| |
| import functools as func |
| import glob |
| import os.path as osp |
| import re |
|
|
| import numpy as np |
|
|
| url_prefix = 'https://github.com/open-mmlab/mmsegmentation/blob/master/' |
|
|
| files = sorted(glob.glob('../configs/*/README.md')) |
|
|
| stats = [] |
| titles = [] |
| num_ckpts = 0 |
|
|
| for f in files: |
| url = osp.dirname(f.replace('../', url_prefix)) |
|
|
| with open(f, 'r') as content_file: |
| content = content_file.read() |
|
|
| title = content.split('\n')[0].replace('#', '').strip() |
| ckpts = set(x.lower().strip() |
| for x in re.findall(r'https?://download.*\.pth', content) |
| if 'mmsegmentation' in x) |
| if len(ckpts) == 0: |
| continue |
|
|
| _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)] |
| assert len(_papertype) > 0 |
| papertype = _papertype[0] |
|
|
| paper = set([(papertype, title)]) |
|
|
| titles.append(title) |
| num_ckpts += len(ckpts) |
| statsmsg = f""" |
| \t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts) |
| """ |
| stats.append((paper, ckpts, statsmsg)) |
|
|
| allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats]) |
| msglist = '\n'.join(x for _, _, x in stats) |
|
|
| papertypes, papercounts = np.unique([t for t, _ in allpapers], |
| return_counts=True) |
| countstr = '\n'.join( |
| [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)]) |
|
|
| modelzoo = f""" |
| # Model Zoo Statistics |
| |
| * Number of papers: {len(set(titles))} |
| {countstr} |
| |
| * Number of checkpoints: {num_ckpts} |
| {msglist} |
| """ |
|
|
| with open('modelzoo_statistics.md', 'w') as f: |
| f.write(modelzoo) |
|
|
