# 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) @test_utils.test_all_strategies 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()