84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test configs for conv2d_transpose."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow.compat.v1 as tf
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from tensorflow.lite.testing.zip_test_utils import create_tensor_data
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from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
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from tensorflow.lite.testing.zip_test_utils import register_make_test_function
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@register_make_test_function()
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def make_conv2d_transpose_tests(options):
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"""Make a set of tests to do transpose_conv."""
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test_parameters = [{
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"input_shape": [[1, 50, 54, 3]],
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"filter_shape": [[1, 1, 8, 3], [1, 2, 8, 3], [1, 3, 8, 3], [1, 4, 8, 3]],
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"output_shape": [[1, 100, 108, 8]],
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"dynamic_output_shape": [True, False],
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}, {
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"input_shape": [[1, 16, 1, 512]],
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"filter_shape": [[4, 1, 512, 512]],
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"output_shape": [[1, 32, 1, 512]],
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"dynamic_output_shape": [True, False],
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}, {
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"input_shape": [[1, 128, 128, 1]],
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"filter_shape": [[4, 4, 1, 1]],
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"output_shape": [[1, 256, 256, 1]],
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"dynamic_output_shape": [True, False],
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}]
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def build_graph(parameters):
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"""Build a transpose_conv graph given `parameters`."""
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input_tensor = tf.compat.v1.placeholder(
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dtype=tf.float32, name="input", shape=parameters["input_shape"])
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filter_tensor = tf.compat.v1.placeholder(
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dtype=tf.float32, name="filter", shape=parameters["filter_shape"])
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input_tensors = [input_tensor, filter_tensor]
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if parameters["dynamic_output_shape"]:
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output_shape = tf.compat.v1.placeholder(dtype=tf.int32, shape=[4])
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input_tensors.append(output_shape)
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else:
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output_shape = parameters["output_shape"]
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out = tf.nn.conv2d_transpose(
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input_tensor,
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filter_tensor,
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output_shape=output_shape,
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padding="SAME",
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strides=(1, 2, 2, 1))
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return input_tensors, [out]
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def build_inputs(parameters, sess, inputs, outputs):
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values = [
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create_tensor_data(np.float32, parameters["input_shape"]),
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create_tensor_data(np.float32, parameters["filter_shape"])
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]
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if parameters["dynamic_output_shape"]:
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values.append(np.array(parameters["output_shape"]))
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return values, sess.run(outputs, feed_dict=dict(zip(inputs, values)))
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make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
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