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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 where."""
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_where_tests(options):
28  """Make a set of tests to do where."""
29
30  test_parameters = [
31      {
32          "input_dtype": [tf.float32, tf.int32],
33          "input_shape_set": [([1, 2, 3, 4], [1, 2, 3, 4]),],
34          "use_where_v2": [False, True],
35          "fully_quantize": [False],
36      },
37      {
38          "input_dtype": [tf.float32, tf.int32],
39          "input_shape_set": [([], []),],
40          "use_where_v2": [],
41          "fully_quantize": [False],
42      },
43      {
44          "input_dtype": [tf.float32],
45          "input_shape_set": [([1, 2, 3, 4], [1, 2, 3, 4]), ([], []),],
46          "use_where_v2": [False, True],
47          "fully_quantize": [True],
48      },
49  ]
50
51  # High dimension broadcasting support in MLIR converter.
52  if options.use_experimental_converter:
53    test_parameters = test_parameters + [
54        {
55            "input_dtype": [tf.float32, tf.int32],
56            "input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1]),],
57            "use_where_v2": [True],
58            "fully_quantize": [False],
59        },
60        {
61            "input_dtype": [tf.float32],
62            "input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1]),],
63            "use_where_v2": [True],
64            "fully_quantize": [True],
65        },
66    ]
67
68  def build_graph(parameters):
69    """Build the where op testing graph."""
70    input_value1 = tf.compat.v1.placeholder(
71        dtype=parameters["input_dtype"],
72        name="input2",
73        shape=parameters["input_shape_set"][0])
74    input_value2 = tf.compat.v1.placeholder(
75        dtype=parameters["input_dtype"],
76        name="input3",
77        shape=parameters["input_shape_set"][1])
78    less = tf.less(input_value1, input_value2)
79    where = tf.where_v2 if parameters["use_where_v2"] else tf.where
80    out = where(less, input_value1, input_value2)
81    return [input_value1, input_value2], [out]
82
83  def build_inputs(parameters, sess, inputs, outputs):
84    input_value1 = create_tensor_data(parameters["input_dtype"],
85                                      parameters["input_shape_set"][0],
86                                      min_value=-1, max_value=1)
87    input_value2 = create_tensor_data(parameters["input_dtype"],
88                                      parameters["input_shape_set"][1],
89                                      min_value=-1, max_value=1)
90    return [input_value1, input_value2], sess.run(
91        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
92
93  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
94