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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()
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