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main_train_cdiffmr.py
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main_train_cdiffmr.py
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'''
# -----------------------------------------
Main Program for Training
CDiff for MRI_Recon
by XXX
# -----------------------------------------
'''
import os
import sys
import math
import argparse
import random
import cv2
import numpy as np
import logging
import time
import torch
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from utils import utils_logger
from utils import utils_image as util
from utils import utils_option as option
from utils.utils_dist import get_dist_info, init_dist
from utils import utils_early_stopping
from data.select_dataset import define_Dataset
from models.select_model import define_Model
from tensorboardX import SummaryWriter
from collections import OrderedDict
from skimage.transform import resize
import lpips
import wandb
def main(json_path=''):
'''
# ----------------------------------------
# Step--1 (prepare opt)
# ----------------------------------------
'''
parser = argparse.ArgumentParser()
parser.add_argument('--opt', type=str, default=json_path, help='Path to option JSON file.')
parser.add_argument('--launcher', default='pytorch', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
# parser.add_argument('--dist', default=False)
opt = option.parse(parser.parse_args().opt, is_train=True)
# opt['dist'] = parser.parse_args().dist
# distributed settings
if opt['dist']:
init_dist('pytorch')
opt['rank'], opt['world_size'] = get_dist_info()
if opt['rank'] == 0:
util.mkdirs((path for key, path in opt['path'].items() if 'pretrained' not in key))
# update opt
init_iter_model_DM, init_path_model_DM = option.find_last_checkpoint(opt['path']['models'], net_type='model_DM')
init_iter_model_EMA, init_path_model_EMA = option.find_last_checkpoint(opt['path']['models'], net_type='model_EMA')
init_iter_optimizer_DM, init_path_optimizer_DM = option.find_last_checkpoint(opt['path']['models'], net_type='optimizer_DM')
current_step = max(init_iter_model_DM, init_iter_model_EMA, init_iter_optimizer_DM)
if not opt["use_pretrain_weight"]:
opt['path']['pretrained_model_DM'] = init_path_model_DM
opt['path']['pretrained_model_EMA'] = init_path_model_EMA
opt['path']['pretrained_optimizer_DM'] = init_path_optimizer_DM
# save opt to a '../option.json' file
if opt['rank'] == 0:
option.save(opt)
# return None for missing key
opt = option.dict_to_nonedict(opt)
# Do not support DDP when using WANDB Sweep
if opt['wandb']['is_sweep']:
assert opt['rank'] == 0, 'Do not support DDP when using WANDB Sweep'
# configure logger
if opt['rank'] == 0:
# wandb init
os.environ['WANDB_MODE'] = opt['wandb']['mode']
wandb.init(project=opt['wandb']['project_name'], entity="XXX")
# sweep parameter
# check here when changing sweep yaml
if opt['wandb']['is_sweep']:
pass
# opt['train']['model_DM_optimizer_lr'] = wandb.config.model_DM_optimizer_lr
# opt['train']['model_DM_optimizer_type'] = wandb.config.model_DM_optimizer_type
# opt['datasets']['train']['dataloader_batch_size'] = wandb.config.batch_size
# logger
logger_name = 'train'
utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name+'.log'))
logger = logging.getLogger(logger_name)
logger.info(option.dict2str(opt))
# tensorbordX log
logger_tensorboard = SummaryWriter(os.path.join(opt['path']['log']))
# wandb logger
wandb.config.update(opt)
wandb.define_metric("TRAIN/step")
wandb.define_metric('TRAIN/Learning Rate', step_metric="TRAIN/step")
wandb.define_metric('TRAIN LOSS/model_DM_loss', step_metric="TRAIN/step")
wandb.define_metric('TRAIN LOSS/model_DM_loss_image', step_metric="TRAIN/step")
wandb.define_metric('TRAIN LOSS/model_DM_loss_frequency', step_metric="TRAIN/step")
wandb.define_metric('TRAIN LOSS/model_DM_loss_preceptual', step_metric="TRAIN/step")
wandb.define_metric("VAL/step")
wandb.define_metric('VAL LOSS/model_DM_loss', step_metric="VAL/step")
wandb.define_metric('VAL LOSS/model_DM_loss_image', step_metric="VAL/step")
wandb.define_metric('VAL LOSS/model_DM_loss_frequency', step_metric="VAL/step")
wandb.define_metric('VAL LOSS/model_DM_loss_preceptual', step_metric="VAL/step")
wandb.define_metric('VAL METRICS/SSIM', step_metric="VAL/step")
wandb.define_metric('VAL METRICS/PSNR', step_metric="VAL/step")
wandb.define_metric('VAL METRICS/LPIPS', step_metric="VAL/step")
wandb.define_metric('VAL METRICS/FID', step_metric="VAL/step")
# set seed
seed = opt['manual_seed']
if seed is None:
seed = random.randint(1, 10000)
print('Random seed: {}'.format(seed))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
'''
# ----------------------------------------
# Step--2 (creat dataloader)
# ----------------------------------------
'''
# ----------------------------------------
# 1) create_dataset
# 2) creat_dataloader for train and test
# ----------------------------------------
if 'val' not in list(opt['datasets'].keys()):
opt['datasets']['val'] = opt['datasets']['test']
del opt['datasets']['test']
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train':
train_set = define_Dataset(dataset_opt)
train_size = int(math.ceil(len(train_set) / dataset_opt['dataloader_batch_size']))
if opt['rank'] == 0:
logger.info('Number of train images: {:,d}, iters: {:,d}'.format(len(train_set), train_size))
if opt['dist']:
train_sampler = DistributedSampler(train_set, shuffle=dataset_opt['dataloader_shuffle'], drop_last=True, seed=seed)
