Force object annotiation to be a list (#1790)

* Force object annotiation to be an array

* Remove unecessary parentheses

* Change object check

* Remove check for list

* Add test coverage to xml parsing

* Tidy up whitespace

test/fakedata_generation.py

@@ -258,3 +258,15 @@ def svhn_root():
         _make_mat(os.path.join(root, "extra_32x32.mat"))
 
         yield root
+
+@contextlib.contextmanager
+def voc_root():
+    with get_tmp_dir() as tmp_dir:
+        voc_dir = os.path.join(tmp_dir, 'VOCdevkit',
+                'VOC2012','ImageSets','Main')
+        os.makedirs(voc_dir)
+        train_file = os.path.join(voc_dir,'train.txt')
+        with open(train_file, 'w') as f:
+            f.write('test')
+
+        yield tmp_dir

test/test_datasets.py

@@ -9,7 +9,8 @@ from torch._utils_internal import get_file_path_2
 import torchvision
 from common_utils import get_tmp_dir
 from fakedata_generation import mnist_root, cifar_root, imagenet_root, \
-    cityscapes_root, svhn_root
+    cityscapes_root, svhn_root, voc_root
+import xml.etree.ElementTree as ET
 
 
 try:
@@ -210,6 +211,32 @@ class Tester(unittest.TestCase):
             dataset = torchvision.datasets.SVHN(root, split="extra")
             self.generic_classification_dataset_test(dataset, num_images=2)
 
+    @mock.patch('torchvision.datasets.voc.download_extract')
+    def test_voc_parse_xml(self, mock_download_extract):
+        with voc_root() as root:
+            dataset = torchvision.datasets.VOCDetection(root)
+
+            single_object_xml = """<annotation>
+              <object>
+                <name>cat</name>
+              </object>
+            </annotation>"""
+            multiple_object_xml = """<annotation>
+              <object>
+                <name>cat</name>
+              </object>
+              <object>
+                <name>dog</name>
+              </object>
+            </annotation>"""
+            single_object_parsed = dataset.parse_voc_xml(ET.fromstring(single_object_xml
+                ))
+            multiple_object_parsed = dataset.parse_voc_xml(ET.fromstring(multiple_object_xml))
+
+            self.assertEqual(single_object_parsed, {'annotation': {'object':[{'name': 'cat'}]}})
+            self.assertEqual(multiple_object_parsed, {'annotation':
+                {'object':[{'name': 'cat'}, {'name': 'dog'}]}})
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/datasets/voc.py

@@ -218,6 +218,8 @@ class VOCDetection(VisionDataset):
             for dc in map(self.parse_voc_xml, children):
                 for ind, v in dc.items():
                     def_dic[ind].append(v)
+            if node.tag == 'annotation':
+                def_dic['object'] = [def_dic['object']]
             voc_dict = {
                 node.tag:
                     {ind: v[0] if len(v) == 1 else v