MVTecAD
A Pytorch loader for MVTecAD dataset.
It strictly follows the code style of common Pytorch datasets, such as torchvision.datasets.CIFAR10. Therefore, there is no need to worry about the consistence of your code !!!
Showcase
from torch.utils.data import DataLoader
from torchvision import transforms
from mvtec_ad import MVTecAD
def _convert_label(x):
'''
convert anomaly label. 0: normal; 1: anomaly.
:param x (int): class label
:return: 0 or 1
'''
return 0 if x == 0 else 1
if __name__ == '__main__':
# define transforms
transform = transforms.Compose([transforms.Resize((300, 300)), transforms.ToTensor()])
target_transform = transforms.Lambda(_convert_label)
# load data
mvtec = MVTecAD('data',
subset_name='bottle',
train=True,
transform=transform,
mask_transform=transform,
target_transform=target_transform,
download=True)
# feed to data loader
data_loader = DataLoader(mvtec,
batch_size=2,
shuffle=True,
num_workers=8,
pin_memory=True,
drop_last=True)
# obtain in batch
for idx, (image, mask, target) in enumerate(data_loader):
print(idx, target)
Licence
This repository is under GPL V3.
About
Homepage: https://liujiyuan13.github.io
Email: [email protected]
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