def create_target_assigner(reference, stage=None, negative_class_weight=1.0, use_matmul_gather=False): """Factory function for creating standard target assigners. Args: reference: string referencing the type of TargetAssigner. stage: string denoting stage: {proposal, detection}. negative_class_weight: classification weight to be associated to negative anchors (default: 1.0) use_matmul_gather: whether to use matrix multiplication based gather which are better suited for TPUs. Returns: TargetAssigner: desired target assigner. Raises: ValueError: if combination reference+stage is invalid. """ if reference == 'Multibox' and stage == 'proposal': similarity_calc = sim_calc.NegSqDistSimilarity() matcher = bipartite_matcher.GreedyBipartiteMatcher() box_coder = mean_stddev_box_coder.MeanStddevBoxCoder() elif reference == 'FasterRCNN' and stage == 'proposal': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.7, unmatched_threshold=0.3, force_match_for_each_row=True, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FasterRCNN' and stage == 'detection': similarity_calc = sim_calc.IouSimilarity() # Uses all proposals with IOU < 0.5 as candidate negatives. matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, negatives_lower_than_unmatched=True, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FastRCNN': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.1, force_match_for_each_row=False, negatives_lower_than_unmatched=False, use_matmul_gather=use_matmul_gather) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder() else: raise ValueError('No valid combination of reference and stage.') return TargetAssigner(similarity_calc, matcher, box_coder, negative_class_weight=negative_class_weight)
def build(region_similarity_calculator_config): """Builds region similarity calculator based on the configuration. Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See core/region_similarity_calculator.proto for details. Args: region_similarity_calculator_config: RegionSimilarityCalculator configuration proto. Returns: region_similarity_calculator: RegionSimilarityCalculator object. Raises: ValueError: On unknown region similarity calculator. """ if not isinstance( region_similarity_calculator_config, region_similarity_calculator_pb2.RegionSimilarityCalculator): raise ValueError( 'region_similarity_calculator_config not of type ' 'region_similarity_calculator_pb2.RegionsSimilarityCalculator') similarity_calculator = region_similarity_calculator_config.WhichOneof( 'region_similarity') if similarity_calculator == 'iou_similarity': return region_similarity_calculator.IouSimilarity() if similarity_calculator == 'ioa_similarity': return region_similarity_calculator.IoaSimilarity() if similarity_calculator == 'neg_sq_dist_similarity': return region_similarity_calculator.NegSqDistSimilarity() raise ValueError('Unknown region similarity calculator.')
def _get_target_assigner(self): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.5) box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1) return targetassigner.TargetAssigner(similarity_calc, matcher, box_coder)
def test_get_correct_pairwise_similarity_based_on_iou(self): corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]]) corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0], [0.0, 0.0, 20.0, 20.0]]) exp_output = [[2.0 / 16.0, 0, 6.0 / 400.0], [1.0 / 16.0, 0.0, 5.0 / 400.0]] boxes1 = box_list.BoxList(corners1) boxes2 = box_list.BoxList(corners2) iou_similarity_calculator = region_similarity_calculator.IouSimilarity() iou_similarity = iou_similarity_calculator.compare(boxes1, boxes2) with self.test_session() as sess: iou_output = sess.run(iou_similarity) self.assertAllClose(iou_output, exp_output)
def graph_fn(anchor_means, groundtruth_box_corners): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.3) box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder) anchors_boxlist = box_list.BoxList(anchor_means) groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners) result = target_assigner.assign(anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None) (cls_targets, cls_weights, reg_targets, reg_weights, _) = result return (cls_targets, cls_weights, reg_targets, reg_weights)
def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_keypoints): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.5) box_coder = keypoint_box_coder.KeypointBoxCoder( num_keypoints=6, scale_factors=[10.0, 10.0, 5.0, 5.0]) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder) anchors_boxlist = box_list.BoxList(anchor_means) groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners) groundtruth_boxlist.add_field(fields.BoxListFields.keypoints, groundtruth_keypoints) result = target_assigner.assign(anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None) (cls_targets, cls_weights, reg_targets, reg_weights, _) = result return (cls_targets, cls_weights, reg_targets, reg_weights)
def graph_fn(anchor_means, groundtruth_box_corners, groundtruth_labels, groundtruth_weights): similarity_calc = region_similarity_calculator.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5, unmatched_threshold=0.5) box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1) unmatched_class_label = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder) anchors_boxlist = box_list.BoxList(anchor_means) groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners) result = target_assigner.assign( anchors_boxlist, groundtruth_boxlist, groundtruth_labels, unmatched_class_label=unmatched_class_label, groundtruth_weights=groundtruth_weights) (_, cls_weights, _, reg_weights, _) = result return (cls_weights, reg_weights)
