Same transform for a paired image?

1

Hello all, I have a paired image such as img1, img2. I want to apply the same transform during training for these images as

transform = transforms.Compose([
           transforms.RandomHorizontalFlip(),
           transforms.ToTensor(),
]
img1 = transform(img1)
img2 = transform(img2)

Is it possible to do it in the data loader of pytorch?

2

You can concatenate the images along the channel dim and then apply the transform. You can check kornia, it does exactly what you want.

3

Alternatively, you could also use the functional API from torchvision as given in this example.

1 Like
4

Does the approach work on distributed training?

5

If you are using multiple processes, each process would still apply the same transformations on the data and target. The transformations will be different for each sample (in a single process and in a distributed setup).
Could you explain a bit more, what your use case is and what you would like to do?

6

Thanks @ptrblck. We use 16 workers and the current dataloader is

 train_transform = transforms.Compose([
           transforms.RandomHorizontalFlip(),
           transforms.ToTensor(),
           transforms.Normalize(mean = RGB_MEAN,
                            std = RGB_STD),])
    dataset_train = FaceDataset(DATA_ROOT, RECORD_DIR, train_transform)
    train_sampler = torch.utils.data.distributed.DistributedSampler(dataset_train)
    train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=batch_size, shuffle = (train_sampler is None), num_workers=workers, pin_memory=True, sampler=train_sampler, drop_last=True)
    SAMPLE_NUMS = dataset_train.get_sample_num_of_each_class()
    NUM_CLASS = len(train_loader.dataset.classes)

And the current dataloader is refered from https://github.com/HuangYG123/CurricularFace/blob/8b2f47318117995aa05490c05b455b113489917e/dataset/datasets.py#L92

path, target = self.imgs[index]
sample = Image.open(path)
sample = sample.convert("RGB")
if self.transform is not None:
     sample = self.transform(sample)

Is it fine to replace your customized transformation for the above code?

7

Yes, it should be alright.
Note that using multiple workers is not a distributed setup, which we define as using multiple devices or nodes.