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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": [
46              ([1, 2, 3, 4], [1, 2, 3, 4]),
47              ([], []),
48          ],
49          "use_where_v2": [False, True],
50          "fully_quantize": [True],
51      },
52  ]
53
54  # High dimension broadcasting support in MLIR converter.
55  if options.use_experimental_converter:
56    test_parameters = test_parameters + [
57        {
58            "input_dtype": [tf.float32, tf.int32],
59            "input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1]),
60                                ([8, 7, 6, 5, 4, 3, 2, 1], [None, 3, 2, 1]),
61                                ([8, 7, 6, 5, None, 3, 2, 1], [None, 3, 2, 1])],
62            "use_where_v2": [True],
63            "fully_quantize": [False],
64            "dynamic_size_value": [4, 1],
65        },
66        {
67            "input_dtype": [tf.float32],
68            "input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1])],
69            "use_where_v2": [True],
70            "fully_quantize": [True],
71            "dynamic_size_value": [4],
72        },
73        {
74            "input_dtype": [tf.float32, tf.int32],
75            "input_shape_set": [([], []), ([1], []), ([], [1])],
76            "use_where_v2": [False, True],
77            "fully_quantize": [False],
78        },
79    ]
80
81  def populate_dynamic_shape(parameters, input_shape):
82    return [
83        parameters["dynamic_size_value"] if x is None else x
84        for x in input_shape
85    ]
86
87  def build_graph(parameters):
88    """Build the where op testing graph."""
89    input_value1 = tf.compat.v1.placeholder(
90        dtype=parameters["input_dtype"],
91        name="input2",
92        shape=parameters["input_shape_set"][0])
93    input_value2 = tf.compat.v1.placeholder(
94        dtype=parameters["input_dtype"],
95        name="input3",
96        shape=parameters["input_shape_set"][1])
97    less = tf.less(input_value1, input_value2)
98    where = tf.where_v2 if parameters["use_where_v2"] else tf.where
99    out = where(less, input_value1, input_value2)
100    return [input_value1, input_value2], [out]
101
102  def build_inputs(parameters, sess, inputs, outputs):
103    input_shape_1 = populate_dynamic_shape(parameters,
104                                           parameters["input_shape_set"][0])
105    input_shape_2 = populate_dynamic_shape(parameters,
106                                           parameters["input_shape_set"][1])
107
108    input_value1 = create_tensor_data(
109        parameters["input_dtype"], input_shape_1, min_value=-1, max_value=1)
110    input_value2 = create_tensor_data(
111        parameters["input_dtype"], input_shape_2, min_value=-1, max_value=1)
112    return [input_value1, input_value2], sess.run(
113        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
114
115  if options.use_experimental_converter:
116    expected_tf_failures = 4
117  else:
118    expected_tf_failures = 0
119
120  make_zip_of_tests(
121      options,
122      test_parameters,
123      build_graph,
124      build_inputs,
125      expected_tf_failures=expected_tf_failures)
126