graphviz类似于excel,想要使用它需要先进行本地的安装。
官方网站
可以选择安装
msi或者zip,我在win8.1下使用msi安装失败,所以下载的zip版本,然后把bin文件添加进系统路径,供python调用
然后需要在python的环境中安装graphviz-2.38-hfd603c8_2.tar.bz2,具体可以在清华的镜像服务器中找到
https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/win-64/
现在之后利用 conda install --use-local path 进行本地的安装即可
生成pytorch的结构
visualize.py
from graphviz import Digraph
import torch
from torch.autograd import Variable
import grpc
def make_dot(var, params=None):
""" Produces Graphviz representation of PyTorch autograd graph
Blue nodes are the Variables that require grad, orange are Tensors
saved for backward in torch.autograd.Function
Args:
var: output Variable
params: dict of (name, Variable) to add names to node that
require grad (TODO: make optional)
"""
if params is not None:
assert isinstance(params.values()[0], Variable)
param_map = {id(v): k for k, v in params.items()}
node_attr = dict(style='filled',
shape='box',
align='left',
fontsize='12',
ranksep='0.1',
height='0.2')
dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))
seen = set()
def size_to_str(size):
return '('+(', ').join(['%d' % v for v in size])+')'
def add_nodes(var):
if var not in seen:
if torch.is_tensor(var):
dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')
elif hasattr(var, 'variable'):
u = var.variable
name = param_map[id(u)] if params is not None else ''
node_name = '%s\n %s' % (name, size_to_str(u.size()))
dot.node(str(id(var)), node_name, fillcolor='lightblue')
else:
dot.node(str(id(var)), str(type(var).__name__))
seen.add(var)
if hasattr(var, 'next_functions'):
for u in var.next_functions:
if u[0] is not None:
dot.edge(str(id(u[0])), str(id(var)))
add_nodes(u[0])
if hasattr(var, 'saved_tensors'):
for t in var.saved_tensors:
dot.edge(str(id(t)), str(id(var)))
add_nodes(t)
add_nodes(var.grad_fn)
return dot
if __name__ == '__main__':
dot = Digraph(comment='The Round Table')
# 添加圆点 A, A的标签是 King Arthur
dot.node('A', 'king')
dot.view() # 后面这句就注释了,也可以使用这个命令查看效果
调用
my_res = resnet18(input_channels=3)
board_state = torch.rand(1, 3, 70, 66)
resout = my_res(board_state)
g = make_dot(resout)
g.render('convLstm3_resnet18', view=True)