ultralytics 8.2.59 use Results.save_txt for validation (#14496)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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7 changed files with 83 additions and 32 deletions
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@ -160,8 +160,12 @@ class DetectionValidator(BaseValidator):
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if self.args.save_json:
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self.pred_to_json(predn, batch["im_file"][si])
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if self.args.save_txt:
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file = self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt'
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self.save_one_txt(predn, self.args.save_conf, pbatch["ori_shape"], file)
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self.save_one_txt(
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predn,
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self.args.save_conf,
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pbatch["ori_shape"],
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self.save_dir / "labels" / f'{Path(batch["im_file"][si]).stem}.txt',
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)
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def finalize_metrics(self, *args, **kwargs):
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"""Set final values for metrics speed and confusion matrix."""
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@ -261,12 +265,14 @@ class DetectionValidator(BaseValidator):
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def save_one_txt(self, predn, save_conf, shape, file):
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"""Save YOLO detections to a txt file in normalized coordinates in a specific format."""
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gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
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for *xyxy, conf, cls in predn.tolist():
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xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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with open(file, "a") as f:
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f.write(("%g " * len(line)).rstrip() % line + "\n")
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from ultralytics.engine.results import Results
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Results(
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np.zeros((shape[0], shape[1]), dtype=np.uint8),
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path=None,
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names=self.names,
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boxes=predn[:, :6],
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).save_txt(file, save_conf=save_conf)
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def pred_to_json(self, predn, filename):
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"""Serialize YOLO predictions to COCO json format."""
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