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!13548 add example and run script

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From: @zhujingxuan
Reviewed-by: 
Signed-off-by:
pull/13548/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 8968c1ef71 3233f76c04
commit c9f1be1244
23 changed files with 1586 additions and 102 deletions
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8
mindspore/lite/micro/coder/operator_library/CMakeLists.txt
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@ -53,12 +53,8 @@ include(${MICRO_CMAKE_PATH}/package_wrapper.cmake)
list(APPEND OP_FILES ${NNACL_OPS} ${WRAPPER_SRC} ${RUNTIME_SRC})
if(PLATFORM_ARM64)
set(LIB_PATH "${OPERATOR_LIBRARY_PATH}/lib/arm64")
elseif(PLATFORM_ARM32)
set(LIB_PATH "${OPERATOR_LIBRARY_PATH}/lib/arm32a")
else()
set(LIB_PATH "${OPERATOR_LIBRARY_PATH}/lib/x86")
set(LIB_PATH "${OPERATOR_LIBRARY_PATH}/lib")
if(NOT PLATFORM_ARM64 AND NOT PLATFORM_ARM32)
list(APPEND OP_FILES ${CMSIS_OPS})
endif()

60
mindspore/lite/micro/example/mnist/benchmark/CMakeLists.txt
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cmake_minimum_required(VERSION 3.14)
project(benchmark)
if(NOT DEFINED MODEL_LIB)
message(FATAL_ERROR "MODEL_LIB not set")
endif()
get_filename_component(MODEL_LIB ${MODEL_LIB} ABSOLUTE BASE_DIR ${CMAKE_CURRENT_BINARY_DIR})
function(parse_lib_info lib_full_path lib_name lib_path)
string(FIND "${lib_full_path}" "/" POS REVERSE)
math(EXPR POS "${POS} + 1")
string(SUBSTRING ${lib_full_path} 0 ${POS} path)
set(${lib_path} ${path} PARENT_SCOPE)
string(SUBSTRING ${lib_full_path} "${POS}" "-1" name)
set(${lib_name} ${name} PARENT_SCOPE)
endfunction(parse_lib_info)
parse_lib_info(${MODEL_LIB} MODEL_LIB_NAME MODEL_LIB_PATH)
message("project name: ${MODEL_LIB_NAME}")
option(MICRO_BUILD_ARM64 "build android arm64" OFF)
option(MICRO_BUILD_ARM32A "build android arm32" OFF)
if(MICRO_BUILD_ARM64 OR MICRO_BUILD_ARM32A)
add_compile_definitions(ENABLE_NEON)
add_compile_definitions(ENABLE_ARM)
endif()
if(MICRO_BUILD_ARM64)
add_compile_definitions(ENABLE_ARM64)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -march=armv8.2-a+dotprod")
endif()
if(MICRO_BUILD_ARM32A)
add_compile_definitions(ENABLE_ARM32)
add_definitions(-mfloat-abi=softfp -mfpu=neon)
endif()
set(CMAKE_C_FLAGS "${CMAKE_ENABLE_C99} ${CMAKE_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++17")
if("${CMAKE_BUILD_TYPE}" STREQUAL "Debug")
message(STATUS "build benchmark with debug info")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DDebug -g")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DDebug -g")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fvisibility=default")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=default")
else()
set(CMAKE_C_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes \
-Wno-deprecated-declarations -Wno-missing-braces ${CMAKE_C_FLAGS}")
set(CMAKE_CXX_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes \
-Wno-deprecated-declarations -Wno-missing-braces -Wno-overloaded-virtual ${CMAKE_CXX_FLAGS}")
endif()
link_directories(${MODEL_LIB_PATH})
include(benchmark.cmake)
add_executable(benchmark ${SRC_FILES})
target_link_libraries(benchmark ${MODEL_LIB_NAME} -lm -pthread)

