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1# Copyright 2021 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 conv3d_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
22
23from tensorflow.lite.testing.zip_test_utils import create_tensor_data
24from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
25from tensorflow.lite.testing.zip_test_utils import register_make_test_function
26
27
28@register_make_test_function()
29def make_conv3d_transpose_tests(options):
30  """Make a set of tests to do conv3d_transpose."""
31
32  test_parameters = [{
33      "shape_dtype": [tf.int32, tf.int64],
34      "input_dtype": [tf.float32],
35      "input_shape": [[2, 3, 4, 5, 2], [2, 5, 6, 8, 2]],
36      "filter_shape": [[2, 2, 2, 3, 2], [1, 2, 2, 3, 2]],
37      "strides": [(1, 1, 1, 1, 1), (1, 1, 1, 2, 1), (1, 1, 2, 2, 1),
38                  (1, 2, 1, 2, 1), (1, 2, 2, 2, 1)],
39      "dilations": [(1, 1, 1, 1, 1)],
40      "padding": ["SAME", "VALID"],
41  }]
42
43  def build_graph(parameters):
44    """Build the exp op testing graph."""
45    output_shape = tf.compat.v1.placeholder(
46        dtype=parameters["shape_dtype"], name="input", shape=[5])
47    input_tensor = tf.compat.v1.placeholder(
48        dtype=parameters["input_dtype"],
49        name="input",
50        shape=parameters["input_shape"])
51    filter_tensor = tf.compat.v1.placeholder(
52        dtype=parameters["input_dtype"],
53        name="filter",
54        shape=parameters["filter_shape"])
55
56    out = tf.nn.conv3d_transpose(
57        input_tensor,
58        filter_tensor,
59        output_shape,
60        strides=parameters["strides"],
61        dilations=parameters["dilations"],
62        padding=parameters["padding"])
63    return [input_tensor, filter_tensor, output_shape], [out]
64
65  def calculate_output_shape(parameters):
66
67    def calculate_shape(idx):
68      input_size = parameters["input_shape"][idx]
69      filter_size = parameters["filter_shape"][idx - 1]
70      stride = parameters["strides"][idx]
71      if parameters["padding"] == "SAME":
72        return (input_size - 1) * stride + 1
73      else:
74        return (input_size - 1) * stride + filter_size
75
76    output_shape_values = [parameters["input_shape"][0]]
77    output_shape_values.append(calculate_shape(1))
78    output_shape_values.append(calculate_shape(2))
79    output_shape_values.append(calculate_shape(3))
80    output_shape_values.append(parameters["filter_shape"][3])
81    return np.dtype(
82        parameters["shape_dtype"].as_numpy_dtype()).type(output_shape_values)
83
84  def build_inputs(parameters, sess, inputs, outputs):
85    values = [
86        create_tensor_data(
87            parameters["input_dtype"],
88            parameters["input_shape"],
89            min_value=-100,
90            max_value=9),
91        create_tensor_data(
92            parameters["input_dtype"],
93            parameters["filter_shape"],
94            min_value=-3,
95            max_value=3),
96        calculate_output_shape(parameters)
97    ]
98    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
99
100  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
101