thirdparty/Metric3D/training/mono/tools/train.py

import os
import os.path as osp
import time
import sys
CODE_SPACE=os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(CODE_SPACE)
#os.chdir(CODE_SPACE)
import argparse
import copy
import mmcv
import torch
import torch.distributed as dist
import torch.multiprocessing as mp

try:
    from mmcv.utils import Config, DictAction
except:
    from mmengine import Config, DictAction
import socket
import subprocess
from datetime import timedelta
import random
import numpy as np
import logging

from mono.datasets.distributed_sampler import log_canonical_transfer_info
from mono.utils.comm import init_env, collect_env
from mono.utils.logger import setup_logger
from mono.utils.db import load_data_info, reset_ckpt_path
from mono.utils.do_train import do_train


def parse_args():
    parser = argparse.ArgumentParser(description='Train a segmentor')
    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('--tensorboard-dir', help='the dir to save tensorboard logs')
    parser.add_argument(
        '--load-from', help='the checkpoint file to load weights from')
    parser.add_argument(
        '--resume-from', help='the checkpoint file to resume from')
    parser.add_argument(
        '--no-validate',
        action='store_true',
        help='whether not to evaluate the checkpoint during training')
    parser.add_argument(
        '--gpu-ids',
        type=int,
        nargs='+',
        help='ids of gpus to use '
        '(only applicable to non-distributed training)')
    parser.add_argument('--seed', type=int, default=88, help='random seed')
    parser.add_argument(
        '--deterministic',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    parser.add_argument(
        '--use-tensorboard',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    parser.add_argument(
        '--options', nargs='+', action=DictAction, help='custom options')
    parser.add_argument('--node_rank', type=int, default=0)
    parser.add_argument('--nnodes', 
                        type=int, 
                        default=1, 
                        help='number of nodes')
    parser.add_argument(
        '--launcher', choices=['None', 'pytorch', 'slurm', 'mpi', 'ror'], default='slurm',
        help='job launcher') 
    parser.add_argument('--local_rank', 
                        type=int, 
                        default=0, 
                        help='rank')  
    parser.add_argument('--experiment_name', default='debug', help='the experiment name for mlflow')
    args = parser.parse_args()
    return args

  
def set_random_seed(seed, deterministic=False):
    """Set random seed.
    Args:
        @seed (int): Seed to be used.
        @deterministic (bool): Whether to set the deterministic option for
            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
            to True and `torch.backends.cudnn.benchmark` to False.
            Default: False.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    #if deterministic:
    #    torch.backends.cudnn.deterministic = True
    #    torch.backends.cudnn.benchmark = False

def main(args):
    os.chdir(CODE_SPACE)
    cfg = Config.fromfile(args.config)
    cfg.dist_params.nnodes = args.nnodes
    cfg.dist_params.node_rank = args.node_rank
    cfg.deterministic = args.deterministic
    if args.options is not None:
        cfg.merge_from_dict(args.options)
    # set cudnn_benchmark
    #if cfg.get('cudnn_benchmark', False) and args.launcher != 'ror':
    #    torch.backends.cudnn.benchmark = True
    # The flag below controls whether to allow TF32 on matmul. This flag defaults to False
    # in PyTorch 1.12 and later.
    # torch.backends.cuda.matmul.allow_tf32 = False
    # The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
    # torch.backends.cudnn.allow_tf32 = False

    # 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 + timestamp as default work_dir if cfg.work_dir is None
        cfg.work_dir = osp.join('./work_dirs',
                                osp.splitext(osp.basename(args.config))[0], 
                                args.timestamp)
    # tensorboard_dir is determined in this priority: CLI > segment in file > filename
    if args.tensorboard_dir is not None:
        cfg.tensorboard_dir = args.tensorboard_dir
    elif cfg.get('tensorboard_dir', None) is None:
        # use cfg.work_dir + 'tensorboard' as default tensorboard_dir if cfg.tensorboard_dir is None
        cfg.tensorboard_dir = osp.join(cfg.work_dir, 'tensorboard')

    # ckpt path
    if args.load_from is not None:
        cfg.load_from = args.load_from
    # resume training
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    
    # create work_dir and tensorboard_dir
    os.makedirs(osp.abspath(cfg.work_dir), exist_ok=True)
    os.makedirs(os.path.abspath(cfg.tensorboard_dir), exist_ok=True)
    
