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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import warnings
import numpy as np
import torch
import monai
from monai.config import NdarrayOrTensor
from monai.data.utils import AFFINE_TOL
from monai.transforms.utils_pytorch_numpy_unification import allclose
from monai.utils import LazyAttr, convert_to_numpy, convert_to_tensor, look_up_option
__all__ = ["resample", "combine_transforms"]
class Affine:
"""A class to represent an affine transform matrix."""
__slots__ = ("data",)
def __init__(self, data):
self.data = data
@staticmethod
def is_affine_shaped(data):
"""Check if the data is an affine matrix."""
if isinstance(data, Affine):
return True
if isinstance(data, DisplacementField):
return False
if not hasattr(data, "shape") or len(data.shape) < 2:
return False
return data.shape[-1] in (3, 4) and data.shape[-1] == data.shape[-2]
class DisplacementField:
"""A class to represent a dense displacement field."""
__slots__ = ("data",)
def __init__(self, data):
self.data = data
@staticmethod
def is_ddf_shaped(data):
"""Check if the data is a DDF."""
if isinstance(data, DisplacementField):
return True
if isinstance(data, Affine):
return False
if not hasattr(data, "shape") or len(data.shape) < 3:
return False
return not Affine.is_affine_shaped(data)
def combine_transforms(left: torch.Tensor, right: torch.Tensor) -> torch.Tensor:
"""Given transforms A and B to be applied to x, return the combined transform (AB), so that A(B(x)) becomes AB(x)"""
if Affine.is_affine_shaped(left) and Affine.is_affine_shaped(right): # linear transforms
left = convert_to_tensor(left.data if isinstance(left, Affine) else left, wrap_sequence=True)
right = convert_to_tensor(right.data if isinstance(right, Affine) else right, wrap_sequence=True)
return torch.matmul(left, right)
if DisplacementField.is_ddf_shaped(left) and DisplacementField.is_ddf_shaped(
right
): # adds DDFs, do we need metadata if metatensor input?
left = convert_to_tensor(left.data if isinstance(left, DisplacementField) else left, wrap_sequence=True)
right = convert_to_tensor(right.data if isinstance(right, DisplacementField) else right, wrap_sequence=True)
return left + right
raise NotImplementedError
def affine_from_pending(pending_item):
"""Extract the affine matrix from a pending transform item."""
if isinstance(pending_item, (torch.Tensor, np.ndarray)):
return pending_item
if isinstance(pending_item, dict):
return pending_item[LazyAttr.AFFINE]
return pending_item
def kwargs_from_pending(pending_item):
"""Extract kwargs from a pending transform item."""
if not isinstance(pending_item, dict):
return {}
ret = {
LazyAttr.INTERP_MODE: pending_item.get(LazyAttr.INTERP_MODE, None), # interpolation mode
LazyAttr.PADDING_MODE: pending_item.get(LazyAttr.PADDING_MODE, None), # padding mode
}
if LazyAttr.SHAPE in pending_item:
ret[LazyAttr.SHAPE] = pending_item[LazyAttr.SHAPE]
if LazyAttr.DTYPE in pending_item:
ret[LazyAttr.DTYPE] = pending_item[LazyAttr.DTYPE]
return ret # adding support of pending_item['extra_info']??
def is_compatible_apply_kwargs(kwargs_1, kwargs_2):
"""Check if two sets of kwargs are compatible (to be combined in `apply`)."""
return True
def requires_interp(matrix, atol=AFFINE_TOL):
"""
Check whether the transformation matrix suggests voxel-wise interpolation.
Returns None if the affine matrix suggests interpolation.
Otherwise, the matrix suggests that the resampling could be achieved by simple array operations
such as flip/permute/pad_nd/slice; in this case this function returns axes information about simple axes
operations.
Args:
matrix: the affine matrix to check.
atol: absolute tolerance for checking if the matrix is close to an integer.
"""
matrix = convert_to_numpy(matrix, wrap_sequence=True)
s = matrix[:, -1]
if not np.allclose(s, np.round(s), atol=atol):
return None
ndim = len(matrix) - 1
ox, oy = [], [0]
for x, r in enumerate(matrix[:ndim, :ndim]):
for y, c in enumerate(r):
if np.isclose(c, -1, atol=atol) or np.isclose(c, 1, atol=atol):
y_channel = y + 1 # the returned axis index starting with channel dim
if x in ox or y_channel in oy:
return None
ox.append(x)
oy.append(y_channel)
elif not np.isclose(c, 0.0, atol=atol):
return None
return oy
__override_lazy_keywords = {*list(LazyAttr), "atol"}
def resample(data: torch.Tensor, matrix: NdarrayOrTensor, kwargs: dict | None = None):
"""
Resample `data` using the affine transformation defined by ``matrix``.
