利用pytorch模型可视化以及参数计算

我们在设计完程序以后希望能对我们的模型进行可视化，pytorch这里似乎没有提供相应的包直接进行调用，参考一些博客，下面把代码贴出来：

import torch
from torch.autograd import Variable
import torch.nn as nn
from graphviz import Digraph
 
 
def make_dot(var, params=None):
   
    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
 

我们在我们的模型下面直接进行调用就可以了，例如：

if __name__ == "__main__":
    model = DeepLab(backbone='resnet', output_stride=16)
    input = torch.rand(1, 3, 53, 53)
    output = model(input)
    g = make_dot(output)
    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))

模型部分可视化结果：

                 

参数计算：