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