Args:
data: input data to be resampled.
matrix: affine transformation matrix.
kwargs: currently supports (see also: ``monai.utils.enums.LazyAttr``)
- "lazy_shape" for output spatial shape
- "lazy_padding_mode"
- "lazy_interpolation_mode" (this option might be ignored when ``mode="auto"``.)
- "lazy_align_corners"
- "lazy_dtype" (dtype for resampling computation; this might be ignored when ``mode="auto"``.)
- "atol" for tolerance for matrix floating point comparison.
- "lazy_resample_mode" for resampling backend, default to `"auto"`. Setting to other values will use the
`monai.transforms.SpatialResample` for resampling.
See Also:
:py:class:`monai.transforms.SpatialResample`
"""
if not Affine.is_affine_shaped(matrix):
raise NotImplementedError(f"Calling the dense grid resample API directly not implemented, {matrix.shape}.")
if isinstance(data, monai.data.MetaTensor) and data.pending_operations:
warnings.warn("data.pending_operations is not empty, the resampling output may be incorrect.")
kwargs = kwargs or {}
for k in kwargs:
look_up_option(k, __override_lazy_keywords)
atol = kwargs.get("atol", AFFINE_TOL)
mode = kwargs.get(LazyAttr.RESAMPLE_MODE, "auto")
init_kwargs = {
"dtype": kwargs.get(LazyAttr.DTYPE, data.dtype),
"align_corners": kwargs.get(LazyAttr.ALIGN_CORNERS, False),
}
ndim = len(matrix) - 1
img = convert_to_tensor(data=data, track_meta=monai.data.get_track_meta())
init_affine = monai.data.to_affine_nd(ndim, img.affine)
spatial_size = kwargs.get(LazyAttr.SHAPE, None)
out_spatial_size = img.peek_pending_shape() if spatial_size is None else spatial_size
out_spatial_size = convert_to_numpy(out_spatial_size, wrap_sequence=True)
call_kwargs = {
"spatial_size": out_spatial_size,
"dst_affine": init_affine @ monai.utils.convert_to_dst_type(matrix, init_affine)[0],
"mode": kwargs.get(LazyAttr.INTERP_MODE),
"padding_mode": kwargs.get(LazyAttr.PADDING_MODE),
}
axes = requires_interp(matrix, atol=atol)
if axes is not None and mode == "auto" and not init_kwargs["align_corners"]:
matrix_np = np.round(convert_to_numpy(matrix, wrap_sequence=True))
full_transpose = np.argsort(axes).tolist()
if not np.allclose(full_transpose, np.arange(len(full_transpose))):
img = img.permute(full_transpose[: len(img.shape)])
in_shape = img.shape[1 : ndim + 1] # requires that ``img`` has empty pending operations
matrix_np[:ndim] = matrix_np[[x - 1 for x in full_transpose[1:]]]
flip = [idx + 1 for idx, val in enumerate(matrix_np[:ndim]) if val[idx] == -1]
if flip:
img = torch.flip(img, dims=flip) # todo: if on cpu, using the np.flip is faster?
for f in flip:
ind_f = f - 1
matrix_np[ind_f, ind_f] = 1
matrix_np[ind_f, -1] = in_shape[ind_f] - 1 - matrix_np[ind_f, -1]
if not np.all(out_spatial_size > 0):
raise ValueError(f"Resampling out_spatial_size should be positive, got {out_spatial_size}.")
if (
allclose(matrix_np, np.eye(len(matrix_np)), atol=atol)
and len(in_shape) == len(out_spatial_size)
and allclose(convert_to_numpy(in_shape, wrap_sequence=True), out_spatial_size)
):
img.affine = call_kwargs["dst_affine"]
img = img.to(torch.float32) # consistent with monai.transforms.spatial.functional.spatial_resample
return img
img = monai.transforms.crop_or_pad_nd(img, matrix_np, out_spatial_size, mode=call_kwargs["padding_mode"])
img = img.to(torch.float32) # consistent with monai.transforms.spatial.functional.spatial_resample
img.affine = call_kwargs["dst_affine"]
return img
resampler = monai.transforms.SpatialResample(**init_kwargs)
resampler.lazy = False # resampler is a lazytransform
with resampler.trace_transform(False): # don't track this transform in `img`
return resampler(img=img, **call_kwargs)
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