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| # Copyright 2018 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # 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 lstm_object_detection.tensorflow.model_builder.""" | |
| import tensorflow.compat.v1 as tf | |
| from google.protobuf import text_format | |
| from lstm_object_detection import model_builder | |
| from lstm_object_detection.meta_architectures import lstm_ssd_meta_arch | |
| from lstm_object_detection.protos import pipeline_pb2 as internal_pipeline_pb2 | |
| from object_detection.protos import pipeline_pb2 | |
| class ModelBuilderTest(tf.test.TestCase): | |
| def create_train_model(self, model_config, lstm_config): | |
| """Builds a DetectionModel based on the model config. | |
| Args: | |
| model_config: A model.proto object containing the config for the desired | |
| DetectionModel. | |
| lstm_config: LstmModel config proto that specifies LSTM train/eval | |
| configs. | |
| Returns: | |
| DetectionModel based on the config. | |
| """ | |
| return model_builder.build(model_config, lstm_config, is_training=True) | |
| def create_eval_model(self, model_config, lstm_config): | |
| """Builds a DetectionModel based on the model config. | |
| Args: | |
| model_config: A model.proto object containing the config for the desired | |
| DetectionModel. | |
| lstm_config: LstmModel config proto that specifies LSTM train/eval | |
| configs. | |
| Returns: | |
| DetectionModel based on the config. | |
| """ | |
| return model_builder.build(model_config, lstm_config, is_training=False) | |
| def get_model_configs_from_proto(self): | |
| """Creates a model text proto for testing. | |
| Returns: | |
| A dictionary of model configs. | |
| """ | |
| model_text_proto = """ | |
| [lstm_object_detection.protos.lstm_model] { | |
| train_unroll_length: 4 | |
| eval_unroll_length: 4 | |
| } | |
| model { | |
| ssd { | |
| feature_extractor { | |
| type: 'lstm_ssd_mobilenet_v1' | |
| conv_hyperparams { | |
| regularizer { | |
| l2_regularizer { | |
| } | |
| } | |
| initializer { | |
| truncated_normal_initializer { | |
| } | |
| } | |
| } | |
| } | |
| negative_class_weight: 2.0 | |
| box_coder { | |
| faster_rcnn_box_coder { | |
| } | |
| } | |
| matcher { | |
| argmax_matcher { | |
| } | |
| } | |
| similarity_calculator { | |
| iou_similarity { | |
| } | |
| } | |
| anchor_generator { | |
| ssd_anchor_generator { | |
| aspect_ratios: 1.0 | |
| } | |
| } | |
| image_resizer { | |
| fixed_shape_resizer { | |
| height: 320 | |
| width: 320 | |
| } | |
| } | |
| box_predictor { | |
| convolutional_box_predictor { | |
| conv_hyperparams { | |
| regularizer { | |
| l2_regularizer { | |
| } | |
| } | |
| initializer { | |
| truncated_normal_initializer { | |
| } | |
| } | |
| } | |
| } | |
| } | |
| normalize_loc_loss_by_codesize: true | |
| loss { | |
| classification_loss { | |
| weighted_softmax { | |
| } | |
| } | |
| localization_loss { | |
| weighted_smooth_l1 { | |
| } | |
| } | |
| } | |
| } | |
| }""" | |
| pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() | |
| text_format.Merge(model_text_proto, pipeline_config) | |
| configs = {} | |
| configs['model'] = pipeline_config.model | |
| configs['lstm_model'] = pipeline_config.Extensions[ | |
| internal_pipeline_pb2.lstm_model] | |
| return configs | |
| def get_interleaved_model_configs_from_proto(self): | |
| """Creates an interleaved model text proto for testing. | |
| Returns: | |
| A dictionary of model configs. | |
| """ | |
| model_text_proto = """ | |
| [lstm_object_detection.protos.lstm_model] { | |
| train_unroll_length: 4 | |
| eval_unroll_length: 10 | |
| lstm_state_depth: 320 | |
| depth_multipliers: 1.4 | |
| depth_multipliers: 0.35 | |
| pre_bottleneck: true | |
| low_res: true | |
| train_interleave_method: 'RANDOM_SKIP_SMALL' | |
| eval_interleave_method: 'SKIP3' | |
| } | |
| model { | |
| ssd { | |
| feature_extractor { | |
| type: 'lstm_ssd_interleaved_mobilenet_v2' | |
| conv_hyperparams { | |
| regularizer { | |
| l2_regularizer { | |
| } | |
| } | |
| initializer { | |
| truncated_normal_initializer { | |
| } | |
| } | |
| } | |
| } | |
