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