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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 TensorScatterUpdate."""
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_tensor_scatter_update_tests(options):
29  """Make a set of tests to do tensor_scatter_update."""
30
31  test_parameters = [{
32      "input_dtype": [tf.float32, tf.int32, tf.int64],
33      "input_shape": [[14], [2, 4, 7]],
34      "updates_count": [1, 3, 5],
35  }]
36
37  def build_graph(parameters):
38    """Build the tensor_scatter_update op testing graph."""
39    input_tensor = tf.compat.v1.placeholder(
40        dtype=parameters["input_dtype"],
41        name="input",
42        shape=parameters["input_shape"])
43    # The indices will be a list of "input_shape".
44    indices_tensor = tf.compat.v1.placeholder(
45        dtype=tf.int32,
46        name="indices",
47        shape=([parameters["updates_count"],
48                len(parameters["input_shape"])]))
49    # The updates will be a list of scalar, shaped of "updates_count".
50    updates_tensors = tf.compat.v1.placeholder(
51        dtype=parameters["input_dtype"],
52        name="updates",
53        shape=[parameters["updates_count"]])
54
55    out = tf.tensor_scatter_nd_update(input_tensor, indices_tensor,
56                                      updates_tensors)
57    return [input_tensor, indices_tensor, updates_tensors], [out]
58
59  def build_inputs(parameters, sess, inputs, outputs):
60    indices = set()
61    while len(indices) < parameters["updates_count"]:
62      loc = []
63      for d in parameters["input_shape"]:
64        loc.append(np.random.randint(0, d))
65      indices.add(tuple(loc))
66
67    values = [
68        create_tensor_data(parameters["input_dtype"],
69                           parameters["input_shape"]),
70        np.array(list(indices), dtype=np.int32),
71        create_tensor_data(
72            parameters["input_dtype"],
73            parameters["updates_count"],
74            min_value=-3,
75            max_value=3)
76    ]
77    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
78
79  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
80