Hi @JunlinHan

I have a dataset contains single image and I am simply applying your YOCO technique to visualize image generated by YOCO. I just get a single output sometimes the output is same image as input and sometimes flip+cut. Is that correct?

Code

import torch
from torchvision.utils import save_image
import torchvision.transforms as transforms
from torchvision import datasets
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader

training_data = datasets.ImageFolder(root="/media/cvpr/CM_22/dataset/train", transform=ToTensor())
train_loader = DataLoader(training_data, batch_size=64, shuffle=True)


def YOCO(images, aug, h, w):
    images = torch.cat((aug(images[:, :, :, 0:int(w / 2)]), aug(images[:, :, :, int(w / 2):w])), dim=3) if \
        torch.rand(1) > 0.5 else torch.cat((aug(images[:, :, 0:int(h / 2), :]), aug(images[:, :, int(h / 2):h, :])),
                                           dim=2)
    return images


for i, (images, target) in enumerate(train_loader):
    aug = torch.nn.Sequential(
        transforms.RandomHorizontalFlip(), )
    _, _, h, w = images.shape
    # perform augmentations with YOCO
    images = YOCO(images, aug, h, w)
    save_image(images, 'img' + str(i) + '.png')

Input image:

Output Image

Hi, thanks for your inspiring work! I got a question here. Have you split the training dataset into train and val? From the code you shared, it seems that you directly used the test dataset for validation by which the model with the best performance is picked： val_loader = torch.utils.data.DataLoader( datasets.CIFAR10('../data', train=False, transform=transform_test), batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True) I am not sure if it is ok and I am really confused, because I notice some researchers make the split while some others do not.

Dear @JunlinHan,

Did you run all experiments multiple times than average and put them into the table? Like training resnet18 fives times and then averaging the accuracy and putting it into the table. Why I am asking this question because every time when I am training resnet18 I got different accuracy and error rate (almost 1% fluctuation).

Regards, Khawar

Dear @JunlinHan

Thank you for your work. I am working on your paper and reproducing the results given in the paper. I have trained a model based on your configuration for cifar-10 but I cannot find an error-rate file. How we can evaluate the top-1 error-rate? Like this

print('Test\t  Prec@1: {top1.avg:.3f} (Err: {error:.3f} )\n'
          .format(top1=top1, error=100 - top1.avg))