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import numpy as np
from light_training.dataloading.dataset import get_test_loader_from_test
import torch
import torch.nn as nn
from monai.networks.nets.basic_unet import BasicUNet
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 monai.utils import set_determinism
from light_training.utils.files_helper import save_new_model_and_delete_last
from models.uent3d import UNet3D
from monai.networks.nets.segresnet import SegResNet
from models.transbts.TransBTS_downsample8x_skipconnection import TransBTS
from einops import rearrange
from models.modelgenesis.unet3d import UNet3DModelGen
from models.transvw.models.ynet3d import UNet3DTransVW
from monai.networks.nets.basic_unet import BasicUNet
from monai.networks.nets.attentionunet import AttentionUnet
from light_training.loss.compound_losses import DC_and_CE_loss
from light_training.loss.dice import MemoryEfficientSoftDiceLoss
from light_training.evaluation.metric import dice
set_determinism(123)
from light_training.loss.compound_losses import DC_and_CE_loss
import os
from medpy import metric
from light_training.prediction import Predictor
data_dir = "./data/fullres/test"
env = "pytorch"
max_epoch = 1000
batch_size = 2
val_every = 2
num_gpus = 1
device = "cuda:2"
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
def get_input(self, batch):
image = batch["data"]
label = batch["seg"]
properties = batch["properties"]
# label = self.convert_labels(label)
del batch
return image, label, properties
def define_model_diffunet(self):
from models.nnunet_denoise_ddp_infer.get_unet3d_denoise_uncer_edge import DiffUNet
model = DiffUNet(1, 10, 3, 1, bta=True)
model_path = "/home/xingzhaohu/zongweizhou/logs_gpu4/diffunet/model/final_model_0.8384.pt"
new_sd = self.filte_state_dict(torch.load(model_path, map_location="cpu"))
model.load_state_dict(new_sd, strict=False)
model.eval()
window_infer = SlidingWindowInferer(roi_size=patch_size,
sw_batch_size=2,
overlap=0.3,
progress=True,
mode="gaussian")
predictor = Predictor(window_infer=window_infer,
mirror_axes=[0,1,2])
save_path = "./prediction_results/diffunet_ep1000_test"
os.makedirs(save_path, exist_ok=True)
return model, predictor, save_path
def validation_step(self, batch):
image, label, properties = self.get_input(batch)
print(properties['spacing'])
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).cpu()
model_output = model_output.argmax(dim=0)
model_output = predictor.predict_noncrop_probability(model_output, properties)
print(f"save shape is {model_output.shape}")
seg_list = ["aorta", "gall_bladder", "kidney_left",
"kidney_right", "liver", "pancreas",
"postcava", "spleen", "stomach"]
save_path = os.path.join(save_path, properties['name'][0], "predictions")
# print(f"save_path is {save_path}")
os.makedirs(save_path, exist_ok=True)
for i in range(1, len(seg_list) + 1):
model_output_c = model_output == i
predictor.save_to_nii(model_output_c,
raw_spacing=properties['spacing'],
case_name=seg_list[i-1],
save_dir=save_path)
return 0
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__)
test_ds = get_test_loader_from_test(data_dir=data_dir)
trainer.validation_single_gpu(test_ds)
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