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ultralytics 8.0.76 minor fixes and improvements (#2004)

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Co-authored-by: Seungtaek Kim <seungtaek.kim.94@gmail.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: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Ercalvez <45692523+Ercalvez@users.noreply.github.com>
Co-authored-by: Erwan CALVEZ <ecalvez@enib.fr>
This commit is contained in:
Glenn Jocher
2023-04-13 20:19:42 +02:00
committed by GitHub
parent 48c4483795
commit 4916014af2
15 changed files with 63 additions and 26 deletions
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2
ultralytics/yolo/engine/model.py
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@ -116,7 +116,7 @@ class YOLO:
@staticmethod
def is_hub_model(model):
return any((
model.startswith('https://hub.ultralytics.com/models/'),
model.startswith('https://hub.ultra'), # i.e. https://hub.ultralytics.com/models/MODEL_ID
[len(x) for x in model.split('_')] == [42, 20], # APIKEY_MODELID
len(model) == 20 and not Path(model).exists() and all(x not in model for x in './\\'))) # MODELID

2
ultralytics/yolo/engine/results.py
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@ -207,7 +207,7 @@ class Results(SimpleClass):
if pred_masks and show_masks:
if img_gpu is None:
img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
img_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.masks.device).permute(
img_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device).permute(
2, 0, 1).flip(0).contiguous() / 255
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in pred_boxes.cls], im_gpu=img_gpu)

4
ultralytics/yolo/engine/trainer.py
View File

@ -632,9 +632,9 @@ def check_amp(model):
def amp_allclose(m, im):
# All close FP32 vs AMP results
a = m(im, device=device, verbose=False)[0].boxes.boxes # FP32 inference
a = m(im, device=device, verbose=False)[0].boxes.data # FP32 inference
with torch.cuda.amp.autocast(True):
b = m(im, device=device, verbose=False)[0].boxes.boxes # AMP inference
b = m(im, device=device, verbose=False)[0].boxes.data # AMP inference
del m
return a.shape == b.shape and torch.allclose(a, b.float(), atol=0.5) # close to 0.5 absolute tolerance