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 graph_fn(similarity): matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=3.) match = matcher.match(similarity) matched_cols = match.matched_column_indicator() unmatched_cols = match.unmatched_column_indicator() match_results = match.match_results return (matched_cols, unmatched_cols, match_results)
def build(matcher_config): """Builds a matcher object based on the matcher config. Args: matcher_config: A matcher.proto object containing the config for the desired Matcher. Returns: Matcher based on the config. Raises: ValueError: On empty matcher proto. """ if not isinstance(matcher_config, matcher_pb2.Matcher): raise ValueError('matcher_config not of type matcher_pb2.Matcher.') if matcher_config.WhichOneof('matcher_oneof') == 'argmax_matcher': matcher = matcher_config.argmax_matcher matched_threshold = unmatched_threshold = None if not matcher.ignore_thresholds: matched_threshold = matcher.matched_threshold unmatched_threshold = matcher.unmatched_threshold return argmax_matcher.ArgMaxMatcher( matched_threshold=matched_threshold, unmatched_threshold=unmatched_threshold, negatives_lower_than_unmatched=matcher. negatives_lower_than_unmatched, force_match_for_each_row=matcher.force_match_for_each_row, use_matmul_gather=matcher.use_matmul_gather) if matcher_config.WhichOneof('matcher_oneof') == 'bipartite_matcher': matcher = matcher_config.bipartite_matcher return bipartite_matcher.GreedyBipartiteMatcher( matcher.use_matmul_gather) raise ValueError('Empty matcher.')
def graph_fn(similarity): matcher = argmax_matcher.ArgMaxMatcher( matched_threshold=3., unmatched_threshold=2., negatives_lower_than_unmatched=False) match = matcher.match(similarity) matched_cols = match.matched_column_indicator() unmatched_cols = match.unmatched_column_indicator() match_results = match.match_results return (matched_cols, unmatched_cols, match_results)
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 graph_fn(similarity_matrix): matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=None) match = matcher.match(similarity_matrix) return match.unmatched_column_indicator()
def test_invalid_arguments_unmatched_thres_larger_than_matched_thres(self): with self.assertRaises(ValueError): argmax_matcher.ArgMaxMatcher(matched_threshold=1, unmatched_threshold=2)
def test_invalid_arguments_no_matched_threshold(self): with self.assertRaises(ValueError): argmax_matcher.ArgMaxMatcher(matched_threshold=None, unmatched_threshold=4)
def test_invalid_arguments_corner_case_negatives_lower_than_thres_false(self): with self.assertRaises(ValueError): argmax_matcher.ArgMaxMatcher(matched_threshold=1, unmatched_threshold=1, negatives_lower_than_unmatched=False)
def test_valid_arguments_corner_case(self): argmax_matcher.ArgMaxMatcher(matched_threshold=1, unmatched_threshold=1)
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)