Making large AI models cheaper, faster and more accessible
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import os
from pathlib import Path
import pytest
from torchvision import transforms, datasets
from torch.utils.data import DataLoader
@pytest.mark.cpu
def test_cifar10_dataset():
# build transform
transform_pipeline = [transforms.ToTensor()]
transform_pipeline = transforms.Compose(transform_pipeline)
# build dataset
dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
# build dataloader
dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=2)
data_iter = iter(dataloader)
img, label = data_iter.next()
if __name__ == '__main__':
test_cifar10_dataset()