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)