203 lines
7.0 KiB
Python
203 lines
7.0 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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from functools import partial
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from typing import Optional
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import numpy as np
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import torch
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from mmengine.config import Config, DictAction
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from mmengine.data import pseudo_collate, worker_init_fn
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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 load_checkpoint
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from mmengine.utils import mkdir_or_exist
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from torch.utils.data import DataLoader
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from mmselfsup.models.utils import Extractor
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from mmselfsup.registry import DATA_SAMPLERS, DATASETS, 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(
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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('--seed', type=int, default=0, help='random seed')
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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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# 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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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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# 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'extract_{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 the dataset
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dataset_cfg = Config.fromfile(args.dataset_config)
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extract_dataloader_cfg = dataset_cfg.get('extract_dataloader')
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extract_dataset_cfg = extract_dataloader_cfg.pop('extract_dataset')
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if isinstance(extract_dataset_cfg, dict):
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dataset = DATASETS.build(extract_dataset_cfg)
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if hasattr(dataset, 'full_init'):
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dataset.full_init()
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# build sampler
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sampler_cfg = extract_dataloader_cfg.pop('sampler')
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if isinstance(sampler_cfg, dict):
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sampler = DATA_SAMPLERS.build(
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sampler_cfg, default_args=dict(dataset=dataset, seed=args.seed))
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# build dataloader
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init_fn: Optional[partial]
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if args.seed is not None:
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init_fn = partial(
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worker_init_fn,
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num_workers=extract_dataloader_cfg.get('num_workers'),
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rank=get_rank(),
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seed=args.seed)
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else:
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init_fn = None
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data_loader = DataLoader(
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dataset=dataset,
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sampler=sampler,
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collate_fn=pseudo_collate,
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worker_init_fn=init_fn,
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**extract_dataloader_cfg)
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# build the model
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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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model = MODELS.build(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 hasattr(cfg.model.backbone, 'init_cfg'):
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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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)
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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 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 = Extractor(
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extract_dataloader=data_loader,
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seed=args.seed,
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dist_mode=distributed,
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pool_cfg=dataset_cfg.pool_cfg)
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outputs = extractor(model)
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# run
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rank = get_rank()
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mkdir_or_exist(f'{cfg.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'{cfg.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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