Update vi_backbones.py

vi_backbones.py

@@ -35,6 +35,25 @@ class vi_backbones(datasets.GeneratorBasedBuilder):
         html = response.text
         return BeautifulSoup(html, 'html.parser')
 
+    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'})
+        params_label_span = size_span.find_next_sibling(
+            'span', string='"num_params"')
+        params_span = params_label_span.find_next_sibling(
+            'span', {'class': 'mi'})
+        m_url = str(url_span.text[1:-2])
+        input_size = int(size_span.text)
+        num_params = int(params_span.text)
+        m_dict = {
+            'ver': m_ver,
+            'type': m_type,
+            'input_size': input_size,
+            'num_params': num_params,
+            'url': m_url
+        }
+        return m_dict, params_span
+
     def _generate_dataset(self, url):
 
         torch_page = self._parse_url(url)
@@ -52,37 +71,22 @@ class vi_backbones(datasets.GeneratorBasedBuilder):
                 model_page = self._parse_url(url + href)
                 divs = model_page.select('div.viewcode-block')
 
-                for div in divs:
-                    div_id = str(div['id'])
-                    if div_id.__contains__('_Weights'):
-                        m_ver = div_id.split('_Weight')[0].lower()
-                        m_type = re.search('[a-zA-Z]+', m_ver).group(0)
-                        ints = div.find_all('span', {'class': 'mi'})
-                        m_urls = div.find_all('span', {'class': 's2'})
-                        input_size = int(ints[0].text)
-                        num_params = int(ints[1].text)
-                        m_url = m_urls[0].text
-                        m_dict = {
-                            'ver': m_ver,
-                            'type': m_type,
-                            'input_size': input_size,
-                            'num_params': num_params,
-                            'url': m_url
-                        }
-                        in1k_v1.append(m_dict)
-
-                        if len(ints) > 2 and len(m_urls) > 1:
-                            input_size = int(ints[2].text)
-                            num_params = int(ints[3].text)
-                            m_url = m_urls[1].text
-                            m_dict = {
-                                'ver': m_ver,
-                                'type': m_type,
-                                'input_size': input_size,
-                                'num_params': num_params,
-                                'url': m_url
-                            }
-                            in1k_v2.append(m_dict)
+            for div in divs:
+                div_id = str(div['id'])
+                if div_id.__contains__('_Weights'):
+                    m_ver = div_id.split('_Weight')[0].lower()
+                    m_type = re.search('[a-zA-Z]+', m_ver).group(0)
+                    in1k_v1_span = div.find('span', string='IMAGENET1K_V1')
+                    m_dict, in1k_v1_num_params = self._info_on_dataset(
+                        m_ver, m_type, in1k_v1_span)
+                    in1k_v1.append(m_dict)
+
+                    in1k_v2_span = in1k_v1_num_params.find_next_sibling(
+                        'span', string='IMAGENET1K_V2')
+                    if in1k_v2_span != None:
+                        m_dict, _ = self._info_on_dataset(
+                            m_ver, m_type, in1k_v2_span)
+                        in1k_v2.append(m_dict)
 
         return in1k_v1, in1k_v2