可视化工具Graphviz

一.安装
Graphviz http://www.graphviz.org/
mac用户建议直接用homebrew来安装，官网上版本比较旧

1.安装homebrew
打开终端复制、粘贴以下命令：
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

2.安装Graphviz
homebrew安装完毕后运行 brew install graphviz即可

安装完后事例代码：

import torchvision.models as models
import torch
from torchsummary import summary
from torch.autograd import Variable
import torch
from torch.autograd import Variable
from graphviz import Digraph
import os

def make_dot(var, params=None):
    if params is not None:
        assert all(isinstance(p, Variable) for p in params.values())
        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]) + ')'

    output_nodes = (var.grad_fn,) if not isinstance(var, tuple) else tuple(v.grad_fn for v in var)

    def add_nodes(var):
        if var not in seen:
            if torch.is_tensor(var):
                # note: this used to show .saved_tensors in pytorch0.2, but stopped
                # working as it was moved to ATen and Variable-Tensor merged
                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')
            elif var in output_nodes:
                dot.node(str(id(var)), str(type(var).__name__), fillcolor='darkolivegreen1')
            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)

    if isinstance(var, tuple):
        for v in var:
            add_nodes(v.grad_fn)
    else:
        add_nodes(var.grad_fn)
    return dot

if __name__=="__main__":

    os.environ["PATH"] += os.pathsep + '/Library/Python/2.7/site-packages'

    ## visual model
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model = models.vgg()
    model =model.to(device)

    x = Variable(torch.randn(1, 3, 224,224))
    vis_graph = make_dot(model(x), params=dict(model.named_parameters()))
    vis_graph.view()

3、安装python的对应的包 sudo pip install graphviz
用pip安装的Graphviz,但是Graphviz不是一个python tool，你仍然需要安装GraphViz‘s executables. 查阅资料后发现,原来我没有安装GraphViz‘s executables

显示网络结构的工具torchsummary

sudo pip install torchsummary 进行安装