generation/data/custom_dataset_data_loader.py [27:49]:
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    print("dataset [%s] was created" % (dataset.name()))
    dataset.initialize(opt)
    return dataset

class CustomDatasetDataLoader(BaseDataLoader):
    def name(self):
        return 'CustomDatasetDataLoader'

    def initialize(self, opt):
        BaseDataLoader.initialize(self, opt)
        self.dataset = CreateDataset(opt)
        self.dataloader = torch.utils.data.DataLoader(
            self.dataset,
            batch_size=opt.batchSize,
            shuffle=not opt.serial_batches,
            # shuffle=False,
            num_workers=int(opt.nThreads))

    def load_data(self):
        return self.dataloader

    def __len__(self):
        return min(len(self.dataset), self.opt.max_dataset_size)
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separate_vae/data/custom_dataset_data_loader.py [24:45]:
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    print("dataset [%s] was created" % (dataset.name()))
    dataset.initialize(opt)
    return dataset

class CustomDatasetDataLoader(BaseDataLoader):
    def name(self):
        return 'CustomDatasetDataLoader'

    def initialize(self, opt):
        BaseDataLoader.initialize(self, opt)
        self.dataset = CreateDataset(opt)
        self.dataloader = torch.utils.data.DataLoader(
            self.dataset,
            batch_size=opt.batchSize,
            shuffle=not opt.serial_batches,
            num_workers=int(opt.nThreads))

    def load_data(self):
        return self.dataloader

    def __len__(self):
        return min(len(self.dataset), self.opt.max_dataset_size)
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