Hi, I just found a much better solution using kornia 87, the augmentation library that supports direct tensor and works on cuda too.
Here is the simplified solution:
import torch
import kornia as K

x = torch.randn(1, 3, 200, 200).cuda()
K.augmentation.RandomResizedCrop((65, 65))(x)

Note that this augmentation methods can be used in the exact same way as nn.Module within nn.Sequential layer.
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