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Update ViT ops.py to torch.long (#3508)

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Co-authored-by: Laughing-q <1185102784@qq.com>
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Glenn Jocher 2023-07-04 10:08:22 +02:00 committed by GitHub
parent 8a11eda4a9
commit 31b46bf2b4
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2 changed files with 4 additions and 4 deletions
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4
ultralytics/vit/utils/loss.py
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@ -284,11 +284,11 @@ class RTDETRDetectionLoss(DETRLoss):
idx_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0) idx_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
for i, num_gt in enumerate(gt_groups): for i, num_gt in enumerate(gt_groups):
if num_gt > 0: if num_gt > 0:
gt_idx = torch.arange(end=num_gt, dtype=torch.int32) + idx_groups[i] gt_idx = torch.arange(end=num_gt, dtype=torch.long) + idx_groups[i]
gt_idx = gt_idx.repeat(dn_num_group) gt_idx = gt_idx.repeat(dn_num_group)
assert len(dn_pos_idx[i]) == len(gt_idx), 'Expected the same length, ' assert len(dn_pos_idx[i]) == len(gt_idx), 'Expected the same length, '
f'but got {len(dn_pos_idx[i])} and {len(gt_idx)} respectively.' f'but got {len(dn_pos_idx[i])} and {len(gt_idx)} respectively.'
dn_match_indices.append((dn_pos_idx[i], gt_idx)) dn_match_indices.append((dn_pos_idx[i], gt_idx))
else: else:
dn_match_indices.append((torch.zeros([0], dtype=torch.int32), torch.zeros([0], dtype=torch.int32))) dn_match_indices.append((torch.zeros([0], dtype=torch.long), torch.zeros([0], dtype=torch.long)))
return dn_match_indices return dn_match_indices

4
ultralytics/vit/utils/ops.py
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@ -71,7 +71,7 @@ class HungarianMatcher(nn.Module):
bs, nq, nc = pred_scores.shape bs, nq, nc = pred_scores.shape
if sum(gt_groups) == 0: if sum(gt_groups) == 0:
return [(torch.tensor([], dtype=torch.int32), torch.tensor([], dtype=torch.int32)) for _ in range(bs)] return [(torch.tensor([], dtype=torch.long), torch.tensor([], dtype=torch.long)) for _ in range(bs)]
# We flatten to compute the cost matrices in a batch # We flatten to compute the cost matrices in a batch
# [batch_size * num_queries, num_classes] # [batch_size * num_queries, num_classes]
@ -107,7 +107,7 @@ class HungarianMatcher(nn.Module):
indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(gt_groups, -1))] indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(gt_groups, -1))]
gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0) gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
# (idx for queries, idx for gt) # (idx for queries, idx for gt)
return [(torch.tensor(i, dtype=torch.int32), torch.tensor(j, dtype=torch.int32) + gt_groups[k]) return [(torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
for k, (i, j) in enumerate(indices)] for k, (i, j) in enumerate(indices)]
def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None): def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):