YOLOX导出onnx文件

本是古典何须时尚 2022-08-28 13:53 223阅读 0赞

# 导出onnx文件 #

第一步先确认类别，在yolox/exp/yolox\_base.py下面，导出模型的类别数和这里的num\_classes保持一致。

![image-20210918161656112][]

本例使用yolo\_s模型，yolox自带导出onnx的文件，直接执行命令：

python tools/export_onnx.py --output-name yolox_s.onnx -f  exps/default/yolox_s.py -c YOLOX_outputs/yolox_voc_s/best_ckpt.pth

export\_onnx.py

#!/usr/bin/env python3
    # -*- coding:utf-8 -*-
    # Copyright (c) Megvii, Inc. and its affiliates.
    
    import argparse
    import os
    from loguru import logger
    
    import torch
    from torch import nn
    
    from yolox.exp import get_exp
    from yolox.models.network_blocks import SiLU
    from yolox.utils import replace_module
    
    
    def make_parser():
        parser = argparse.ArgumentParser("YOLOX onnx deploy")
        parser.add_argument(
            "--output-name", type=str, default="yolox.onnx", help="output name of models"
        )
        parser.add_argument(
            "--input", default="images", type=str, help="input node name of onnx model"
        )
        parser.add_argument(
            "--output", default="output", type=str, help="output node name of onnx model"
        )
        parser.add_argument(
            "-o", "--opset", default=11, type=int, help="onnx opset version"
        )
        parser.add_argument("--batch-size", type=int, default=1, help="batch size")
        parser.add_argument(
            "--dynamic", action="store_true", help="whether the input shape should be dynamic or not"
        )
        parser.add_argument("--no-onnxsim", action="store_true", help="use onnxsim or not")
        parser.add_argument(
            "-f",
            "--exp_file",
            default=None,
            type=str,
            help="expriment description file",
        )
        parser.add_argument("-expn", "--experiment-name", type=str, default=None)
        parser.add_argument("-n", "--name", type=str, default=None, help="model name")
        parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt path")
        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()
        logger.info("args value: {}".format(args))
        exp = get_exp(args.exp_file, args.name)
        exp.merge(args.opts)
    
        if not args.experiment_name:
            args.experiment_name = exp.exp_name
    
        model = exp.get_model()
        if args.ckpt is None:
            file_name = os.path.join(exp.output_dir, args.experiment_name)
            ckpt_file = os.path.join(file_name, "best_ckpt.pth")
        else:
            ckpt_file = args.ckpt
    
        # load the model state dict
        ckpt = torch.load(ckpt_file, map_location="cpu")
    
        model.eval()
        if "model" in ckpt:
            ckpt = ckpt["model"]
        model.load_state_dict(ckpt)
        model = replace_module(model, nn.SiLU, SiLU)
        model.head.decode_in_inference = False
    
        logger.info("loading checkpoint done.")
        dummy_input = torch.randn(args.batch_size, 3, exp.test_size[0], exp.test_size[1])
    
        torch.onnx._export(
            model,
            dummy_input,
            args.output_name,
            input_names=[args.input],
            output_names=[args.output],
            dynamic_axes={ args.input: { 0: 'batch'},
                          args.output: { 0: 'batch'}} if args.dynamic else None,
            opset_version=args.opset,
        )
        logger.info("generated onnx model named {}".format(args.output_name))
    
        if not args.no_onnxsim:
            import onnx
    
            from onnxsim import simplify
    
            input_shapes = { args.input: list(dummy_input.shape)} if args.dynamic else None
            
            # use onnxsimplify to reduce reduent model.
            onnx_model = onnx.load(args.output_name)
            model_simp, check = simplify(onnx_model,
                                         dynamic_input_shape=args.dynamic,
                                         input_shapes=input_shapes)
            assert check, "Simplified ONNX model could not be validated"
            onnx.save(model_simp, args.output_name)
            logger.info("generated simplified onnx model named {}".format(args.output_name))
    
    
    if __name__ == "__main__":
        main()

[image-20210918161656112]: /images/20220828/b36a3414350e4332b277bf336247ce7d.png