• 2024年4月27日14:32:30----0.9.2

参照模型

以pytorch官网的tutorial为观察对象，链接是https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html

模型代码如下

python">import torch.nn as nn
import torch.nn.functional as Fclass RNN(nn.Module):def __init__(self, input_size, hidden_size, output_size):super(RNN, self).__init__()self.hidden_size = hidden_sizeself.i2h = nn.Linear(input_size, hidden_size)self.h2h = nn.Linear(hidden_size, hidden_size)self.h2o = nn.Linear(hidden_size, output_size)self.softmax = nn.LogSoftmax(dim=1)def forward(self, input, hidden):hidden = F.tanh(self.i2h(input) + self.h2h(hidden))output = self.h2o(hidden)output = self.softmax(output)return output, hiddendef initHidden(self):return torch.zeros(1, self.hidden_size)n_hidden = 128
rnn = RNN(n_letters, n_hidden, n_categories)

安装Netron

pip install netron即可

其他安装方式参考链接
https://blog.csdn.net/m0_49963403/article/details/136242313

写netron代码

随便找一个地方打个点，如sample方法中

python">import netron
max_length = 20# Sample from a category and starting letter
def sample(category, start_letter='A'):with torch.no_grad():  # no need to track history in samplingcategory_tensor = categoryTensor(category)input = inputTensor(start_letter)hidden = rnn.initHidden()output_name = start_letterfor i in range(max_length):
#             print("category_tensor",category_tensor.size())
#             print("input[0]",input[0].size())
#             print("hidden",hidden.size())output, hidden = rnn(category_tensor, input[0], hidden)torch.onnx.export(rnn,(category_tensor, input[0], hidden) , f='AlexNet1.onnx')   #导出 .onnx 文件netron.start('AlexNet1.onnx') #展示结构图break
#             print("output",output.size())
#             print("hidden",hidden.size())
#             print("====================")topv, topi = output.topk(1)topi = topi[0][0]if topi == n_letters - 1:breakelse:letter = all_letters[topi]output_name += letterinput = inputTensor(letter)return output_name# Get multiple samples from one category and multiple starting letters
def samples(category, start_letters='ABC'):for start_letter in start_letters:print(sample(category, start_letter))breaksamples('Russian', 'RUS')

运行查看结果

结果是在浏览器中，运行成功后会显示：
Serving ‘AlexNet.onnx’ at http://localhost:8080

打开这个网页就可以看见模型结构，如下图

需要关注的地方

1. 关于参数
   如果模型是一个参数的情况下，如下使用就可以了

python">import torch
from torchvision.models import AlexNet
import netron
model = AlexNet()
input = torch.ones((1,3,224,224))
torch.onnx.export(model, input, f='AlexNet.onnx')
netron.start('AlexNet.onnx')

如果模型有多个参数的情况下，则需要如下用括号括起来，如本文中的例子

python">torch.onnx.export(rnn,(category_tensor, input[0], hidden) , f='AlexNet1.onnx')   #导出 .onnx 文件
netron.start('AlexNet1.onnx') #展示结构图

2. 如果运行过程中发现报错找不到模型
   有可能是你手动删除了生成的模型，最好的方法是重新生成这个模型，再运行