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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 reshape."""
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_reshape_tests(options):
29  """Make a set of tests to do reshape."""
30
31  # All shapes below are suitable for tensors with 420 elements.
32  test_parameters = [{
33      "dtype": [tf.float32, tf.int32],
34      "input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]],
35      "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
36      "constant_shape": [True, False],
37      "fully_quantize": [False],
38  }, {
39      "dtype": [tf.float32],
40      "input_shape": [[1]],
41      "output_shape": [[]],
42      "constant_shape": [True, False],
43      "fully_quantize": [False],
44  }, {
45      "dtype": [tf.float32],
46      "input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]],
47      "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]],
48      "constant_shape": [True],
49      "fully_quantize": [True],
50  }]
51
52  if options.use_experimental_converter:
53    test_parameters = test_parameters + [
54        # Zero in input shape.
55        {
56            "dtype": [tf.float32],
57            "input_shape": [[1, 4, 0]],
58            "output_shape": [[2, -1], [2, 0, -1]],
59            "constant_shape": [True, False],
60            "fully_quantize": [False],
61        }
62    ]
63
64  def build_graph(parameters):
65    """Build the graph for reshape tests."""
66    input_tensor = tf.compat.v1.placeholder(
67        dtype=parameters["dtype"],
68        name="input",
69        shape=parameters["input_shape"])
70
71    # Get shape as either a placeholder or constants.
72    if parameters["constant_shape"]:
73      output_shape = parameters["output_shape"]
74      input_tensors = [input_tensor]
75    else:
76      # The shape of the shape tensor.
77      shape_tensor_shape = [len(parameters["output_shape"])]
78      output_shape = tf.compat.v1.placeholder(
79          dtype=tf.int32, name="output_shape", shape=shape_tensor_shape)
80      input_tensors = [input_tensor, output_shape]
81    out = tf.reshape(input_tensor, shape=output_shape)
82    return input_tensors, [out]
83
84  def build_inputs(parameters, sess, inputs, outputs):
85    """Build inputs for reshape op."""
86
87    values = [
88        create_tensor_data(
89            parameters["dtype"],
90            parameters["input_shape"],
91            min_value=-1,
92            max_value=1)
93    ]
94    if not parameters["constant_shape"]:
95      values.append(np.array(parameters["output_shape"]))
96
97    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
98
99  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
100