# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp import time import mmcv import torch from mmcv import DictAction from mmcv.parallel import MMDataParallel, MMDistributedDataParallel from mmcv.runner import get_dist_info, init_dist, load_checkpoint from mmselfsup.datasets import build_dataloader, build_dataset from mmselfsup.models import build_algorithm from mmselfsup.models.utils import ExtractProcess, knn_classifier from mmselfsup.utils import get_root_logger 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) 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) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # set cudnn_benchmark if 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, **cfg.dist_params) # create work_dir and init the logger before other steps timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) knn_work_dir = osp.join(cfg.work_dir, 'knn/') mmcv.mkdir_or_exist(osp.abspath(knn_work_dir)) log_file = osp.join(knn_work_dir, f'knn_{timestamp}.log') 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_train = build_dataset(dataset_cfg.data.train) dataset_val = build_dataset(dataset_cfg.data.val) if 'imgs_per_gpu' in cfg.data: logger.warning('"imgs_per_gpu" is deprecated. ' 'Please use "samples_per_gpu" instead') if 'samples_per_gpu' in cfg.data: logger.warning( f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and ' f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"' f'={cfg.data.imgs_per_gpu} is used in this experiments') else: logger.warning( 'Automatically set "samples_per_gpu"="imgs_per_gpu"=' f'{cfg.data.imgs_per_gpu} in this experiments') cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu data_loader_train = build_dataloader( dataset_train, samples_per_gpu=dataset_cfg.data.samples_per_gpu, workers_per_gpu=dataset_cfg.data.workers_per_gpu, dist=distributed, shuffle=False) data_loader_val = build_dataloader( dataset_val, samples_per_gpu=dataset_cfg.data.samples_per_gpu, workers_per_gpu=dataset_cfg.data.workers_per_gpu, dist=distributed, shuffle=False) # build the model model = build_algorithm(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 not distributed: model = MMDataParallel(model, device_ids=[0]) else: model = MMDistributedDataParallel( model.cuda(), device_ids=[torch.cuda.current_device()], broadcast_buffers=False) model.eval() # build extraction processor and run extractor = ExtractProcess() train_feats = extractor.extract( model, data_loader_train, distributed=distributed)['feat'] val_feats = extractor.extract( model, data_loader_val, distributed=distributed)['feat'] train_feats = torch.from_numpy(train_feats) val_feats = torch.from_numpy(val_feats) train_labels = torch.LongTensor(dataset_train.data_source.get_gt_labels()) val_labels = torch.LongTensor(dataset_val.data_source.get_gt_labels()) logger.info('Features are extracted! Start k-NN classification...') rank, _ = get_dist_info() 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()