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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 hardswish."""
16from __future__ import absolute_import
17from __future__ import division
18from __future__ import print_function
19
20import functools
21
22import numpy as np
23import tensorflow as tf
24from tensorflow.lite.testing.zip_test_utils import create_tensor_data
25from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
26from tensorflow.lite.testing.zip_test_utils import register_make_test_function
27
28
29def _tflite_convert_verify_num_ops(tflite_convert_function, *args, **kwargs):
30  """Verifies that the result of the conversion is a single op."""
31  num_ops = kwargs.pop("num_ops", 2)
32  result = tflite_convert_function(*args, **kwargs)
33  tflite_model_binary = result[0]
34  if not result[0]:
35    tf.compat.v1.logging.error(result[1])  # stderr from running tflite_convert.
36    raise RuntimeError("Failed to bulid model: \n\n" + result[1])
37  interpreter = tf.lite.Interpreter(model_content=tflite_model_binary)
38  interpreter.allocate_tensors()
39  if len(interpreter.get_tensor_details()) != num_ops:
40    raise RuntimeError(
41        "Expected to generate two node graph got %s " %
42        "\n".join(str(x) for x in interpreter.get_tensor_details()))
43  return result
44
45
46@register_make_test_function()
47def make_hardswish_tests(options):
48  """Make a set of tests to do hardswish."""
49
50  # Chose a set of parameters
51  test_parameters = [{
52      "input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3],
53                      [3, 15, 14, 3], [3, 1, 2, 4, 6], [2, 2, 3, 4, 5, 6]],
54  }]
55
56  def build_graph(parameters):
57    inp = tf.compat.v1.placeholder(
58        dtype=tf.float32, name="input", shape=parameters["input_shape"])
59    out = inp * tf.nn.relu6(inp + np.float32(3)) * np.float32(1. / 6.)
60
61    return [inp], [out]
62
63  def build_inputs(parameters, sess, inputs, outputs):
64    input_values = create_tensor_data(
65        np.float32, parameters["input_shape"], min_value=-10, max_value=10)
66    return [input_values], sess.run(
67        outputs, feed_dict=dict(zip(inputs, [input_values])))
68
69  # Add additional validation if we are using toco.
70  # Flex doesn't yet support this.
71  if not options.run_with_flex:
72    options.tflite_convert_function = functools.partial(
73        _tflite_convert_verify_num_ops,
74        options.tflite_convert_function,
75        num_ops=2)
76  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
77