This package provides a function that generates a dot file from a PyTorch autograd graph.
Saves into result_file a dot file corresponding to the
autograd graph for the Variable variable. The
dictionary variable_labels associates strings to some
variables, which will be used in the resulting graph.
A typical use is provided in mlp.py:
from torch import nn
from torch.nn import functional as fn
from torch import Tensor
from torch.nn import Module
import agtree2dot
class MLP(Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(MLP, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.fc1(x)
x = fn.tanh(x)
x = self.fc2(x)
return x
mlp = MLP(10, 20, 1)
input = Tensor(100, 10).normal_()
target = Tensor(100, 1).normal_()
output = mlp(input)
criterion = nn.MSELoss()
loss = criterion(output, target)
agtree2dot.save_dot(loss,
{
input: 'input',
target: 'target',
loss: 'loss',
mlp.fc1.weight: 'weight1',
mlp.fc1.bias: 'bias1',
mlp.fc2.weight: 'weight2',
mlp.fc2.bias: 'bias2',
},
open('./mlp.dot', 'w'))
print('Generated mlp.dot')
try:
subprocess.check_call(['dot', 'mlp.dot', '-Lg', '-T', 'pdf', '-o', 'mlp.pdf' ])
except subprocess.CalledProcessError:
print('Calling the dot command failed. Is Graphviz installed?')
sys.exit(1)
print('Generated mlp.pdf')which would generate a file mlp.dot and try to generate mlp.pdf from it with Graphviz tools.