model.py

from torch.utils.data import Dataset
from torchvision import transforms
from PIL import Image
import os

class CustomDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.classes = sorted(os.listdir(root_dir))
        self.class_to_idx = {cls: i for i, cls in enumerate(self.classes)}
        self.images = self.load_images()

    def load_images(self):
        images = []
        for cls in self.classes:
            class_path = os.path.join(self.root_dir, cls)
            for img_name in os.listdir(class_path):
                img_path = os.path.join(class_path, img_name)
                images.append((img_path, self.class_to_idx[cls]))
        return images

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        img_path, label = self.images[idx]
        image = Image.open(img_path).convert("L")  # グレースケールに変換

        if self.transform:
            image = self.transform(image)

        return image, label