画pytorch模型图,以及参数计算的方法

import torchfrom torch.autograd import Variableimport torch.nn as nnfrom graphviz import Digraph  class CNN(nn.Module):  def __init__(self):    super(CNN, self).__init__()    self.conv1 = nn.Sequential(      nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2),      nn.ReLU(),      nn.MaxPool2d(kernel_size=2)    )    self.conv2 = nn.Sequential(      nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),      nn.ReLU(),      nn.MaxPool2d(kernel_size=2)    )    self.out = nn.Linear(32*7*7, 10)   def forward(self, x):    x = self.conv1(x)    x = self.conv2(x)    x = x.view(x.size(0), -1) # (batch, 32*7*7)    out = self.out(x)    return out  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  if __name__ == '__main__':  net = CNN()  x = Variable(torch.randn(1, 1, 28, 28))  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)) 

  net = CNN()  x = Variable(torch.randn(1, 1, 28, 28))  y = net(x)  g = make_dot(y)  g.view()

该层的结构：[16, 1, 5, 5]该层参数和：400该层的结构：[16]该层参数和：16该层的结构：[32, 16, 5, 5]该层参数和：12800该层的结构：[32]该层参数和：32该层的结构：[10, 1568]该层参数和：15680该层的结构：[10]该层参数和：10总参数数量和：28938