!14132 fix cmsis gesture of pooling opcoder

From: @zoloft
Reviewed-by: @wangchengyuan
Signed-off-by: @wangchengyuan

mindspore/lite/micro/coder/generator/component/const_blocks/model.cc

@@ -20,7 +20,7 @@ namespace mindspore::lite::micro {
 
 const char *model_header = R"RAW(
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -66,12 +66,11 @@ class MModel : public Model {
 
 Model *Model::Import(const char *model_buf, size_t size) {
   MS_NULLPTR_IF_NULL(model_buf);
-  MModel *model = new (std::nothrow) MModel();
-  MS_NULLPTR_IF_NULL(model);
   if (size == 0) {
-    delete model;
     return nullptr;
   }
+  MModel *model = new (std::nothrow) MModel();
+  MS_NULLPTR_IF_NULL(model);
   model->buf = reinterpret_cast<char *>(malloc(size));
   if (model->buf == nullptr) {
     delete model;

mindspore/lite/micro/coder/opcoders/cmsis-nn/int8/pooling_int8_coder.cc

@@ -69,7 +69,7 @@ int PoolingInt8Coder::SetParameters() {
   dim_src_height_ = input_tensor_->Height();
   dim_src_width_ = input_tensor_->Width();
   dim_dst_height_ = output_tensor_->DimensionSize(1);
-  dim_src_width_ = output_tensor_->DimensionSize(2);
+  dim_dst_width_ = output_tensor_->DimensionSize(2);
   ch_src_ = input_tensor_->Channel();
 
   stride_height_ = pooling_parameter_->stride_h_;

mindspore/lite/micro/coder/opcoders/nnacl/fp32/convolution_fp32_coder.cc

@@ -117,7 +117,8 @@ int ConvolutionFP32Coder::DoCode(CoderContext *const context) {
                   "PreSum4x16Int8Peroc.S",
                   "PreSum4x16Int8Pert.S",
                   "IndirectGemmInt16to32_8x4.S",
-                  "MatmulInt8.S"};
+                  "MatmulInt8.S",
+                  "MatmulFp32Opt12x4.S"};
     } else if (target_ == kARM64) {
       asmFiles = {"MatmulFp32.S",          "MatmulFp32Opt.S",      "PreSum4x16Int8Peroc.S",       "MatVecMulFp32.S",
                   "PreSum4x16Int8Peroc.S", "PreSum4x16Int8Pert.S", "IndirectGemmInt16to32_8x4.S", "MatmulInt8.S"};

mindspore/lite/micro/coder/opcoders/nnacl/int8/conv2d_int8_coder.cc

@@ -203,9 +203,16 @@ int Conv2DINT8Coder::DoCode(CoderContext *const context) {
   code.CodeFunction("memset", matmul_packed_input_, 0, matmul_packed_input_size_);
   code.CodeStruct("conv_param", *conv_param_);
 
-  code.CodeBaseStruct("ConvolutionInt8Args", kRunArgs, input_tensor_, packed_input_, matmul_packed_input_,
-                      packed_weight_, bias_data_, output_tensor_, filter_zp_ptr_, input_sum_,
-                      "(ConvParameter *)&conv_param", matmul_func_, "GetSupportOptFlag()");
+  if (target_ == kARM64) {
+    code.CodeBaseStruct("ConvolutionInt8Args", kRunArgs, input_tensor_, packed_input_, matmul_packed_input_,
+                        packed_weight_, bias_data_, output_tensor_, filter_zp_ptr_, input_sum_,
+                        "(ConvParameter *)&conv_param", matmul_func_, "GetSupportOptFlag()");
+  } else {
+    code.CodeBaseStruct("ConvolutionInt8Args", kRunArgs, input_tensor_, packed_input_, matmul_packed_input_,
+                        packed_weight_, bias_data_, output_tensor_, filter_zp_ptr_, input_sum_,
+                        "(ConvParameter *)&conv_param", matmul_func_, support_optimize_);
+  }
+
   if (support_parallel_) {
     code.CodeFunction(kParallelLaunch, gThreadPool, "ConvolutionInt8Run", kRunArgsAddr, gThreadNum);
   } else {

