def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(self): similarity_calc = region_similarity_calculator.NegSqDistSimilarity() matcher = bipartite_matcher.GreedyBipartiteMatcher() box_coder = mean_stddev_box_coder.MeanStddevBoxCoder() unmatched_class_label = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32) target_assigner = targetassigner.TargetAssigner( similarity_calc, matcher, box_coder) prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8], [0, 0.5, .5, 1.0], [.75, 0, 1.0, .25]]) priors = box_list.BoxList(prior_means) box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.8], [0.5, 0.5, 0.9, 0.9], [.75, 0, .95, .27]] boxes = box_list.BoxList(tf.constant(box_corners)) groundtruth_labels = tf.constant( [[0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0]], tf.float32) with self.assertRaisesRegexp(ValueError, 'Unequal shapes'): target_assigner.assign(priors, boxes, groundtruth_labels, unmatched_class_label=unmatched_class_label, num_valid_rows=3)
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 test_get_expected_matches_with_only_one_valid_row(self): similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]]) num_valid_rows = 1 expected_match_results = [-1, -1, 0] matcher = bipartite_matcher.GreedyBipartiteMatcher() match = matcher.match(similarity_matrix, num_valid_rows=num_valid_rows) with self.test_session() as sess: match_results_out = sess.run(match._match_results) self.assertAllEqual(match_results_out, expected_match_results)
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)