ultralytics 8.1.9 replace .size(0) with .shape[0] (#7957)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/utils/metrics.py

@@ -331,7 +331,7 @@ class ConfusionMatrix:
             gt_bboxes (Array[M, 4]): Ground truth bounding boxes with xyxy format.
             gt_cls (Array[M]): The class labels.
         """
-        if gt_cls.size(0) == 0:  # Check if labels is empty
+        if gt_cls.shape[0] == 0:  # Check if labels is empty
             if detections is not None:
                 detections = detections[detections[:, 4] > self.conf]
                 detection_classes = detections[:, 5].int()

ultralytics/utils/tal.py

@@ -56,8 +56,8 @@ class TaskAlignedAssigner(nn.Module):
             fg_mask (Tensor): shape(bs, num_total_anchors)
             target_gt_idx (Tensor): shape(bs, num_total_anchors)
         """
-        self.bs = pd_scores.size(0)
-        self.n_max_boxes = gt_bboxes.size(1)
+        self.bs = pd_scores.shape[0]
+        self.n_max_boxes = gt_bboxes.shape[1]
 
         if self.n_max_boxes == 0:
             device = gt_bboxes.device

ultralytics/utils/torch_utils.py

@@ -190,7 +190,7 @@ def fuse_conv_and_bn(conv, bn):
     fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
 
     # Prepare spatial bias
-    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_conv = torch.zeros(conv.weight.shape[0], device=conv.weight.device) if conv.bias is None else conv.bias
     b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
     fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
 
@@ -221,7 +221,7 @@ def fuse_deconv_and_bn(deconv, bn):
     fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))
 
     # Prepare spatial bias
-    b_conv = torch.zeros(deconv.weight.size(1), device=deconv.weight.device) if deconv.bias is None else deconv.bias
+    b_conv = torch.zeros(deconv.weight.shape[1], device=deconv.weight.device) if deconv.bias is None else deconv.bias
     b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
     fuseddconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)