mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
b6585cc1f1
* update knn * update * fix bugs of knn * update entrance scripts * update configs and related codes * update sampler config * remove redundance * update * update docs * fix lint * update logic of loading ckpt
185 lines
6.5 KiB
Python
185 lines
6.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import copy
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import os
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import os.path as osp
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import time
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import torch
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from mmengine.config import Config, DictAction
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from mmengine.dist import get_rank, init_dist
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from mmengine.logging import MMLogger
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from mmengine.model.wrappers import MMDistributedDataParallel, is_model_wrapper
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from mmengine.runner import Runner, load_checkpoint
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from mmengine.utils import mkdir_or_exist
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from mmselfsup.evaluation.functional import knn_eval
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from mmselfsup.models.utils import Extractor
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from mmselfsup.registry import MODELS
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from mmselfsup.utils import register_all_modules
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def parse_args():
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parser = argparse.ArgumentParser(description='KNN evaluation')
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parser.add_argument('config', help='train config file path')
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parser.add_argument('--checkpoint', default=None, help='checkpoint file')
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parser.add_argument(
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'--dataset-config',
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default='configs/benchmarks/classification/knn_imagenet.py',
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help='knn dataset config file path')
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parser.add_argument(
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'--work-dir', type=str, default=None, help='the dir to save results')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='job launcher')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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# KNN settings
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parser.add_argument(
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'--num-knn',
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default=[10, 20, 100, 200],
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nargs='+',
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type=int,
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help='Number of NN to use. 20 usually works the best.')
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parser.add_argument(
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'--temperature',
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default=0.07,
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type=float,
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help='Temperature used in the voting coefficient.')
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parser.add_argument(
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'--use-cuda',
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default=True,
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type=bool,
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help='Store the features on GPU. Set to False if you encounter OOM')
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parser.add_argument('--local_rank', type=int, default=0)
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parser.add_argument('--seed', type=int, default=0, help='random seed')
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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return args
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def main():
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args = parse_args()
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# register all modules in mmselfsup into the registries
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register_all_modules()
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# load config
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# set cudnn_benchmark
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if cfg.env_cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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cfg.work_dir = osp.join('./work_dirs/benchmarks/knn/',
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osp.splitext(osp.basename(args.config))[0])
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher)
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# create work_dir
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mkdir_or_exist(osp.abspath(cfg.work_dir))
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# init the logger before other steps
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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log_file = osp.join(cfg.work_dir, f'knn_{timestamp}.log')
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logger = MMLogger.get_instance(
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'mmselfsup',
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logger_name='mmselfsup',
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log_file=log_file,
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log_level=cfg.log_level)
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# build dataloader
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dataset_cfg = Config.fromfile(args.dataset_config)
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data_loader_train = Runner.build_dataloader(
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dataloader=dataset_cfg.train_dataloader, seed=args.seed)
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data_loader_val = Runner.build_dataloader(
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dataloader=dataset_cfg.val_dataloader, seed=args.seed)
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# build the model
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model = MODELS.build(cfg.model)
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# model is determined in this priority: checkpoint > init_cfg > random
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if args.checkpoint is not None:
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logger.info(f'Use checkpoint: {args.checkpoint} to extract features')
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load_checkpoint(model, args.checkpoint, map_location='cpu')
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elif getattr(model.backbone.init_cfg, 'type', None) == 'Pretrained':
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model.init_weights()
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else:
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logger.warning(
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'No pretrained or checkpoint is given, use random init.')
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if torch.cuda.is_available():
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model = model.cuda()
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if distributed:
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model = MMDistributedDataParallel(
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module=model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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if is_model_wrapper(model):
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model = model.module
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# build extractor and extract features
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extractor_train = Extractor(
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extract_dataloader=data_loader_train,
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seed=args.seed,
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dist_mode=distributed,
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pool_cfg=copy.deepcopy(dataset_cfg.pool_cfg))
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extractor_val = Extractor(
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extract_dataloader=data_loader_val,
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seed=args.seed,
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dist_mode=distributed,
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pool_cfg=copy.deepcopy(dataset_cfg.pool_cfg))
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train_feats = extractor_train(model)
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logger.info('Features from train dataset are extracted.')
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val_feats = extractor_val(model)
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logger.info('Features from validation dataset are extracted.')
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# run knn
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rank = get_rank()
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if rank == 0:
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for key in train_feats.keys():
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train_feats = train_feats[key]
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val_feats = val_feats[key]
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train_labels = torch.LongTensor(
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data_loader_train.dataset.get_gt_labels()).to(
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train_feats.device)
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val_labels = torch.LongTensor(
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data_loader_val.dataset.get_gt_labels()).to(val_feats.device)
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logger.info(f'Start k-NN classification of key "{key}".')
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for k in args.num_knn:
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top1, top5 = knn_eval(train_feats, train_labels, val_feats,
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val_labels, k, args.temperature)
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logger.info(f'Reasults of "{key}", {k}-NN classifier: '
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f'Top1 - {top1}, Top5 - {top5}.')
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if __name__ == '__main__':
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main()
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