Update README.md
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README.md
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@@ -141,13 +141,15 @@ def process_imgs(imgs: torch.Tensor, tile_ids: list[str]) -> torch.Tensor:
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batch_stack = np.concatenate([batch_tiles_coordinates, batch_features], axis=1)
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return batch_stack
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def save_features(
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"""Save features to disk."""
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logger.success(f"Successfully saved features for slide: {slide_id}")
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# Create the dataset and dataloader without actually loading the files to disk (`streaming=True`)
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batch_stack = np.concatenate([batch_tiles_coordinates, batch_features], axis=1)
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return batch_stack
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def save_features(slide_features: list[np.ndarray], slide_id: str):
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"""Save features to disk."""
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slide_features_export_dir = Path(export_dir / slide_id)
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slide_features_export_path = slides_features_export_dir / "features.npy"
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slide_features_export_dir.mkdir(exist_ok=True, parents=True)
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output_slide_features = np.concatenate(slide_features, axis=0).astype(np.float32)
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slide_num_tiles = output_slide_features.shape[0]
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assert slide_num_tiles == num_tiles, f"Output features for slide {slide_id} contains {slide_num_tiles} < {num_tiles}."
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np.save(slides_features_export_path, output_slide_features)
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logger.success(f"Successfully saved features for slide: {slide_id}")
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# Create the dataset and dataloader without actually loading the files to disk (`streaming=True`)
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