# 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()