If your targets are the standard CIFAR targets you could try something like this

transform = transforms.Compose(transforms.ToTensor(), nn.MaxPool2d())
cifar = torchvision.datasets.CIFAR100(path_to_CIFAR_data, transform=transform)
dataloader = torch.utils.data.DataLoader(cifar, batch_size=args.bs 1)

I’m not sure if nn.MaxPool2d would work but maybe it will.
However as a side note, why not apply max pooling to the regular dataset, i.e. use nn.MaxPool2d as the first layer of your NN
If they aren’t you can check the tutorial on custom datasets (http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#dataset-class 5). In summary you inherit from torch.utils.data.Dataset and implement len, getitem and init to read and transform your data