face_alignment_cpp

2D and 3D Face alignment library build using pytorch https://www.adrianbulat.com c++ implementation

Contents

1. Requirements
2. Build
3. Usage

Requirements

• Pytorch (tag: pytorch v1.4)
• Libtorch
• OpenCV

Build

Step 1

Export your pytorch model to torch script file, We will simply use resnet50 in this demo

Step 2

Write your C++ program, check the file prediction.cpp for more detial.

PS: module->to(at::kCUDA) and input_tensor.to(at::kCUDA) will switch your model & tensor to GPU mode,
comment out them if you just want to use CPU mode.

Step 3

Write a CMakeLists.txt, the version of OpenCV must the same as your libtorch. Otherwise, you may get the compile error:

error: undefined reference to `cv::imread(std::string const&, int)'

check issues 14684 and issues 14620 for more details.

Usage

• run model_trace.py, then you will get a file resnet50.pt
• compile your cpp program, you need to use -DCMAKE_PREFIX_PATH=/absolute/path/to/libtorch, for example:

mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH=/home/cgilab/pytorch/torch/lib/tmp_install ..
make

• test your program

classifier <path-to-exported-script-module> <path-to-lable-file>