81d6155030e5f58ce3bd1781dc939fd56d6eed01,train.py,,,#,17

Before Change

    dataset = torchvision.datasets.ImageFolder(opt.dataroot, 
        transform=transforms.Compose([
            transforms.Resize((opt.loadSize,opt.loadSize)),
            transforms.RandomResizedCrop(opt.fineSize),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor()]))
    dataset_loader = torch.utils.data.DataLoader(dataset,batch_size=opt.batchSize, shuffle=True)

    // data_loader = CreateDataLoader(opt)
    // dataset = data_loader.load_data()

After Change

            transforms.RandomChoice([transforms.Resize(opt.loadSize,interpolation=1),
                transforms.Resize(opt.loadSize,interpolation=2),
                transforms.Resize(opt.loadSize,interpolation=3),
                transforms.Resize((opt.loadSize,opt.loadSize),interpolation=1),
                transforms.Resize((opt.loadSize,opt.loadSize),interpolation=2),
                transforms.Resize((opt.loadSize,opt.loadSize),interpolation=3)]),
            transforms.RandomResizedCrop(opt.fineSize),
            transforms.RandomChoice([transforms.ColorJitter(brightness=.1,contrast=.1,saturation=.1,hue=.1),
                transforms.ColorJitter(brightness=0,contrast=0,saturation=.1,hue=.2),
                transforms.ColorJitter(brightness=0,contrast=0,saturation=0,hue=0),]),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor()]))
    dataset_loader = torch.utils.data.DataLoader(dataset,batch_size=opt.batchSize, shuffle=True)

    // data_loader = CreateDataLoader(opt)
    // dataset = data_loader.load_data()

Instances