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imagenet.py
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import os
from torchvision.datasets import ImageFolder, ImageNet
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from default_paths import DATA_IMAGENET, DATA_IMAGENET_A, DATA_IMAGENET_SKETCH, DATA_IMAGENET_V2, DATA_VAL_IMAGENET200A
from torchvision.transforms import ToTensor
from imagenetv2_pytorch import ImageNetV2Dataset
class ImageNetDataModule(LightningDataModule):
def __init__(self, batch_size=16, num_workers=12, shuffle=True, *args, **kwargs) -> None:
super().__init__()
self.batch_size = batch_size
self.num_workers = num_workers
self.shuffle = shuffle
self.preprocess = ToTensor()
def setup(self, stage=None):
self.dataset_val = ImageNet(root=DATA_IMAGENET, split="val", transform=self.preprocess)
def train_dataloader(self):
raise NotImplementedError
def val_dataloader(self):
return DataLoader(
self.dataset_val,
self.batch_size,
shuffle=False,
num_workers=self.num_workers,
)
def test_dataloader(self):
raise NotImplementedError
def get_all_ood_dataloaders(self):
imagenet_sketch = ImageFolder(root=DATA_IMAGENET_SKETCH, transform=self.preprocess)
imagenet_v2_a = ImageNetV2Dataset(
variant="matched-frequency", location=DATA_IMAGENET_V2, transform=self.preprocess
)
return [
(
"imagenet_v2_a",
DataLoader(imagenet_v2_a, self.batch_size, shuffle=False, num_workers=self.num_workers),
),
(
"imagenet_sketch",
DataLoader(imagenet_sketch, self.batch_size, shuffle=False, num_workers=self.num_workers),
),
]
@property
def num_classes(self):
return 1000
imagenet_val_location = DATA_IMAGENET / "val"
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