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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 local_response_norm."""
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_local_response_norm_tests(options):
29  """Make a set of tests to do local_response_norm."""
30
31  # Chose a set of parameters
32  test_parameters = [{
33      "input_shape": [[1, 1, 1, 1], [1, 3, 4, 3], [3, 15, 14, 3]],
34      "depth_radius": [None, 0, 1, 3, 5],
35      "bias": [None, 0.3, -0.1],
36      "alpha": [None, 2, -3],
37      "beta": [None, 0.25, 2],
38  }]
39
40  def build_graph(parameters):
41    input_tensor = tf.compat.v1.placeholder(
42        dtype=tf.float32, name="input", shape=parameters["input_shape"])
43    out = tf.nn.local_response_normalization(
44        input_tensor,
45        depth_radius=parameters["depth_radius"],
46        bias=parameters["bias"],
47        alpha=parameters["alpha"],
48        beta=parameters["beta"])
49    return [input_tensor], [out]
50
51  def build_inputs(parameters, sess, inputs, outputs):
52    input_values = create_tensor_data(
53        np.float32, parameters["input_shape"], min_value=-4, max_value=10)
54    return [input_values], sess.run(
55        outputs, feed_dict=dict(zip(inputs, [input_values])))
56
57  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
58