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Tencent / TNN

TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.
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ONNX模型转TNN模型后在安卓端推理结果错误 #1140

Closed bestpower closed 3 years ago

bestpower commented 3 years ago

1. 环境（environment）

1、模型转换工具编译： 编译脚本路径 TNN/tools/onnx2tnn/onnx-converter/build.sh

2、模型转换 转换脚本路径及命令 python3 TNN/tools/convert2tnn/converter.py onnx2tnn $ONNX_MODEL_DIR/${onnx_model} -optimize -v v3.0 -o $OUTPUT_TNN_MODEL_DIR/

3、推理库编译 编译命令 export ANDROID_NDK=/home/wyu/SDK/NDK/android-ndk-r19c cd TNN/scripts rm -rf release ./build_android.sh cp -r release $OUTPUT_TNN_LID_DIR

4. 编译日志(build log)

1、转换工具编译日志
-- The C compiler identification is GNU 7.5.0
-- The CXX compiler identification is GNU 7.5.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- The ASM compiler identification is GNU
-- Found assembler: /usr/bin/cc
-- >>>>>>>>>>>>>
-- TNN BUILD INFO:
--  System: Linux
--  Processor: x86_64
--  Cpu:    ON
--  X86:    OFF
--  Arm:    OFF
--  Arm82:  OFF
--  Metal:  OFF
--  OpenCL: OFF
--  CUDA:   OFF
--  DSP:    OFF
--  Atlas:  OFF
--  TensorRT:   OFF
--  HuaweiNPU:  OFF
--  RKNPU:  OFF
--  OpenVINO:   OFF
--  OpenMP: OFF
--  TEST:   OFF
--  --Unit Test:    OFF
--  Quantization:   OFF
--  ModelCheck: OFF
--  DEBUG:  ON
--  PROFILE:    OFF
--  BENCHMARK:  OFF
--  BENCHMARK Layer:    OFF
--  Model Converter:    OFF
--  ONNX2TNN Converter: ON
--  TNN2MEM:    OFF
--  BENCHMARK Test Lib: OFF
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Looking for pthread_create
-- Looking for pthread_create - not found
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE  
-- Found Protobuf: /usr/local/lib/libprotobuf.so;-lpthread (found version "3.5.1") 
-- Found PythonInterp: /usr/bin/python3.6 (found version "3.6.9") 
-- Found PythonLibs: /usr/lib/x86_64-linux-gnu/libpython3.6m.so
-- pybind11 v2.4.dev4
-- Configuring done
-- Generating done
-- Build files have been written to: /home/wyu/github_projects/TNN/tools/onnx2tnn/onnx-converter/build
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[100%] Linking CXX shared module onnx2tnn.cpython-36m-x86_64-linux-gnu.so
[100%] Built target onnx2tnn
/home/wyu/github_projects/TNN/tools/onnx2tnn/onnx-converter/build
Compiled onnx2tnn successfully !

2、转换日志
----------  convert model, please wait a moment ----------

Converter ONNX to TNN Model...

Converter ONNX to TNN check_onnx_dim...

Converter ONNX to TNN check_onnx_dim...

Converter ONNX to TNN model succeed!

3、推理库编译日志
******************** step 1: update version.h ********************
/home/wyu/github_projects/TNN/scripts/version
Target: tnn
Commit Branch: hotfix_issue_1109
Commit Date: 2021-07-07
Commit Hash: ebc079cf

******************** step 2: start build rpn arm32 ********************
remove build32
armeabi-v7a
-- android-14 is unsupported. Using minimum supported version 16.
-- Check for working C compiler: /home/wyu/SDK/NDK/android-ndk-r19c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang
-- Check for working C compiler: /home/wyu/SDK/NDK/android-ndk-r19c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /home/wyu/SDK/NDK/android-ndk-r19c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang++
-- Check for working CXX compiler: /home/wyu/SDK/NDK/android-ndk-r19c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- The ASM compiler identification is Clang
-- Found assembler: /home/wyu/SDK/NDK/android-ndk-r19c/toolchains/llvm/prebuilt/linux-x86_64/bin/clang
-- Found OpenMP_C: -fopenmp=libomp (found version "3.1") 
-- Found OpenMP_CXX: -fopenmp=libomp (found version "3.1") 
-- Found OpenMP: TRUE (found version "3.1")  
-- >>>>>>>>>>>>>
-- TNN BUILD INFO:
--  System: Android
--  Processor: armv7-a
--  Cpu:    ON
--  X86:    OFF
--  Arm:    ON
--  Arm82:  OFF
--  Metal:  OFF
--  OpenCL: ON
--  CUDA:   OFF
--  DSP:    OFF
--  Atlas:  OFF
--  TensorRT:   OFF
--  HuaweiNPU:  OFF
--  RKNPU:  OFF
--  OpenVINO:   OFF
--  OpenMP: ON
--  TEST:   ON
--  --Unit Test:    OFF
--  Quantization:   OFF
--  ModelCheck: OFF
--  DEBUG:  OFF
--  PROFILE:    OFF
--  BENCHMARK:  OFF
--  BENCHMARK Layer:    OFF
--  Model Converter:    OFF
--  ONNX2TNN Converter: OFF
--  TNN2MEM:    OFF
--  BENCHMARK Test Lib: OFF
-- Found PythonInterp: /usr/bin/python (found version "3.6.9") 
Generate OpenCL Source done !!! 

