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 LowLightFDataset, LowLightFDatasetEval 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('-m1', '--model1', type=str, default='INet', help='Model1 Name') parser.add_argument('-m2', '--model2', type=str, default='NSNet', help='Model1 Name') parser.add_argument('-m1w', '--model1_weight', type=str, default=None, help='Model Name') parser.add_argument('--comment', type=str, default='default', help='Project comment') parser.add_argument('--graph', action='store_true') parser.add_argument('--no_sche', action='store_true') parser.add_argument('--sampling', action='store_true') parser.add_argument('--slope', type=float, default=2.) parser.add_argument('--lr', type=float, default=0.001) 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 class ModelNSNet(nn.Module): def __init__(self, model1, model2): super().__init__() self.texture_loss = models.MSELoss() self.model_ianet = model1(in_channels=1, out_channels=1) self.model_nsnet = model2(in_channels=2, out_channels=1) assert opt.model1_weight is not None self.load_weight(self.model_ianet, opt.model1_weight) self.model_ianet.eval() self.eps = 1e-2 def load_weight(self, model, weight_pth): state_dict = torch.load(weight_pth) ret = model.load_state_dict(state_dict, strict=True) print(ret) def noise_syn(self, illumi, strength): return torch.exp(-illumi) * strength def forward(self, image, image_gt, training=True): with torch.no_grad(): image = image.squeeze(0) 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) illumi = self.model_ianet(texture_in_down) illumi = F.interpolate(illumi, scale_factor=2, mode='bicubic', align_corners=True) attention = self.noise_syn(illumi, strength=0.1) noise = torch.normal(mean=0., std=attention) noisy_gt = torch.clamp(texture_gt + noise, 0., 1.) texture_res = self.model_nsnet(torch.cat([noisy_gt, attention], dim=1)) restor_loss = self.texture_loss(texture_res, texture_gt - noisy_gt) texture_ns = noisy_gt + texture_res psnr = PSNR(texture_ns, texture_gt) ssim = SSIM(texture_ns, texture_gt).item() return noisy_gt, texture_ns, texture_res, illumi, restor_loss, psnr, ssim def train(opt): if torch.cuda.is_available(): torch.cuda.manual_seed(42) else: torch.manual_seed(42) # params.project_name = params.project_name + str(time.time()).replace('.', '') 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')) training_generator = DataLoader(training_set, **training_params) val_set = LowLightFDatasetEval(os.path.join(opt.data_path, 'eval')) val_generator = DataLoader(val_set, **val_params) model1 = getattr(models, opt.model1) model2 = getattr(models, opt.model2) writer = SingleSummaryWriter(opt.log_path + f'/{datetime.datetime.now().strftime("%Y%m%d-%H%M%S")}/') model = ModelNSNet(model1, model2) print(model) 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.model_nsnet.parameters(), opt.lr) else: optimizer = torch.optim.SGD(model.model_nsnet.parameters(), opt.lr, momentum=0.9, nesterov=True) scheduler = CosineLR(optimizer, opt.lr, opt.num_epochs) epoch = 0 step = 0 model.model_nsnet.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) saver.base_url = os.path.join(opt.saved_path, 'results', '%03d' % epoch) if not opt.sampling: 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 = data.cuda() target = target.cuda() optimizer.zero_grad() noisy_gt, texture_ns, texture_res, illumi, \ restor_loss, psnr, ssim = model(data, target, training=True) loss = restor_loss loss.backward() optimizer.step() epoch_loss.append(float(loss)) progress_bar.set_description( 'Step: {}. Epoch: {}/{}. Iteration: {}/{}. restor_loss: {:.5f}, psnr: {:.5f}, ssim: {:.5f}'.format( step, epoch, opt.num_epochs, iter + 1, num_iter_per_epoch, restor_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 not opt.no_sche: scheduler.step() if epoch % opt.val_interval == 0: model.model_nsnet.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() noisy_gt, texture_ns, texture_res, \ illumi, restor_loss, psnr, ssim = model(data, target, training=False) texture_gt, _, _ = torch.split(kornia.color.rgb_to_ycbcr(target), 1, dim=1) saver.save_image(noisy_gt, name=os.path.splitext(name[0])[0] + '_in') saver.save_image(texture_ns, name=os.path.splitext(name[0])[0] + '_ns') saver.save_image(texture_res, name=os.path.splitext(name[0])[0] + '_res') saver.save_image(illumi, name=os.path.splitext(name[0])[0] + '_ill') saver.save_image(target, name=os.path.splitext(name[0])[0] + '_gt') loss = restor_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.model2}_{"%03d" % epoch}_{psnr}_{ssim}_{step}.pth') model.model_nsnet.train() except KeyboardInterrupt: save_checkpoint(model, f'{opt.model2}_{epoch}_{step}_keyboardInterrupt.pth') writer.close() writer.close() def save_checkpoint(model, name): if isinstance(model, nn.DataParallel): torch.save(model.module.model_nsnet.state_dict(), os.path.join(opt.saved_path, name)) else: torch.save(model.model_nsnet.state_dict(), os.path.join(opt.saved_path, name)) if __name__ == '__main__': opt = get_args() train(opt)