pytorch 模型可视化的例子

from graphviz import Digraphimport torchfrom 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/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

import torchfrom torch.autograd import Variablefrom models import *from visualize import make_dotx = Variable(torch.rand(1, 3, 256, 256))model = GeneratorUNet()y = model(x)g = make_dot(y)g.view()