5f6e2c4a115a6a706cc011b3bf9ed9e3ef149d98,data/unaligned_data_loader.py,UnalignedDataLoader,initialize,#UnalignedDataLoader#Any#,54

Before Change

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

        // 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
        self.paired_data = PairedData(data_loader_A, data_loader_B, self.opt.max_dataset_size)

    def name(self):

After Change

                           transforms.ToTensor(),
                           transforms.Normalize((0.5, 0.5, 0.5),
                                                (0.5, 0.5, 0.5))]
        if opt.isTrain and not opt.no_flip:
            transformations.insert(1, transforms.RandomHorizontalFlip())
        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
        self.paired_data = PairedData(data_loader_A, data_loader_B, self.opt.max_dataset_size)

    def name(self):

Instances