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I am using pytorch to build DCGAN which i aim to train on custom dataset.

I have already posted a question on training DCGAN on small dataset, and of course answer was data augmentation.

But i have a problem. I am coding in PyTorch and i want to perform several different transfomrations on existing ImageFolder object which represents my loaded dataset.

Idea was to perform, lets say around 5 different transformations, and after performing each transformation i want to expand my dataset by adding the newly transformed images to it. Here is pseudo code:

list_of_transformations = ['random_crop','center_crop','color_jitter','mirror','grayscale']
dataset = ImageFolder('path_to_dataset')
for t in list_of_transformations
       dataset += dataset.t()

I am guessing this might not be the best idea, or is even considered 'stupid', but i am new to Deep Learning and Python in general. Any help would be appreciated! Thank you in advance!

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I've just found out about the class torch.utils.data.ConcatDataset([datasets]) . Its easy to use and documentation is here : http://pytorch.org/docs/master/data.html

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