How to draw lr log without train dataset

[vansin]

new tools/fake_train.py as follow

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

from mmengine.config import Config, DictAction
from mmengine.utils.dl_utils.visualize import fake_run


def parse_args():
    parser = argparse.ArgumentParser(description='Train a detector')
    parser.add_argument('config', help='train config file path')
    parser.add_argument('--work-dir', help='the dir to save logs and models')
    parser.add_argument(
        '--amp',
        action='store_true',
        default=False,
        help='enable automatic-mixed-precision training')
    parser.add_argument(
        '--auto-scale-lr',
        action='store_true',
        help='enable automatically scaling LR.')
    parser.add_argument(
        '--resume',
        action='store_true',
        help='resume from the latest checkpoint in the work_dir automatically')
    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.')
    parser.add_argument(
        '--launcher',
        choices=['none', 'pytorch', 'slurm', 'mpi'],
        default='none',
        help='job launcher')
    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()

    # register all modules in mmdet into the registries
    # do not init the default scope here because it will be init in the runner
    # register_all_modules(init_default_scope=False)
    # load config
    cfg = Config.fromfile(args.config)

    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    # start training


    # 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

        cfg.work_dir = osp.join('./work_dirs',
                                osp.splitext(args.config.split('configs/')[1])[0])

        # cfg.work_dir = osp.join('./work_dirs',
        #                         osp.splitext(osp.basename(args.config))[0])



    fake_run(cfg)

if __name__ == '__main__':
    main()

use follow command to fake run

pip install future tensorboard
python tools/fake_train.py configs/balloon/faster_rcnn/faster-rcnn_r101_fpn_1x_coco.py
tensorboard --logdir=temp_dir

[mzr1996]

You can try the scheduler visualization tool in MMCLS, https://github.com/open-mmlab/mmclassification/blob/dev-1.x/tools/visualizations/vis_scheduler.py