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Split transform (#4153)
* Redesign whole slide image reading (#4107) * Redesign BaseWSIReader, WSIReader, CuCIMWSIReader Signed-off-by: Behrooz <[email protected]> * Add unittests for WSIReader Signed-off-by: Behrooz <[email protected]> * Add image mode for output validation Signed-off-by: Behrooz <[email protected]> * Update docs Signed-off-by: Behrooz <[email protected]> * Update references to new WSIReader Signed-off-by: Behrooz <[email protected]> * Remove legacy WSIReader Signed-off-by: Behrooz <[email protected]> * Update unittests Signed-off-by: Behrooz <[email protected]> * Update docs Signed-off-by: Behrooz <[email protected]> * sort imports Signed-off-by: Behrooz <[email protected]> * Clean up imports Signed-off-by: Behrooz <[email protected]> * Update docstrings Signed-off-by: Behrooz <[email protected]> * Update docs and docstrings Signed-off-by: Behrooz <[email protected]> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix a typo Signed-off-by: Behrooz <[email protected]> * Remove redundant checking Signed-off-by: Behrooz <[email protected]> * Update read and other methods Signed-off-by: Behrooz <[email protected]> * Update wsireader to support multi image and update docstrings Signed-off-by: Behrooz <[email protected]> * Make workaround for CuImage objects Signed-off-by: Behrooz <[email protected]> * Add unittests for multi image reading Signed-off-by: Behrooz <[email protected]> * Update a note about cucim Signed-off-by: Behrooz <[email protected]> * Update type hints and docstrings Signed-off-by: Behrooz <[email protected]> * Implement Split transform Signed-off-by: Behrooz <[email protected]> * Add unittests Signed-off-by: Behrooz <[email protected]> * Update formatting Signed-off-by: Behrooz <[email protected]> * Implement SplitDict Signed-off-by: Behrooz <[email protected]> * Add unittests for SplitDict Signed-off-by: Behrooz <[email protected]> * Add docs Signed-off-by: Behrooz <[email protected]> * Remove images from docs Signed-off-by: Behrooz <[email protected]> * Address all comments Signed-off-by: Behrooz <[email protected]> * Add example and size check Signed-off-by: Behrooz <[email protected]> * Update docs Signed-off-by: Behrooz <[email protected]> * Revert references to new wsireader Signed-off-by: Behrooz <[email protected]> * Add missing comma Signed-off-by: Behrooz <[email protected]>
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‎docs/source/transforms.rst‎

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@@ -737,6 +737,13 @@ Spatial
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:members:
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:special-members: __call__
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`GridSplit`
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"""""""""""
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.. autoclass:: GridSplit
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:members:
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:special-members: __call__
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Smooth Field
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^^^^^^^^^^^^
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:members:
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:special-members: __call__
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`GridSplitd`
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""""""""""""
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.. autoclass:: GridSplitd
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:members:
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:special-members: __call__
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`RandRotate90d`
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"""""""""""""""
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.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/RandRotate90d.png

‎monai/transforms/__init__.py‎

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AffineGrid,
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Flip,
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GridDistortion,
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GridSplit,
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Orientation,
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Rand2DElastic,
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Rand3DElastic,
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GridDistortiond,
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GridDistortionD,
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GridDistortionDict,
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GridSplitd,
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GridSplitD,
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GridSplitDict,
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Orientationd,
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OrientationD,
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OrientationDict,

