• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
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 is_finite."""
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_is_finite_tests(options):
29  """Make a set of tests to do is_finite."""
30
31  test_parameters = [
32      {
33          "input_shape": [[100], [3, 15, 14, 3]],
34      },
35  ]
36
37  def build_graph(parameters):
38    """Build the graph for the test case."""
39
40    input_tensor = tf.compat.v1.placeholder(
41        dtype=tf.float32, name="input", shape=parameters["input_shape"])
42    out = tf.math.is_finite(input_tensor)
43    return [input_tensor], [out]
44
45  def build_inputs(parameters, sess, inputs, outputs):
46    """Build the inputs for the test case."""
47    input_values = create_tensor_data(
48        np.float32, parameters["input_shape"], min_value=-10, max_value=10)
49
50    # Inject NaN and Inf value.
51    def random_index(shape):
52      result = []
53      for dim in shape:
54        result.append(np.random.randint(low=0, high=dim))
55      return tuple(result)
56
57    input_values[random_index(input_values.shape)] = np.Inf
58    input_values[random_index(input_values.shape)] = -np.Inf
59    input_values[random_index(input_values.shape)] = np.NAN
60    input_values[random_index(input_values.shape)] = tf.float32.max
61    input_values[random_index(input_values.shape)] = tf.float32.min
62
63    return [input_values], sess.run(
64        outputs, feed_dict=dict(zip(inputs, [input_values])))
65
66  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
67