Class PatchDataset

monai/data/grid_dataset.py:366–440

Yields patches from data read from an image dataset. The patches are generated by a user-specified callable `patch_func`, and are optionally post-processed by `transform`. For example, to generate random patch samples from an image dataset: .. code-block:: python impor

class PatchDataset(IterableDataset):
    """
    Yields patches from data read from an image dataset.
    The patches are generated by a user-specified callable `patch_func`,
    and are optionally post-processed by `transform`.
    For example, to generate random patch samples from an image dataset:

    .. code-block:: python

        import numpy as np

        from monai.data import PatchDataset, DataLoader
        from monai.transforms import RandSpatialCropSamples, RandShiftIntensity

        # image dataset
        images = [np.arange(16, dtype=float).reshape(1, 4, 4),
                  np.arange(16, dtype=float).reshape(1, 4, 4)]
        # image patch sampler
        n_samples = 5
        sampler = RandSpatialCropSamples(roi_size=(3, 3), num_samples=n_samples,
                                         random_center=True, random_size=False)
        # patch-level intensity shifts
        patch_intensity = RandShiftIntensity(offsets=1.0, prob=1.0)
        # construct the patch dataset
        ds = PatchDataset(dataset=images,
                          patch_func=sampler,
                          samples_per_image=n_samples,
                          transform=patch_intensity)

        # use the patch dataset, length: len(images) x samplers_per_image
        print(len(ds))

        >>> 10

        for item in DataLoader(ds, batch_size=2, shuffle=True, num_workers=2):
            print(item.shape)

        >>> torch.Size([2, 1, 3, 3])

    """

    def __init__(
        self, data: Sequence, patch_func: Callable, samples_per_image: int = 1, transform: Callable | None = None
    ) -> None:
        """
        Args:
            data: an image dataset to extract patches from.
            patch_func: converts an input image (item from dataset) into a sequence of image patches.
                patch_func(dataset[idx]) must return a sequence of patches (length `samples_per_image`).
            samples_per_image: `patch_func` should return a sequence of `samples_per_image` elements.
            transform: transform applied to each patch.
        """
        super().__init__(data=data, transform=None)

        self.patch_func = patch_func
        if samples_per_image <= 0:
            raise ValueError("sampler_per_image must be a positive integer.")
        self.samples_per_image = int(samples_per_image)