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deeplab2 / model /layers /axial_block_groups_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 axial_block_groups."""

	import numpy as np
	import tensorflow as tf

	from deeplab2.model import test_utils
	from deeplab2.model.layers import axial_block_groups


	class AxialBlockGroupsTest(tf.test.TestCase):

	def test_axial_attention_follows_bottleneck_block(self):
	layer = axial_block_groups.BlockGroup(
	filters=512,
	num_blocks=2,
	name='block_group',
	original_resnet_stride=2,
	original_resnet_input_stride=16,
	use_axial_beyond_stride=32,
	output_stride=16)
	_, pixel_output, memory_output = layer((tf.zeros([2, 65, 65, 1024]),
	tf.zeros([2, 128, 147])))
	self.assertListEqual(pixel_output.get_shape().as_list(),
	[2, 65, 65, 2048])
	self.assertListEqual(memory_output.get_shape().as_list(),
	[2, 128, 147])

	def test_global_attention_follows_basic_block(self):
	layer = axial_block_groups.BlockGroup(
	filters=256,
	num_blocks=2,
	name='block_group',
	backbone_type='wider_resnet',
	original_resnet_stride=2,
	original_resnet_input_stride=8,
	use_global_beyond_stride=16,
	positional_encoding_type='1D')

	_, pixel_output, memory_output = layer((tf.zeros([2, 65, 65, 32]),
	tf.zeros([2, 128, 147])))
	self.assertListEqual(pixel_output.get_shape().as_list(),
	[2, 33, 33, 1024])
	self.assertListEqual(memory_output.get_shape().as_list(),
	[2, 128, 147])

	def test_atrous_consistency_basic_block(self):
	tf.random.set_seed(0)
	pixel_inputs = test_utils.create_test_input(2, 11, 11, 3)
	# Dense feature extraction followed by subsampling.
	layer1 = axial_block_groups.BlockGroup(
	filters=2,
	num_blocks=2,
	name='stage3',
	backbone_type='wider_resnet',
	original_resnet_stride=2,
	original_resnet_input_stride=8,
	output_stride=8,
	use_axial_beyond_stride=0,
	use_global_beyond_stride=0,
	use_transformer_beyond_stride=0)
	# Create the weights
	layer1((pixel_inputs, None))
	weights = layer1.get_weights()
	# Set the batch norm gamma as non-zero so that the 3x3 convolution affects
	# the output.
	for index in range(len(weights)):
	if np.sum(weights[index]) == 0.0:
	weights[index] = weights[index] + 1
	layer1.set_weights(weights)
	_, pixel_outputs, _ = layer1((pixel_inputs, None))
	output = pixel_outputs[:, ::2, ::2, :]
	# Feature extraction at the nominal network rate.
	layer2 = axial_block_groups.BlockGroup(
	filters=2,
	num_blocks=2,
	name='stage3',
	backbone_type='wider_resnet',
	original_resnet_stride=2,
	original_resnet_input_stride=8,
	output_stride=16,
	use_axial_beyond_stride=0,
	use_global_beyond_stride=0,
	use_transformer_beyond_stride=0)
	# Create the weights
	layer2((pixel_inputs, None))
	# Make the two networks use the same weights.
	layer2.set_weights(layer1.get_weights())
	_, expected, _ = layer2((pixel_inputs, None))
	self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)

	def test_atrous_consistency_bottleneck_block(self):
	tf.random.set_seed(0)
	pixel_inputs = test_utils.create_test_input(2, 11, 11, 3)
	# Dense feature extraction followed by subsampling.
	layer1 = axial_block_groups.BlockGroup(
	filters=2,
	num_blocks=2,
	name='stage3',
	backbone_type='wider_resnet',
	original_resnet_stride=2,
	original_resnet_input_stride=16,
	output_stride=16,
	use_axial_beyond_stride=0,
	use_global_beyond_stride=0,
	use_transformer_beyond_stride=0)
	# Create the weights
	layer1((pixel_inputs, None))
	weights = layer1.get_weights()
	# Set the batch norm gamma as non-zero so that the 3x3 convolution affects
	# the output.
	for index in range(len(weights)):
	if np.sum(weights[index]) == 0.0:
	weights[index] = weights[index] + 1
	layer1.set_weights(weights)
	_, pixel_outputs, _ = layer1((pixel_inputs, None))
	output = pixel_outputs[:, ::2, ::2, :]
	# Feature extraction at the nominal network rate.
	layer2 = axial_block_groups.BlockGroup(
	filters=2,
	num_blocks=2,
	name='stage3',
	backbone_type='wider_resnet',
	original_resnet_stride=2,
	original_resnet_input_stride=16,
	output_stride=32,
	use_axial_beyond_stride=0,
	use_global_beyond_stride=0,
	use_transformer_beyond_stride=0)
	# Create the weights
	layer2((pixel_inputs, None))
	# Make the two networks use the same weights.
	layer2.set_weights(layer1.get_weights())
	_, expected, _ = layer2((pixel_inputs, None))
	self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)

	def test_use_se_sac_recompute_drop_path_schedule(self):
	_ = axial_block_groups.BlockGroup(
	filters=512,
	num_blocks=2,
	name='block_group',
	original_resnet_stride=2,
	original_resnet_input_stride=8,
	use_axial_beyond_stride=0,
	use_squeeze_and_excite=True, # True
	use_sac_beyond_stride=16, # True
	recompute_within_stride=16, # True
	drop_path_beyond_stride=16,
	drop_path_schedule='linear', # 1.0, 0.85
	output_stride=16)

	def test_nouse_se_sac_recompute_drop_path_schedule(self):
	_ = axial_block_groups.BlockGroup(
	filters=512,
	num_blocks=2,
	name='block_group',
	original_resnet_stride=2,
	original_resnet_input_stride=8,
	use_axial_beyond_stride=0,
	use_squeeze_and_excite=False, # False
	use_sac_beyond_stride=32, # False
	recompute_within_stride=8, # False
	drop_path_beyond_stride=32, # 1.0, 1.0
	drop_path_schedule='constant',
	output_stride=16)

	if __name__ == '__main__':
	tf.test.main()