DAMOYOLO windows 单卡训练

最近达摩院放出了目前最能打的yolo算法，时间和精度都得到了提升

 目前代码已经开源：

代码地址：GitHub - tinyvision/DAMO-YOLO: DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.

        代码预设仅支持分布式训练，对于硬件资源有限的小伙伴来说，算法的训练就不是太友好了，但是对于想要尝试的小伙伴还是有办法的

一、修改train中的代码

#!/usr/bin/env python
# Copyright (C) Alibaba Group Holding Limited. All rights reserved.
import argparse
import copy
import os
import torch
from loguru import logger
 
from damo.apis import Trainer
from damo.config.base import parse_config
from damo.utils import synchronize
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12345'
 
def make_parser():
    """
    Create a parser with some common arguments used by users.
    Returns:
        argparse.ArgumentParser
    """
 
    parser = argparse.ArgumentParser('Damo-Yolo train parser')
 
    parser.add_argument(
        '-f',
        '--config_file',
        default=r'G:\xxx\DAMO-YOLO\configs\damoyolo_tinynasL20_T.py', # xxx自己的路径
        type=str,
        help='plz input your config file',
    )
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument('--tea_config', type=str, default=None)
    parser.add_argument('--tea_ckpt', type=str, default=None)
    parser.add_argument(
        'opts',
        help='Modify config options using the command-line',
        default=None,
        nargs=argparse.REMAINDER,
    )
    return parser
 
 
@logger.catch
def main():
    args = make_parser().parse_args()
 
    torch.cuda.set_device(args.local_rank)
    # torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=torch.cuda.device_count(), rank=args.local_rank)
 
    try:
        world_size = torch.cuda.device_count()  # int(os.environ["WORLD_SIZE"])
        rank = args.local_rank  # int(os.environ["RANK"])
        # distributed.init_process_group("nccl")
        torch.distributed.init_process_group("gloo",rank=rank,world_size=world_size)
    except KeyError:
        world_size = torch.cuda.device_count()
        rank = args.local_rank
        torch.distributed.init_process_group(
            backend="nccl",
            init_method='env://',
            rank=rank,
            world_size=world_size,
        )
    synchronize()
    if args.tea_config is not None:
        tea_config = parse_config(args.tea_config)
    else:
        tea_config = None
 
    config = parse_config(args.config_file)
    config.merge(args.opts)
 
 
    trainer = Trainer(config, args, tea_config)
    trainer.train(args.local_rank)
 
 
if __name__ == '__main__':
    main()

1、增加 

os.environ['MASTER_ADDR'] = 'localhost'

os.environ['MASTER_PORT'] = '12345'

        否则会报：

ValueError: Error initializing torch.distributed using env:// rendezvous: environment variable MASTER_ADDR expected, but not set

or

ValueError: Error initializing torch.distributed using env:// rendezvous: environment variable MASTER_PORT expected, but not set

2、windows不支持nccl backbone所以init_process_group中改为‘gloo’

二、改配置configs\xxx.py

        如damoyolo_tinynasL20_T.py找到代码17行的

self.train.batch_size = 256 --->调小即可

        ps：建议设置为8， 训练过程中占用显存较大

三、改数据集路径

damo\config\paths_catalog.py

        找到代码的第8行修改

DATA_DIR = r'G:\xxx\train_data'  

        同时还要修改第38行的路径，改成绝对路径即可，否则也会报如下错误

ImportError: G:\xxx\DAMO-YOLO\configs\damoyolo_tinynasL20_T.py doesn't contains class named 'Config'

        到这里基本上就能在windows端使用单卡运行起来了