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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import torch
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from ultralytics.yolo.data.augment import LetterBox
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from ultralytics.yolo.engine.predictor import BasePredictor
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from ultralytics.yolo.engine.results import Results
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from ultralytics.yolo.utils import ops
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class RTDETRPredictor(BasePredictor):
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def postprocess(self, preds, img, orig_imgs):
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"""Postprocess predictions and returns a list of Results objects."""
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bboxes, scores = preds[:2] # (1, bs, 300, 4), (1, bs, 300, nc)
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bboxes, scores = bboxes.squeeze_(0), scores.squeeze_(0)
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results = []
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for i, bbox in enumerate(bboxes): # (300, 4)
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bbox = ops.xywh2xyxy(bbox)
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score, cls = scores[i].max(-1, keepdim=True) # (300, 1)
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idx = score.squeeze(-1) > self.args.conf # (300, )
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if self.args.classes is not None:
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idx = (cls == torch.tensor(self.args.classes, device=cls.device)).any(1) & idx
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pred = torch.cat([bbox, score, cls], dim=-1)[idx] # filter
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
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oh, ow = orig_img.shape[:2]
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if not isinstance(orig_imgs, torch.Tensor):
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pred[..., [0, 2]] *= ow
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pred[..., [1, 3]] *= oh
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path = self.batch[0]
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img_path = path[i] if isinstance(path, list) else path
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results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
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return results
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def pre_transform(self, im):
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"""Pre-transform input image before inference.
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Args:
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im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
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Return: A list of transformed imgs.
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"""
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# The size must be square(640) and scaleFilled.
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return [LetterBox(self.imgsz, auto=False, scaleFill=True)(image=x) for x in im]
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