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#!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import functools as func
import glob
import re
from os.path import basename, splitext
import numpy as np
import titlecase
def title2anchor(name):
return re.sub(r'-+', '-', re.sub(r'[^a-zA-Z0-9]', '-',
name.strip().lower())).strip('-')
# Count algorithms
files = sorted(glob.glob('*_models.md'))
stats = []
for f in files:
with open(f, 'r') as content_file:
content = content_file.read()
# Remove the blackquote notation from the paper link under the title
# for better layout in readthedocs
expr = r'(^## \s*?.*?\s+?)>\s*?(\[.*?\]\(.*?\))'
content = re.sub(expr, r'\1\2', content, flags=re.MULTILINE)
with open(f, 'w') as content_file:
content_file.write(content)
# title
title = content.split('\n')[0].replace('#', '')
# count papers
exclude_papertype = ['ABSTRACT', 'IMAGE']
exclude_expr = ''.join(f'(?!{s})' for s in exclude_papertype)
expr = rf'<!-- \[{exclude_expr}([A-Z]+?)\] -->'\
r'\s*\n.*?\btitle\s*=\s*{(.*?)}'
papers = set(
(papertype, titlecase.titlecase(paper.lower().strip()))
for (papertype, paper) in re.findall(expr, content, re.DOTALL))
print(papers)
# paper links
revcontent = '\n'.join(list(reversed(content.splitlines())))
paperlinks = {}
for _, p in papers:
q = p.replace('\\', '\\\\').replace('?', '\\?')
paper_link = title2anchor(
re.search(
rf'\btitle\s*=\s*{{\s*{q}\s*}}.*?\n## (.*?)\s*[,;]?\s*\n',
revcontent, re.DOTALL | re.IGNORECASE).group(1))
paperlinks[p] = f'[{p}]({splitext(basename(f))[0]}.html#{paper_link})'
paperlist = '\n'.join(
sorted(f' - [{t}] {paperlinks[x]}' for t, x in papers))
# count configs
configs = set(x.lower().strip()
for x in re.findall(r'https.*configs/.*\.py', content))
# count ckpts
ckpts = set(x.lower().strip()
for x in re.findall(r'https://download.*\.pth', content)
if 'mmocr' in x)
statsmsg = f"""
## [{title}]({f})
* Number of checkpoints: {len(ckpts)}
* Number of configs: {len(configs)}
* Number of papers: {len(papers)}
{paperlist}
"""
stats.append((papers, configs, ckpts, statsmsg))
allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _, _ in stats])
allconfigs = func.reduce(lambda a, b: a.union(b), [c for _, c, _, _ in stats])
allckpts = func.reduce(lambda a, b: a.union(b), [c for _, _, c, _ 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"""
# Statistics
* Number of checkpoints: {len(allckpts)}
* Number of configs: {len(allconfigs)}
* Number of papers: {len(allpapers)}
{countstr}
{msglist}
"""
with open('modelzoo.md', 'w') as f:
f.write(modelzoo)
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