mirror of
https://github.com/open-mmlab/mmsegmentation.git
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d35fbbdb47
* Modify default work dir when training. * Refactor gather_models.py. * Add train and test matching list. * Regression benchmark list. * lower readme name to upper readme name. * Add url check tool and model inference test tool. * Modify tool name. * Support duplicate mode of log json url check. * Add regression benchmark evaluation automatic tool. * Add train script generator. * Only Support script running. * Add evaluation results gather. * Add exec Authority. * Automatically make checkpoint root folder. * Modify gather results save path. * Coarse-grained train results gather tool. * Complete benchmark train script. * Make some little modifications. * Fix checkpoint urls. * Fix unet checkpoint urls. * Fix fast scnn & fcn checkpoint url. * Fix fast scnn checkpoint urls. * Fix fast scnn url. * Add differential results calculation. * Add differential results of regression benchmark train results. * Add an extra argument to select model. * Update nonlocal_net & hrnet checkpoint url. * Fix checkpoint url of hrnet and Fix some tta evaluation results and modify gather models tool. * Modify fast scnn checkpoint url. * Resolve new comments. * Fix url check status code bug. * Resolve some comments. * Modify train scripts generator. * Modify work_dir of regression benchmark results. * model gather tool modification.
92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import glob
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import os.path as osp
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import mmcv
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from mmcv import Config
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Gather benchmarked model evaluation results')
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parser.add_argument('config', help='test config file path')
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parser.add_argument(
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'root',
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type=str,
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help='root path of benchmarked models to be gathered')
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parser.add_argument(
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'--out',
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type=str,
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default='benchmark_evaluation_info.json',
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help='output path of gathered metrics and compared '
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'results to be stored')
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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root_path = args.root
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metrics_out = args.out
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result_dict = {}
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cfg = Config.fromfile(args.config)
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for model_key in cfg:
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model_infos = cfg[model_key]
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if not isinstance(model_infos, list):
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model_infos = [model_infos]
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for model_info in model_infos:
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previous_metrics = model_info['metric']
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config = model_info['config'].strip()
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fname, _ = osp.splitext(osp.basename(config))
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# Load benchmark evaluation json
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metric_json_dir = osp.join(root_path, fname)
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if not osp.exists(metric_json_dir):
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print(f'{metric_json_dir} not existed.')
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continue
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json_list = glob.glob(osp.join(metric_json_dir, '*.json'))
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if len(json_list) == 0:
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print(f'There is no eval json in {metric_json_dir}.')
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continue
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log_json_path = list(sorted(json_list))[-1]
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metric = mmcv.load(log_json_path)
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if config not in metric.get('config', {}):
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print(f'{config} not included in {log_json_path}')
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continue
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# Compare between new benchmark results and previous metrics
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differential_results = dict()
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new_metrics = dict()
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for record_metric_key in previous_metrics:
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if record_metric_key not in metric['metric']:
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raise KeyError('record_metric_key not exist, please '
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'check your config')
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old_metric = previous_metrics[record_metric_key]
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new_metric = round(metric['metric'][record_metric_key] * 100,
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2)
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differential = new_metric - old_metric
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flag = '+' if differential > 0 else '-'
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differential_results[
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record_metric_key] = f'{flag}{abs(differential):.2f}'
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new_metrics[record_metric_key] = new_metric
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result_dict[config] = dict(
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differential=differential_results,
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previous=previous_metrics,
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new=new_metrics)
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if metrics_out:
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mmcv.dump(result_dict, metrics_out, indent=4)
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print('===================================')
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for config_name, metrics in result_dict.items():
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print(config_name, metrics)
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print('===================================')
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