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import torch
import numpy as np
import SimpleITK
import os
import sys
from monai.inferers import SlidingWindowInferer
class Customalgorithm(): # SegmentationAlgorithm is not inherited in this class anymore
def __init__(self):
"""
Do not modify the `self.input_dir` and `self.output_dir`.
(Check https://grand-challenge.org/algorithms/interfaces/)
"""
self.input_dir = "/input/"
self.output_dir = "/output/images/head-neck-segmentation/"
# self.out_spacing = [3.0, 0.54199219, 0.54199219]
self.out_spacing = [3.0, 1.0, 1.0]
# self.device = "cpu"
self.device = torch.device("cuda")
self.patch_size = [64, 128, 128]
def filte_state_dict(self, sd):
if "module" in sd :
sd = sd["module"]
new_sd = {}
for k, v in sd.items():
k = str(k)
new_k = k[7:] if k.startswith("module") else k
new_sd[new_k] = v
del sd
return new_sd
def convert_mha_to_nii(self, mha_input_path, nii_out_path): # nnUNet specific
img = SimpleITK.ReadImage(mha_input_path)
print(img.GetSize())
SimpleITK.WriteImage(img, nii_out_path, True)
def convert_nii_to_mha(self, nii_input_path, mha_out_path): # nnUNet specific
img = SimpleITK.ReadImage(nii_input_path)
SimpleITK.WriteImage(img, mha_out_path, True)
def read(self, mha_path):
img = SimpleITK.ReadImage(mha_path)
spacing = img.GetSpacing()
raw_size = SimpleITK.GetArrayFromImage(img).shape
img = SimpleITK.GetArrayFromImage(img)[None,].astype(np.float32)
properties = {
"spacing": spacing,
"raw_size": raw_size
}
return img, properties
def check_gpu(self):
"""
Check if GPU is available. Note that the Grand Challenge only has one available GPU.
"""
print('Checking GPU availability')
is_available = torch.cuda.is_available()
print('Available: ' + str(is_available))
print(f'Device count: {torch.cuda.device_count()}')
if is_available:
print(f'Current device: {torch.cuda.current_device()}')
print('Device name: ' + torch.cuda.get_device_name(0))
print('Device memory: ' +
str(torch.cuda.get_device_properties(0).total_memory))
def load_inputs(self): # use two modalities input data
"""
Read input data (two modalities) from `self.input_dir` (/input/).
Please do not modify the path for CT and contrast-CT images.
"""
ct_mha = os.listdir(os.path.join(self.input_dir, 'images/head-neck-ct/'))[0]
ctc_mha = os.listdir(os.path.join(self.input_dir, 'images/head-neck-contrast-enhanced-ct/'))[0]
uuid = os.path.splitext(ct_mha)[0]
img, properties = self.read(os.path.join(self.input_dir, 'images/head-neck-ct/', ct_mha))
img_c, _ = self.read(os.path.join(self.input_dir, 'images/head-neck-contrast-enhanced-ct/', ctc_mha))
data = np.concatenate([img, img_c], axis=0)
del img
del img_c
# data is (2, d, w, h)
return uuid, data, properties
def crop(self, data, properties):
from light_training.preprocessing.cropping.cropping import crop_to_nonzero
seg = np.zeros_like(data)
shape_before_cropping = data.shape[1:]
## crop
properties['shape_before_cropping'] = shape_before_cropping
# this command will generate a segmentation. This is important because of the nonzero mask which we may need
data, seg, bbox = crop_to_nonzero(data, seg)
del seg
properties['bbox_used_for_cropping'] = bbox
return data, properties
def resample(self, data, properties):
from light_training.preprocessing.resampling.default_resampling import compute_new_shape, resample_data_or_seg_to_shape
