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36 lines
1.4 KiB
36 lines
1.4 KiB
2 years ago
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import torch
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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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from ultralytics.yolo.utils.ops import xyxy2xywh
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class NASPredictor(BasePredictor):
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def postprocess(self, preds_in, img, orig_imgs):
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"""Postprocesses predictions and returns a list of Results objects."""
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# Cat boxes and class scores
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boxes = xyxy2xywh(preds_in[0][0])
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preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
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preds = ops.non_max_suppression(preds,
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self.args.conf,
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self.args.iou,
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agnostic=self.args.agnostic_nms,
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max_det=self.args.max_det,
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classes=self.args.classes)
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
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if not isinstance(orig_imgs, torch.Tensor):
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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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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