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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 max_deeplab.""" | |
| import tensorflow as tf | |
| from deeplab2 import common | |
| from deeplab2 import config_pb2 | |
| from deeplab2.model.decoder import max_deeplab | |
| def _create_max_deeplab_example_proto(num_non_void_classes=19): | |
| semantic_decoder = config_pb2.DecoderOptions( | |
| feature_key='feature_semantic', atrous_rates=[6, 12, 18]) | |
| auxiliary_semantic_head = config_pb2.HeadOptions( | |
| output_channels=num_non_void_classes, head_channels=256) | |
| pixel_space_head = config_pb2.HeadOptions( | |
| output_channels=128, head_channels=256) | |
| max_deeplab_options = config_pb2.ModelOptions.MaXDeepLabOptions( | |
| pixel_space_head=pixel_space_head, | |
| auxiliary_semantic_head=auxiliary_semantic_head) | |
| # Add features from lowest to highest. | |
| max_deeplab_options.auxiliary_low_level.add( | |
| feature_key='res3', channels_project=64) | |
| max_deeplab_options.auxiliary_low_level.add( | |
| feature_key='res2', channels_project=32) | |
| return config_pb2.ModelOptions( | |
| decoder=semantic_decoder, max_deeplab=max_deeplab_options) | |
| class MaXDeeplabTest(tf.test.TestCase): | |
| def test_max_deeplab_decoder_output_shape(self): | |
| num_non_void_classes = 19 | |
| num_mask_slots = 127 | |
| model_options = _create_max_deeplab_example_proto( | |
| num_non_void_classes=num_non_void_classes) | |
| decoder = max_deeplab.MaXDeepLab( | |
| max_deeplab_options=model_options.max_deeplab, | |
| ignore_label=255, | |
| decoder_options=model_options.decoder) | |
| input_dict = { | |
| 'res2': | |
| tf.random.uniform([2, 17, 17, 256]), | |
| 'res3': | |
| tf.random.uniform([2, 9, 9, 512]), | |
| 'transformer_class_feature': | |
| tf.random.uniform([2, num_mask_slots, 256]), | |
| 'transformer_mask_feature': | |
| tf.random.uniform([2, num_mask_slots, 256]), | |
| 'feature_panoptic': | |
| tf.random.uniform([2, 17, 17, 256]), | |
| 'feature_semantic': | |
| tf.random.uniform([2, 5, 5, 2048]) | |
| } | |
| resulting_dict = decoder(input_dict) | |
| self.assertListEqual( | |
| resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), | |
| [2, 17, 17, 19]) # Stride 4 | |
| self.assertListEqual( | |
| resulting_dict[ | |
| common.PRED_PIXEL_SPACE_NORMALIZED_FEATURE_KEY].shape.as_list(), | |
| [2, 17, 17, 128]) # Stride 4 | |
| self.assertListEqual( | |
| resulting_dict[ | |
| common.PRED_TRANSFORMER_CLASS_LOGITS_KEY].shape.as_list(), | |
| # Non-void classes and a void class. | |
| [2, num_mask_slots, num_non_void_classes + 1]) | |
| self.assertListEqual( | |
| resulting_dict[common.PRED_PIXEL_SPACE_MASK_LOGITS_KEY].shape.as_list(), | |
| [2, 17, 17, num_mask_slots]) # Stride 4. | |
| if __name__ == '__main__': | |
| tf.test.main() | |