Bread / train_IAN.py
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import argparse
import datetime
import os
import traceback
import kornia
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader
from tqdm.autonotebook import tqdm
import models
from datasets import LowLightDataset, LowLightFDataset
from models import PSNR, SSIM, CosineLR
from tools import SingleSummaryWriter
from tools import saver, mutils
def get_args():
parser = argparse.ArgumentParser('Breaking Downing the Darkness')
parser.add_argument('--num_gpus', type=int, default=1, help='number of gpus being used')
parser.add_argument('--num_workers', type=int, default=12, help='num_workers of dataloader')
parser.add_argument('--batch_size', type=int, default=1, help='The number of images per batch among all devices')
parser.add_argument('-m', '--model', type=str, default='INet',
help='Model Name')
parser.add_argument('--comment', type=str, default='default',
help='Project comment')
parser.add_argument('--graph', action='store_true')
parser.add_argument('--scratch', action='store_true')
parser.add_argument('--lr', type=float, default=0.01)
parser.add_argument('--no_sche', action='store_true')
parser.add_argument('--optim', type=str, default='adam', help='select optimizer for training, '
'suggest using \'admaw\' until the'
' very final stage then switch to \'sgd\'')
parser.add_argument('--num_epochs', type=int, default=500)
parser.add_argument('--val_interval', type=int, default=1, help='Number of epoches between valing phases')
parser.add_argument('--save_interval', type=int, default=500, help='Number of steps between saving')
parser.add_argument('--data_path', type=str, default='./data/LOL',
help='the root folder of dataset')
parser.add_argument('--log_path', type=str, default='logs/')
parser.add_argument('--saved_path', type=str, default='logs/')
args = parser.parse_args()
return args
def compute_gradient(img):
gradx = img[..., 1:, :] - img[..., :-1, :]
grady = img[..., 1:] - img[..., :-1]
return gradx, grady
class ModelINet(nn.Module):
def __init__(self, model):
super().__init__()
self.restor_loss = models.MSELoss()
self.wtv_loss = models.WTVLoss2()
self.model = model(in_channels=1, out_channels=1)
self.eps = 1e-2
def forward(self, image, image_gt, training=True):
if training:
image = image.squeeze(0)
image_gt = image_gt.repeat(8, 1, 1, 1)
texture_in, _, _ = torch.split(kornia.color.rgb_to_ycbcr(image), 1, dim=1)
texture_gt, _, _ = torch.split(kornia.color.rgb_to_ycbcr(image_gt), 1, dim=1)
texture_in_down = F.interpolate(texture_in, scale_factor=0.5, mode='bicubic', align_corners=True)
texture_gt_down = F.interpolate(texture_gt, scale_factor=0.5, mode='bicubic', align_corners=True)
illumi = self.model(texture_in_down)
texture_out = texture_in_down / torch.clamp_min(illumi, self.eps)
restor_loss = self.restor_loss(texture_out, texture_gt_down)
restor_loss += self.restor_loss(texture_in_down, texture_gt_down * illumi)
tv_loss = self.wtv_loss(illumi, texture_in_down)
if training:
psnr = 0.0
ssim = 0.0
else:
illumi = F.interpolate(illumi, scale_factor=2, mode='bicubic', align_corners=True)
texture_out = texture_in / torch.clamp_min(illumi, self.eps)
psnr = PSNR(texture_out, texture_gt)
ssim = SSIM(texture_out, texture_gt).item()
return texture_out, illumi, restor_loss, tv_loss, psnr, ssim
def train(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(42)
else:
torch.manual_seed(42)
timestamp = mutils.get_formatted_time()
opt.saved_path = opt.saved_path + f'/{opt.comment}/{timestamp}'
opt.log_path = opt.log_path + f'/{opt.comment}/{timestamp}/tensorboard/'
os.makedirs(opt.log_path, exist_ok=True)
os.makedirs(opt.saved_path, exist_ok=True)
training_params = {'batch_size': opt.batch_size,
'shuffle': True,
'drop_last': True,
'num_workers': opt.num_workers}
val_params = {'batch_size': 1,
'shuffle': False,
'drop_last': True,
'num_workers': opt.num_workers}
training_set = LowLightFDataset(os.path.join(opt.data_path, 'train'), image_split='images_aug',
targets_split='targets')
training_generator = DataLoader(training_set, **training_params)
val_set = LowLightDataset(os.path.join(opt.data_path, 'eval'), targets_split='targets')
val_generator = DataLoader(val_set, **val_params)
model = getattr(models, opt.model)
model = ModelINet(model)
print(model)
# load last weights
writer = SingleSummaryWriter(opt.log_path + f'/{datetime.datetime.now().strftime("%Y%m%d-%H%M%S")}/')
if opt.num_gpus > 0:
model = model.cuda()
if opt.num_gpus > 1:
model = nn.DataParallel(model)
if opt.optim == 'adam':
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
else:
optimizer = torch.optim.SGD(model.parameters(), opt.lr, momentum=0.9, nesterov=True)
scheduler = CosineLR(optimizer, opt.lr, opt.num_epochs)
epoch = 0
step = 0
model.train()
num_iter_per_epoch = len(training_generator)
try:
for epoch in range(opt.num_epochs):
last_epoch = step // num_iter_per_epoch
if epoch < last_epoch:
continue
epoch_loss = []
progress_bar = tqdm(training_generator)
for iter, (data, target, name) in enumerate(progress_bar):
if iter < step - last_epoch * num_iter_per_epoch:
progress_bar.update()
continue
try:
if opt.num_gpus == 1:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
texture_out, texture_attention, restor_loss, \
tv_loss, psnr, ssim = model(data, target, training=True)
loss = restor_loss + tv_loss
loss.backward()
optimizer.step()
epoch_loss.append(float(loss))
progress_bar.set_description(
'Step: {}. Epoch: {}/{}. Iteration: {}/{}. var: {:.5f}, res_loss: {:.5f}, tv_loss: {:.5f}, psnr: {:.3f}, ssim: {:.3f}'.format(
step, epoch, opt.num_epochs, iter + 1, num_iter_per_epoch, torch.var(texture_attention),
restor_loss.item(),
tv_loss.item(), psnr, ssim))
writer.add_scalar('Loss/train', loss, step)
writer.add_scalar('PSNR/train', psnr, step)
writer.add_scalar('SSIM/train', ssim, step)
# log learning_rate
current_lr = optimizer.param_groups[0]['lr']
writer.add_scalar('learning_rate', current_lr, step)
step += 1
except Exception as e:
print('[Error]', traceback.format_exc())
print(e)
continue
if opt.no_sche:
scheduler.step()
saver.base_url = os.path.join(opt.saved_path, 'results', '%03d' % epoch)
if epoch % opt.val_interval == 0:
model.eval()
loss_ls = []
psnrs = []
ssims = []
for iter, (data, target, name) in enumerate(val_generator):
with torch.no_grad():
if opt.num_gpus == 1:
data = data.cuda()
target = target.cuda()
texture_in, _, _ = torch.split(kornia.color.rgb_to_ycbcr(data), 1, dim=1)
texture_gt, _, _ = torch.split(kornia.color.rgb_to_ycbcr(target), 1, dim=1)
texture_out, texture_attention, restor_loss, \
tv_loss, psnr, ssim = model(data, target, training=False)
saver.save_image(texture_out, name=os.path.splitext(name[0])[0] + '_out')
saver.save_image(texture_in, name=os.path.splitext(name[0])[0] + '_in')
saver.save_image(texture_gt, name=os.path.splitext(name[0])[0] + '_gt')
saver.save_image(texture_attention, name=os.path.splitext(name[0])[0] + '_att')
loss = restor_loss + tv_loss
loss_ls.append(loss.item())
psnrs.append(psnr)
ssims.append(ssim)
loss = np.mean(np.array(loss_ls))
psnr = np.mean(np.array(psnrs))
ssim = np.mean(np.array(ssims))
print(
'Val. Epoch: {}/{}. Loss: {:1.5f}, psnr: {:.5f}, ssim: {:.5f}'.format(
epoch, opt.num_epochs, loss, psnr, ssim))
writer.add_scalar('Loss/val', loss, step)
writer.add_scalar('PSNR/val', psnr, step)
writer.add_scalar('SSIM/val', ssim, step)
save_checkpoint(model, f'{opt.model}_{"%03d" % epoch}_{psnr}_{ssim}_{step}.pth')
model.train()
except KeyboardInterrupt:
save_checkpoint(model, f'{opt.model}_{epoch}_{step}_keyboardInterrupt.pth')
writer.close()
writer.close()
def save_checkpoint(model, name):
if isinstance(model, nn.DataParallel):
torch.save(model.module.model.state_dict(), os.path.join(opt.saved_path, name))
else:
torch.save(model.model.state_dict(), os.path.join(opt.saved_path, name))
if __name__ == '__main__':
opt = get_args()
train(opt)