocGeneratorVisitor.index)]) - visitor( - 'submodule2.DocGeneratorVisitor', - doc_generator_visitor.DocGeneratorVisitor, - [('index', doc_generator_visitor.DocGeneratorVisitor.index), - ('index2', doc_generator_visitor.DocGeneratorVisitor.index)]) - visitor( - 'DocGeneratorVisitor2', - doc_generator_visitor.DocGeneratorVisitor, - [('index', doc_generator_visitor.DocGeneratorVisitor.index), - ('index2', doc_generator_visitor.DocGeneratorVisitor.index)]) - - # The shorter path should be master, or if equal, the lexicographically - # first will be. - self.assertEqual( - {'DocGeneratorVisitor2': sorted(['submodule.DocGeneratorVisitor', - 'submodule2.DocGeneratorVisitor', - 'DocGeneratorVisitor2']), - 'DocGeneratorVisitor2.index': sorted([ - 'submodule.DocGeneratorVisitor.index', - 'submodule.DocGeneratorVisitor.index2', - 'submodule2.DocGeneratorVisitor.index', - 'submodule2.DocGeneratorVisitor.index2', - 'DocGeneratorVisitor2.index', - 'DocGeneratorVisitor2.index2' - ]), - }, visitor.duplicates) - self.assertEqual({ - 'submodule.DocGeneratorVisitor': 'DocGeneratorVisitor2', - 'submodule.DocGeneratorVisitor.index': 'DocGeneratorVisitor2.index', - 'submodule.DocGeneratorVisitor.index2': 'DocGeneratorVisitor2.index', - 'submodule2.DocGeneratorVisitor': 'DocGeneratorVisitor2', - 'submodule2.DocGeneratorVisitor.index': 'DocGeneratorVisitor2.index', - 'submodule2.DocGeneratorVisitor.index2': 'DocGeneratorVisitor2.index', - 'DocGeneratorVisitor2.index2': 'DocGeneratorVisitor2.index' + def test_duplicates_module_class_depth(self): + + class Parent(object): + + class Nested(object): + pass + + tf = types.ModuleType('tf') + tf.Parent = Parent + tf.submodule = types.ModuleType('submodule') + tf.submodule.Parent = Parent + + visitor = generate_lib.extract( + [('tf', tf)], + private_map={}, + do_not_descend_map={}, + visitor_cls=NoDunderVisitor) + + self.assertEqual({ + 'tf.submodule.Parent': + sorted([ + 'tf.Parent', + 'tf.submodule.Parent', + ]), + 'tf.submodule.Parent.Nested': + sorted([ + 'tf.Parent.Nested', + 'tf.submodule.Parent.Nested', + ]), + }, visitor.duplicates) + + self.assertEqual({ + 'tf.Parent.Nested': 'tf.submodule.Parent.Nested', + 'tf.Parent': 'tf.submodule.Parent', + }, visitor.duplicate_of) + + self.assertEqual({ + id(Parent): 'tf.submodule.Parent', + id(Parent.Nested): 'tf.submodule.Parent.Nested', + id(tf): 'tf', + id(tf.submodule): 'tf.submodule', + }, visitor.reverse_index) + + def test_duplicates_contrib(self): + + class Parent(object): + pass + + tf = types.ModuleType('tf') + tf.contrib = types.ModuleType('contrib') + tf.submodule = types.ModuleType('submodule') + tf.contrib.Parent = Parent + tf.submodule.Parent = Parent + + visitor = generate_lib.extract( + [('tf', tf)], + private_map={}, + do_not_descend_map={}, + visitor_cls=NoDunderVisitor) + + self.assertEqual({ + 'tf.submodule.Parent': + sorted(['tf.contrib.Parent', 'tf.submodule.Parent']), + }, visitor.duplicates) + + self.assertEqual({ + 'tf.contrib.Parent': 'tf.submodule.Parent', + }, visitor.duplicate_of) + + self.assertEqual({ + id(tf): 'tf', + id(tf.submodule): 'tf.submodule', + id(Parent): 