ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -37,7 +37,7 @@ class HungarianMatcher(nn.Module):
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"""
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super().__init__()
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if cost_gain is None:
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cost_gain = {'class': 1, 'bbox': 5, 'giou': 2, 'mask': 1, 'dice': 1}
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cost_gain = {"class": 1, "bbox": 5, "giou": 2, "mask": 1, "dice": 1}
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self.cost_gain = cost_gain
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self.use_fl = use_fl
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self.with_mask = with_mask
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@ -86,7 +86,7 @@ class HungarianMatcher(nn.Module):
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# Compute the classification cost
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pred_scores = pred_scores[:, gt_cls]
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if self.use_fl:
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neg_cost_class = (1 - self.alpha) * (pred_scores ** self.gamma) * (-(1 - pred_scores + 1e-8).log())
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neg_cost_class = (1 - self.alpha) * (pred_scores**self.gamma) * (-(1 - pred_scores + 1e-8).log())
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pos_cost_class = self.alpha * ((1 - pred_scores) ** self.gamma) * (-(pred_scores + 1e-8).log())
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cost_class = pos_cost_class - neg_cost_class
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else:
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@ -99,9 +99,11 @@ class HungarianMatcher(nn.Module):
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cost_giou = 1.0 - bbox_iou(pred_bboxes.unsqueeze(1), gt_bboxes.unsqueeze(0), xywh=True, GIoU=True).squeeze(-1)
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# Final cost matrix
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C = self.cost_gain['class'] * cost_class + \
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self.cost_gain['bbox'] * cost_bbox + \
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self.cost_gain['giou'] * cost_giou
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C = (
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self.cost_gain["class"] * cost_class
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+ self.cost_gain["bbox"] * cost_bbox
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+ self.cost_gain["giou"] * cost_giou
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)
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# Compute the mask cost and dice cost
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if self.with_mask:
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C += self._cost_mask(bs, gt_groups, masks, gt_mask)
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@ -111,10 +113,11 @@ class HungarianMatcher(nn.Module):
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C = C.view(bs, nq, -1).cpu()
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indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(gt_groups, -1))]
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gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
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# (idx for queries, idx for gt)
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return [(torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
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for k, (i, j) in enumerate(indices)]
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gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0) # (idx for queries, idx for gt)
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return [
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(torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
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for k, (i, j) in enumerate(indices)
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]
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# This function is for future RT-DETR Segment models
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# def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
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@ -147,14 +150,9 @@ class HungarianMatcher(nn.Module):
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# return C
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def get_cdn_group(batch,
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num_classes,
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num_queries,
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class_embed,
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num_dn=100,
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cls_noise_ratio=0.5,
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box_noise_scale=1.0,
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training=False):
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def get_cdn_group(
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batch, num_classes, num_queries, class_embed, num_dn=100, cls_noise_ratio=0.5, box_noise_scale=1.0, training=False
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):
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"""
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Get contrastive denoising training group. This function creates a contrastive denoising training group with positive
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and negative samples from the ground truths (gt). It applies noise to the class labels and bounding box coordinates,
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@ -180,7 +178,7 @@ def get_cdn_group(batch,
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if (not training) or num_dn <= 0:
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return None, None, None, None
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gt_groups = batch['gt_groups']
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gt_groups = batch["gt_groups"]
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total_num = sum(gt_groups)
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max_nums = max(gt_groups)
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if max_nums == 0:
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@ -190,9 +188,9 @@ def get_cdn_group(batch,
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num_group = 1 if num_group == 0 else num_group
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# Pad gt to max_num of a batch
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bs = len(gt_groups)
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gt_cls = batch['cls'] # (bs*num, )
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gt_bbox = batch['bboxes'] # bs*num, 4
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b_idx = batch['batch_idx']
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gt_cls = batch["cls"] # (bs*num, )
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gt_bbox = batch["bboxes"] # bs*num, 4
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b_idx = batch["batch_idx"]
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# Each group has positive and negative queries.
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dn_cls = gt_cls.repeat(2 * num_group) # (2*num_group*bs*num, )
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@ -245,16 +243,21 @@ def get_cdn_group(batch,
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# Reconstruct cannot see each other
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for i in range(num_group):
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if i == 0:
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attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), max_nums * 2 * (i + 1):num_dn] = True
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attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), max_nums * 2 * (i + 1) : num_dn] = True
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if i == num_group - 1:
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attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), :max_nums * i * 2] = True
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attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), : max_nums * i * 2] = True
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else:
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attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), max_nums * 2 * (i + 1):num_dn] = True
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attn_mask[max_nums * 2 * i:max_nums * 2 * (i + 1), :max_nums * 2 * i] = True
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attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), max_nums * 2 * (i + 1) : num_dn] = True
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attn_mask[max_nums * 2 * i : max_nums * 2 * (i + 1), : max_nums * 2 * i] = True
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dn_meta = {
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'dn_pos_idx': [p.reshape(-1) for p in pos_idx.cpu().split(list(gt_groups), dim=1)],
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'dn_num_group': num_group,
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'dn_num_split': [num_dn, num_queries]}
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"dn_pos_idx": [p.reshape(-1) for p in pos_idx.cpu().split(list(gt_groups), dim=1)],
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"dn_num_group": num_group,
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"dn_num_split": [num_dn, num_queries],
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}
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return padding_cls.to(class_embed.device), padding_bbox.to(class_embed.device), attn_mask.to(
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class_embed.device), dn_meta
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return (
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padding_cls.to(class_embed.device),
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padding_bbox.to(class_embed.device),
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attn_mask.to(class_embed.device),
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dn_meta,
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)
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