datasets.py

import os
import os.path
import torch.utils.data as data
from PIL import Image

def make_dataset(root, type='GT'):
    image_path = os.path.join(root, 'Imgs')
    mask_path = os.path.join(root, type)
    img_list = [os.path.splitext(f)[0] for f in os.listdir(image_path) if f.endswith('.jpg')]
    img_list.sort()
    return [(os.path.join(image_path, img_name + '.jpg'), os.path.join(mask_path, img_name + '.png')) for img_name in img_list]

class ImageFolder(data.Dataset):
    # image and gt should be in the same folder and have same filename except extended name (jpg and png respectively)
    def __init__(self, root, joint_transform=None, transform=None, target_transform=None, index=None, type='GT'):
        self.root = root
        self.imgs = make_dataset(root, type=type)
        if index is not None:
            self.imgs = [self.imgs[i] for i in index]
        self.joint_transform = joint_transform
        self.transform = transform
        self.target_transform = target_transform

    def __getitem__(self, index):
        img_path, gt_path = self.imgs[index]
        img = Image.open(img_path).convert('RGB')
        target = Image.open(gt_path).convert('L')
        if self.joint_transform is not None:
            img, target = self.joint_transform(img, target)
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

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