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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 deeplabv3plus.""" | |
| import numpy as np | |
| import tensorflow as tf | |
| from deeplab2 import common | |
| from deeplab2 import config_pb2 | |
| from deeplab2.model.decoder import deeplabv3plus | |
| from deeplab2.utils import test_utils | |
| def _create_deeplabv3plus_model(high_level_feature_name, low_level_feature_name, | |
| low_level_channels_project, | |
| aspp_output_channels, decoder_output_channels, | |
| atrous_rates, num_classes, **kwargs): | |
| decoder_options = config_pb2.DecoderOptions( | |
| feature_key=high_level_feature_name, | |
| decoder_channels=decoder_output_channels, | |
| aspp_channels=aspp_output_channels, | |
| atrous_rates=atrous_rates) | |
| deeplabv3plus_options = config_pb2.ModelOptions.DeeplabV3PlusOptions( | |
| low_level=config_pb2.LowLevelOptions( | |
| feature_key=low_level_feature_name, | |
| channels_project=low_level_channels_project), | |
| num_classes=num_classes) | |
| return deeplabv3plus.DeepLabV3Plus(decoder_options, deeplabv3plus_options, | |
| **kwargs) | |
| class Deeplabv3PlusTest(tf.test.TestCase): | |
| def test_deeplabv3plus_feature_key_not_present(self): | |
| deeplabv3plus_decoder = _create_deeplabv3plus_model( | |
| high_level_feature_name='not_in_features_dict', | |
| low_level_feature_name='in_feature_dict', | |
| low_level_channels_project=128, | |
| aspp_output_channels=64, | |
| decoder_output_channels=64, | |
| atrous_rates=[6, 12, 18], | |
| num_classes=80) | |
| input_dict = dict() | |
| input_dict['in_feature_dict'] = tf.random.uniform(shape=(2, 65, 65, 32)) | |
| with self.assertRaises(KeyError): | |
| _ = deeplabv3plus_decoder(input_dict) | |
| def test_deeplabv3plus_output_shape(self): | |
| list_of_num_classes = [2, 19, 133] | |
| for num_classes in list_of_num_classes: | |
| deeplabv3plus_decoder = _create_deeplabv3plus_model( | |
| high_level_feature_name='high', | |
| low_level_feature_name='low', | |
| low_level_channels_project=128, | |
| aspp_output_channels=64, | |
| decoder_output_channels=128, | |
| atrous_rates=[6, 12, 18], | |
| num_classes=num_classes) | |
| input_dict = dict() | |
| input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32)) | |
| input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16)) | |
| expected_shape = [2, 129, 129, num_classes] | |
| logit_tensor = deeplabv3plus_decoder(input_dict) | |
| self.assertListEqual( | |
| logit_tensor[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), | |
| expected_shape) | |
| def test_deeplabv3plus_feature_extraction_consistency(self): | |
| deeplabv3plus_decoder = _create_deeplabv3plus_model( | |
| high_level_feature_name='high', | |
| low_level_feature_name='low', | |
| low_level_channels_project=128, | |
| aspp_output_channels=96, | |
| decoder_output_channels=64, | |
| atrous_rates=[6, 12, 18], | |
| num_classes=80) | |
| input_dict = dict() | |
| input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32)) | |
| input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16)) | |
| reference_logits_tensor = deeplabv3plus_decoder( | |
| input_dict, training=False) | |
| logits_tensor_to_compare = deeplabv3plus_decoder(input_dict, training=False) | |
| np.testing.assert_equal( | |
| reference_logits_tensor[common.PRED_SEMANTIC_LOGITS_KEY].numpy(), | |
| logits_tensor_to_compare[common.PRED_SEMANTIC_LOGITS_KEY].numpy()) | |
| def test_deeplabv3plus_pool_size_setter(self): | |
| deeplabv3plus_decoder = _create_deeplabv3plus_model( | |
| high_level_feature_name='high', | |
| low_level_feature_name='low', | |
| low_level_channels_project=128, | |
| aspp_output_channels=96, | |
| decoder_output_channels=64, | |
| atrous_rates=[6, 12, 18], | |
| num_classes=80) | |
| pool_size = (10, 10) | |
| deeplabv3plus_decoder.set_pool_size(pool_size) | |
| self.assertTupleEqual(deeplabv3plus_decoder._aspp._aspp_pool._pool_size, | |
| pool_size) | |
| def test_deeplabv3plus_sync_bn(self, strategy): | |
| input_dict = dict() | |
| input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32)) | |
| input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16)) | |
| with strategy.scope(): | |
| for bn_layer in test_utils.NORMALIZATION_LAYERS: | |
| deeplabv3plus_decoder = _create_deeplabv3plus_model( | |
| high_level_feature_name='high', | |
| low_level_feature_name='low', | |
| low_level_channels_project=128, | |
| aspp_output_channels=96, | |
| decoder_output_channels=64, | |
| atrous_rates=[6, 12, 18], | |
| num_classes=80, | |
| bn_layer=bn_layer) | |
| _ = deeplabv3plus_decoder(input_dict) | |
| def test_deeplabv3plus_pool_size_resetter(self): | |
| deeplabv3plus_decoder = _create_deeplabv3plus_model( | |
| high_level_feature_name='high', | |
| low_level_feature_name='low', | |
| low_level_channels_project=128, | |
| aspp_output_channels=96, | |
| decoder_output_channels=64, | |
| atrous_rates=[6, 12, 18], | |
| num_classes=80) | |
| pool_size = (None, None) | |
| deeplabv3plus_decoder.reset_pooling_layer() | |
| self.assertTupleEqual(deeplabv3plus_decoder._aspp._aspp_pool._pool_size, | |
| pool_size) | |
| def test_deeplabv3plus_ckpt_items(self): | |
| deeplabv3plus_decoder = _create_deeplabv3plus_model( | |
| high_level_feature_name='high', | |
| low_level_feature_name='low', | |
| low_level_channels_project=128, | |
| aspp_output_channels=96, | |
| decoder_output_channels=64, | |
| atrous_rates=[6, 12, 18], | |
| num_classes=80) | |
| ckpt_dict = deeplabv3plus_decoder.checkpoint_items | |
| self.assertIn(common.CKPT_DEEPLABV3PLUS_ASPP, ckpt_dict) | |
| self.assertIn(common.CKPT_DEEPLABV3PLUS_PROJECT_CONV_BN_ACT, ckpt_dict) | |
| self.assertIn(common.CKPT_DEEPLABV3PLUS_FUSE, ckpt_dict) | |
| self.assertIn(common.CKPT_SEMANTIC_LAST_LAYER, ckpt_dict) | |
| if __name__ == '__main__': | |
| tf.test.main() | |