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https://github.com/iwatake2222/InferenceHelper
C++ Helper Class for Deep Learning Inference Frameworks: TensorFlow Lite, TensorRT, OpenCV, OpenVINO, ncnn, MNN, SNPE, Arm NN, NNabla, ONNX Runtime, LibTorch, TensorFlow
https://github.com/iwatake2222/InferenceHelper
cpp deep-learning deeplearning inference mnn ncnn nnabla opencv tensorflow tensorrt
Last synced: 13 days ago
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C++ Helper Class for Deep Learning Inference Frameworks: TensorFlow Lite, TensorRT, OpenCV, OpenVINO, ncnn, MNN, SNPE, Arm NN, NNabla, ONNX Runtime, LibTorch, TensorFlow
- Host: GitHub
- URL: https://github.com/iwatake2222/InferenceHelper
- Owner: iwatake2222
- License: apache-2.0
- Created: 2020-12-08T09:27:27.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-04-09T04:43:56.000Z (over 2 years ago)
- Last Synced: 2024-08-01T03:27:40.736Z (3 months ago)
- Topics: cpp, deep-learning, deeplearning, inference, mnn, ncnn, nnabla, opencv, tensorflow, tensorrt
- Language: C++
- Homepage:
- Size: 4.24 MB
- Stars: 270
- Watchers: 17
- Forks: 55
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Inference Helper
- This is a wrapper of deep learning frameworks especially for inference
- This class provides a common interface to use various deep learnig frameworks, so that you can use the same application code
## Supported frameworks
- TensorFlow Lite
- TensorFlow Lite with delegate (XNNPACK, GPU, EdgeTPU, NNAPI)
- TensorRT (GPU, DLA)
- OpenCV(dnn) (with GPU)
- OpenVINO with OpenCV (xml+bin)
- ncnn (with Vulkan)
- MNN (with Vulkan)
- SNPE (Snapdragon Neural Processing Engine SDK (Qualcomm Neural Processing SDK for AI v1.51.0))
- Arm NN
- NNabla (with CUDA)
- ONNX Runtime (with CUDA)
- LibTorch (with CUDA)
- TensorFlow (with GPU)
## Supported targets
- Windows 10 (Visual Studio 2019 x64)
- Linux (x64, armv7, aarch64)
- Android (armeabi-v7a, arm64-v8a)
## CI Status
| Framework | Windows (x64) | Linux (x64) | Linux (armv7) | Linux (aarch64) | Android (aarch64) |
|---------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|------------------------------------------------|
| | [](https://github.com/iwatake2222/InferenceHelper/actions/workflows/ci_windows.yml) | [](https://github.com/iwatake2222/InferenceHelper/actions/workflows/ci_ubuntu.yml) | [](https://github.com/iwatake2222/InferenceHelper/actions/workflows/ci_arm.yml) | [](https://github.com/iwatake2222/InferenceHelper/actions/workflows/ci_arm.yml) | [](https://github.com/iwatake2222/InferenceHelper/actions/workflows/ci_android.yml) |
| TensorFlow Lite |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [ ] Test
| TensorFlow Lite + XNNPACK |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [ ] Test
| TensorFlow Lite + EdgeTPU |
- [x] Build
- [ ] Test
- [x] Build
- [ ] Test
- [x] Build
- [ ] Test
- [x] Build
- [ ] Test
| TensorFlow Lite + GPU | No library | No library | No library | No library |
- [x] Build
- [ ] Test
| TensorFlow Lite + NNAPI | Unsupported | Unsupported | Unsupported | Unsupported |
- [x] Build
- [ ] Test
| TensorRT |
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
| OpenCV(dnn) |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [ ] Build
- [ ] Test
- [x] Build
- [x] Test
- [x] Build
- [ ] Test
| OpenVINO with OpenCV |
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
| ncnn |
- [x] Build
- [ ] Test
- [x] Build
- [x] Test
- [x] Build
- [ ] Test
| MNN |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [ ] Test
| SNPE | Unsupported | Unsupported |
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
- [ ] Build
- [ ] Test
| Arm NN | Unsupported |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
| NNabla |
- [x] Build
- [x] Test
| ONNX Runtime |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [x] Test
- [x] Build
- [ ] Test
| LibTorch |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
| TensorFlow |
- [x] Build
- [x] Test
- [x] Build
- [x] Test
* Unchedked(blank) doesn't mean that the framework is unsupported. Unchecked just means that the framework is not tested in CI. For instance, TensorRT on Windows/Linux works and I confirmed it in my PC, but can't run it in CI.
