def test_raise_value_error_on_empty_anchor_genertor(self):
anchor_generator_text_proto = """
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
with self.assertRaises(ValueError):
anchor_generator_builder.build(anchor_generator_proto)
def test_build_ssd_anchor_generator_with_defaults(self):
anchor_generator_text_proto = """
ssd_anchor_generator {
aspect_ratios: [1.0]
}
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
anchor_generator_object = anchor_generator_builder.build(
anchor_generator_proto)
self.assertTrue(
isinstance(
anchor_generator_object,
multiple_grid_anchor_generator.MultipleGridAnchorGenerator))
for actual_scales, expected_scales in zip(
list(anchor_generator_object._scales), [(0.1, 0.2, 0.2),
(0.35, 0.418),
(0.499, 0.570),
(0.649, 0.721),
(0.799, 0.871),
(0.949, 0.974)]):
self.assert_almost_list_equal(expected_scales,
actual_scales,
delta=1e-2)
for actual_aspect_ratio, expected_aspect_ratio in zip(
list(anchor_generator_object._aspect_ratios),
[(1.0, 2.0, 0.5)] + 5 * [(1.0, 1.0)]):
self.assert_almost_list_equal(expected_aspect_ratio,
actual_aspect_ratio)
with self.test_session() as sess:
base_anchor_size = sess.run(
anchor_generator_object._base_anchor_size)
self.assertAllClose(base_anchor_size, [1.0, 1.0])
def test_build_grid_anchor_generator_with_non_default_parameters(self):
anchor_generator_text_proto = """
grid_anchor_generator {
height: 128
width: 512
height_stride: 10
width_stride: 20
height_offset: 30
width_offset: 40
scales: [0.4, 2.2]
aspect_ratios: [0.3, 4.5]
}
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
anchor_generator_object = anchor_generator_builder.build(
anchor_generator_proto)
self.assertTrue(
isinstance(anchor_generator_object,
grid_anchor_generator.GridAnchorGenerator))
self.assert_almost_list_equal(anchor_generator_object._scales,
[0.4, 2.2])
self.assert_almost_list_equal(anchor_generator_object._aspect_ratios,
[0.3, 4.5])
with self.test_session() as sess:
base_anchor_size, anchor_offset, anchor_stride = sess.run([
anchor_generator_object._base_anchor_size,
anchor_generator_object._anchor_offset,
anchor_generator_object._anchor_stride
])
self.assertAllEqual(anchor_offset, [30, 40])
self.assertAllEqual(anchor_stride, [10, 20])
self.assertAllEqual(base_anchor_size, [128, 512])
def _build_ssd_model(ssd_config, is_training):
"""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.
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(ssd_config.feature_extractor,
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)
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) = losses_builder.build(ssd_config.loss)
normalize_loss_by_num_matches = ssd_config.normalize_loss_by_num_matches
return ssd_meta_arch.SSDMetaArch(
is_training,
anchor_generator,
ssd_box_predictor,
box_coder,
feature_extractor,
matcher,
region_similarity_calculator,
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)
def test_build_ssd_anchor_generator_with_non_default_parameters(self):
anchor_generator_text_proto = """
ssd_anchor_generator {
num_layers: 2
min_scale: 0.3
max_scale: 0.8
aspect_ratios: [2.0]
height_stride: 16
height_stride: 32
width_stride: 20
width_stride: 30
height_offset: 8
height_offset: 16
width_offset: 0
width_offset: 10
}
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
anchor_generator_object = anchor_generator_builder.build(
anchor_generator_proto)
self.assertTrue(
isinstance(
anchor_generator_object,
multiple_grid_anchor_generator.MultipleGridAnchorGenerator))
for actual_scales, expected_scales in zip(
list(anchor_generator_object._scales), [(0.1, 0.3, 0.3),
(0.8, 0.894)]):
self.assert_almost_list_equal(expected_scales,
actual_scales,
