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!14131 fix cmsis gesture of pooling opcoder

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From: @zoloft
Reviewed-by: @wangchengyuan
Signed-off-by: @wangchengyuan
pull/14131/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent fc603155d3 aa5da5ecc6
commit 6c5361fb5c
9 changed files with 145 additions and 113 deletions
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5
mindspore/lite/micro/coder/generator/component/const_blocks/model.cc
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@ -66,12 +66,11 @@ class MModel : public Model {
Model *Model::Import(const char *model_buf, size_t size) { Model *Model::Import(const char *model_buf, size_t size) {
MS_NULLPTR_IF_NULL(model_buf); MS_NULLPTR_IF_NULL(model_buf);
MModel *model = new (std::nothrow) MModel();
MS_NULLPTR_IF_NULL(model);
if (size == 0) { if (size == 0) {
delete model;
return nullptr; return nullptr;
} }
MModel *model = new (std::nothrow) MModel();
MS_NULLPTR_IF_NULL(model);
model->buf = reinterpret_cast<char *>(malloc(size)); model->buf = reinterpret_cast<char *>(malloc(size));
if (model->buf == nullptr) { if (model->buf == nullptr) {
delete model; delete model;

2
mindspore/lite/micro/coder/opcoders/cmsis-nn/int8/pooling_int8_coder.cc
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@ -69,7 +69,7 @@ int PoolingInt8Coder::SetParameters() {
dim_src_height_ = input_tensor_->Height(); dim_src_height_ = input_tensor_->Height();
dim_src_width_ = input_tensor_->Width(); dim_src_width_ = input_tensor_->Width();
dim_dst_height_ = output_tensor_->DimensionSize(1); 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(); ch_src_ = input_tensor_->Channel();
stride_height_ = pooling_parameter_->stride_h_; stride_height_ = pooling_parameter_->stride_h_;

3
mindspore/lite/micro/coder/opcoders/nnacl/fp32/convolution_fp32_coder.cc
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@ -117,7 +117,8 @@ int ConvolutionFP32Coder::DoCode(CoderContext *const context) {
"PreSum4x16Int8Peroc.S", "PreSum4x16Int8Peroc.S",
"PreSum4x16Int8Pert.S", "PreSum4x16Int8Pert.S",
"IndirectGemmInt16to32_8x4.S", "IndirectGemmInt16to32_8x4.S",
"MatmulInt8.S"}; "MatmulInt8.S",
"MatmulFp32Opt12x4.S"};
} else if (target_ == kARM64) { } else if (target_ == kARM64) {
asmFiles = {"MatmulFp32.S", "MatmulFp32Opt.S", "PreSum4x16Int8Peroc.S", "MatVecMulFp32.S", asmFiles = {"MatmulFp32.S", "MatmulFp32Opt.S", "PreSum4x16Int8Peroc.S", "MatVecMulFp32.S",
"PreSum4x16Int8Peroc.S", "PreSum4x16Int8Pert.S", "IndirectGemmInt16to32_8x4.S", "MatmulInt8.S"}; "PreSum4x16Int8Peroc.S", "PreSum4x16Int8Pert.S", "IndirectGemmInt16to32_8x4.S", "MatmulInt8.S"};

13
mindspore/lite/micro/coder/opcoders/nnacl/int8/conv2d_int8_coder.cc
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@ -203,9 +203,16 @@ int Conv2DINT8Coder::DoCode(CoderContext *const context) {
code.CodeFunction("memset", matmul_packed_input_, 0, matmul_packed_input_size_); code.CodeFunction("memset", matmul_packed_input_, 0, matmul_packed_input_size_);
code.CodeStruct("conv_param", *conv_param_); code.CodeStruct("conv_param", *conv_param_);
code.CodeBaseStruct("ConvolutionInt8Args", kRunArgs, input_tensor_, packed_input_, matmul_packed_input_, if (target_ == kARM64) {
packed_weight_, bias_data_, output_tensor_, filter_zp_ptr_, input_sum_, code.CodeBaseStruct("ConvolutionInt8Args", kRunArgs, input_tensor_, packed_input_, matmul_packed_input_,
"(ConvParameter *)&conv_param", matmul_func_, support_optimize_); 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_) { if (support_parallel_) {
code.CodeFunction(kParallelLaunch, gThreadPool, "ConvolutionInt8Run", kRunArgsAddr, gThreadNum); code.CodeFunction(kParallelLaunch, gThreadPool, "ConvolutionInt8Run", kRunArgsAddr, gThreadNum);
} else { } else {

5
mindspore/lite/micro/coder/opcoders/serializers/nnacl_serializer/nnacl_stream_utils.cc
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@ -31,9 +31,10 @@ std::ostream &operator<<(std::ostream &code, const ::QuantArg &quant_arg) {
return code; return code;
} }
std::ostream &operator<<(std::ostream &code, const OpParameter &tile) { std::ostream &operator<<(std::ostream &code, const OpParameter &parameter) {
code << "{ \"\"" code << "{ \"\""
<< ", " << tile.type_ << ", " << gThreadNum << "}"; << ", " << std::boolalpha << parameter.infer_flag_ << ", " << parameter.type_ << ", " << gThreadNum << ", "
<< parameter.quant_type_ << "}";
return code; return code;
} }

