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- # Ultralytics YOLO 🚀, AGPL-3.0 license
- import torch
- from ultralytics.engine.predictor import BasePredictor
- from ultralytics.engine.results import Results
- from ultralytics.utils import ops
- class NASPredictor(BasePredictor):
- def postprocess(self, preds_in, img, orig_imgs):
- """Postprocess predictions and returns a list of Results objects."""
- # Cat boxes and class scores
- boxes = ops.xyxy2xywh(preds_in[0][0])
- preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
- preds = ops.non_max_suppression(preds,
- self.args.conf,
- self.args.iou,
- agnostic=self.args.agnostic_nms,
- max_det=self.args.max_det,
- classes=self.args.classes)
- results = []
- is_list = isinstance(orig_imgs, list) # input images are a list, not a torch.Tensor
- for i, pred in enumerate(preds):
- orig_img = orig_imgs[i] if is_list else orig_imgs
- if is_list:
- 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
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