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nirps.py
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nirps.py
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import torch
from configuration.config import parse_arguments_nirps
import yaml
import os
from tools.utilize import *
from data_io.brats import BraTS2021
from data_io.ixi import IXI
from torch.utils.data import DataLoader
from arch_centralized.cyclegan import CycleGAN
from arch_centralized.munit import Munit
from arch_centralized.unit import Unit
import warnings
warnings.filterwarnings("ignore")
class NIRPS(object):
def __init__(self, args):
self.args = args
def load_config(self):
with open('./configuration/nirps/3_dataset_base/{}.yaml'.format(self.args.dataset), 'r') as f:
config_model = yaml.load(f, Loader=yaml.SafeLoader)
with open('./configuration/nirps/2_train_base/centralized_training.yaml', 'r') as f:
config_train = yaml.load(f, Loader=yaml.SafeLoader)
with open('./configuration/nirps/1_model_base/{}.yaml'.format(self.args.model), 'r') as f:
config_dataset = yaml.load(f, Loader=yaml.SafeLoader)
config = override_config(config_model, config_train)
config = override_config(config, config_dataset)
self.para_dict = merge_config(config, self.args)
self.args = extract_config(self.args)
def preliminary(self):
print('---------------------')
print(self.args)
print('---------------------')
print(self.para_dict)
print('---------------------')
seed_everything(self.para_dict['seed'])
device, device_ids = parse_device_list(self.para_dict['gpu_ids'], int(self.para_dict['gpu_id']))
self.device = torch.device("cuda", device)
self.file_path = record_path(self.para_dict)
#save log
save_arg(self.para_dict, self.file_path)
save_script(__file__, self.file_path)
self.fid_stats_from_a_to_b = '{}/{}/{}_{}_fid_stats.npz'.format(
self.para_dict['fid_dir'], self.para_dict['dataset'], self.para_dict['source_domain'], self.para_dict['target_domain'])
self.fid_stats_from_b_to_a = '{}/{}/{}_{}_fid_stats.npz'.format(
self.para_dict['fid_dir'], self.para_dict['dataset'], self.para_dict['target_domain'], self.para_dict['source_domain'])
if not os.path.exists(self.fid_stats_from_a_to_b):
os.system(r'python3 fid_stats.py --dataset {} --source-domain {} --target-domain {} --gpu-id {} --valid-path {}'.format(
self.para_dict['dataset'], self.para_dict['source_domain'], self.para_dict['target_domain'], self.para_dict['gpu_id'], self.para_dict['valid_path']))
if not os.path.exists(self.fid_stats_from_a_to_b):
raise NotImplementedError('FID Still Not be Implemented Yet')
else:
print('fid stats from a to b: {}'.format(self.fid_stats_from_a_to_b))
if not os.path.exists(self.fid_stats_from_b_to_a):
os.system(r'python3 fid_stats.py --dataset {} --source-domain {} --target-domain {} --gpu-id {} --valid-path {}'.format(
self.para_dict['dataset'], self.para_dict['target_domain'], self.para_dict['source_domain'], self.para_dict['gpu_id'], self.para_dict['valid_path']))
if not os.path.exists(self.fid_stats_from_b_to_a):
raise NotImplementedError('FID Still Not be Implemented Yet')
else:
print('fid stats from b to a: {}'.format(self.fid_stats_from_b_to_a))
print('work dir: {}'.format(self.file_path))
def setup_folder(self):
# fp: file path
self.dataset_fp = os.path.join('nirps', self.para_dict['dataset'])
create_folders(self.dataset_fp)
self.source_modality_fp = os.path.join(self.dataset_fp, self.para_dict['source_domain'])
create_folders(self.source_modality_fp)
self.target_modality_fp = os.path.join(self.dataset_fp, self.para_dict['target_domain'])
create_folders(self.target_modality_fp)
self.model_source_fp = os.path.join(self.source_modality_fp, self.para_dict['model'])
self.model_target_fp = os.path.join(self.target_modality_fp, self.para_dict['model'])