2
mindspore/lite/micro/example/mnist_stm32f746/README.md
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@ -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： 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。 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。

3
mindspore/lite/micro/example/mnist_x86/mnist.sh
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@ -71,7 +71,7 @@ gen_mnist() {
${CODEGEN_PATH}/codegen --codePath=${BASEPATH}/build --modelPath=${BASEPATH}/build/${MNIST_FILE} ${CODEGEN_PATH}/codegen --codePath=${BASEPATH}/build --modelPath=${BASEPATH}/build/${MNIST_FILE}
} }
mkdir -p build mkdir -p ${BASEPATH}/build
get_version get_version
download_inference download_inference
@ -85,6 +85,7 @@ if [[ "${GEN}" == "ON" ]]; then
fi fi
# 1. build benchmark # 1. build benchmark
rm -rf ${BASEPATH}/build/benchmark
mkdir -p ${BASEPATH}/build/benchmark && cd ${BASEPATH}/build/benchmark || exit 1 mkdir -p ${BASEPATH}/build/benchmark && cd ${BASEPATH}/build/benchmark || exit 1
cmake -DPKG_PATH=${PKG_PATH} ${BENCHMARK_PATH} cmake -DPKG_PATH=${PKG_PATH} ${BENCHMARK_PATH}
make make

4
mindspore/lite/micro/example/mobilenetv2/README.md
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@ -86,7 +86,7 @@ mkdir mobilenetv2/build && cd mobilenetv2/build
```bash ```bash
cmake -DCMAKE_BUILD_TYPE=Release \ 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_ABI="arm64-v8a" \
-DANDROID_TOOLCHAIN_NAME="aarch64-linux-android-clang" \ -DANDROID_TOOLCHAIN_NAME="aarch64-linux-android-clang" \
-DANDROID_NATIVE_API_LEVEL="19" \ -DANDROID_NATIVE_API_LEVEL="19" \
@ -99,7 +99,7 @@ make
```bash ```bash
cmake -DCMAKE_BUILD_TYPE=Release \ 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_ABI="armeabi-v7a" \
-DANDROID_TOOLCHAIN_NAME="clang" \ -DANDROID_TOOLCHAIN_NAME="clang" \
-DANDROID_NATIVE_API_LEVEL="19" \ -DANDROID_NATIVE_API_LEVEL="19" \