-- Build TNN OpenCL
-- Looking for C++ include unistd.h
-- Looking for C++ include unistd.h - found
-- Looking for C++ include stdint.h
-- Looking for C++ include stdint.h - found
-- Looking for C++ include inttypes.h
-- Looking for C++ include inttypes.h - found
-- Looking for C++ include sys/types.h
-- Looking for C++ include sys/types.h - found
-- Looking for C++ include sys/stat.h
-- Looking for C++ include sys/stat.h - found
-- Looking for C++ include fnmatch.h
-- Looking for C++ include fnmatch.h - found
-- Looking for C++ include stddef.h
-- Looking for C++ include stddef.h - found
-- Check size of uint32_t
-- Check size of uint32_t - done
-- Looking for strtoll
-- Looking for strtoll - found
-- Configuring done
-- Generating done
-- Build files have been written to: /home/wyu/github_projects/TNN/scripts/build32
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******************** step 4: add version attr ********************
******************** step 4: copy to release ********************
build done!

5. 详细描述bug 情况 (Describe the bug)

   编译出来的推理库和转换的模型放到安卓端应用里推理得不到正确结果，模型是做人脸检测（RetinaFace）的，节点输出的各个维度都对，但是generate后没有任何人脸信息的输出，应该是模型转换或者输入预处理还是有问题。经验证原始onnx模型没有问题，在通过mnn框架的转换与部署到安卓端推理可以得到正确的结果，而在tnn上就不行，而generate代码用的是同一套。麻烦帮忙分析一下问题出在哪。

模型加载与推理部分核心代码：

//模型与推理网络加载 JNIEXPORT JNICALL jint TNN_CLASSIFY(init)(JNIEnv *env, jobject thiz, jstring modelPath, jstring protoPath) {

std::string protoContent, modelContent;
std::string modelPathStr(jstring2string(env, modelPath));
std::string protoPathStr(jstring2string(env, protoPath));
protoContent = fdLoadFile(protoPathStr);
modelContent = fdLoadFile(modelPathStr);

TNN_NS::Status status = TNN_NS::TNN_OK;
TNN_NS::ModelConfig config = TNN_NS::ModelConfig();
config.model_type = TNN_NS::MODEL_TYPE_TNN;
config.params = {protoContent, modelContent};

auto net = std::make_shared<TNN_NS::TNN>();
status = net->Init(config);

device_type_ = TNN_NS::DEVICE_ARM;

TNN_NS::InputShapesMap shapeMap = TNN_NS::InputShapesMap();
TNN_NS::NetworkConfig network_config = TNN_NS::NetworkConfig();
network_config.library_path = {""};
network_config.device_type = device_type_;

    faceDetect_net_ = net;
    TNN_NS::DimsVector target_dims = {1, 3, 640, 640};
    shapeMap.insert(make_pair("data", target_dims));
    auto instance_0 = faceDetect_net_->CreateInst(network_config, status, shapeMap);
    instance_0->SetCpuNumThreads(4);
    faceDetect_instance_ = instance_0;

if (status != TNN_NS::TNN_OK) {
    LOGE("TNN init failed %d", (int) status);
    return -1;
}
return 0;

}

//模型推理代码 //输入参数主要为待检测图像bitmap对象、待检测图像宽度、待检测图像高度 JNIEXPORT JNICALL jint TNN_CLASSIFY(getFaceDetect)(JNIEnv *env, jobject thiz, jobject imageSource, jint width, jint height) {

jint ret = -1;
TNN_NS::Status status = TNN_NS::TNN_OK;
AndroidBitmapInfo sourceInfocolor;
void *sourcePixelscolor;
int origin_width = width;
int origin_height = height;

if (AndroidBitmap_getInfo(env, imageSource, &sourceInfocolor) < 0) {
    return nullptr;
}

if (sourceInfocolor.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
    return nullptr;
}

if (AndroidBitmap_lockPixels(env, imageSource, &sourcePixelscolor) < 0) {
    return nullptr;
}

