import torch
from torchvision import datasets, transforms
MNIST_PATH = '/home/dingxiaohan/datasets/torch_mnist/'
CIFAR10_PATH = '/home/dingxiaohan/datasets/cifar-10-batches-py/'
CH_PATH = '/home/dingxiaohan/datasets/torch_ch/'
SVHN_PATH = '/home/dingxiaohan/datasets/torch_svhn/'
class InfiniteDataLoader(torch.utils.data.DataLoader):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Initialize an iterator over the dataset.
self.dataset_iterator = super().__iter__()
def __iter__(self):
return self
def __next__(self):
try:
batch = next(self.dataset_iterator)
except StopIteration:
# Dataset exhausted, use a new fresh iterator.
self.dataset_iterator = super().__iter__()
batch = next(self.dataset_iterator)
return batch
def create_dataset(dataset_name, subset, batch_size):
assert dataset_name in ['imagenet', 'cifar10', 'ch', 'svhn', 'mnist']
assert subset in ['train', 'val']
if dataset_name == 'imagenet':
raise ValueError('TODO')
# copied from https://github.com/pytorch/examples/blob/master/mnist/main.py
elif dataset_name == 'mnist':
if subset == 'train':
return InfiniteDataLoader(datasets.MNIST(MNIST_PATH, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])), batch_size=batch_size, shuffle=True)
else:
return InfiniteDataLoader(datasets.MNIST(MNIST_PATH, train=False, transform=transforms.Compose([
transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=batch_size, shuffle=False)
elif dataset_name == 'cifar10':
if subset == 'train':
return InfiniteDataLoader(datasets.CIFAR10(CIFAR10_PATH, train=True, download=False,
transform=transforms.Compose([
transforms.Pad(padding=(4, 4, 4, 4)),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])),
batch_size=batch_size, shuffle=True)
else:
return InfiniteDataLoader(datasets.CIFAR10(CIFAR10_PATH, train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])),
batch_size=batch_size, shuffle=False)
elif dataset_name == 'ch':
if subset == 'train':
return InfiniteDataLoader(datasets.CIFAR100(CH_PATH, train=True, download=True,
transform=transforms.Compose([
transforms.Pad(padding=(4, 4, 4, 4)),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])),
batch_size=batch_size, shuffle=True)
else:
return InfiniteDataLoader(datasets.CIFAR100(CH_PATH, train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])),
batch_size=batch_size, shuffle=False)
else:
assert False
def num_train_examples_per_epoch(dataset_name):
if dataset_name == 'imagenet':
return 1281167
elif dataset_name == 'mnist':
return 60000
elif dataset_name in ['cifar10', 'ch']:
return 50000
else:
assert False
