212 lines
7.2 KiB
Python
212 lines
7.2 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 hashlib
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import json
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import os
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import os.path as osp
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import shutil
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import mmcv
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import torch
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# build schedule look-up table to automatically find the final model
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RESULTS_LUT = ['mIoU', 'mAcc', 'aAcc']
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def calculate_file_sha256(file_path):
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"""calculate file sha256 hash code."""
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with open(file_path, 'rb') as fp:
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sha256_cal = hashlib.sha256()
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sha256_cal.update(fp.read())
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return sha256_cal.hexdigest()
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def process_checkpoint(in_file, out_file):
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checkpoint = torch.load(in_file, map_location='cpu')
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# remove optimizer for smaller file size
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if 'optimizer' in checkpoint:
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del checkpoint['optimizer']
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# if it is necessary to remove some sensitive data in checkpoint['meta'],
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# add the code here.
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torch.save(checkpoint, out_file)
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# The hash code calculation and rename command differ on different system
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# platform.
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sha = calculate_file_sha256(out_file)
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final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
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os.rename(out_file, final_file)
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# Remove prefix and suffix
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final_file_name = osp.split(final_file)[1]
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final_file_name = osp.splitext(final_file_name)[0]
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return final_file_name
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def get_final_iter(config):
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iter_num = config.split('_')[-2]
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assert iter_num.endswith('k')
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return int(iter_num[:-1]) * 1000
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def get_final_results(log_json_path, iter_num):
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result_dict = dict()
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last_iter = 0
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with open(log_json_path, 'r') as f:
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for line in f.readlines():
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log_line = json.loads(line)
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if 'mode' not in log_line.keys():
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continue
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# When evaluation, the 'iter' of new log json is the evaluation
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# steps on single gpu.
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flag1 = ('aAcc' in log_line) or (log_line['mode'] == 'val')
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flag2 = (last_iter == iter_num - 50) or (last_iter == iter_num)
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if flag1 and flag2:
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result_dict.update({
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key: log_line[key]
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for key in RESULTS_LUT if key in log_line
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})
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return result_dict
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last_iter = log_line['iter']
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def parse_args():
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parser = argparse.ArgumentParser(description='Gather benchmarked models')
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parser.add_argument(
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'-f', '--config-name', type=str, help='Process the selected config.')
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parser.add_argument(
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'-w',
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'--work-dir',
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default='work_dirs/',
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type=str,
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help='Ckpt storage root folder of benchmarked models to be gathered.')
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parser.add_argument(
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'-c',
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'--collect-dir',
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default='work_dirs/gather',
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type=str,
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help='Ckpt collect root folder of gathered models.')
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parser.add_argument(
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'--all', action='store_true', help='whether include .py and .log')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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work_dir = args.work_dir
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collect_dir = args.collect_dir
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selected_config_name = args.config_name
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mmcv.mkdir_or_exist(collect_dir)
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# find all models in the root directory to be gathered
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raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True))
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# filter configs that is not trained in the experiments dir
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used_configs = []
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for raw_config in raw_configs:
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config_name = osp.splitext(osp.basename(raw_config))[0]
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if osp.exists(osp.join(work_dir, config_name)):
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if (selected_config_name is None
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or selected_config_name == config_name):
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used_configs.append(raw_config)
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print(f'Find {len(used_configs)} models to be gathered')
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# find final_ckpt and log file for trained each config
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# and parse the best performance
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model_infos = []
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for used_config in used_configs:
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config_name = osp.splitext(osp.basename(used_config))[0]
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exp_dir = osp.join(work_dir, config_name)
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# check whether the exps is finished
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final_iter = get_final_iter(used_config)
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final_model = 'iter_{}.pth'.format(final_iter)
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model_path = osp.join(exp_dir, final_model)
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# skip if the model is still training
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if not osp.exists(model_path):
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print(f'{used_config} train not finished yet')
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continue
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# get logs
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log_json_paths = glob.glob(osp.join(exp_dir, '*.log.json'))
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log_json_path = log_json_paths[0]
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model_performance = None
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for idx, _log_json_path in enumerate(log_json_paths):
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model_performance = get_final_results(_log_json_path, final_iter)
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if model_performance is not None:
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log_json_path = _log_json_path
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break
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if model_performance is None:
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print(f'{used_config} model_performance is None')
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continue
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model_time = osp.split(log_json_path)[-1].split('.')[0]
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model_infos.append(
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dict(
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config_name=config_name,
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results=model_performance,
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iters=final_iter,
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model_time=model_time,
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log_json_path=osp.split(log_json_path)[-1]))
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# publish model for each checkpoint
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publish_model_infos = []
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for model in model_infos:
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config_name = model['config_name']
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model_publish_dir = osp.join(collect_dir, config_name)
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publish_model_path = osp.join(model_publish_dir,
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config_name + '_' + model['model_time'])
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trained_model_path = osp.join(work_dir, config_name,
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'iter_{}.pth'.format(model['iters']))
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if osp.exists(model_publish_dir):
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for file in os.listdir(model_publish_dir):
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if file.endswith('.pth'):
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print(f'model {file} found')
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model['model_path'] = osp.abspath(
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osp.join(model_publish_dir, file))
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break
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if 'model_path' not in model:
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print(f'dir {model_publish_dir} exists, no model found')
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else:
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mmcv.mkdir_or_exist(model_publish_dir)
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# convert model
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final_model_path = process_checkpoint(trained_model_path,
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publish_model_path)
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model['model_path'] = final_model_path
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new_json_path = f'{config_name}_{model["log_json_path"]}'
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# copy log
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shutil.copy(
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osp.join(work_dir, config_name, model['log_json_path']),
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osp.join(model_publish_dir, new_json_path))
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if args.all:
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new_txt_path = new_json_path.rstrip('.json')
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shutil.copy(
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osp.join(work_dir, config_name,
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model['log_json_path'].rstrip('.json')),
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osp.join(model_publish_dir, new_txt_path))
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if args.all:
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# copy config to guarantee reproducibility
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raw_config = osp.join('./configs', f'{config_name}.py')
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mmcv.Config.fromfile(raw_config).dump(
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osp.join(model_publish_dir, osp.basename(raw_config)))
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publish_model_infos.append(model)
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models = dict(models=publish_model_infos)
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mmcv.dump(models, osp.join(collect_dir, 'model_infos.json'), indent=4)
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if __name__ == '__main__':
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main()
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