//orgin dims
TNN_NS::DimsVector origin_dims = {1, 4, origin_height, origin_width};
//target dims
TNN_NS::DimsVector target_dims = {1, 3, 640, 640};

auto input_mat = std::make_shared<TNN_NS::Mat>(device_type_, TNN_NS::N8UC4, origin_dims, sourcePixelscolor);
//here add the resize
auto target_mat = std::make_shared<TNN_NS::Mat>(device_type_, TNN_NS::N8UC3, target_dims);
TNN_NS::ResizeParam param = TNN_NS::ResizeParam();
TNN_NS::MatUtils::Resize(*input_mat, *target_mat, param, nullptr);

// step 1. set input mat
TNN_NS::MatConvertParam input_cvt_param = TNN_NS::MatConvertParam();
auto input_status = faceDetect_instance_->SetInputMat(target_mat, input_cvt_param, "data");//输入节点

if (input_status != TNN_NS::TNN_OK) {return nullptr;}

// step 2. Forward
status = faceDetect_instance_->ForwardAsync(nullptr);
if (status != TNN_NS::TNN_OK) {return nullptr;}

// step 3. get output mat
std::shared_ptr<TNN_NS::Mat> score_stride8 = nullptr;
std::shared_ptr<TNN_NS::Mat> bbox_stride8 = nullptr;
std::shared_ptr<TNN_NS::Mat> landmark_stride8 = nullptr;
std::shared_ptr<TNN_NS::Mat> score_stride16 = nullptr;
std::shared_ptr<TNN_NS::Mat> bbox_stride16 = nullptr;
std::shared_ptr<TNN_NS::Mat> landmark_stride16 = nullptr;
std::shared_ptr<TNN_NS::Mat> score_stride32 = nullptr;
std::shared_ptr<TNN_NS::Mat> bbox_stride32 = nullptr;
std::shared_ptr<TNN_NS::Mat> landmark_stride32 = nullptr;

TNN_NS::MatConvertParam output_cvt_param = TNN_NS::MatConvertParam();

/**
 * stride 8
 **/
status = faceDetect_instance_->GetOutputMat(score_stride8, output_cvt_param, "face_rpn_cls_prob_reshape_stride8");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *score_stride8_data = static_cast<float*>(score_stride8->GetData());
LOGE("score_stride8 shape: %d %d %d %d", score_stride8->GetBatch(), score_stride8->GetChannel(), score_stride8->GetHeight(), score_stride8->GetWidth());

status = faceDetect_instance_->GetOutputMat(bbox_stride8, output_cvt_param, "face_rpn_bbox_pred_stride8");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *bbox_stride8_data = static_cast<float*>(bbox_stride8->GetData());
LOGE("bbox_stride8 shape: %d %d %d %d", bbox_stride8->GetBatch(), bbox_stride8->GetChannel(), bbox_stride8->GetHeight(), bbox_stride8->GetWidth());

status = faceDetect_instance_->GetOutputMat(landmark_stride8, output_cvt_param, "face_rpn_landmark_pred_stride8");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *landmark_stride8_data = static_cast<float*>(landmark_stride8->GetData());
LOGE("landmark_stride8 shape: %d %d %d %d", landmark_stride8->GetBatch(), landmark_stride8->GetChannel(), landmark_stride8->GetHeight(), landmark_stride8->GetWidth());

/**
 * stride 16
 **/
status = faceDetect_instance_->GetOutputMat(score_stride16, output_cvt_param, "face_rpn_cls_prob_reshape_stride16");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *score_stride16_data = static_cast<float*>(score_stride16->GetData());
LOGE("score_stride16 shape: %d %d %d %d", score_stride16->GetBatch(), score_stride16->GetChannel(), score_stride16->GetHeight(), score_stride16->GetWidth());

status = faceDetect_instance_->GetOutputMat(bbox_stride16, output_cvt_param, "face_rpn_bbox_pred_stride16");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *bbox_stride16_data = static_cast<float*>(bbox_stride16->GetData());
LOGE("bbox_stride16 shape: %d %d %d %d", bbox_stride16->GetBatch(), bbox_stride16->GetChannel(), bbox_stride16->GetHeight(), bbox_stride16->GetWidth());

status = faceDetect_instance_->GetOutputMat(landmark_stride16, output_cvt_param, "face_rpn_landmark_pred_stride16");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *landmark_stride16_data = static_cast<float*>(landmark_stride16->GetData());
LOGE("landmark_stride16 shape: %d %d %d %d", landmark_stride16->GetBatch(), landmark_stride16->GetChannel(), landmark_stride16->GetHeight(), landmark_stride16->GetWidth());

