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import re
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import shutil
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from collections import defaultdict
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from pathlib import Path
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from modelindex.load_model_index import load
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from modelindex.models.Result import Result
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from tabulate import tabulate
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from utils import replace_link
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MMACT_ROOT = Path(__file__).absolute().parents[2]
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PAPERS_ROOT = Path('model_zoo')
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GITHUB_PREFIX = 'https://github.com/open-mmlab/mmaction2/blob/main/'
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MODELZOO_TEMPLATE = """\
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# 模型库统计
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在本页面中，我们列举了我们支持的[所有算法](#所有已支持的算法)。你可以点击链接跳转至对应的模型详情页面。
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另外，我们还列出了我们提供的所有模型权重文件。你可以使用排序和搜索功能找到需要的模型权重，并使用链接跳转至模型详情页面。
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## 所有已支持的算法
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* 论文数量：{num_papers}
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{type_msg}
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* 模型权重文件数量：{num_ckpts}
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{paper_msg}
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"""
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METRIC_ALIAS = {
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'Top 1 Accuracy': 'Top-1 (%)',
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'Top 5 Accuracy': 'Top-5 (%)',
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}
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TASK_MAP = dict(
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detection='时空行为检测模型',
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localization='时序动作定位模型',
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recognition='行为识别模型',
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skeleton='基于骨骼点的行为识别模型',
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retrieval='视频检索模型',
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recognition_audio='基于声音的行为识别模型')
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model_index = load(str(MMACT_ROOT / 'model-index.yml'))
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def build_collections(model_index):
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col_by_name = {}
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for col in model_index.collections:
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setattr(col, 'models', [])
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col_by_name[col.name] = col
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for model in model_index.models:
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col = col_by_name[model.in_collection]
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col.models.append(model)
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setattr(model, 'collection', col)
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if model.results is None:
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setattr(model, 'tasks', [])
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else:
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setattr(model, 'tasks', [result.task for result in model.results])
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build_collections(model_index)
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model2title = dict()
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def count_papers(collections):
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total_num_ckpts = 0
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type_count = defaultdict(int)
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paper_msgs = []
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for collection in collections:
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with open(MMACT_ROOT / collection.readme) as f:
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readme = f.read()
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ckpts = set(x.lower().strip()
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for x in re.findall(r'\[ckpt.*\]\((https?.*)\)', readme))
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total_num_ckpts += len(ckpts)
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title = collection.paper['Title']
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papertype = collection.data.get('type', 'Algorithm')
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type_count[papertype] += 1
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readme_title = re.search(r'^#\s+.+', readme)
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readme = Path(collection.filepath).parents[1].with_suffix('.md').name
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model = Path(collection.filepath).parent.name
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model2title[model] = readme_title.group()[2:].replace(' ', '-')
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paper_msgs.append(f'\t- [{papertype}] [{title}]({PAPERS_ROOT / readme}'
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f'#{model2title[model]}) ({len(ckpts)} ckpts)')
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type_msg = '\n'.join(
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[f'\t- {type_}: {count}' for type_, count in type_count.items()])
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paper_msg = '\n'.join(paper_msgs)
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modelzoo = MODELZOO_TEMPLATE.format(
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num_papers=len(collections),
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num_ckpts=total_num_ckpts,
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type_msg=type_msg,
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paper_msg=paper_msg,
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)
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with open('modelzoo_statistics.md', 'w') as f:
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f.write(modelzoo)
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count_papers(model_index.collections)
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def generate_paper_page(collection):
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with open(MMACT_ROOT / collection.readme) as f:
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content = f.read()
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readme_path = Path(collection.filepath)
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copy = PAPERS_ROOT / readme_path.parents[1].with_suffix('.md').name
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if not copy.exists():
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with open(copy, 'w') as copy_file:
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task = readme_path.parents[1].name
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head_content = f'# {TASK_MAP[task]}\n'
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copy_file.write(head_content)
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def lower_heading(match):
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return '#' + match.group()
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content = replace_link(r'\[([^\]]+)\]\(([^)]+)\)', '[{}]({})', content,
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Path(collection.readme))
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content = replace_link(r'\[([^\]]+)\]: (.*)', '[{}]: {}', content,
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Path(collection.readme))
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content = re.sub(r'^#+\s+.+', lower_heading, content, flags=re.M)
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with open(copy, 'a') as copy_file:
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copy_file.write(content)
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if PAPERS_ROOT.exists():