97
mindspore/lite/micro/example/mnist/benchmark/benchmark.cc
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@ -0,0 +1,97 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <iostream>
#include <string>
#include <cstring>
#include "include/lite_session.h"
#include "include/ms_tensor.h"
#include "include/errorcode.h"
#include "load_input.h"
using namespace mindspore;
void usage() {
printf(
"-- mindspore benchmark params usage:\n"
"args[0]: executable file\n"
"args[1]: inputs binary file\n"
"args[2]: model weight binary file\n"
"args[3]: loop count for performance test\n"
"args[4]: runtime thread num\n"
"args[5]: runtime thread bind mode\n\n");
}
int main(int argc, const char **argv) {
if (argc < 2) {
std::cout << "input command is invalid\n" << std::endl;
usage();
return lite::RET_ERROR;
}
std::cout << "start run benchmark" << std::endl;
const char *model_buffer = nullptr;
int model_size = 0;
// read .net file by ReadBinaryFile;
if (argc >= 3) {
model_buffer = static_cast<const char *>(ReadInputData(argv[2], &model_size));
}
session::LiteSession *session = mindspore::session::LiteSession::CreateSession(model_buffer, model_size, nullptr);
if (session == nullptr) {
std::cerr << "create lite session failed" << std::endl;
return lite::RET_ERROR;
}
// set model inputs tensor data
std::vector<tensor::MSTensor *> inputs = session->GetInputs();
size_t inputs_num = inputs.size();
void *inputs_binbuf[inputs_num];
int inputs_size[inputs_num];
for (size_t i = 0; i < inputs_num; ++i) {
inputs_size[i] = inputs[i]->Size();
}
int ret = ReadInputsFile(const_cast<char *>(argv[1]), inputs_binbuf, inputs_size, inputs_num);
if (ret != lite::RET_OK) {
return lite::RET_ERROR;
}
for (size_t i = 0; i < inputs_num; ++i) {
void *input_data = inputs[i]->MutableData();
memcpy(input_data, inputs_binbuf[i], inputs_size[i]);
}
ret = session->RunGraph();
if (ret != lite::RET_OK) {
return lite::RET_ERROR;
}
auto outputs = session->GetOutputs();
std::cout << "output size: " << outputs.size() << std::endl;
for (const auto &item : outputs) {
auto output = item.second;
std::cout << "name: " << output->tensor_name() << ", size: " << output->Size() << std::endl;
}
std::cout << "run benchmark success" << std::endl;
delete session;
for (size_t i = 0; i < inputs_num; ++i) {
free(inputs_binbuf[i]);
}
return lite::RET_OK;
}

8
mindspore/lite/micro/example/mnist/benchmark/benchmark.cmake
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@ -0,0 +1,8 @@
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../src/)
include_directories(${HEADER_PATH})
set(SRC_FILES
benchmark.cc
load_input.c
debug_utils.c
)