def _create_model(self, model_fn=ssd_meta_arch.SSDMetaArch, apply_hard_mining=True, normalize_loc_loss_by_codesize=False, add_background_class=True, random_example_sampling=False, weight_regression_loss_by_score=False, use_expected_classification_loss_under_sampling=False, minimum_negative_sampling=1, desired_negative_sampling_ratio=3, use_keras=False, predict_mask=False, use_static_shapes=False, nms_max_size_per_class=5): is_training = False num_classes = 1 mock_anchor_generator = MockAnchorGenerator2x2() if use_keras: mock_box_predictor = test_utils.MockKerasBoxPredictor( is_training, num_classes, predict_mask=predict_mask) else: mock_box_predictor = test_utils.MockBoxPredictor( is_training, num_classes, predict_mask=predict_mask) mock_box_coder = test_utils.MockBoxCoder() if use_keras: fake_feature_extractor = FakeSSDKerasFeatureExtractor() else: fake_feature_extractor = FakeSSDFeatureExtractor() mock_matcher = test_utils.MockMatcher() region_similarity_calculator = sim_calc.IouSimilarity() encode_background_as_zeros = False def image_resizer_fn(image): return [tf.identity(image), tf.shape(image)] classification_loss = losses.WeightedSigmoidClassificationLoss() localization_loss = losses.WeightedSmoothL1LocalizationLoss() non_max_suppression_fn = functools.partial( post_processing.batch_multiclass_non_max_suppression, score_thresh=-20.0, iou_thresh=1.0, max_size_per_class=nms_max_size_per_class, max_total_size=nms_max_size_per_class, use_static_shapes=use_static_shapes) classification_loss_weight = 1.0 localization_loss_weight = 1.0 negative_class_weight = 1.0 normalize_loss_by_num_matches = False hard_example_miner = None if apply_hard_mining: # This hard example miner is expected to be a no-op. hard_example_miner = losses.HardExampleMiner( num_hard_examples=None, iou_threshold=1.0) random_example_sampler = None if random_example_sampling: random_example_sampler = sampler.BalancedPositiveNegativeSampler( positive_fraction=0.5) target_assigner_instance = target_assigner.TargetAssigner( region_similarity_calculator, mock_matcher, mock_box_coder, negative_class_weight=negative_class_weight, weight_regression_loss_by_score=weight_regression_loss_by_score) expected_classification_loss_under_sampling = None if use_expected_classification_loss_under_sampling: expected_classification_loss_under_sampling = functools.partial( ops.expected_classification_loss_under_sampling, minimum_negative_sampling=minimum_negative_sampling, desired_negative_sampling_ratio=desired_negative_sampling_ratio ) code_size = 4 model = model_fn( is_training=is_training, anchor_generator=mock_anchor_generator, box_predictor=mock_box_predictor, box_coder=mock_box_coder, feature_extractor=fake_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=tf.identity, classification_loss=classification_loss, localization_loss=localization_loss, classification_loss_weight=classification_loss_weight, localization_loss_weight=localization_loss_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=False, normalize_loc_loss_by_codesize=normalize_loc_loss_by_codesize, freeze_batchnorm=False, inplace_batchnorm_update=False, add_background_class=add_background_class, random_example_sampler=random_example_sampler, expected_classification_loss_under_sampling= expected_classification_loss_under_sampling) return model, num_classes, mock_anchor_generator.num_anchors( ), code_size
def create_target_assigner(reference, stage=None, negative_class_weight=1.0, unmatched_cls_target=None): """Factory function for creating standard target assigners. Args: reference: string referencing the type of TargetAssigner. stage: string denoting stage: {proposal, detection}. negative_class_weight: classification weight to be associated to negative anchors (default: 1.0) unmatched_cls_target: a float32 tensor with shape [d_1, d_2, ..., d_k] which is consistent with the classification target for each anchor (and can be empty for scalar targets). This shape must thus be compatible with the groundtruth labels that are passed to the Assign function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]). If set to None, unmatched_cls_target is set to be 0 for each anchor. Returns: TargetAssigner: desired target assigner. Raises: ValueError: if combination reference+stage is invalid. """ if reference == 'Multibox' and stage == 'proposal': similarity_calc = sim_calc.NegSqDistSimilarity() matcher = bipartite_matcher.GreedyBipartiteMatcher() box_coder = mean_stddev_box_coder.MeanStddevBoxCoder() elif reference == 'FasterRCNN' and stage == 'proposal': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.7, unmatched_threshold=0.3, force_match_for_each_row=True) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FasterRCNN' and stage == 'detection': similarity_calc = sim_calc.IouSimilarity() # Uses all proposals with IOU < 0.5 as candidate negatives. matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.5, negatives_lower_than_unmatched=True) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder( scale_factors=[10.0, 10.0, 5.0, 5.0]) elif reference == 'FastRCNN': similarity_calc = sim_calc.IouSimilarity() matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=0.5, unmatched_threshold=0.1, force_match_for_each_row=False, negatives_lower_than_unmatched=False) box_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder() else: raise ValueError('No valid combination of reference and stage.') return TargetAssigner(similarity_calc, matcher, box_coder, negative_class_weight=negative_class_weight, unmatched_cls_target=unmatched_cls_target)