Spaces:
Runtime error
Runtime error
| # 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() | |