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train_ohaze.py
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train_ohaze.py
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# coding: utf-8
import argparse
import os
import datetime
from tqdm import tqdm
import torch
from torch import nn
from torch import optim
from torch.backends import cudnn
from torch.utils.data import DataLoader
import torch.cuda.amp as amp
from model import DM2FNet_woPhy
from tools.config import OHAZE_ROOT
from datasets import OHazeDataset
from tools.utils import AvgMeter, check_mkdir, sliding_forward
from skimage.metrics import peak_signal_noise_ratio, structural_similarity
def parse_args():
parser = argparse.ArgumentParser(description='Train a DM2FNet')
parser.add_argument(
'--gpus', type=str, default='0', help='gpus to use ')
parser.add_argument('--ckpt-path', default='./ckpt', help='checkpoint path')
parser.add_argument(
'--exp-name',
default='O-Haze',
help='experiment name.')
args = parser.parse_args()
return args
cfgs = {
'use_physical': True,
'iter_num': 20000,
'train_batch_size': 16,
'last_iter': 0,
'lr': 2e-4,
'lr_decay': 0.9,
'weight_decay': 2e-5,
'momentum': 0.9,
'snapshot': '',
'val_freq': 2000,
'crop_size': 512,
}
def main():
net = DM2FNet_woPhy().cuda().train()
# net = DataParallel(net)
optimizer = optim.Adam([
{'params': [param for name, param in net.named_parameters()
if name[-4:] == 'bias' and param.requires_grad],
'lr': 2 * cfgs['lr']},
{'params': [param for name, param in net.named_parameters()
if name[-4:] != 'bias' and param.requires_grad],
'lr': cfgs['lr'], 'weight_decay': cfgs['weight_decay']}
])
if len(cfgs['snapshot']) > 0:
print('training resumes from \'%s\'' % cfgs['snapshot'])
net.load_state_dict(torch.load(os.path.join(args.ckpt_path,
args.exp_name, cfgs['snapshot'] + '.pth')))
optimizer.load_state_dict(torch.load(os.path.join(args.ckpt_path,
args.exp_name, cfgs['snapshot'] + '_optim.pth')))
optimizer.param_groups[0]['lr'] = 2 * cfgs['lr']
optimizer.param_groups[1]['lr'] = cfgs['lr']
check_mkdir(args.ckpt_path)
check_mkdir(os.path.join(args.ckpt_path, args.exp_name))
open(log_path, 'w').write(str(cfgs) + '\n\n')
train(net, optimizer)
def train(net, optimizer):
curr_iter = cfgs['last_iter']
scaler = amp.GradScaler()
torch.cuda.empty_cache()
while curr_iter <= cfgs['iter_num']:
train_loss_record = AvgMeter()
loss_x_jf_record = AvgMeter()
loss_x_j1_record, loss_x_j2_record = AvgMeter(), AvgMeter()
loss_x_j3_record, loss_x_j4_record = AvgMeter(), AvgMeter()
for data in train_loader:
optimizer.param_groups[0]['lr'] = 2 * cfgs['lr'] * (1 - float(curr_iter) / cfgs['iter_num']) \
** cfgs['lr_decay']
optimizer.param_groups[1]['lr'] = cfgs['lr'] * (1 - float(curr_iter) / cfgs['iter_num']) \
** cfgs['lr_decay']
haze, gt, _ = data
batch_size = haze.size(0)
haze, gt = haze.cuda(), gt.cuda()
optimizer.zero_grad()
with amp.autocast():
x_jf, x_j1, x_j2, x_j3, x_j4 = net(haze)
loss_x_jf = criterion(x_jf, gt)
loss_x_j1 = criterion(x_j1, gt)
loss_x_j2 = criterion(x_j2, gt)
loss_x_j3 = criterion(x_j3, gt)
loss_x_j4 = criterion(x_j4, gt)
loss = loss_x_jf + loss_x_j1 + loss_x_j2 + loss_x_j3 + loss_x_j4
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
# loss.backward()
# optimizer.step()
train_loss_record.update(loss.item(), batch_size)
loss_x_jf_record.update(loss_x_jf.item(), batch_size)
loss_x_j1_record.update(loss_x_j1.item(), batch_size)
loss_x_j2_record.update(loss_x_j2.item(), batch_size)
loss_x_j3_record.update(loss_x_j3.item(), batch_size)
loss_x_j4_record.update(loss_x_j4.item(), batch_size)
curr_iter += 1
log = '[iter %d], [train loss %.5f], [loss_x_fusion %.5f], [loss_x_j1 %.5f], ' \
'[loss_x_j2 %.5f], [loss_x_j3 %.5f], [loss_x_j4 %.5f], [lr %.13f]' % \
(curr_iter, train_loss_record.avg, loss_x_jf_record.avg,
loss_x_j1_record.avg, loss_x_j2_record.avg, loss_x_j3_record.avg, loss_x_j4_record.avg,
optimizer.param_groups[1]['lr'])
print(log)
open(log_path, 'a').write(log + '\n')
if curr_iter == 1 or (curr_iter + 1) % cfgs['val_freq'] == 0:
validate(net, curr_iter, optimizer)
torch.cuda.empty_cache()
if curr_iter > cfgs['iter_num']:
break
def validate(net, curr_iter, optimizer):
print('validating...')
net.eval()
loss_record = AvgMeter()
psnr_record, ssim_record = AvgMeter(), AvgMeter()
with torch.no_grad():
for data in tqdm(val_loader):
haze, gt, _ = data
haze, gt = haze.cuda(), gt.cuda()
dehaze = sliding_forward(net, haze)
loss = criterion(dehaze, gt)
loss_record.update(loss.item(), haze.size(0))
for i in range(len(haze)):
r = dehaze[i].cpu().numpy().transpose([1, 2, 0]) # data range [0, 1]
g = gt[i].cpu().numpy().transpose([1, 2, 0])
psnr = peak_signal_noise_ratio(g, r)
ssim = structural_similarity(g, r, data_range=1, multichannel=True,
gaussian_weights=True, sigma=1.5, use_sample_covariance=False)
psnr_record.update(psnr)
ssim_record.update(ssim)
snapshot_name = 'iter_%d_loss_%.5f_lr_%.6f' % (curr_iter + 1, loss_record.avg, optimizer.param_groups[1]['lr'])
log = '[validate]: [iter {}], [loss {:.5f}] [PSNR {:.4f}] [SSIM {:.4f}]'.format(
curr_iter + 1, loss_record.avg, psnr_record.avg, ssim_record.avg)
print(log)
open(log_path, 'a').write(log + '\n')
torch.save(net.state_dict(),
os.path.join(args.ckpt_path, args.exp_name, snapshot_name + '.pth'))
torch.save(optimizer.state_dict(),
os.path.join(args.ckpt_path, args.exp_name, snapshot_name + '_optim.pth'))
net.train()
if __name__ == '__main__':
args = parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
cudnn.benchmark = True
torch.cuda.set_device(int(args.gpus))
train_dataset = OHazeDataset(OHAZE_ROOT, 'train_crop_512')
train_loader = DataLoader(train_dataset, batch_size=cfgs['train_batch_size'], num_workers=4,
shuffle=True, drop_last=True)
val_dataset = OHazeDataset(OHAZE_ROOT, 'val')
val_loader = DataLoader(val_dataset, batch_size=1)
criterion = nn.L1Loss().cuda()
log_path = os.path.join(args.ckpt_path, args.exp_name, str(datetime.datetime.now()) + '.txt')
main()