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,
add_background_class=True):
"""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.
add_background_class: Whether to add an implicit background class to one-hot
encodings of groundtruth labels. Set to false if using groundtruth labels
with an explicit background class or using multiclass scores instead of
truth in the case of distillation.
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,
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
ssd_box_predictor = box_predictor_builder.build(hyperparams_builder.build,
ssd_config.box_predictor,
is_training, num_classes)
anchor_generator = anchor_generator_builder.build(
ssd_config.anchor_generator)
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) = 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
return ssd_meta_arch.SSDMetaArch(
is_training,
anchor_generator,
ssd_box_predictor,
box_coder,
feature_extractor,
matcher,
region_similarity_calculator,
encode_background_as_zeros,
negative_class_weight,
image_resizer_fn,
non_max_suppression_fn,
score_conversion_fn,
classification_loss,
localization_loss,
classification_weight,
localization_weight,
normalize_loss_by_num_matches,
hard_example_miner,
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=add_background_class,
random_example_sampler=random_example_sampler)