create_folders(self.model_source_fp)
create_folders(self.model_target_fp)
self.model_source_gt_fp = os.path.join(self.model_source_fp, 'gt')
self.model_target_gt_fp = os.path.join(self.model_target_fp, 'gt')
for i in range(self.para_dict['num_epoch']):
epoch_model_source_fp = os.path.join(self.model_source_fp, str(i))
epoch_model_target_fp = os.path.join(self.model_target_fp, str(i))
create_folders(epoch_model_source_fp)
create_folders(epoch_model_target_fp)
def load_data(self):
self.normal_transform = [{'degrees':0, 'translate':[0.00, 0.00],
'scale':[1.00, 1.00],
'size':(self.para_dict['size'], self.para_dict['size'])},
{'degrees':0, 'translate':[0.00, 0.00],
'scale':[1.00, 1.00],
'size':(self.para_dict['size'], self.para_dict['size'])}]
if self.para_dict['dataset'] == 'brats2021':
assert self.para_dict['source_domain'] in ['t1', 't2', 'flair']
assert self.para_dict['target_domain'] in ['t1', 't2', 'flair']
self.train_dataset = BraTS2021(root=self.para_dict['data_path'],
modalities=[self.para_dict['source_domain'], self.para_dict['target_domain']],
extract_slice=[self.para_dict['es_lower_limit'], self.para_dict['es_higher_limit']],
noise_type='normal',
learn_mode='train',
transform_data=self.normal_transform,
client_weights=[1.0],
data_mode=self.para_dict['data_mode'],
data_num=self.para_dict['data_num'],
data_paired_weight=1.0,
data_moda_ratio=1.0,
data_moda_case='case1')
self.valid_dataset = BraTS2021(root=self.para_dict['valid_path'],
modalities=[self.para_dict['source_domain'], self.para_dict['target_domain']],
noise_type='normal',
learn_mode='test',
extract_slice=[self.para_dict['es_lower_limit'], self.para_dict['es_higher_limit']],
transform_data=self.normal_transform,
data_mode='paired',
assigned_data=True,
assigned_images=None)
elif self.para_dict['dataset'] == 'ixi':
assert self.para_dict['source_domain'] in ['t2', 'pd']
assert self.para_dict['target_domain'] in ['t2', 'pd']
self.train_dataset = IXI(root=self.para_dict['data_path'],
modalities=[self.para_dict['source_domain'], self.para_dict['target_domain']],
extract_slice=[self.para_dict['es_lower_limit'], self.para_dict['es_higher_limit']],
noise_type='normal',
learn_mode='train',
transform_data=self.normal_transform,
data_mode=self.para_dict['data_mode'],
data_num=self.para_dict['data_num'],
data_paired_weight=1.0,
client_weights=[1.0],
dataset_splited=True,
data_moda_ratio=1.0,
data_moda_case='case1')
self.valid_dataset = IXI(root=self.para_dict['data_path'],
modalities=[self.para_dict['source_domain'], self.para_dict['target_domain']],
extract_slice=[self.para_dict['es_lower_limit'], self.para_dict['es_higher_limit']],
noise_type='normal',
learn_mode='test',
transform_data=self.normal_transform,
data_mode='paired',
dataset_splited=True)
else:
raise NotImplementedError('This Dataset Has Not Been Implemented Yet')
self.train_loader = DataLoader(self.train_dataset,
batch_size=self.para_dict['batch_size'],
num_workers=self.para_dict['num_workers'],
shuffle=True)
self.valid_loader = DataLoader(self.valid_dataset,
num_workers=self.para_dict['num_workers'],
batch_size=1,
shuffle=False)
self.assigned_loader = None
def init_model(self):
if self.para_dict['model'] == 'cyclegan':
self.trainer = CycleGAN(self.para_dict, self.train_loader, self.valid_loader,
self.assigned_loader, self.device, self.file_path)
elif self.para_dict['model'] == 'munit':
self.trainer = Munit(self.para_dict, self.train_loader, self.valid_loader,
self.assigned_loader, self.device, self.file_path)
elif self.para_dict['model'] == 'unit':
self.trainer = Unit(self.para_dict, self.train_loader, self.valid_loader,
self.assigned_loader, self.device, self.file_path)
else:
raise ValueError('Model is invalid!')
