Taking subsets of a pytorch dataset

Question

I have a network which I want to train on some dataset (as an example, say CIFAR10). I can create data loader object via

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

My question is as follows: Suppose I want to make several different training iterations. Let's say I want at first to train the network on all images in odd positions, then on all images in even positions and so on. In order to do that, I need to be able to access to those images. Unfortunately, it seems that trainset does not allow such access. That is, trying to do trainset[:1000] or more generally trainset[mask] will throw an error.

I could do instead

trainset.train_data=trainset.train_data[mask]
trainset.train_labels=trainset.train_labels[mask]

and then

trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                              shuffle=True, num_workers=2)

However, that will force me to create a new copy of the full dataset in each iteration (as I already changed trainset.train_data so I will need to redefine trainset). Is there some way to avoid it?

Ideally, I would like to have something "equivalent" to

trainloader = torch.utils.data.DataLoader(trainset[mask], batch_size=4,
                                              shuffle=True, num_workers=2)

Answer 1

torch.utils.data.Subset is easier, supports shuffle, and doesn't require writing your own sampler:

import torchvision
import torch

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=None)

evens = list(range(0, len(trainset), 2))
odds = list(range(1, len(trainset), 2))
trainset_1 = torch.utils.data.Subset(trainset, evens)
trainset_2 = torch.utils.data.Subset(trainset, odds)

trainloader_1 = torch.utils.data.DataLoader(trainset_1, batch_size=4,
                                            shuffle=True, num_workers=2)
trainloader_2 = torch.utils.data.DataLoader(trainset_2, batch_size=4,
                                            shuffle=True, num_workers=2)