Merge pull request #12 from pytorch/cifar10fix

cifar10/100 only load train/test and not both

torchvision/datasets/cifar.py

@@ -42,35 +42,35 @@ class CIFAR10(data.Dataset):
                                + ' You can use download=True to download it')
                 
         # now load the picked numpy arrays
-        self.train_data = []
-        self.train_labels = []
-        for fentry in self.train_list:
-            f = fentry[0]
+        if self.train:
+            self.train_data = []
+            self.train_labels = []
+            for fentry in self.train_list:
+                f = fentry[0]
+                file = os.path.join(root, self.base_folder, f)
+                fo = open(file, 'rb')
+                entry = pickle.load(fo)
+                self.train_data.append(entry['data'])
+                if 'labels' in entry:
+                    self.train_labels += entry['labels']
+                else:
+                    self.train_labels += entry['fine_labels']
+                fo.close()
+
+            self.train_data = np.concatenate(self.train_data)
+            self.train_data = self.train_data.reshape((50000, 3, 32, 32))
+        else:
+            f = self.test_list[0][0]
             file = os.path.join(root, self.base_folder, f)
             fo = open(file, 'rb')
             entry = pickle.load(fo)
-            self.train_data.append(entry['data'])
+            self.test_data = entry['data']
             if 'labels' in entry:
-                self.train_labels += entry['labels']
+                self.test_labels = entry['labels']
             else:
-                self.train_labels += entry['fine_labels']
+                self.test_labels = entry['fine_labels']
             fo.close()
-
-        self.train_data = np.concatenate(self.train_data)
-
-        f = self.test_list[0][0]
-        file = os.path.join(root, self.base_folder, f)
-        fo = open(file, 'rb')
-        entry = pickle.load(fo)
-        self.test_data = entry['data']
-        if 'labels' in entry:
-            self.test_labels = entry['labels']
-        else:
-            self.test_labels = entry['fine_labels']
-        fo.close()
-
-        self.train_data = self.train_data.reshape((50000, 3, 32, 32))
-        self.test_data = self.test_data.reshape((10000, 3, 32, 32))
+            self.test_data = self.test_data.reshape((10000, 3, 32, 32))
 
     def __getitem__(self, index):
         if self.train: