This package provides a function that generates a dot file from a PyTorch autograd graph.



agtree2dot.save_dot(variable, variable_labels, result_file)

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

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)

                        input: 'input',
                        target: 'target',
                        loss: 'loss',
                        mlp.fc1.weight: 'weight1',
                        mlp.fc1.bias: 'bias1',
                        mlp.fc2.weight: 'weight2',
                        mlp.fc2.bias: 'bias2',
                    open('./', 'w'))


    subprocess.check_call(['dot', '', '-Lg', '-T', 'pdf', '-o', 'mlp.pdf' ])

except subprocess.CalledProcessError:
    print('Calling the dot command failed. Is Graphviz installed?')

print('Generated mlp.pdf')

which would generate a file and try to generate mlp.pdf from it with Graphviz tools.