""" Copyright (c) 2022 Samsung Electronics Co., Ltd. Author: Abhijith Punnappurath (abhijith.p@samsung.com) Licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License, (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://creativecommons.org/licenses/by-nc/4.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. For conditions of distribution and use, see the accompanying LICENSE.md file. """ from utils.unet import UNet import torch import torch.nn as nn import torch.optim as optim import torchvision from utils.dataset_raw import DatasetRAW from torch.optim import lr_scheduler import time import os import argparse from torch.utils.tensorboard import SummaryWriter def parse_args(): parser = argparse.ArgumentParser(description='Day-to-night train') parser.add_argument( '--data-dir', default='data', type=str, help='folder of training and validation images') parser.add_argument( '--savefoldername', default='models', type=str, help='folder to save trained models to') parser.add_argument( '--which-input', default='clean_raw', type=str, help='clean_raw or noisy_raw') parser.add_argument( '--wb-illum', default='asn', type=str, help='asn (as-shot-neutral) or avg (average)') parser.add_argument( '--patch-size', type=int, default=64, help='patch size') parser.add_argument( '--stride', type=int, default=64, help='stride when cropping patches') parser.add_argument( '--batch-size', type=int, default=128, help='batch size') parser.add_argument( '--lr', type=float, default=0.001, help='learning rate') parser.add_argument( '--milestones', default='400', type=str, help='milestones as comma separated string') parser.add_argument( '--num-epochs', type=int, default=500, help='number of epochs') parser.add_argument( '--tboard-freq', type=int, default=200, help='frequency of writing to tensorboard') parser.add_argument('--on-cuda', default=False, action='store_true', help='False: load each batch on cuda, True: load all data directly on cuda') args = parser.parse_args() print(args) return args def mypsnr(img1, img2): mse = torch.mean(((img1 * 255.0).floor() - (img2 * 255.0).floor()) ** 2, dim=[1, 2, 3]) # mse[(mse==0).nonzero()]=0.05 # deprecated warning after pytorch 1.5 mse[torch.nonzero((mse == 0), as_tuple=True)] = 0.05 psnrout = torch.mean(20 * torch.log10(255.0 / torch.sqrt(mse))) return psnrout def main(args): milestones = [item for item in args.milestones.split(',')] for i in range(len(milestones)): milestones[i] = int(milestones[i]) savefoldername = args.savefoldername writer = SummaryWriter(os.path.join('./tensorboard', savefoldername)) modsavepath = os.path.join('./models/', savefoldername) if not (os.path.exists(modsavepath) and os.path.isdir(modsavepath)): os.makedirs(modsavepath) image_datasets = { x: DatasetRAW(os.path.join(args.data_dir, x), args.batch_size, args.patch_size, args.stride, args.wb_illum, args.on_cuda, args.which_input) for x in ['train', 'val']} dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=args.batch_size, shuffle=True, num_workers=0) for x in ['train', 'val']} dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} epoch_loss = {x: 0.0 for x in ['train', 'val']} epoch_psnr = {x: 0.0 for x in ['train', 'val']} device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = UNet(in_channels=3, out_channels=3, init_features=32) model = model.to(device) criterion = nn.L1Loss() params = model.parameters() optimizer = optim.Adam(params, lr=args.lr) scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=milestones, gamma=0.1) # training loop starts here since = time.time() best_loss = 10 ** 6 best_psnr = 0.0 for epoch in range(args.num_epochs): print('Epoch {}/{}'.format(epoch, args.num_epochs - 1)) print('-' * 10) running_loss_tboard = 0.0 running_psnr_tboard = 0.0 # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': model.train() # Set model to training mode else: model.eval() # Set model to evaluate mode running_loss = 0.0 running_psnr = 0.0 # Iterate over data. for i, (inputs, targets) in enumerate(dataloaders[phase]): if not args.on_cuda: inputs = inputs.to(device) targets = targets.to(device) # zero the parameter gradients optimizer.zero_grad() # forward # track history if only in train with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) loss = criterion(outputs, targets) # psnrout = mypsnr((torch.clip(outputs, 0, 1)), targets) psnrout = mypsnr(outputs, targets) # backward + optimize only if in training phase if phase == 'train': loss.backward() optimizer.step() running_loss_tboard += loss.item() running_psnr_tboard += psnrout.item() if (i+1) % args.tboard_freq == 0: # every tboard_freq mini-batches... # ...log the running loss writer.add_scalar('loss', running_loss_tboard / args.tboard_freq, epoch * len(dataloaders[phase]) + i) writer.add_scalar('psnr', running_psnr_tboard / args.tboard_freq, epoch * len(dataloaders[phase]) + i) running_loss_tboard = 0.0 running_psnr_tboard = 0.0 # statistics running_loss += loss.item() * inputs.size(0) running_psnr += psnrout.item() * inputs.size(0) if phase == 'train': scheduler.step() epoch_loss[phase] = running_loss / dataset_sizes[phase] epoch_psnr[phase] = running_psnr / dataset_sizes[phase] if phase == 'val': # ...log the running loss writer.add_scalars('loss', {'train': epoch_loss['train'], 'val': epoch_loss['val']}, (epoch + 1) * len(dataloaders['train'])) writer.add_scalars('psnr', {'train': epoch_psnr['train'], 'val': epoch_psnr['val']}, (epoch + 1) * len(dataloaders['train'])) # img_grid = torchvision.utils.make_grid(torch.cat((inputs, torch.clip(outputs, 0, 1), # targets), 2), normalize=True, range=(0, 1)) img_grid = torchvision.utils.make_grid(torch.cat((inputs, outputs, targets), 2), normalize=True, range=(0, 1)) # write to tensorboard writer.add_image('val_epoch_' + str(epoch), img_grid) print('{} Loss: {:.6f} PSNR: {:.4f}'.format( phase, epoch_loss[phase], epoch_psnr[phase])) # save the model if phase == 'val' and epoch_loss[phase] < best_loss: best_loss = epoch_loss[phase] best_psnr = epoch_psnr[phase] torch.save(model.state_dict(), os.path.join(modsavepath, 'bestmodel.pt')) print() time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best val loss: {:4f}'.format(best_loss)) print('Best val psnr: {:4f}'.format(best_psnr)) if __name__ == "__main__": args = parse_args() main(args)