这个是用demo这个代码测试的，需要先训练一个pth文件夹，训练之后再调用pth文件夹进行测试。测试的代码文件名是：image_demo_new.py，代码如系所示：

# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParserfrom mmdet.apis import (async_inference_detector, inference_detector,init_detector, show_result_pyplot)
import cv2
import osdef parse_args():parser = ArgumentParser()parser.add_argument('img', default=None,help='Image file')parser.add_argument('config', help='Config file')parser.add_argument('checkpoint', help='Checkpoint file')parser.add_argument('--out-file', default=None, help='Path to output file')parser.add_argument('--device', default='cuda:0', help='Device used for inference')parser.add_argument('--palette',default='coco',choices=['coco', 'voc', 'citys', 'random'],help='Color palette used for visualization')parser.add_argument('--score-thr', type=float, default=0.3, help='bbox score threshold')parser.add_argument('--async-test',action='store_true',help='whether to set async options for async inference.')args = parser.parse_args()return argsdef main(args):outdir='./demo_out1'imgdir='./image/fire'for imgf in os.listdir(imgdir):if not imgf.endswith('jpg'):continue# build the model from a config file and a checkpoint filemodel = init_detector(args.config, args.checkpoint, device=args.device)# test a single imageresult = inference_detector(model, imgdir + '/'+ imgf)# import pdb;pdb.set_trace()if hasattr(model, 'module'):model = model.moduleimg = model.show_result(imgdir + '/'+ imgf,result,score_thr=args.score_thr,show=False)cv2.imwrite('{}/{}'.format(outdir,imgf),img)if __name__ == '__main__':args = parse_args()main(args)

记得自己新建“demo_out1”这个文件夹
测试完成之后会保存到一个新的文件夹下面，文件名是：demo_out1，测试代码文件夹名称是： imgdir='./image/fire'，运行的代码如下所示：python demo/image_demo_new.py image/fire/image_733.jpg configs/ddod_log_ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco.py configs/ddod_log_ddod_r50_fpn_1x_coco/epoch_100.pth --device cpu

测试我是直接用cpu的，没用GPU,界面截图如下所示：