I’m currently using a UNet for image regression and want to explore data augmentation. However, it appears to me that pytorch transformations (as implemented below) replace the original image in the dataset. Considering that the total amount of data in the dataset stays the same, won’t the model’s performance be similar? Don’t know if that assumption is correct, though. Also, are the transforms refreshed per epoch?

```
rotation_angle = 90
rotation_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomRotation(degrees=rotation_angle),
transforms.ToTensor()
])
dataset = FullDataset(dir_input, dir_mask, transform=rotation_transform)
```

FullDataset **getitem()** snippit:

```
if self.transform:
aug_input = self.transform(input)
aug_mask = self.transform(mask)
return aug_input, aug_mask
```