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 softmax."""
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_softmax_tests(options):
28  """Make a set of tests to do softmax."""
29
30  test_parameters = [{
31      "dtype": [tf.float32],
32      "input_shape": [[1, 3, 4, 3], [2, 3], [3], [1, 4], [1, 1, 5],
33                      [1, 1, 1, 6]],
34      "dim": [-1, 0],
35      "fully_quantize": [False, True],
36  }, {
37      "dtype": [tf.float32],
38      "input_shape": [[4, 7]],
39      "dim": [-1, 1],
40      "fully_quantize": [False, True],
41  }]
42
43  def build_graph(parameters):
44    input_tensor = tf.compat.v1.placeholder(
45        dtype=parameters["dtype"],
46        name="input",
47        shape=parameters["input_shape"])
48    out = tf.nn.softmax(input_tensor, dim=parameters["dim"])
49    return [input_tensor], [out]
50
51  def build_inputs(parameters, sess, inputs, outputs):
52    input_values = create_tensor_data(
53        parameters["dtype"],
54        parameters["input_shape"],
55        min_value=-1,
56        max_value=1)
57    return [input_values], sess.run(
58        outputs, feed_dict=dict(zip(inputs, [input_values])))
59
60  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
61