mindspore/lite/micro/coder/opcoders/nnacl/int8/matmul_base_int8_coder.cc

@@ -58,11 +58,11 @@ int MatMulBaseInt8Coder::InitTmpBuffer() {
 MatMulBaseInt8Coder::~MatMulBaseInt8Coder() { FreeQuantParam(); }
 
 void MatMulBaseInt8Coder::ResizeParameter() {
-  param_->row_align_ = UP_ROUND(param_->row_, C4NUM);
-  param_->col_align_ = UP_ROUND(param_->col_, C4NUM);
+  param_->row_align_ = UP_ROUND(param_->row_, row_tile_);
+  param_->col_align_ = UP_ROUND(param_->col_, col_tile_);
   param_->deep_16_ = UP_ROUND(param_->deep_, C16NUM);
-  thread_count_ = MSMIN(param_->op_parameter_.thread_num_, UP_DIV(param_->col_align_, C4NUM));
-  thread_stride_ = UP_DIV(UP_DIV(param_->col_align_, C4NUM), thread_count_);
+  thread_count_ = MSMIN(param_->op_parameter_.thread_num_, UP_DIV(param_->col_align_, col_tile_));
+  thread_stride_ = UP_DIV(UP_DIV(param_->col_align_, col_tile_), thread_count_);
 }
 
 void MatMulBaseInt8Coder::FreeQuantParam() {
@@ -138,6 +138,12 @@ int MatMulBaseInt8Coder::InitQuantParam() {
 void MatMulBaseInt8Coder::InitParameter() {
   param_->a_const_ = (input_tensor_ != nullptr);
   param_->b_const_ = (filter_tensor_ != nullptr);
+  row_tile_ = C4NUM;
+  if (target_ == kARM32A) {
+    col_tile_ = C2NUM;
+  } else {
+    col_tile_ = C4NUM;
+  }
 }
 
 int MatMulBaseInt8Coder::InitBias() {
@@ -189,6 +195,7 @@ int MatMulBaseInt8Coder::DoCode(CoderContext *const context) {
                            param_->deep_, param_->col_, param_->col_align_, param_->deep_16_, quant_.input_.zp_,
                            "init_filter_zp", bias_ptr_, param_->b_transpose_, filter_per_channel_);
   } else {
+    code.CodeArray("init_filter_zp", quant_.filter_zp_, weight_quant_num_, false);
     code.CodeFunction("InitInt8MatrixB", filter_tensor_, weight_bias_sums_, pack_b_ptr_, param_->batch, param_->deep_,
                       param_->col_, param_->col_align_, param_->deep_16_, quant_.input_.zp_, "init_filter_zp",
                       bias_ptr_, param_->b_transpose_, filter_per_channel_);
@@ -216,7 +223,7 @@ int MatMulBaseInt8Coder::DoCode(CoderContext *const context) {
     std::string batch_b_ptr_str = pack_b_ptr_str + "+" + std::to_string(i * param_->col_align_ * param_->deep_16_);
     std::string batch_c_ptr_str = c_ptr_str + "+" + std::to_string(i * param_->row_ * param_->col_);
 
-    int stride = thread_stride_ * C4NUM;
+    int stride = thread_stride_ * col_tile_;
     int cur_stride = task_id * stride;
     int res_stride = param_->col_ - cur_stride;
     int cur_oc = MSMIN(stride, res_stride);

mindspore/lite/micro/coder/opcoders/nnacl/int8/matmul_base_int8_coder.h

@@ -65,6 +65,8 @@ class MatMulBaseInt8Coder : public OperatorCoder {
 
  private:
   int weight_quant_num_{0};
+  int row_tile_{C4NUM};
+  int col_tile_{C4NUM};
 };
 }  // namespace mindspore::lite::micro::nnacl
 #endif  // MINDSPORE_LITE_MICRO_CODER_OPCODERS_NNACL_INT8_MATMUL_BASE_INT8_CODER_H_

mindspore/lite/micro/coder/opcoders/serializers/nnacl_serializer/nnacl_stream_utils.cc

@@ -31,9 +31,10 @@ std::ostream &operator<<(std::ostream &code, const ::QuantArg &quant_arg) {
   return code;
 }
 