* `No Library` means a pre-built library is not provided so that I cannot confirm it in CI. It may work if you build a library by yourself.
## Sample projects
- https://github.com/iwatake2222/InferenceHelper_Sample
- https://github.com/iwatake2222/play_with_tflite
- https://github.com/iwatake2222/play_with_tensorrt
- https://github.com/iwatake2222/play_with_ncnn
- https://github.com/iwatake2222/play_with_mnn
# Usage
Please refer to https://github.com/iwatake2222/InferenceHelper_Sample
## Installation
- Add this repository into your project (Using `git submodule` is recommended)
- Download prebuilt libraries
- `sh third_party/download_prebuilt_libraries.sh`
## Additional steps
You need some additional steps if you use the frameworks listed below
### Additional steps: OpenCV / OpenVINO
- Install OpenCV or OpenVINO
- You may need to set/modify `OpenCV_DIR` and `PATH` environment variable
- To use OpenVINO, you may need to run `C:\Program Files (x86)\Intel\openvino_2021\bin\setupvars.bat` or `source /opt/intel/openvino_2021/bin/setupvars.sh`
### Additional steps: TensorRT
- Install CUDA + cuDNN
- Install TensorRT 8.x
### Additional steps: Tensorflow Lite (EdgeTPU)
- Install the following library
- Linux: https://github.com/google-coral/libedgetpu/releases/download/release-grouper/edgetpu_runtime_20210726.zip
- Windows: https://github.com/google-coral/libedgetpu/releases/download/release-frogfish/edgetpu_runtime_20210119.zip
- the latest version doesn't work
- it may be better to delete `C:\Windows\System32\edgetpu.dll` to ensure the program uses our pre-built library
### Additional steps: ncnn
- Install Vulkan
- You need Vulkan even if you don't use it because the pre-built libraries require it. Otherwise you need to build libraries by yourself disabling Vulkan
- https://vulkan.lunarg.com/sdk/home
- Windows
- https://sdk.lunarg.com/sdk/download/latest/windows/vulkan-sdk.exe
- It's better to check `(Optional) Debuggable Shader API Libraries -64-bit` , so that you can use Debug in Visual Studio
- Linux (x64)
```sh
wget https://sdk.lunarg.com/sdk/download/latest/linux/vulkan-sdk.tar.gz
tar xzvf vulkan-sdk.tar.gz
export VULKAN_SDK=$(pwd)/1.2.198.1/x86_64
sudo apt install -y vulkan-utils libvulkan1 libvulkan-dev
```
### Additional steps: SNPE
- Download library from https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk/tools
- Extract `snpe-1.51.0.zip` , then place `lib` and `include` folders to `third_party/snpe_prebuilt`
### Note:
- `Debug` mode in Visual Studio doesn't work for ncnn, NNabla and LibTorch because debuggable libraries are not provided
- `Debug` will cause unexpected bahavior, so use `Release` or `RelWithDebInfo`
- See `third_party/download_prebuilt_libraries.sh` and `third_party/cmakes/*` to check which libraries are being used. For instance, libraries without GPU(CUDA/Vulkan) are used to be safe. So, if you want to use GPU, modify these files.
