def val_dataloader(self) -> DataLoader:
"""Uses the part of the train split of imagenet2012 that was not used for training via
`num_imgs_per_val_class`
Args:
batch_size: the batch size
transforms: the transforms
"""
transforms = self.val_transform(
) if self.val_transforms is None else self.val_transforms
dataset = UnlabeledImagenet(
self.data_dir,
num_imgs_per_class_val_split=self.num_imgs_per_val_class,
meta_dir=self.meta_dir,
split="val",
transform=transforms,
)
loader: DataLoader = DataLoader(
dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
drop_last=self.drop_last,
pin_memory=self.pin_memory,
)
return loader
def imagenet(dataset_root, nb_classes, split: str = 'train'):
assert split in ('train', 'val')
dataset = UnlabeledImagenet(
dataset_root,
nb_classes=nb_classes,
split=split,
transform=amdim_transforms.AMDIMTrainTransformsImageNet128(),
)
return dataset
def imagenet(dataset_root,
nb_classes,
patch_size,
patch_overlap,
split: str = 'train'):
assert split in ('train', 'val')
train_transform = amdim_transforms.TransformsImageNet128Patches(
patch_size=patch_size, overlap=patch_overlap)
dataset = UnlabeledImagenet(
dataset_root,
nb_classes=nb_classes,
split=split,
transform=train_transform,
)
return dataset
def train_dataloader(self,
num_images_per_class: int = -1,
add_normalize: bool = False) -> DataLoader:
transforms = self._default_transforms(
) if self.train_transforms is None else self.train_transforms
dataset = UnlabeledImagenet(self.data_dir,
num_imgs_per_class=num_images_per_class,
meta_dir=self.meta_dir,
split='train',
transform=transforms)
loader: DataLoader = DataLoader(dataset,
batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=self.num_workers,
drop_last=self.drop_last,
pin_memory=self.pin_memory)
return loader
def test_dataloader(self) -> DataLoader:
"""
Uses the validation split of imagenet2012 for testing
"""
transforms = self.val_transform(
) if self.test_transforms is None else self.test_transforms
dataset = UnlabeledImagenet(self.data_dir,
num_imgs_per_class=-1,
meta_dir=self.meta_dir,
split='test',
transform=transforms)
loader: DataLoader = DataLoader(dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
drop_last=self.drop_last,
pin_memory=self.pin_memory)
return loader
def train_dataloader(self) -> DataLoader:
"""
Uses the train split of imagenet2012 and puts away a portion of it for the validation split
"""
transforms = self.train_transform(
) if self.train_transforms is None else self.train_transforms
dataset = UnlabeledImagenet(
self.data_dir,
num_imgs_per_class=-1,
num_imgs_per_class_val_split=self.num_imgs_per_val_class,
meta_dir=self.meta_dir,
split='train',
transform=transforms)
loader: DataLoader = DataLoader(dataset,
batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=self.num_workers,
drop_last=self.drop_last,
pin_memory=self.pin_memory)
return loader