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ultralytics 8.0.89 SAM predict and auto-annotate (#2298)

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This commit is contained in:
Glenn Jocher
2023-04-28 00:36:50 +02:00
committed by GitHub
parent 3e118f6170
commit 243fc4b1fe
44 changed files with 2915 additions and 440 deletions
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8
ultralytics/yolo/v8/classify/predict.py
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@ -9,8 +9,14 @@ from ultralytics.yolo.utils import DEFAULT_CFG, ROOT
class ClassificationPredictor(BasePredictor):
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
super().__init__(cfg, overrides, _callbacks)
self.args.task = 'classify'
def preprocess(self, img):
"""Converts input image to model-compatible data type."""
if not isinstance(img, torch.Tensor):
img = torch.stack([self.transforms(im) for im in img], dim=0)
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
@ -19,7 +25,7 @@ class ClassificationPredictor(BasePredictor):
results = []
for i, pred in enumerate(preds):
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
path, _, _, _, _ = self.batch
path = self.batch[0]
img_path = path[i] if isinstance(path, list) else path
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))