mnist_model/database.py

from pathlib import Path

import torchvision
from torchvision.transforms import v2
import torch

data_path = Path(__file__).resolve().parent.parent.parent / 'data'

print(data_path)

def transform():
    return v2.Compose([
        v2.ToImage(), 
        v2.ToDtype(torch.float32, scale=True),
        v2.Pad(2),
        v2.Normalize((0.1307,), (0.3081,))
    ])

def add_noise(noise_factor=0.5):
    def add_noise_to_image(x):
        noise = torch.randn_like(x) * noise_factor
        return torch.clamp(x + noise, 0., 1.)
    return v2.Lambda(add_noise_to_image)

def train_transform():
    return v2.Compose([
        v2.ToImage(), 
        v2.ToDtype(torch.float32, scale=True),
        v2.Pad(2),
        v2.RandomAffine(degrees=5, translate=(0.2, 0.2), scale=(0.5, 1.2)),
        v2.Normalize((0.1307,), (0.3081,))
    ])


def get_dataset(val_split=0.2):
    train_dataset = torchvision.datasets.MNIST(
        root=str(data_path), train=True, transform=train_transform(), download=True
    )

    test_dataset = torchvision.datasets.MNIST(
        root=str(data_path), train=False, transform=transform(), download=True
    )

    return train_dataset, test_dataset