221
mindspore/lite/micro/example/mobilenetv2/mobilenetv2.sh
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@ -15,111 +15,132 @@
# ============================================================================ # ============================================================================
set -e set -e
CURRENT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" usage()
MINDSPORE_ROOT_DIR=${${CURRENT_DIR}%%/mindspore/lite/micro/example/mobilenetv2} {
echo "Usage:"
echo "bash build.sh [-I arm64|arm32]"
echo "Options:"
echo " -I download and build for arm64 or arm32, default arm64"
}
OUTPUT_DIR=${1:-${MINDSPORE_ROOT_DIR}/output} LITE_PLATFORM="arm64"
THREAD_NUM=${2:-32} while getopts 'I:' OPT
MODULE_NAME=mobilenetv2 do
OUTPUT_IR=Reshape-64.ir OPTARG=$(echo ${OPTARG} | tr '[A-Z]' '[a-z]')
CALIB_OUT=${CURRENT_DIR}/Reshape-64.out 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: ${CURRENT_DIR}" BASEPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
echo "packed output dir is :${OUTPUT_DIR}" MINDSPORE_ROOT_DIR=${BASEPATH%%/mindspore/lite/micro/example/mobilenetv2}
if [ ! -d "${OUTPUT_DIR}" ]; then echo "current dir is: ${BASEPATH}"
echo "folder ${OUTPUT_DIR} does not exist"
return 1 MOBILE_NAME=mobilenetv2
fi MOBILE_FILE=${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 if [ ! -e ${BASEPATH}/build/${MINDSPORE_FILE} ]; then
WORKSPACE=${CURRENT_DIR}/build wget -c -O ${BASEPATH}/build/${MINDSPORE_FILE} --no-check-certificate ${MINDSPORE_LITE_DOWNLOAD_URL}
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
fi 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 download_mobile() {
cp ${OUTPUT_DIR}/mindspore-lite-*-codegen-linux-x64.tar.gz ${WORKSPACE}/ || exit 1 local MOBILE_DOWNLOAD_URL=https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_imagenet/r1.2/${MOBILE_FILE}
cd ${WORKSPACE} || exit 1
tar -zxf mindspore-lite-*-codegen-linux-x64.tar.gz || exit 1 if [ ! -e ${BASEPATH}/build/${MOBILE_FILE} ]; then
cd mindspore-lite-*-codegen-linux-x64 || exit 1 wget -c -O ${BASEPATH}/build/${MOBILE_FILE} --no-check-certificate ${MOBILE_DOWNLOAD_URL}
mv operator_library/ ${WORKSPACE}/ || exit 1 fi
mv codegen ${WORKSPACE}/ || exit 1 }
cd -
rm -r mindspore-lite-*-codegen-linux-x64 || exit 1 gen_mobile() {
rm mindspore-lite-*-codegen-linux-x64.tar.gz || exit 1 local CODEGEN_FILE_NAME="mindspore-lite-${VERSION_STR}-inference-linux-x64"
local CODEGEN_FILE="${CODEGEN_FILE_NAME}.tar.gz"
# convert model local CODEGEN_LITE_DOWNLOAD_URL="https://ms-release.obs.cn-north-4.myhuaweicloud.com/${VERSION_STR}/MindSpore/lite/release/linux/${CODEGEN_FILE}"
cp ${OUTPUT_DIR}/mindspore-lite-*-converter-linux-x64.tar.gz ${WORKSPACE}/ || exit 1
cd ${WORKSPACE} || exit 1 # if [ ! -e ${BASEPATH}/build/${CODEGEN_FILE} ]; then
tar -zxf mindspore-lite-*-converter-linux-x64.tar.gz || exit 1 # wget -c -O ${BASEPATH}/build/${CODEGEN_FILE} --no-check-certificate ${CODEGEN_LITE_DOWNLOAD_URL}
rm mindspore-lite-*-converter-linux-x64.tar.gz || exit 1 # fi
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 cp ${OUTPUT_DIR}/${CODEGEN_FILE} ${BASEPATH}/build || exit 1
converter/converter_lite --fmk=TFLITE \
--modelFile=${CURRENT_DIR}/mobilenet_v2_1.0_224_quant.tflite \ tar xzvf ${BASEPATH}/build/${CODEGEN_FILE} -C ${BASEPATH}/build/ || exit 1
--outputFile=${WORKSPACE}/mobilenet_v2 rm ${BASEPATH}/build/${CODEGEN_FILE} || exit 1
cd - CODEGEN_PATH=${BASEPATH}/build/${CODEGEN_FILE_NAME}/tools/codegen
rm -rf mindspore-lite-*-converter-linux-x64 || exit 1 if [[ "${LITE_PLATFORM}" == "arm64" ]]; then
local TARGET=ARM64
# generate code else
${WORKSPACE}/codegen --modelPath=${WORKSPACE}/mobilenet_v2.ms \ local TARGET=ARM32A
--moduleName=${MODULE_NAME} \ fi
--isWeightFile=true \ ${CODEGEN_PATH}/codegen --codePath=${BASEPATH}/build --modelPath=${BASEPATH}/build/${MOBILE_FILE} --target=${TARGET}
--debugMode=true }
rm codegen
mkdir -p ${BASEPATH}/build
if [ ! -d "${PROJECT_DIR}" ]; then
echo "folder ${PROJECT_DIR} does not exist" get_version
return 1 download_inference
fi
cd ${PROJECT_DIR} || exit 1 echo "downloading ${MOBILE_FILE}!"
download_mobile
# 1. build static lib.a echo "generating mobilenetv2"
echo -e "building static library" gen_mobile
mkdir -p src/build && cd src/build || exit 1 BENCHMARK_PATH=${BASEPATH}/build/${MOBILE_NAME}
OP_HEADER_PATH=${WORKSPACE}/operator_library/include
OP_LIB=${WORKSPACE}/operator_library/lib/x86/libops.a # build benchmark
echo "Head Path: ${OP_HEADER_PATH}" rm -rf ${BASEPATH}/build/benchmark
echo "Lib Path: ${OP_LIB}" mkdir -p ${BASEPATH}/build/benchmark && cd ${BASEPATH}/build/benchmark || exit 1
cmake -DCMAKE_BUILD_TYPE=Debug \
-DOP_LIB=${OP_LIB} \ if [[ "${LITE_PLATFORM}" == "arm64" ]]; then
-DOP_HEADER_PATH=${OP_HEADER_PATH} .. echo "making arm64"
make -j${THREAD_NUM} cmake -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
# 2. build benchmark -DANDROID_ABI="arm64-v8a" \
cd ${PROJECT_DIR}/benchmark && mkdir -p build && cd build || exit 1 -DANDROID_TOOLCHAIN_NAME="aarch64-linux-android-clang" \
cmake -DMODEL_LIB="${PROJECT_DIR}/src/build/libnet.a" .. -DANDROID_NATIVE_API_LEVEL="19" \
make -j${THREAD_NUM} -DMICRO_BUILD_ARM64=ON \
-DPKG_PATH=${PKG_PATH} ${BENCHMARK_PATH}
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}"
else else
echo -e "run benchmark failed: ${MODULE_NAME}" cmake -DCMAKE_BUILD_TYPE=Release \
exit 1 -DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
fi -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

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