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import os
import re
import requests
import datasets
from bs4 import BeautifulSoup
_DBNAME = os.path.basename(__file__).split('.')[0]
_HOMEPAGE = "https://huggingface.co/datasets/george-chou/" + _DBNAME
_URL = 'https://pytorch.org/vision/main/_modules/'
class vi_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"),
"num_params": datasets.Value("int64"),
"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 _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'):
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()
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)
return in1k_v1, in1k_v2
def _split_generators(self, dl_manager):
in1k_v1, in1k_v2 = self._generate_dataset(_URL)
return [
datasets.SplitGenerator(
name="IMAGENET1K_V1",
gen_kwargs={
"files": in1k_v1,
},
),
datasets.SplitGenerator(
name="IMAGENET1K_V2",
gen_kwargs={
"files": in1k_v2,
},
),
]
def _generate_examples(self, files):
for i, model in enumerate(files):
yield i, {
"ver": model['ver'],
"type": model['type'],
"input_size": model['input_size'],
"num_params": model['num_params'],
"url": model['url'],
}
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