GitHub - yang0000qi/InferenceHelper: Helper Class for Deep Learning Inference Frameworks: TensorFlow Lite, TensorRT, OpenCV, ncnn, MNN, SNPE, Arm NN, NNAbla

This is a helper class for deep learning frameworks especially for inference
This class provides an 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)
OpenCV(dnn) with GPU
ncnn
MNN
SNPE (Snapdragon Neural Processing Engine SDK (Qualcomm Neural Processing SDK for AI v1.51.0))
Arm NN
NNabla
NNabla with CUDA

Supported targets

Windows 10 (Visual Studio 2017 x64, Visual Studio 2019 x64)
Linux (x64, armv7, aarch64)
Android (armv7, aarch64)

Tested Environment

Framework	Windows (x64)	Linux (x64)	Linux (armv7)	Linux (aarch64)	Android (aarch64)
OpenCV(dnn)	OK	OK	OK	OK	not tested
TensorFlow Lite	OK	OK	OK	OK	OK
TensorFlow Lite + XNNPACK	OK	OK	OK	OK	OK
TensorFlow Lite + GPU	not supported	OK	OK	OK	OK
TensorFlow Lite + EdgeTPU	OK	not tested	OK	OK	not supported
TensorFlow Lite + NNAPI	not supported	not supported	not supported	not supported	OK
TensorRT	not tested	not tested	not tested	OK	not supported
ncnn	OK	OK	OK	OK	OK
MNN	OK	OK	OK	OK	OK
SNPE	not supported	not supported	not tested	OK	OK
Arm NN	not supported	OK	not supported	OK	not supported
NNabla	OK	note tested	not supported	OK	not tested
NNabla + CUDA	OK	note tested	not supported	OK	not tested
Note	Visual Studio 2017, 2019	Xubuntu 18.04	Raspberry Pi	Jetson Xavier NX	Pixel 4a

Sample project

https://github.com/iwatake2222/InferenceHelper_Sample

Related projects

Usage

Installation

Add this repository into your project (Using git submodule is recommended)
Download prebuilt libraries
- Download prebuilt libraries (third_party.zip) from https://github.com/iwatake2222/InferenceHelper/releases/
- Extract it to third_party

Extra steps

You need some extra steps if you use the frameworks listed below

Extra steps: Tensorflow Lite

Download header files

git submodule init
git submodule update
cd third_party/tensorflow
chmod +x tensorflow/lite/tools/make/download_dependencies.sh
tensorflow/lite/tools/make/download_dependencies.sh

Extra 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

Extra steps: NNabla

Prepare library:
- If you use pre-built library:
  - Download library from https://nnabla.org/ja/install/#cpplib_list
  - Extract nnabla-cpplib-1.20.1-xxx.zip or nnabla-cpplib-cuda_xxx-1.20.1-xxx.zip into third_party/nnabla_prebuilt/xxx
- If you build library by yourself:
  - Follow the instruction:
    - https://github.com/sony/nnabla/blob/master/doc/build/build_cpp_utils.md
    - https://github.com/sony/nnabla-ext-cuda/blob/master/doc/build/build.md
  - Copy the generated library files into third_party/nnabla_prebuilt/xxx
For Windows users:
- You need to install CUDA and cuDNN
- Extract archive.dll (libarchive-v3.5.2-win64.zip) to the same folder as executable file
  - https://github.com/libarchive/libarchive/releases
- You will get exception error if you run the project with Debug mode in Visual Studio
  - use Release or ReleaseWithDebug mode

Project settings in CMake

Add InferenceHelper to your project

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

# OpenCV (dnn)
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
cmake .. -DINFERENCE_HELPER_ENABLE_NCNN=on
# MNN
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

Enable/Disable preprocess using OpenCV:
- By disabling this option, InferenceHelper is not dependent on OpenCV
```
cmake .. -INFERENCE_HELPER_ENABLE_PRE_PROCESS_BY_OPENCV=off
```

APIs

InferenceHelper

Enumeration

typedef enum {
    kOpencv,
    kOpencvGpu,
    kTensorflowLite,
    kTensorflowLiteXnnpack,
    kTensorflowLiteGpu,
    kTensorflowLiteEdgetpu,
    kTensorflowLiteNnapi,
    kTensorrt,
    kNcnn,
    kMnn,
    kSnpe,
    kArmnn,
    kNnabla,
    kNnablaCuda,
} HelperType;

static InferenceHelper* Create(const HelperType helper_type)

Create InferenceHelper instance for the selected framework

std::unique_ptr<InferenceHelper> 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

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

inference_helper->SetNumThreads(4);

int32_t SetCustomOps(const std::vector<std::pair<const char, const void>>& custom_ops)

Set custom ops
This function needs to be called before initialize

std::vector<std::pair<const char*, const void*>> custom_ops;
custom_ops.push_back(std::pair<const char*, const void*>("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

std::vector<InputTensorInfo> 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<OutputTensorInfo> 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

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.

inference_helper->PreProcess(input_tensor_list);

int32_t Process(std::vector& output_tensor_info_list)

Run inference

inference_helper->Process(output_tensor_info_list)

TensorInfo (InputTensorInfo, OutputTensorInfo)

Enumeration

enum {
    kTensorTypeNone,
    kTensorTypeUint8,
    kTensorTypeInt8,
    kTensorTypeFp32,
    kTensorTypeInt32,
    kTensorTypeInt64,
};

Properties

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<int32_t> 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

enum {
    kDataTypeImage,
    kDataTypeBlobNhwc,  // data_ which already finished preprocess(color conversion, resize, normalize_, etc.)
    kDataTypeBlobNchw,
};

Properties

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

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)

const float* val_float = output_tensor_list[0].GetDataAsFloat();

License

InferenceHelper
https://github.com/iwatake2222/InferenceHelper
Licensed under the Apache License, Version 2.0

Acknowledgements

This project utilizes OSS (Open Source Software)
- NOTICE.md

Supported frameworks

Supported targets

Tested Environment

Sample project

Related projects

Usage

Installation

Extra steps

Extra steps: Tensorflow Lite

Extra steps: SNPE

Extra steps: NNabla

Project settings in CMake

CMake options

APIs

InferenceHelper

Enumeration

static InferenceHelper* Create(const HelperType helper_type)

static void PreProcessByOpenCV(const InputTensorInfo& input_tensor_info, bool is_nchw, cv::Mat& img_blob)

int32_t SetNumThreads(const int32_t num_threads)

int32_t SetCustomOps(const std::vector<std::pair<const char*, const void*>>& custom_ops)

int32_t Initialize(const std::string& model_filename, std::vector& input_tensor_info_list, std::vector& output_tensor_info_list)

int32_t Finalize(void)

int32_t PreProcess(const std::vector& input_tensor_info_list)

int32_t Process(std::vector& output_tensor_info_list)

TensorInfo (InputTensorInfo, OutputTensorInfo)

Enumeration

Properties

InputTensorInfo

Enumeration

Properties

OutputTensorInfo

Properties

float* GetDataAsFloat()

License

Acknowledgements

int32_t SetCustomOps(const std::vector<std::pair<const char, const void>>& custom_ops)