-std::ostream &operator<<(std::ostream &code, const OpParameter &tile) {
+std::ostream &operator<<(std::ostream &code, const OpParameter &parameter) {
   code << "{ \"\""
-       << ", " << tile.type_ << ", " << gThreadNum << "}";
+       << ", " << std::boolalpha << parameter.infer_flag_ << ", " << parameter.type_ << ", " << gThreadNum << ", "
+       << parameter.quant_type_ << "}";
   return code;
 }
 

mindspore/lite/micro/coder/operator_library/wrapper/int8/matmul_int8_wrapper.c

@@ -38,10 +38,18 @@ void InitInt8MatrixB(int8_t *weight_ptr, int32_t *weight_bias_sums_batch_, int8_
     int8_t *cur_b_pack = dst_ptr + i * col_align * deep_16;
     int32_t *cur_sums = weight_bias_sums_batch_ + i * col_align;
     if (b_transpose) {
+#ifdef ENABLE_ARM32
+      RowMajor2Row2x16MajorInt8(cur_b, cur_b_pack, col, deep);
+#else
       RowMajor2Row16x4MajorInt8(cur_b, cur_b_pack, col, deep);
+#endif
       CalcWeightBiasSums(cur_b, deep, col, input_zp, weight_zp, bias_ptr, cur_sums, ColMajor, filter_per_channel);
     } else {
+#ifdef ENABLE_ARM32
+      RowMajor2Col16x2MajorInt8(cur_b, cur_b_pack, deep, col);
+#else
       RowMajor2Col16x4MajorInt8(cur_b, deep, col, cur_b_pack);
+#endif
       CalcWeightBiasSums(cur_b, deep, col, input_zp, weight_zp, bias_ptr, cur_sums, RowMajor, false);
     }
   }

mindspore/lite/micro/example/mnist_stm32f746/README.md

@@ -64,6 +64,8 @@
     
 ```
 
+  > 在使用过程中，我们注意到引入Softmax相关的CMSIS算子文件时，头文件中需要加入`arm_nnfunctions.h`,使用者可以稍作注意。
+
 生成代码工程目录如下：
 
 模型推理对外API头文件可由mindspore团队发布的[Release包](https://www.mindspore.cn/tutorial/lite/zh-CN/master/use/downloads.html)中获取。

mindspore/lite/micro/example/mnist_x86/mnist.sh

@@ -71,7 +71,7 @@ gen_mnist() {
     ${CODEGEN_PATH}/codegen --codePath=${BASEPATH}/build --modelPath=${BASEPATH}/build/${MNIST_FILE}
 }
 
-mkdir -p build
+mkdir -p ${BASEPATH}/build
 
 get_version
 download_inference
@@ -85,6 +85,7 @@ if [[ "${GEN}" == "ON" ]]; then
 fi
 
 # 1. build benchmark
+rm -rf ${BASEPATH}/build/benchmark
 mkdir -p ${BASEPATH}/build/benchmark && cd ${BASEPATH}/build/benchmark || exit 1
 cmake -DPKG_PATH=${PKG_PATH} ${BENCHMARK_PATH}
 make

mindspore/lite/micro/example/mobilenetv2/README.md

@@ -86,7 +86,7 @@ mkdir mobilenetv2/build && cd mobilenetv2/build
 
