168 lines
6.3 KiB
Python
168 lines
6.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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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 mmcv
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import numpy as np
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import torch
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from mmcv import DictAction
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import get_dist_info, init_dist, load_checkpoint
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from mmselfsup.datasets import build_dataloader, build_dataset
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from mmselfsup.models import build_algorithm
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from mmselfsup.models.utils import ExtractProcess
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from mmselfsup.utils import get_root_logger
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMSelfSup extract features of a model')
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parser.add_argument('config', help='test 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/svm_voc07.py',
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help='extract dataset config file path')
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parser.add_argument(
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'--layer-ind',
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type=str,
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help='layer indices, separated by comma, e.g., "0,1,2,3,4"')
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parser.add_argument(
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'--work_dir',
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type=str,
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default=None,
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help='the dir to save logs and models')
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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('--local_rank', type=int, default=0)
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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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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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cfg = mmcv.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.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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work_type = args.config.split('/')[1]
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cfg.work_dir = osp.join('./work_dirs', work_type,
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osp.splitext(osp.basename(args.config))[0])
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# get out_indices from args
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layer_ind = [int(idx) for idx in args.layer_ind.split(',')]
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cfg.model.backbone.out_indices = layer_ind
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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, **cfg.dist_params)
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# create work_dir
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mmcv.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'extract_{timestamp}.log')
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logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
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# build the dataloader
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dataset_cfg = mmcv.Config.fromfile(args.dataset_config)
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dataset = build_dataset(dataset_cfg.data.extract)
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if 'imgs_per_gpu' in cfg.data:
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logger.warning('"imgs_per_gpu" is deprecated. '
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'Please use "samples_per_gpu" instead')
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if 'samples_per_gpu' in cfg.data:
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logger.warning(
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f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
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f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
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f'={cfg.data.imgs_per_gpu} is used in this experiments')
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else:
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logger.warning(
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'Automatically set "samples_per_gpu"="imgs_per_gpu"='
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f'{cfg.data.imgs_per_gpu} in this experiments')
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cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=dataset_cfg.data.samples_per_gpu,
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workers_per_gpu=dataset_cfg.data.workers_per_gpu,
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dist=distributed,
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shuffle=False)
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# build the model
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model = build_algorithm(cfg.model)
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model.init_weights()
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# model is determined in this priority: init_cfg > checkpoint > random
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if getattr(cfg.model.backbone.init_cfg, 'type', None) == 'Pretrained':
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logger.info(
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f'Use pretrained model: '
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f'{cfg.model.backbone.init_cfg.checkpoint} to extract features')
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elif 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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else:
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logger.info('No pretrained or checkpoint is given, use random init.')
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if not distributed:
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model = MMDataParallel(model, device_ids=[0])
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else:
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model = MMDistributedDataParallel(
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model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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# build extraction processor
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extractor = ExtractProcess(
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pool_type='specified', backbone='resnet50', layer_indices=layer_ind)
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# run
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outputs = extractor.extract(model, data_loader, distributed=distributed)
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rank, _ = get_dist_info()
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mmcv.mkdir_or_exist(f'{args.work_dir}/features/')
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if rank == 0:
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for key, val in outputs.items():
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split_num = len(dataset_cfg.split_name)
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split_at = dataset_cfg.split_at
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for ss in range(split_num):
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output_file = f'{args.work_dir}/features/' \
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f'{dataset_cfg.split_name[ss]}_{key}.npy'
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if ss == 0:
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np.save(output_file, val[:split_at[0]])
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elif ss == split_num - 1:
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np.save(output_file, val[split_at[-1]:])
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else:
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np.save(output_file, val[split_at[ss - 1]:split_at[ss]])
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if __name__ == '__main__':
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main()
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