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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 global_batch_norm."""
16from __future__ import absolute_import
17from __future__ import division
18from __future__ import print_function
19
20import tensorflow.compat.v1 as tf
21from tensorflow.lite.testing.zip_test_utils import create_tensor_data
22from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
23from tensorflow.lite.testing.zip_test_utils import register_make_test_function
24
25
26@register_make_test_function()
27def make_global_batch_norm_tests(options):
28  """Make a set of tests to do batch_norm_with_global_normalization."""
29
30  test_parameters = [{
31      "dtype": [tf.float32],
32      "input_shape": [[1, 1, 6, 2], [3, 4, 5, 4]],
33      "epsilon": [0.1, 0.0001],
34      "scale_after": [True, False],
35  }]
36
37  def build_graph(parameters):
38    """Build the global batch norm testing graph."""
39    input_shape = parameters["input_shape"]
40    scale_shape = input_shape[3]
41
42    scale = create_tensor_data(parameters["dtype"], scale_shape)
43    offset = create_tensor_data(parameters["dtype"], scale_shape)
44    mean = create_tensor_data(parameters["dtype"], scale_shape)
45    variance = create_tensor_data(parameters["dtype"], scale_shape)
46
47    x = create_tensor_data(parameters["dtype"], parameters["input_shape"])
48    x_norm = tf.nn.batch_norm_with_global_normalization(
49        x, mean, variance, scale, offset, parameters["epsilon"],
50        parameters["scale_after"])
51
52    input_tensor = tf.compat.v1.placeholder(
53        dtype=parameters["dtype"],
54        name="input",
55        shape=parameters["input_shape"])
56    out = tf.add(input_tensor, x_norm)
57    return [input_tensor], [out]
58
59  def build_inputs(parameters, sess, inputs, outputs):
60    input_value = create_tensor_data(parameters["dtype"],
61                                     parameters["input_shape"])
62    return [input_value], sess.run(
63        outputs, feed_dict=dict(zip(inputs, [input_value])))
64
65  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
66