def val_dataloader(self): if self.hparams.dataset_name == 'CIFAR10': dataset = AMDIMPretraining.cifar10_val(self.hparams.data_dir) if self.hparams.dataset_name == 'stl_10': dataset = self.val_split if self.hparams.dataset_name == 'imagenet_128': dataset = AMDIMPretraining.imagenet_val(self.hparams.data_dir, self.hparams.nb_classes) # LOADER loader = DataLoader( dataset=dataset, batch_size=self.hparams.batch_size, pin_memory=True, drop_last=True, num_workers=16, ) return loader
def val_dataloader(self): kwargs = dict(nb_classes=self.hparams.nb_classes) if self.hparams.datamodule == 'imagenet2012' else {} dataset = AMDIMPretraining.get_dataset(self.hparams.datamodule, self.hparams.data_dir, split='val', **kwargs) # LOADER loader = DataLoader( dataset=dataset, batch_size=self.hparams.batch_size, pin_memory=True, drop_last=True, num_workers=16, ) return loader