本文對(duì)比一下兩種pth文件轉(zhuǎn)為onnx的區(qū)別以及onnx文件在NETRON中的圖

只有參數(shù)的pth文件：cat_dog.pth

既有參數(shù)又有模型結(jié)構(gòu)的pth文件：cat_dog_model_args.pth

既有參數(shù)又有模型結(jié)構(gòu)的onnx文件：cat_dog_model_args.onnx

cat_dog_model.pth 在NETRON中的圖（無(wú)網(wǎng)絡(luò)架構(gòu)）

由于沒(méi)有網(wǎng)絡(luò)結(jié)構(gòu)，所以不能通過(guò)代碼將其轉(zhuǎn)為onnx文件

cat_dog_model_args.pth 在NETRON中的圖

cat_dog_model_args.onnx在NETRON中的圖

先將cat_dog_model_args.pth 轉(zhuǎn)為cat_dog_model_args.onnx
代碼：

import torch import torchvision dummy_input = torch.randn(1, 3, 224, 224) model = torch.load('D:\***\swin_transformer_flower\cat_dog_model_args.pth') model.eval() input_names = ["input"] output_names = ["output"] torch.onnx.export(model,dummy_input,"cat_dog_model_args.onnx",verbose=True,input_names=input_names,output_names=output_names)

