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4_predict_brats2023.py
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4_predict_brats2023.py
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from light_training.dataloading.dataset import get_train_test_loader_from_test_list
import torch
from monai.networks.nets.swin_unetr import SwinUNETR
from monai.inferers import SlidingWindowInferer
from light_training.evaluation.metric import dice
from light_training.trainer import Trainer
from light_training.evaluation.metric import dice
import os
from light_training.prediction import Predictor
data_dir = "./data/fullres/train"
env = "pytorch"
max_epoch = 1000
batch_size = 2
val_every = 2
num_gpus = 1
device = "cuda:6"
patch_size = [128, 128, 128]
class BraTSTrainer(Trainer):
def __init__(self, env_type, max_epochs, batch_size, device="cpu", val_every=1, num_gpus=1, logdir="./logs/", master_ip='localhost', master_port=17750, training_script="train.py"):
super().__init__(env_type, max_epochs, batch_size, device, val_every, num_gpus, logdir, master_ip, master_port, training_script)
self.patch_size = patch_size
self.augmentation = False
def convert_labels(self, labels):
## TC, WT and ET
result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
return torch.cat(result, dim=1).float()
def get_input(self, batch):
image = batch["data"]
label = batch["seg"]
properties = batch["properties"]
label = self.convert_labels(label)
# label = label[:, 0].long()
return image, label, properties
def define_model_diffunet(self):
from diffunet.diffunet_model import DiffUNet
model = DiffUNet(4, 4)
model_path = ""
new_sd = self.filte_state_dict(torch.load(model_path, map_location="cpu"))
model.load_state_dict(new_sd)
model.eval()
window_infer = SlidingWindowInferer(roi_size=patch_size,
sw_batch_size=2,
overlap=0.5,
progress=True,
mode="gaussian")
predictor = Predictor(window_infer=window_infer,
mirror_axes=[0,1,2])
save_path = "./prediction_results/diffunet"
os.makedirs(save_path, exist_ok=True)
return model, predictor, save_path
def convert_labels_dim0(self, labels):
## TC, WT and ET
result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
return torch.cat(result, dim=0).float()
def validation_step(self, batch):
image, label, properties = self.get_input(batch)
ddim = True
model, predictor, save_path = self.define_model_diffunet()
if ddim:
model_output = predictor.maybe_mirror_and_predict(image, model, device=device, ddim=True)
else :
model_output = predictor.maybe_mirror_and_predict(image, model, device=device)
model_output = predictor.predict_raw_probability(model_output,
properties=properties)
model_output = model_output.argmax(dim=0)[None]
model_output = self.convert_labels_dim0(model_output)
label = label[0]
c = 3
dices = []
for i in range(0, c):
output_i = model_output[i].cpu().numpy()
label_i = label[i].cpu().numpy()
d = dice(output_i, label_i)
dices.append(d)
print(dices)
model_output = predictor.predict_noncrop_probability(model_output, properties)
predictor.save_to_nii(model_output,
raw_spacing=[1,1,1],
case_name = properties['name'][0],
save_dir=save_path)
return 0
def convert_labels_dim0(self, labels):
## TC, WT and ET
result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
return torch.cat(result, dim=0).float()
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
if __name__ == "__main__":
trainer = BraTSTrainer(env_type=env,
max_epochs=max_epoch,
batch_size=batch_size,
device=device,
logdir="",
val_every=val_every,
num_gpus=num_gpus,
master_port=17751,
training_script=__file__)
from test_list_brats2023 import test_list
train_ds, test_ds = get_train_test_loader_from_test_list(data_dir=data_dir, test_list=test_list)
trainer.validation_single_gpu(test_ds)