pytorch网络结构可视化

• 1.首先安装包python-Graphviz:

conda install -n pytorch python-graphviz

• 2.保存以下代码到自己的项目路径，并保存为：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

• 3.使用方法：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

class simpleconv3(nn.Module):
    def __init__(self):
        super(simpleconv3,self).__init__()
        self.conv1 = nn.Conv2d(3, 12, 3, 2)
        self.bn1 = nn.BatchNorm2d(12)
        self.conv2 = nn.Conv2d(12, 24, 3, 2)
        self.bn2 = nn.BatchNorm2d(24)
        self.conv3 = nn.Conv2d(24, 48, 3, 2)
        self.bn3 = nn.BatchNorm2d(48)
        self.fc1 = nn.Linear(48 * 5 * 5 , 1200)
        self.fc2 = nn.Linear(1200 , 128)
        self.fc3 = nn.Linear(128 , 2)

    def forward(self , x):
        x = F.relu(self.bn1(self.conv1(x)))
        #print "bn1 shape",x.shape
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        x = x.view(-1 , 48 * 5 * 5) 
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

if __name__ == '__main__':
    import torch
    from torch.autograd import Variable
    from visualize import  make_dot
    x = Variable(torch.randn(1,3,48,48))
    model = simpleconv3()
    y = model(x)
    print(y.data)
    g = make_dot(y)
#     g.view()
    g.render('simpleconv3Visualize', view=True)

打印结果：

simpleconv3Visualize.pdf