forked from open-mmlab/mmpose
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Fix] fix hand readthedocs page (open-mmlab#1416)
* fix hand readthedocs page * fix docs and collect.py * fix doc index
- Loading branch information
Showing
7 changed files
with
318 additions
and
6 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| #!/usr/bin/env python | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import os | ||
| import os.path as osp | ||
| import re | ||
| from glob import glob | ||
|
|
||
| from titlecase import titlecase | ||
|
|
||
| os.makedirs('topics', exist_ok=True) | ||
| os.makedirs('papers', exist_ok=True) | ||
|
|
||
|
|
||
| def _parse_task(task): | ||
| """Parse task name. | ||
| Data modality is represented by a string of 4 or 5 parts like: | ||
| - 2d_kpt_sview_rgb_img | ||
| - gesture_sview_rgbd_vid | ||
| """ | ||
|
|
||
| parts = task.split('_') | ||
| if len(parts) == 5: | ||
| pass | ||
| elif len(parts) == 4: | ||
| # The first part "spatial dimension" is optional | ||
| parts = [''] + parts | ||
| else: | ||
| raise ValueError('Invalid modality') | ||
|
|
||
| return parts | ||
|
|
||
|
|
||
| # Step 1: get subtopics: a mix of topic and task | ||
| minisections = [ | ||
| x.split(osp.sep)[-2:] for x in glob('../../configs/*/*') | ||
| if '_base_' not in x | ||
| ] | ||
| alltopics = sorted(list(set(x[0] for x in minisections))) | ||
| subtopics = [] | ||
| for topic in alltopics: | ||
| tasks = [_parse_task(x[1]) for x in minisections if x[0] == topic] | ||
| valid_ids = [] | ||
| for i in range(len(tasks[0])): | ||
| if len(set(x[i] for x in tasks)) > 1: | ||
| valid_ids.append(i) | ||
| if len(valid_ids) > 0: | ||
| for task in tasks: | ||
| appendix = ','.join( | ||
| [task[i].title() for i in valid_ids if task[i]]) | ||
| subtopic = [ | ||
| f'{titlecase(topic)}({appendix})', | ||
| topic, | ||
| '_'.join(t for t in task if t), | ||
| ] | ||
| subtopics.append(subtopic) | ||
| else: | ||
| subtopics.append([titlecase(topic), topic, '_'.join(tasks[0])]) | ||
|
|
||
| contents = {} | ||
| for subtopic, topic, task in sorted(subtopics): | ||
| # Step 2: get all datasets | ||
| datasets = sorted( | ||
| list( | ||
| set( | ||
| x.split(osp.sep)[-2] | ||
| for x in glob(f'../../configs/{topic}/{task}/*/*/')))) | ||
| contents[subtopic] = {d: {} for d in datasets} | ||
| for dataset in datasets: | ||
| # Step 3: get all settings: algorithm + backbone + trick | ||
| for file in glob(f'../../configs/{topic}/{task}/*/{dataset}/*.md'): | ||
| keywords = (file.split(osp.sep)[-3], | ||
| *file.split(osp.sep)[-1].split('_')[:-1]) | ||
| with open(file, 'r', encoding='utf-8') as f: | ||
| contents[subtopic][dataset][keywords] = f.read() | ||
|
|
||
| # Step 4: write files by topic | ||
| for subtopic, datasets in contents.items(): | ||
| lines = [f'# {subtopic}', ''] | ||
| for dataset, keywords in datasets.items(): | ||
| if len(keywords) == 0: | ||
| continue | ||
| lines += [ | ||
| '<hr/>', '<br/><br/>', '', f'## {titlecase(dataset)} Dataset', '' | ||
| ] | ||
| for keyword, info in keywords.items(): | ||
| keyword_strs = [titlecase(x.replace('_', ' ')) for x in keyword] | ||
| lines += [ | ||
| '<br/>', '', | ||
| (f'### {" + ".join(keyword_strs)}' | ||
| f' on {titlecase(dataset)}'), '', info, '' | ||
| ] | ||
|
|
||
| with open(f'topics/{subtopic.lower()}.md', 'w', encoding='utf-8') as f: | ||
| f.write('\n'.join(lines)) | ||
|
|
||
| # Step 5: write files by paper | ||
| allfiles = [x.split(osp.sep)[-2:] for x in glob('../en/papers/*/*.md')] | ||
| sections = sorted(list(set(x[0] for x in allfiles))) | ||
| for section in sections: | ||
| lines = [f'# {titlecase(section)}', ''] | ||
| files = [f for s, f in allfiles if s == section] | ||
| for file in files: | ||
| with open( | ||
| f'../en/papers/{section}/{file}', 'r', encoding='utf-8') as f: | ||
| keyline = [ | ||
| line for line in f.readlines() if line.startswith('<summary') | ||
| ][0] | ||
| papername = re.sub(r'\<.*?\>', '', keyline).strip() | ||
| paperlines = [] | ||
| for subtopic, datasets in contents.items(): | ||
| for dataset, keywords in datasets.items(): | ||
| keywords = {k: v for k, v in keywords.items() if keyline in v} | ||
| if len(keywords) == 0: | ||
| continue | ||
| for keyword, info in keywords.items(): | ||
| keyword_strs = [ | ||
| titlecase(x.replace('_', ' ')) for x in keyword | ||
| ] | ||
| paperlines += [ | ||
| '<br/>', '', | ||
| (f'### {" + ".join(keyword_strs)}' | ||
| f' on {titlecase(dataset)}'), '', info, '' | ||
| ] | ||
| if len(paperlines) > 0: | ||
| lines += ['<hr/>', '<br/><br/>', '', f'## {papername}', ''] | ||
| lines += paperlines | ||
