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from ultralytics.models.yolo.detect import DetectionPredictor |
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import torch |
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from ultralytics.utils import ops |
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from ultralytics.engine.results import Results |
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class YOLOv10DetectionPredictor(DetectionPredictor): |
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def postprocess(self, preds, img, orig_imgs): |
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if isinstance(preds, dict): |
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preds = preds["one2one"] |
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if isinstance(preds, (list, tuple)): |
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preds = preds[0] |
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if preds.shape[-1] == 6: |
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pass |
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else: |
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preds = preds.transpose(-1, -2) |
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bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1]-4) |
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bboxes = ops.xywh2xyxy(bboxes) |
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preds = torch.cat([bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1) |
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mask = preds[..., 4] > self.args.conf |
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if self.args.classes is not None: |
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mask = mask & (preds[..., 5:6] == torch.tensor(self.args.classes, device=preds.device).unsqueeze(0)).any(2) |
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preds = [p[mask[idx]] for idx, p in enumerate(preds)] |
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if not isinstance(orig_imgs, list): |
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) |
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results = [] |
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for i, pred in enumerate(preds): |
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orig_img = orig_imgs[i] |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) |
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img_path = self.batch[0][i] |
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results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred)) |
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return results |
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