train_loader = DataLoader(train_set,
batch_size=dataset_opt['dataloader_batch_size']//opt['num_gpu'],
shuffle=False,
num_workers=dataset_opt['dataloader_num_workers']//opt['num_gpu'],
drop_last=True,
pin_memory=False,
sampler=train_sampler)
else:
train_loader = DataLoader(train_set,
batch_size=dataset_opt['dataloader_batch_size'],
shuffle=dataset_opt['dataloader_shuffle'],
num_workers=dataset_opt['dataloader_num_workers'],
drop_last=True,
pin_memory=False)
elif phase == 'val':
val_set = define_Dataset(dataset_opt)
val_loader = DataLoader(val_set,
batch_size=1,
shuffle=False,
num_workers=1,
drop_last=False,
pin_memory=False)
else:
raise NotImplementedError("Phase [%s] is not recognized." % phase)
'''
# ----------------------------------------
# Step--3 (initialize model)
# ----------------------------------------
'''
# define model
model = define_Model(opt)
model.init_train()
# define LPIPS function
loss_fn_alex = lpips.LPIPS(net='alex').to(model.device)
# define early stopping
if opt['train']['is_early_stopping']:
early_stopping = utils_early_stopping.EarlyStopping(patience=opt['train']['early_stopping_num'])
# record
if opt['rank'] == 0:
logger.info(model.info_network())
logger.info(model.info_params())
'''
# ----------------------------------------
# Step--4 (main training)
# ----------------------------------------
'''
# if it is not set, keep running
max_iter_epoch = opt['train']['max_iter_epoch'] if opt['train']['max_iter_epoch'] else 100000000000
print(f"max iterative step: {max_iter_epoch}")
for epoch in range(max_iter_epoch):
if opt['dist']:
train_sampler.set_epoch(epoch)
for i, train_data in enumerate(train_loader):
current_step += 1
# -------------------------------
# 1) update learning rate
# -------------------------------
model.update_learning_rate(current_step)
# -------------------------------
# 2) feed patch pairs
# -------------------------------
model.feed_data(train_data)
# -------------------------------
# 3) optimize parameters
# -------------------------------
model.optimize_parameters(current_step)
# -------------------------------
# 4) training information
# -------------------------------
if current_step % opt['train']['checkpoint_print'] == 0 and opt['rank'] == 0:
logs = model.current_log()
message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(epoch, current_step, model.current_learning_rate())
for k, v in logs.items():
message += '{:s}: {:.3e} '.format(k, v)
logger.info(message)
# record train loss
log_wandb = {'TRAIN/step': current_step, 'TRAIN/Learning Rate': model.current_learning_rate(),}
logger_tensorboard.add_scalar('Learning Rate', model.current_learning_rate(), global_step=current_step)
logger_tensorboard.add_scalar('TRAIN Generator LOSS/model_DM_loss', logs['model_DM_loss'], global_step=current_step)
log_wandb['TRAIN LOSS/model_DM_loss'] = logs['model_DM_loss']
if 'model_DM_loss_image' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Generator LOSS/model_DM_loss_image', logs['model_DM_loss_image'], global_step=current_step)
log_wandb['TRAIN LOSS/model_DM_loss_image'] = logs['model_DM_loss_image']
if 'model_DM_loss_frequency' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Generator LOSS/model_DM_loss_frequency', logs['model_DM_loss_frequency'], global_step=current_step)
log_wandb['TRAIN LOSS/model_DM_loss_frequency'] = logs['model_DM_loss_frequency']
if 'model_DM_loss_preceptual' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Generator LOSS/model_DM_loss_preceptual', logs['model_DM_loss_preceptual'], global_step=current_step)
log_wandb['TRAIN LOSS/model_DM_loss_preceptual'] = logs['model_DM_loss_preceptual']
wandb.log(log_wandb)
# -------------------------------
# 5) save model
# -------------------------------
if current_step % opt['train']['checkpoint_save'] == 0 and opt['rank'] == 0:
logger.info('Saving the model.')
model.save(current_step)
# -------------------------------
# 6) testing
# -------------------------------
if current_step % opt['train']['checkpoint_test'] == 0 and opt['rank'] == 0:
# create folder for FID
# img_dir_tmp_x_t = os.path.join(opt['path']['images'], 'temp_x_t')
# util.mkdir(img_dir_tmp_x_t)
# img_dir_tmp_x_start = os.path.join(opt['path']['images'], 'temp_x_start')
# util.mkdir(img_dir_tmp_x_start)
# img_dir_tmp_x_direct_recon = os.path.join(opt['path']['images'], 'temp_x_direct_recon')
# util.mkdir(img_dir_tmp_x_direct_recon)
# img_dir_tmp_x_recon = os.path.join(opt['path']['images'], 'temp_x_recon')
# util.mkdir(img_dir_tmp_x_recon)
# create result dict
test_results = OrderedDict()
test_results['direct_recon'] = OrderedDict()
test_results['direct_recon']['psnr'] = []
test_results['direct_recon']['ssim'] = []
test_results['direct_recon']['lpips'] = []
test_results['recon'] = OrderedDict()
test_results['recon']['psnr'] = []
test_results['recon']['ssim'] = []
test_results['recon']['lpips'] = []
test_results['model_DM_loss'] = []
test_results['model_DM_loss_image'] = []
test_results['model_DM_loss_frequency'] = []
test_results['model_DM_loss_preceptual'] = []
for idx, test_data in enumerate(val_loader):
pass
print("Training Stop")
if __name__ == '__main__':
# pass
# main()
############################## PENDING ##############################
############################## TRAINING ##############################
main("options/CDiffMR/FastMRI/ksu/train_CDiffMR_FastMRIKneePD_m.0.4.s2.ksu.cran.LogSR.d.1.0.cplx.2ch_DEBUG.json")
############################## TRAINED ##############################