v0.16~preview.128.12+239

.gitignore

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+_build
+*.install
+*.merlin
+_opam
+
+__pycache__
+*.annot
+*.cmo
+*.cma
+*.cmi
+*.a
+*.o
+*.cmx
+*.cmxs
+*.cmxa
+*.swp
+*.ipynb_checkpoints
+
+data/
+
+*.byte
+*.native
+
+setup.data
+setup.log
+torch.install
+
+# generated files
+src/wrapper/cxx_flags.sexp
+src/wrapper/c_library_flags.sexp
+src/wrapper/torch_stubs.c
+src/wrapper/torch_bindings.ml
+src/wrapper/torch_generated.ml
+src/wrapper/torch_bindings_generated.ml

.ocamlformat

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+profile = janestreet
+let-binding-spacing = compact

CONTRIBUTING.md

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+This repository contains open source software that is developed and
+maintained by [Jane Street][js].
+
+Contributions to this project are welcome and should be submitted via
+GitHub pull requests.
+
+Signing contributions
+---------------------
+
+We require that you sign your contributions. Your signature certifies
+that you wrote the patch or otherwise have the right to pass it on as
+an open-source patch. The rules are pretty simple: if you can certify
+the below (from [developercertificate.org][dco]):
+
+```
+Developer Certificate of Origin
+Version 1.1
+
+Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
+1 Letterman Drive
+Suite D4700
+San Francisco, CA, 94129
+
+Everyone is permitted to copy and distribute verbatim copies of this
+license document, but changing it is not allowed.
+
+
+Developer's Certificate of Origin 1.1
+
+By making a contribution to this project, I certify that:
+
+(a) The contribution was created in whole or in part by me and I
+    have the right to submit it under the open source license
+    indicated in the file; or
+
+(b) The contribution is based upon previous work that, to the best
+    of my knowledge, is covered under an appropriate open source
+    license and I have the right under that license to submit that
+    work with modifications, whether created in whole or in part
+    by me, under the same open source license (unless I am
+    permitted to submit under a different license), as indicated
+    in the file; or
+
+(c) The contribution was provided directly to me by some other
+    person who certified (a), (b) or (c) and I have not modified
+    it.
+
+(d) I understand and agree that this project and the contribution
+    are public and that a record of the contribution (including all
+    personal information I submit with it, including my sign-off) is
+    maintained indefinitely and may be redistributed consistent with
+    this project or the open source license(s) involved.
+```
+
+Then you just add a line to every git commit message:
+
+```
+Signed-off-by: Joe Smith <[email protected]>
+```
+
+Use your real name (sorry, no pseudonyms or anonymous contributions.)
+
+If you set your `user.name` and `user.email` git configs, you can sign
+your commit automatically with git commit -s.
+
+[dco]: http://developercertificate.org/
+[js]: https://opensource.janestreet.com/

LICENSE.md

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+The MIT License
+
+Copyright (c) 2022--2023 Jane Street Group, LLC <[email protected]>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSE_NOTES

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+Some minimal contributions to ocaml-torch, originally contributed under the Apache 2.0 license, have been sublicensed under the MIT license, such that the whole of ocaml-torch is now licensed or sublicensed under the MIT license.

Makefile

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+INSTALL_ARGS := $(if $(PREFIX),--prefix $(PREFIX),)
+
+default:
+	dune build
+
+install:
+	dune install $(INSTALL_ARGS)
+
+uninstall:
+	dune uninstall $(INSTALL_ARGS)
+
+reinstall: uninstall install
+
+clean:
+	dune clean
+
+.PHONY: default install uninstall reinstall clean

