def __init__(
self,
mode=GridSampleMode.BILINEAR.value,
padding_mode=GridSamplePadMode.BORDER.value,
):
"""
For pytorch native APIs, the possible values are:
- mode: ``"nearest"``, ``"bilinear"``, ``"bicubic"``.
- padding_mode: ``"zeros"``, ``"border"``, ``"reflection"``
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
For MONAI C++/CUDA extensions, the possible values are:
- mode: ``"nearest"``, ``"bilinear"``, ``"bicubic"``, 0, 1, ...
- padding_mode: ``"zeros"``, ``"border"``, ``"reflection"``, 0, 1, ...
See also: :py:class:`monai.networks.layers.grid_pull`
"""
super(Warp, self).__init__()
# resolves _interp_mode for different methods
if USE_COMPILED:
if mode in (inter.value for inter in GridSampleMode):
mode = GridSampleMode(mode)
if mode == GridSampleMode.BILINEAR:
mode = 1
elif mode == GridSampleMode.NEAREST:
mode = 0
elif mode == GridSampleMode.BICUBIC:
mode = 3
else:
mode = 1 # default to linear
self._interp_mode = mode
else:
warnings.warn(
"monai.networks.blocks.Warp: Using PyTorch native grid_sample."
)
self._interp_mode = GridSampleMode(mode).value
# resolves _padding_mode for different methods
if USE_COMPILED:
if padding_mode in (pad.value for pad in GridSamplePadMode):
padding_mode = GridSamplePadMode(padding_mode)
if padding_mode == GridSamplePadMode.ZEROS:
padding_mode = 7
elif padding_mode == GridSamplePadMode.BORDER:
padding_mode = 0
elif padding_mode == GridSamplePadMode.REFLECTION:
padding_mode = 1
else:
padding_mode = 0 # default to nearest
self._padding_mode = padding_mode
else:
self._padding_mode = GridSamplePadMode(padding_mode).value
def __init__(
self,
output_dir: str = "./",
output_postfix: str = "seg",
output_ext: str = ".nii.gz",
resample: bool = True,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
dtype: Optional[np.dtype] = None,
) -> None:
"""
Args:
output_dir: output image directory.
output_postfix: a string appended to all output file names.
output_ext: output file extension name.
resample: whether to resample before saving the data array.
mode: {``"bilinear"``, ``"nearest"``}
This option is used when ``resample = True``.
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
This option is used when ``resample = True``.
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
dtype: convert the image data to save to this data type.
If None, keep the original type of data.
"""
self.output_dir = output_dir
self.output_postfix = output_postfix
self.output_ext = output_ext
self.resample = resample
self.mode: GridSampleMode = GridSampleMode(mode)
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
self.dtype = dtype
self._data_index = 0
def __init__(
self,
spatial_dims: int,
mode: int = 1,
padding_mode: Optional[Union[GridSamplePadMode, str]] = GridSamplePadMode.ZEROS,
):
"""
Args:
spatial_dims: {2, 3}. number of spatial dimensions
mode: interpolation mode to calculate output values, defaults to 1.
Possible values are::
- 0 or 'nearest' or InterpolationType.nearest
- 1 or 'linear' or InterpolationType.linear
- 2 or 'quadratic' or InterpolationType.quadratic
- 3 or 'cubic' or InterpolationType.cubic
- 4 or 'fourth' or InterpolationType.fourth
- etc.
