CIFAR: permanent 'data' and 'targets' fields (#594)

torchvision/datasets/cifar.py

@@ -51,13 +51,6 @@ class CIFAR10(data.Dataset):
         'md5': '5ff9c542aee3614f3951f8cda6e48888',
     }
 
-    @property
-    def targets(self):
-        if self.train:
-            return self.train_labels
-        else:
-            return self.test_labels
-
     def __init__(self, root, train=True,
                  transform=None, target_transform=None,
                  download=False):
@@ -73,44 +66,30 @@ class CIFAR10(data.Dataset):
             raise RuntimeError('Dataset not found or corrupted.' +
                                ' You can use download=True to download it')
 
-        # now load the picked numpy arrays
         if self.train:
-            self.train_data = []
-            self.train_labels = []
-            for fentry in self.train_list:
-                f = fentry[0]
-                file = os.path.join(self.root, self.base_folder, f)
-                fo = open(file, 'rb')
-                if sys.version_info[0] == 2:
-                    entry = pickle.load(fo)
-                else:
-                    entry = pickle.load(fo, encoding='latin1')
-                self.train_data.append(entry['data'])
-                if 'labels' in entry:
-                    self.train_labels += entry['labels']
+            downloaded_list = self.train_list
         else:
-                    self.train_labels += entry['fine_labels']
-                fo.close()
+            downloaded_list = self.test_list
 
-            self.train_data = np.concatenate(self.train_data)
-            self.train_data = self.train_data.reshape((50000, 3, 32, 32))
-            self.train_data = self.train_data.transpose((0, 2, 3, 1))  # convert to HWC
-        else:
-            f = self.test_list[0][0]
-            file = os.path.join(self.root, self.base_folder, f)
-            fo = open(file, 'rb')
+        self.data = []
+        self.targets = []
+
+        # now load the picked numpy arrays
+        for file_name, checksum in downloaded_list:
+            file_path = os.path.join(self.root, self.base_folder, file_name)
+            with open(file_path, 'rb') as f:
                 if sys.version_info[0] == 2:
-                entry = pickle.load(fo)
+                    entry = pickle.load(f)
                 else:
-                entry = pickle.load(fo, encoding='latin1')
-            self.test_data = entry['data']
+                    entry = pickle.load(f, encoding='latin1')
+                self.data.append(entry['data'])
                 if 'labels' in entry:
-                self.test_labels = entry['labels']
+                    self.targets.extend(entry['labels'])
                 else:
-                self.test_labels = entry['fine_labels']
-            fo.close()
-            self.test_data = self.test_data.reshape((10000, 3, 32, 32))
-            self.test_data = self.test_data.transpose((0, 2, 3, 1))  # convert to HWC
+                    self.targets.extend(entry['fine_labels'])
+
+        self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
+        self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC
 
         self._load_meta()
 
@@ -135,10 +114,7 @@ class CIFAR10(data.Dataset):
         Returns:
             tuple: (image, target) where target is index of the target class.
         """
-        if self.train:
-            img, target = self.train_data[index], self.train_labels[index]
-        else:
-            img, target = self.test_data[index], self.test_labels[index]
+        img, target = self.data[index], self.targets[index]
 
         # doing this so that it is consistent with all other datasets
         # to return a PIL Image
@@ -153,10 +129,7 @@ class CIFAR10(data.Dataset):
         return img, target
 
     def __len__(self):
-        if self.train:
-            return len(self.train_data)
-        else:
-            return len(self.test_data)
+        return len(self.data)
 
     def _check_integrity(self):
         root = self.root
@@ -174,16 +147,11 @@ class CIFAR10(data.Dataset):
             print('Files already downloaded and verified')
             return
 
-        root = self.root
-        download_url(self.url, root, self.filename, self.tgz_md5)
+        download_url(self.url, self.root, self.filename, self.tgz_md5)
 
         # extract file
-        cwd = os.getcwd()
-        tar = tarfile.open(os.path.join(root, self.filename), "r:gz")
-        os.chdir(root)
-        tar.extractall()
-        tar.close()
-        os.chdir(cwd)
+        with tarfile.open(os.path.join(self.root, self.filename), "r:gz") as tar:
+            tar.extractall(path=self.root)
 
     def __repr__(self):
         fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'