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1# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Test configs for conv2d_transpose."""
16import numpy as np
17import tensorflow.compat.v1 as tf
18from tensorflow.lite.testing.zip_test_utils import create_tensor_data
19from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
20from tensorflow.lite.testing.zip_test_utils import register_make_test_function
21
22
23@register_make_test_function()
24def make_conv2d_transpose_tests(options):
25  """Make a set of tests to do transpose_conv."""
26
27  test_parameters = [{
28      "input_shape": [[1, 50, 54, 3]],
29      "filter_shape": [[1, 1, 8, 3], [1, 2, 8, 3], [1, 3, 8, 3], [1, 4, 8, 3]],
30      "output_shape": [[1, 100, 108, 8]],
31      "dynamic_output_shape": [True, False],
32  }, {
33      "input_shape": [[1, 16, 1, 512]],
34      "filter_shape": [[4, 1, 512, 512]],
35      "output_shape": [[1, 32, 1, 512]],
36      "dynamic_output_shape": [True, False],
37  }, {
38      "input_shape": [[1, 128, 128, 1]],
39      "filter_shape": [[4, 4, 1, 1]],
40      "output_shape": [[1, 256, 256, 1]],
41      "dynamic_output_shape": [True, False],
42  }]
43
44  def build_graph(parameters):
45    """Build a transpose_conv graph given `parameters`."""
46    input_tensor = tf.compat.v1.placeholder(
47        dtype=tf.float32, name="input", shape=parameters["input_shape"])
48
49    filter_tensor = tf.compat.v1.placeholder(
50        dtype=tf.float32, name="filter", shape=parameters["filter_shape"])
51
52    input_tensors = [input_tensor, filter_tensor]
53
54    if parameters["dynamic_output_shape"]:
55      output_shape = tf.compat.v1.placeholder(dtype=tf.int32, shape=[4])
56      input_tensors.append(output_shape)
57    else:
58      output_shape = parameters["output_shape"]
59
60    out = tf.nn.conv2d_transpose(
61        input_tensor,
62        filter_tensor,
63        output_shape=output_shape,
64        padding="SAME",
65        strides=(1, 2, 2, 1))
66
67    return input_tensors, [out]
68
69  def build_inputs(parameters, sess, inputs, outputs):
70    values = [
71        create_tensor_data(np.float32, parameters["input_shape"]),
72        create_tensor_data(np.float32, parameters["filter_shape"])
73    ]
74    if parameters["dynamic_output_shape"]:
75      values.append(np.array(parameters["output_shape"]))
76
77    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
78
79  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
80