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train_DID.py
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train_DID.py
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# -*- coding: utf-8 -*-
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0,1,2,3"
import argparse
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, RandomSampler
from tensorboardX import SummaryWriter
from tqdm import tqdm
from utils import AverageMeter
from datasets.DID_Dataloader import TrainData_for_DID, TestData_for_DID
from numpy import *
from random import sample
import time
from models import *
from utils.utils import *
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='FADformer', type=str, help='model name')
parser.add_argument('--num_workers', default=16, type=int, help='number of workers')
parser.add_argument('--save_dir', default='./saved_models/', type=str, help='path to models saving')
parser.add_argument('--data_dir', default='./data/', type=str, help='path to dataset')
parser.add_argument('--log_dir', default='./logs/', type=str, help='path to logs')
parser.add_argument('--exp', default='did', type=str, help='experiment setting')
args = parser.parse_args()
torch.manual_seed(8001)
def train(train_loader, network, criterion, optimizer, contrastive):
losses_l1 = AverageMeter()
losses_con = AverageMeter()
torch.cuda.empty_cache()
network.train()
for batch in train_loader:
source_img = batch['source'].cuda()
target_img = batch['target'].cuda()
output = network(source_img)
l1loss = criterion(output, target_img)
con_loss = contrastive(output, target_img, source_img)
# loss = l1loss
loss = l1loss + 1e-1 * con_loss
losses_l1.update(l1loss.item())
losses_con.update(con_loss.item())
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(network.parameters(), 0.01)
optimizer.step()
return losses_l1.avg, losses_con.avg
def valid(val_loader_full, network):
PSNR_full = AverageMeter()
SSIM_full = AverageMeter()
# torch.cuda.empty_cache()
network.eval()
for batch in val_loader_full:
source_img = batch['source'].cuda()
target_img = batch['target'].cuda()
with torch.no_grad():
output = network(source_img).clamp_(0, 1)
psnr_full, sim = calculate_psnr_torch(target_img, output)
PSNR_full.update(psnr_full.item(), source_img.size(0))
ssim_full = sim
SSIM_full.update(ssim_full.item(), source_img.size(0))
return PSNR_full.avg, SSIM_full.avg
if __name__ == '__main__':
setting_filename = os.path.join('configs', args.exp, args.model + '.json')
print(setting_filename)
if not os.path.exists(setting_filename):
setting_filename = os.path.join('configs', args.exp, 'default.json')
with open(setting_filename, 'r') as f:
setting = json.load(f)
device_index = [0, 1, 2, 3]
network = eval(args.model.replace('-', '_'))()
network = nn.DataParallel(network, device_ids=device_index).cuda()
criterion = nn.L1Loss()
contrastive = FCR()
if setting['optimizer'] == 'adam':
optimizer = torch.optim.Adam(network.parameters(), lr=setting['lr'], eps=1e-8)
elif setting['optimizer'] == 'adamw':
optimizer = torch.optim.AdamW(network.parameters(), lr=setting['lr'])
else:
raise Exception("ERROR: wrunsupported optimizer")
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=setting['epochs'],
eta_min=1e-5)
train_dir = './datasets/DIDTrain'
test_dir = './datasets/DIDTest'
train_dataset = TrainData_for_DID(256, train_dir)
train_loader = DataLoader(train_dataset,
batch_size=setting['batch_size'],
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
test_dataset = TestData_for_DID(8, test_dir)
test_loader = DataLoader(test_dataset,
batch_size=setting['batch_size']*5,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
save_dir = os.path.join(args.save_dir, args.exp)
os.makedirs(save_dir, exist_ok=True)
# change test_str when you development new exp
timestamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
test_str = f"_fadformer_t{timestamp}"
if not os.path.exists(os.path.join(save_dir, args.model + test_str + '.pth')):
print('==> Start training, current model name: ' + args.model)
writer = SummaryWriter(log_dir=os.path.join(args.log_dir, args.exp, args.model, test_str))
best_psnr = 0
# you need to use the matlib code to re-evaluate psnr and ssim, followed by drsformer
best_ssim = 0
for epoch in tqdm(range(setting['epochs'] + 1)):
train_loss, train_loss_con = train(train_loader, network, criterion, optimizer, contrastive)
writer.add_scalar('train_loss', train_loss, epoch)
writer.add_scalar('train_constrative', train_loss_con, epoch)
scheduler.step()
if epoch % setting['eval_freq'] == 0:
avg_psnr, avg_ssim = valid(test_loader, network)
writer.add_scalar('valid_psnr', avg_psnr, epoch)
writer.add_scalar('valid_ssim', avg_ssim, epoch)
torch.save({'state_dict': network.state_dict()},
os.path.join(save_dir, args.model + test_str + '_newest' + '.pth'))
if avg_psnr > best_psnr:
best_psnr = avg_psnr
torch.save({'state_dict': network.state_dict()},
os.path.join(save_dir, args.model + test_str + '_best' + '.pth'))
writer.add_scalar('best_psnr', best_psnr, epoch)
if avg_ssim > best_ssim:
best_ssim = avg_ssim
writer.add_scalar('best_ssim', best_ssim, epoch)
else:
print('==> Existing trained model')
exit(1)