mmselfsup/tools/benchmarks/classification/svm_voc07/extract.py

# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import time

import mmcv
import numpy as np
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 MultiExtractProcess
from mmselfsup.utils import get_root_logger


def parse_args():
    parser = argparse.ArgumentParser(
        description='MMSelfSup extract features of a model')
    parser.add_argument('config', help='test config file path')
    parser.add_argument('--checkpoint', default=None, help='checkpoint file')
    parser.add_argument(
        '--dataset-config',
        default='configs/benchmarks/classification/svm_voc07.py',
        help='extract dataset config file path')
    parser.add_argument(
        '--layer-ind',
        type=str,
        help='layer indices, separated by comma, e.g., "0,1,2,3,4"')
    parser.add_argument(
        '--work_dir',
        type=str,
        default=None,
        help='the dir to save logs and models')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    parser.add_argument('--local_rank', type=int, default=0)
    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.')
    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])

    # get out_indices from args
    layer_ind = [int(idx) for idx in args.layer_ind.split(',')]
    cfg.model.backbone.out_indices = layer_ind

    # 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
    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
    # init the logger before other steps
    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
    log_file = osp.join(cfg.work_dir, f'extract_{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 = build_dataset(dataset_cfg.data.extract)
    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 = build_dataloader(
        dataset,
        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 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)

    # build extraction processor
    extractor = MultiExtractProcess(
        pool_type='specified', backbone='resnet50', layer_indices=layer_ind)

    # run
    outputs = extractor.extract(model, data_loader, distributed=distributed)
    rank, _ = get_dist_info()
    mmcv.mkdir_or_exist(f'{args.work_dir}/features/')
    if rank == 0:
        for key, val in outputs.items():
            split_num = len(dataset_cfg.split_name)
            split_at = dataset_cfg.split_at
            for ss in range(split_num):
                output_file = f'{args.work_dir}/features/' \
                              f'{dataset_cfg.split_name[ss]}_{key}.npy'
                if ss == 0:
                    np.save(output_file, val[:split_at[0]])
                elif ss == split_num - 1:
                    np.save(output_file, val[split_at[-1]:])
                else:
                    np.save(output_file, val[split_at[ss - 1]:split_at[ss]])


if __name__ == '__main__':
    main()