cksac / tch-rs

Rust bindings for PyTorch

tch-rs

Rust bindings for PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorch). It aims at staying as close as possible to the original C++ api. More idiomatic rust bindings could then be developed on top of this. The documentation can be found on docs.rs.

The code generation part for the C api on top of libtorch comes from ocaml-torch.

Getting Started

This crate requires the C++ version of PyTorch (libtorch) to be available on your system. You can either install it manually and let the build script know about it via the LIBTORCH environment variable. If not set, the build script will try downloading and extracting a pre-built binary version of libtorch.

Libtorch Manual Install

• Get libtorch from the PyTorch website download section and extract the content of the zip file.
• Add the following to your .bashrc or equivalent.

export LIBTORCH=/path/to/libtorch
export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH

• You should now be able to run some examples, e.g. cargo run --example basics.

Examples

Writing a Simple Neural Network

The following code defines a simple model with one hidden layer.

struct Net {
    fc1: nn::Linear,
    fc2: nn::Linear,
}

impl Net {
    fn new(vs: &mut nn::VarStore) -> Net {
        let fc1 = nn::Linear::new(vs, IMAGE_DIM, HIDDEN_NODES);
        let fc2 = nn::Linear::new(vs, HIDDEN_NODES, LABELS);
        Net { fc1, fc2 }
    }
}

impl nn::Module for Net {
    fn forward(&self, xs: &Tensor) -> Tensor {
        xs.apply(&self.fc1).relu().apply(&self.fc2)
    }
}

This model can be trained on the MNIST dataset by running the following command.

cargo run --example mnist

More details on the training loop can be found in the detailed tutorial.

Using some Pre-Trained Model

The pretrained-models example illustrates how to use some pre-trained computer vision model on an image. The weights - which have been extracted from the PyTorch implementation - can be downloaded here resnet18.ot and here resnet34.ot.

The example can then be run via the following command:

cargo run --example pretrained-models -- resnet18.ot tiger.jpg

This should print the top 5 imagenet categories for the image. The code for this example is pretty simple.

    // First the image is loaded and resized to 224x224.
    let image = imagenet::load_image_and_resize(image_file)?;

    // A variable store is created to hold the model parameters.
    let vs = tch::nn::VarStore::new(tch::Device::Cpu);

    // Then the model is built on this variable store, and the weights are loaded.
    let resnet18 = tch::vision::resnet::resnet18(vs.root(), imagenet::CLASS_COUNT);
    vs.load(weight_file)?;

    // Apply the forward pass of the model to get the logits and convert them
    // to probabilities via a softmax.
    let output = resnet18
        .forward_t(&image.unsqueeze(0), /*train=*/ false)
        .softmax(-1);

    // Finally print the top 5 categories and their associated probabilities.
    for (probability, class) in imagenet::top(&output, 5).iter() {
        println!("{:50} {:5.2}%", class, 100.0 * probability)
    }

Further examples include:

• A simplified version of char-rnn illustrating character level language modeling using Recurrent Neural Networks.
• Neural style transfer uses a pre-trained VGG-16 model to compose an image in the style of another image (pre-trained weights: vgg16.ot).
• Some ResNet examples on CIFAR-10.
• A tutorial showing how to deploy/run some Python trained models using TorchScript JIT.