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shutil.rmtree(PAPERS_ROOT)
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PAPERS_ROOT.mkdir(exist_ok=True)
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for collection in model_index.collections:
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generate_paper_page(collection)
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def scatter_results(models):
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model_result_pairs = []
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for model in models:
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if model.results is None:
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result = Result(task=None, dataset=None, metrics={})
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model_result_pairs.append((model, result))
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else:
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for result in model.results:
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model_result_pairs.append((model, result))
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return model_result_pairs
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def generate_summary_table(task, model_result_pairs, title=None):
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metrics = set()
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for model, result in model_result_pairs:
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if result.task == task:
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metrics = metrics.union(result.metrics.keys())
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metrics = sorted(list(metrics))
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rows = []
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def convert2float(number):
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units = {'M': 1e6, 'G': 1e9, 'T': 1e12}
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if isinstance(number, str):
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num = float(number.rstrip('MGT'))
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number = num * units[number[-1]]
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return number
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for model, result in model_result_pairs:
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if result.task != task:
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continue
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name = model.name
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if model.metadata.parameters is not None:
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params = convert2float(model.metadata.parameters)
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params = f'{params / 1e6:.2f}'
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else:
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params = None
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if model.metadata.flops is not None:
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flops = convert2float(model.metadata.flops)
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flops = f'{flops / 1e9:.2f}'
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else:
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flops = None
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readme = Path(
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model.collection.filepath).parents[1].with_suffix('.md').name
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model = Path(model.collection.filepath).parent.name
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page = f'[链接]({PAPERS_ROOT / readme}#{model2title[model]})'
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model_metrics = []
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for metric in metrics:
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model_metrics.append(str(result.metrics.get(metric, '')))
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rows.append([name, params, flops, *model_metrics, page])
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with open('modelzoo_statistics.md', 'a') as f:
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if title is not None:
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f.write(f'\n{title}')
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f.write("""\n```{table}\n:class: model-summary\n""")
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header = [
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'模型',
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'参数量 (M)',
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'Flops (G)',
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*[METRIC_ALIAS.get(metric, metric) for metric in metrics],
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'Readme',
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]
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table_cfg = dict(
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tablefmt='pipe',
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floatfmt='.2f',
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numalign='right',
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stralign='center')
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f.write(tabulate(rows, header, **table_cfg))
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f.write('\n```\n')
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def generate_dataset_wise_table(task, model_result_pairs, title=None):
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dataset_rows = defaultdict(list)
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for model, result in model_result_pairs:
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if result.task == task:
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dataset_rows[result.dataset].append((model, result))
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if title is not None:
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with open('modelzoo_statistics.md', 'a') as f:
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f.write(f'\n{title}')
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for dataset, pairs in dataset_rows.items():
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generate_summary_table(task, pairs, title=f'### {dataset}')
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model_result_pairs = scatter_results(model_index.models)
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generate_dataset_wise_table(
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task='Action Recognition',
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model_result_pairs=model_result_pairs,
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title='## 行为识别',
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)
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generate_dataset_wise_table(
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task='Action Detection',
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model_result_pairs=model_result_pairs,
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title='## 时空行为检测',
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)
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generate_dataset_wise_table(
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task='Skeleton-based Action Recognition',
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model_result_pairs=model_result_pairs,
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title='## 骨骼点行为识别',
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)
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generate_dataset_wise_table(
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task='Video Retrieval',
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model_result_pairs=model_result_pairs,
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title='## 视频检索',
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)
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generate_dataset_wise_table(
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task='Temporal Action Localization',
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model_result_pairs=model_result_pairs,
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title='## 时序动作定位',
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)
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