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Update val max_dets=args.max_det=300
(#3051)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@ -223,7 +223,7 @@ class DetectionValidator(BaseValidator):
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def plot_predictions(self, batch, preds, ni):
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def plot_predictions(self, batch, preds, ni):
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"""Plots predicted bounding boxes on input images and saves the result."""
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"""Plots predicted bounding boxes on input images and saves the result."""
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plot_images(batch['img'],
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plot_images(batch['img'],
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*output_to_target(preds, max_det=15),
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*output_to_target(preds, max_det=self.args.max_det),
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paths=batch['im_file'],
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paths=batch['im_file'],
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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names=self.names,
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names=self.names,
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@ -156,7 +156,7 @@ class PoseValidator(DetectionValidator):
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"""Plots predictions for YOLO model."""
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"""Plots predictions for YOLO model."""
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pred_kpts = torch.cat([p[:, 6:].view(-1, *self.kpt_shape)[:15] for p in preds], 0)
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pred_kpts = torch.cat([p[:, 6:].view(-1, *self.kpt_shape)[:15] for p in preds], 0)
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plot_images(batch['img'],
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plot_images(batch['img'],
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*output_to_target(preds, max_det=15),
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*output_to_target(preds, max_det=self.args.max_det),
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kpts=pred_kpts,
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kpts=pred_kpts,
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paths=batch['im_file'],
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paths=batch['im_file'],
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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@ -179,13 +179,14 @@ class SegmentationValidator(DetectionValidator):
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def plot_predictions(self, batch, preds, ni):
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def plot_predictions(self, batch, preds, ni):
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"""Plots batch predictions with masks and bounding boxes."""
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"""Plots batch predictions with masks and bounding boxes."""
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plot_images(batch['img'],
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plot_images(
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*output_to_target(preds[0], max_det=15),
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batch['img'],
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torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
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*output_to_target(preds[0], max_det=15), # not set to self.args.max_det due to slow plotting speed
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paths=batch['im_file'],
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torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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paths=batch['im_file'],
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names=self.names,
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fname=self.save_dir / f'val_batch{ni}_pred.jpg',
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on_plot=self.on_plot) # pred
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names=self.names,
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on_plot=self.on_plot) # pred
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self.plot_masks.clear()
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self.plot_masks.clear()
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def pred_to_json(self, predn, filename, pred_masks):
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def pred_to_json(self, predn, filename, pred_masks):
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