This repository has been deprecated.
Future development will be in the GTN org repository.
Quickstart | Installation | Documentation
GTN is a framework for automatic differentiation with weighted finite-state transducers. The framework is written in C++ and has bindings to Python.
The goal of GTN is to make adding and experimenting with structure in learning
algorithms much simpler. This structure is encoded as weighted automata, either
acceptors (WFSAs) or transducers (WFSTs). With gtn you can dynamically construct complex
graphs from operations on simpler graphs. Automatic differentiation gives gradients with respect to any input or intermediate graph
with a single call to gtn.backward.
Also checkout the repository gtn_applications which consists of GTN applications to Handwriting Recognition (HWR), Automatic Speech Recognition (ASR) etc.
First install the python bindings.
The following is a minimal example of building two WFSAs with gtn, constructing a simple function on the graphs, and computing gradients.
import gtn
# Make some graphs:
g1 = gtn.Graph()
g1.add_node(True) # Add a start node
g1.add_node() # Add an internal node
g1.add_node(False, True) # Add an accepting node
# Add arcs with (src node, dst node, label):
g1.add_arc(0, 1, 1)
g1.add_arc(0, 1, 2)
g1.add_arc(1, 2, 1)
g1.add_arc(1, 2, 0)
g2 = gtn.Graph()
g2.add_node(True, True)
g2.add_arc(0, 0, 1)
g2.add_arc(0, 0, 0)
# Compute a function of the graphs:
intersection = gtn.intersect(g1, g2)
score = gtn.forward_score(intersection)
# Visualize the intersected graph:
gtn.draw(intersection, "intersection.pdf")
# Backprop:
gtn.backward(score)
# Print gradients of arc weights
print(g1.grad().weights_to_list()) # [1.0, 0.0, 1.0, 0.0]- A C++ compiler with good C++14 support (e.g. g++ >= 5)
cmake>= 3.5.1, andmake
Install the Python bindings with
pip install gtn
First, clone the project:
git clone [email protected]:facebookresearch/gtn.git && cd gtn
Create a build directory and run CMake and make:
mkdir -p build && cd build
cmake ..
make -j $(nproc)
Run tests with:
make test
Run make install to install.
Setting up your environment:
conda create -n gtn_env
conda activate gtn_env
Required dependencies:
cd bindings/python
conda install setuptools
Use one of the following commands for installation:
python setup.py install
or, to install in editable mode (for dev):
python setup.py develop
Python binding tests can be run with make test, or with
python -m unittest discover bindings/python/test
Run a simple example:
python bindings/python/examples/simple_graph.py
GTN is licensed under a MIT license. See LICENSE.
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