mirror of
https://github.com/open-mmlab/mmsegmentation.git
synced 2025-06-03 22:03:48 +08:00
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.
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
import argparse
|
|
import glob
|
|
import os.path as osp
|
|
|
|
import mmcv
|
|
from gather_models import get_final_results
|
|
from mmcv import Config
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(
|
|
description='Gather benchmarked models train results')
|
|
parser.add_argument('config', help='test config file path')
|
|
parser.add_argument(
|
|
'root',
|
|
type=str,
|
|
help='root path of benchmarked models to be gathered')
|
|
parser.add_argument(
|
|
'--out',
|
|
type=str,
|
|
default='benchmark_train_info.json',
|
|
help='output path of gathered metrics to be stored')
|
|
|
|
args = parser.parse_args()
|
|
return args
|
|
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_args()
|
|
|
|
root_path = args.root
|
|
metrics_out = args.out
|
|
|
|
evaluation_cfg = Config.fromfile(args.config)
|
|
|
|
result_dict = {}
|
|
for model_key in evaluation_cfg:
|
|
model_infos = evaluation_cfg[model_key]
|
|
if not isinstance(model_infos, list):
|
|
model_infos = [model_infos]
|
|
for model_info in model_infos:
|
|
config = model_info['config']
|
|
|
|
# benchmark train dir
|
|
model_name = osp.split(osp.dirname(config))[1]
|
|
config_name = osp.splitext(osp.basename(config))[0]
|
|
exp_dir = osp.join(root_path, model_name, config_name)
|
|
if not osp.exists(exp_dir):
|
|
print(f'{config} hasn\'t {exp_dir}')
|
|
continue
|
|
|
|
# parse config
|
|
cfg = mmcv.Config.fromfile(config)
|
|
total_iters = cfg.runner.max_iters
|
|
exp_metric = cfg.evaluation.metric
|
|
if not isinstance(exp_metric, list):
|
|
exp_metrics = [exp_metric]
|
|
|
|
# determine whether total_iters ckpt exists
|
|
ckpt_path = f'iter_{total_iters}.pth'
|
|
if not osp.exists(osp.join(exp_dir, ckpt_path)):
|
|
print(f'{config} hasn\'t {ckpt_path}')
|
|
continue
|
|
|
|
# only the last log json counts
|
|
log_json_path = list(
|
|
sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1]
|
|
|
|
# extract metric value
|
|
model_performance = get_final_results(log_json_path, total_iters)
|
|
if model_performance is None:
|
|
print(f'log file error: {log_json_path}')
|
|
continue
|
|
|
|
differential_results = dict()
|
|
old_results = dict()
|
|
new_results = dict()
|
|
for metric_key in model_performance:
|
|
if metric_key in ['mIoU']:
|
|
metric = round(model_performance[metric_key] * 100, 2)
|
|
old_metric = model_info['metric'][metric_key]
|
|
old_results[metric_key] = old_metric
|
|
new_results[metric_key] = metric
|
|
differential = metric - old_metric
|
|
flag = '+' if differential > 0 else '-'
|
|
differential_results[
|
|
metric_key] = f'{flag}{abs(differential):.2f}'
|
|
result_dict[config] = dict(
|
|
differential_results=differential_results,
|
|
old_results=old_results,
|
|
new_results=new_results,
|
|
)
|
|
|
|
# 4 save or print results
|
|
if metrics_out:
|
|
mmcv.dump(result_dict, metrics_out, indent=4)
|
|
print('===================================')
|
|
for config_name, metrics in result_dict.items():
|
|
print(config_name, metrics)
|
|
print('===================================')
|