unaligned_dataset.py

import os.path
import torchvision.transforms as transforms
from data.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
import PIL
from pdb import set_trace as st


class UnalignedDataset(BaseDataset):
    def initialize(self, opt):
        self.opt = opt
        self.root = opt.dataroot
        self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
        self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')

        self.A_paths = make_dataset(self.dir_A)
        self.B_paths = make_dataset(self.dir_B)

        self.A_paths = sorted(self.A_paths)
        self.B_paths = sorted(self.B_paths)
        self.A_size = len(self.A_paths)
        self.B_size = len(self.B_paths)

        transform_list = []
        if opt.resize_or_crop == 'resize_and_crop':
            osize = [opt.loadSize, opt.loadSize]
            transform_list.append(transforms.Scale(osize, Image.BICUBIC))

        if opt.isTrain and not opt.no_flip:
            transform_list.append(transforms.RandomHorizontalFlip())

        if opt.resize_or_crop != 'no_resize':
            transform_list.append(transforms.RandomCrop(opt.fineSize))

        transform_list += [transforms.ToTensor(),
                           transforms.Normalize((0.5, 0.5, 0.5),
                                                (0.5, 0.5, 0.5))]
        self.transform = transforms.Compose(transform_list)

    def __getitem__(self, index):
        A_path = self.A_paths[index % self.A_size]
        B_path = self.B_paths[index % self.B_size]

        A_img = Image.open(A_path).convert('RGB')
        B_img = Image.open(B_path).convert('RGB')

        A_img = self.transform(A_img)
        B_img = self.transform(B_img)

        return {'A': A_img, 'B': B_img,
                'A_paths': A_path, 'B_paths': B_path}

    def __len__(self):
        return max(self.A_size, self.B_size)

    def name(self):
        return 'UnalignedDataset'