## Project settings in CMake
- Add InferenceHelper to your project
```cmake
set(INFERENCE_HELPER_DIR ${CMAKE_CURRENT_LIST_DIR}/../../InferenceHelper/)
add_subdirectory(${INFERENCE_HELPER_DIR}/inference_helper inference_helper)
target_include_directories(${LibraryName} PUBLIC ${INFERENCE_HELPER_DIR}/inference_helper)
target_link_libraries(${LibraryName} InferenceHelper)
```
## CMake options
- Deep learning framework:
- You can enable multiple options althoguh the following example enables just one option
```sh
# OpenCV (dnn), OpenVINO
cmake .. -DINFERENCE_HELPER_ENABLE_OPENCV=on
# Tensorflow Lite
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE=on
# Tensorflow Lite (XNNPACK)
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_XNNPACK=on
# Tensorflow Lite (GPU)
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_GPU=on
# Tensorflow Lite (EdgeTPU)
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_EDGETPU=on
# Tensorflow Lite (NNAPI)
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_NNAPI=on
# TensorRT
cmake .. -DINFERENCE_HELPER_ENABLE_TENSORRT=on
# ncnn, ncnn + vulkan
cmake .. -DINFERENCE_HELPER_ENABLE_NCNN=on
# MNN (+ Vulkan)
cmake .. -DINFERENCE_HELPER_ENABLE_MNN=on
# SNPE
cmake .. -DINFERENCE_HELPER_ENABLE_SNPE=on
# Arm NN
cmake .. -DINFERENCE_HELPER_ENABLE_ARMNN=on
# NNabla
cmake .. -DINFERENCE_HELPER_ENABLE_NNABLA=on
# NNabla with CUDA
cmake .. -DINFERENCE_HELPER_ENABLE_NNABLA_CUDA=on
# ONNX Runtime
cmake .. -DINFERENCE_HELPER_ENABLE_ONNX_RUNTIME=on
# ONNX Runtime with CUDA
cmake .. -DINFERENCE_HELPER_ENABLE_ONNX_RUNTIME_CUDA=on
# LibTorch
cmake .. -DINFERENCE_HELPER_ENABLE_LIBTORCH=on
# LibTorch with CUDA
cmake .. -DINFERENCE_HELPER_ENABLE_LIBTORCH_CUDA=on
# TensorFlow
cmake .. -DINFERENCE_HELPER_ENABLE_TENSORFLOW=on
# TensorFlow with GPU
cmake .. -DINFERENCE_HELPER_ENABLE_TENSORFLOW_GPU=on
```
- Enable/Disable preprocess using OpenCV:
- By disabling this option, InferenceHelper is not dependent on OpenCV
```sh
cmake .. -INFERENCE_HELPER_ENABLE_PRE_PROCESS_BY_OPENCV=off
```
# Structure
# APIs
## InferenceHelper
### Enumeration
```c++
typedef enum {
kOpencv,
kOpencvGpu,
kTensorflowLite,
kTensorflowLiteXnnpack,
kTensorflowLiteGpu,
kTensorflowLiteEdgetpu,
kTensorflowLiteNnapi,
kTensorrt,
kNcnn,
kNcnnVulkan,
kMnn,
kSnpe,
kArmnn,
kNnabla,
kNnablaCuda,
kOnnxRuntime,
kOnnxRuntimeCuda,
kLibtorch,
kLibtorchCuda,
kTensorflow,
kTensorflowGpu,
} HelperType;
```
### static InferenceHelper* Create(const HelperType helper_type)
- Create InferenceHelper instance for the selected framework
```c++
std::unique_ptr inference_helper(InferenceHelper::Create(InferenceHelper::kTensorflowLite));
```
### static void PreProcessByOpenCV(const InputTensorInfo& input_tensor_info, bool is_nchw, cv::Mat& img_blob)
- Run preprocess (convert image to blob(NCHW or NHWC))
- This is just a helper function. You may not use this function.
- Available when `INFERENCE_HELPER_ENABLE_PRE_PROCESS_BY_OPENCV=on`
```c++
InferenceHelper::PreProcessByOpenCV(input_tensor_info, false, img_blob);
```
### int32_t SetNumThreads(const int32_t num_threads)
- Set the number of threads to be used
- This function needs to be called before initialize
```c++
inference_helper->SetNumThreads(4);
```
### int32_t SetCustomOps(const std::vector>& custom_ops)
- Set custom ops
- This function needs to be called before initialize
```c++
std::vector> custom_ops;
custom_ops.push_back(std::pair("Convolution2DTransposeBias", (const void*)mediapipe::tflite_operations::RegisterConvolution2DTransposeBias()));
inference_helper->SetCustomOps(custom_ops);
```
### int32_t Initialize(const std::string& model_filename, std::vector& input_tensor_info_list, std::vector& output_tensor_info_list)
- Initialize inference helper
- Load model
- Set tensor information
```c++
std::vector input_tensor_list;
InputTensorInfo input_tensor_info("input", TensorInfo::TENSOR_TYPE_FP32, false); /* name, data_type, NCHW or NHWC */