216
mindspore/lite/micro/example/mnist/benchmark/debug_utils.c
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@ -0,0 +1,216 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <inttypes.h>
#include "debug_utils.h"
#define UP_DIV(x, y) (((x) + (y) - (1)) / (y))
static const unsigned int kPrintNums = 20;
static const unsigned int kLineSplitNum = 44;
static const unsigned int kLineNum = 45;
unsigned int GetTensorElementSize(const MicroTensor *tensor) {
unsigned int ans = 1;
if (tensor->format == Format_NC4HW4) {
for (unsigned int i = 0; i < tensor->ndim; ++i) {
unsigned int dim = tensor->dim[i];
if (i == 1) {
dim = UP_DIV(dim, 4) * 4;
}
ans *= dim;
}
} else {
for (unsigned int i = 0; i < tensor->ndim; ++i) {
ans *= tensor->dim[i];
}
}
return ans;
}
static const char *const TypeNames[] = {"DT_FLOAT", "DT_FLOAT16", "DT_INT8", "DT_INT32", "DT_UINT8", "DT_INT16",
"", "", "DT_UINT32", "DT_INT64", "DT_UINT16", "",
"", "", "", "", "DT_UNDEFINED", ""};
const char *EnumNameFormat(enum Format e) {
switch (e) {
case Format_NCHW:
return "NCHW";
case Format_NHWC:
return "NHWC";
case Format_HWKC:
return "HWKC";
case Format_HWCK:
return "HWCK";
case Format_KCHW:
return "KCHW";
case Format_CKHW:
return "CKHW";
case Format_KHWC:
return "KHWC";
case Format_CHWK:
return "CHWK";
case Format_NC4HW4:
return "NC4HW4";
case Format_NUM_OF_FORMAT:
return "NUM_OF_FORMAT";
default:
return "";
}
}
void PrintTensorData(MicroTensor *tensor) {
void *data = tensor->data;
unsigned int elenums = GetTensorElementSize(tensor);
if (data == NULL || elenums == 0) {
MICRO_ERROR("print tensor data failed");
return;
}
switch (tensor->type) {
case DataType_DT_FLOAT: {
float *addr = (float *)(data);
for (int i = 0; i < elenums && i < kPrintNums; ++i) {
printf("%f, ", addr[i]);
}
break;
}
case DataType_DT_INT32: {
int32_t *addr = (int32_t *)(data);
for (int i = 0; i < elenums && i < kPrintNums; ++i) {
printf("%d, ", addr[i]);
}
break;
}
case DataType_DT_INT8: {
int8_t *addr = (int8_t *)(data);
for (int i = 0; i < elenums && i < kPrintNums; ++i) {
printf("%d, ", addr[i]);
}
break;
}
case DataType_DT_UINT32: {
uint32_t *addr = (uint32_t *)(data);
for (int i = 0; i < elenums && i < kPrintNums; ++i) {
printf("%u, ", addr[i]);
}
break;
}
case DataType_DT_UINT8: {
uint8_t *addr = (uint8_t *)(data);
for (int i = 0; i < elenums && i < kPrintNums; ++i) {
printf("%u, ", addr[i]);
}
break;
}
default:
MICRO_ERROR("unsupported data type %d", tensor->type);
}
printf("\n");
}
void PrintDataToFile(const void *data, const size_t elenums, const enum DataType type, FILE *file) {
if (data == NULL || elenums == 0) {
MICRO_ERROR("print tensor data to file failed");
return;
}
switch (type) {
case DataType_DT_FLOAT: {
float *addr = (float *)(data);
for (int i = 0; i < elenums; ++i) {
fprintf(file, "%0.15f, ", addr[i]);
if (i % kLineNum == kLineSplitNum) {
fprintf(file, "\n");
}
}
break;
}
case DataType_DT_INT32: {
int32_t *addr = (int32_t *)(data);
for (int i = 0; i < elenums; ++i) {
fprintf(file, "%d, ", addr[i]);
if (i % kLineNum == kLineSplitNum) {
fprintf(file, "\n");
}
}
break;
}
case DataType_DT_INT8: {
int8_t *addr = (int8_t *)(data);
for (int i = 0; i < elenums; ++i) {
fprintf(file, "%d, ", addr[i]);
if (i % kLineNum == kLineSplitNum) {
fprintf(file, "\n");
}
}
break;
}
case DataType_DT_UINT32: {
uint32_t *addr = (uint32_t *)(data);
for (int i = 0; i < elenums; ++i) {
fprintf(file, "%u, ", addr[i]);
if (i % kLineNum == kLineSplitNum) {
fprintf(file, "\n");
}
}
break;
}
case DataType_DT_UINT8: {
uint8_t *addr = (uint8_t *)(data);
for (int i = 0; i < elenums; ++i) {
fprintf(file, "%u, ", addr[i]);
if (i % kLineNum == kLineSplitNum) {
fprintf(file, "\n");
}
}
break;
}
default:
MICRO_ERROR("unsupported data type %d", type);
}
fprintf(file, "\n");
}
void PrintTensor(MicroTensor *tensor, FILE *output_file, const char *is_input) {
if (output_file == NULL) {
MICRO_ERROR("output file is NULL");
return;
}
fprintf(output_file, "%s ", is_input);
for (int i = 0; i < tensor->ndim; ++i) {
fprintf(output_file, "%u, ", tensor->dim[i]);
}
fprintf(output_file, "\n");
const char *type = TypeNames[tensor->type];
const char *format = EnumNameFormat(tensor->format);
unsigned int tensorSize = GetTensorElementSize(tensor);
fprintf(output_file, "%s type:%s, format:%s, elementSize: %u\n", is_input, type, format, tensorSize);
fprintf(output_file, "%s Data:\n", is_input);
PrintDataToFile(tensor->data, tensorSize, tensor->type, output_file);
(void)fflush(output_file);
}
uint64_t GetTimeUs() {
const int USEC = 1000000;
const int MSEC = 1000;
struct timespec ts = {0, 0};
if (clock_gettime(CLOCK_MONOTONIC, &ts) != 0) {
return 0;
}
uint64_t retval = (uint64_t)((ts.tv_sec * USEC) + (ts.tv_nsec / MSEC));
return retval;
}

34
mindspore/lite/micro/example/mnist/benchmark/debug_utils.h
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@ -0,0 +1,34 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_LITE_MICRO_MICRODEBUGUTIL_H_
#define MINDSPORE_LITE_MICRO_MICRODEBUGUTIL_H_
#include <stdio.h>
#include <sys/time.h>
#include <time.h>
#include <stdint.h>
#include "microtensor.h"
void PrintTensor(MicroTensor *tensor, FILE *output_file, const char *is_input);
void PrintTensorData(MicroTensor *tensor);
uint64_t GetTimeUs();
#endif // MINDSPORE_LITE_MICRO_MICRODEBUGUTIL_H_

