-#!/usr/bin/env python-for-pytorch
+#########################################################################
+# This program is free software: you can redistribute it and/or modify #
+# it under the terms of the version 3 of the GNU General Public License #
+# as published by the Free Software Foundation. #
+# #
+# This program is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of #
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU #
+# General Public License for more details. #
+# #
+# You should have received a copy of the GNU General Public License #
+# along with this program. If not, see <http://www.gnu.org/licenses/>. #
+# #
+# Written by and Copyright (C) Francois Fleuret #
+# Contact <francois.fleuret@idiap.ch> for comments & bug reports #
+#########################################################################
import torch
-import math, sys, re
-
-from torch import nn
-from torch.nn import functional as fn
-
-from torch import Tensor
-from torch.autograd import Variable
-from torch.nn.parameter import Parameter
-from torch.nn import Module
+import sys, re
######################################################################
if k > 0:
if not for_input: result = ' |' + result
result += ' { <' + label + '0> 0'
- for j in range(1, k+1):
+ for j in range(1, k + 1):
result += " | " + '<' + label + str(j) + '> ' + str(j)
result += " } "
if for_input: result = result + '| '
######################################################################
def add_link(node_list, link_list, u, nu, v, nv):
- link = Link(u, nu, v, nv)
- link_list.append(link)
- node_list[u].max_in = max(node_list[u].max_in, nu)
- node_list[v].max_out = max(node_list[u].max_out, nv)
+ if u is not None and v is not None:
+ link = Link(u, nu, v, nv)
+ link_list.append(link)
+ node_list[u].max_in = max(node_list[u].max_in, nu)
+ node_list[v].max_out = max(node_list[v].max_out, nv)
######################################################################
-def build_ag_graph_lists(u, node_labels, out, node_list, link_list):
+def fill_graph_lists(u, node_labels, node_list, link_list):
- if not u in node_list:
+ if u is not None and not u in node_list:
node = Node(len(node_list) + 1,
(u in node_labels and node_labels[u]) or \
re.search('<class \'(.*\.|)([a-zA-Z0-9_]*)\'>', str(type(u))).group(2))
node_list[u] = node
- if isinstance(u, torch.autograd.Variable):
- build_ag_graph_lists(u.grad_fn, node_labels, out, node_list, link_list)
+ if hasattr(u, 'grad_fn'):
+ fill_graph_lists(u.grad_fn, node_labels, node_list, link_list)
add_link(node_list, link_list, u, 0, u.grad_fn, 0)
- else:
- if hasattr(u, 'next_functions'):
- i = 0
- for v, j in u.next_functions:
- build_ag_graph_lists(v, node_labels, out, node_list, link_list)
- add_link(node_list, link_list, u, i, v, j)
- i += 1
+
+ if hasattr(u, 'variable'):
+ fill_graph_lists(u.variable, node_labels, node_list, link_list)
+ add_link(node_list, link_list, u, 0, u.variable, 0)
+
+ if hasattr(u, 'next_functions'):
+ for i, (v, j) in enumerate(u.next_functions):
+ fill_graph_lists(v, node_labels, node_list, link_list)
+ add_link(node_list, link_list, u, i, v, j)
######################################################################
for n in node_list:
node = node_list[n]
- out.write(
- ' ' + \
- str(node.id) + ' [shape=record,label="{ ' + \
- slot_string(node.max_out, for_input = True) + \
- node.label + \
- slot_string(node.max_in, for_input = False) + \
- ' }"]\n'
- )
+ if isinstance(n, torch.autograd.Variable):
+ out.write(
+ ' ' + \
+ str(node.id) + ' [shape=note,style=filled, fillcolor="#e0e0ff",label="' + \
+ node.label + ' ' + re.search('torch\.Size\((.*)\)', str(n.data.size())).group(1) + \
+ '"]\n'
+ )
+ else:
+ out.write(
+ ' ' + \
+ str(node.id) + ' [shape=record,style=filled, fillcolor="#f0f0f0",label="{ ' + \
+ slot_string(node.max_out, for_input = True) + \
+ node.label + \
+ slot_string(node.max_in, for_input = False) + \
+ ' }"]\n'
+ )
for n in link_list:
out.write(' ' + \
######################################################################
def save_dot(x, node_labels = {}, out = sys.stdout):
- node_list = {}
- link_list = []
- build_ag_graph_lists(x, node_labels, out, node_list, link_list)
+ node_list, link_list = {}, []
+ fill_graph_lists(x, node_labels, node_list, link_list)
print_dot(node_list, link_list, out)
######################################################################
-
-# x = Variable(torch.rand(5))
-# y = torch.topk(x, 3)
-# l = torch.sqrt(torch.norm(y[0]) + torch.norm(5.0 * y[1].float()))
-
-# save_dot(l, { l: 'variable l' }, open('/tmp/test.dot', 'w'))
projects / agtree2dot.git / blobdiff