ultralytics 8.0.50
AMP check and YOLOv5u YAMLs (#1263)
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>
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@ -14,12 +14,12 @@ class DetectionPredictor(BasePredictor):
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return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))
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def preprocess(self, img):
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img = torch.from_numpy(img).to(self.model.device)
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img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
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img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
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img /= 255 # 0 - 255 to 0.0 - 1.0
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return img
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def postprocess(self, preds, img, orig_img):
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def postprocess(self, preds, img, orig_imgs):
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preds = ops.non_max_suppression(preds,
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self.args.conf,
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self.args.iou,
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@ -29,7 +29,7 @@ class DetectionPredictor(BasePredictor):
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
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shape = orig_img.shape
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
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path, _, _, _, _ = self.batch
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