refactor: remove invalid loss_cardinality

models/anchor_detr.py

@@ -168,20 +168,6 @@ def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
  losses['class_error'] = 100 - accuracy(src_logits[idx], target_classes_o)[0]
  return losses
 
- @torch.no_grad()
- def loss_cardinality(self, outputs, targets, indices, num_boxes):
- """ Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes
- This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients
- """
- pred_logits = outputs['pred_logits']
- device = pred_logits.device
- tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device)
- # Count the number of predictions that are NOT "no-object" (which is the last class)
- card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)
- card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())
- losses = {'cardinality_error': card_err}
- return losses
-
  def loss_boxes(self, outputs, targets, indices, num_boxes):
  """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss
  targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]
@@ -247,7 +233,6 @@ def _get_tgt_permutation_idx(self, indices):
  def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
  loss_map = {
  'labels': self.loss_labels,
- 'cardinality': self.loss_cardinality,
  'boxes': self.loss_boxes,
  'masks': self.loss_masks
  }
@@ -382,7 +367,7 @@ def build(args):
  aux_weight_dict.update({k + f'_enc': v for k, v in weight_dict.items()})
  weight_dict.update(aux_weight_dict)
 
- losses = ['labels', 'boxes', 'cardinality']
+ losses = ['labels', 'boxes']
  if args.masks:
  losses += ["masks"]
  # num_classes, matcher, weight_dict, losses, focal_alpha=0.25