09640bc683a23130c29ba4ab6dc53e20c2a48fa5,tensorflow/python/grappler/layout_optimizer_test.py,LayoutOptimizerTest,testConv3DBackpropInput,#LayoutOptimizerTest#,1432

Before Change

  @test_util.deprecated_graph_mode_only
  def testConv3DBackpropInput(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      x_3d = array_ops.reshape(conv, [-1, 4, 14, 14, 1])
      filters = random_ops.truncated_normal([2, 2, 2, 1, 1], seed=0)
      strides_val = [1, 1, 1, 1, 1]
      shape = array_ops.shape(x_3d)
      conv3d_grad = gen_nn_ops.conv3d_backprop_input_v2(shape, filters, x_3d,
                                                        strides_val, "SAME")
      output = array_ops.identity(conv3d_grad)

      with session.Session(config=_get_config(False)) as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw("Conv2D-0", nodes)
      self._assert_vec_ndhwc_to_ncdhw("Conv3DBackpropInputV2-0", nodes)
      self._assert_trans_ndhwc_to_ncdhw("Conv3DBackpropInputV2-2", nodes)
      self._assert_trans_ncdhw_to_ndhwc("Conv3DBackpropInputV2-0-0", nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

  @test_util.deprecated_graph_mode_only
  def testConv3DBackpropFilter(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)

After Change

  @test_util.deprecated_graph_mode_only
  def testConv3DBackpropInput(self):
    if not test.is_gpu_available(cuda_only=True):
      self.skipTest("GPU required")
    random_seed.set_random_seed(0)
    dy = random_ops.truncated_normal([2, 2, 14, 14, 1], seed=0)
    w = random_ops.truncated_normal([2, 2, 2, 1, 1], seed=0)
    strides = [1, 1, 1, 1, 1]

Instances