pytorch 模型可视化的例子

如下所示：

一. visualize.py

from graphviz import Digraph
import torch
from torch.autograd import Variable

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

二. 使用步骤

import torch
from torch.autograd import Variable
from models import *
from visualize import make_dot
x = Variable(torch.rand(1, 3, 256, 256))
model = GeneratorUNet()
y = model(x)
g = make_dot(y)
g.view()

三. 效果展示

来源：https://blog.csdn.net/weixin_42445501/article/details/81221362