| 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'], | |
| } | |