'tf.submodule.Parent', + id(tf.contrib): 'tf.contrib', + }, visitor.reverse_index) + + def test_duplicates_defining_class(self): + + class Parent(object): + obj1 = object() + + class Child(Parent): + pass + + tf = types.ModuleType('tf') + tf.Parent = Parent + tf.Child = Child + + visitor = generate_lib.extract( + [('tf', tf)], + private_map={}, + do_not_descend_map={}, + visitor_cls=NoDunderVisitor) + + self.assertEqual({ + 'tf.Parent.obj1': sorted([ + 'tf.Parent.obj1', + 'tf.Child.obj1', + ]), + }, visitor.duplicates) + + self.assertEqual({ + 'tf.Child.obj1': 'tf.Parent.obj1', }, visitor.duplicate_of) + + self.assertEqual({ + id(tf): 'tf', + id(Parent): 'tf.Parent', + id(Child): 'tf.Child', + id(Parent.obj1): 'tf.Parent.obj1', + }, visitor.reverse_index) + + def test_duplicates_module_depth(self): + + class Parent(object): + pass + + tf = types.ModuleType('tf') + tf.submodule = types.ModuleType('submodule') + tf.submodule.submodule2 = types.ModuleType('submodule2') + tf.Parent = Parent + tf.submodule.submodule2.Parent = Parent + + visitor = generate_lib.extract( + [('tf', tf)], + private_map={}, + do_not_descend_map={}, + visitor_cls=NoDunderVisitor) + + self.assertEqual({ + 'tf.Parent': sorted(['tf.Parent', 'tf.submodule.submodule2.Parent']), + }, visitor.duplicates) + + self.assertEqual({ + 'tf.submodule.submodule2.Parent': 'tf.Parent' + }, visitor.duplicate_of) + + self.assertEqual({ + id(tf): 'tf', + id(tf.submodule): 'tf.submodule', + id(tf.submodule.submodule2): 'tf.submodule.submodule2', + id(Parent): 'tf.Parent', + }, visitor.reverse_index) + + def test_duplicates_name(self): + + class Parent(object): + obj1 = object() + + Parent.obj2 = Parent.obj1 + + tf = types.ModuleType('tf') + tf.submodule = types.ModuleType('submodule') + tf.submodule.Parent = Parent + + visitor = generate_lib.extract( + [('tf', tf)], + private_map={}, + do_not_descend_map={}, + visitor_cls=NoDunderVisitor) + + self.assertEqual({ + 'tf.submodule.Parent.obj1': + sorted([ + 'tf.submodule.Parent.obj1', + 'tf.submodule.Parent.obj2', + ]), + }, visitor.duplicates) + + self.assertEqual({ + 'tf.submodule.Parent.obj2': 'tf.submodule.Parent.obj1', + }, visitor.duplicate_of) + self.assertEqual({ - id(doc_generator_visitor.DocGeneratorVisitor): 'DocGeneratorVisitor2', - id(doc_generator_visitor.DocGeneratorVisitor.index): - 'DocGeneratorVisitor2.index', + id(tf): 'tf', + id(tf.submodule): 'tf.submodule', + id(Parent): 'tf.submodule.Parent', + id(Parent.obj1): 'tf.submodule.Parent.obj1', }, visitor.reverse_index) if __name__ == '__main__': diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py index 4f70a6936490dab833dd32c30598f2e6f493feaa..4bc8cbf4b435463f6fed32bdbd69328d4708e845 100644 --- a/tensorflow/tools/docs/generate_lib.py +++ b/tensorflow/tools/docs/generate_lib.py @@ -235,12 +235,16 @@ def add_dict_to_dict(add_from, add_to): # Exclude some libraries in contrib from the documentation altogether. def _get_default_private_map(): - return {'tf.test': ['mock']} + return { + 'tf.contrib.autograph': ['utils', 'operators'], + 'tf.test': ['mock'], + 'tf.compat': ['v1', 