# Copyright (c) OpenMMLab. All rights reserved. import argparse import copy import os import os.path as osp import time import torch from mmengine import Runner from mmengine.config import Config, DictAction from mmengine.dist import get_rank, init_dist from mmengine.logging import MMLogger from mmengine.model.wrappers import MMDistributedDataParallel, is_model_wrapper from mmengine.runner import load_checkpoint from mmengine.utils import mkdir_or_exist from mmselfsup.evaluation.functional import knn_classifier from mmselfsup.models.utils import Extractor from mmselfsup.registry import MODELS from mmselfsup.utils import register_all_modules def parse_args(): parser = argparse.ArgumentParser(description='KNN evaluation') parser.add_argument('config', help='train config file path') parser.add_argument('--checkpoint', default=None, help='checkpoint file') parser.add_argument( '--dataset-config', default='configs/benchmarks/classification/knn_imagenet.py', help='knn dataset config file path') parser.add_argument( '--work-dir', type=str, default=None, help='the dir to save results') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher') 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.') # KNN settings parser.add_argument( '--num-knn', default=[10, 20, 100, 200], nargs='+', type=int, help='Number of NN to use. 20 usually works the best.') parser.add_argument( '--temperature', default=0.07, type=float, help='Temperature used in the voting coefficient.') parser.add_argument( '--use-cuda', default=True, type=bool, help='Store the features on GPU. Set to False if you encounter OOM') parser.add_argument('--local_rank', type=int, default=0) parser.add_argument('--seed', type=int, default=0, help='random seed') 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 knn_work_dir = osp.join(cfg.work_dir, 'knn/') mkdir_or_exist(osp.abspath(knn_work_dir)) # init the logger before other steps timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) log_file = osp.join(knn_work_dir, f'knn_{timestamp}.log') logger = MMLogger.get_instance( 'mmselfsup', logger_name='mmselfsup', log_file=log_file, log_level=cfg.log_level) # build dataloader dataset_cfg = Config.fromfile(args.dataset_config) data_loader_train = Runner.build_dataloader( dataloader=dataset_cfg.train_dataloader, seed=args.seed) data_loader_val = Runner.build_dataloader( dataloader=dataset_cfg.val_dataloader, seed=args.seed) # build the model 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_train = Extractor( extract_dataloader=data_loader_train, seed=args.seed, dist_mode=distributed, pool_cfg=copy.deepcopy(dataset_cfg.pool_cfg)) extractor_val = Extractor( extract_dataloader=data_loader_val, seed=args.seed, dist_mode=distributed, pool_cfg=copy.deepcopy(dataset_cfg.pool_cfg)) train_feats = extractor_train(model)['feat5'] val_feats = extractor_val(model)['feat5'] train_feats = torch.from_numpy(train_feats) val_feats = torch.from_numpy(val_feats) train_labels = torch.LongTensor(data_loader_train.dataset.get_gt_labels()) val_labels = torch.LongTensor(data_loader_val.dataset.get_gt_labels()) logger.info('Features are extracted! Start k-NN classification...') # run knn rank = get_rank() if rank == 0: if args.use_cuda: train_feats = train_feats.cuda() val_feats = val_feats.cuda() train_labels = train_labels.cuda() val_labels = val_labels.cuda() for k in args.num_knn: top1, top5 = knn_classifier(train_feats, train_labels, val_feats, val_labels, k, args.temperature) logger.info( f'{k}-NN classifier result: Top1: {top1}, Top5: {top5}') if __name__ == '__main__': main()