| negative_class_weight: 2.0 | |
| box_coder { | |
| faster_rcnn_box_coder { | |
| } | |
| } | |
| matcher { | |
| argmax_matcher { | |
| } | |
| } | |
| similarity_calculator { | |
| iou_similarity { | |
| } | |
| } | |
| anchor_generator { | |
| ssd_anchor_generator { | |
| aspect_ratios: 1.0 | |
| } | |
| } | |
| image_resizer { | |
| fixed_shape_resizer { | |
| height: 320 | |
| width: 320 | |
| } | |
| } | |
| box_predictor { | |
| convolutional_box_predictor { | |
| conv_hyperparams { | |
| regularizer { | |
| l2_regularizer { | |
| } | |
| } | |
| initializer { | |
| truncated_normal_initializer { | |
| } | |
| } | |
| } | |
| } | |
| } | |
| normalize_loc_loss_by_codesize: true | |
| loss { | |
| classification_loss { | |
| weighted_softmax { | |
| } | |
| } | |
| localization_loss { | |
| weighted_smooth_l1 { | |
| } | |
| } | |
| } | |
| } | |
| }""" | |
| pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() | |
| text_format.Merge(model_text_proto, pipeline_config) | |
| configs = {} | |
| configs['model'] = pipeline_config.model | |
| configs['lstm_model'] = pipeline_config.Extensions[ | |
| internal_pipeline_pb2.lstm_model] | |
| return configs | |
| def test_model_creation_from_valid_configs(self): | |
| configs = self.get_model_configs_from_proto() | |
| # Test model properties. | |
| self.assertEqual(configs['model'].ssd.negative_class_weight, 2.0) | |
| self.assertTrue(configs['model'].ssd.normalize_loc_loss_by_codesize) | |
| self.assertEqual(configs['model'].ssd.feature_extractor.type, | |
| 'lstm_ssd_mobilenet_v1') | |
| model = self.create_train_model(configs['model'], configs['lstm_model']) | |
| # Test architechture type. | |
| self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch) | |
| # Test LSTM unroll length. | |
| self.assertEqual(model.unroll_length, 4) | |
| model = self.create_eval_model(configs['model'], configs['lstm_model']) | |
| # Test architechture type. | |
| self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch) | |
| # Test LSTM configs. | |
| self.assertEqual(model.unroll_length, 4) | |
| def test_interleaved_model_creation_from_valid_configs(self): | |
| configs = self.get_interleaved_model_configs_from_proto() | |
| # Test model properties. | |
| self.assertEqual(configs['model'].ssd.negative_class_weight, 2.0) | |
| self.assertTrue(configs['model'].ssd.normalize_loc_loss_by_codesize) | |
| self.assertEqual(configs['model'].ssd.feature_extractor.type, | |
| 'lstm_ssd_interleaved_mobilenet_v2') | |
| model = self.create_train_model(configs['model'], configs['lstm_model']) | |
| # Test architechture type. | |
| self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch) | |
| # Test LSTM configs. | |
| self.assertEqual(model.unroll_length, 4) | |
| self.assertEqual(model._feature_extractor.lstm_state_depth, 320) | |
| self.assertAllClose(model._feature_extractor.depth_multipliers, (1.4, 0.35)) | |
| self.assertTrue(model._feature_extractor.pre_bottleneck) | |
| self.assertTrue(model._feature_extractor.low_res) | |
| self.assertEqual(model._feature_extractor.interleave_method, | |
| 'RANDOM_SKIP_SMALL') | |
| model = self.create_eval_model(configs['model'], configs['lstm_model']) | |
| # Test architechture type. | |
| self.assertIsInstance(model, lstm_ssd_meta_arch.LSTMSSDMetaArch) | |
| # Test LSTM configs. | |
| self.assertEqual(model.unroll_length, 10) | |
| self.assertEqual(model._feature_extractor.lstm_state_depth, 320) | |
| self.assertAllClose(model._feature_extractor.depth_multipliers, (1.4, 0.35)) | |
| self.assertTrue(model._feature_extractor.pre_bottleneck) | |
| self.assertTrue(model._feature_extractor.low_res) | |
| self.assertEqual(model._feature_extractor.interleave_method, 'SKIP3') | |
| def test_model_creation_from_invalid_configs(self): | |
| configs = self.get_model_configs_from_proto() | |
| # Test model build failure with wrong input configs. | |
| with self.assertRaises(AttributeError): | |
| _ = self.create_train_model(configs['model'], configs['model']) | |
| with self.assertRaises(AttributeError): | |
| _ = self.create_eval_model(configs['model'], configs['model']) | |
| if __name__ == '__main__': | |
| tf.test.main() | |