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
"""
super(Warp, self).__init__()
if spatial_dims not in [2, 3]:
raise ValueError(f"got unsupported spatial_dims={spatial_dims}, only support 2-d and 3-d input")
self.spatial_dims = spatial_dims
if mode < 0:
raise ValueError(f"do not support negative mode, got mode={mode}")
self.mode = mode
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
def __init__(
self,
spacing: Union[Tuple[float, float], float],
max_tumor_size: float,
magnitude_range: Tuple[float, float],
prob: float = 0.1,
spatial_size: Optional[Union[Sequence[int], int]] = None,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode,
str] = GridSamplePadMode.REFLECTION,
as_tensor_output: bool = False,
device: Optional[torch.device] = None,
) -> None:
self.deform_grid = TumorGrowthGrid(
spacing=spacing,
max_tumor_size=max_tumor_size,
magnitude_range=magnitude_range,
as_tensor_output=True,
device=device,
)
self.resampler = Resample(as_tensor_output=as_tensor_output,
device=device)
self.spatial_size = spatial_size
self.mode: GridSampleMode = GridSampleMode(mode)
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
self.prob = prob
self.do_transform = False
def __init__(
self,
spatial_size: Optional[Union[Sequence[int], int]] = None,
normalized: bool = False,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.ZEROS,
align_corners: bool = False,
reverse_indexing: bool = True,
) -> None:
"""
Apply affine transformations with a batch of affine matrices.
When `normalized=False` and `reverse_indexing=True`,
it does the commonly used resampling in the 'pull' direction
following the ``scipy.ndimage.affine_transform`` convention.
In this case `theta` is equivalent to (ndim+1, ndim+1) input ``matrix`` of ``scipy.ndimage.affine_transform``,
operates on homogeneous coordinates.
See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.affine_transform.html
When `normalized=True` and `reverse_indexing=False`,
it applies `theta` to the normalized coordinates (coords. in the range of [-1, 1]) directly.
This is often used with `align_corners=False` to achieve resolution-agnostic resampling,
thus useful as a part of trainable modules such as the spatial transformer networks.
See also: https://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html
Args:
spatial_size: output spatial shape, the full output shape will be
`[N, C, *spatial_size]` where N and C are inferred from the `src` input of `self.forward`.
normalized: indicating whether the provided affine matrix `theta` is defined
for the normalized coordinates. If `normalized=False`, `theta` will be converted
to operate on normalized coordinates as pytorch affine_grid works with the normalized
coordinates.
mode: {``"bilinear"``, ``"nearest"``}
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
Padding mode for outside grid values. Defaults to ``"zeros"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
align_corners: see also https://pytorch.org/docs/stable/nn.functional.html#grid-sample.
reverse_indexing: whether to reverse the spatial indexing of image and coordinates.
set to `False` if `theta` follows pytorch's default "D, H, W" convention.
set to `True` if `theta` follows `scipy.ndimage` default "i, j, k" convention.
"""
super().__init__()
self.spatial_size = ensure_tuple(
spatial_size) if spatial_size is not None else None
self.normalized = normalized
self.mode: GridSampleMode = GridSampleMode(mode)
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
self.align_corners = align_corners
self.reverse_indexing = reverse_indexing
def __init__(
self,
output_dir: str = "./",
output_postfix: str = "seg",
output_ext: str = ".nii.gz",
resample: bool = True,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
align_corners: bool = False,
dtype: DtypeLike = np.float64,
output_dtype: DtypeLike = np.float32,
squeeze_end_dims: bool = True,
) -> None:
"""
Args:
output_dir: output image directory.
output_postfix: a string appended to all output file names.
output_ext: output file extension name.
resample: whether to resample before saving the data array.
mode: {``"bilinear"``, ``"nearest"``}
This option is used when ``resample = True``.
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
This option is used when ``resample = True``.
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
If None, use the data type of input data.
output_dtype: data type for saving data. Defaults to ``np.float32``.
squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel
has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and
then if C==1, it will be saved as (H,W,D). If D also ==1, it will be saved as (H,W). If false,
image will always be saved as (H,W,D,C).
"""
self.output_dir = output_dir
self.output_postfix = output_postfix
self.output_ext = output_ext
self.resample = resample
self.mode: GridSampleMode = GridSampleMode(mode)
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
self.align_corners = align_corners
self.dtype = dtype
self.output_dtype = output_dtype
self._data_index = 0
self.squeeze_end_dims = squeeze_end_dims
def __init__(
self,
output_dir: str = "./",
output_postfix: str = "seg",
output_ext: str = ".nii.gz",
resample: bool = True,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
align_corners: bool = False,
dtype: DtypeLike = np.float64,
output_dtype: DtypeLike = np.float32,
) -> None:
"""
Args:
output_dir: output image directory.
output_postfix: a string appended to all output file names.
output_ext: output file extension name.
resample: whether to resample before saving the data array.
mode: {``"bilinear"``, ``"nearest"``}
This option is used when ``resample = True``.
