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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()
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