/**
 * stride 32
 **/
status = faceDetect_instance_->GetOutputMat(score_stride32, output_cvt_param, "face_rpn_cls_prob_reshape_stride32");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *score_stride32_data = static_cast<float*>(score_stride32->GetData());
LOGE("score_stride32 shape: %d %d %d %d", score_stride32->GetBatch(), score_stride32->GetChannel(), score_stride32->GetHeight(), score_stride32->GetWidth());

status = faceDetect_instance_->GetOutputMat(bbox_stride32, output_cvt_param, "face_rpn_bbox_pred_stride32");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *bbox_stride32_data = static_cast<float*>(bbox_stride32->GetData());
LOGE("bbox_stride32 shape: %d %d %d %d", bbox_stride32->GetBatch(), bbox_stride32->GetChannel(), bbox_stride32->GetHeight(), bbox_stride32->GetWidth());

status = faceDetect_instance_->GetOutputMat(landmark_stride32, output_cvt_param, "face_rpn_landmark_pred_stride32");
if (status != TNN_NS::TNN_OK) {return nullptr;}
const auto *landmark_stride32_data = static_cast<float*>(landmark_stride32->GetData());
LOGE("landmark_stride32 shape: %d %d %d %d", landmark_stride32->GetBatch(), landmark_stride32->GetChannel(), landmark_stride32->GetHeight(), landmark_stride32->GetWidth());

AndroidBitmap_unlockPixels(env, imageSource);

/*
... 推理结果generate融合代码
*/

env->DeleteLocalRef(imageSource);
ret = 0；
return ret;

}

6. 运行日志（runtime log）

输出的9个节点的nchw sized的数据如下

score_stride8 shape: 1 4 80 80
bbox_stride8 shape: 1 8 80 80
landmark_stride8 shape: 1 20 80 80
score_stride16 shape: 1 4 40 40
bbox_stride16 shape: 1 8 40 40
landmark_stride16 shape: 1 20 40 40
score_stride32 shape: 1 4 20 20
bbox_stride32 shape: 1 8 20 20
landmark_stride32 shape: 1 20 20 20

与mnn框架推理一致，但在generate后未有任何人脸数据输出，而mnn可以正常检测到且结果正确。

模型详见附件文件！ models.zip

bestpower commented 3 years ago

1109

该问题是在这里问题的基础上提出的 补充：在输入到jni推理函数getFaceDetect前，已经在安卓端作了如下处理：

//计算原图最大边长
Bitmap bitmapBW ;
int max_size = Math.max(currentBitmap.getWidth(), currentBitmap.getHeight());
if(max_size>640) {
    //以最大边长的正方形为背景图
    bitmapBW = Bitmap.createBitmap(max_size, max_size, Bitmap.Config.ARGB_8888);
    bwCanvas = new Canvas(bitmapBW);
    bwCanvas.drawARGB(255, 0, 0, 0);
    //将原图绘制在背景图左上位上
    bwCanvas.drawBitmap(currentBitmap, 0, 0, null);
   //将正方形背景图尺寸缩放到640x640
    bitmapBW = ImageProcessUtil.scaleBitmap(bitmapBW, 640, 640, 0, false);
}else{
    //以640边长的正方形为背景图
    bitmapBW = Bitmap.createBitmap(640, 640, Bitmap.Config.ARGB_8888);
    bwCanvas = new Canvas(bitmapBW);
    bwCanvas.drawARGB(255, 0, 0, 0);
    //将原图绘制在背景图左上位上
    bwCanvas.drawBitmap(currentBitmap, 0, 0, null);
}
......
//以bitmapBW作为待检测的Bitmap输入对象
bestpower commented 3 years ago

如果不方便从头分析的话，能否先帮忙看一下是否是模型加载和推理部分的代码有问题：

JNIEXPORT JNICALL jint
TNN_CLASSIFY(init)(JNIEnv *env, jobject thiz, jstring modelPath, jstring protoPath) {

      std::string protoContent, modelContent;
      std::string modelPathStr(jstring2string(env, modelPath));
      std::string protoPathStr(jstring2string(env, protoPath));
      protoContent = fdLoadFile(protoPathStr);
      modelContent = fdLoadFile(modelPathStr);

