pytorch显示网络结构

转发：https://blog.csdn.net/gyguo95/article/details/78821617

首先要安装

graphviz

• 这种方法需要安装python-graphviz: 
  conda install -n pytorch python-graphviz

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
 %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

　　

import ResNet34
import numpy as np
import torch
from torch.autograd import Variable
from visualize import make_dot
from ResNet34 import NetG
import torch as t

class Config(object):

    nz = 500 # 噪声维度
    ngf = 64  # 生成器feature map数
    ndf = 64  # 判别器feature map数
    gen_search_num = 3  # 从512张生成的图片中保存最好的64张
    g_every = 5  # 每5个batch训练一次生成器
    gen_mean = 0  # 噪声的均值
    gen_std = 2  # 噪声的方差
    gen_num = 1
    batch_size = 256
    gpu = False  # 是否使用GPU
    gen_img = '2018.png'

if __name__ == '__main__':

    opt = Config()
    a = NetG(opt)
    noises = t.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std)
    noises = Variable(noises, volatile=True)
    y = a(noises)
    print(y.size())
    g = make_dot(y)
    g.view()
    #g.render('here', view=False)