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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."""
16from __future__ import absolute_import
17from __future__ import division
18from __future__ import print_function
19
20import numpy as np
21import tensorflow.compat.v1 as tf
22from tensorflow.lite.testing.zip_test_utils import create_tensor_data
23from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
24from tensorflow.lite.testing.zip_test_utils import register_make_test_function
25
26
27@register_make_test_function()
28def make_conv2d_transpose_tests(options):
29  """Make a set of tests to do transpose_conv."""
30
31  test_parameters = [{
32      "input_shape": [[1, 50, 54, 3]],
33      "filter_shape": [[1, 1, 8, 3], [1, 2, 8, 3], [1, 3, 8, 3], [1, 4, 8, 3]],
34      "output_shape": [[1, 100, 108, 8]],
35      "dynamic_output_shape": [True, False],
36  }, {
37      "input_shape": [[1, 16, 1, 512]],
38      "filter_shape": [[4, 1, 512, 512]],
39      "output_shape": [[1, 32, 1, 512]],
40      "dynamic_output_shape": [True, False],
41  }, {
42      "input_shape": [[1, 128, 128, 1]],
43      "filter_shape": [[4, 4, 1, 1]],
44      "output_shape": [[1, 256, 256, 1]],
45      "dynamic_output_shape": [True, False],
46  }]
47
48  def build_graph(parameters):
49    """Build a transpose_conv graph given `parameters`."""
50    input_tensor = tf.compat.v1.placeholder(
51        dtype=tf.float32, name="input", shape=parameters["input_shape"])
52
53    filter_tensor = tf.compat.v1.placeholder(
54        dtype=tf.float32, name="filter", shape=parameters["filter_shape"])
55
56    input_tensors = [input_tensor, filter_tensor]
57
58    if parameters["dynamic_output_shape"]:
59      output_shape = tf.compat.v1.placeholder(dtype=tf.int32, shape=[4])
60      input_tensors.append(output_shape)
61    else:
62      output_shape = parameters["output_shape"]
63
64    out = tf.nn.conv2d_transpose(
65        input_tensor,
66        filter_tensor,
67        output_shape=output_shape,
68        padding="SAME",
69        strides=(1, 2, 2, 1))
70
71    return input_tensors, [out]
72
73  def build_inputs(parameters, sess, inputs, outputs):
74    values = [
75        create_tensor_data(np.float32, parameters["input_shape"]),
76        create_tensor_data(np.float32, parameters["filter_shape"])
77    ]
78    if parameters["dynamic_output_shape"]:
79      values.append(np.array(parameters["output_shape"]))
80
81    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
82
83  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
84