text: # Copyright 2019 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.
  # ==============================================================================
  """Test configs for transpose_conv."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  from tensorflow.lite.testing.zip_test_utils import create_tensor_data
  from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
  from tensorflow.lite.testing.zip_test_utils import register_make_test_function
  
  
  # Since compute output_shape is fairly complicated for
  # tf.nn.conv2d_transpose input_sizes argument, so we here first perform a
  # "conv2d" operation to get the output, then we use the output to feed in
  # tf.nn.conv2d_backprop_input.
  # This test will depend on the "conv2d" operation's correctness.
  @register_make_test_function()
  def make_transpose_conv_tests(options):
    """Make a set of tests to do transpose_conv."""
  
    # Tensorflow only supports equal strides
    test_parameters = [{
        "input_shape": [[1, 3, 4, 1], [1, 10, 10, 3], [3, 20, 20, 1]],
        "filter_size": [[1, 1], [1, 2], [3, 3]],
        "strides": [[1, 1, 1, 1], [1, 3, 3, 1]],
        "padding": ["SAME", "VALID"],
        "data_format": ["NHWC"],
        "channel_multiplier": [1, 2],
    }]
  
    def get_tensor_shapes(parameters):
      input_shape = parameters["input_shape"]
      filter_size = parameters["filter_size"]
      filter_shape = filter_size + [
          input_shape[3], parameters["channel_multiplier"]
      ]
      return [input_shape, filter_shape]
  
    def build_graph(parameters):
      """Build a transpose_conv graph given `parameters`."""
      input_shape, filter_shape = get_tensor_shapes(parameters)
      input_tensor = tf.placeholder(
          dtype=tf.float32, name="input", shape=input_shape)
  
      filter_input = tf.placeholder(
          dtype=tf.float32, name="filter", shape=filter_shape)
  
      conv_outputs = tf.nn.conv2d(
          input_tensor,
          filter_input,
          strides=parameters["strides"],
          padding=parameters["padding"],
          data_format=parameters["data_format"])
      out = tf.nn.conv2d_backprop_input(
          input_shape,
          filter_input,
          conv_outputs,
          strides=parameters["strides"],
          padding=parameters["padding"],
          data_format=parameters["data_format"])
      input_tensors = [input_tensor, filter_input]
      return input_tensors, [out]
  
    def build_inputs(parameters, sess, inputs, outputs):
      input_shape, filter_shape = get_tensor_shapes(parameters)
      values = [
          create_tensor_data(np.float32, input_shape),
          create_tensor_data(np.float32, filter_shape)
      ]
      return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
  
    make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
  