delta=1e-2)
for actual_aspect_ratio, expected_aspect_ratio in zip(
list(anchor_generator_object._aspect_ratios), [(1.0, 2.0, 0.5),
(2.0, 1.0)]):
self.assert_almost_list_equal(expected_aspect_ratio,
actual_aspect_ratio)
for actual_strides, expected_strides in zip(
list(anchor_generator_object._anchor_strides), [(16, 20),
(32, 30)]):
self.assert_almost_list_equal(expected_strides, actual_strides)
for actual_offsets, expected_offsets in zip(
list(anchor_generator_object._anchor_offsets), [(8, 0),
(16, 10)]):
self.assert_almost_list_equal(expected_offsets, actual_offsets)
with self.test_session() as sess:
base_anchor_size = sess.run(
anchor_generator_object._base_anchor_size)
self.assertAllClose(base_anchor_size, [1.0, 1.0])
def test_build_ssd_anchor_generator_with_custom_interpolated_scale(self):
anchor_generator_text_proto = """
ssd_anchor_generator {
aspect_ratios: [0.5]
interpolated_scale_aspect_ratio: 0.5
reduce_boxes_in_lowest_layer: false
}
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
anchor_generator_object = anchor_generator_builder.build(
anchor_generator_proto)
self.assertTrue(
isinstance(
anchor_generator_object,
multiple_grid_anchor_generator.MultipleGridAnchorGenerator))
for actual_aspect_ratio, expected_aspect_ratio in zip(
list(anchor_generator_object._aspect_ratios),
6 * [(0.5, 0.5)]):
self.assert_almost_list_equal(expected_aspect_ratio,
actual_aspect_ratio)
def test_build_grid_anchor_generator_with_defaults(self):
anchor_generator_text_proto = """
grid_anchor_generator {
}
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
anchor_generator_object = anchor_generator_builder.build(
anchor_generator_proto)
self.assertTrue(
isinstance(anchor_generator_object,
grid_anchor_generator.GridAnchorGenerator))
self.assertListEqual(anchor_generator_object._scales, [])
self.assertListEqual(anchor_generator_object._aspect_ratios, [])
with self.test_session() as sess:
base_anchor_size, anchor_offset, anchor_stride = sess.run([
anchor_generator_object._base_anchor_size,
anchor_generator_object._anchor_offset,
anchor_generator_object._anchor_stride
])
self.assertAllEqual(anchor_offset, [0, 0])
self.assertAllEqual(anchor_stride, [16, 16])
self.assertAllEqual(base_anchor_size, [256, 256])
def test_build_ssd_anchor_generator_with_custom_scales(self):
anchor_generator_text_proto = """
ssd_anchor_generator {
aspect_ratios: [1.0]
scales: [0.1, 0.15, 0.2, 0.4, 0.6, 0.8]
reduce_boxes_in_lowest_layer: false
}
"""
anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
anchor_generator_object = anchor_generator_builder.build(
anchor_generator_proto)
self.assertTrue(
isinstance(
anchor_generator_object,
multiple_grid_anchor_generator.MultipleGridAnchorGenerator))
for actual_scales, expected_scales in zip(
list(anchor_generator_object._scales),
[(0.1, math.sqrt(0.1 * 0.15)), (0.15, math.sqrt(0.15 * 0.2)),
(0.2, math.sqrt(0.2 * 0.4)), (0.4, math.sqrt(0.4 * 0.6)),
(0.6, math.sqrt(0.6 * 0.8)), (0.8, math.sqrt(0.8 * 1.0))]):
self.assert_almost_list_equal(expected_scales,
actual_scales,
delta=1e-2)
def _build_faster_rcnn_model(frcnn_config, is_training):
"""Builds a Faster R-CNN or R-FCN detection model based on the model config.
Builds R-FCN model if the second_stage_box_predictor in the config is of type
`rfcn_box_predictor` else builds a Faster R-CNN model.
Args:
frcnn_config: A faster_rcnn.proto object containing the config for the
desired FasterRCNNMetaArch or RFCNMetaArch.
is_training: True if this model is being built for training purposes.
Returns:
FasterRCNNMetaArch based on the config.
Raises:
ValueError: If frcnn_config.type is not recognized (i.e. not registered in
model_class_map).
