基于C++开发的resnet算法内含模型可以直接部署

## 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) ```sh # Install minimal prerequisites (Ubuntu 18.04 as reference) $ sudo apt update && sudo apt install -y cmake g++ wget unzip # Download and unpack sources $ wget -O opencv.zip https://github.com/opencv/opencv/archive/4.x.zip $ unzip opencv.zip # Create build directory $ mkdir -p build && cd build # Configure $ cmake ../opencv-4.x # Build $ cmake --build . ``` - **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 <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!! :love_letter: