Delete cv_backbones.py

cv_backbones.py

@@ -1,147 +0,0 @@
-import os
-import re
-import requests
-import datasets
-from bs4 import BeautifulSoup
-
-_DBNAME = os.path.basename(__file__).split('.')[0]
-_HOMEPAGE = "https://huggingface.co/datasets/monet-joe/" + _DBNAME
-_URL = 'https://pytorch.org/vision/main/_modules/'
-
-
-class cv_backbones(datasets.GeneratorBasedBuilder):
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
-                {
-                    "ver": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "input_size": datasets.Value("int16"),
-                    "url": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=("ver", "type"),
-            homepage=_HOMEPAGE,
-            license="mit"
-        )
-
-    def _parse_url(self, url):
-        response = requests.get(url)
-        html = response.text
-        return BeautifulSoup(html, 'html.parser')
-
-    def _special_type(self, m_ver):
-        m_type = re.search('[a-zA-Z]+', m_ver).group(0)
-
-        if m_type == 'wide' or m_type == 'resnext':
-            return 'resnet'
-
-        elif m_type == 'swin':
-            return 'swin_transformer'
-
-        elif 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'})
-        m_url = str(url_span.text[1:-1])
-        input_size = int(size_span.text)
-        m_dict = {
-            'ver': m_ver,
-            'type': m_type,
-            'input_size': input_size,
-            'url': m_url
-        }
-        return m_dict, size_span
-
-    def _generate_dataset(self, url):
-        torch_page = self._parse_url(url)
-        article = torch_page.find('article', {'id': 'pytorch-article'})
-        ul = article.find('ul').find('ul')
-        in1k_v1, in1k_v2 = [], []
-
-        for li in ul.find_all('li'):
-            name = str(li.text)
-            if name.__contains__('torchvision.models.') and len(name.split('.')) == 3:
-
-                if name.__contains__('_api') or \
-                    name.__contains__('feature_extraction') or \
-                        name.__contains__('maxvit'):
-                    continue
-
-                href = li.find('a').get('href')
-                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()
-
-                        if m_ver.__contains__('swin_v2_'):
-                            continue
-
-                        m_type = self._special_type(m_ver)
-
-                        in1k_v1_span = div.find(
-                            name='span',
-                            attrs={'class': 'n'},
-                            string='IMAGENET1K_V1'
-                        )
-
-                        if in1k_v1_span == None:
-                            continue
-
-                        m_dict, size_span = self._info_on_dataset(
-                            m_ver,
-                            m_type,
-                            in1k_v1_span
-                        )
-                        in1k_v1.append(m_dict)
-
-                        in1k_v2_span = size_span.find_next_sibling(
-                            name='span',
-                            attrs={'class': 'n'},
-                            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
-
-    def _split_generators(self, _):
-        in1k_v1, in1k_v2 = self._generate_dataset(_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name="IMAGENET1K_V1",
-                gen_kwargs={
-                    "subset": in1k_v1,
-                },
-            ),
-            datasets.SplitGenerator(
-                name="IMAGENET1K_V2",
-                gen_kwargs={
-                    "subset": in1k_v2,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, subset):
-        for i, model in enumerate(subset):
-            yield i, {
-                "ver": model['ver'],
-                "type": model['type'],
-                "input_size": model['input_size'],
-                "url": model['url'],
-            }