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最简单的方法当然可以直接print(net),但是这样网络比较复杂的时候效果不太好,看着比较乱;以前使用caffe的时候有一个网站可以在线生成网络框图,tensorflow可以用tensor board,keras中可以用model.summary()、或者plot_model()。pytorch没有这样的API,但是可以用代码来完成。
(1)安装环境:graphviz
conda install -n pytorch python-graphviz
或:
sudo apt-get install graphviz
或者从官网下载,按此好代码教程。
(2)生成网络结构的代码:
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)打印网络结构:
import torch from torch.autograd import Variable import torch.nn as nn from graphviz import Digraph class CNN(nn.module): def __init__(self): ****** def forward(self,x): ****** return out ***************************** def make_dot(): #复制上面的代码 ***************************** if __name__ == '__main__': net = CNN() x = Variable(torch.randn(1, 1, 1024,1024)) y = net(x) g = make_dot(y) g.view() params = list(net.parameters()) k = 0 for i in params: l = 1 print("该层的结构:" + str(list(i.size()))) for j in i.size(): l *= j print("该层参数和:" + str(l)) k = k + l print("总参数数量和:" + str(k))
(4)结果展示(例如这是一个resnet block类型的网络):
以上这篇pytorch打印网络结构的实例就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。