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deeplab2 / model /layers /blocks_test.py

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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()