 ```bash
 cmake -DCMAKE_BUILD_TYPE=Release \
--DCMAKE_TOOLCHAIN_FILE="${ANDRIOD_NDK}/build/cmake/android.toolchain.cmake" \
+-DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
 -DANDROID_ABI="arm64-v8a" \
 -DANDROID_TOOLCHAIN_NAME="aarch64-linux-android-clang" \
 -DANDROID_NATIVE_API_LEVEL="19" \
@@ -99,7 +99,7 @@ make
 
 ```bash
 cmake -DCMAKE_BUILD_TYPE=Release \
--DCMAKE_TOOLCHAIN_FILE="${ANDRIOD_NDK}/build/cmake/android.toolchain.cmake" \
+-DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
 -DANDROID_ABI="armeabi-v7a" \
 -DANDROID_TOOLCHAIN_NAME="clang" \
 -DANDROID_NATIVE_API_LEVEL="19" \

mindspore/lite/micro/example/mobilenetv2/mobilenetv2.sh

@@ -15,111 +15,130 @@
 # ============================================================================
 set -e
 
-CURRENT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
-MINDSPORE_ROOT_DIR=${${CURRENT_DIR}%%/mindspore/lite/micro/example/mobilenetv2}
+usage()
+{
+  echo "Usage:"
+  echo "bash build.sh [-I arm64|arm32]"
+  echo "Options:"
+  echo "    -I download and build for arm64 or arm32, default arm64"
+}
 
-OUTPUT_DIR=${1:-${MINDSPORE_ROOT_DIR}/output}
-THREAD_NUM=${2:-32}
-MODULE_NAME=mobilenetv2
-OUTPUT_IR=Reshape-64.ir
-CALIB_OUT=${CURRENT_DIR}/Reshape-64.out
+LITE_PLATFORM="arm64"
+while getopts 'I:' OPT
+do
+    OPTARG=$(echo ${OPTARG} | tr '[A-Z]' '[a-z]')
+    case $OPT in
+        I)
+            if [[ "$OPTARG" == "arm64" ]]; then
+              LITE_PLATFORM="arm64"
+            elif [[ "$OPTARG" == "arm32" ]]; then
+              LITE_PLATFORM="arm32"
+            else
+              echo "-I parameter must be arm64 or arm32"
+              exit 1
+            fi
+            ;;
+        *)
+            echo "Unknown option ${opt}!"
+            usage
+            exit 1
+    esac
+done
 
-echo "current dir is: ${CURRENT_DIR}"
-echo "packed output dir is :${OUTPUT_DIR}"
+BASEPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+MINDSPORE_ROOT_DIR=${BASEPATH%%/mindspore/lite/micro/example/mobilenetv2}
 
-if [ ! -d "${OUTPUT_DIR}" ]; then
-  echo "folder ${OUTPUT_DIR} does not exist"
-  return 1
-fi
+echo "current dir is: ${BASEPATH}"
+
+MOBILE_NAME=mobilenetv2
+MOBILE_FILE=${MOBILE_NAME}.ms
+
+get_version() {
+    local VERSION_HEADER=${MINDSPORE_ROOT_DIR}/mindspore/lite/include/version.h
+    local VERSION_MAJOR=$(grep "const int ms_version_major =" ${VERSION_HEADER} | tr -dc "[0-9]")
+    local VERSION_MINOR=$(grep "const int ms_version_minor =" ${VERSION_HEADER} | tr -dc "[0-9]")
+    local VERSION_REVISION=$(grep "const int ms_version_revision =" ${VERSION_HEADER} | tr -dc "[0-9]")
+    VERSION_STR=${VERSION_MAJOR}.${VERSION_MINOR}.${VERSION_REVISION}
+}
+
+download_inference() {
+    if [[ "${LITE_PLATFORM}" == "arm64" ]]; then
+        local ARM_NAME=aarch64
+    else
+        local ARM_NAME=aarch32
+    fi
+    MINDSPORE_FILE_NAME="mindspore-lite-${VERSION_STR}-inference-android-${ARM_NAME}"
+    local MINDSPORE_FILE="${MINDSPORE_FILE_NAME}.tar.gz"
