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5983 update to use np.linalg for the small affine inverse #5967

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merged 11 commits into from
Feb 13, 2023
Merged

5983 update to use np.linalg for the small affine inverse #5967

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c601e45
update to use np.linalg
wyli Feb 9, 2023
5ac5fa9
update
wyli Feb 9, 2023
91dc115
update integration results
wyli Feb 9, 2023
c72d537
update answers
wyli Feb 9, 2023
ecfec2e
update results
wyli Feb 9, 2023
1130539
5762 pprint head and tail bundle script (#5969)
wyli Feb 10, 2023
fac6848
Added callable options for iteration_log and epoch_log in StatsHandle…
vfdev-5 Feb 10, 2023
01d80e7
Merge branch 'dev' into numpy-inverse
wyli Feb 13, 2023
5a5634e
Merge branch 'dev' into numpy-inverse
wyli Feb 13, 2023
565f93c
remove unused ansers
wyli Feb 13, 2023
af3e1d3
update based on comments
wyli Feb 13, 2023
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9 changes: 5 additions & 4 deletions monai/networks/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,14 +22,15 @@
from copy import deepcopy
from typing import Any

import numpy as np
import torch
import torch.nn as nn

from monai.apps.utils import get_logger
from monai.config import PathLike
from monai.utils.misc import ensure_tuple, save_obj, set_determinism
from monai.utils.module import look_up_option, pytorch_after
from monai.utils.type_conversion import convert_to_tensor
from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor

__all__ = [
"one_hot",
Expand Down Expand Up @@ -185,7 +186,7 @@ def predict_segmentation(logits: torch.Tensor, mutually_exclusive: bool = False,

def normalize_transform(
shape,
device: torch.device | None = None,
device: torch.device | str | None = None,
dtype: torch.dtype | None = None,
align_corners: bool = False,
zero_centered: bool = False,
Expand Down Expand Up @@ -264,8 +265,8 @@ def to_norm_affine(
raise ValueError(f"affine suggests {sr}D, got src={len(src_size)}D, dst={len(dst_size)}D.")

src_xform = normalize_transform(src_size, affine.device, affine.dtype, align_corners, zero_centered)
dst_xform = normalize_transform(dst_size, affine.device, affine.dtype, align_corners, zero_centered)
return src_xform @ affine @ torch.inverse(dst_xform)
dst_xform = normalize_transform(dst_size, "cpu", affine.dtype, align_corners, zero_centered)
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return src_xform @ affine @ convert_to_dst_type(np.linalg.inv(dst_xform.numpy()), dst=affine)[0]
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def normal_init(
Expand Down
29 changes: 13 additions & 16 deletions monai/transforms/spatial/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,6 @@
fall_back_tuple,
issequenceiterable,
optional_import,
pytorch_after,
)
from monai.utils.deprecate_utils import deprecated_arg
from monai.utils.enums import GridPatchSort, PytorchPadMode, TraceKeys, TransformBackends, WSIPatchKeys
Expand Down Expand Up @@ -272,14 +271,12 @@ def __call__(
)

