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https://github.com/THU-MIG/yolov10.git
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46 lines
2.1 KiB
Python
46 lines
2.1 KiB
Python
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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import torch.nn.functional as F
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class YOLOv10SegPredictor(DetectionPredictor):
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def postprocess(self, preds, img, orig_imgs):
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coef,proto = None,None
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if isinstance(preds, dict):
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coef = preds['coef']
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proto = preds['proto']
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preds = preds["one2one"]
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if isinstance(preds, (list, tuple)):
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preds = preds[0] # [1,5,6006] coef[1,32,6006] proto[1,32,104,176]
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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) # [1,6006,5]
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coef = coef.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, scores, labels,segmask = ops.v10segpostprocess([preds,coef,proto], 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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segmask = [p[mask[idx]] for idx, p in enumerate(segmask)]
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if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a 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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seg = segmask[i]
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cc,hh,ww = seg.shape
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seg = F.interpolate(seg[None], (hh*4, ww*4), mode="bilinear", align_corners=False)[0].gt_(0)
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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,masks=seg))
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return results
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