    # init the logger before other steps
    cfg.log_file = osp.join(cfg.work_dir, f'{args.timestamp}.log')
    logger = setup_logger(cfg.log_file)

    # init the meta dict to record some important information such as
    # environment info and seed, which will be logged
    meta = dict()
    # log env info
    env_info_dict = collect_env()
    env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()])
    dash_line = '-' * 60 + '\n'
    logger.info('Environment info:\n' + dash_line + env_info + '\n' +
                dash_line)
    meta['env_info'] = env_info

    # log some basic info
    # logger.info(f'Config:\n{cfg.pretty_text}')

    # mute online evaluation
    if args.no_validate:
        cfg.evaluation.online_eval = False


    cfg.seed = args.seed
    meta['seed'] = args.seed
    meta['exp_name'] = osp.basename(args.config)

    # load data info
    data_info = {}
    load_data_info('data_server_info', data_info=data_info)
    cfg.db_info = data_info
    # update check point info
    reset_ckpt_path(cfg.model, data_info)

    # log data transfer to canonical space info``
    # log_canonical_transfer_info(cfg)
    
    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'None':
        cfg.distributed = False
    else:
        cfg.distributed = True
    init_env(args.launcher, cfg)
    logger.info(f'Distributed training: {cfg.distributed}')
    logger.info(cfg.dist_params)
    # dump config
    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
    
    cfg.experiment_name = args.experiment_name

    if not cfg.distributed:
        main_worker(0, cfg)
    else:
        # distributed training
        if args.launcher == 'slurm': 
            mp.spawn(main_worker, nprocs=cfg.dist_params.num_gpus_per_node, args=(cfg, args.launcher))
        elif args.launcher == 'pytorch':
            main_worker(args.local_rank, cfg, args.launcher)

def main_worker(local_rank: int, cfg: dict, launcher: str='slurm'):
    logger = setup_logger(cfg.log_file)
    if cfg.distributed:
        if launcher == 'slurm':
            torch.set_num_threads(8) # without it, the spawn method is much slower than the launch method 
            cfg.dist_params.global_rank = cfg.dist_params.node_rank * cfg.dist_params.num_gpus_per_node + local_rank
            cfg.dist_params.local_rank = local_rank
            os.environ['RANK']=str(cfg.dist_params.global_rank)
        else:
            torch.set_num_threads(1)
        
        torch.cuda.set_device(local_rank)
        default_timeout = timedelta(minutes=10)
        dist.init_process_group(
                backend=cfg.dist_params.backend,
                init_method=cfg.dist_params.dist_url,
                world_size=cfg.dist_params.world_size,
                rank=cfg.dist_params.global_rank,)
                #timeout=default_timeout,)
        dist.barrier()

    # if cfg.distributed:
        
    #     cfg.dist_params.global_rank = cfg.dist_params.node_rank * cfg.dist_params.num_gpus_per_node + local_rank
    #     cfg.dist_params.local_rank = local_rank
    #     os.environ['RANK']=str(cfg.dist_params.global_rank)
        
    #     if launcher == 'ror':
    #         init_torch_process_group(use_hvd=False)
    #     else:
    #         #torch.set_num_threads(4) # without it, the spawn method maybe much slower than the launch method 
    #         torch.cuda.set_device(local_rank)
    #         default_timeout = timedelta(minutes=30)
    #         dist.init_process_group(
    #             backend=cfg.dist_params.backend,
    #             init_method=cfg.dist_params.dist_url,
    #             world_size=cfg.dist_params.world_size,
    #             rank=cfg.dist_params.global_rank,)
    #             #timeout=default_timeout,)
    
    # set random seeds
    if cfg.seed is not None:
        logger.info(f'Set random seed to {cfg.seed}, deterministic: 'f'{cfg.deterministic}')
        set_random_seed(cfg.seed, deterministic=cfg.deterministic)
    # with torch.autograd.set_detect_anomaly(True):
    do_train(local_rank, cfg)


if __name__=='__main__':
    # load args
    args = parse_args()
    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
    args.timestamp = timestamp
    print(args.work_dir, args.tensorboard_dir)
    main(args)