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from copy import deepcopy
from typing import Union, Tuple, List
import numpy as np
import torch
from einops import rearrange
from torch.nn import functional as F
from data.default_resampling import determine_do_sep_z_and_axis
ANISO_THRESHOLD = 3 # determines when a sample is considered anisotropic (3 means that the spacing in the low
# resolution axis must be 3x as large as the next largest spacing)
def resample_torch_simple(
data: Union[torch.Tensor, np.ndarray],
new_shape: Union[Tuple[int, ...], List[int], np.ndarray],
is_seg: bool = False,
num_threads: int = 4,
device: torch.device = torch.device('cpu'),
memefficient_seg_resampling: bool = False,
mode='linear'
):
if mode == 'linear':
if data.ndim == 4:
torch_mode = 'trilinear'
elif data.ndim == 3:
torch_mode = 'bilinear'
else:
raise RuntimeError
else:
torch_mode = mode
if isinstance(new_shape, np.ndarray):
new_shape = [int(i) for i in new_shape]
if all([i == j for i, j in zip(new_shape, data.shape[1:])]):
return data
else:
n_threads = torch.get_num_threads()
torch.set_num_threads(num_threads)
new_shape = tuple(new_shape)
with torch.no_grad():
input_was_numpy = isinstance(data, np.ndarray)
if input_was_numpy:
data = torch.from_numpy(data).to(device)
else:
orig_device = deepcopy(data.device)
data = data.to(device)
if is_seg:
unique_values = torch.unique(data)
result_dtype = torch.int8 if max(unique_values) < 127 else torch.int16
result = torch.zeros((data.shape[0], *new_shape), dtype=result_dtype, device=device)
if not memefficient_seg_resampling:
# believe it or not, the implementation below is 3x as fast (at least on Liver CT and on CPU)
# Why? Because argmax is slow. The implementation below immediately sets most locations and only lets the
# uncertain ones be determined by argmax
# unique_values = torch.unique(data)
# result = torch.zeros((len(unique_values), data.shape[0], *new_shape), dtype=torch.float16)
# for i, u in enumerate(unique_values):
# result[i] = F.interpolate((data[None] == u).float() * 1000, new_shape, mode='trilinear', antialias=False)[0]
# result = unique_values[result.argmax(0)]
result_tmp = torch.zeros((len(unique_values), data.shape[0], *new_shape), dtype=torch.float16,
device=device)
scale_factor = 1000
done_mask = torch.zeros_like(result, dtype=torch.bool, device=device)
for i, u in enumerate(unique_values):
result_tmp[i] = \
F.interpolate((data[None] == u).float() * scale_factor, new_shape, mode=torch_mode,
antialias=False)[0]
mask = result_tmp[i] > (0.7 * scale_factor)
result[mask] = u.item()
done_mask |= mask
if not torch.all(done_mask):
# print('resolving argmax', torch.sum(~done_mask), "voxels to go")
result[~done_mask] = unique_values[result_tmp[:, ~done_mask].argmax(0)].to(result_dtype)
else:
for i, u in enumerate(unique_values):
if u == 0:
pass
result[F.interpolate((data[None] == u).float(), new_shape, mode=torch_mode, antialias=False)[
0] > 0.5] = u
else:
result = F.interpolate(data[None].float(), new_shape, mode=torch_mode, antialias=False)[0]
if input_was_numpy:
result = result.cpu().numpy()
else:
result = result.to(orig_device)
torch.set_num_threads(n_threads)
return result
def resample_torch_fornnunet(
data: Union[torch.Tensor, np.ndarray],
new_shape: Union[Tuple[int, ...], List[int], np.ndarray],
current_spacing: Union[Tuple[float, ...], List[float], np.ndarray],
new_spacing: Union[Tuple[float, ...], List[float], np.ndarray],
is_seg: bool = False,
num_threads: int = 4,
device: torch.device = torch.device('cpu'),
memefficient_seg_resampling: bool = False,
force_separate_z: Union[bool, None] = None,
separate_z_anisotropy_threshold: float = ANISO_THRESHOLD,
mode='linear',
aniso_axis_mode='nearest-exact'
):
"""
data must be c, x, y, z
"""
assert data.ndim == 4, "data must be c, x, y, z"
new_shape = [int(i) for i in new_shape]
orig_shape = data.shape
do_separate_z, axis = determine_do_sep_z_and_axis(force_separate_z, current_spacing, new_spacing,
separate_z_anisotropy_threshold)
# print('shape', data.shape, 'current_spacing', current_spacing, 'new_spacing', new_spacing, 'do_separate_z', do_separate_z, 'axis', axis)
if do_separate_z:
was_numpy = isinstance(data, np.ndarray)
if was_numpy:
data = torch.from_numpy(data)
if isinstance(axis, list):
assert len(axis) == 1
axis = axis[0]
else:
pass
tmp = "xyz"
axis_letter = tmp[axis]
others_int = [i for i in range(3) if i != axis]
others = [tmp[i] for i in others_int]
# reshape by overloading c channel
data = rearrange(data, f"c x y z -> (c {axis_letter}) {others[0]} {others[1]}")
# reshape in-plane
tmp_new_shape = [new_shape[i] for i in others_int]
data = resample_torch_simple(data, tmp_new_shape, is_seg=is_seg, num_threads=num_threads, device=device,
memefficient_seg_resampling=memefficient_seg_resampling, mode=mode)
data = rearrange(data, f"(c {axis_letter}) {others[0]} {others[1]} -> c x y z",
**{
axis_letter: orig_shape[axis + 1],
others[0]: tmp_new_shape[0],
others[1]: tmp_new_shape[1]
}
)
# reshape out of plane w/ nearest
data = resample_torch_simple(data, new_shape, is_seg=is_seg, num_threads=num_threads, device=device,
memefficient_seg_resampling=memefficient_seg_resampling, mode=aniso_axis_mode)
if was_numpy:
data = data.numpy()
return data
else:
return resample_torch_simple(data, new_shape, is_seg, num_threads, device, memefficient_seg_resampling)
if __name__ == '__main__':
torch.set_num_threads(16)
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