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Merged
mehdidc merged 1 commit into from
Jun 11, 2025
Merged

Update imagenetv2 urls #137

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53 changes: 10 additions & 43 deletions clip_benchmark/datasets/imagenetv2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,54 +12,21 @@
from torch.utils.data import Dataset, DataLoader
from torchvision.datasets import ImageFolder

URLS = {"matched-frequency" : "https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-matched-frequency.tar.gz",
"threshold-0.7" : "https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-threshold0.7.tar.gz",
"top-images": "https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-top-images.tar.gz",
"val": "https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenet_validation.tar.gz"}
URLS = {
"matched-frequency" : "https://huggingface.co/datasets/vaishaal/ImageNetV2/resolve/main/imagenetv2-matched-frequency.tar.gz",
"threshold-0.7" : "https://huggingface.co/datasets/vaishaal/ImageNetV2/resolve/main/imagenetv2-threshold0.7.tar.gz",
"top-images": "https://huggingface.co/datasets/vaishaal/ImageNetV2/resolve/main/imagenetv2-top-images.tar.gz",
}

FNAMES = {"matched-frequency" : "imagenetv2-matched-frequency-format-val",
"threshold-0.7" : "imagenetv2-threshold0.7-format-val",
"top-images": "imagenetv2-top-images-format-val",
"val": "imagenet_validation"}
FNAMES = {
"matched-frequency" : "imagenetv2-matched-frequency-format-val",
"threshold-0.7" : "imagenetv2-threshold0.7-format-val",
"top-images": "imagenetv2-top-images-format-val",
}


V2_DATASET_SIZE = 10000
VAL_DATASET_SIZE = 50000

class ImageNetValDataset(Dataset):
def __init__(self, transform=None, location="."):
self.dataset_root = pathlib.Path(f"{location}/imagenet_validation/")
self.tar_root = pathlib.Path(f"{location}/imagenet_validation.tar.gz")
self.fnames = list(self.dataset_root.glob("**/*.JPEG"))
self.transform = transform
if not self.dataset_root.exists() or len(self.fnames) != VAL_DATASET_SIZE:
if not self.tar_root.exists():
print(f"Dataset imagenet-val not found on disk, downloading....")
response = requests.get(URLS["val"], stream=True)
total_size_in_bytes= int(response.headers.get('content-length', 0))
block_size = 1024 #1 Kibibyte
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
with open(self.tar_root, 'wb') as f:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
f.write(data)
progress_bar.close()
if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
assert False, f"Downloading from {URLS[variant]} failed"
print("Extracting....")
tarfile.open(self.tar_root).extractall(f"{location}")
shutil.move(f"{location}/{FNAMES['val']}", self.dataset_root)

self.dataset = ImageFolder(self.dataset_root)

def __len__(self):
return len(self.dataset)

def __getitem__(self, i):
img, label = self.dataset[i]
if self.transform is not None:
img = self.transform(img)
return img, label

class ImageNetV2Dataset(Dataset):
def __init__(self, variant="matched-frequency", transform=None, location="."):
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