如下所示：importioimporttorchimporttorch.onnxfrommodels.C3AEModelimportPlainC3AENetCBAMdevice=torch.device("cuda:0"
如下所示：
import io
import torch
import torch.onnx
from models.C3AEModel import PlainC3AENetCBAM
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def test():
model = PlainC3AENetCBAM()
pthfile = r'/home/joy/Projects/models/emotion/PlainC3AENet.pth'
loaded_model = torch.load(pthfile, map_location='cpu')
# try:
# loaded_model.eval()
# except AttributeError as error:
# print(error)
model.load_state_dict(loaded_model['state_dict'])
# model = model.to(device)
#data type nchw
dummy_input1 = torch.randn(1, 3, 64, 64)
# dummy_input2 = torch.randn(1, 3, 64, 64)
# dummy_input3 = torch.randn(1, 3, 64, 64)
input_names = [ "actual_input_1"]
output_names = [ "output1" ]
# torch.onnx.export(model, (dummy_input1, dummy_input2, dummy_input3), "C3AE.onnx", verbose=True, input_names=input_names, output_names=output_names)
torch.onnx.export(model, dummy_input1, "C3AE_emotion.onnx", verbose=True, input_names=input_names, output_names=output_names)
if __name__ == "__main__":
test()
直接将PlainC3AENetCBAM替换成需要转换的模型，然后修改pthfile，输入和onnx模型名字然后执行即可。
注意：上面代码中注释的dummy_input2，dummy_input3，torch.onnx.export对应的是多个输入的例子。
在转换过程中遇到的问题汇总
RuntimeError: Failed to export an ONNX attribute, since it's not constant, please try to make things (e.g., kernel size) static if possible
在转换过程中遇到RuntimeError: Failed to export an ONNX attribute, since it's not constant, please try to make things (e.g., kernel size) static if possible的错误。
根据报的错误日志信息打开/home/joy/.tensorflow/venv/lib/python3.6/site-packages/torch/onnx/symbolic_helper.py，在相应位置添加print之后，可以定位到具体哪个op出问题。
例如：
在相应位置添加
print（v.node()）输出信息如下：
%124 : Long() = onnx::Gather[axis=0](%122, %121), scope: PlainC3AENetCBAM/Bottleneck[cbam]/CBAM[cbam]/ChannelGate[ChannelGate] # /home/joy/Projects/models/emotion/WhatsTheemotion/models/cbam.py:46:0原因是pytorch中的tensor.size(1)方式onnx识别不了，需要修改成常量。
以上这篇Pytorch模型转onnx模型实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
Pytorch 模型 onnx