def save_models(self, fp=None, epoch=None):
if self.para_dict['model'] == 'cyclegan':
gener_from_a_to_b, gener_from_b_to_a, discr_from_a_to_b, discr_from_b_to_a = self.trainer.get_model()
save_model_per_epoch(gener_from_a_to_b, '{}/checkpoint/g_from_a_to_b'.format(fp), self.para_dict, epoch)
save_model_per_epoch(gener_from_b_to_a, '{}/checkpoint/g_from_b_to_a'.format(fp), self.para_dict, epoch)
elif self.para_dict['model'] == 'munit' or self.para_dict['model'] == 'unit':
gener_from_a_to_b_enc, gener_from_a_to_b_dec, gener_from_b_to_a_enc, gener_from_b_to_a_dec, discr_from_a_to_b, discr_from_b_to_a = self.trainer.get_model()
save_model(gener_from_a_to_b_enc, '{}/checkpoint/g_from_a_to_b_enc'.format(fp), self.para_dict, epoch)
save_model(gener_from_a_to_b_dec, '{}/checkpoint/g_from_a_to_b_dec'.format(fp), self.para_dict, epoch)
save_model(gener_from_b_to_a_enc, '{}/checkpoint/g_from_b_to_a_enc'.format(fp), self.para_dict, epoch)
save_model(gener_from_b_to_a_dec, '{}/checkpoint/g_from_b_to_a_dec'.format(fp), self.para_dict, epoch)
def work_flow(self):
self.trainer.train_epoch()
# evaluation from a to b
if self.para_dict['general_evaluation']:
(source_mae, source_psnr, source_ssim, source_fid, target_mae, target_psnr, target_ssim, target_fid) = self.trainer.evaluation(direction='both')
src_infor = '[Epoch {}/{}] [{}] mae: {:.4f} psnr: {:.4f} ssim: {:.4f} fid: {:.4f}'.format(
self.epoch+1, self.para_dict['num_epoch'], self.para_dict['source_domain'], source_mae, source_psnr, source_ssim, source_fid)
tag_infor = '[Epoch {}/{}] [{}] mae: {:.4f} psnr: {:.4f} ssim: {:.4f} fid: {:.4f}'.format(
self.epoch+1, self.para_dict['num_epoch'], self.para_dict['target_domain'], target_mae, target_psnr, target_ssim, target_fid)
print(src_infor)
print(tag_infor)
save_log(src_infor, self.file_path, description='both')
save_log(tag_infor, self.file_path, description='both')
for i in range(int(self.para_dict['num_epoch'])):
epoch_model_source_fp = os.path.join(self.model_source_fp, str(i))
epoch_model_target_fp = os.path.join(self.model_target_fp, str(i))
self.save_models(fp=epoch_model_source_fp, epoch=i)
self.save_models(fp=epoch_model_target_fp, epoch=i)
self.trainer.infer_nirps_generated(src_epoch_path=epoch_model_source_fp,
tag_epoch_path=epoch_model_target_fp,
data_loader=self.valid_loader)
self.trainer.infer_nirps_gt(src_gt_path=self.model_source_gt_fp,
tag_gt_path=self.model_target_gt_fp,
data_loader=self.valid_loader)
def run_work_flow(self):
self.load_config()
self.preliminary()
self.setup_folder()
self.load_data()
self.init_model()
print('---------------------')
for epoch in range(self.para_dict['num_epoch']):
self.epoch = epoch
self.work_flow()
print('work dir: {}'.format(self.file_path))
with open('{}/log_finished.txt'.format(self.para_dict['work_dir']), 'a') as f:
print('\n---> work dir {}'.format(self.file_path), file=f)
print(self.args, file=f)
print('---------------------')
if __name__ == '__main__':
args = parse_arguments_nirps()
for key_arg in ['dataset', 'model', 'source_domain', 'target_domain']:
if not vars(args)[key_arg]:
raise ValueError('Parameter {} must be refered!'.format(key_arg))
work = NIRPS(args=args)
work.run_work_flow()