|
|
||
| with open(f'papers/{section}.md', 'w', encoding='utf-8') as f: | ||
| f.write('\n'.join(lines)) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| #!/usr/bin/env python | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import os | ||
| import os.path as osp | ||
| import re | ||
| from glob import glob | ||
|
|
||
| from titlecase import titlecase | ||
|
|
||
| os.makedirs('topics', exist_ok=True) | ||
| os.makedirs('papers', exist_ok=True) | ||
|
|
||
|
|
||
| def _parse_task(task): | ||
| """Parse task name. | ||
| Data modality is represented by a string of 4 or 5 parts like: | ||
| - 2d_kpt_sview_rgb_img | ||
| - gesture_sview_rgbd_vid | ||
| """ | ||
|
|
||
| parts = task.split('_') | ||
| if len(parts) == 5: | ||
| pass | ||
| elif len(parts) == 4: | ||
| # The first part "spatial dimension" is optional | ||
| parts = [''] + parts | ||
| else: | ||
| raise ValueError('Invalid modality') | ||
|
|
||
| return parts | ||
|
|
||
|
|
||
| # Step 1: get subtopics: a mix of topic and task | ||
| minisections = [ | ||
| x.split(osp.sep)[-2:] for x in glob('../../configs/*/*') | ||
| if '_base_' not in x | ||
| ] | ||
| alltopics = sorted(list(set(x[0] for x in minisections))) | ||
| subtopics = [] | ||
| for topic in alltopics: | ||
| tasks = [_parse_task(x[1]) for x in minisections if x[0] == topic] | ||
| valid_ids = [] | ||
| for i in range(len(tasks[0])): | ||
| if len(set(x[i] for x in tasks)) > 1: | ||
| valid_ids.append(i) | ||
| if len(valid_ids) > 0: | ||
| for task in tasks: | ||
| appendix = ','.join( | ||
| [task[i].title() for i in valid_ids if task[i]]) | ||
| subtopic = [ | ||
| f'{titlecase(topic)}({appendix})', | ||
| topic, | ||
| '_'.join(t for t in task if t), | ||
| ] | ||
| subtopics.append(subtopic) | ||
| else: | ||
| subtopics.append([titlecase(topic), topic, '_'.join(tasks[0])]) | ||
|
|
||
| contents = {} | ||
| for subtopic, topic, task in sorted(subtopics): | ||
| # Step 2: get all datasets | ||
| datasets = sorted( | ||
| list( | ||
| set( | ||
| x.split(osp.sep)[-2] | ||
| for x in glob(f'../../configs/{topic}/{task}/*/*/')))) | ||
| contents[subtopic] = {d: {} for d in datasets} | ||
| for dataset in datasets: | ||
| # Step 3: get all settings: algorithm + backbone + trick | ||
| for file in glob(f'../../configs/{topic}/{task}/*/{dataset}/*.md'): | ||
| keywords = (file.split(osp.sep)[-3], | ||
| *file.split(osp.sep)[-1].split('_')[:-1]) | ||
| with open(file, 'r', encoding='utf-8') as f: | ||
| contents[subtopic][dataset][keywords] = f.read() | ||
|
|
||
| # Step 4: write files by topic | ||
| for subtopic, datasets in contents.items(): | ||
| lines = [f'# {subtopic}', ''] | ||
| for dataset, keywords in datasets.items(): | ||
| if len(keywords) == 0: | ||
| continue | ||
| lines += [ | ||
| '<hr/>', '<br/><br/>', '', f'## {titlecase(dataset)} Dataset', '' | ||
| ] | ||
| for keyword, info in keywords.items(): | ||
| keyword_strs = [titlecase(x.replace('_', ' ')) for x in keyword] | ||
| lines += [ | ||
| '<br/>', '', | ||
| (f'### {" + ".join(keyword_strs)}' | ||
| f' on {titlecase(dataset)}'), '', info, '' | ||
| ] | ||
|
|
||
| with open(f'topics/{subtopic.lower()}.md', 'w', encoding='utf-8') as f: | ||
| f.write('\n'.join(lines)) | ||
|
|
||
| # Step 5: write files by paper | ||
| allfiles = [x.split(osp.sep)[-2:] for x in glob('../en/papers/*/*.md')] | ||
| sections = sorted(list(set(x[0] for x in allfiles))) | ||
| for section in sections: | ||
| lines = [f'# {titlecase(section)}', ''] | ||
| files = [f for s, f in allfiles if s == section] | ||
| for file in files: | ||
| with open( | ||
| f'../en/papers/{section}/{file}', 'r', encoding='utf-8') as f: | ||
| keyline = [ | ||
| line for line in f.readlines() if line.startswith('<summary') | ||
| ][0] | ||
| papername = re.sub(r'\<.*?\>', '', keyline).strip() | ||
| paperlines = [] | ||
| for subtopic, datasets in contents.items(): | ||
| for dataset, keywords in datasets.items(): | ||
| keywords = {k: v for k, v in keywords.items() if keyline in v} | ||
| if len(keywords) == 0: | ||
| continue | ||
| for keyword, info in keywords.items(): | ||
| keyword_strs = [ | ||
| titlecase(x.replace('_', ' ')) for x in keyword | ||
| ] | ||
| paperlines += [ | ||
| '<br/>', '', | ||
| (f'### {" + ".join(keyword_strs)}' | ||
| f' on {titlecase(dataset)}'), '', info, '' | ||
| ] | ||
| if len(paperlines) > 0: | ||
| lines += ['<hr/>', '<br/><br/>', '', f'## {papername}', ''] | ||
| lines += paperlines | ||
|
|
||
| with open(f'papers/{section}.md', 'w', encoding='utf-8') as f: | ||
| f.write('\n'.join(lines)) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.