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import os |
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import re |
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import requests |
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import datasets |
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from bs4 import BeautifulSoup |
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_DBNAME = os.path.basename(__file__).split('.')[0] |
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_HOMEPAGE = "https://huggingface.co/datasets/george-chou/" + _DBNAME |
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_URL = 'https://pytorch.org/vision/main/_modules/' |
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class vi_backbones(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"ver": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"input_size": datasets.Value("int16"), |
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"url": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("ver", "type"), |
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homepage=_HOMEPAGE, |
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license="mit" |
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) |
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def _parse_url(self, url): |
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response = requests.get(url) |
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html = response.text |
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return BeautifulSoup(html, 'html.parser') |
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def _info_on_dataset(self, m_ver, m_type, in1k_span): |
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url_span = in1k_span.find_next_sibling('span', {'class': 's2'}) |
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size_span = url_span.find_next_sibling('span', {'class': 'mi'}) |
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m_url = str(url_span.text[1:-2]) |
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input_size = int(size_span.text) |
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m_dict = { |
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'ver': m_ver, |
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'type': m_type, |
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'input_size': input_size, |
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'url': m_url |
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} |
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return m_dict, size_span |
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def _generate_dataset(self, url): |
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torch_page = self._parse_url(url) |
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article = torch_page.find('article', {'id': 'pytorch-article'}) |
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ul = article.find('ul').find('ul') |
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in1k_v1, in1k_v2 = [], [] |
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for li in ul.find_all('li'): |
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name = str(li.text) |
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if name.__contains__('torchvision.models.') and len(name.split('.')) == 3: |
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if name.__contains__('_api') or name.__contains__('feature_extraction'): |
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continue |
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href = li.find('a').get('href') |
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model_page = self._parse_url(url + href) |
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divs = model_page.select('div.viewcode-block') |
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for div in divs: |
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div_id = str(div['id']) |
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if div_id.__contains__('_Weights'): |
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m_ver = div_id.split('_Weight')[0].lower() |
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m_type = re.search('[a-zA-Z]+', m_ver).group(0) |
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in1k_v1_span = div.find('span', string='IMAGENET1K_V1') |
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m_dict, size_span = self._info_on_dataset( |
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m_ver, m_type, in1k_v1_span) |
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in1k_v1.append(m_dict) |
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in1k_v2_span = size_span.find_next_sibling( |
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'span', string='IMAGENET1K_V2') |
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if in1k_v2_span != None: |
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m_dict, _ = self._info_on_dataset( |
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m_ver, m_type, in1k_v2_span) |
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in1k_v2.append(m_dict) |
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return in1k_v1, in1k_v2 |
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def _split_generators(self, dl_manager): |
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in1k_v1, in1k_v2 = self._generate_dataset(_URL) |
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return [ |
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datasets.SplitGenerator( |
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name="IMAGENET1K_V1", |
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gen_kwargs={ |
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"files": in1k_v1, |
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}, |
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), |
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datasets.SplitGenerator( |
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name="IMAGENET1K_V2", |
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gen_kwargs={ |
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"files": in1k_v2, |
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}, |
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), |
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] |
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def _generate_examples(self, files): |
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for i, model in enumerate(files): |
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yield i, { |
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"ver": model['ver'], |
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"type": model['type'], |
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"input_size": model['input_size'], |
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"url": model['url'], |
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} |
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