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main_train_tesganmr.py
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main_train_tesganmr.py
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'''
# -----------------------------------------
Main Program for Training
TES-GAN (ST-GAN+) for MRI_Recon (EMBC2022)
by Jiahao Huang (j.huang21@imperial.ac.uk)
# -----------------------------------------
'''
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
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_G, init_path_G = option.find_last_checkpoint(opt['path']['models'], net_type='G')
init_iter_D, init_path_D = option.find_last_checkpoint(opt['path']['models'], net_type='D')
init_iter_D_g, init_path_D_g = option.find_last_checkpoint(opt['path']['models'], net_type='D_g')
init_iter_E, init_path_E = option.find_last_checkpoint(opt['path']['models'], net_type='E')
opt['path']['pretrained_netG'] = init_path_G
opt['path']['pretrained_netD'] = init_path_D
opt['path']['pretrained_netD_g'] = init_path_D_g
opt['path']['pretrained_netE'] = init_path_E
init_iter_optimizerG, init_path_optimizerG = option.find_last_checkpoint(opt['path']['models'], net_type='optimizerG')
init_iter_optimizerD, init_path_optimizerD = option.find_last_checkpoint(opt['path']['models'], net_type='optimizerD')
init_iter_optimizerD_g, init_path_optimizerD_g = option.find_last_checkpoint(opt['path']['models'], net_type='optimizerD_g')
opt['path']['pretrained_optimizerG'] = init_path_optimizerG
opt['path']['pretrained_optimizerD'] = init_path_optimizerD
opt['path']['pretrained_optimizerD_g'] = init_path_optimizerD_g
current_step = max(init_iter_G, init_iter_D, init_iter_D_g, init_iter_E, init_iter_optimizerG, init_iter_optimizerD, init_iter_optimizerD_g)
# 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)
# configure logger
if opt['rank'] == 0:
# 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']))
# 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
# ----------------------------------------
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 == 'test':
test_set = define_Dataset(dataset_opt)
test_loader = DataLoader(test_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)
# ----------------------------------------
'''
for epoch in range(100000000): # keep running
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
logger_tensorboard.add_scalar('Learning Rate', model.current_learning_rate(), global_step=current_step)
logger_tensorboard.add_scalar('TRAIN Generator LOSS/G_loss', logs['G_loss'], global_step=current_step)
if 'G_loss_image' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Generator LOSS/G_loss_image', logs['G_loss_image'], global_step=current_step)
if 'G_loss_frequency' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Generator LOSS/G_loss_frequency', logs['G_loss_frequency'], global_step=current_step)
if 'G_loss_preceptual' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Generator LOSS/G_loss_preceptual', logs['G_loss_preceptual'], global_step=current_step)
if 'G_loss_adversarial' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Generator LOSS/G_loss_adversarial', logs['G_loss_adversarial'], global_step=current_step)
if 'D_loss_real' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Discriminator LOSS/D_loss_real', logs['D_loss_real'], global_step=current_step)
if 'D_loss_fake' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Discriminator LOSS/D_loss_fake', logs['D_loss_fake'], global_step=current_step)
if 'D_g_loss_real' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Discriminator_g LOSS/D_g_loss_real', logs['D_g_loss_real'], global_step=current_step)
if 'D_g_loss_fake' in logs.keys():
logger_tensorboard.add_scalar('TRAIN Discriminator_g LOSS/D_g_loss_fake', logs['D_g_loss_fake'], global_step=current_step)
# -------------------------------
# 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_H = os.path.join(opt['path']['images'], 'tempH')
util.mkdir(img_dir_tmp_H)
img_dir_tmp_E = os.path.join(opt['path']['images'], 'tempE')
util.mkdir(img_dir_tmp_E)
img_dir_tmp_L = os.path.join(opt['path']['images'], 'tempL')
util.mkdir(img_dir_tmp_L)
# create result dict
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['lpips'] = []
test_results['G_loss'] = []
test_results['G_loss_adversarial'] = []
test_results['G_loss_image'] = []
test_results['G_loss_frequency'] = []
test_results['G_loss_preceptual'] = []
