Taking subsets of a pytorch dataset
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)


You can define a custom sampler for the dataset loader avoiding recreating the dataset (just creating a new loader for each different sampling).

class YourSampler(Sampler):
    def __init__(self, mask):
        self.mask = mask

    def __iter__(self):
        return (self.indices[i] for i in torch.nonzero(self.mask))

    def __len__(self):
        return len(self.mask)

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)

sampler1 = YourSampler(your_mask)
sampler2 = YourSampler(your_other_mask)
trainloader_sampler1 = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          sampler = sampler1, shuffle=False, num_workers=2)
trainloader_sampler2 = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          sampler = sampler2, shuffle=False, num_workers=2)


PS: You can find more info here: http://pytorch.org/docs/master/_modules/torch/utils/data/sampler.html#Sampler
