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--- |
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task_categories: |
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- image-classification |
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size_categories: |
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- 100K<n<1M |
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--- |
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[CINIC10](https://github.com/BayesWatch/cinic-10) dataset with interface of [CIFAR10](https://github.com/pytorch/vision/blob/main/torchvision/datasets/cifar.py). |
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It is faster than the common CINIC10 due to the fact that all images are loaded into RAM while initing dataset instance. |
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You should save `cinic10.py` from this repo in local directory. And then import the CINIC10 class from it: |
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``` |
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import torchvision |
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import torch |
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from torchvision transforms |
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from cinic10 import CINIC10 |
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data_mean = [0.47889522, 0.47227842, 0.43047404] |
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data_std = [0.24205776, 0.23828046, 0.25874835] |
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transform_train = transforms.Compose([ |
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transforms.RandomCrop(32, padding=4), |
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transforms.Resize(32), |
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transforms.RandomHorizontalFlip(), |
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transforms.ToTensor(), |
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transforms.Normalize(data_mean, data_std), |
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]) |
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transform_test = transforms.Compose([ |
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transforms.Resize(32), |
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transforms.ToTensor(), |
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transforms.Normalize(data_mean, data_std), |
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]) |
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batch_size = 64 |
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num_workers = 4 |
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trainset = CINIC10(root='./data', train=True, download=True, transform=transform_train) |
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trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers) |
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testset = CINIC10(root='./data', train=False, download=True, transform=transform_test) |
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testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers) |
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``` |
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