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 and his PR, 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
Preparation
Step 0: Dependencies
Make sure LibTorch and OpenCV have been installed correctly.
- Install OpenCV: for Linux, for Mac OS
- Get LibTorch: download LibTorch package from the official website, then unpack it, for example:
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.zipStep 1: JIT Model
Export torch script file, we use resnet18/resnet50 in this demo. (see model_trace.py)
Step 2: Cpp Program
Write C++ application program. (see 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. (check cppdocs for more details)
Build
- run
model_trace.py, you will get a converted modelresnet50.pt. - compile your cpp program, you need to use
-DCMAKE_PREFIX_PATH=/absolute/path/to/libtorch, for example:
mkdir build
cd build
# change "/home/bigballon/libtorch" to your libtorch path
cmake -DCMAKE_PREFIX_PATH=/home/bigballon/libtorch ..
makePS: If you get the compile error: error: undefined reference to `cv::imread(std::string const&, int)', check issues 14684 and issues 14620 for more details.
Usage
classifier <path-to-exported-script-module> <path-to-lable-file>
# 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!! 💌