cv4e_lecture13/dataset.py

# -*- coding: utf-8 -*-
'''
    Model implementation.
    We'll be using a "simple" ResNet-18 for image classification here.

    2022 Benjamin Kellenberger
'''
from os.path import abspath

import torch
from torchvision import datasets
from torchvision.transforms import Compose, Resize, ToTensor


def load(cfg):
    """
    Load the MNIST dataset from PyTorch (download if needed) and return a DataLoader

    MNIST is a sample dataset for machine learning, each image is 28-pixels high and 28-pixels wide (1 color channel)
    """
    root = abspath('datasets')

    train = torch.utils.data.DataLoader(
        datasets.MNIST(
            root,
            train=True,
            transform=Compose([Resize(cfg['image_size']), ToTensor()]),
            download=True,
        ),
        batch_size=cfg.get('batch_size'),
        shuffle=True,
        num_workers=cfg.get('num_workers'),
    )

    test = torch.utils.data.DataLoader(
        datasets.MNIST(
            root,
            train=False,
            transform=Compose([Resize(cfg['image_size']), ToTensor()]),
            download=True,
        ),
        batch_size=cfg.get('batch_size'),
        shuffle=False,
        num_workers=cfg.get('num_workers'),
    )

    return train, test