add special type func & models filter

vi_backbones.py

@@ -4,6 +4,7 @@ import hashlib
 import requests
 import datasets
 from bs4 import BeautifulSoup
+import torchvision.models as models
 
 
 _DBNAME = os.path.basename(__file__).split('.')[0]
@@ -58,11 +59,22 @@ class vi_backbones(datasets.GeneratorBasedBuilder):
         html = response.text
         return BeautifulSoup(html, 'html.parser')
 
+    def _special_type(self, m_type):
+        if m_type == 'wide' or m_type == 'resnext':
+            return 'resnet'
+
+        if m_type == 'swin':
+            return 'swin_transformer'
+
+        if m_type == 'inception':
+            return 'googlenet'
+
+        return m_type
+
     def _info_on_dataset(self, m_ver, m_type, in1k_span):
         url_span = in1k_span.find_next_sibling('span', {'class': 's2'})
         size_span = url_span.find_next_sibling('span', {'class': 'mi'})
-        if m_type == 'wide' or m_type == 'resnext':
-            m_type = 'resnet'
+        m_type = self._special_type(m_type)
         m_url = str(url_span.text[1:-1])
         input_size = int(size_span.text)
         m_dict = {
@@ -93,6 +105,9 @@ class vi_backbones(datasets.GeneratorBasedBuilder):
                     div_id = str(div['id'])
                     if div_id.__contains__('_Weights'):
                         m_ver = div_id.split('_Weight')[0].lower()
+                        if not hasattr(models, m_ver):
+                            continue
+
                         m_type = re.search('[a-zA-Z]+', m_ver).group(0)
                         in1k_v1_span = div.find(
                             'span', {'class': 'n'}, string='IMAGENET1K_V1')