安裝graphviz
pip install graphviz
sudo apt install graphviz
dot -version
若安裝成功則會顯示:
Using device: dot:dot:core
The plugin configuration file:
/usr/lib/x86_64-linux-gnu/graphviz/config6a
was successfully loaded.
render : cairo dot dot_json fig gd json json0 map mp pic pov ps svg tk vml vrml xdot xdot_json
layout : circo dot fdp neato nop nop1 nop2 osage patchwork sfdp twopi
textlayout : textlayout
device : canon cmap cmapx cmapx_np dot dot_json eps fig gd gd2 gif gv imap imap_np ismap jpe jpeg jpg json json0 mp pdf pic plain plain-ext png pov ps ps2 svg svgz tk vml vmlz vrml wbmp x11 xdot xdot1.2 xdot1.4 xdot_json xlib
loadimage : (lib) eps gd gd2 gif jpe jpeg jpg png ps svg xbm
添加環境變量
export PATH=$PATH:/usr/lib/x86_64-linux-gnu/graphviz
生成網絡圖片
運行下面代碼
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchviz import make_dot
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Sequential( #input_size=(1*28*28)
nn.Conv2d(1, 6, 5, 1, 2),
nn.ReLU(), #(6*28*28)
nn.MaxPool2d(kernel_size=2, stride=2), #output_size=(6*14*14)
)
self.conv2 = nn.Sequential(
nn.Conv2d(6, 16, 5),
nn.ReLU(), #(16*10*10)
nn.MaxPool2d(2, 2) #output_size=(16*5*5)
)
self.fc1 = nn.Sequential(
nn.Linear(16 * 5 * 5, 120),
nn.ReLU()
)
self.fc2 = nn.Sequential(
nn.Linear(120, 84),
nn.ReLU()
)
self.fc3 = nn.Linear(84, 10)
# 定義前向傳播過程,輸入爲x
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
# nn.Linear()的輸入輸出都是維度爲一的值,所以要把多維度的tensor展平成一維
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
dummy_input = Variable(torch.rand(13, 1, 28, 28)) #假設輸入13張1*28*28的圖片
model = LeNet()
y = model(dummy_input)
g = make_dot(y)
g.render('LeNet_model', view=False)
會生成一個LeNet_model
和LeNet_model.pdf
,LeNet_model
即爲網絡圖:
參考:
pytorch 網絡結構可視化方法彙總(三種實現方法詳解)
ExecutableNotFound: failed to execute [‘dot’, ‘-Tsvg’], make sure the Graphviz executables are on yo