try:
_s = convert_to_tensor(src_affine_, track_meta=False, device=torch.device("cpu"))
_d = convert_to_tensor(dst_affine, track_meta=False, device=torch.device("cpu"))
xform = (
torch.linalg.solve(_s, _d) if pytorch_after(1, 8, 0) else torch.solve(_d, _s).solution # type: ignore
)
_s = convert_to_numpy(src_affine_)
_d = convert_to_numpy(dst_affine)
xform = np.linalg.solve(_s, _d)
except (np.linalg.LinAlgError, RuntimeError) as e:
raise ValueError("src affine is not invertible.") from e
xform = to_affine_nd(spatial_rank, xform).to(device=img.device, dtype=_dtype)
raise ValueError(f"src affine is not invertible {_s}, {_d}.") from e
xform = convert_to_tensor(to_affine_nd(spatial_rank, xform)).to(device=img.device, dtype=_dtype)
# no resampling if it's identity transform
if allclose(xform, torch.eye(len(xform)), atol=AFFINE_TOL) and allclose(spatial_size, in_spatial_size):
return self._post_process(
Expand All @@ -293,12 +290,12 @@ def __call__(
xform_shape = [-1] + in_spatial_size
img = img.reshape(xform_shape) # type: ignore
if isinstance(mode, int):
dst_xform_1 = normalize_transform(spatial_size, xform.device, xform.dtype, True, True)[0] # to (-1, 1)
dst_xform_1 = normalize_transform(spatial_size, "cpu", xform.dtype, True, True)[0].numpy() # to (-1, 1)
if not align_corners:
norm = create_scale(spatial_rank, [(max(d, 2) - 1) / d for d in spatial_size], xform.device, "torch")
dst_xform_1 = norm.to(xform.dtype) @ dst_xform_1 # type: ignore # scaling (num_step - 1) / num_step
dst_xform_d = normalize_transform(spatial_size, xform.device, xform.dtype, align_corners, False)[0]
xform = xform @ torch.inverse(dst_xform_d) @ dst_xform_1
norm = create_scale(spatial_rank, [(max(d, 2) - 1) / d for d in spatial_size])
dst_xform_1 = norm.astype(float) @ dst_xform_1 # type: ignore # scaling (num_step - 1) / num_step
dst_xform_d = normalize_transform(spatial_size, "cpu", xform.dtype, align_corners, False)[0].numpy()
xform @= convert_to_dst_type(np.linalg.solve(dst_xform_d, dst_xform_1), xform)[0]
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affine_xform = Affine(
affine=xform, spatial_size=spatial_size, normalized=True, image_only=True, dtype=_dtype # type: ignore
)
Expand Down Expand Up @@ -1084,7 +1081,7 @@ def inverse_transform(self, data: torch.Tensor, transform) -> torch.Tensor:
padding_mode = transform[TraceKeys.EXTRA_INFO]["padding_mode"]
align_corners = transform[TraceKeys.EXTRA_INFO]["align_corners"]
dtype = transform[TraceKeys.EXTRA_INFO]["dtype"]
inv_rot_mat = linalg_inv(fwd_rot_mat)
inv_rot_mat = linalg_inv(convert_to_numpy(fwd_rot_mat))

xform = AffineTransform(
normalized=False,
Expand Down Expand Up @@ -2281,7 +2278,7 @@ def inverse(self, data: torch.Tensor) -> torch.Tensor:
fwd_affine = transform[TraceKeys.EXTRA_INFO]["affine"]
mode = transform[TraceKeys.EXTRA_INFO]["mode"]
padding_mode = transform[TraceKeys.EXTRA_INFO]["padding_mode"]
inv_affine = linalg_inv(fwd_affine)
inv_affine = linalg_inv(convert_to_numpy(fwd_affine))
inv_affine = convert_to_dst_type(inv_affine, data, dtype=inv_affine.dtype)[0]

affine_grid = AffineGrid(affine=inv_affine)
Expand Down Expand Up @@ -2520,7 +2517,7 @@ def inverse(self, data: torch.Tensor) -> torch.Tensor:
fwd_affine = transform[TraceKeys.EXTRA_INFO]["affine"]
mode = transform[TraceKeys.EXTRA_INFO]["mode"]
padding_mode = transform[TraceKeys.EXTRA_INFO]["padding_mode"]
inv_affine = linalg_inv(fwd_affine)
inv_affine = linalg_inv(convert_to_numpy(fwd_affine))
inv_affine = convert_to_dst_type(inv_affine, data, dtype=inv_affine.dtype)[0]
affine_grid = AffineGrid(affine=inv_affine)
grid, _ = affine_grid(orig_size)
Expand Down
2 changes: 1 addition & 1 deletion monai/transforms/utils.py
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Expand Up @@ -828,7 +828,7 @@ def _create_shear(spatial_dims: int, coefs: Sequence[float] | float, eye_func=np
def create_scale(
spatial_dims: int,
scaling_factor: Sequence[float] | float,
device: torch.device | None = None,
device: torch.device | str | None = None,
backend=TransformBackends.NUMPY,
) -> NdarrayOrTensor:
"""
Expand Down
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