xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ultralytics 8.0.56 PyTorch 2.0 support and minor fixes (#1538)

Browse Source 
Co-authored-by: N-Friederich <127681326+N-Friederich@users.noreply.github.com>
Co-authored-by: Uhrendoktor <36703334+Uhrendoktor@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Aman Agarwal <amanag.11@gmail.com>
Co-authored-by: ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com>
Co-authored-by: Nadav Eidelstein <30617226+nodeav@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-03-24 00:22:20 +01:00
committed by GitHub
parent 8c8c43922c
commit 28e48be5b6
18 changed files with 149 additions and 88 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
ultralytics/yolo/v8/detect/train.py
View File

@ -134,6 +134,7 @@ class Loss:
else:
i = targets[:, 0] # image index
_, counts = i.unique(return_counts=True)
counts = counts.to(dtype=torch.int32)
out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
for j in range(batch_size):
matches = i == j

13
ultralytics/yolo/v8/detect/val.py
View File

@ -108,8 +108,9 @@ class DetectionValidator(BaseValidator):
# Save
if self.args.save_json:
self.pred_to_json(predn, batch['im_file'][si])
# if self.args.save_txt:
# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
if self.args.save_txt:
file = self.save_dir / 'labels' / f'{Path(batch["im_file"][si]).stem}.txt'
self.save_one_txt(predn, self.args.save_conf, shape, file)
def finalize_metrics(self, *args, **kwargs):
self.metrics.speed = self.speed
@ -197,6 +198,14 @@ class DetectionValidator(BaseValidator):
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names) # pred
def save_one_txt(self, predn, save_conf, shape, file):
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(file, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
def pred_to_json(self, predn, filename):
stem = Path(filename).stem
image_id = int(stem) if stem.isnumeric() else stem

2
ultralytics/yolo/v8/segment/train.py
View File

@ -79,7 +79,7 @@ class SegLoss(Loss):
# targets
try:
batch_idx = batch['batch_idx'].view(-1, 1)
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes'].to(dtype)), 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)