OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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247 lines
9.0 KiB
247 lines
9.0 KiB
# 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 warnings |
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import mmcv |
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import torch |
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import torch.distributed as dist |
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from mmcv import Config, DictAction |
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from mmcv.runner import get_dist_info, init_dist |
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from mmcv.utils import get_git_hash |
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from mmdet import __version__ |
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from mmdet.apis import init_random_seed, set_random_seed, train_detector |
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from mmdet.datasets import build_dataset |
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from mmdet.models import build_detector |
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from mmdet.utils import (collect_env, get_device, get_root_logger, |
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replace_cfg_vals, rfnext_init_model, |
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setup_multi_processes, update_data_root) |
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def parse_args(): |
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parser = argparse.ArgumentParser(description='Train a detector') |
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parser.add_argument('config', help='train config file path') |
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parser.add_argument('--work-dir', help='the dir to save logs and models') |
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parser.add_argument( |
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'--resume-from', help='the checkpoint file to resume from') |
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parser.add_argument( |
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'--auto-resume', |
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action='store_true', |
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help='resume from the latest checkpoint automatically') |
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parser.add_argument( |
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'--no-validate', |
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action='store_true', |
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help='whether not to evaluate the checkpoint during training') |
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group_gpus = parser.add_mutually_exclusive_group() |
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group_gpus.add_argument( |
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'--gpus', |
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type=int, |
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help='(Deprecated, please use --gpu-id) number of gpus to use ' |
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'(only applicable to non-distributed training)') |
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group_gpus.add_argument( |
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'--gpu-ids', |
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type=int, |
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nargs='+', |
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help='(Deprecated, please use --gpu-id) ids of gpus to use ' |
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'(only applicable to non-distributed training)') |
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group_gpus.add_argument( |
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'--gpu-id', |
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type=int, |
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default=0, |
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help='id of gpu to use ' |
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'(only applicable to non-distributed training)') |
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parser.add_argument('--seed', type=int, default=None, help='random seed') |
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parser.add_argument( |
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'--diff-seed', |
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action='store_true', |
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help='Whether or not set different seeds for different ranks') |
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parser.add_argument( |
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'--deterministic', |
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action='store_true', |
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help='whether to set deterministic options for CUDNN backend.') |
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parser.add_argument( |
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'--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 (deprecate), ' |
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'change to --cfg-options instead.') |
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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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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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'--auto-scale-lr', |
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action='store_true', |
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help='enable automatically scaling LR.') |
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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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if args.options and args.cfg_options: |
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raise ValueError( |
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'--options and --cfg-options cannot be both ' |
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'specified, --options is deprecated in favor of --cfg-options') |
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if args.options: |
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warnings.warn('--options is deprecated in favor of --cfg-options') |
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args.cfg_options = args.options |
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return args |
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def main(): |
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args = parse_args() |
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cfg = Config.fromfile(args.config) |
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# replace the ${key} with the value of cfg.key |
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cfg = replace_cfg_vals(cfg) |
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# update data root according to MMDET_DATASETS |
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update_data_root(cfg) |
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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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if args.auto_scale_lr: |
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if 'auto_scale_lr' in cfg and \ |
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'enable' in cfg.auto_scale_lr and \ |
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'base_batch_size' in cfg.auto_scale_lr: |
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cfg.auto_scale_lr.enable = True |
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else: |
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warnings.warn('Can not find "auto_scale_lr" or ' |
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'"auto_scale_lr.enable" or ' |
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'"auto_scale_lr.base_batch_size" in your' |
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' configuration file. Please update all the ' |
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'configuration files to mmdet >= 2.24.1.') |
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# set multi-process settings |
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setup_multi_processes(cfg) |
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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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cfg.work_dir = osp.join('./work_dirs', |
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osp.splitext(osp.basename(args.config))[0]) |
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if args.resume_from is not None: |
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cfg.resume_from = args.resume_from |
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cfg.auto_resume = args.auto_resume |
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if args.gpus is not None: |
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cfg.gpu_ids = range(1) |
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warnings.warn('`--gpus` is deprecated because we only support ' |
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'single GPU mode in non-distributed training. ' |
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'Use `gpus=1` now.') |
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if args.gpu_ids is not None: |
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cfg.gpu_ids = args.gpu_ids[0:1] |
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warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. ' |
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'Because we only support single GPU mode in ' |
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'non-distributed training. Use the first GPU ' |
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'in `gpu_ids` now.') |
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if args.gpus is None and args.gpu_ids is None: |
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cfg.gpu_ids = [args.gpu_id] |
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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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# re-set gpu_ids with distributed training mode |
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_, world_size = get_dist_info() |
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cfg.gpu_ids = range(world_size) |
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# create work_dir |
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mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) |
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# dump config |
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cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) |
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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'{timestamp}.log') |
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logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) |
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# init the meta dict to record some important information such as |
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# environment info and seed, which will be logged |
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meta = dict() |
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# log env info |
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env_info_dict = collect_env() |
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env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()]) |
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dash_line = '-' * 60 + '\n' |
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logger.info('Environment info:\n' + dash_line + env_info + '\n' + |
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dash_line) |
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meta['env_info'] = env_info |
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meta['config'] = cfg.pretty_text |
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# log some basic info |
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logger.info(f'Distributed training: {distributed}') |
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logger.info(f'Config:\n{cfg.pretty_text}') |
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cfg.device = get_device() |
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# set random seeds |
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seed = init_random_seed(args.seed, device=cfg.device) |
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seed = seed + dist.get_rank() if args.diff_seed else seed |
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logger.info(f'Set random seed to {seed}, ' |
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f'deterministic: {args.deterministic}') |
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set_random_seed(seed, deterministic=args.deterministic) |
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cfg.seed = seed |
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meta['seed'] = seed |
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meta['exp_name'] = osp.basename(args.config) |
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model = build_detector( |
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cfg.model, |
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train_cfg=cfg.get('train_cfg'), |
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test_cfg=cfg.get('test_cfg')) |
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model.init_weights() |
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# init rfnext if 'RFSearchHook' is defined in cfg |
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rfnext_init_model(model, cfg=cfg) |
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datasets = [build_dataset(cfg.data.train)] |
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if len(cfg.workflow) == 2: |
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assert 'val' in [mode for (mode, _) in cfg.workflow] |
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val_dataset = copy.deepcopy(cfg.data.val) |
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val_dataset.pipeline = cfg.data.train.get( |
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'pipeline', cfg.data.train.dataset.get('pipeline')) |
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datasets.append(build_dataset(val_dataset)) |
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if cfg.checkpoint_config is not None: |
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# save mmdet version, config file content and class names in |
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# checkpoints as meta data |
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cfg.checkpoint_config.meta = dict( |
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mmdet_version=__version__ + get_git_hash()[:7], |
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CLASSES=datasets[0].CLASSES) |
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# add an attribute for visualization convenience |
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model.CLASSES = datasets[0].CLASSES |
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train_detector( |
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model, |
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datasets, |
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cfg, |
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distributed=distributed, |
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validate=(not args.no_validate), |
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timestamp=timestamp, |
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meta=meta) |
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if __name__ == '__main__': |
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main()
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