unaligned_data_loader.py 文件源码

def initialize(self, opt):
        BaseDataLoader.initialize(self, opt)
        transformations = [transforms.Scale(opt.loadSize),
                           transforms.RandomCrop(opt.fineSize),
                           transforms.ToTensor(),
                           transforms.Normalize((0.5, 0.5, 0.5),
                                                (0.5, 0.5, 0.5))]
        transform = transforms.Compose(transformations)

        # Dataset A
        dataset_A = ImageFolder(root=opt.dataroot + '/' + opt.phase + 'A',
                                transform=transform, return_paths=True)
        data_loader_A = torch.utils.data.DataLoader(
            dataset_A,
            batch_size=self.opt.batchSize,
            shuffle=not self.opt.serial_batches,
            num_workers=int(self.opt.nThreads))

        # Dataset B
        dataset_B = ImageFolder(root=opt.dataroot + '/' + opt.phase + 'B',
                                transform=transform, return_paths=True)
        data_loader_B = torch.utils.data.DataLoader(
            dataset_B,
            batch_size=self.opt.batchSize,
            shuffle=not self.opt.serial_batches,
            num_workers=int(self.opt.nThreads))
        self.dataset_A = dataset_A
        self.dataset_B = dataset_B
        flip = opt.isTrain and not opt.no_flip
        self.paired_data = PairedData(data_loader_A, data_loader_B, 
                                      self.opt.max_dataset_size, flip)