使用netron工具可视化pytorch模型_netron identity

netron是微软小哥lutzroeder的一个广受好评的开源项目，地址https://github.com/lutzroeder/Netro

可惜，默认支持的格式中并不包括pytorch，可能当年小哥面试facebook被拒了，:)

Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Core ML (.mlmodel), Caffe(.caffemodel, .prototxt), Caffe2 (predict_net.pb, predict_net.pbtxt), MXNet (.model, -symbol.json), NCNN (.param) and TensorFlow Lite (.tflite).

1. 安装netron

pip install netron

2. 测试代码

由于不支持默认的pytorch模型格式(.pth)，因此需要存为onnx，庆幸pytorch支持！

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
 
import netron
 
 
class model(nn.Module):
    def __init__(self):
        super(model, self).__init__()
        self.block1 = nn.Sequential(
            nn.Conv2d(64, 64, 3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 32, 1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, 3, padding=1, bias=False),
            nn.BatchNorm2d(64)
        )
 
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1, bias=False)
        self.output = nn.Sequential(
            nn.Conv2d(64, 1, 3, padding=1, bias=True),
            nn.Sigmoid()
        )
 
    def forward(self, x):
        x = self.conv1(x)
        identity = x
        x = F.relu(self.block1(x) + identity)
        x = self.output(x)
        return x
 
 
d = torch.rand(1, 3, 416, 416)
m = model()
o = m(d)
 
onnx_path = "onnx_model_name.onnx"
torch.onnx.export(m, d, onnx_path)
 
netron.start(onnx_path)
 
 
#############################################################或者
 
import torch.onnx
 
import netron
 
d = torch.rand(1, 3, 224, 224,dtype=torch.float)
m = model_ft
o = model_ft(d)
 
onnx_path = "onnx_model_name.onnx"
torch.onnx.export(m, d, onnx_path)
 
netron.start(onnx_path)

3. 结果

执行上面代码后，会调用本地浏览器打开，形式和tensorboard差不多

Serving 'onnx_model_name.onnx' at http://localhost:8080