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,
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 = "trans",
output_ext: str = ".nii.gz",
resample: bool = True,
mode: Union[GridSampleMode, InterpolateMode, str] = "nearest",
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
scale: Optional[int] = None,
dtype: DtypeLike = np.float64,
output_dtype: DtypeLike = np.float32,
save_batch: bool = False,
squeeze_end_dims: bool = True,
data_root_dir: str = "",
print_log: bool = True,
) -> None:
self.saver: Union[NiftiSaver, PNGSaver]
if output_ext in (".nii.gz", ".nii"):
self.saver = NiftiSaver(
output_dir=output_dir,
output_postfix=output_postfix,
output_ext=output_ext,
resample=resample,
mode=GridSampleMode(mode),
padding_mode=padding_mode,
dtype=dtype,
output_dtype=output_dtype,
squeeze_end_dims=squeeze_end_dims,
data_root_dir=data_root_dir,
print_log=print_log,
)
elif output_ext == ".png":
self.saver = PNGSaver(
output_dir=output_dir,
output_postfix=output_postfix,
output_ext=output_ext,
resample=resample,
mode=InterpolateMode(mode),
scale=scale,
data_root_dir=data_root_dir,
print_log=print_log,
)
else:
raise ValueError(f"unsupported output extension: {output_ext}.")
self.save_batch = save_batch
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: str = "./",
output_postfix: str = "seg",
output_ext: str = ".nii.gz",
resample: bool = True,
mode: Union[GridSampleMode, InterpolateMode, str] = "nearest",
padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
scale: Optional[int] = None,
dtype: Optional[np.dtype] = np.float64,
output_dtype: Optional[np.dtype] = np.float32,
batch_transform: Callable = lambda x: x,
output_transform: Callable = lambda x: x,
name: Optional[str] = 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: This option is used when ``resample = True``. Defaults to ``"nearest"``.
- NIfTI files {``"bilinear"``, ``"nearest"``}
Interpolation mode to calculate output values.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
- PNG files {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
The interpolation mode.
See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
padding_mode: This option is used when ``resample = True``. Defaults to ``"border"``.
- NIfTI files {``"zeros"``, ``"border"``, ``"reflection"``}
Padding mode for outside grid values.
See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
- PNG files
This option is ignored.
scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling
[0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.
It's used for PNG format only.
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``, it's used for Nifti format only.
output_dtype: data type for saving data. Defaults to ``np.float32``, it's used for Nifti format only.
batch_transform: a callable that is used to transform the
ignite.engine.batch into expected format to extract the meta_data dictionary.
output_transform: a callable that is used to transform the
ignite.engine.output into the form expected image data.
The first dimension of this transform's output will be treated as the
batch dimension. Each item in the batch will be saved individually.
name: identifier of logging.logger to use, defaulting to `engine.logger`.
"""
self.saver: Union[NiftiSaver, PNGSaver]
if output_ext in (".nii.gz", ".nii"):
self.saver = NiftiSaver(
output_dir=output_dir,
output_postfix=output_postfix,
output_ext=output_ext,
resample=resample,
mode=GridSampleMode(mode),
padding_mode=padding_mode,
dtype=dtype,
output_dtype=output_dtype,
)
elif output_ext == ".png":
self.saver = PNGSaver(
output_dir=output_dir,
output_postfix=output_postfix,
output_ext=output_ext,
resample=resample,
mode=InterpolateMode(mode),
scale=scale,
)
self.batch_transform = batch_transform
self.output_transform = output_transform
self.logger = logging.getLogger(name)
self._name = name
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