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import torch
import numpy as np
import SimpleITK as sitk
import os
from light_training.preprocessing.resampling.default_resampling import resample_data_or_seg_to_shape
from scipy import ndimage
import skimage.measure as measure
class dummy_context(object):
def __enter__(self):
pass
def __exit__(self, exc_type, exc_val, exc_tb):
pass
def large_connected_domain(label):
cd, num = measure.label(label, return_num=True, connectivity=1)
volume = np.zeros([num])
for k in range(num):
volume[k] = ((cd == (k + 1)).astype(np.uint8)).sum()
volume_sort = np.argsort(volume)
# print(volume_sort)
label = (cd == (volume_sort[-1] + 1)).astype(np.uint8)
label = ndimage.binary_fill_holes(label)
label = label.astype(np.uint8)
return label
class Predictor:
def __init__(self, window_infer, mirror_axes=None) -> None:
self.window_infer = window_infer
self.mirror_axes = mirror_axes
@staticmethod
def predict_raw_probability(model_output, properties):
if len(model_output.shape) == 5:
model_output = model_output[0]
shape_before_resample = model_output.shape
if isinstance(model_output, torch.Tensor):
model_output = model_output.cpu().numpy()
spacing = properties["spacing"]
new_spacing = [spacing[0].item(), spacing[1].item(), spacing[2].item()]
new_spacing_trans = new_spacing[::-1]
print(f"current spacing is {[0.5, 0.70410156, 0.70410156]}, new_spacing is {new_spacing_trans}")
shape_after_cropping_before_resample = properties["shape_after_cropping_before_resample"]
d, w, h = shape_after_cropping_before_resample[0].item(), shape_after_cropping_before_resample[1].item(), shape_after_cropping_before_resample[2].item()
# model_output = torch.nn.functional.interpolate(model_output, mode="nearest", size=(d, w, h))
model_output = resample_data_or_seg_to_shape(model_output,
new_shape=(d, w, h),
current_spacing=[0.5, 0.70410156, 0.70410156],
new_spacing=new_spacing_trans,
is_seg=False,
order=1,
order_z=0)
shape_after_resample = model_output.shape
print(f"before resample shape: {shape_before_resample}, after resample shape: {shape_after_resample}")
return model_output
@staticmethod
def apply_nonlinear(model_output, nonlinear_type="softmax"):
if isinstance(model_output, np.ndarray):
model_output = torch.from_numpy(model_output)
assert len(model_output.shape) == 4
assert nonlinear_type in ["softmax", "sigmoid"]
if nonlinear_type == "softmax":
model_output = torch.softmax(model_output, dim=0)
model_output = model_output.argmax(dim=0)
else :
model_output = torch.sigmoid(model_output)
return model_output.numpy()
@staticmethod
def predict_noncrop_probability(model_output, properties):
assert len(model_output.shape) == 3
shape_before_cropping = properties["shape_before_cropping"]
none_crop_pred = np.zeros([shape_before_cropping[0], shape_before_cropping[1], shape_before_cropping[2]], dtype=np.uint8)
bbox_used_for_cropping = properties["bbox_used_for_cropping"]
none_crop_pred[
bbox_used_for_cropping[0][0]: bbox_used_for_cropping[0][1],
bbox_used_for_cropping[1][0]: bbox_used_for_cropping[1][1],
bbox_used_for_cropping[2][0]: bbox_used_for_cropping[2][1]] = model_output
return model_output
def maybe_mirror_and_predict(self, x, model, **kwargs) -> torch.Tensor:
# mirror_axes = [0, 1, 2]
window_infer = self.window_infer
device = next(model.parameters()).device
with torch.no_grad():
prediction = window_infer(x, model, **kwargs)
mirror_axes = self.mirror_axes
if mirror_axes is not None:
# check for invalid numbers in mirror_axes
# x should be 5d for 3d images and 4d for 2d. so the max value of mirror_axes cannot exceed len(x.shape) - 3
assert max(mirror_axes) <= len(x.shape) - 3, 'mirror_axes does not match the dimension of the input!'
num_predictons = 2 ** len(mirror_axes)
if 0 in mirror_axes:
prediction += torch.flip(window_infer(torch.flip(x, (2,)), model, **kwargs), (2,))
if 1 in mirror_axes:
prediction += torch.flip(window_infer(torch.flip(x, (3,)), model, **kwargs), (3,))
if 2 in mirror_axes:
prediction += torch.flip(window_infer(torch.flip(x, (4,)), model, **kwargs), (4,))
if 0 in mirror_axes and 1 in mirror_axes:
prediction += torch.flip(window_infer(torch.flip(x, (2, 3)), model, **kwargs), (2, 3))
if 0 in mirror_axes and 2 in mirror_axes:
prediction += torch.flip(window_infer(torch.flip(x, (2, 4)), model, **kwargs), (2, 4))
if 1 in mirror_axes and 2 in mirror_axes:
prediction += torch.flip(window_infer(torch.flip(x, (3, 4)), model, **kwargs), (3, 4))
if 0 in mirror_axes and 1 in mirror_axes and 2 in mirror_axes:
prediction += torch.flip(window_infer(torch.flip(x, (2, 3, 4)), model, **kwargs), (2, 3, 4))
prediction /= num_predictons
return prediction
def save_to_nii(self, return_output,
raw_spacing,
save_dir,
case_name,
postprocess=False):
return_output = return_output.astype(np.uint8)
# # postprocessing
if postprocess:
return_output = large_connected_domain(return_output)
return_output = sitk.GetImageFromArray(return_output)
return_output.SetSpacing((raw_spacing[0].item(), raw_spacing[1].item(), raw_spacing[2].item()))
sitk.WriteImage(return_output, os.path.join(save_dir, f"{case_name}.nii.gz")) |