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run_2d_upl.py
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from utils import parse_config, set_random,niiDataset
from unet import UNet
from torch.utils.data import DataLoader
import torch
import matplotlib
import os
import argparse
from test_run import test
from metrics import dice_eval
import numpy as np
matplotlib.use('Agg')
def get_data_loader(config,dataset,target):
batch_size = config['train']['batch_size']
data_root_mms = config['train']['data_root_mms']
data_root_fb = config['train']['data_root_fb']
if dataset == 'mms':
train_img = data_root_mms+'/train/img/{}'.format(target)
train_lab = data_root_mms+'/train/lab/{}'.format(target)
valid_img = data_root_mms+'/valid/img/{}'.format(target)
valid_lab = data_root_mms+'/valid/lab/{}'.format(target)
test_img = data_root_mms+'/test/img/{}'.format(target)
test_lab = data_root_mms+'/test/lab/{}'.format(target)
elif dataset == 'fb':
train_img = data_root_fb+'/image/train'
train_lab = data_root_fb+'/label/train'
valid_img = data_root_fb+'/image/valid'
valid_lab = data_root_fb+'/label/valid'
test_img = data_root_fb+'/image/test'
test_lab = data_root_fb+'/label/test'
train_test = niiDataset(train_img,train_lab, dataset=dataset, target = target, phase = 'train')
train_loader = DataLoader(train_test, batch_size = batch_size,shuffle=True, drop_last=True)
val_dataset = niiDataset(valid_img,valid_lab, dataset=dataset, target = target, phase = 'valid')
valid_loader = DataLoader(val_dataset, batch_size=1,shuffle=False, drop_last=False)
test_dataset = niiDataset(test_img,test_lab, dataset=dataset, target = target, phase = 'test')
test_loader = DataLoader(test_dataset, batch_size=1,shuffle=False, drop_last=False)
return train_loader,valid_loader,test_loader
def train(config,train_loader,valid_loader,test_loader,target,list_data):
# load exp_name
exp_name = config['train']['exp_name']
dataset = config['train']['dataset']
if dataset=='fb':
num_classes = config['network']['n_classes_fb']
elif dataset=='mms':
num_classes = config['network']['n_classes_mms']
# load model
device = torch.device('cuda:{}'.format(config['train']['gpu']))
upl_model = UNet(config).to(device)
upl_model.train()
if dataset == 'fb':
upl_model.load_state_dict(torch.load(config['train']['source_model_root_fb'],map_location='cpu'),strict=False)
elif dataset == 'mms':
upl_model.load_state_dict(torch.load(config['train']['source_model_root_mms'],map_location='cpu'),strict=False)
dec1 = upl_model.aux_dec1.state_dict()
upl_model.aux_dec2.load_state_dict(dec1)
upl_model.aux_dec3.load_state_dict(dec1)
upl_model.aux_dec4.load_state_dict(dec1)
# load train details
num_epochs = config['train']['num_epochs']
valid_epochs = config['train']['valid_epoch']
j = 0
best_dice = 0.
for epoch in range(num_epochs):
for i, (B, B_label, _,_) in enumerate(train_loader):
B = B.to(device).detach()
B_label = B_label.to(device).detach()
if config['train']['train_target']:
upl_model.save_nii(B)
upl_model.trian_target(B)
else:
upl_model.train_source(B,B_label)
if (epoch) % valid_epochs == 0:
current_dice = 0.
with torch.no_grad():
upl_model.eval()
for it,(xt,xt_label,xt_name,lab_Imag) in enumerate(valid_loader):
xt = xt.to(device)
xt_label = xt_label.numpy().squeeze().astype(np.uint8)
output,_ = upl_model.test_with_name(xt,xt_name)
output = output.squeeze(0)
output = torch.argmax(output,dim=1)
output_ = output.cpu().numpy()
xt = xt.detach().cpu().numpy().squeeze()
output = output_.squeeze()
one_case_dice = dice_eval(output,xt_label,num_classes) * 100
one_case_dice = np.array(one_case_dice)
one_case_dice = np.mean(one_case_dice,axis=0)
current_dice += one_case_dice
if (current_dice / (it+1)) > best_dice:
best_dice = current_dice / (it+1)
model_dir = "save_model_revised_TMI/" + str(exp_name+'_'+target)
if(not os.path.exists(model_dir)):
os.mkdir(model_dir)
best_epoch = '{}/model-{}-{}-{}.pth'.format(model_dir, 'best', str(j+1), best_dice)
torch.save(upl_model.state_dict(), best_epoch)
torch.save(upl_model.state_dict(), '{}/model-{}.pth'.format(model_dir, 'latest'))
upl_model.load_state_dict(torch.load(best_epoch,map_location='cpu'),strict=False)
upl_model.eval()
test(config,upl_model,valid_loader,test_loader,list_data)
return list_data
def mian():
# load config
# load config
parser = argparse.ArgumentParser(description='config file')
parser.add_argument('--config', type=str, default="./config\train2d.cfg",
help='Path to the configuration file')
args = parser.parse_args()
config = args.config
config = parse_config(config)
list_data = []
print(config)
dataset = config['train']['dataset']
for dataset in ['mms','fb']:
if dataset == 'mms':
for target in ['B','C','D']:
config['train']['dataset'] = dataset
list_data.append(dataset)
list_data.append(target)
train_loader,valid_loader,test_loader = get_data_loader(config,dataset,target)
list_data = train(config,train_loader,valid_loader,test_loader,target,list_data)
elif dataset == 'fb':
for target in ['fb']:
config['train']['dataset'] = dataset
list_data.append(dataset)
list_data.append(target)
train_loader,valid_loader,test_loader = get_data_loader(config,dataset,target)
list_data = train(config,train_loader,valid_loader,test_loader,target,list_data)
with open('result_data/source_wo_en.txt', 'w') as file:
for line in list_data:
file.write(line + "\n")
if __name__ == '__main__':
set_random()
mian()