BIGBALLON / Pytorch Cpp

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

• Contents
• Preparation
  • Step 0: Dependencies
  • Step 1: JIT Model
  • Step 2: Cpp Program
  • Step 3: CMakeLists
• Build
• Usage

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.zip

Step 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 model resnet50.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 ..
make

PS: 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!! 💌