train.py

import os
import argparse

import albumentations
from albumentations import HorizontalFlip, Resize, RandomResizedCrop

import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.optim import lr_scheduler

import processing
from utils import build_loss, misc
from model.build_model import build_model
from datasets.build_dataset import dataset_generator


def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument('--workers', type=int, default=8,
                        metavar='N', help='Dataloader threads.')

    parser.add_argument('--batch_size', type=int, default=16,
                        help='You can override model batch size by specify positive number.')

    parser.add_argument('--device', type=str, default='cuda',
                        help="Whether use cuda, 'cuda' or 'cpu'.")

    parser.add_argument('--epochs', type=int, default=60,
                        help='Epochs number.')

    parser.add_argument('--lr', type=int, default=1e-4,
                        help='Learning rate.')

    parser.add_argument('--save_path', type=str, default="./logs",
                        help='Where to save logs and checkpoints.')

    parser.add_argument('--dataset_path', type=str, default=r".\iHarmony4",
                        help='Dataset path.')

    parser.add_argument('--print_freq', type=int, default=100,
                        help='Number of iterations then print.')

    parser.add_argument('--base_size', type=int, default=256,
                        help='Base size. Resolution of the image input into the Encoder')

    parser.add_argument('--input_size', type=int, default=256,
                        help='Input size. Resolution of the image that want to be generated by the Decoder')

    parser.add_argument('--INR_input_size', type=int, default=256,
                        help='INR input size. Resolution of the image that want to be generated by the Decoder. '
                             'Should be the same as `input_size`')

    parser.add_argument('--INR_MLP_dim', type=int, default=32,
                        help='Number of channels for INR linear layer.')

    parser.add_argument('--LUT_dim', type=int, default=7,
                        help='Dim of the output LUT. Refer to https://ieeexplore.ieee.org/abstract/document/9206076')

    parser.add_argument('--activation', type=str, default='leakyrelu_pe',
                        help='INR activation layer type: leakyrelu_pe, sine')

    parser.add_argument('--pretrained', type=str,
                        default=None,
                        help='Pretrained weight path')

    parser.add_argument('--param_factorize_dim', type=int,
                        default=10,
                        help='The intermediate dimensions of the factorization of the predicted MLP parameters. '
                             'Refer to https://arxiv.org/abs/2011.12026')

    parser.add_argument('--embedding_type', type=str,
                        default="CIPS_embed",
                        help='Which embedding_type to use.')

    parser.add_argument('--optim', type=str,
                        default='adamw',
                        help='Which optimizer to use.')

    parser.add_argument('--INRDecode', action="store_false",
                        help='Whether INR decoder. Set it to False if you want to test the baseline '
                             '(https://github.com/SamsungLabs/image_harmonization)')

    parser.add_argument('--isMoreINRInput', action="store_false",
                        help='Whether to cat RGB and mask. See Section 3.4 in the paper.')

    parser.add_argument('--hr_train', action="store_true",
                        help='Whether use hr_train. See section 3.4 in the paper.')

    parser.add_argument('--isFullRes', action="store_true",
                        help='Whether for original resolution. See section 3.4 in the paper.')

    opt = parser.parse_args()

    opt.save_path = misc.increment_path(os.path.join(opt.save_path, "exp1"))

    try:
        import wandb
        opt.wandb = True
        wandb.init(config=opt, project="INR_Harmonization", name=os.path.basename(opt.save_path))

    except:
        opt.wandb = False

    return opt


def main_process(opt):
    logger = misc.create_logger(os.path.join(opt.save_path, "log.txt"))
    cudnn.benchmark = True

    trainset_path = os.path.join(opt.dataset_path, "IHD_train.txt")
    valset_path = os.path.join(opt.dataset_path, "IHD_test.txt")

    opt.transform_mean = [.5, .5, .5]
    opt.transform_var = [.5, .5, .5]
    torch_transform = transforms.Compose([transforms.ToTensor(),
                                          transforms.Normalize(opt.transform_mean, opt.transform_var)])

    trainset_alb_transform = albumentations.Compose(
        [
            RandomResizedCrop(opt.input_size, opt.input_size, scale=(0.5, 1.0)),
            HorizontalFlip()],
        additional_targets={'real_image': 'image', 'object_mask': 'image'}
    )

    valset_alb_transform = albumentations.Compose([Resize(opt.input_size, opt.input_size)],
                                                  additional_targets={'real_image': 'image', 'object_mask': 'image'})

    trainset = dataset_generator(trainset_path, trainset_alb_transform, torch_transform, opt, mode='Train')

    valset = dataset_generator(valset_path, valset_alb_transform, torch_transform, opt, mode='Val')

    train_loader = DataLoader(trainset, opt.batch_size, shuffle=True, drop_last=True,
                              pin_memory=True,
                              num_workers=opt.workers, persistent_workers=True)

    val_loader = DataLoader(valset, opt.batch_size, shuffle=False, drop_last=False, pin_memory=True,
                            num_workers=opt.workers, persistent_workers=True)

    model = build_model(opt).to(opt.device)

    loss_fn = build_loss.loss_generator()

    optimizer_params = {
        'lr': opt.lr,
        'weight_decay': 1e-2
    }
    optimizer = misc.get_optimizer(model, opt.optim, optimizer_params)

    scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=opt.lr, total_steps=opt.epochs * len(train_loader),
                                        pct_start=0.0)

    processing.train(train_loader, val_loader, model, optimizer, scheduler, loss_fn, logger, opt)


if __name__ == '__main__':
    opt = parse_args()
    os.makedirs(opt.save_path, exist_ok=True)
    main_process(opt)