34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
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import os, sys, torch
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import torchvision.transforms as transforms
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from TieredImageNet import TieredImageNet
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from MetaBatchSampler import MetaBatchSampler
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root_dir = os.environ['TORCH_HOME'] + '/tiered-imagenet'
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print ('root : {:}'.format(root_dir))
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means, stds = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
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lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(84, padding=8), transforms.ToTensor(), transforms.Normalize(means, stds)]
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transform = transforms.Compose(lists)
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dataset = TieredImageNet(root_dir, 'val-test', transform)
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image, label = dataset[111]
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print ('image shape = {:}, label = {:}'.format(image.size(), label))
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print ('image : min = {:}, max = {:} ||| label : {:}'.format(image.min(), image.max(), label))
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sampler = MetaBatchSampler(dataset.labels, 250, 100, 10)
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dataloader = torch.utils.data.DataLoader(dataset, batch_sampler=sampler)
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print ('the length of dataset : {:}'.format( len(dataset) ))
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print ('the length of loader : {:}'.format( len(dataloader) ))
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for images, labels in dataloader:
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print ('images : {:}'.format( images.size() ))
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print ('labels : {:}'.format( labels.size() ))
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for i in range(3):
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print ('image-value-[{:}] : {:} ~ {:}, mean={:}, std={:}'.format(i, images[:,i].min(), images[:,i].max(), images[:,i].mean(), images[:,i].std()))
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print('-----')
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