# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp import time from functools import partial from typing import Optional import numpy as np import torch from mmengine.config import Config, DictAction from mmengine.data import pseudo_collate, worker_init_fn from mmengine.dist import get_rank, init_dist from mmengine.model.wrappers import MMDistributedDataParallel, is_model_wrapper from mmengine.runner import load_checkpoint from mmengine.utils import mkdir_or_exist from torch.utils.data import DataLoader from mmselfsup.models.utils import Extractor from mmselfsup.registry import DATA_SAMPLERS, DATASETS, MODELS from mmselfsup.utils import get_root_logger, register_all_modules def parse_args(): parser = argparse.ArgumentParser( description='MMSelfSup extract features of a model') parser.add_argument('config', help='test config file path') parser.add_argument('--checkpoint', default=None, help='checkpoint file') parser.add_argument( '--dataset-config', default='configs/benchmarks/classification/svm_voc07.py', help='extract dataset config file path') parser.add_argument( '--layer-ind', type=str, help='layer indices, separated by comma, e.g., "0,1,2,3,4"') parser.add_argument( '--work-dir', type=str, default=None, help='the dir to save logs and models') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) parser.add_argument('--seed', type=int, default=0, help='random seed') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. If the value to ' 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' 'Note that the quotation marks are necessary and that no white space ' 'is allowed.') args = parser.parse_args() if 'LOCAL_RANK' not in os.environ: os.environ['LOCAL_RANK'] = str(args.local_rank) return args def main(): args = parse_args() # register all modules in mmselfsup into the registries register_all_modules() # load config cfg = Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # set cudnn_benchmark if cfg.env_cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True # work_dir is determined in this priority: CLI > segment in file > filename if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: # use config filename as default work_dir if cfg.work_dir is None work_type = args.config.split('/')[1] cfg.work_dir = osp.join('./work_dirs', work_type, osp.splitext(osp.basename(args.config))[0]) # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher) # create work_dir mkdir_or_exist(osp.abspath(cfg.work_dir)) # init the logger before other steps timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(cfg.work_dir, f'extract_{timestamp}.log') logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) # build the dataset dataset_cfg = Config.fromfile(args.dataset_config) extract_dataloader_cfg = dataset_cfg.get('extract_dataloader') extract_dataset_cfg = extract_dataloader_cfg.pop('extract_dataset') if isinstance(extract_dataset_cfg, dict): dataset = DATASETS.build(extract_dataset_cfg) if hasattr(dataset, 'full_init'): dataset.full_init() # build sampler sampler_cfg = extract_dataloader_cfg.pop('sampler') if isinstance(sampler_cfg, dict): sampler = DATA_SAMPLERS.build( sampler_cfg, default_args=dict(dataset=dataset, seed=args.seed)) # build dataloader init_fn: Optional[partial] if args.seed is not None: init_fn = partial( worker_init_fn, num_workers=extract_dataloader_cfg.get('num_workers'), rank=get_rank(), seed=args.seed) else: init_fn = None data_loader = DataLoader( dataset=dataset, sampler=sampler, collate_fn=pseudo_collate, worker_init_fn=init_fn, **extract_dataloader_cfg) # build the model # get out_indices from args layer_ind = [int(idx) for idx in args.layer_ind.split(',')] cfg.model.backbone.out_indices = layer_ind model = MODELS.build(cfg.model) model.init_weights() # model is determined in this priority: init_cfg > checkpoint > random if hasattr(cfg.model.backbone, 'init_cfg'): if getattr(cfg.model.backbone.init_cfg, 'type', None) == 'Pretrained': logger.info( f'Use pretrained model: ' f'{cfg.model.backbone.init_cfg.checkpoint} to extract features' ) elif args.checkpoint is not None: logger.info(f'Use checkpoint: {args.checkpoint} to extract features') load_checkpoint(model, args.checkpoint, map_location='cpu') else: logger.info('No pretrained or checkpoint is given, use random init.') if torch.cuda.is_available(): model = model.cuda() if distributed: model = MMDistributedDataParallel( module=model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) if is_model_wrapper(model): model = model.module # build extractor and extract features extractor = Extractor( extract_dataloader=data_loader, seed=args.seed, dist_mode=distributed, pool_cfg=dataset_cfg.pool_cfg) outputs = extractor(model) # run rank = get_rank() mkdir_or_exist(f'{cfg.work_dir}/features/') if rank == 0: for key, val in outputs.items(): split_num = len(dataset_cfg.split_name) split_at = dataset_cfg.split_at for ss in range(split_num): output_file = f'{cfg.work_dir}/features/' \ f'{dataset_cfg.split_name[ss]}_{key}.npy' if ss == 0: np.save(output_file, val[:split_at[0]]) elif ss == split_num - 1: np.save(output_file, val[split_at[-1]:]) else: np.save(output_file, val[split_at[ss - 1]:split_at[ss]]) if __name__ == '__main__': main()