for idx, test_data in enumerate(test_loader):
with torch.no_grad():
img_info = test_data['img_info'][0]
img_dir = os.path.join(opt['path']['images'], img_info)
# testing and adjust resolution
model.feed_data(test_data)
model.check_windowsize()
model.test()
model.recover_windowsize()
model.record_loss_for_val()
logs = model.current_log()
test_results['G_loss'].append(logs['G_loss'])
test_results['G_loss_adversarial'].append(logs['G_loss_adversarial'])
test_results['G_loss_image'].append(logs['G_loss_image'])
test_results['G_loss_frequency'].append(logs['G_loss_frequency'])
test_results['G_loss_preceptual'].append(logs['G_loss_preceptual'])
# acquire test result
results = model.current_results_gpu()
# calculate LPIPS (GPU | torch.tensor)
L_img = results['L']
E_img = results['E']
H_img = results['H']
current_lpips = util.calculate_lpips_single(loss_fn_alex, H_img, E_img).data.squeeze().float().cpu().numpy()
# calculate PSNR SSIM (CPU | np.float)
L_img = util.tensor2float(L_img)
E_img = util.tensor2float(E_img)
H_img = util.tensor2float(H_img)
current_psnr = util.calculate_psnr_single(H_img, E_img, border=0)
current_ssim = util.calculate_ssim_single(H_img, E_img, border=0)
# record metrics
test_results['psnr'].append(current_psnr)
test_results['ssim'].append(current_ssim)
test_results['lpips'].append(current_lpips)
# save samples
if idx < 5:
util.mkdir(img_dir)
cv2.imwrite(os.path.join(img_dir, 'ZF_{:05d}.png'.format(current_step)), np.clip(L_img, 0, 1) * 255)
cv2.imwrite(os.path.join(img_dir, 'Recon_{:05d}.png'.format(current_step)), np.clip(E_img, 0, 1) * 255)
cv2.imwrite(os.path.join(img_dir, 'GT_{:05d}.png'.format(current_step)), np.clip(H_img, 0, 1) * 255)
if opt['datasets']['test']['resize_for_fid']:
resize_for_fid = opt['datasets']['test']['resize_for_fid']
cv2.imwrite(os.path.join(img_dir_tmp_L, 'ZF_{:05d}.png'.format(idx)), resize(np.clip(L_img, 0, 1), (resize_for_fid[0], resize_for_fid[1])) * 255)
cv2.imwrite(os.path.join(img_dir_tmp_E, 'Recon_{:05d}.png'.format(idx)), resize(np.clip(E_img, 0, 1), (resize_for_fid[0], resize_for_fid[1])) * 255)
cv2.imwrite(os.path.join(img_dir_tmp_H, 'GT_{:05d}.png'.format(idx)), resize(np.clip(H_img, 0, 1), (resize_for_fid[0], resize_for_fid[1])) * 255)
else:
cv2.imwrite(os.path.join(img_dir_tmp_L, 'ZF_{:05d}.png'.format(idx)), np.clip(L_img, 0, 1) * 255)
cv2.imwrite(os.path.join(img_dir_tmp_E, 'Recon_{:05d}.png'.format(idx)), np.clip(E_img, 0, 1) * 255)
cv2.imwrite(os.path.join(img_dir_tmp_H, 'GT_{:05d}.png'.format(idx)), np.clip(H_img, 0, 1) * 255)
# summarize psnr/ssim/lpips
ave_psnr = np.mean(test_results['psnr'])
# std_psnr = np.std(test_results['psnr'], ddof=1)
ave_ssim = np.mean(test_results['ssim'])
# std_ssim = np.std(test_results['ssim'], ddof=1)
ave_lpips = np.mean(test_results['lpips'])
# std_lpips = np.std(test_results['lpips'], ddof=1)
# calculate FID
if opt['dist']:
# DistributedDataParallel (If multiple GPUs are used to train, use the 2nd GPU for FID calculation.)
log = os.popen("{} -m pytorch_fid {} {} ".format(
sys.executable,
img_dir_tmp_H,
img_dir_tmp_E)).read()
else:
# DataParallel (If multiple GPUs are used to train, use the 2nd GPU for FID calculation for unbalance of GPU menory use.)
if len(opt['gpu_ids']) > 1:
log = os.popen("{} -m pytorch_fid --device cuda:1 {} {} ".format(
sys.executable,
img_dir_tmp_H,
img_dir_tmp_E)).read()
else:
log = os.popen("{} -m pytorch_fid {} {} ".format(
sys.executable,
img_dir_tmp_H,
img_dir_tmp_E)).read()
print(log)
fid = eval(log.replace('FID: ', ''))
# testing log
logger.info('<epoch:{:3d}, iter:{:8,d}, Average PSNR : {:<.2f}; Average Average SSIM : {:<.4f}; LPIPS : {:<.4f}; FID : {:<.2f}'
.format(epoch, current_step, ave_psnr, ave_ssim, ave_lpips, fid))
logger_tensorboard.add_scalar('VALIDATION PSNR', ave_psnr, global_step=current_step)
logger_tensorboard.add_scalar('VALIDATION SSIM', ave_ssim, global_step=current_step)
logger_tensorboard.add_scalar('VALIDATION LPIPS', ave_lpips, global_step=current_step)
logger_tensorboard.add_scalar('VALIDATION FID', fid, global_step=current_step)
# # early stopping
# if opt['train']['is_early_stopping']:
# early_stopping(ave_psnr, model, epoch, current_step)
# if early_stopping.is_save:
# logger.info('Saving the model by early stopping')
# model.save(f'best_{current_step}')
# if early_stopping.early_stop:
# print("Early stopping!")
# break
print("Training Stop")
if __name__ == '__main__':
main()