'v2'], + } # Exclude members of some libraries. def _get_default_do_not_descend_map(): - # TODO(wicke): Shrink this list once the modules get sealed. + # TODO(markdaoust): Use docs_controls decorators, locally, instead. return { 'tf': ['cli', 'lib', 'wrappers'], 'tf.contrib': [ @@ -284,10 +288,13 @@ def _get_default_do_not_descend_map(): } -def extract(py_modules, private_map, do_not_descend_map): +def extract(py_modules, + private_map, + do_not_descend_map, + visitor_cls=doc_generator_visitor.DocGeneratorVisitor): """Extract docs from tf namespace and write them to disk.""" # Traverse the first module. - visitor = doc_generator_visitor.DocGeneratorVisitor(py_modules[0][0]) + visitor = visitor_cls(py_modules[0][0]) api_visitor = public_api.PublicAPIVisitor(visitor) api_visitor.set_root_name(py_modules[0][0]) add_dict_to_dict(private_map, api_visitor.private_map) diff --git a/tensorflow/tools/lib_package/BUILD b/tensorflow/tools/lib_package/BUILD index 44d8a37a8f5b9172bdcf5a571be9a4ca73a63819..b450bc42c541cf51249c462d12255d79edf353c1 100644 --- a/tensorflow/tools/lib_package/BUILD +++ b/tensorflow/tools/lib_package/BUILD @@ -4,7 +4,9 @@ package(default_visibility = ["//visibility:private"]) load("@bazel_tools//tools/build_defs/pkg:pkg.bzl", "pkg_tar") +load("@local_config_syslibs//:build_defs.bzl", "if_not_system_lib") load("//tensorflow:tensorflow.bzl", "tf_binary_additional_srcs") +load("//tensorflow:tensorflow.bzl", "if_cuda") load("//third_party/mkl:build_defs.bzl", "if_mkl") genrule( @@ -113,11 +115,8 @@ genrule( "//third_party/hadoop:LICENSE.txt", "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", - "@aws//:LICENSE", "@boringssl//:LICENSE", - "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", "@com_googlesource_code_re2//:LICENSE", - "@cub_archive//:LICENSE.TXT", "@curl//:COPYING", "@double_conversion//:LICENSE", "@eigen_archive//:COPYING.MPL2", @@ -125,13 +124,8 @@ genrule( "@fft2d//:fft/readme.txt", "@gemmlowp//:LICENSE", "@gif_archive//:COPYING", - "@grpc//:LICENSE", - "@grpc//third_party/address_sorting:LICENSE", - "@grpc//third_party/nanopb:LICENSE.txt", "@highwayhash//:LICENSE", - "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", - "@libxsmm_archive//:LICENSE.md", "@llvm//:LICENSE.TXT", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", @@ -141,10 +135,42 @@ genrule( "@protobuf_archive//:LICENSE", "@snappy//:COPYING", "@zlib_archive//:zlib.h", - ] + if_mkl([ + ] + select({ + "//tensorflow:with_aws_support": [ + "@aws//:LICENSE", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow:with_gcp_support": [ + "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow:with_jemalloc_linux_x86_64": [ + "@jemalloc//:COPYING", + ], + "//tensorflow:with_jemalloc_linux_ppc64le": [ + "@jemalloc//:COPYING", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow/core/kernels:xsmm": [ + "@libxsmm_archive//:LICENSE.md", + ], + "//conditions:default": [], + }) + if_cuda([ + "@cub_archive//:LICENSE.TXT", + ]) + if_mkl([ "//third_party/mkl:LICENSE", "//third_party/mkl_dnn:LICENSE", - ]), + ]) + if_not_system_lib( + "grpc", + [ + "@grpc//:LICENSE", + "@grpc//third_party/nanopb:LICENSE.txt", + "@grpc//third_party/address_sorting:LICENSE", + ], + ), outs = ["include/tensorflow/c/LICENSE"], cmd = "$(location :concat_licenses.sh) $(SRCS) >$@", tools = [":concat_licenses.sh"], @@ -156,11 +182,8 @@ genrule( "//third_party/hadoop:LICENSE.txt", "//third_party/eigen3:LICENSE", "//third_party/fft2d:LICENSE", - "@aws//:LICENSE", "@boringssl//:LICENSE", - "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", "@com_googlesource_code_re2//:LICENSE", - "@cub_archive//:LICENSE.TXT", "@curl//:COPYING", "@double_conversion//:LICENSE", "@eigen_archive//:COPYING.MPL2", @@ -169,9 +192,7 @@ genrule( "@gemmlowp//:LICENSE", "@gif_archive//:COPYING", "@highwayhash//:LICENSE", - "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", - "@libxsmm_archive//:LICENSE.md", "@llvm//:LICENSE.TXT", "@lmdb//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", @@ -181,7 +202,32 @@ genrule( "@protobuf_archive//:LICENSE", "@snappy//:COPYING", "@zlib_archive//:zlib.h", - ] + if_mkl([ + ] + select({ + "//tensorflow:with_aws_support": [ + "@aws//:LICENSE", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow:with_gcp_support": [ + "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow:with_jemalloc_linux_x86_64": [ + "@jemalloc//:COPYING", + ], + "//tensorflow:with_jemalloc_linux_ppc64le": [ + "@jemalloc//:COPYING", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow/core/kernels:xsmm": [ + "@libxsmm_archive//:LICENSE.md", + ], + "//conditions:default": [], + }) + if_cuda([ + "@cub_archive//:LICENSE.TXT", + ]) + if_mkl([ "//third_party/mkl:LICENSE", "//third_party/mkl_dnn:LICENSE", ]), diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index 06ee2307e50061765398fc63d899b0292af7fd28..ef7ae1aa255bbc2a3a9570ee7b8e640020acde7e 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -131,13 +131,9 @@ filegroup( "@absl_py//absl/flags:LICENSE", "@arm_neon_2_x86_sse//:LICENSE", "@astor_archive//:LICENSE", - "@aws//:LICENSE", "@boringssl//:LICENSE", - "@com_github_googleapis_googleapis//:LICENSE", - "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", "@com_google_absl//:LICENSE", "@com_googlesource_code_re2//:LICENSE", - "@cub_archive//:LICENSE.TXT", "@curl//:COPYING", "@double_conversion//:LICENSE", "@eigen_archive//:COPYING.MPL2", @@ -148,12 +144,8 @@ filegroup( "@gemmlowp//:LICENSE", "@gif_archive//:COPYING", "@highwayhash//:LICENSE", - "@jemalloc//:COPYING", "@jpeg//:LICENSE.md", - "@kafka//:LICENSE", - "@libxsmm_archive//:LICENSE.md", "@lmdb//:LICENSE", - "@local_config_nccl//:LICENSE", "@local_config_sycl//sycl:LICENSE.text", "@nasm//:LICENSE", "@nsync//:LICENSE", @@ -166,7 +158,39 @@ filegroup( "@termcolor_archive//:COPYING.txt", "@zlib_archive//:zlib.h", "@org_python_pypi_backports_weakref//:LICENSE", - ] + if_mkl([ + ] + select({ + "//tensorflow:with_aws_support": [ + "@aws//:LICENSE", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow:with_gcp_support": [ + "@com_github_googleapis_googleapis//:LICENSE", + "@com_github_googlecloudplatform_google_cloud_cpp//:LICENSE", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow:with_jemalloc_linux_x86_64": [ + "@jemalloc//:COPYING", + ], + "//tensorflow:with_jemalloc_linux_ppc64le": [ + "@jemalloc//:COPYING", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow:with_kafka_support": [ + "@kafka//:LICENSE", + ], + "//conditions:default": [], + }) + select({ + "//tensorflow/core/kernels:xsmm": [ + "@libxsmm_archive//:LICENSE.md", + ], + "//conditions:default": [], + }) + if_cuda([ + "@cub_archive//:LICENSE.TXT", + "@local_config_nccl//:LICENSE", + ]) + if_mkl([ "//third_party/mkl:LICENSE", "//third_party/mkl_dnn:LICENSE", ]) + if_not_system_lib( diff --git a/tensorflow/tools/pip_package/pip_smoke_test.py b/tensorflow/tools/pip_package/pip_smoke_test.py index 401f833dbd6ae404af000714219cae482a31129b..bfc007bc391fc3964a087b305bdb3684cc614631 100644 --- a/tensorflow/tools/pip_package/pip_smoke_test.py +++ b/tensorflow/tools/pip_package/pip_smoke_test.py @@ -90,6 +90,7 @@ BLACKLIST = [ "//tensorflow/contrib/lite/python:interpreter.py", "//tensorflow/contrib/lite/python:interpreter_test.py", "//tensorflow/contrib/ffmpeg:test_data", + "//tensorflow/contrib/hadoop:test_data", "//tensorflow/contrib/factorization/examples:mnist", "//tensorflow/contrib/factorization/examples:mnist.py", "//tensorflow/contrib/factorization:factorization_py_CYCLIC_DEPENDENCIES_THAT_NEED_TO_GO", # pylint:disable=line-too-long diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 085f3dd88a924cb2c12c7e6240f4207dfd290fe5..5e179079c576ca23db87038442b9be9990fbc5ab 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,12 +45,14 @@ DOCLINES = __doc__.split('\n') # This version string is semver compatible, but incompatible with pip. # For pip, we will remove all '-' characters from this string, and use the # result for pip. -_VERSION = '1.10.0-rc1' +_VERSION = '1.10.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', 'astor >= 0.6.0', 'gast >= 0.2.0', + 'keras_applications == 1.0.4', + 'keras_preprocessing == 1.0.2', 'numpy >= 1.13.3, <= 1.14.5', 'six >= 1.10.0', 'protobuf >= 3.6.0', diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 1ed56975efdc297262eada63c1574e886e656c3d..217910c04f09987deff7dfac9e535ab493d7421b 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -157,11 +157,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "com_googlesource_code_re2", urls = [ - "https://mirror.bazel.build/github.com/google/re2/archive/2018-04-01.tar.gz", - "https://github.com/google/re2/archive/2018-04-01.tar.gz", + "https://mirror.bazel.build/github.com/google/re2/archive/2018-07-01.tar.gz", + "https://github.com/google/re2/archive/2018-07-01.tar.gz", ], - sha256 = "2f945446b71336e7f5a2bcace1abcf0b23fbba368266c6a1be33de3de3b3c912", - strip_prefix = "re2-2018-04-01", + sha256 = "803c7811146edeef8f91064de37c6f19136ff01a2a8cdb3230e940b2fd9f07fe", + strip_prefix = "re2-2018-07-01", system_build_file = clean_dep("//third_party/systemlibs:re2.BUILD"), ) @@ -486,11 +486,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/36f54002c931a026f490f9fb074c11d91e3487a2.