mirror of https://github.com/open-mmlab/mmocr.git
95 lines
2.7 KiB
Python
95 lines
2.7 KiB
Python
#!/usr/bin/env python
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import functools as func
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import glob
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import re
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from os.path import basename, splitext
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import numpy as np
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import titlecase
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def anchor(name):
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return re.sub(r'-+', '-', re.sub(r'[^a-zA-Z0-9]', '-',
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name.strip().lower())).strip('-')
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# Count algorithms
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files = sorted(glob.glob('*_models.md'))
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# files = sorted(glob.glob('docs/*_models.md'))
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stats = []
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for f in files:
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with open(f, 'r') as content_file:
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content = content_file.read()
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# title
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title = content.split('\n')[0].replace('#', '')
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# count papers
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papers = set((papertype, titlecase.titlecase(paper.lower().strip()))
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for (papertype, paper) in re.findall(
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r'\n\s*\[([A-Z]+?)\]\s*\n.*?\btitle\s*=\s*{(.*?)}',
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content, re.DOTALL))
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# paper links
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revcontent = '\n'.join(list(reversed(content.splitlines())))
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paperlinks = {}
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for _, p in papers:
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print(p)
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q = p.replace('\\', '\\\\').replace('?', '\\?')
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paperlinks[p] = ' '.join(
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(f'[⇨]({splitext(basename(f))[0]}.html#{anchor(paperlink)})'
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for paperlink in re.findall(
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rf'\btitle\s*=\s*{{\s*{q}\s*}}.*?\n## (.*?)\s*[,;]?\s*\n',
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revcontent, re.DOTALL | re.IGNORECASE)))
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print(' ', paperlinks[p])
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paperlist = '\n'.join(
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sorted(f' - [{t}] {x} ({paperlinks[x]})' for t, x in papers))
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# count configs
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configs = set(x.lower().strip()
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for x in re.findall(r'https.*configs/.*\.py', content))
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# count ckpts
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ckpts = set(x.lower().strip()
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for x in re.findall(r'https://download.*\.pth', content)
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if 'mmaction' in x)
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statsmsg = f"""
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## [{title}]({f})
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* Number of checkpoints: {len(ckpts)}
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* Number of configs: {len(configs)}
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* Number of papers: {len(papers)}
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{paperlist}
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"""
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stats.append((papers, configs, ckpts, statsmsg))
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allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _, _ in stats])
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allconfigs = func.reduce(lambda a, b: a.union(b), [c for _, c, _, _ in stats])
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allckpts = func.reduce(lambda a, b: a.union(b), [c for _, _, c, _ in stats])
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msglist = '\n'.join(x for _, _, _, x in stats)
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papertypes, papercounts = np.unique([t for t, _ in allpapers],
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return_counts=True)
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countstr = '\n'.join(
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[f' - {t}: {c}' for t, c in zip(papertypes, papercounts)])
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modelzoo = f"""
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# Overview
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* Number of checkpoints: {len(allckpts)}
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* Number of configs: {len(allconfigs)}
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* Number of papers: {len(allpapers)}
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{countstr}
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For supported datasets, see [datasets overview](datasets.md).
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{msglist}
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"""
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with open('modelzoo.md', 'w') as f:
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f.write(modelzoo)
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