      TNN_NS::Status status = TNN_NS::TNN_OK;
      TNN_NS::ModelConfig config = TNN_NS::ModelConfig();
      config.model_type = TNN_NS::MODEL_TYPE_TNN;
      config.params = {protoContent, modelContent};

      auto net = std::make_shared<TNN_NS::TNN>();
      status = net->Init(config);

      device_type_ = TNN_NS::DEVICE_ARM;

      TNN_NS::InputShapesMap shapeMap = TNN_NS::InputShapesMap();
      TNN_NS::NetworkConfig network_config = TNN_NS::NetworkConfig();
      network_config.library_path = {""};
      network_config.device_type = device_type_;

      faceDetect_net_ = net;
      TNN_NS::DimsVector target_dims = {1, 3, 640, 640};
      shapeMap.insert(make_pair("data", target_dims));
      auto instance_0 = faceDetect_net_->CreateInst(network_config, status, shapeMap);
      instance_0->SetCpuNumThreads(4);
      faceDetect_instance_ = instance_0;

      if (status != TNN_NS::TNN_OK) {
          LOGE("TNN init failed %d", (int) status);
          return -1;
      }
      return 0;
}

JNIEXPORT JNICALL jint
TNN_CLASSIFY(getFaceDetect)(JNIEnv *env, jobject thiz, jobject imageSource, jint width, jint height) {

      jint ret = -1;
      TNN_NS::Status status = TNN_NS::TNN_OK;
      AndroidBitmapInfo sourceInfocolor;
      void *sourcePixelscolor;
      int origin_width = width;
      int origin_height = height;

      if (AndroidBitmap_getInfo(env, imageSource, &sourceInfocolor) < 0) {
          return nullptr;
      }

      if (sourceInfocolor.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
          return nullptr;
      }

      if (AndroidBitmap_lockPixels(env, imageSource, &sourcePixelscolor) < 0) {
          return nullptr;
      }

      //orgin dims
      TNN_NS::DimsVector origin_dims = {1, 4, origin_height, origin_width};
      //target dims
      TNN_NS::DimsVector target_dims = {1, 3, 640, 640};

      auto input_mat = std::make_shared<TNN_NS::Mat>(device_type_, TNN_NS::N8UC4, origin_dims, sourcePixelscolor);
      //here add the resize
      auto target_mat = std::make_shared<TNN_NS::Mat>(device_type_, TNN_NS::N8UC3, target_dims);
      TNN_NS::ResizeParam param = TNN_NS::ResizeParam();
      TNN_NS::MatUtils::Resize(*input_mat, *target_mat, param, nullptr);

      // step 1. set input mat
      TNN_NS::MatConvertParam input_cvt_param = TNN_NS::MatConvertParam();
      auto input_status = faceDetect_instance_->SetInputMat(target_mat, input_cvt_param, "data");//输入节点

      if (input_status != TNN_NS::TNN_OK) {return nullptr;}

      // step 2. Forward
      status = faceDetect_instance_->ForwardAsync(nullptr);
      if (status != TNN_NS::TNN_OK) {return nullptr;}

      // step 3. get output mat
      std::shared_ptr<TNN_NS::Mat> score_stride8 = nullptr;
      std::shared_ptr<TNN_NS::Mat> bbox_stride8 = nullptr;
      std::shared_ptr<TNN_NS::Mat> landmark_stride8 = nullptr;
      std::shared_ptr<TNN_NS::Mat> score_stride16 = nullptr;
      std::shared_ptr<TNN_NS::Mat> bbox_stride16 = nullptr;
      std::shared_ptr<TNN_NS::Mat> landmark_stride16 = nullptr;
      std::shared_ptr<TNN_NS::Mat> score_stride32 = nullptr;
      std::shared_ptr<TNN_NS::Mat> bbox_stride32 = nullptr;
      std::shared_ptr<TNN_NS::Mat> landmark_stride32 = nullptr;

      TNN_NS::MatConvertParam output_cvt_param = TNN_NS::MatConvertParam();

