def _build_localization_loss(loss_config):
"""Builds a localization loss based on the loss config.
Args:
loss_config: A losses_pb2.LocalizationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.LocalizationLoss):
raise ValueError(
'loss_config not of type losses_pb2.LocalizationLoss.')
loss_type = loss_config.WhichOneof('localization_loss')
if loss_type == 'weighted_l2':
return losses.WeightedL2LocalizationLoss()
if loss_type == 'weighted_smooth_l1':
return losses.WeightedSmoothL1LocalizationLoss(
loss_config.weighted_smooth_l1.delta)
if loss_type == 'weighted_iou':
return losses.WeightedIOULocalizationLoss()
if loss_type == 'l1_localization_loss':
return losses.L1LocalizationLoss()
if loss_type == 'weighted_giou':
return losses.WeightedGIOULocalizationLoss()
raise ValueError('Empty loss config.')
def build_center_net_meta_arch():
feature_extractor = center_net_resnet_feature_extractor.\
CenterNetResnetFeatureExtractor('resnet_v2_101')
image_resizer_fn = functools.partial(
preprocessor.resize_to_range,
min_dimension=128,
max_dimension=128,
pad_to_max_dimesnion=True)
object_detection_params = center_net_meta_arch.ObjectDetectionParams(
classification_loss=losses.PenaltyReducedLogisticFocalLoss(
alpha=1.0, beta=1.0),
classification_loss_weight=1.0,
localization_loss=losses.L1LocalizationLoss(),
offset_loss_weight=1.0,
scale_loss_weight=0.1,
min_box_overlap_iou=1.0,
max_box_predictions=5)
return center_net_meta_arch.CenterNetMetaArch(
is_training=True,
add_summaries=False,
num_classes=10,
feature_extractor=feature_extractor,
image_resizer_fn=image_resizer_fn,
object_detection_params=object_detection_params)
def build_meta_arch(predict_full_resolution_masks=False, use_dice_loss=False):
"""Builds the DeepMAC meta architecture."""
feature_extractor = DummyFeatureExtractor(channel_means=(1.0, 2.0, 3.0),
channel_stds=(10., 20., 30.),
bgr_ordering=False,
num_feature_outputs=2,
stride=4)
image_resizer_fn = functools.partial(preprocessor.resize_to_range,
min_dimension=128,
max_dimension=128,
pad_to_max_dimesnion=True)
object_center_params = center_net_meta_arch.ObjectCenterParams(
classification_loss=losses.WeightedSigmoidClassificationLoss(),
object_center_loss_weight=1.0,
min_box_overlap_iou=1.0,
max_box_predictions=5,
use_labeled_classes=False)
if use_dice_loss:
classification_loss = losses.WeightedDiceClassificationLoss(False)
else:
classification_loss = losses.WeightedSigmoidClassificationLoss()
deepmac_params = deepmac_meta_arch.DeepMACParams(
classification_loss=classification_loss,
dim=8,
task_loss_weight=1.0,
pixel_embedding_dim=2,
allowed_masked_classes_ids=[],
mask_size=16,
mask_num_subsamples=-1,
use_xy=True,
network_type='hourglass10',
use_instance_embedding=True,
num_init_channels=8,
predict_full_resolution_masks=predict_full_resolution_masks,
postprocess_crop_size=128,
max_roi_jitter_ratio=0.0,
roi_jitter_mode='random')
object_detection_params = center_net_meta_arch.ObjectDetectionParams(
localization_loss=losses.L1LocalizationLoss(),
offset_loss_weight=1.0,
scale_loss_weight=0.1)
return deepmac_meta_arch.DeepMACMetaArch(
is_training=True,
add_summaries=False,
num_classes=6,
feature_extractor=feature_extractor,
object_center_params=object_center_params,
deepmac_params=deepmac_params,
object_detection_params=object_detection_params,
image_resizer_fn=image_resizer_fn)
def test_returns_correct_loss(self):
loss = losses.L1LocalizationLoss()
pred = [[0.1, 0.2], [0.7, 0.5]]
target = [[0.9, 1.0], [0.1, 0.4]]
weights = [[1.0, 0.0], [1.0, 1.0]]
np.testing.assert_allclose(loss._compute_loss(pred, target, weights),
[[0.8, 0.0], [0.6, 0.1]],
rtol=1e-6)
def build_meta_arch(**override_params):
"""Builds the DeepMAC meta architecture."""
params = dict(predict_full_resolution_masks=False,
use_instance_embedding=True,
mask_num_subsamples=-1,
network_type='hourglass10',
use_xy=True,
pixel_embedding_dim=2,
dice_loss_prediction_probability=False,
color_consistency_threshold=0.5,
use_dice_loss=False,
box_consistency_loss_normalize='normalize_auto',
box_consistency_tightness=False,
task_loss_weight=1.0,
color_consistency_loss_weight=1.0,
box_consistency_loss_weight=1.0,
num_init_channels=8,
dim=8,
allowed_masked_classes_ids=[],
mask_size=16,
postprocess_crop_size=128,
max_roi_jitter_ratio=0.0,
roi_jitter_mode='random',
color_consistency_dilation=2,
color_consistency_warmup_steps=0,
color_consistency_warmup_start=0)
params.update(override_params)
feature_extractor = DummyFeatureExtractor(channel_means=(1.0, 2.0, 3.0),
channel_stds=(10., 20., 30.),
bgr_ordering=False,
num_feature_outputs=2,
stride=4)
image_resizer_fn = functools.partial(preprocessor.resize_to_range,
min_dimension=128,
max_dimension=128,
pad_to_max_dimesnion=True)
object_center_params = center_net_meta_arch.ObjectCenterParams(
classification_loss=losses.WeightedSigmoidClassificationLoss(),
object_center_loss_weight=1.0,
min_box_overlap_iou=1.0,
max_box_predictions=5,
use_labeled_classes=False)
use_dice_loss = params.pop('use_dice_loss')
dice_loss_prediction_prob = params.pop('dice_loss_prediction_probability')
if use_dice_loss:
classification_loss = losses.WeightedDiceClassificationLoss(
squared_normalization=False,
is_prediction_probability=dice_loss_prediction_prob)
else:
classification_loss = losses.WeightedSigmoidClassificationLoss()
deepmac_params = deepmac_meta_arch.DeepMACParams(
classification_loss=classification_loss, **params)
object_detection_params = center_net_meta_arch.ObjectDetectionParams(
localization_loss=losses.L1LocalizationLoss(),
offset_loss_weight=1.0,
scale_loss_weight=0.1)
return deepmac_meta_arch.DeepMACMetaArch(
is_training=True,
add_summaries=False,
num_classes=6,
feature_extractor=feature_extractor,
object_center_params=object_center_params,
deepmac_params=deepmac_params,
object_detection_params=object_detection_params,
image_resizer_fn=image_resizer_fn)
