How to sample minibatches without using for loop?

1

Hello, everyone! How can I define a function that takes a dataset and an index list as input and returns a tensor?

For example, given a dataset defined as below

import torch
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
dataroot = "datasets/celebA/"
image_size = 64
dataset = dset.ImageFolder(root=dataroot,
                           transform=transforms.Compose([
                               transforms.Resize(image_size),
                               transforms.CenterCrop(image_size),
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
                           ]))

I want to define a function

def minibatch(dataset,indices):
    """

    :param dataset:
    :param indices: an index list
    :return: a N*C*H*W tensor that contains feature vectors corresponding to samples with these indices
    """
2

You can order dataloader indices as you want and use typicall pytorch dataloader. You will sequentially load batches with the mixtures that you want.

3

I find out this is actually very easy :sweat_smile:


def batch(batch_size=batch_size, lower=0, upper=n_sample):
    indices = np.random.randint(low=lower, high=upper, size=batch_size)
    batch_data = torch.FloatTensor(batch_size, nc, image_size, image_size)
    for i in range(batch_size):
        sample, target = dataset[indices[i]]
        batch_data[i] = sample
    return batch_data
4

Once you load the dataset, create a dataloader, and since the dataloader and then you can do something like:

a = iter(dataloader)
b = next(a)

where b is a minibatch.

An example:

    dataset = VQA_dataset(ROOT_DIR, train = False)


    loader = torch.utils.data.DataLoader(
                    dataset,
                    batch_size= 3,
                    shuffle=True,
                            )
    data = next(iter(loader))
    v, bb, spat, obs, a, q,  q_len, item = data


    torch.save(data, DUMMY_DATA)
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