ocaml-torch provides some ocaml bindings for the PyTorch tensor library. This brings to OCaml NumPy-like tensor computations with GPU acceleration and tape-based automatic differentiation.
These bindings use the PyTorch C++ API and are mostly automatically generated. The current GitHub tip and the opam package v0.7 corresponds to PyTorch v1.8.0.
The opam package can be installed using the following command. This automatically installs the CPU version of libtorch.
opam install torch
Here is a sample utop session.
Build a Simple Example
To build a first torch program, create a file
example.ml with the
open Torch let () = let tensor = Tensor.randn [ 4; 2 ] in Tensor.print tensor
Then create a
dune file with the following content:
(executables (names example) (libraries torch))
dune exec example.exe to compile the program and run it!
Alternatively you can first compile the code via
dune build example.exe then run the executable
_build/default/example.exe (note that building the bytecode target
not work on macos).
- MNIST tutorial.
- Finetuning a ResNet-18 model.
- Generative Adversarial Networks.
- Running some Python model.
Below is an example of a linear model trained on the MNIST dataset (full code).
(* Create two tensors to store model weights. *) let ws = Tensor.zeros [image_dim; label_count] ~requires_grad:true in let bs = Tensor.zeros [label_count] ~requires_grad:true in let model xs = Tensor.(mm xs ws + bs) in for index = 1 to 100 do (* Compute the cross-entropy loss. *) let loss = Tensor.cross_entropy_for_logits (model train_images) ~targets:train_labels in Tensor.backward loss; (* Apply gradient descent, disable gradient tracking for these. *) Tensor.(no_grad (fun () -> ws -= grad ws * f learning_rate; bs -= grad bs * f learning_rate)); (* Compute the validation error. *) let test_accuracy = Tensor.(argmax (model test_images) = test_labels) |> Tensor.to_kind ~kind:(T Float) |> Tensor.sum |> Tensor.float_value |> fun sum -> sum /. test_samples in printf "%d %f %.2f%%\n%!" index (Tensor.float_value loss) (100. *. test_accuracy); done
- Some ResNet examples on CIFAR-10.
- A simplified version of char-rnn illustrating character level language modeling using Recurrent Neural Networks.
- Neural Style Transfer applies the style of an image to the content of another image. This uses some deep Convolutional Neural Network.
Models and Weights
Various pre-trained computer vision models are implemented in the vision library. The weight files can be downloaded at the following links:
- ResNet-18 weights.
- ResNet-34 weights.
- ResNet-50 weights.
- ResNet-101 weights.
- ResNet-152 weights.
- DenseNet-121 weights.
- DenseNet-161 weights.
- DenseNet-169 weights.
- SqueezeNet 1.0 weights.
- SqueezeNet 1.1 weights.
- VGG-13 weights.
- VGG-16 weights.
- AlexNet weights.
- Inception-v3 weights.
- MobileNet-v2 weights.
- EfficientNet b0 weights, b1 weights, b2 weights, b3 weights, b4 weights.
Running the pre-trained models on some sample images can the easily be done via the following commands.
dune exec examples/pretrained/predict.exe path/to/resnet18.ot tiger.jpg
Natural Language Processing models based on BERT can be found in the ocaml-torch repo.
Alternative Installation Option
This alternative way to install ocaml-torch could be useful to run with GPU acceleration enabled.
Download and extract the libtorch library then to build all the examples run:
export LIBTORCH=/path/to/libtorch git clone https://github.com/LaurentMazare/ocaml-torch.git cd ocaml-torch make all