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https://github.com/BIGBALLON/PyTorch-CPP
PyTorch C++ inference with LibTorch
https://github.com/BIGBALLON/PyTorch-CPP
cpp demo imagenet inference libtorch opencv pytorch pytorch-cpp
Last synced: about 2 months ago
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PyTorch C++ inference with LibTorch
- Host: GitHub
- URL: https://github.com/BIGBALLON/PyTorch-CPP
- Owner: BIGBALLON
- Created: 2019-01-18T05:22:25.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-11-27T12:33:43.000Z (about 4 years ago)
- Last Synced: 2024-08-01T03:28:10.565Z (5 months ago)
- Topics: cpp, demo, imagenet, inference, libtorch, opencv, pytorch, pytorch-cpp
- Language: C++
- Homepage:
- Size: 530 KB
- Stars: 331
- Watchers: 8
- Forks: 69
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
This demo will demonstrate how to use LibTorch to build your C++ application.
**[UPDATE 2019/01/18]** : Init the repo, test with PyTorch1.0.
**[UPDATE 2020/02/22]** : Thanks for [Ageliss](https://github.com/Ageliss) and his [PR](https://github.com/BIGBALLON/PyTorch-CPP/pull/4), which update this demo to fit LibTorch1.4.0 and OpenCV4.0.
**[UPDATE 2020/04/15]** : Retest this tutorial with **OpenCV4.3**/**PyTorch1.4**/**LibTorch1.4**, update readme for beginner.
**[UPDATE 2020/04/25]** : Update ``CMakeLists.txt`` to fit **C++14,** retest with **CUDA10.0**/**PyTorch1.5**/**LibTorch1.5**.
**[UPDATE 2020/11/08]** : Improve readme, retest with **PyTorch1.7**/ **CUDA10.2+cuDNNv7.6.5** and **CUDA11.0+cuDNNv8.0.4**
## Contents
- [Contents](#contents)
- [Preparation](#preparation)
- [Step 0: Dependencies](#step-0-dependencies)
- [Step 1: JIT Model](#step-1-jit-model)
- [Step 2: Cpp Program](#step-2-cpp-program)
- [Step 3: CMakeLists](#step-3-cmakelists)
- [Build](#build)
- [Usage](#usage)
## Preparation
### Step 0: Dependencies
**Make sure** LibTorch and OpenCV have been installed correctly.
- **Install OpenCV**: for [Linux](https://docs.opencv.org/master/d7/d9f/tutorial_linux_install.html), for [Mac OS](https://docs.opencv.org/master/d0/db2/tutorial_macos_install.html)
- **Get LibTorch**: download LibTorch package from the official [website](https://pytorch.org/get-started/locally/), then unpack it, for example:
```bash
cd path_to_your_workspace
wget https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.7.0.zip
unzip libtorch-cxx11-abi-shared-with-deps-1.7.0.zip
```
### Step 1: JIT Model
Export torch script file, we use ``resnet18``/``resnet50`` in this demo. (see [model_trace.py](./model_trace.py))
### Step 2: Cpp Program
Write C++ application program. (see [prediction.cpp](./prediction.cpp))
**PS**: ``module->to(at::kCUDA)`` and ``input_tensor.to(at::kCUDA)`` will switch your model & tensors to GPU mode, comment out them if you just want to use CPU.
### Step 3: CMakeLists
Write a [CMakeLists.txt](./CMakeLists.txt). (check [cppdocs](https://pytorch.org/cppdocs/) for more details)
## Build
- run ``model_trace.py``, you will get a converted model ``resnet50.pt``.
- compile your cpp program, you need to use ``-DCMAKE_PREFIX_PATH=/absolute/path/to/libtorch``, for example:
```bash
mkdir build
cd build
# change "/home/bigballon/libtorch" to your libtorch path
cmake -DCMAKE_PREFIX_PATH=/home/bigballon/libtorch ..
make
```
**PS**: If you get the compile error: ``error: undefined reference to `cv::imread(std::string const&, int)'``, check [issues 14684](https://github.com/pytorch/pytorch/issues/14684) and [issues 14620](https://github.com/pytorch/pytorch/issues/14620) for more details.
## Usage
```bash
classifier
# example:
# ./classifier ../resnet18.pt ../label.txt
```
```
> ./classifier ../resnet18.pt ../label
== Switch to GPU mode
== Model [../resnet18.pt] loaded!
== Label loaded! Let's try it
== Input image path: [enter Q to exit]
../pic/dog.jpg
== image size: [976 x 549] ==
== simply resize: [224 x 224] ==
============= Top-1 =============
Label: beagle
With Probability: 97.0629%
============= Top-2 =============
Label: Walker hound, Walker foxhound
With Probability: 1.30952%
============= Top-3 =============
Label: English foxhound
With Probability: 0.434456%
```
```
../pic/shark.jpg
== image size: [800 x 500] ==
== simply resize: [224 x 224] ==
============= Top-1 =============
Label: tiger shark, Galeocerdo cuvieri
With Probability: 67.672%
============= Top-2 =============
Label: hammerhead, hammerhead shark
With Probability: 16.4908%
============= Top-3 =============
Label: great white shark, white shark, man-eater, man-eating shark
With Probability: 15.7808%
== Input image path: [enter Q to exit]
Q
```
```
> ./classifier ../resnet50.pt ../label
== Switch to GPU mode
== Model [../resnet50.pt] loaded!
== Label loaded! Let's try it
== Input image path: [enter Q to exit]
../pic/dog.jpg
== image size: [976 x 549] ==
== simply resize: [224 x 224] ==
============= Top-1 =============
Label: beagle
With Probability: 99.1227%
============= Top-2 =============
Label: Walker hound, Walker foxhound
With Probability: 0.469356%
============= Top-3 =============
Label: English foxhound
With Probability: 0.110916%
== Input image path: [enter Q to exit]
../pic/shark.jpg
== image size: [800 x 500] ==
== simply resize: [224 x 224] ==
============= Top-1 =============
Label: tiger shark, Galeocerdo cuvieri
With Probability: 92.2599%
============= Top-2 =============
Label: great white shark, white shark, man-eater, man-eating shark
With Probability: 5.94252%
============= Top-3 =============
Label: hammerhead, hammerhead shark
With Probability: 1.77417%
== Input image path: [enter Q to exit]
Q
```
Take it easy!! :love_letter: