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ultralytics 8.0.55 unified YOLOv8 model YAMLs (#1475)

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This commit is contained in:
Glenn Jocher
2023-03-20 13:54:20 +01:00
committed by GitHub
parent 701fba4770
commit 25cc07401f
45 changed files with 203 additions and 896 deletions
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10
ultralytics/yolo/v8/segment/predict.py
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@ -76,17 +76,17 @@ class SegmentationPredictor(DetectionPredictor):
# Write results
for j, d in enumerate(reversed(det)):
cls, conf, id = d.cls.squeeze(), d.conf.squeeze(), None if d.id is None else int(d.id.item())
c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
if self.args.save_txt: # Write to file
seg = mask.segments[len(det) - j - 1].copy().reshape(-1) # reversed mask.segments, (n,2) to (n*2)
line = (cls, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
line = (c, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
with open(f'{self.txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if self.args.save or self.args.save_crop or self.args.show: # Add bbox to image
c = int(cls) # integer class
if self.args.save or self.args.show: # Add bbox to image
name = ('' if id is None else f'id:{id} ') + self.model.names[c]
label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}')
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) if self.args.boxes else None
if self.args.boxes:
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if self.args.save_crop:
save_one_box(d.xyxy,
imc,

16
ultralytics/yolo/v8/segment/train.py
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@ -122,13 +122,15 @@ class SegLoss(Loss):
xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy,
marea) # seg loss
# WARNING: Uncomment lines below in case of Multi-GPU DDP unused gradient errors
# else:
# loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
# else:
# loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy, marea) # seg
# WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
else:
loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
# WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
else:
loss[1] += proto.sum() * 0 + pred_masks.sum() * 0
loss[0] *= self.hyp.box # box gain
loss[1] *= self.hyp.box / batch_size # seg gain