input_tensor_info.tensor_dims = { 1, 224, 224, 3 };
input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
input_tensor_info.data = img_src.data;
input_tensor_info.image_info.width = img_src.cols;
input_tensor_info.image_info.height = img_src.rows;
input_tensor_info.image_info.channel = img_src.channels();
input_tensor_info.image_info.crop_x = 0;
input_tensor_info.image_info.crop_y = 0;
input_tensor_info.image_info.crop_width = img_src.cols;
input_tensor_info.image_info.crop_height = img_src.rows;
input_tensor_info.image_info.is_bgr = false;
input_tensor_info.image_info.swap_color = false;
input_tensor_info.normalize.mean[0] = 0.485f; /* https://github.com/onnx/models/tree/master/vision/classification/mobilenet#preprocessing */
input_tensor_info.normalize.mean[1] = 0.456f;
input_tensor_info.normalize.mean[2] = 0.406f;
input_tensor_info.normalize.norm[0] = 0.229f;
input_tensor_info.normalize.norm[1] = 0.224f;
input_tensor_info.normalize.norm[2] = 0.225f;
input_tensor_list.push_back(input_tensor_info);
std::vector output_tensor_list;
output_tensor_list.push_back(OutputTensorInfo("MobilenetV2/Predictions/Reshape_1", TensorInfo::TENSOR_TYPE_FP32));
inference_helper->initialize("mobilenet_v2_1.0_224.tflite", input_tensor_list, output_tensor_list);
```
### int32_t Finalize(void)
- Finalize inference helper
```c++
inference_helper->Finalize();
```
### int32_t PreProcess(const std::vector& input_tensor_info_list)
- Run preprocess
- Call this function before invoke
- Call this function even if the input data is already pre-processed in order to copy data to memory
- **Note** : Some frameworks don't support crop, resize. So, it's better to resize image before calling preProcess.
```c++
inference_helper->PreProcess(input_tensor_list);
```
### int32_t Process(std::vector& output_tensor_info_list)
- Run inference
```c++
inference_helper->Process(output_tensor_info_list)
```
## TensorInfo (InputTensorInfo, OutputTensorInfo)
### Enumeration
```c++
enum {
kTensorTypeNone,
kTensorTypeUint8,
kTensorTypeInt8,
kTensorTypeFp32,
kTensorTypeInt32,
kTensorTypeInt64,
};
```
### Properties
```c++
std::string name; // [In] Set the name_ of tensor
int32_t id; // [Out] Do not modify (Used in InferenceHelper)
int32_t tensor_type; // [In] The type of tensor (e.g. kTensorTypeFp32)
std::vector tensor_dims; // InputTensorInfo: [In] The dimentions of tensor. (If empty at initialize, the size is updated from model info.)
// OutputTensorInfo: [Out] The dimentions of tensor is set from model information
bool is_nchw; // [IN] NCHW or NHWC
```
## InputTensorInfo
### Enumeration
```c++
enum {
kDataTypeImage,
kDataTypeBlobNhwc, // data_ which already finished preprocess(color conversion, resize, normalize_, etc.)
kDataTypeBlobNchw,
};
```
### Properties
```c++
void* data; // [In] Set the pointer to image/blob
int32_t data_type; // [In] Set the type of data_ (e.g. kDataTypeImage)
struct {
int32_t width;
int32_t height;
int32_t channel;
int32_t crop_x;
int32_t crop_y;
int32_t crop_width;
int32_t crop_height;
bool is_bgr; // used when channel == 3 (true: BGR, false: RGB)
bool swap_color;
} image_info; // [In] used when data_type_ == kDataTypeImage
struct {
float mean[3];
float norm[3];
} normalize; // [In] used when data_type_ == kDataTypeImage
```
## OutputTensorInfo
### Properties
```c++
void* data; // [Out] Pointer to the output data_
struct {
float scale;
uint8_t zero_point;
} quant; // [Out] Parameters for dequantization (convert uint8 to float)
```
### float* GetDataAsFloat()
- Get output data in the form of FP32
- When tensor type is INT8 (quantized), the data is converted to FP32 (dequantized)
```c++
const float* val_float = output_tensor_list[0].GetDataAsFloat();
```
# License
- InferenceHelper
- https://github.com/iwatake2222/InferenceHelper
- Copyright 2020 iwatake2222
- Licensed under the Apache License, Version 2.0
# Acknowledgements
- This project utilizes OSS (Open Source Software)
- [NOTICE.md](NOTICE.md)