‎monai/transforms/spatial/array.py‎

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import numpy as np
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import torch
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from numpy.lib.stride_tricks import as_strided
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from monai.config import USE_COMPILED, DtypeLike
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from monai.config.type_definitions import NdarrayOrTensor
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"Orientation",
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"Flip",
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"GridDistortion",
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"GridSplit",
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"Resize",
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"Rotate",
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"Zoom",
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if not self._do_transform:
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return img
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return self.grid_distortion(img, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode)
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class GridSplit(Transform):
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"""
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Split the image into patches based on the provided grid in 2D.
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Args:
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grid: a tuple define the shape of the grid upon which the image is split. Defaults to (2, 2)
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size: a tuple or an integer that defines the output patch sizes.
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If it's an integer, the value will be repeated for each dimension.
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The default is None, where the patch size will be inferred from the grid shape.
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Example:
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Given an image (torch.Tensor or numpy.ndarray) with size of (3, 10, 10) and a grid of (2, 2),
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it will return a Tensor or array with the size of (4, 3, 5, 5).
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Here, if the `size` is provided, the returned shape will be (4, 3, size, size)
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Note: This transform currently support only image with two spatial dimensions.
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"""
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backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
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def __init__(self, grid: Tuple[int, int] = (2, 2), size: Optional[Union[int, Tuple[int, int]]] = None):
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# Grid size
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self.grid = grid
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# Patch size
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self.size = None if size is None else ensure_tuple_rep(size, len(self.grid))
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def __call__(self, image: NdarrayOrTensor) -> NdarrayOrTensor:
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if self.grid == (1, 1) and self.size is None:
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if isinstance(image, torch.Tensor):
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return torch.stack([image])
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elif isinstance(image, np.ndarray):
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return np.stack([image]) # type: ignore
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else:
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raise ValueError(f"Input type [{type(image)}] is not supported.")
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size, steps = self._get_params(image.shape[1:])
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patches: NdarrayOrTensor
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if isinstance(image, torch.Tensor):
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patches = (
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image.unfold(1, size[0], steps[0])
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.unfold(2, size[1], steps[1])
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.flatten(1, 2)
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.transpose(0, 1)
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.contiguous()
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)
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elif isinstance(image, np.ndarray):
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x_step, y_step = steps
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c_stride, x_stride, y_stride = image.strides
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n_channels = image.shape[0]
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patches = as_strided(
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image,
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shape=(*self.grid, n_channels, size[0], size[1]),
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strides=(x_stride * x_step, y_stride * y_step, c_stride, x_stride, y_stride),
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writeable=False,
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)
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# flatten the first two dimensions
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patches = patches.reshape(np.prod(patches.shape[:2]), *patches.shape[2:])
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# make it a contiguous array
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patches = np.ascontiguousarray(patches)
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else:
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raise ValueError(f"Input type [{type(image)}] is not supported.")
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return patches
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def _get_params(self, image_size: Union[Sequence[int], np.ndarray]):
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"""
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Calculate the size and step required for splitting the image
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Args:
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The size of the input image
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"""
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if self.size is not None:
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# Set the split size to the given default size
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if any(self.size[i] > image_size[i] for i in range(len(self.grid))):
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raise ValueError("The image size ({image_size})is smaller than the requested split size ({self.size})")
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split_size = self.size
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else:
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# infer each sub-image size from the image size and the grid
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split_size = tuple(image_size[i] // self.grid[i] for i in range(len(self.grid)))
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steps = tuple(
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(image_size[i] - split_size[i]) // (self.grid[i] - 1) if self.grid[i] > 1 else image_size[i]
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for i in range(len(self.grid))
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)
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return split_size, steps

‎monai/transforms/spatial/dictionary.py‎

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AffineGrid,
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Flip,
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GridDistortion,
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GridSplit,
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Orientation,
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Rand2DElastic,
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Rand3DElastic,
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"ZoomDict",
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"RandZoomD",
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"RandZoomDict",
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"GridSplitd",
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"GridSplitD",
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"GridSplitDict",
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]
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GridSampleModeSequence = Union[Sequence[Union[GridSampleMode, str]], GridSampleMode, str]
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return d
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class GridSplitd(MapTransform):
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"""
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Split the image into patches based on the provided grid in 2D.
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Args:
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keys: keys of the corresponding items to be transformed.
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grid: a tuple define the shape of the grid upon which the image is split. Defaults to (2, 2)
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size: a tuple or an integer that defines the output patch sizes.
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If it's an integer, the value will be repeated for each dimension.
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The default is None, where the patch size will be inferred from the grid shape.
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allow_missing_keys: don't raise exception if key is missing.
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Note: This transform currently support only image with two spatial dimensions.
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"""
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backend = GridSplit.backend
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def __init__(
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self,
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keys: KeysCollection,
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grid: Tuple[int, int] = (2, 2),
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size: Optional[Union[int, Tuple[int, int]]] = None,
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allow_missing_keys: bool = False,
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):
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super().__init__(keys, allow_missing_keys)
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self.splitter = GridSplit(grid=grid, size=size)
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def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
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d = dict(data)
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for key in self.key_iterator(d):
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d[key] = self.splitter(d[key])
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return d
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SpatialResampleD = SpatialResampleDict = SpatialResampled
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ResampleToMatchD = ResampleToMatchDict = ResampleToMatchd
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SpacingD = SpacingDict = Spacingd
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RandRotateD = RandRotateDict = RandRotated
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ZoomD = ZoomDict = Zoomd
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RandZoomD = RandZoomDict = RandZoomd
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GridSplitD = GridSplitDict = GridSplitd

