def _build_classification_loss(loss_config):
"""Builds a classification loss based on the loss config.
Args:
loss_config: A losses_pb2.ClassificationLoss object.
Returns:
Loss based on the config.
Raises:
ValueError: On invalid loss_config.
"""
if not isinstance(loss_config, losses_pb2.ClassificationLoss):
raise ValueError(
'loss_config not of type losses_pb2.ClassificationLoss.')
loss_type = loss_config.WhichOneof('classification_loss')
if loss_type == 'weighted_sigmoid':
return losses.WeightedSigmoidClassificationLoss()
elif loss_type == 'weighted_sigmoid_focal':
config = loss_config.weighted_sigmoid_focal
alpha = None
if config.HasField('alpha'):
alpha = config.alpha
return losses.SigmoidFocalClassificationLoss(gamma=config.gamma,
alpha=alpha)
elif loss_type == 'weighted_softmax':
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
elif loss_type == 'weighted_logits_softmax':
config = loss_config.weighted_logits_softmax
return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
logit_scale=config.logit_scale)
elif loss_type == 'bootstrapped_sigmoid':
config = loss_config.bootstrapped_sigmoid
return losses.BootstrappedSigmoidClassificationLoss(
alpha=config.alpha,
bootstrap_type=('hard' if config.hard_bootstrap else 'soft'))
elif loss_type == 'penalty_reduced_logistic_focal_loss':
config = loss_config.penalty_reduced_logistic_focal_loss
return losses.PenaltyReducedLogisticFocalLoss(alpha=config.alpha,
beta=config.beta)
elif loss_type == 'weighted_dice_classification_loss':
config = loss_config.weighted_dice_classification_loss
return losses.WeightedDiceClassificationLoss(
squared_normalization=config.squared_normalization,
is_prediction_probability=config.is_prediction_probability)
else:
raise ValueError('Empty loss config.')
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 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)