pytorch 模型可视化的例子

如下所示：

一. 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 = '%sn %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

二. 使用步骤

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

三. 效果展示

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