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/36f54002c931a026f490f9fb074c11d91e3487a2.tar.gz", ], - sha256 = "c6cbb21acd46e3e00faa8c379595ecffb99ef77622da17f29371db2bfad1d3d3", - strip_prefix = "llvm-7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428", + sha256 = "e360a9e9b0d4f1adedcdb89fc1efc171f68e250c115ddfaeb82d71edef7a10c8", + strip_prefix = "llvm-36f54002c931a026f490f9fb074c11d91e3487a2", build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) @@ -521,11 +521,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""): tf_http_archive( name = "boringssl", urls = [ - "https://mirror.bazel.build/github.com/google/boringssl/archive/f4fa779521475a98c1586dff349eb44934d5f281.tar.gz", - "https://github.com/google/boringssl/archive/f4fa779521475a98c1586dff349eb44934d5f281.tar.gz", + "https://mirror.bazel.build/github.com/google/boringssl/archive/45c4a87ae97eb95a8fc2906c035d6a8d0e02e1b8.tar.gz", + "https://github.com/google/boringssl/archive/45c4a87ae97eb95a8fc2906c035d6a8d0e02e1b8.tar.gz", ], - sha256 = "813d3ae5a11f8391941f716172c4438f888953d9f15ab609e1ee8f291a4e42d9", - strip_prefix = "boringssl-f4fa779521475a98c1586dff349eb44934d5f281", + sha256 = "972e8d8a9d1daf9892fff7155312b1af46b4754446575a7b285e62f917424c78", + strip_prefix = "boringssl-45c4a87ae97eb95a8fc2906c035d6a8d0e02e1b8", ) tf_http_archive( diff --git a/third_party/jpeg/jpeg.BUILD b/third_party/jpeg/jpeg.BUILD index 663a2187336d4a558a42f9fb6c4017a360976050..b36295ad06450d5207127e205e393621e0e23842 100644 --- a/third_party/jpeg/jpeg.BUILD +++ b/third_party/jpeg/jpeg.BUILD @@ -272,8 +272,10 @@ cc_library( "jchuff.h", "jconfig.h", "jdct.h", + "jerror.h", "jinclude.h", "jmorecfg.h", + "jpegint.h", "jpeglib.h", "jsimd.h", "jsimddct.h", diff --git a/third_party/kafka/BUILD b/third_party/kafka/BUILD index 75792b0d87366c304ca29f95f943114ee482dfcd..3c50b8cf52d125665461341ea7910ba801cfbb7b 100644 --- a/third_party/kafka/BUILD +++ b/third_party/kafka/BUILD @@ -15,6 +15,7 @@ cc_library( "src-cpp/KafkaConsumerImpl.cpp", "src-cpp/MessageImpl.cpp", "src-cpp/MetadataImpl.cpp", + "src-cpp/ProducerImpl.cpp", "src-cpp/QueueImpl.cpp", "src-cpp/RdKafka.cpp", "src-cpp/TopicImpl.cpp", @@ -130,7 +131,15 @@ cc_library( "src/tinycthread.h", "src/xxhash.c", "src/xxhash.h", - ], + ] + select({ + "@org_tensorflow//tensorflow:windows": [ + "src/rdkafka_sasl_win32.c", + "src/rdwin32.h", + "src/regexp.c", + "src/regexp.h", + ], + "//conditions:default": [], + }), hdrs = [ "config.h", "src-cpp/rdkafkacpp.h", @@ -138,15 +147,25 @@ cc_library( "src/lz4.c", "src/snappy_compat.h", ], - copts = [ - "-Iexternal/kafka/src", - "-Iexternal/kafka/src-cpp", - ], - defines = [ - ], - linkopts = [ - "-lpthread", + copts = select({ + "@org_tensorflow//tensorflow:windows": [ + "-DWIN32_LEAN_AND_MEAN", + "-DWITHOUT_WIN32_CONFIG", + "-DWITH_ZLIB=1", + "-DWITH_SSL=1", + "-DWITH_SNAPPY=1", + ], + "//conditions:default": [], + }), + defines = ["LIBRDKAFKA_STATICLIB"], + includes = [ + "src", + "src-cpp", ], + linkopts = select({ + "@org_tensorflow//tensorflow:windows": ["-defaultlib:crypt32.lib"], + "//conditions:default": ["-lpthread"], + }), visibility = ["//visibility:public"], deps = [ "@boringssl//:ssl",