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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 relu6."""
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_relu6_tests(options):
29  """Make a set of tests to do relu6."""
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                      [3, 1, 2, 4, 6], [2, 2, 3, 4, 5, 6]],
35      "fully_quantize": [True, False],
36      "input_range": [(-2, 8)]
37  }]
38
39  def build_graph(parameters):
40    input_tensor = tf.compat.v1.placeholder(
41        dtype=tf.float32, name="input", shape=parameters["input_shape"])
42    out = tf.nn.relu6(input_tensor)
43    return [input_tensor], [out]
44
45  def build_inputs(parameters, sess, inputs, outputs):
46    min_value, max_value = parameters["input_range"]
47    input_values = create_tensor_data(np.float32, parameters["input_shape"],
48                                      min_value, max_value)
49    return [input_values], sess.run(
50        outputs, feed_dict=dict(zip(inputs, [input_values])))
51
52  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
53