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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 conv_with_shared_weights."""
16import numpy as np
17import tensorflow.compat.v1 as tf
18from tensorflow.lite.testing.zip_test_utils import create_tensor_data
19from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
20from tensorflow.lite.testing.zip_test_utils import register_make_test_function
21
22
23@register_make_test_function()
24def make_conv_with_shared_weights_tests(options):
25  """Make a test where 2 Conv ops shared the same constant weight tensor."""
26
27  test_parameters = [{
28      "input_shape": [[1, 10, 10, 3]],
29      "filter_shape": [[3, 3]],
30      "strides": [[1, 1, 1, 1]],
31      "dilations": [[1, 1, 1, 1]],
32      "padding": ["SAME"],
33      "data_format": ["NHWC"],
34      "channel_multiplier": [1],
35      "dynamic_range_quantize": [False, True],
36  }]
37
38  def get_tensor_shapes(parameters):
39    input_shape = parameters["input_shape"]
40    filter_size = parameters["filter_shape"]
41    filter_shape = filter_size + [
42        input_shape[3], parameters["channel_multiplier"]
43    ]
44    return [input_shape, filter_shape]
45
46  def build_graph(parameters):
47    """Build a conv graph given `parameters`."""
48    input_shape, filter_shape = get_tensor_shapes(parameters)
49    input_tensor = tf.compat.v1.placeholder(
50        dtype=tf.float32, name="input", shape=input_shape)
51    input_tensors = [input_tensor]
52
53    # Construct a constant weights tensor which will be used by both Conv2D.
54    filter_tensor = tf.constant(
55        create_tensor_data(np.float32, filter_shape), dtype=tf.float32)
56
57    # Ensure that FuseBinaryIntoFollowingAffine works with an input which
58    # is shared by multiple affine ops.
59    conv_input = input_tensor + 0.1
60
61    # Construct 2 Conv2D operations which use exactly the same input and
62    # weights.
63    result1 = tf.nn.conv2d(
64        conv_input,
65        filter_tensor,
66        strides=parameters["strides"],
67        dilations=parameters["dilations"],
68        padding=parameters["padding"],
69        data_format=parameters["data_format"])
70    result2 = tf.nn.conv2d(
71        conv_input,
72        filter_tensor,
73        strides=parameters["strides"],
74        dilations=parameters["dilations"],
75        padding=parameters["padding"],
76        data_format=parameters["data_format"])
77    # Add MUL ops after Conv2D ops. These MUL ops should be fused into the
78    # weights of Conv2D.
79    result1 = result1 * 2
80    result2 = result2 * 3
81    # Add the 2 results up.
82    out = result1 + result2
83    return input_tensors, [out]
84
85  def build_inputs(parameters, sess, inputs, outputs):
86    # Build list of input values either containing 1 tensor (input) or 2 tensors
87    # (input, filter) based on whether filter is constant or variable input.
88    input_shape, unused_filter_shape = get_tensor_shapes(parameters)
89    values = [create_tensor_data(np.float32, input_shape)]
90    return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
91
92  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
93