最近我自己的电脑上面尝试训练yolox_s和yolox_nano模型，使用的都是我自己的数据集，只有1类，以下博客就是我自己环境配置、训练还有遇到的问题的过程，这些我都写在了一个博客上面：
https://blog.csdn.net/ELSA001/article/details/120918082?spm=1001.2014.3001.5501
这些问题我都一一解决了，但是我想要把训练好的yolox_nano.pth模型转换成yolox_nano.onnx文件，然后转换成ncnn相关的文件来部署到安卓机上面，但是我在把yolox_nano.pth模型转换成yolox_nano.onnx文件的时候遇到了问题。
报错信息如下：

RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for YOLOX:
Missing key(s) in state_dict: "backbone.backbone.dark2.0.dconv.conv.weight", "backbone.backbone.dark2.0.dconv.bn.weight".....
Unexpected key(s) in state_dict: "backbone.backbone.dark2.0.conv.weight", "backbone.backbone.dark2.0.bn.weight"......

报错图片：
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完整报错信息如下：

(torch_G) E:\YOLOX>python tools/export_onnx.py -n yolox-nano -c weights/yolox_nano.pth --output-name weights/yolox_nano.onnx
2021-10-27 12:50:15.825 | INFO     | __main__:main:59 - args value: Namespace(batch_size=1, ckpt='weights/yolox_nano.pth', dynamic=False, exp_file=None, experiment_name=None, input='images', name='yolox-nano', no_onnxsim=False, opset=11, opts=[], output='output', output_name='weights/yolox_nano.onnx')
2021-10-27 12:50:16.038 | ERROR    | __main__:<module>:116 - An error has been caught in function '<module>', process 'MainProcess' (70928), thread 'MainThread' (70764):
Traceback (most recent call last):> File "tools\export_onnx.py", line 116, in <module>main()└ <function main at 0x000001A3D70D64C0>File "tools\export_onnx.py", line 79, in mainmodel.load_state_dict(ckpt)│     │               └ OrderedDict([('backbone.backbone.stem.conv.conv.weight', tensor([[[[ 1.2789e-02,  1.7050e-02,  2.4743e-02],│     │                           [ 6.535...│     └ <function Module.load_state_dict at 0x000001A3D64469D0>└ YOLOX((backbone): YOLOPAFPN((backbone): CSPDarknet((stem): Focus((conv): BaseConv((conv): ...File "E:\Anaconda3\envs\torch_G\lib\site-packages\torch\nn\modules\module.py", line 1223, in load_state_dictraise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(RuntimeError: Error(s) in loading state_dict for YOLOX:Missing key(s) in state_dict: "backbone.backbone.dark2.0.dconv.conv.weight", "backbone.backbone.dark2.0.dconv.bn.weight", "backbone.backbone.dark2.0.dconv.bn.bias", "backbone.backbone.dark2.0.dconv.bn.running_mean", "backbone.backbone.dark2.0.dconv.bn.running_var", "backbone.backbone.dark2.0.pconv.conv.weight", "backbone.backbone.dark2.0.pconv.bn.weight", "backbone.backbone.dark2.0.pconv.bn.bias", "backbone.backbone.dark2.0.pconv.bn.running_mean", "backbone.backbone.dark2.0.pconv.bn.running_var", "backbone.backbone.dark2.1.m.0.conv2.dconv.conv.weight", "backbone.backbone.dark2.1.m.0.conv2.dconv.bn.weight", "backbone.backbone.dark2.1.m.0.conv2.dconv.bn.bias", "backbone.backbone.dark2.1.m.0.conv2.dconv.bn.running_mean", "backbone.backbone.dark2.1.m.0.conv2.dconv.bn.running_var", "backbone.backbone.dark2.1.m.0.conv2.pconv.conv.weight", "backbone.backbone.dark2.1.m.0.conv2.pconv.bn.weight", "backbone.backbone.dark2.1.m.0.conv2.pconv.bn.bias", "backbone.backbone.dark2.1.m.0.conv2.pconv.bn.running_mean", "backbone.backbone.dark2.1.m.0.conv2.pconv.bn.running_var", "backbone.backbone.dark3.0.dconv.conv.weight", "backbone.backbone.dark3.0.dconv.bn.weight", "backbone.backbone.dark3.0.dconv.bn.bias", 