I am a little bit confused about the data augmentation performed in PyTorch. Now, as far as I know, when we are performing data augmentation, we are KEEPING our original dataset, and then adding other versions of it (Flipping, Cropping...etc). But that doesn&#39;t seem like happening in PyTorch. As far as I understood from the references, when we use `data.transforms` in PyTorch, then it applies them one by one. So for example:

&lt;!-- language: lang-python --&gt;

    data_transforms = {
        &#39;train&#39;: transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
        &#39;val&#39;: transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
    }

Here , for the training, we are first randomly cropping the image and resizing it to shape `(224,224)`. Then we are taking these `(224,224)` images and horizontally flipping them. Therefore, our dataset is now containing ONLY the horizontally flipped images, so our original images are lost in this case. 

Am I right? Is this understanding correct? If not, then where do we tell PyTorch in this code above (taken from Official Documentation) to keep the original images and resize them to the expected shape `(224,224)`?

Thanks 
 

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