(一)pytorch視覺化
阿新 • • 發佈:2018-12-09
import torch from torch.autograd import Variable import torch.nn as nn from graphviz import Digraph #複製該函式 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 model = nn.Sequential() model.add_module('liner_1', nn.Linear(4, 16)) model.add_module('tanh', nn.Tanh()) model.add_module('liner_2', nn.Linear(16, 2)) model.add_module('liner_3', nn.Linear(2, 1)) x = Variable(torch.randn(2,4)) y = model(x) #顯示 g = make_dot(y) g.view()
(其中,右邊代表是輸入,左邊是輸出)