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"head.reg_convs.1.1.pconv.conv.weight", "head.reg_convs.1.1.pconv.bn.weight", "head.reg_convs.1.1.pconv.bn.bias", "head.reg_convs.1.1.pconv.bn.running_mean", "head.reg_convs.1.1.pconv.bn.running_var", "head.reg_convs.2.0.dconv.conv.weight", "head.reg_convs.2.0.dconv.bn.weight", "head.reg_convs.2.0.dconv.bn.bias", "head.reg_convs.2.0.dconv.bn.running_mean", "head.reg_convs.2.0.dconv.bn.running_var", "head.reg_convs.2.0.pconv.conv.weight", "head.reg_convs.2.0.pconv.bn.weight", "head.reg_convs.2.0.pconv.bn.bias", "head.reg_convs.2.0.pconv.bn.running_mean", "head.reg_convs.2.0.pconv.bn.running_var", "head.reg_convs.2.1.dconv.conv.weight", "head.reg_convs.2.1.dconv.bn.weight", "head.reg_convs.2.1.dconv.bn.bias", "head.reg_convs.2.1.dconv.bn.running_mean", "head.reg_convs.2.1.dconv.bn.running_var", "head.reg_convs.2.1.pconv.conv.weight", "head.reg_convs.2.1.pconv.bn.weight", "head.reg_convs.2.1.pconv.bn.bias", "head.reg_convs.2.1.pconv.bn.running_mean", "head.reg_convs.2.1.pconv.bn.running_var".Unexpected key(s) in state_dict: "backbone.backbone.dark2.0.conv.weight", "backbone.backbone.dark2.0.bn.weight", "backbone.backbone.dark2.0.bn.bias", "backbone.backbone.dark2.0.bn.running_mean", "backbone.backbone.dark2.0.bn.running_var", "backbone.backbone.dark2.0.bn.num_batches_tracked", "backbone.backbone.dark2.1.m.0.conv2.conv.weight", "backbone.backbone.dark2.1.m.0.conv2.bn.weight", "backbone.backbone.dark2.1.m.0.conv2.bn.bias", "backbone.backbone.dark2.1.m.0.conv2.bn.running_mean", "backbone.backbone.dark2.1.m.0.conv2.bn.running_var", "backbone.backbone.dark2.1.m.0.conv2.bn.num_batches_tracked", "backbone.backbone.dark3.0.conv.weight", "backbone.backbone.dark3.0.bn.weight", "backbone.backbone.dark3.0.bn.bias", "backbone.backbone.dark3.0.bn.running_mean", "backbone.backbone.dark3.0.bn.running_var", "backbone.backbone.dark3.0.bn.num_batches_tracked", "backbone.backbone.dark3.1.m.0.conv2.conv.weight", "backbone.backbone.dark3.1.m.0.conv2.bn.weight", "backbone.backbone.dark3.1.m.0.conv2.bn.bias", "backbone.backbone.dark3.1.m.0.conv2.bn.running_mean", "backbone.backbone.dark3.1.m.0.conv2.bn.running_var", "backbone.backbone.dark3.1.m.0.conv2.bn.num_batches_tracked", "backbone.backbone.dark3.1.m.1.conv2.conv.weight", "backbone.backbone.dark3.1.m.1.conv2.bn.weight", "backbone.backbone.dark3.1.m.1.conv2.bn.bias", "backbone.backbone.dark3.1.m.1.conv2.bn.running_mean", "backbone.backbone.dark3.1.m.1.conv2.bn.running_var", "backbone.backbone.dark3.1.m.1.conv2.bn.num_batches_tracked", "backbone.backbone.dark3.1.m.2.conv2.conv.weight", "backbone.backbone.dark3.1.m.2.conv2.bn.weight", "backbone.backbone.dark3.1.m.2.conv2.bn.bias", "backbone.backbone.dark3.1.m.2.conv2.bn.running_mean", "backbone.backbone.dark3.1.m.2.conv2.bn.running_var", "backbone.backbone.dark3.1.m.2.conv2.bn.num_batches_tracked", "backbone.backbone.dark4.0.conv.weight", "backbone.backbone.dark4.0.bn.weight", "backbone.backbone.dark4.0.bn.bias", "backbone.backbone.dark4.0.bn.running_mean", "backbone.backbone.dark4.0.bn.running_var", "backbone.backbone.dark4.0.bn.num_batches_tracked", "backbone.backbone.dark4.1.m.0.conv2.conv.weight", "backbone.backbone.dark4.1.m.0.conv2.bn.weight", "backbone.backbone.dark4.1.m.0.conv2.bn.bias", "backbone.backbone.dark4.1.m.0.conv2.bn.running_mean", "backbone.backbone.dark4.1.m.0.conv2.bn.running_var", "backbone.backbone.dark4.1.m.0.conv2.bn.num_batches_tracked", "backbone.backbone.dark4.1.m.1.conv2.conv.weight", "backbone.backbone.dark4.1.m.1.conv2.bn.weight", "backbone.backbone.dark4.1.m.1.conv2.bn.bias", "backbone.backbone.dark4.1.m.1.conv2.bn.running_mean", "backbone.backbone.dark4.1.m.1.conv2.bn.running_var", "backbone.backbone.dark4.1.m.1.conv2.bn.num_batches_tracked", "backbone.backbone.dark4.1.m.2.conv2.conv.weight", "backbone.backbone.dark4.1.m.2.conv2.bn.weight", "backbone.backbone.dark4.1.m.2.conv2.bn.bias", "backbone.backbone.dark4.1.m.2.conv2.bn.running_mean", "backbone.backbone.dark4.1.m.2.conv2.bn.running_var", "backbone.backbone.dark4.1.m.2.conv2.bn.num_batches_tracked", "backbone.backbone.dark5.0.conv.weight", "backbone.backbone.dark5.0.bn.weight", "backbone.backbone.dark5.0.bn.bias", "backbone.backbone.dark5.0.bn.running_mean", "backbone.backbone.dark5.0.bn.running_var", "backbone.backbone.dark5.0.bn.num_batches_tracked", "backbone.backbone.dark5.2.m.0.conv2.conv.weight", "backbone.backbone.dark5.2.m.0.conv2.bn.weight", "backbone.backbone.dark5.2.m.0.conv2.bn.bias", "backbone.backbone.dark5.2.m.0.conv2.bn.running_mean", "backbone.backbone.dark5.2.m.0.conv2.bn.running_var", "backbone.backbone.dark5.2.m.0.conv2.bn.num_batches_tracked", "backbone.C3_p4.m.0.conv2.conv.weight", "backbone.C3_p4.m.0.conv2.bn.weight", "backbone.C3_p4.m.0.conv2.bn.bias", "backbone.C3_p4.m.0.conv2.bn.running_mean", "backbone.C3_p4.m.0.conv2.bn.running_var", "backbone.C3_p4.m.0.conv2.bn.num_batches_tracked", "backbone.C3_p3.m.0.conv2.conv.weight", "backbone.C3_p3.m.0.conv2.bn.weight", "backbone.C3_p3.m.0.conv2.bn.bias", "backbone.C3_p3.m.0.conv2.bn.running_mean", "backbone.C3_p3.m.0.conv2.bn.running_var", "backbone.C3_p3.m.0.conv2.bn.num_batches_tracked", "backbone.bu_conv2.conv.weight", "backbone.bu_conv2.bn.weight", "backbone.bu_conv2.bn.bias", "backbone.bu_conv2.bn.running_mean", "backbone.bu_conv2.bn.running_var", "backbone.bu_conv2.bn.num_batches_tracked", "backbone.C3_n3.m.0.conv2.conv.weight", "backbone.C3_n3.m.0.conv2.bn.weight", "backbone.C3_n3.m.0.conv2.bn.bias", "backbone.C3_n3.m.0.conv2.bn.running_mean", "backbone.C3_n3.m.0.conv2.bn.running_var", "backbone.C3_n3.m.0.conv2.bn.num_batches_tracked", "backbone.bu_conv1.conv.weight", "backbone.bu_conv1.bn.weight", "backbone.bu_conv1.bn.bias", "backbone.bu_conv1.bn.running_mean", "backbone.bu_conv1.bn.running_var", "backbone.bu_conv1.bn.num_batches_tracked", "backbone.C3_n4.m.0.conv2.conv.weight", "backbone.C3_n4.m.0.conv2.bn.weight", "backbone.C3_n4.m.0.conv2.bn.bias", "backbone.C3_n4.m.0.conv2.bn.running_mean", "backbone.C3_n4.m.0.conv2.bn.running_var", "backbone.C3_n4.m.0.conv2.bn.num_batches_tracked", "head.cls_convs.0.0.conv.weight", "head.cls_convs.0.0.bn.weight", "head.cls_convs.0.0.bn.bias", "head.cls_convs.0.0.bn.running_mean", "head.cls_convs.0.0.bn.running_var", "head.cls_convs.0.0.bn.num_batches_tracked", "head.cls_convs.0.1.conv.weight", "head.cls_convs.0.1.bn.weight", "head.cls_convs.0.1.bn.bias", "head.cls_convs.0.1.bn.running_mean", "head.cls_convs.0.1.bn.running_var", "head.cls_convs.0.1.bn.num_batches_tracked", "head.cls_convs.1.0.conv.weight", "head.cls_convs.1.0.bn.weight", "head.cls_convs.1.0.bn.bias", "head.cls_convs.1.0.bn.running_mean", "head.cls_convs.1.0.bn.running_var", "head.cls_convs.1.0.bn.num_batches_tracked", "head.cls_convs.1.1.conv.weight", "head.cls_convs.1.1.bn.weight", "head.cls_convs.1.1.bn.bias", "head.cls_convs.1.1.bn.running_mean", "head.cls_convs.1.1.bn.running_var", "head.cls_convs.1.1.bn.num_batches_tracked", "head.cls_convs.2.0.conv.weight", "head.cls_convs.2.0.bn.weight", "head.cls_convs.2.0.bn.bias", "head.cls_convs.2.0.bn.running_mean", "head.cls_convs.2.0.bn.running_var", "head.cls_convs.2.0.bn.num_batches_tracked", "head.cls_convs.2.1.conv.weight", "head.cls_convs.2.1.bn.weight", "head.cls_convs.2.1.bn.bias", "head.cls_convs.2.1.bn.running_mean", "head.cls_convs.2.1.bn.running_var", "head.cls_convs.2.1.bn.num_batches_tracked", "head.reg_convs.0.0.conv.weight", "head.reg_convs.0.0.bn.weight", "head.reg_convs.0.0.bn.bias", "head.reg_convs.0.0.bn.running_mean", "head.reg_convs.0.0.bn.running_var", "head.reg_convs.0.0.bn.num_batches_tracked", "head.reg_convs.0.1.conv.weight", "head.reg_convs.0.1.bn.weight", "head.reg_convs.0.1.bn.bias", "head.reg_convs.0.1.bn.running_mean", "head.reg_convs.0.1.bn.running_var", "head.reg_convs.0.1.bn.num_batches_tracked", "head.reg_convs.1.0.conv.weight", "head.reg_convs.1.0.bn.weight", "head.reg_convs.1.0.bn.bias", "head.reg_convs.1.0.bn.running_mean", "head.reg_convs.1.0.bn.running_var", "head.reg_convs.1.0.bn.num_batches_tracked", "head.reg_convs.1.1.conv.weight", "head.reg_convs.1.1.bn.weight", "head.reg_convs.1.1.bn.bias", "head.reg_convs.1.1.bn.running_mean", "head.reg_convs.1.1.bn.running_var", "head.reg_convs.1.1.bn.num_batches_tracked", "head.reg_convs.2.0.conv.weight", "head.reg_convs.2.0.bn.weight", "head.reg_convs.2.0.bn.bias", "head.reg_convs.2.0.bn.running_mean", "head.reg_convs.2.0.bn.running_var", "head.reg_convs.2.0.bn.num_batches_tracked", "head.reg_convs.2.1.conv.weight", "head.reg_convs.2.1.bn.weight", "head.reg_convs.2.1.bn.bias", "head.reg_convs.2.1.bn.running_mean", "head.reg_convs.2.1.bn.running_var", "head.reg_convs.2.1.bn.num_batches_tracked".

这些报错信息都是我在没有改变export_onnx.py时候的报错，我之前还把官网上面的80类的coco数据集的yolox_nano.pth模型转换成yolox_nano.onnx文件，但是可以成功转换：
在这里插入图片描述
在这里插入图片描述
而我已经修改了yolox_base.py里面的num_classes、depth、width还有input_size这些：
在这里插入图片描述
但是还是不能转换！
我看了网上很多教程，发现可以使用replace操作把权重文件的backbone.去掉，然后我操作了一下export_onnx.py这个文件：
把它改成这样：
在这里插入图片描述
但是依然有报错，只是稍微不一样了而已：
在这里插入图片描述
在这里插入图片描述
我发现这新的错误里面的有些变了，有些却没有变。
我训练好的的yolox_nano.pth是没问题的，可以正常预测（推理）：
在这里插入图片描述
在这里插入图片描述
我认为是我使用自己的1类的数据集加上单卡训练加上没有使用半精度训练的原因，不过我之前还试过直接把model. load_state_dict(ckpt)这个代码直接注释掉：
在这里插入图片描述
这个model. load_state_dict(ckpt)的代码应该是加载训练好的模型的，但是我没有使用这个然后进行转换，发现可以转换成功：
在这里插入图片描述
在这里插入图片描述
在这里插入图片描述
onnx文件打开看发现也没问题：
最后我也成功转换了ncnn文件：
在这里插入图片描述
但最后部署到安卓上面的时候就出问题了，完全检测不到：
在这里插入图片描述
我认为这明显就是.pth文件转换.onnx文件的时候出了问题，因为我完全就是没有加载ckpt模型。
我在这里卡了好长时间，希望有过使用自己的数据集训练过yolox_nano.pth模型来部署到安卓上面的大佬可以教我一下，十分感谢，这个部署对我来说很重要。

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