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