README.md

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-# torch
+# ocaml-torch
+__ocaml-torch__ provides some ocaml bindings for the [PyTorch](https://pytorch.org) 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](https://pytorch.org/cppdocs/) and are
+mostly automatically generated. The current GitHub tip and the opam package v0.7
+corresponds to PyTorch **v1.13.0**.
+
+On Linux note that you will need the PyTorch version using the appropriate cxx11 abi depending on your g++ version.
+[cpu version](https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.13.0%2Bcpu.zip),
+[cuda 11.6 version](https://download.pytorch.org/libtorch/cu116/libtorch-cxx11-abi-shared-with-deps-1.13.0%2Bcu116.zip).
+
+## Opam Installation
+
+The [opam](https://opam.ocaml.org/) package can be installed using the following command.
+This automatically installs the CPU version of libtorch.
+
+```bash
+opam install torch
+```
+
+You can then compile some sample code, see some instructions below.
+__ocaml-torch__ can also be used in interactive mode via
+[utop](https://github.com/ocaml-community/utop) or
+[ocaml-jupyter](https://github.com/akabe/ocaml-jupyter).
+
+Here is a sample utop session.
+
+![utop](./images/utop.png)
+
+
+### Build a Simple Example
+
+To build a first torch program, create a file `example.ml` with the
+following content.
+
+```ocaml
+open Torch
+
+let () =
+  let tensor = Tensor.randn [ 4; 2 ] in
+  Tensor.print tensor
+```
+
+Then create a `dune` file with the following content:
+
+```ocaml
+(executables
+  (names example)
+  (libraries torch))
+```
+
+Run `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 `example.bc` may
+not work on macos).
+
+## Tutorials and Examples
+
+* [MNIST tutorial](./examples/mnist/README.md).
+* [Finetuning a ResNet-18 model](./examples/pretrained/README.md).
+* [Generative Adversarial Networks](./examples/gan/README.md).
+* [Running some Python model](./examples/jit/README.md).
+
+Some more advanced applications from external repos:
+* An [OCaml port of mini-dalle](https://github.com/ArulselvanMadhavan/mini_dalle) by Arulselvan Madhavan.
+* Natural Language Processing models based on BERT can be found in the
+[ocaml-bert repo](https://github.com/LaurentMazare/ocaml-bert).
+
+## Sample Code
+
+Below is an example of a linear model trained on the MNIST dataset ([full
+code](./examples/mnist/README.md)).
+
+```ocaml
+  (* 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 ~dim:(-1) (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](./examples/cifar/README.md).
+* A simplified version of
+  [char-rnn](./examples/char_rnn/README.md)
+  illustrating character level language modeling using Recurrent Neural Networks.
+* [Neural Style Transfer](./examples/neural_transfer/README.md)
+  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](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet18.ot).
+* ResNet-34 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet34.ot).
+* ResNet-50 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet50.ot).
+* ResNet-101 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet101.ot).
+* ResNet-152 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/resnet152.ot).
+* DenseNet-121 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/densenet121.ot).
+* DenseNet-161 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/densenet161.ot).
+* DenseNet-169 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/densenet169.ot).
+* SqueezeNet 1.0 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/squeezenet1_0.ot).
+* SqueezeNet 1.1 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/squeezenet1_1.ot).
+* VGG-13 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/vgg13.ot).
+* VGG-16 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/vgg16.ot).
+* AlexNet [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/alexnet.ot).
+* Inception-v3 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/inception-v3.ot).
+* MobileNet-v2 [weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/mobilenet-v2.ot).
+* EfficientNet
+  [b0 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b0.ot),
+  [b1 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b1.ot),
+  [b2 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b2.ot),
+  [b3 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b3.ot),
+  [b4 weights](https://github.com/LaurentMazare/ocaml-torch/releases/download/v0.1-unstable/efficientnet-b4.ot).
+
+Running the pre-trained models on some sample images can the easily be done via the following commands.
+```bash
+dune exec examples/pretrained/predict.exe path/to/resnet18.ot images/tiger.jpg
+```
+
+## Acknowledgements
+
+Many thanks to [@LaurentMazare](https://github.com/LaurentMazare) for the [original
+work](https://github.com/LaurentMazare/ocaml-torch) of ocaml-torch.

bin/dune

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+(executables (names tensor_tools)
+ (libraries base cmdliner npy torch_core stdio torch torch_vision)
+ (preprocess (pps ppx_jane)))