# crop, remember to store size before cropping!
shape_before_resample = data.shape[1:]
properties['shape_after_cropping_before_resample'] = shape_before_resample
new_shape = compute_new_shape(data.shape[1:], original_spacing_trans, self.out_spacing)
assert len(data.shape) == 4
data = resample_data_or_seg_to_shape(data, new_shape,
original_spacing,
self.out_spacing,
order=3,
order_z=0)
properties['shape_after_resample'] = new_shape
return data, properties
def preprocess(self, data, properties, crop_first=True):
from light_training.process_framework.norm import norm_func
original_spacing = list(properties['spacing'])
## 由于old spacing读出来是反的，因此这里需要转置一下
original_spacing_trans = original_spacing[::-1]
properties["original_spacing_trans"] = original_spacing_trans
properties["target_spacing_trans"] = self.out_spacing
if crop_first:
data, properties = self.crop(data, properties)
data = norm_func(data)
if not crop_first:
data, properties = self.crop(data, properties)
data, properties = self.resample(data, properties)
data = data[None,]
data = torch.from_numpy(data)
return data, properties
def predict(self, data, properties, uid):
torch.cuda.empty_cache()
from models.nnunet3d import NNUNetWrapper
model = NNUNetWrapper(norm="ins")
new_sd = self.filte_state_dict(torch.load("./weight/unet3d_0_addaug_bs2_ep1000_ds_gpu4/final_model_0.8552.pt", map_location="cpu"))
model.load_state_dict(new_sd)
del new_sd
torch.cuda.empty_cache()
# data = data.to(self.deivce)
# model.to(self.device)
model.eval()
window_infer = SlidingWindowInferer(roi_size=self.patch_size,
sw_batch_size=1,
overlap=0.5,
progress=True,
mode="gaussian")
predictor = Predictor(window_infer, mirror_axes=None)
try:
ensemble_output = predictor.maybe_mirror_and_predict(data, model, self.device)
except RuntimeError:
ensemble_output = predictor.maybe_mirror_and_predict(data, model, torch.device("cpu"))
torch.cuda.empty_cache()
del model
del data
print(f"prediction done")
ensemble_output = predictor.predict_raw_probability(ensemble_output, properties)
print(f"non linear....")
# ensemble_output = predictor.apply_nonlinear(ensemble_output, nonlinear_type="sigmoid")
ensemble_output = ensemble_output > 0
print(f"restore crop...")
ensemble_output = predictor.predict_noncrop_probability(ensemble_output, properties)
raw_spacing = properties["spacing"]
case_name = uid
print(f"uuid is {uid}")
os.makedirs(os.path.dirname(self.output_dir), exist_ok=True)
print(f"saving....")
predictor.save_to_nii_multi_organ(ensemble_output,
raw_spacing,
save_dir=self.output_dir,
case_name=case_name,
postprocess=False)
# """
# load the model and checkpoint, and generate the predictions. You can replace this part with your own model.
# """
# predict_from_folder_segrap2023(self.weight, self.nii_path, self.result_path, 0, 0, 1)
# print("nnUNet segmentation done!")
# if not os.path.exists(os.path.join(self.result_path, self.nii_seg_file)):
# print('waiting for nnUNet segmentation to be created')
# while not os.path.exists(os.path.join(self.result_path, self.nii_seg_file)):
# import time
# print('.', end='')
# time.sleep(5)
# # print(cproc) # since nnUNet_predict call is split into prediction and postprocess, a pre-mature exit code is received but segmentation file not yet written. This hack ensures that all spawned subprocesses are finished before being printed.
# print('Prediction finished !')
def post_process(self):
self.check_gpu()
print('Start processing')
uuid, data, properties = self.load_inputs()
data, properties = self.preprocess(data, properties)
print(properties)
print('Start prediction')
self.predict(data, properties, uuid)
# print('Start output writing')
# self.write_outputs(uuid)
def process(self):
"""
Read inputs from /input, process with your algorithm and write to /output
"""
self.post_process()
if __name__ == "__main__":
Customalgorithm().process()
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