+    local MINDSPORE_LITE_DOWNLOAD_URL="https://ms-release.obs.cn-north-4.myhuaweicloud.com/${VERSION_STR}/MindSpore/lite/release/linux/${MINDSPORE_FILE}"
 
-# rm if already exist
-WORKSPACE=${CURRENT_DIR}/build
-rm -rf ${WORKSPACE}
-mkdir ${WORKSPACE} || exit 1
-PROJECT_DIR=${WORKSPACE}/${MODULE_NAME}
-
-compare_output() {
-  local OUTPUT_FILE=$1
-  local CALIB_FILE=$2
-  if [[ ! -f "${OUTPUT_FILE}" || ! -f "${CALIB_FILE}" ]]; then
-    echo "file ${OUTPUT_FILE}, ${CALIB_FILE} does not exist, pwd $(pwd)"
-    exit 1
-  fi
-  lines=$(cat ${CALIB_FILE} | wc -l)
-  for ((i = 1; i <= $lines; i++)); do
-    line1=$(awk 'NR=="'${i}'"{print $0}' ${CALIB_FILE})
-    line2=$(awk 'NR=="'${i}'"{print $0}' ${OUTPUT_FILE})
-    if [[ "${line1}" != "${line2}" ]]; then
-      echo -e "file ${OUTPUT_FILE}, ${CALIB_FILE}, compare failed! line: ${i}"
-      exit 1
+    if [ ! -e ${BASEPATH}/build/${MINDSPORE_FILE} ]; then
+      wget -c -O ${BASEPATH}/build/${MINDSPORE_FILE} --no-check-certificate ${MINDSPORE_LITE_DOWNLOAD_URL}
     fi
-  done
-  echo -e "compare success, ${OUTPUT_FILE}, ${CALIB_FILE}"
+
+    tar xzvf ${BASEPATH}/build/${MINDSPORE_FILE} -C ${BASEPATH}/build/ || exit 1
+    rm ${BASEPATH}/build/${MINDSPORE_FILE} || exit 1
+    PKG_PATH=${BASEPATH}/build/${MINDSPORE_FILE_NAME}
 }
 
-# cp oplib and codegen
-cp ${OUTPUT_DIR}/mindspore-lite-*-codegen-linux-x64.tar.gz ${WORKSPACE}/ || exit 1
-cd ${WORKSPACE} || exit 1
-tar -zxf mindspore-lite-*-codegen-linux-x64.tar.gz || exit 1
-cd mindspore-lite-*-codegen-linux-x64 || exit 1
-mv operator_library/ ${WORKSPACE}/ || exit 1
-mv codegen ${WORKSPACE}/ || exit 1
-cd -
-rm -r mindspore-lite-*-codegen-linux-x64 || exit 1
-rm mindspore-lite-*-codegen-linux-x64.tar.gz || exit 1
-
-# convert model
-cp ${OUTPUT_DIR}/mindspore-lite-*-converter-linux-x64.tar.gz ${WORKSPACE}/ || exit 1
-cd ${WORKSPACE} || exit 1
-tar -zxf mindspore-lite-*-converter-linux-x64.tar.gz || exit 1
-rm mindspore-lite-*-converter-linux-x64.tar.gz || exit 1
-cd mindspore-lite-*-converter-linux-x64 || exit 1
-export LD_LIBRARY_PATH=./lib/:./third_party/protobuf/lib:./third_party/flatbuffers/lib:./third_party/glog/lib
-converter/converter_lite --fmk=TFLITE \
-                         --modelFile=${CURRENT_DIR}/mobilenet_v2_1.0_224_quant.tflite \
-                         --outputFile=${WORKSPACE}/mobilenet_v2
-cd -
-rm -rf mindspore-lite-*-converter-linux-x64 || exit 1
-
-# generate code
-${WORKSPACE}/codegen --modelPath=${WORKSPACE}/mobilenet_v2.ms \
-                     --moduleName=${MODULE_NAME} \
-                     --isWeightFile=true \
-                     --debugMode=true
-rm codegen
-
-if [ ! -d "${PROJECT_DIR}" ]; then
-  echo "folder ${PROJECT_DIR} does not exist"
-  return 1
-fi
-cd ${PROJECT_DIR} || exit 1
-
-# 1. build static lib.a
-echo -e "building static library"
-mkdir -p src/build && cd src/build || exit 1
-OP_HEADER_PATH=${WORKSPACE}/operator_library/include
-OP_LIB=${WORKSPACE}/operator_library/lib/x86/libops.a
-echo "Head Path: ${OP_HEADER_PATH}"
-echo "Lib Path: ${OP_LIB}"
