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ultralytics 8.0.50 AMP check and YOLOv5u YAMLs (#1263)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Troy <wudashuo@vip.qq.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Huijae Lee <46982469+ZeroAct@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-03-06 11:39:26 +01:00
committed by GitHub
parent 3861e6c82a
commit f0d8e4718b
29 changed files with 440 additions and 83 deletions
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9
ultralytics/yolo/v8/classify/predict.py
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@ -14,14 +14,13 @@ class ClassificationPredictor(BasePredictor):
return Annotator(img, example=str(self.model.names), pil=True)
def preprocess(self, img):
img = (img if isinstance(img, torch.Tensor) else torch.Tensor(img)).to(self.model.device)
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
return img
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
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_imgs):
results = []
for i, pred in enumerate(preds):
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
path, _, _, _, _ = self.batch
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))

6
ultralytics/yolo/v8/detect/predict.py
View File

@ -14,12 +14,12 @@ class DetectionPredictor(BasePredictor):
return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))
def preprocess(self, img):
img = torch.from_numpy(img).to(self.model.device)
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
img /= 255 # 0 - 255 to 0.0 - 1.0
return img
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_imgs):
preds = ops.non_max_suppression(preds,
self.args.conf,
self.args.iou,
@ -29,7 +29,7 @@ class DetectionPredictor(BasePredictor):
results = []
for i, pred in enumerate(preds):
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
shape = orig_img.shape
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
path, _, _, _, _ = self.batch

4
ultralytics/yolo/v8/segment/predict.py
View File

@ -10,7 +10,7 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
class SegmentationPredictor(DetectionPredictor):
def postprocess(self, preds, img, orig_img):
def postprocess(self, preds, img, orig_imgs):
# TODO: filter by classes
p = ops.non_max_suppression(preds[0],
self.args.conf,
@ -22,7 +22,7 @@ class SegmentationPredictor(DetectionPredictor):
results = []
proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
for i, pred in enumerate(p):
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
shape = orig_img.shape
path, _, _, _, _ = self.batch
img_path = path[i] if isinstance(path, list) else path