可視化工具Graphviz

一.安裝
Graphviz http://www.graphviz.org/
mac用戶建議直接用homebrew來安裝骡澈，官網(wǎng)上版本比較舊

1.安裝homebrew
打開終端復(fù)制、粘貼以下命令：
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

2.安裝Graphviz
homebrew安裝完畢后運行 brew install graphviz即可

安裝完后事例代碼：

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储狭、安裝python的對應(yīng)的包 sudo pip install graphviz
用pip安裝的Graphviz,但是Graphviz不是一個python tool，你仍然需要安裝GraphViz‘s executables. 查閱資料后發(fā)現(xiàn),原來我沒有安裝GraphViz‘s executables

顯示網(wǎng)絡(luò)結(jié)構(gòu)的工具torchsummary

sudo pip install torchsummary 進行安裝