運(yùn)行以上代碼
輸出

graph(%input : Float(1:150528, 3:50176, 224:224, 224:1, requires_grad=0, device=cpu),%features.0.weight : Float(64:27, 3:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.0.bias : Float(64:1, requires_grad=0, device=cpu),%features.2.weight : Float(64:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.2.bias : Float(64:1, requires_grad=0, device=cpu),%features.5.weight : Float(128:576, 64:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.5.bias : Float(128:1, requires_grad=0, device=cpu),%features.7.weight : Float(128:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.7.bias : Float(128:1, requires_grad=0, device=cpu),%features.10.weight : Float(256:1152, 128:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.10.bias : Float(256:1, requires_grad=0, device=cpu),%features.12.weight : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.12.bias : Float(256:1, requires_grad=0, device=cpu),%features.14.weight : Float(256:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.14.bias : Float(256:1, requires_grad=0, device=cpu),%features.17.weight : Float(512:2304, 256:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.17.bias : Float(512:1, requires_grad=0, device=cpu),%features.19.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.19.bias : Float(512:1, requires_grad=0, device=cpu),%features.21.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.21.bias : Float(512:1, requires_grad=0, device=cpu),%features.24.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.24.bias : Float(512:1, requires_grad=0, device=cpu),%features.26.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.26.bias : Float(512:1, requires_grad=0, device=cpu),%features.28.weight : Float(512:4608, 512:9, 3:3, 3:1, requires_grad=0, device=cpu),%features.28.bias : Float(512:1, requires_grad=0, device=cpu),%classifier.0.weight : Float(100:25088, 25088:1, requires_grad=1, device=cpu),%classifier.0.bias : Float(100:1, requires_grad=1, device=cpu),%classifier.3.weight : Float(2:100, 100:1, requires_grad=1, device=cpu),%classifier.3.bias : Float(2:1, requires_grad=1, device=cpu)):%31 : Float(1:3211264, 64:50176, 224:224, 224:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%input, %features.0.weight, %features.0.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%32 : Float(1:3211264, 64:50176, 224:224, 224:1, requires_grad=0, device=cpu) = onnx::Relu(%31) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%33 : Float(1:3211264, 64:50176, 224:224, 224:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%32, %features.2.weight, %features.2.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%34 : Float(1:3211264, 64:50176, 224:224, 224:1, requires_grad=0, device=cpu) = onnx::Relu(%33) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%35 : Float(1:802816, 64:12544, 112:112, 112:1, requires_grad=0, device=cpu) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%34) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:586:0%36 : Float(1:1605632, 128:12544, 112:112, 112:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%35, %features.5.weight, %features.5.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%37 : Float(1:1605632, 128:12544, 112:112, 112:1, requires_grad=0, device=cpu) = onnx::Relu(%36) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%38 : Float(1:1605632, 128:12544, 112:112, 112:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%37, %features.7.weight, %features.7.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%39 : Float(1:1605632, 128:12544, 112:112, 112:1, requires_grad=0, device=cpu) = onnx::Relu(%38) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%40 : Float(1:401408, 128:3136, 56:56, 56:1, requires_grad=0, device=cpu) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%39) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:586:0%41 : Float(1:802816, 256:3136, 56:56, 56:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%40, %features.10.weight, %features.10.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%42 : Float(1:802816, 256:3136, 56:56, 56:1, requires_grad=0, device=cpu) = onnx::Relu(%41) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%43 : Float(1:802816, 256:3136, 56:56, 56:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%42, %features.12.weight, %features.12.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%44 : Float(1:802816, 256:3136, 56:56, 56:1, requires_grad=0, device=cpu) = onnx::Relu(%43) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%45 : Float(1:802816, 256:3136, 56:56, 56:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%44, %features.14.weight, %features.14.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%46 : Float(1:802816, 256:3136, 56:56, 56:1, requires_grad=0, device=cpu) = onnx::Relu(%45) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%47 : Float(1:200704, 256:784, 28:28, 28:1, requires_grad=0, device=cpu) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%46) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:586:0%48 : Float(1:401408, 512:784, 28:28, 28:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%47, %features.17.weight, %features.17.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%49 : Float(1:401408, 512:784, 28:28, 28:1, requires_grad=0, device=cpu) = onnx::Relu(%48) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%50 : Float(1:401408, 512:784, 28:28, 28:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%49, %features.19.weight, %features.19.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%51 : Float(1:401408, 512:784, 28:28, 28:1, requires_grad=0, device=cpu) = onnx::Relu(%50) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%52 : Float(1:401408, 512:784, 28:28, 28:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%51, %features.21.weight, %features.21.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%53 : Float(1:401408, 512:784, 28:28, 28:1, requires_grad=0, device=cpu) = onnx::Relu(%52) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%54 : Float(1:100352, 512:196, 14:14, 14:1, requires_grad=0, device=cpu) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%53) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:586:0%55 : Float(1:100352, 512:196, 14:14, 14:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%54, %features.24.weight, %features.24.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%56 : Float(1:100352, 512:196, 14:14, 14:1, requires_grad=0, device=cpu) = onnx::Relu(%55) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%57 : Float(1:100352, 512:196, 14:14, 14:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%56, %features.26.weight, %features.26.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%58 : Float(1:100352, 512:196, 14:14, 14:1, requires_grad=0, device=cpu) = onnx::Relu(%57) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%59 : Float(1:100352, 512:196, 14:14, 14:1, requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%58, %features.28.weight, %features.28.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\modules\conv.py:420:0%60 : Float(1:100352, 512:196, 14:14, 14:1, requires_grad=0, device=cpu) = onnx::Relu(%59) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1134:0%61 : Float(1:25088, 512:49, 7:7, 7:1, requires_grad=0, device=cpu) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%60) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:586:0%62 : Float(1:25088, 512:49, 7:7, 7:1, requires_grad=0, device=cpu) = onnx::AveragePool[kernel_shape=[1, 1], strides=[1, 1]](%61) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:936:0%63 : Float(1:25088, 25088:1, requires_grad=0, device=cpu) = onnx::Flatten[axis=1](%62) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torchvision\models\vgg.py:45:0%64 : Float(1:100, 100:1, requires_grad=1, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%63, %classifier.0.weight, %classifier.0.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1690:0%65 : Float(1:100, 100:1, requires_grad=1, device=cpu) = onnx::Relu(%64) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:983:0%output : Float(1:2, 2:1, requires_grad=1, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1](%65, %classifier.3.weight, %classifier.3.bias) # C:\Users\deep\anaconda3\envs\swin\lib\site-packages\torch\nn\functional.py:1690:0return (%output)Process finished with exit code 0

圖片居中方法：
參考:CSDN博客文章中圖片居中
即只需要在圖片下方代碼頁(yè)最后加上#pic_center即可

總結(jié)

以上是生活随笔為你收集整理的使用onnx包将pth文件转换为onnx文件的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺(jué)得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。