update codes

lib/datasets/get_dataset_with_transform.py

@@ -10,7 +10,7 @@ from .TieredImageNet import TieredImageNet
 Dataset2Class = {'cifar10' : 10,
                  'cifar100': 100,
                  'tiered'  : -1,
-                 'imagnet-1k'  : 1000,
+                 'imagenet-1k' : 1000,
                  'imagenet-100': 100}
 
 
@@ -25,8 +25,8 @@ def get_datasets(name, root, cutout):
     std = [x / 255 for x in [68.2, 65.4, 70.4]]
   elif name == 'tiered':
     mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
-  elif name == 'imagnet-1k' or name == 'imagenet-100':
-    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+  elif name == 'imagenet-1k' or name == 'imagenet-100':
+    mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
   else: raise TypeError("Unknow dataset : {:}".format(name))
 
 
@@ -42,7 +42,7 @@ def get_datasets(name, root, cutout):
     if cutout > 0 : lists += [Cutout(cutout)]
     train_transform = transforms.Compose(lists)
     test_transform  = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(mean, std)])
-  elif name == 'imagnet-1k' or name == 'imagenet-100':
+  elif name == 'imagenet-1k' or name == 'imagenet-100':
     normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     train_transform = transforms.Compose([
       transforms.RandomResizedCrop(224),
@@ -57,15 +57,14 @@ def get_datasets(name, root, cutout):
     ])
     test_transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
   else: raise TypeError("Unknow dataset : {:}".format(name))
-    train_data = TieredImageNet(root, 'train-val', train_transform)
-    test_data = None
+
   if name == 'cifar10':
     train_data = dset.CIFAR10(root, train=True, transform=train_transform, download=True)
     test_data  = dset.CIFAR10(root, train=True, transform=test_transform , download=True)
   elif name == 'cifar100':
     train_data = dset.CIFAR100(root, train=True, transform=train_transform, download=True)
     test_data  = dset.CIFAR100(root, train=True, transform=test_transform , download=True)
-  elif name == 'imagnet-1k' or name == 'imagenet-100':
+  elif name == 'imagenet-1k' or name == 'imagenet-100':
     train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
     test_data  = dset.ImageFolder(osp.join(root, 'val'), train_transform)
   else: raise TypeError("Unknow dataset : {:}".format(name))

lib/utils/__init__.py

@@ -4,7 +4,6 @@ from .utils import test_imagenet_data
 from .utils import print_log
 from .evaluation_utils import obtain_accuracy
 from .draw_pts import draw_points
-from .fb_transform import ApplyOffset
 from .gpu_manager import GPUManager
 
 from .save_meta import Save_Meta

lib/utils/fb_transform.py

@@ -1,14 +0,0 @@
-import torch
-import random
-import numpy as np
-
-class ApplyOffset(object):
-  def __init__(self, offset):
-    assert isinstance(offset, int), 'The offset is not right : {}'.format(offset)
-    self.offset = offset
-  def __call__(self, x):
-    if isinstance(x, np.ndarray) and x.dtype == 'uint8':
-      x = x.astype(int)
-    if isinstance(x, np.ndarray) and x.size == 1:
-      x = int(x)
-    return x + self.offset