def test_raise_error_on_empty_matcher(self): matcher_text_proto = """ """ matcher_proto = matcher_pb2.Matcher() text_format.Merge(matcher_text_proto, matcher_proto) with self.assertRaises(ValueError): matcher_builder.build(matcher_proto)
def test_build_bipartite_matcher(self): matcher_text_proto = """ bipartite_matcher { } """ matcher_proto = matcher_pb2.Matcher() text_format.Merge(matcher_text_proto, matcher_proto) matcher_object = matcher_builder.build(matcher_proto) self.assertTrue( isinstance(matcher_object, bipartite_matcher.GreedyBipartiteMatcher))
def test_build_arg_max_matcher_with_defaults(self): matcher_text_proto = """ argmax_matcher { } """ matcher_proto = matcher_pb2.Matcher() text_format.Merge(matcher_text_proto, matcher_proto) matcher_object = matcher_builder.build(matcher_proto) self.assertTrue( isinstance(matcher_object, argmax_matcher.ArgMaxMatcher)) self.assertAlmostEqual(matcher_object._matched_threshold, 0.5) self.assertAlmostEqual(matcher_object._unmatched_threshold, 0.5) self.assertTrue(matcher_object._negatives_lower_than_unmatched) self.assertFalse(matcher_object._force_match_for_each_row)
def test_build_arg_max_matcher_with_non_default_parameters(self): matcher_text_proto = """ argmax_matcher { matched_threshold: 0.7 unmatched_threshold: 0.3 negatives_lower_than_unmatched: false force_match_for_each_row: true use_matmul_gather: true } """ matcher_proto = matcher_pb2.Matcher() text_format.Merge(matcher_text_proto, matcher_proto) matcher_object = matcher_builder.build(matcher_proto) self.assertTrue( isinstance(matcher_object, argmax_matcher.ArgMaxMatcher)) self.assertAlmostEqual(matcher_object._matched_threshold, 0.7) self.assertAlmostEqual(matcher_object._unmatched_threshold, 0.3) self.assertFalse(matcher_object._negatives_lower_than_unmatched) self.assertTrue(matcher_object._force_match_for_each_row) self.assertTrue(matcher_object._use_matmul_gather)
def _build_ssd_model(ssd_config, is_training, add_summaries): """Builds an SSD detection model based on the model config. Args: ssd_config: A ssd.proto object containing the config for the desired SSDMetaArch. is_training: True if this model is being built for training purposes. add_summaries: Whether to add tf summaries in the model. Returns: SSDMetaArch based on the config. Raises: ValueError: If ssd_config.type is not recognized (i.e. not registered in model_class_map). """ num_classes = ssd_config.num_classes # Feature extractor feature_extractor = _build_ssd_feature_extractor( feature_extractor_config=ssd_config.feature_extractor, freeze_batchnorm=ssd_config.freeze_batchnorm, is_training=is_training) box_coder = box_coder_builder.build(ssd_config.box_coder) matcher = matcher_builder.build(ssd_config.matcher) region_similarity_calculator = sim_calc.build( ssd_config.similarity_calculator) encode_background_as_zeros = ssd_config.encode_background_as_zeros negative_class_weight = ssd_config.negative_class_weight anchor_generator = anchor_generator_builder.build( ssd_config.anchor_generator) if feature_extractor.is_keras_model: ssd_box_predictor = box_predictor_builder.build_keras( conv_hyperparams_fn=hyperparams_builder.KerasLayerHyperparams, freeze_batchnorm=ssd_config.freeze_batchnorm, inplace_batchnorm_update=False, num_predictions_per_location_list=anchor_generator. num_anchors_per_location(), box_predictor_config=ssd_config.box_predictor, is_training=is_training, num_classes=num_classes, add_background_class=ssd_config.add_background_class) else: ssd_box_predictor = box_predictor_builder.build( hyperparams_builder.build, ssd_config.box_predictor, is_training, num_classes, ssd_config.add_background_class) image_resizer_fn = image_resizer_builder.build(ssd_config.image_resizer) non_max_suppression_fn, score_conversion_fn = post_processing_builder.build( ssd_config.post_processing) (classification_loss, localization_loss, classification_weight, localization_weight, hard_example_miner, random_example_sampler, expected_loss_weights_fn) = losses_builder.build(ssd_config.loss) normalize_loss_by_num_matches = ssd_config.normalize_loss_by_num_matches normalize_loc_loss_by_codesize = ssd_config.normalize_loc_loss_by_codesize equalization_loss_config = ops.EqualizationLossConfig( weight=ssd_config.loss.equalization_loss.weight, exclude_prefixes=ssd_config.loss.equalization_loss.exclude_prefixes) target_assigner_instance = target_assigner.TargetAssigner( region_similarity_calculator, matcher, box_coder, negative_class_weight=negative_class_weight) ssd_meta_arch_fn = ssd_meta_arch.SSDMetaArch kwargs = {} return ssd_meta_arch_fn( is_training=is_training, anchor_generator=anchor_generator, box_predictor=ssd_box_predictor, box_coder=box_coder, feature_extractor=feature_extractor, encode_background_as_zeros=encode_background_as_zeros, image_resizer_fn=image_resizer_fn, non_max_suppression_fn=non_max_suppression_fn, score_conversion_fn=score_conversion_fn, classification_loss=classification_loss, localization_loss=localization_loss, classification_loss_weight=classification_weight, localization_loss_weight=localization_weight, normalize_loss_by_num_matches=normalize_loss_by_num_matches, hard_example_miner=hard_example_miner, target_assigner_instance=target_assigner_instance, add_summaries=add_summaries, normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize, freeze_batchnorm=ssd_config.freeze_batchnorm, inplace_batchnorm_update=ssd_config.inplace_batchnorm_update, add_background_class=ssd_config.add_background_class, explicit_background_class=ssd_config.explicit_background_class, random_example_sampler=random_example_sampler, expected_loss_weights_fn=expected_loss_weights_fn, use_confidences_as_targets=ssd_config.use_confidences_as_targets, implicit_example_weight=ssd_config.implicit_example_weight, equalization_loss_config=equalization_loss_config, **kwargs)