import argparse import importlib import numpy as np import os import os.path as osp import time import mmcv import torch from mmcv.parallel import MMDataParallel, MMDistributedDataParallel from mmcv.runner import get_dist_info, init_dist, load_checkpoint from openselfsup.utils import dist_forward_collect, nondist_forward_collect from openselfsup.datasets import build_dataloader, build_dataset from openselfsup.models import build_model from openselfsup.models.utils import MultiPooling from openselfsup.utils import get_root_logger class ExtractProcess(object): def __init__(self, pool_type='specified', backbone='resnet50', layer_indices=(0, 1, 2, 3, 4)): self.multi_pooling = MultiPooling( pool_type, in_indices=layer_indices, backbone=backbone) def _forward_func(self, model, **x): backbone_feats = model(mode='extract', **x) pooling_feats = self.multi_pooling(backbone_feats) flat_feats = [xx.view(xx.size(0), -1) for xx in pooling_feats] feat_dict = {'feat{}'.format(i + 1): feat.cpu() \ for i, feat in enumerate(flat_feats)} return feat_dict def extract(self, model, data_loader, distributed=False): model.eval() func = lambda **x: self._forward_func(model, **x) if distributed: rank, world_size = get_dist_info() results = dist_forward_collect(func, data_loader, rank, len(data_loader.dataset)) else: results = nondist_forward_collect(func, data_loader, len(data_loader.dataset)) return results def parse_args(): parser = argparse.ArgumentParser( description='OpenSelfSup 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( '--pretrained', default='random', help='pretrained model file, exclusive to --checkpoint') parser.add_argument( '--dataset-config', default='benchmarks/extract_info/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('--port', type=int, default=29500, help='port only works when launcher=="slurm"') 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() cfg = mmcv.Config.fromfile(args.config) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True # update configs according to CLI args if args.work_dir is not None: cfg.work_dir = args.work_dir layer_ind = [int(idx) for idx in args.layer_ind.split(',')] cfg.model.backbone.out_indices = layer_ind # checkpoint and pretrained are exclusive assert args.pretrained == "random" or args.checkpoint is None, \ "Checkpoint and pretrained are exclusive." # check memcached package exists if importlib.util.find_spec('mc') is None: for field in ['train', 'val', 'test']: if hasattr(cfg.data, field): getattr(cfg.data, field).data_source.memcached = False # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True if args.launcher == 'slurm': cfg.dist_params['port'] = args.port init_dist(args.launcher, **cfg.dist_params) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # logger timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(cfg.work_dir, 'extract_{}.log'.format(timestamp)) logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) # build the dataloader dataset_cfg = mmcv.Config.fromfile(args.dataset_config) dataset = build_dataset(dataset_cfg.data.extract) data_loader = build_dataloader( dataset, imgs_per_gpu=dataset_cfg.data.imgs_per_gpu, workers_per_gpu=dataset_cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # specify pretrained model if args.pretrained != 'random': assert isinstance(args.pretrained, str) cfg.model.pretrained = args.pretrained # build the model and load checkpoint model = build_model(cfg.model) if args.checkpoint is not None: logger.info("Use checkpoint: {} to extract features".format( args.checkpoint)) load_checkpoint(model, args.checkpoint, map_location='cpu') elif args.pretrained != "random": logger.info('Use pretrained model: {} to extract features'.format( args.pretrained)) else: logger.info('No checkpoint or pretrained is give, use random init.') if not distributed: model = MMDataParallel(model, device_ids=[0]) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) # build extraction processor extractor = ExtractProcess( pool_type='specified', backbone='resnet50', layer_indices=layer_ind) # run outputs = extractor.extract(model, data_loader, distributed=distributed) rank, _ = get_dist_info() mmcv.mkdir_or_exist("{}/features/".format(args.work_dir)) 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 = "{}/features/{}_{}.npy".format( args.work_dir, dataset_cfg.split_name[ss], key) 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()