      /**
       * stride 8
       **/
      status = faceDetect_instance_->GetOutputMat(score_stride8, output_cvt_param, "face_rpn_cls_prob_reshape_stride8");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *score_stride8_data = static_cast<float*>(score_stride8->GetData());
      LOGE("score_stride8 shape: %d %d %d %d", score_stride8->GetBatch(), score_stride8->GetChannel(), score_stride8->GetHeight(), score_stride8->GetWidth());

      status = faceDetect_instance_->GetOutputMat(bbox_stride8, output_cvt_param, "face_rpn_bbox_pred_stride8");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *bbox_stride8_data = static_cast<float*>(bbox_stride8->GetData());
      LOGE("bbox_stride8 shape: %d %d %d %d", bbox_stride8->GetBatch(), bbox_stride8->GetChannel(), bbox_stride8->GetHeight(), bbox_stride8->GetWidth());

      status = faceDetect_instance_->GetOutputMat(landmark_stride8, output_cvt_param, "face_rpn_landmark_pred_stride8");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *landmark_stride8_data = static_cast<float*>(landmark_stride8->GetData());
      LOGE("landmark_stride8 shape: %d %d %d %d", landmark_stride8->GetBatch(), landmark_stride8->GetChannel(), landmark_stride8->GetHeight(), landmark_stride8->GetWidth());

      /**
       * stride 16
       **/
      status = faceDetect_instance_->GetOutputMat(score_stride16, output_cvt_param, "face_rpn_cls_prob_reshape_stride16");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *score_stride16_data = static_cast<float*>(score_stride16->GetData());
      LOGE("score_stride16 shape: %d %d %d %d", score_stride16->GetBatch(), score_stride16->GetChannel(), score_stride16->GetHeight(), score_stride16->GetWidth());

      status = faceDetect_instance_->GetOutputMat(bbox_stride16, output_cvt_param, "face_rpn_bbox_pred_stride16");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *bbox_stride16_data = static_cast<float*>(bbox_stride16->GetData());
      LOGE("bbox_stride16 shape: %d %d %d %d", bbox_stride16->GetBatch(), bbox_stride16->GetChannel(), bbox_stride16->GetHeight(), bbox_stride16->GetWidth());

      status = faceDetect_instance_->GetOutputMat(landmark_stride16, output_cvt_param, "face_rpn_landmark_pred_stride16");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *landmark_stride16_data = static_cast<float*>(landmark_stride16->GetData());
      LOGE("landmark_stride16 shape: %d %d %d %d", landmark_stride16->GetBatch(), landmark_stride16->GetChannel(), landmark_stride16->GetHeight(), landmark_stride16->GetWidth());

      /**
       * stride 32
       **/
      status = faceDetect_instance_->GetOutputMat(score_stride32, output_cvt_param, "face_rpn_cls_prob_reshape_stride32");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *score_stride32_data = static_cast<float*>(score_stride32->GetData());
      LOGE("score_stride32 shape: %d %d %d %d", score_stride32->GetBatch(), score_stride32->GetChannel(), score_stride32->GetHeight(), score_stride32->GetWidth());

      status = faceDetect_instance_->GetOutputMat(bbox_stride32, output_cvt_param, "face_rpn_bbox_pred_stride32");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *bbox_stride32_data = static_cast<float*>(bbox_stride32->GetData());
      LOGE("bbox_stride32 shape: %d %d %d %d", bbox_stride32->GetBatch(), bbox_stride32->GetChannel(), bbox_stride32->GetHeight(), bbox_stride32->GetWidth());

      status = faceDetect_instance_->GetOutputMat(landmark_stride32, output_cvt_param, "face_rpn_landmark_pred_stride32");
      if (status != TNN_NS::TNN_OK) {return nullptr;}
      const auto *landmark_stride32_data = static_cast<float*>(landmark_stride32->GetData());
      LOGE("landmark_stride32 shape: %d %d %d %d", landmark_stride32->GetBatch(), landmark_stride32->GetChannel(), landmark_stride32->GetHeight(), landmark_stride32->GetWidth());

      AndroidBitmap_unlockPixels(env, imageSource);

      /*
      ... 推理结果generate融合代码
      */

      env->DeleteLocalRef(imageSource);
      ret = 0；
      return ret;
}
shaundai-tencent commented 3 years ago

