nni/retiarii/oneshot/pytorch/darts.py [143:165]:
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        self._init_dataloader()

    def _init_dataloader(self):
        n_train = len(self.dataset)
        split = n_train // 2
        indices = list(range(n_train))
        train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split])
        valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split:])
        self.train_loader = torch.utils.data.DataLoader(self.dataset,
                                                        batch_size=self.batch_size,
                                                        sampler=train_sampler,
                                                        num_workers=self.workers)
        self.valid_loader = torch.utils.data.DataLoader(self.dataset,
                                                        batch_size=self.batch_size,
                                                        sampler=valid_sampler,
                                                        num_workers=self.workers)

    def _train_one_epoch(self, epoch):
        self.model.train()
        meters = AverageMeterGroup()
        for step, ((trn_X, trn_y), (val_X, val_y)) in enumerate(zip(self.train_loader, self.valid_loader)):
            trn_X, trn_y = to_device(trn_X, self.device), to_device(trn_y, self.device)
            val_X, val_y = to_device(val_X, self.device), to_device(val_y, self.device)
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nni/retiarii/oneshot/pytorch/proxyless.py [263:285]:
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        self._init_dataloader()

    def _init_dataloader(self):
        n_train = len(self.dataset)
        split = n_train // 2
        indices = list(range(n_train))
        train_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[:split])
        valid_sampler = torch.utils.data.sampler.SubsetRandomSampler(indices[split:])
        self.train_loader = torch.utils.data.DataLoader(self.dataset,
                                                        batch_size=self.batch_size,
                                                        sampler=train_sampler,
                                                        num_workers=self.workers)
        self.valid_loader = torch.utils.data.DataLoader(self.dataset,
                                                        batch_size=self.batch_size,
                                                        sampler=valid_sampler,
                                                        num_workers=self.workers)

    def _train_one_epoch(self, epoch):
        self.model.train()
        meters = AverageMeterGroup()
        for step, ((trn_X, trn_y), (val_X, val_y)) in enumerate(zip(self.train_loader, self.valid_loader)):
            trn_X, trn_y = to_device(trn_X, self.device), to_device(trn_y, self.device)
            val_X, val_y = to_device(val_X, self.device), to_device(val_y, self.device)
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