Add more algorithms

others/GDAS/lib/datasets/test_dataset.py

@@ -0,0 +1,33 @@
+import os, sys, torch
+import torchvision.transforms as transforms
+
+from TieredImageNet import TieredImageNet
+from MetaBatchSampler import MetaBatchSampler
+
+root_dir = os.environ['TORCH_HOME'] + '/tiered-imagenet'
+print ('root : {:}'.format(root_dir))
+means, stds = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
+
+lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(84, padding=8), transforms.ToTensor(), transforms.Normalize(means, stds)]
+transform = transforms.Compose(lists)
+
+dataset = TieredImageNet(root_dir, 'val-test', transform)
+image, label = dataset[111]
+print ('image shape = {:}, label = {:}'.format(image.size(), label))
+print ('image : min = {:}, max = {:}    ||| label : {:}'.format(image.min(), image.max(), label))
+
+
+sampler = MetaBatchSampler(dataset.labels, 250, 100, 10)
+
+dataloader = torch.utils.data.DataLoader(dataset, batch_sampler=sampler)
+
+print ('the length of dataset : {:}'.format( len(dataset) ))
+print ('the length of loader  : {:}'.format( len(dataloader) ))
+
+for images, labels in dataloader:
+  print ('images : {:}'.format( images.size() ))
+  print ('labels : {:}'.format( labels.size() ))
+  for i in range(3):
+    print ('image-value-[{:}] : {:} ~ {:}, mean={:}, std={:}'.format(i, images[:,i].min(), images[:,i].max(), images[:,i].mean(), images[:,i].std()))
+
+print('-----')