可以通过以下步骤一步步排查问题： 1、排查转出来的TNN模型是否正确，具体是跟onnx模型结果做对比。可以通过在模型转换的时候加上 -align 参数来校验，TNN的模型结果是否和onnx模型结果一致。 2、确定具体TNN执行推理的平台结果是否正确。可以通过model_check工具进行排查（https://github.com/Tencent/TNN/blob/master/doc/cn/development/model_check.md），通过-d指定需要运行的平台，最终看此平台结果是否与模型的参考输出结果一致。 3、如果上述都没有问题，那说明问题不在TNN这边，有可能在预处理或者后处理部分。可以参考的工程进行对比，是不是送入模型的数据有问题。或者模型的输出数据的处理有问题。

bestpower commented 3 years ago

前两步排查是通过了的，关于预处理的方法已经是参考了其他demo来做的，也试了各种配置还是检测不到 现在主要有两点疑问： 1、这个模型在ncnn的预处理中是不需要将输入图像数据缩放即可检测出结果的，貌似在model_check时提示这个模型的输入可以是任意的，tnn是否一定要指定输入尺寸或者有其他什么处理方式？ 2、输出节点数据类型NCHW_FLOAT数组的排列顺序是怎样的，我在mnn框架上也是设置的NCHW类型的输出，打日志它们输出的nchw size是一致的，通过同一套generate代码mnn就可以正确输出结果，而tnn就不行？

bestpower commented 3 years ago

TNN相较于ncnn，对于数据输入预处理的可配置项明显多了很多，之前看了你们源码自带的Demo以及其他人写的Demo，预处理这一块的代码感觉都写的比较随意，看着很乱，我这边参考也只能一个个乱试，到现在也还是检测不出来，浪费了好多时间，最近我们也是在考虑用新的框架，如果大家总在这些问题上卡住得不到解决，那你们这个新框架也是很难推广的，所以关于输入节点数据预处理的流程及其他类似的问题，你们官方能否出一份标准文档？

shaundai-tencent commented 3 years ago

看了一下代码， TNN_NS::MatUtils::Resize(input_mat, target_mat, param, nullptr); 这一步没有检查返回值，这里应该会返回错误，因为输入和输出的mat的mat type不一致，输入是N8UC4，但是输出是N8UC3。这里报错的话，就不会做resize操作，确认一下这里是否正确执行了。

bestpower commented 3 years ago

看了一下代码， TNN_NS::MatUtils::Resize(input_mat, target_mat, param, nullptr); 这一步没有检查返回值，这里应该会返回错误，因为输入和输出的mat的mat type不一致，输入是N8UC4，但是输出是N8UC3。这里报错的话，就不会做resize操作，确认一下这里是否正确执行了。

我这边都试过，Resize操作在java层已经做过了，模型输入如果是N8UC4 {1, 4, 640, 640}会报错，输入N8UC3 {1, 3, 640, 640}会正常执行，但是最后也检测不出来

shaundai-tencent commented 3 years ago

那建议先检查一下，SetInputMat的那个Mat数据是否正确。送入Mat的数据是RGB或者BGR的INT8数据吗？是否需要设置MatConvertParam？

bestpower commented 3 years ago

那建议先检查一下，SetInputMat的那个Mat数据是否正确。送入Mat的数据是RGB或者BGR的INT8数据吗？是否需要设置MatConvertParam？

输入是BGR格式，按你们代码里的解释就是N8UC3，MatConvertParam里就是默认参数，我用ncnn和mnn框架推理都没有mean和normal的设置，其他我就是在java层做了个尺寸变换和背景填充，变成ARGB8888和640*640的Bitmap输入到native代码里

shaundai-tencent commented 3 years ago

那这个模型的输入是RGB 3通道的，为什么要做背景填充。将ARGB8888数据送入native之后，有没有做一个ARGB8888到RGB的转换？

bestpower commented 3 years ago

那这个模型的输入是RGB 3通道的，为什么要做背景填充。将ARGB8888数据送入native之后，有没有做一个ARGB8888到RGB的转换？

做背景填充是因为模型的输入size要求是640x640，而原图的宽高不一定相等，所以先做填充再做尺寸变换来保证输入尺寸的一致性。没有做ARGB8888到RGB的转换，我看框架里是有带CvtColor的方法，但是没有这两个格式转换类型，这种情况该怎么处理？

typedef enum {
    COLOR_CONVERT_NV12TOBGR  = 0x00,
    COLOR_CONVERT_NV12TOBGRA = 0x01,
    COLOR_CONVERT_NV21TOBGR  = 0x02,
    COLOR_CONVERT_NV21TOBGRA = 0x03,
    COLOR_CONVERT_BGRTOGRAY  = 0x04,
    COLOR_CONVERT_BGRATOGRAY = 0x05,
    COLOR_CONVERT_RGBTOGRAY  = 0x06,
    COLOR_CONVERT_RGBATOGRAY = 0x07,
} PUBLIC ColorConversionType;
shaundai-tencent commented 3 years ago

