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| # coding=utf-8 | |
| # Copyright 2021 The Deeplab2 Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Tests for blocks.py.""" | |
| import tensorflow as tf | |
| from deeplab2.model.layers import blocks | |
| class BlocksTest(tf.test.TestCase): | |
| def test_inverted_bottleneck_block_output_shape(self): | |
| batch, height, width, input_channels = 2, 17, 17, 4 | |
| output_channels = 6 | |
| input_tensor = tf.random.uniform( | |
| shape=(batch, height, width, input_channels)) | |
| ivb_block = blocks.InvertedBottleneckBlock( | |
| in_filters=input_channels, | |
| out_filters=output_channels, | |
| expand_ratio=2, | |
| strides=1, | |
| name='inverted_bottleneck', | |
| ) | |
| output_tensor = ivb_block(input_tensor) | |
| self.assertListEqual(output_tensor.get_shape().as_list(), | |
| [batch, height, width, output_channels]) | |
| def test_inverted_bottleneck_block_feature_map_alignment(self): | |
| batch, height, width, input_channels = 2, 17, 17, 128 | |
| output_channels = 256 | |
| input_tensor = tf.random.uniform( | |
| shape=(batch, height, width, input_channels)) | |
| ivb_block1 = blocks.InvertedBottleneckBlock( | |
| in_filters=input_channels, | |
| out_filters=output_channels, | |
| expand_ratio=2, | |
| strides=2, | |
| name='inverted_bottleneck1', | |
| ) | |
| ivb_block1(input_tensor, False) | |
| weights = ivb_block1.get_weights() | |
| output_tensor = ivb_block1(input_tensor, False) | |
| ivb_block2 = blocks.InvertedBottleneckBlock( | |
| in_filters=input_channels, | |
| out_filters=output_channels, | |
| expand_ratio=2, | |
| strides=1, | |
| name='inverted_bottleneck2', | |
| ) | |
| ivb_block2(input_tensor, False) | |
| ivb_block2.set_weights(weights) | |
| expected = ivb_block2(input_tensor, False)[:, ::2, ::2, :] | |
| self.assertAllClose(ivb_block1.get_weights(), ivb_block2.get_weights(), | |
| atol=1e-4, rtol=1e-4) | |
| self.assertAllClose(output_tensor, expected, atol=1e-4, rtol=1e-4) | |
| if __name__ == '__main__': | |
| tf.test.main() | |