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PyTorch adapter for WSI tile datasets. Provides PytorchTileDataset, a Dataset adapter that wraps WSIDataset for use with DataLoader. Two sampling modes are supported:
  • "sequential" — patches in slide-load order (deterministic).
  • "random" — same patches, but indices are shuffled each epoch.
Both modes visit every patch exactly once per epoch. The data argument accepts three forms:
  • A ready-made WSIDataset.
  • A directory path (str or Path) — all supported WSI files inside it are discovered and loaded.
  • A list of file paths — each path is opened as a slide.
Example: 
>>> from bioptimus.data.wsi.torch_dataset import PytorchTileDataset
>>> ds = PytorchTileDataset(wsi_dataset, sampling="sequential")
>>> ds = PytorchTileDataset("slides/", extractor=ext, sampling="random", seed=0)
>>> loader = DataLoader(ds, batch_size=64, num_workers=4)

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SamplingMode

class SamplingMode(str, Enum)
Patch sampling strategy.
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SEQUENTIAL
Iterate over every patch in slide-load order.
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RANDOM
Shuffle the global patch indices each epoch.
Example: 
>>> mode = SamplingMode("random")
>>> mode == SamplingMode.RANDOM
True

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PytorchTileDataset

class PytorchTileDataset(Dataset)
PyTorch-compatible dataset over WSI tiles with configurable ordering. Accepts a WSIDataset, a directory path, or a list of file paths. When a path or list is given, extractor is required so the slides can be opened and tiled automatically. "sequential" (default) Patches are returned in the order slides were loaded — slide 0 patch 0, slide 0 patch 1, …, slide N patch M. "random" The same set of patches, but the global indices are shuffled. Call shuffle between epochs (or at init) to re-randomise. In both modes len() equals the total patch count, and every patch is visited exactly once per full iteration.
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data
One of: - A WSIDataset instance (used directly). - A str or Path pointing to a directory — all supported WSI files are discovered and loaded. - A list of file paths — each is opened as a slide.
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extractor
Required when data is a path or list of paths. A configured TileExtractor used to tile each slide.
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sampling
"sequential" or "random" (default "sequential").
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transform
Optional callable applied to the np.ndarray (H, W, C) patch.
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seed
Optional RNG seed for reproducible shuffling.
Raises:
  • ValueError — If data is a path/list but extractor is not provided.
  • FileNotFoundError — If a directory path contains no supported WSIs.
Example: 
ds = PytorchTileDataset(wsi_dataset, sampling="sequential")
ds = PytorchTileDataset("slides/", extractor=ext, sampling="random", seed=0)
ds = PytorchTileDataset(["a.svs", "b.svs"], extractor=ext)

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shuffle

def shuffle(seed: Optional[int] = None) -> None
Re-shuffles the global index mapping. Call this between epochs to get a different ordering. Has no effect when sampling is "sequential".
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seed
int | None
Optional RNG seed for reproducibility.
Example: 
>>> ds = PytorchTileDataset(dataset, sampling="random", seed=42)
>>> ds.shuffle(seed=123)  # new ordering for next epoch