C++ Implementation of PyTorch Tutorials for Everyone.zip

<h1 align="center"> <img src="images/pytorch_cpp.png" width="50%"> </h1> <p align="center"> C++ Implementation of PyTorch Tutorials for Everyone <br /> <img src="https://img.shields.io/github/license/prabhuomkar/pytorch-cpp"> <img src="https://img.shields.io/badge/libtorch-2.1.1-ee4c2c"> <img src="https://img.shields.io/badge/cmake-3.14-064f8d"> </p> | OS (Compiler)\\LibTorch | 2.1.1 | | :--------------------- | :--------------------------------------------------------------------------------------------------- | | macOS (clang 11, 12, 13) | [](https://github.com/prabhuomkar/pytorch-cpp/actions?query=workflow%3Aci-build-macos) | | Linux (gcc 9, 10, 11) | [](https://github.com/prabhuomkar/pytorch-cpp/actions?query=workflow%3Aci-build-ubuntu) | | Windows (msvc 2019, 2022) | [](https://github.com/prabhuomkar/pytorch-cpp/actions?query=workflow%3Aci-build-windows) | ## Table of Contents This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch _(i.e. Section 1 to 3)_ **Python Tutorial**: [https://github.com/yunjey/pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial) ### 1. Basics * [PyTorch Basics](tutorials/basics/pytorch_basics/main.cpp) * [Linear Regression](tutorials/basics/linear_regression/main.cpp) * [Logistic Regression](tutorials/basics/logistic_regression/main.cpp) * [Feedforward Neural Network](tutorials/basics/feedforward_neural_network/src/main.cpp) ### 2. Intermediate * [Convolutional Neural Network](tutorials/intermediate/convolutional_neural_network/src/main.cpp) * [Deep Residual Network](tutorials/intermediate/deep_residual_network/src/main.cpp) * [Recurrent Neural Network](tutorials/intermediate/recurrent_neural_network/src/main.cpp) * [Bidirectional Recurrent Neural Network](tutorials/intermediate/bidirectional_recurrent_neural_network/src/main.cpp) * [Language Model (RNN-LM)](tutorials/intermediate/language_model/src/main.cpp) ### 3. Advanced * [Generative Adversarial Networks](tutorials/advanced/generative_adversarial_network/main.cpp) * [Variational Auto-Encoder](tutorials/advanced/variational_autoencoder/src/main.cpp) * [Neural Style Transfer](tutorials/advanced/neural_style_transfer/src/main.cpp) * [Image Captioning (CNN-AttentionRNN)](tutorials/advanced/image_captioning/src/main.cpp) ### 4. Interactive Tutorials * [Tensor Slicing](notebooks/tensor_slicing.ipynb) ### 5. Other Popular Tutorials * [Deep Learning with PyTorch: A 60 Minute Blitz](tutorials/popular/blitz) # Getting Started ## Requirements 1. [C++-17](http://www.cplusplus.com/doc/tutorial/introduction/) compatible compiler 2. [CMake](https://cmake.org/download/) (minimum version 3.14) 3. [LibTorch version >= 1.12.0 and <= 2.1.1](https://pytorch.org/cppdocs/installing.html) 4. [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html) ## For Interactive Tutorials **Note**: Interactive Tutorials are currently running on **LibTorch Nightly Version**. So there are some tutorials which can break when working with _nightly version_. ```bash conda create --name pytorch-cpp conda activate pytorch-cpp conda install xeus-cling notebook -c conda-forge ``` ## Clone, build and run tutorials ### In Google Colab [](https://colab.research.google.com/github/prabhuomkar/pytorch-cpp/blob/master/notebooks/pytorch_cpp_colab_notebook.ipynb) ### On Local Machine ```bash git clone https://github.com/prabhuomkar/pytorch-cpp.git cd pytorch-cpp ``` #### Generate build system ```bash cmake -B build #<options> ``` > **_Note for Windows users:_**<br> > Libtorch only supports 64bit Windows and an x64 generator needs to be specified. For Visual Studio this can be done by appending `-A x64` to the above command. Some useful options: | Option | Default | Description | | :------------- |:------------|-----:| | `-D CUDA_V=(11.8\|12.1\|none)` | `none` | Download LibTorch for a CUDA version (`none` = download CPU version). | | `-D LIBTORCH_DOWNLOAD_BUILD_TYPE=(Release\|Debug)` | `Release` | Determines which libtorch build type version to download (only relevant on **Windows**).| | `-D DOWNLOAD_DATASETS=(OFF\|ON)` | `ON` | Download required datasets during build (only if they do not already exist in `pytorch-cpp/data`). | |`-D CREATE_SCRIPTMODULES=(OFF\|ON)` | `OFF` | Create all required scriptmodule files for prelearned models / weights during build. Requires installed python3 with pytorch and torchvision. | | `-D CMAKE_PREFIX_PATH=path/to/libtorch/share/cmake/Torch` | `<empty>` | Skip the downloading of LibTorch and use your own local version (see [Requirements](#requirements)) instead. | | `-D CMAKE_BUILD_TYPE=(Release\|Debug\|...)` | `<empty>` | Determines the CMake build-type for single-configuration generators (see [CMake docs](https://cmake.org/cmake/help/latest/variable/CMAKE_BUILD_TYPE.html)).| <details> <summary><b>Example Linux</b></summary> ##### Aim * Use existing Python, PyTorch (see [Requirements](#requirements)) and torchvision installation. * Download all datasets and create all necessary scriptmodule files. ##### Command ```bash cmake -B build \ -D CMAKE_BUILD_TYPE=Release \ -D CMAKE_PREFIX_PATH=/path/to/libtorch/share/cmake/Torch \ -D CREATE_SCRIPTMODULES=ON ``` </details> <details> <summary><b>Example Windows</b></summary> ##### Aim * Automatically download LibTorch for CUDA 11.8 (Release version) and all necessary datasets. * Do not create scriptmodule files. ##### Command ```bash cmake -B build \ -A x64 \ -D CUDA_V=11.8 ``` </details> #### Build >**_Note for Windows (Visual Studio) users:_** <br> >The CMake script downloads the *Release* version of LibTorch, so `--config Release` has to be appended to the build command. **How dataset download and scriptmodule creation work:** * If `DOWNLOAD_DATASETS` is `ON`, the datasets required by the tutorials you choose to build will be downloaded to `pytorch-cpp/data` (if they do not already exist there). * If `CREATE_SCRIPTMODULES` is `ON`, the scriptmodule files for the prelearned models / weights required by the tutorials you choose to build will be created in the `model` folder of the respective tutorial's source folder (if they do not already exist). #### All tutorials To build all tutorials use ```bash cmake --build build ``` #### All tutorials in a category You can choose to only build tutorials in one of the categories `basics`, `intermediate`, `advanced` or `popular`. For example, if you are only interested in the `basics` tutorials: ```bash cmake --build build --target basics # In general: cmake --build build --target {category} ``` #### Single tutorial You can also choose to only build a single tutorial. For example to build the language model tutorial only: ```bash cmake --build build --target language-model # In general: cmake --build build --target {tutorial-name} ``` >**_Note_**: > The target argument is the tutorial's foldername with all underscores replaced by hyphens. >**_Tip for users of CMake version >= 3.15_**: > You can specify several targets separated by spaces, for example: > ```bash > cmake --build build --target language-model image-captioning > ``` #### Run Tutorials 1. (**IMPORTANT!**) First change into the tutorial's directory within `build/tutorials`. For example, assuming you are in the `pytorch-cpp` directory and want to change to the pytorch basics tutorial folder: ```bash cd build/tutorials/basics/pytorch_basics