def build_faster_rcnn_classification_loss(loss_config):
"""Builds a classification loss for Faster RCNN 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.
"""
loss_type = loss_config.TYPE
config = loss_config.VALUE
# By default, Faster RCNN second stage classifier uses Softmax loss
# with anchor-wise outputs.
return losses.WeightedSoftmaxClassificationLoss(logit_scale=config.logit_scale)
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.
"""
loss_type = loss_config.TYPE
config = loss_config.VALUE
if loss_type == "weighted_sigmoid":
return losses.WeightedSigmoidClassificationLoss()
elif loss_type == "weighted_sigmoid_focal":
if config.alpha > 0:
alpha = config.alpha
else:
alpha = None
return losses.SigmoidFocalClassificationLoss(gamma=config.gamma,
alpha=alpha)
elif loss_type == "weighted_softmax_focal":
if config.alpha > 0:
alpha = config.alpha
else:
alpha = None
return losses.SoftmaxFocalClassificationLoss(gamma=config.gamma,
alpha=alpha)
elif loss_type == "weighted_ghm":
return GHMCLoss(bins=config.bins, momentum=config.momentum)
elif loss_type == "weighted_softmax":
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
elif loss_type == "bootstrapped_sigmoid":
return losses.BootstrappedSigmoidClassificationLoss(
alpha=config.alpha,
bootstrap_type=("hard" if config.hard_bootstrap else "soft"),
)
raise ValueError("Empty loss config.")