040b4cbce7
Some tests are now using unnamed namespaces more aggressively to make as many names internal as possible. PiperOrigin-RevId: 180564422
73 lines
2.6 KiB
C++
73 lines
2.6 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/cc/framework/grad_op_registry.h"
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#include "tensorflow/cc/framework/gradient_checker.h"
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#include "tensorflow/cc/framework/testutil.h"
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#include "tensorflow/cc/gradients/grad_testutil.h"
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#include "tensorflow/cc/ops/standard_ops.h"
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#include "tensorflow/core/framework/tensor_testutil.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/lib/random/random.h"
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namespace tensorflow {
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namespace {
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using ops::Const;
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using ops::DynamicPartition;
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using ops::DynamicStitch;
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using ops::Placeholder;
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class DataFlowGradTest : public ::testing::Test {
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protected:
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DataFlowGradTest() : scope_(Scope::NewRootScope()) {}
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void RunTest(const OutputList& xs, const std::vector<TensorShape>& x_shapes,
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const OutputList& ys, const std::vector<TensorShape>& y_shapes) {
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TF_ASSERT_OK(scope_.status());
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float max_error;
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TF_ASSERT_OK((ComputeGradientError<float, float, float>(
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scope_, xs, x_shapes, ys, y_shapes, &max_error)));
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EXPECT_LT(max_error, 1e-4);
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}
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Scope scope_;
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};
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TEST_F(DataFlowGradTest, DynamicPartitionGrad) {
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TensorShape data_shape({2, 3, 2});
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auto data = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(data_shape));
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auto partitions = Const(scope_, {{2, 1, 0}, {1, 2, 0}});
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auto y = DynamicPartition(scope_, data, partitions, 3);
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TensorShape partition_shape({2, 2});
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RunTest({data}, {data_shape}, y.outputs,
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{partition_shape, partition_shape, partition_shape});
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}
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TEST_F(DataFlowGradTest, DynamicStitchGrad) {
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TensorShape d1_shape({2});
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TensorShape d2_shape({2, 2});
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std::vector<Output> indices = {Const(scope_, 2), Const(scope_, {1, 0})};
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std::vector<Output> data = {
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Placeholder(scope_, DT_FLOAT, Placeholder::Shape(d1_shape)),
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Placeholder(scope_, DT_FLOAT, Placeholder::Shape(d2_shape))};
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auto y = DynamicStitch(scope_, indices, data);
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TensorShape y_shape({3, 2});
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RunTest(data, {d1_shape, d2_shape}, {y}, {y_shape});
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}
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} // namespace
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} // namespace tensorflow
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