def _build_rpn_losses(
self, outputs: Mapping[str, Any],
labels: Mapping[str, Any]) -> Tuple[tf.Tensor, tf.Tensor]:
"""Build losses for Region Proposal Network (RPN)."""
rpn_score_loss_fn = maskrcnn_losses.RpnScoreLoss(
tf.shape(outputs['box_outputs'])[1])
rpn_box_loss_fn = maskrcnn_losses.RpnBoxLoss(
self.task_config.losses.rpn_huber_loss_delta)
rpn_score_loss = tf.reduce_mean(
rpn_score_loss_fn(outputs['rpn_scores'],
labels['rpn_score_targets']))
rpn_box_loss = tf.reduce_mean(
rpn_box_loss_fn(outputs['rpn_boxes'], labels['rpn_box_targets']))
return rpn_score_loss, rpn_box_loss
def build_losses(self,
outputs: Mapping[str, Any],
labels: Mapping[str, Any],
aux_losses: Optional[Any] = None):
"""Build Mask R-CNN losses."""
params = self.task_config
cascade_ious = params.model.roi_sampler.cascade_iou_thresholds
rpn_score_loss_fn = maskrcnn_losses.RpnScoreLoss(
tf.shape(outputs['box_outputs'])[1])
rpn_box_loss_fn = maskrcnn_losses.RpnBoxLoss(
params.losses.rpn_huber_loss_delta)
rpn_score_loss = tf.reduce_mean(
rpn_score_loss_fn(outputs['rpn_scores'],
labels['rpn_score_targets']))
rpn_box_loss = tf.reduce_mean(
rpn_box_loss_fn(outputs['rpn_boxes'], labels['rpn_box_targets']))
frcnn_cls_loss_fn = maskrcnn_losses.FastrcnnClassLoss()
frcnn_box_loss_fn = maskrcnn_losses.FastrcnnBoxLoss(
params.losses.frcnn_huber_loss_delta,
params.model.detection_head.class_agnostic_bbox_pred)
# Final cls/box losses are computed as an average of all detection heads.
frcnn_cls_loss = 0.0
frcnn_box_loss = 0.0
num_det_heads = 1 if cascade_ious is None else 1 + len(cascade_ious)
for cas_num in range(num_det_heads):
frcnn_cls_loss_i = tf.reduce_mean(
frcnn_cls_loss_fn(
outputs['class_outputs_{}'.
format(cas_num) if cas_num else 'class_outputs'],
outputs['class_targets_{}'.
format(cas_num) if cas_num else 'class_targets']))
frcnn_box_loss_i = tf.reduce_mean(
frcnn_box_loss_fn(
outputs['box_outputs_{}'.
format(cas_num) if cas_num else 'box_outputs'],
outputs['class_targets_{}'.
format(cas_num) if cas_num else 'class_targets'],
outputs['box_targets_{}'.
format(cas_num) if cas_num else 'box_targets']))
frcnn_cls_loss += frcnn_cls_loss_i
frcnn_box_loss += frcnn_box_loss_i
frcnn_cls_loss /= num_det_heads
frcnn_box_loss /= num_det_heads
if params.model.include_mask:
mask_loss_fn = maskrcnn_losses.MaskrcnnLoss()
mask_class_targets = outputs['mask_class_targets']
if self._task_config.allowed_mask_class_ids is not None:
# Classes with ID=0 are ignored by mask_loss_fn in loss computation.
mask_class_targets = zero_out_disallowed_class_ids(
mask_class_targets,
self._task_config.allowed_mask_class_ids)
mask_loss = tf.reduce_mean(
mask_loss_fn(outputs['mask_outputs'], outputs['mask_targets'],
mask_class_targets))
else:
mask_loss = 0.0
model_loss = (params.losses.rpn_score_weight * rpn_score_loss +
params.losses.rpn_box_weight * rpn_box_loss +
params.losses.frcnn_class_weight * frcnn_cls_loss +
params.losses.frcnn_box_weight * frcnn_box_loss +
params.losses.mask_weight * mask_loss)
total_loss = model_loss
if aux_losses:
reg_loss = tf.reduce_sum(aux_losses)
total_loss = model_loss + reg_loss
total_loss = params.losses.loss_weight * total_loss
losses = {
'total_loss': total_loss,
'rpn_score_loss': rpn_score_loss,
'rpn_box_loss': rpn_box_loss,
'frcnn_cls_loss': frcnn_cls_loss,
'frcnn_box_loss': frcnn_box_loss,
'mask_loss': mask_loss,
'model_loss': model_loss,
}
return losses