‎tests/test_grid_split.py‎

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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import torch
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from parameterized import parameterized
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from monai.transforms import GridSplit
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from tests.utils import TEST_NDARRAYS, assert_allclose
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A11 = torch.randn(3, 2, 2)
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A12 = torch.randn(3, 2, 2)
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A21 = torch.randn(3, 2, 2)
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A22 = torch.randn(3, 2, 2)
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A1 = torch.cat([A11, A12], 2)
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A2 = torch.cat([A21, A22], 2)
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A = torch.cat([A1, A2], 1)
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TEST_CASE_0 = [{"grid": (2, 2)}, A, torch.stack([A11, A12, A21, A22])]
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TEST_CASE_1 = [{"grid": (2, 1)}, A, torch.stack([A1, A2])]
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TEST_CASE_2 = [{"grid": (1, 2)}, A1, torch.stack([A11, A12])]
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TEST_CASE_3 = [{"grid": (1, 2)}, A2, torch.stack([A21, A22])]
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TEST_CASE_4 = [{"grid": (1, 1), "size": (2, 2)}, A, torch.stack([A11])]
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TEST_CASE_5 = [{"grid": (1, 1), "size": 4}, A, torch.stack([A])]
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TEST_CASE_6 = [{"grid": (2, 2), "size": 2}, A, torch.stack([A11, A12, A21, A22])]
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TEST_CASE_7 = [{"grid": (1, 1)}, A, torch.stack([A])]
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TEST_CASE_8 = [
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{"grid": (2, 2), "size": 2},
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torch.arange(12).reshape(1, 3, 4).to(torch.float32),
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torch.Tensor([[[[0, 1], [4, 5]]], [[[2, 3], [6, 7]]], [[[4, 5], [8, 9]]], [[[6, 7], [10, 11]]]]).to(torch.float32),
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]
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TEST_SINGLE = []
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for p in TEST_NDARRAYS:
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TEST_SINGLE.append([p, *TEST_CASE_0])
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TEST_SINGLE.append([p, *TEST_CASE_1])
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TEST_SINGLE.append([p, *TEST_CASE_2])
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TEST_SINGLE.append([p, *TEST_CASE_3])
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TEST_SINGLE.append([p, *TEST_CASE_4])
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TEST_SINGLE.append([p, *TEST_CASE_5])
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TEST_SINGLE.append([p, *TEST_CASE_6])
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TEST_SINGLE.append([p, *TEST_CASE_7])
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TEST_SINGLE.append([p, *TEST_CASE_8])
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TEST_CASE_MC_0 = [{"grid": (2, 2)}, [A, A], [torch.stack([A11, A12, A21, A22]), torch.stack([A11, A12, A21, A22])]]
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TEST_CASE_MC_1 = [{"grid": (2, 1)}, [A] * 5, [torch.stack([A1, A2])] * 5]
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TEST_CASE_MC_2 = [{"grid": (1, 2)}, [A1, A2], [torch.stack([A11, A12]), torch.stack([A21, A22])]]
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TEST_MULTIPLE = []
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for p in TEST_NDARRAYS:
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TEST_MULTIPLE.append([p, *TEST_CASE_MC_0])
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TEST_MULTIPLE.append([p, *TEST_CASE_MC_1])
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TEST_MULTIPLE.append([p, *TEST_CASE_MC_2])
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class TestGridSplit(unittest.TestCase):
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@parameterized.expand(TEST_SINGLE)
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def test_split_patch_single_call(self, in_type, input_parameters, image, expected):
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input_image = in_type(image)
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splitter = GridSplit(**input_parameters)
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output = splitter(input_image)
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assert_allclose(output, expected, type_test=False)
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@parameterized.expand(TEST_MULTIPLE)
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def test_split_patch_multiple_call(self, in_type, input_parameters, img_list, expected_list):
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splitter = GridSplit(**input_parameters)
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for image, expected in zip(img_list, expected_list):
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input_image = in_type(image)
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output = splitter(input_image)
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assert_allclose(output, expected, type_test=False)
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if __name__ == "__main__":
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unittest.main()

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