MMDetection目标检测框架推理与参数量计算

本文主要是介绍MMDetection目标检测框架推理与参数量计算，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

模型推理

在使用MMDetection框架完成训练后便可以使用训练所得的权重文件进行推理了，具体可以使用MMDetection文件下的demo文件夹的image_demo.py文件。

from argparse import ArgumentParser
from mmengine.logging import print_log
from mmdet.apis import DetInferencerdef parse_args():parser = ArgumentParser()parser.add_argument('--inputs', type=str,default="/home/ubuntu/programs/mmdetection/tools/images/4.jpg", help='Input image file or folder path.')parser.add_argument('--model',type=str,default="/home/ubuntu/programs/mmdetection/output/faster-rcnn_r50_fpn_2x_coco.py",help='Config or checkpoint .pth file or the model name ''and alias defined in metafile. The model configuration ''file will try to read from .pth if the parameter is ''a .pth weights file.')parser.add_argument('--weights', default="/home/ubuntu/programs/mmdetection/output//epoch_24.pth", help='Checkpoint file')parser.add_argument('--out-dir',type=str,default='/home/ubuntu/programs/mmdetection/outputs/',help='Output directory of images or prediction results.')parser.add_argument('--texts', help='text prompt')parser.add_argument('--device', default='cpu', help='Device used for inference')parser.add_argument('--pred-score-thr',type=float,default=0.5,help='bbox score threshold')parser.add_argument('--batch-size', type=int, default=1, help='Inference batch size.')parser.add_argument('--show',action='store_true',help='Display the image in a popup window.')parser.add_argument('--no-save-vis',action='store_true',help='Do not save detection vis results')parser.add_argument('--no-save-pred',action='store_true',help='Do not save detection json results')parser.add_argument('--print-result',action='store_true',help='Whether to print the results.')parser.add_argument('--palette',default='none',choices=['coco', 'voc', 'citys', 'random', 'none'],help='Color palette used for visualization')# only for GLIPparser.add_argument('--custom-entities','-c',action='store_true',help='Whether to customize entity names? ''If so, the input text should be ''"cls_name1 . cls_name2 . cls_name3 ." format')call_args = vars(parser.parse_args())if call_args['no_save_vis'] and call_args['no_save_pred']:call_args['out_dir'] = ''if call_args['model'].endswith('.pth'):print_log('The model is a weight file, automatically ''assign the model to --weights')call_args['weights'] = call_args['model']call_args['model'] = Noneinit_kws = ['model', 'weights', 'device', 'palette']init_args = {}for init_kw in init_kws:init_args[init_kw] = call_args.pop(init_kw)return init_args, call_argsdef main():init_args, call_args = parse_args()inferencer = DetInferencer(**init_args)inferencer(**call_args)if call_args['out_dir'] != '' and not (call_args['no_save_vis']and call_args['no_save_pred']):print_log(f'results have been saved at {call_args["out_dir"]}')
if __name__ == '__main__':main()