RGBA到RGB的转换，直接将Alpah通道（第四个通道）的数据去除就行。重新分配一个存储RGB数据的内存，逐个像素进行重新赋值，取出RGB的值。

bestpower commented 3 years ago

关于输入通道数问题我还有个疑问，我之前看了很多Demo模型文件都是3通道的输入参数，但在对应native代码里看到很多都是写的4通道的数据输入，这些情况是代码有误还是模型本身支持4通道的输入或者自动过滤掉了Alpah通道数据？

shaundai-tencent commented 3 years ago

Demo里面使用的是TNN的API接口，TNN内部不会自动过滤Alpha通道，传入的Mat，数据的排布一定要和MatType对应，比如，实际的数据排布是RGBA，但是传入的MatType是N8UC3，那内部只会按照RGB的数据格式去读取，这样肯定就有问题。Demo里面肯定做了处理，具体是哪个demo写的是4通道输入？

bestpower commented 3 years ago

TNN\examples\android\demo\src\main\jni\cc\blazeface_detector_jni.cc 模型输入要求是{}1, 3, 128, 128}，而代码里写的是{1, 4, 128, 128}

    //orgin dims
    std::vector<int> origin_dims = {1, 4, orig_height, orig_width};
    std::vector<int> resize_dims = {1, 4, target_height, target_width};
    TNN_NS::DeviceType dt = TNN_NS::DEVICE_ARM;
    auto input_mat = std::make_shared<TNN_NS::Mat>(dt, TNN_NS::N8UC4, origin_dims,
                                                   sourcePixelscolor);

    //here add the resize
    auto resize_mat = std::make_shared<TNN_NS::Mat>(dt, TNN_NS::N8UC4, resize_dims);

    TNN_NS::ResizeParam param;
    TNN_NS::MatUtils::Resize(*input_mat, *resize_mat, param, NULL);

TNN\examples\android\demo\src\main\jni\cc\face_detector_jni.cc 模型输入要求是{1, 3, 240, 320}，而代码里写的是{1, 4, 240, 320}

    TNN_NS::DeviceType dt = TNN_NS::DEVICE_ARM;
    TNN_NS::DimsVector target_dims = {1, 4, height, width};
    auto input_mat = std::make_shared<TNN_NS::Mat>(dt, TNN_NS::N8UC4, target_dims, sourcePixelscolor);
shaundai-tencent commented 3 years ago

这种情况是支持的，就是输入的Mat格式是N8UC4，输入的数据也是8UC4的排布，如果模型是3维的，那内部会忽略第四维的数据。总之，输入的Mat的MatType需要和输入的数据排布一致。

bestpower commented 3 years ago

找到出问题的原因了： 1、推理输入问题：按N8UC4和{1,4,640,640}格式输入推理就可以，不用做RGBA到RGB的转换 2、其他问题：在generate代码中，有个写法在tnn工程里调用不被支持，代码执行到那里就会报错，但按N8UC3和{1,3,640,640}的格式输入却不报错，而在ncnn和mnn工程里也不报错，具体原因未知。

shaundai-tencent commented 3 years ago

能否贴一下出错位置的代码？我们排查一下，是否是内部少了异常判断。

bestpower commented 3 years ago

native推理输入按以下写法就可以了，但是MatType写成TNN_NS::N8UC3，origin_dims写成{1, 4, origin_height, origin_width}也不会报错

AndroidBitmapInfo sourceInfocolor;
void *sourcePixelscolor;
int origin_width = width;
int origin_height = height;
if (AndroidBitmap_getInfo(env, imageSource, &sourceInfocolor) < 0) {
    return nullptr;
}
if (sourceInfocolor.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
    return nullptr;
}
if (AndroidBitmap_lockPixels(env, imageSource, &sourcePixelscolor) < 0) {
    return nullptr;
}
TNN_NS::DimsVector origin_dims = {1, 4, origin_height, origin_width};
auto input_mat = std::make_shared<TNN_NS::Mat>(TNN_NS::DEVICE_ARM, TNN_NS::N8UC4, origin_dims, sourcePixelscolor);

generate代码也是在native中执行的，在执行到

#pragma omp parallel sections

语句会报错，做native调试会指向到 __kmp_abort_process libgcc2.c:?，我把这个语句去掉就OK了，报错应该跟TNN库无关，可能跟编译配置有关

shaundai-tencent commented 3 years ago

好的，内部确实没有判断MatType和dims之间是否有矛盾，后面考虑会打印一个错误信息来提示调用者。

bestpower commented 3 years ago

那这个问题先暂时这样了，后面推理如果再出现什么问题再来提