train_IAN.py

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)