"""
num_classes = frcnn_config.num_classes
image_resizer_fn = image_resizer_builder.build(frcnn_config.image_resizer)
feature_extractor = _build_faster_rcnn_feature_extractor(
frcnn_config.feature_extractor, is_training)
first_stage_only = frcnn_config.first_stage_only
first_stage_anchor_generator = anchor_generator_builder.build(
frcnn_config.first_stage_anchor_generator)
first_stage_atrous_rate = frcnn_config.first_stage_atrous_rate
first_stage_box_predictor_arg_scope = hyperparams_builder.build(
frcnn_config.first_stage_box_predictor_conv_hyperparams, is_training)
first_stage_box_predictor_kernel_size = (
frcnn_config.first_stage_box_predictor_kernel_size)
first_stage_box_predictor_depth = frcnn_config.first_stage_box_predictor_depth
first_stage_minibatch_size = frcnn_config.first_stage_minibatch_size
first_stage_positive_balance_fraction = (
frcnn_config.first_stage_positive_balance_fraction)
first_stage_nms_score_threshold = frcnn_config.first_stage_nms_score_threshold
first_stage_nms_iou_threshold = frcnn_config.first_stage_nms_iou_threshold
first_stage_max_proposals = frcnn_config.first_stage_max_proposals
first_stage_loc_loss_weight = (
frcnn_config.first_stage_localization_loss_weight)
first_stage_obj_loss_weight = frcnn_config.first_stage_objectness_loss_weight
initial_crop_size = frcnn_config.initial_crop_size
maxpool_kernel_size = frcnn_config.maxpool_kernel_size
maxpool_stride = frcnn_config.maxpool_stride
second_stage_box_predictor = box_predictor_builder.build(
hyperparams_builder.build,
frcnn_config.second_stage_box_predictor,
is_training=is_training,
num_classes=num_classes)
second_stage_batch_size = frcnn_config.second_stage_batch_size
second_stage_balance_fraction = frcnn_config.second_stage_balance_fraction
(second_stage_non_max_suppression_fn, second_stage_score_conversion_fn
) = post_processing_builder.build(frcnn_config.second_stage_post_processing)
second_stage_localization_loss_weight = (
frcnn_config.second_stage_localization_loss_weight)
second_stage_classification_loss = (
losses_builder.build_faster_rcnn_classification_loss(
frcnn_config.second_stage_classification_loss))
second_stage_classification_loss_weight = (
frcnn_config.second_stage_classification_loss_weight)
second_stage_mask_prediction_loss_weight = (
frcnn_config.second_stage_mask_prediction_loss_weight)
hard_example_miner = None
if frcnn_config.HasField('hard_example_miner'):
hard_example_miner = losses_builder.build_hard_example_miner(
frcnn_config.hard_example_miner,
second_stage_classification_loss_weight,
second_stage_localization_loss_weight)
common_kwargs = {
'is_training': is_training,
'num_classes': num_classes,
'image_resizer_fn': image_resizer_fn,
'feature_extractor': feature_extractor,
'first_stage_only': first_stage_only,
'first_stage_anchor_generator': first_stage_anchor_generator,
'first_stage_atrous_rate': first_stage_atrous_rate,
'first_stage_box_predictor_arg_scope':
first_stage_box_predictor_arg_scope,
'first_stage_box_predictor_kernel_size':
first_stage_box_predictor_kernel_size,
'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
'first_stage_minibatch_size': first_stage_minibatch_size,
'first_stage_positive_balance_fraction':
first_stage_positive_balance_fraction,
'first_stage_nms_score_threshold': first_stage_nms_score_threshold,
'first_stage_nms_iou_threshold': first_stage_nms_iou_threshold,
'first_stage_max_proposals': first_stage_max_proposals,
'first_stage_localization_loss_weight': first_stage_loc_loss_weight,
'first_stage_objectness_loss_weight': first_stage_obj_loss_weight,
'second_stage_batch_size': second_stage_batch_size,
'second_stage_balance_fraction': second_stage_balance_fraction,
'second_stage_non_max_suppression_fn':
second_stage_non_max_suppression_fn,
'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
'second_stage_localization_loss_weight':
second_stage_localization_loss_weight,
'second_stage_classification_loss':
second_stage_classification_loss,
'second_stage_classification_loss_weight':
second_stage_classification_loss_weight,
'hard_example_miner': hard_example_miner}
if isinstance(second_stage_box_predictor, box_predictor.RfcnBoxPredictor):
return rfcn_meta_arch.RFCNMetaArch(
second_stage_rfcn_box_predictor=second_stage_box_predictor,
**common_kwargs)
else:
return faster_rcnn_meta_arch.FasterRCNNMetaArch(
initial_crop_size=initial_crop_size,
maxpool_kernel_size=maxpool_kernel_size,
maxpool_stride=maxpool_stride,
second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
second_stage_mask_prediction_loss_weight=(
second_stage_mask_prediction_loss_weight),
**common_kwargs)