-cmake -DCMAKE_BUILD_TYPE=Debug       \
-      -DOP_LIB=${OP_LIB}             \
-      -DOP_HEADER_PATH=${OP_HEADER_PATH} ..
-make -j${THREAD_NUM}
-
-# 2. build benchmark
-cd ${PROJECT_DIR}/benchmark && mkdir -p build && cd build || exit 1
-cmake -DMODEL_LIB="${PROJECT_DIR}/src/build/libnet.a" ..
-make -j${THREAD_NUM}
-
-echo "net file: ${PROJECT_DIR}/src/${MODULE_NAME}.net"
-# 3. run benchmark
-./benchmark ${CURRENT_DIR}/input_1_224_224_3_uint8.bin ${PROJECT_DIR}/src/${MODULE_NAME}.net
-compare_output ${OUTPUT_IR} ${CALIB_OUT}
-
-RET=$?
-if [[ "${RET}" -eq 0 ]]; then
-  echo -e "run benchmark success: ${MODULE_NAME}"
+download_mobile() {
+    local MOBILE_DOWNLOAD_URL=https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_imagenet/r1.2/${MOBILE_FILE}
+
+    if [ ! -e ${BASEPATH}/build/${MOBILE_FILE} ]; then
+      wget -c -O ${BASEPATH}/build/${MOBILE_FILE} --no-check-certificate ${MOBILE_DOWNLOAD_URL}
+    fi
+}
+
+gen_mobile() {
+    local CODEGEN_FILE_NAME="mindspore-lite-${VERSION_STR}-inference-linux-x64"
+    local CODEGEN_FILE="${CODEGEN_FILE_NAME}.tar.gz"
+    local CODEGEN_LITE_DOWNLOAD_URL="https://ms-release.obs.cn-north-4.myhuaweicloud.com/${VERSION_STR}/MindSpore/lite/release/linux/${CODEGEN_FILE}"
+
+    if [ ! -e ${BASEPATH}/build/${CODEGEN_FILE} ]; then
+      wget -c -O ${BASEPATH}/build/${CODEGEN_FILE} --no-check-certificate ${CODEGEN_LITE_DOWNLOAD_URL}
+    fi
+
+    tar xzvf ${BASEPATH}/build/${CODEGEN_FILE} -C ${BASEPATH}/build/ || exit 1
+    rm ${BASEPATH}/build/${CODEGEN_FILE} || exit 1
+    CODEGEN_PATH=${BASEPATH}/build/${CODEGEN_FILE_NAME}/tools/codegen
+    if [[ "${LITE_PLATFORM}" == "arm64" ]]; then
+        local TARGET=ARM64
+    else
+        local TARGET=ARM32A
+    fi
+    ${CODEGEN_PATH}/codegen --codePath=${BASEPATH}/build --modelPath=${BASEPATH}/build/${MOBILE_FILE} --target=${TARGET}
+}
+
+mkdir -p ${BASEPATH}/build
+
+get_version
+download_inference
+
+echo "downloading ${MOBILE_FILE}!"
+download_mobile
+echo "generating mobilenetv2"
+gen_mobile
+BENCHMARK_PATH=${BASEPATH}/build/${MOBILE_NAME}
+
+# build benchmark
+rm -rf ${BASEPATH}/build/benchmark
+mkdir -p ${BASEPATH}/build/benchmark && cd ${BASEPATH}/build/benchmark || exit 1
+
+if [[ "${LITE_PLATFORM}" == "arm64" ]]; then
+    echo "making arm64"
+    cmake -DCMAKE_BUILD_TYPE=Release \
+          -DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
+          -DANDROID_ABI="arm64-v8a" \
+          -DANDROID_TOOLCHAIN_NAME="aarch64-linux-android-clang" \
+          -DANDROID_NATIVE_API_LEVEL="19" \
+          -DMICRO_BUILD_ARM64=ON \
+          -DPKG_PATH=${PKG_PATH} ${BENCHMARK_PATH}
 else
-  echo -e "run benchmark failed: ${MODULE_NAME}"
-  exit 1
-fi
+    cmake -DCMAKE_BUILD_TYPE=Release \
+          -DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
+          -DANDROID_ABI="armeabi-v7a" \
+          -DANDROID_TOOLCHAIN_NAME="clang" \
+          -DANDROID_NATIVE_API_LEVEL="19" \
+          -DMICRO_BUILD_ARM32=ON \
+          -DPKG_PATH=${PKG_PATH} ${BENCHMARK_PATH}
+fi
+make