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, valid_rows):
matcher = argmax_matcher.ArgMaxMatcher(
matched_threshold=3.,
unmatched_threshold=2.,
force_match_for_each_row=True)
match = matcher.match(similarity, valid_rows)
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(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 __init__(self, categories, iou_threshold=0.5):
"""Constructor.
Args:
categories: A list of dicts, each of which has the following keys -
'id': (required) an integer id uniquely identifying this category.
'name': (required) string representing category name e.g., 'cat', 'dog'.
iou_threshold: Threshold above which to consider a box as matched during
evaluation.
"""
super(CalibrationDetectionEvaluator, self).__init__(categories)
# Constructing target_assigner to match detections to groundtruth.
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(
matched_threshold=iou_threshold, unmatched_threshold=iou_threshold)
box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
self._target_assigner = target_assigner.TargetAssigner(
similarity_calc, matcher, box_coder)
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 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)