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
This option is used when ``resample = True``.
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
If None, use the data type of input data. To be compatible with other modules,
the output data type is always ``np.float32``.
output_dtype: data type for saving data. Defaults to ``np.float32``.
"""
self.output_dir = output_dir
self.output_postfix = output_postfix
self.output_ext = output_ext
self.resample = resample
self.mode: GridSampleMode = GridSampleMode(mode)
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
self.align_corners = align_corners
self.dtype = dtype
self.output_dtype = output_dtype
self._data_index = 0
def __init__(
self,
spatial_dims: int,
mode: Optional[Union[GridSampleMode, str]] = GridSampleMode.BILINEAR,
padding_mode: Optional[Union[GridSamplePadMode, str]] = GridSamplePadMode.ZEROS,
):
"""
Args:
spatial_dims: {2, 3}. number of spatial dimensions
mode: {``"bilinear"``, ``"nearest"``}
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
"""
super(Warp, self).__init__()
if spatial_dims not in [2, 3]:
raise ValueError(f"got unsupported spatial_dims = {spatial_dims}, only support 2-d and 3-d input")
self.spatial_dims = spatial_dims
self.mode: GridSampleMode = GridSampleMode(mode)
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
def __init__(
self,
output_dir: PathLike = "./",
output_postfix: str = "seg",
output_ext: str = ".nii.gz",
resample: bool = True,
mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
align_corners: bool = False,
dtype: DtypeLike = np.float64,
output_dtype: DtypeLike = np.float32,
squeeze_end_dims: bool = True,
data_root_dir: PathLike = "",
separate_folder: bool = True,
print_log: bool = True,
) -> None:
"""
Args:
output_dir: output image directory.
output_postfix: a string appended to all output file names.
output_ext: output file extension name.
resample: whether to convert the data array to it's original coordinate system
based on `original_affine` in the `meta_data`.
mode: {``"bilinear"``, ``"nearest"``}
This option is used when ``resample = True``.
Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
This option is used when ``resample = True``.
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
If None, use the data type of input data.
output_dtype: data type for saving data. Defaults to ``np.float32``.
squeeze_end_dims: if True, any trailing singleton dimensions will be removed (after the channel
has been moved to the end). So if input is (C,H,W,D), this will be altered to (H,W,D,C), and
then if C==1, it will be saved as (H,W,D). If D also ==1, it will be saved as (H,W). If false,
image will always be saved as (H,W,D,C).
data_root_dir: if not empty, it specifies the beginning parts of the input file's
absolute path. it's used to compute `input_file_rel_path`, the relative path to the file from
`data_root_dir` to preserve folder structure when saving in case there are files in different
folders with the same file names. for example:
input_file_name: /foo/bar/test1/image.nii,
postfix: seg
output_ext: nii.gz
output_dir: /output,
data_root_dir: /foo/bar,
output will be: /output/test1/image/image_seg.nii.gz
separate_folder: whether to save every file in a separate folder, for example: if input filename is
`image.nii`, postfix is `seg` and folder_path is `output`, if `True`, save as:
`output/image/image_seg.nii`, if `False`, save as `output/image_seg.nii`. default to `True`.
print_log: whether to print log about the saved NIfTI file path, etc. default to `True`.
"""
self.output_dir = output_dir
self.output_postfix = output_postfix
self.output_ext = output_ext
self.resample = resample
self.mode: GridSampleMode = GridSampleMode(mode)
self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
self.align_corners = align_corners
self.dtype = dtype
self.output_dtype = output_dtype
self._data_index = 0
self.squeeze_end_dims = squeeze_end_dims
self.data_root_dir = data_root_dir
self.separate_folder = separate_folder
self.print_log = print_log