Function TEST_F

tensorflow/core/grappler/optimizers/layout_optimizer_test.cc:225–258

};

TEST_F(LayoutOptimizerTest, Conv2DBackpropInput) {
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
  auto conv = SimpleConv2DBackpropInput(&s, 7, 2, "EXPLICIT");
  Output fetch = ops::Identity(s.WithOpName("Fetch"), {conv});
  GrapplerItem item;
  TF_CHECK_OK(s.ToGraphDef(&item.graph));
  LayoutOptimizer optimizer;
  GraphDef output;

  Status status = optimizer.Optimize(virtual_cluster_.get(), item, &output);
  NodeMap node_map(&output);
  string input_name = "Conv2DBackpropInput-0-LayoutOptimizer";
  auto input_sizes_node = node_map.GetNode(input_name);
  CHECK(input_sizes_node);
  auto conv2d_backprop_node = node_map.GetNode("Conv2DBackpropInput");
  CHECK(conv2d_backprop_node);
  EXPECT_EQ(input_name, conv2d_backprop_node->input(0));
  auto input_sizes = GetAttrValue(*input_sizes_node);
  Tensor input_sizes_expected(DT_INT32, {4});
  test::FillValues<int>(&input_sizes_expected, {128, 3, 7, 7});
  test::ExpectTensorEqual<int>(input_sizes_expected, input_sizes);

  if (gpu_available_) {
    TensorShape filter_shape = GetAttrShape(*node_map.GetNode("Filter"));
    Tensor filter_data = GenerateRandomTensor<DT_FLOAT>(filter_shape);
    std::vector<string> fetch = {"Fetch"};
    auto tensors_expected =
        EvaluateNodes(item.graph, fetch, {{"Filter", filter_data}});
    auto tensors = EvaluateNodes(output, fetch, {{"Filter", filter_data}});
    EXPECT_EQ(1, tensors_expected.size());
    EXPECT_EQ(1, tensors.size());
    test::ExpectTensorNear<float>(tensors_expected[0], tensors[0], 1e-6);
  }
}

TEST_F(LayoutOptimizerTest, Conv2DBackpropInputNonConstInputSizes) {
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();