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ultralytics 8.2.42 NVIDIA TensorRT 10 default (#13943)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: DeepDiver <zhaoxu1015@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher 2024-06-24 21:05:09 +02:00 • committed by GitHub
parent 1d7eeae325
commit 3bb0c5afa3
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GPG key ID: B5690EEEBB952194
10 changed files with 23 additions and 22 deletions
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8
ultralytics/utils/loss.py
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@ -214,7 +214,7 @@ class v8DetectionLoss:
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
# Pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
@ -280,7 +280,7 @@ class v8SegmentationLoss(v8DetectionLoss):
targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
except RuntimeError as e:
raise TypeError(
"ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n"
@ -467,7 +467,7 @@ class v8PoseLoss(v8DetectionLoss):
targets = torch.cat((batch_idx, batch["cls"].view(-1, 1), batch["bboxes"]), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
# Pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
@ -652,7 +652,7 @@ class v8OBBLoss(v8DetectionLoss):
targets = targets[(rw >= 2) & (rh >= 2)] # filter rboxes of tiny size to stabilize training
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 5), 2) # cls, xywhr
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0.0)
except RuntimeError as e:
raise TypeError(
"ERROR ❌ OBB dataset incorrectly formatted or not a OBB dataset.\n"