95
mindspore/lite/micro/example/mnist/benchmark/load_input.c
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@ -0,0 +1,95 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "load_input.h"
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
void *ReadInputData(const char *real_input_path, int *size) {
if (real_input_path == NULL) {
return NULL;
}
if (strstr(real_input_path, ".bin") || strstr(real_input_path, ".net")) {
FILE *file;
file = fopen(real_input_path, "rb+");
if (!file) {
printf("Can't find %s\n", real_input_path);
return NULL;
}
int curr_file_posi = ftell(file);
fseek(file, 0, SEEK_END);
*size = ftell(file);
unsigned char *buf = malloc((*size));
(void)memset(buf, 0, (*size));
fseek(file, curr_file_posi, SEEK_SET);
int read_size = (int)(fread(buf, 1, *size, file));
if (read_size != (*size)) {
printf("read file failed, total file size: %d, read_size: %d\n", (*size), read_size);
fclose(file);
free(buf);
return NULL;
}
fclose(file);
return (void *)buf;
} else {
printf("input data file should be .bin , .net");
return NULL;
}
}
void SaveOutputData(char *final_name, unsigned char *output_data, unsigned int out_size) {
FILE *output_file;
output_file = fopen(final_name, "w");
if (output_file == NULL) {
printf("fopen output file: %s failed\n", final_name);
return;
}
unsigned char str[out_size];
for (unsigned int i = 0; i < out_size; ++i) {
str[i] = output_data[i];
fprintf(output_file, "%d\t", str[i]);
}
fclose(output_file);
}
int ReadInputsFile(char *path, void **buffers, const int *inputs_size, int inputs_num) {
char *inputs_path[inputs_num];
char *delim = ",";
char *token;
int i = 0;
while ((token = strtok_r(path, delim, &path))) {
if (i >= inputs_num) {
printf("inputs num is error, need: %d\n", inputs_num);
return -1;
}
inputs_path[i] = token;
printf("input %d: %s\n", i, inputs_path[i]);
i++;
}
for (i = 0; i < inputs_num; ++i) {
int size = 0;
buffers[i] = ReadInputData(inputs_path[i], &size);
if (size != inputs_size[i] || buffers[i] == NULL) {
printf("size mismatch, %s, input: %d, needed: %d\n", inputs_path[i], size, inputs_size[i]);
return -1;
}
}
return 0;
}

36
mindspore/lite/micro/example/mnist/benchmark/load_input.h
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@ -0,0 +1,36 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MICRO_EXAMPLE_LOAD_INPUT_LOAD_INPUT_H_
#define MICRO_EXAMPLE_LOAD_INPUT_LOAD_INPUT_H_
#ifdef __cplusplus
extern "C" {
#endif
void *ReadInputData(const char *real_input_path, int *size);
void SaveOutputData(char *final_name, unsigned char *output_data, unsigned int out_size);
int ReadInputsFile(char *path, void **buffers, const int *inputs_size, int inputs_num);
#ifdef __cplusplus
}
#endif
#endif // MICRO_EXAMPLE_LOAD_INPUT_LOAD_INPUT_H_

131
mindspore/lite/micro/example/mnist/mnist.sh
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@ -15,111 +15,58 @@
# ============================================================================
set -e
CURRENT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
MINDSPORE_ROOT_DIR=${${CURRENT_DIR}%%/mindspore/lite/micro/example/mnist}
BASEPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
MINDSPORE_ROOT_DIR=${${BASEPATH}%%/mindspore/lite/micro/example/mnist}
OUTPUT_DIR=${1:-${MINDSPORE_ROOT_DIR}/output}
THREAD_NUM=${2:-32}
MODULE_NAME=mnist
OUTPUT_IR=Reshape-64.ir
CALIB_OUT=${CURRENT_DIR}/Reshape-64.out
echo "current dir is: ${BASEPATH}"
echo "current dir is: ${CURRENT_DIR}"
echo "packed output dir is :${OUTPUT_DIR}"
VERSION_HEADER=${MINDSPORE_ROOT_DIR}/mindspore/lite/include/version.h
INPUT_BIN=${BASEPATH}/mnist_input.bin
if [ ! -d "${OUTPUT_DIR}" ]; then
echo "folder ${OUTPUT_DIR} does not exist"
return 1
fi
# rm if already exist
WORKSPACE=${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
fi
done
echo -e "compare success, ${OUTPUT_FILE}, ${CALIB_FILE}"
get_version() {
VERSION_MAJOR=$(grep "const int ms_version_major =" ${VERSION_HEADER} | tr -dc "[0-9]")
VERSION_MINOR=$(grep "const int ms_version_minor =" ${VERSION_HEADER} | tr -dc "[0-9]")
VERSION_REVISION=$(grep "const int ms_version_revision =" ${VERSION_HEADER} | tr -dc "[0-9]")
VERSION_STR=${VERSION_MAJOR}.${VERSION_MINOR}.${VERSION_REVISION}
}
get_version
MINDSPORE_FILE_NAME="mindspore-lite-${VERSION_STR}-inference-linux-x64"
MINDSPORE_FILE="${MINDSPORE_FILE_NAME}.tar.gz"
MINDSPORE_LITE_DOWNLOAD_URL="https://ms-release.obs.cn-north-4.myhuaweicloud.com/${VERSION_STR}/MindSpore/lite/release/linux/${MINDSPORE_FILE}"
# 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}/mnist.tflite \
--outputFile=${WORKSPACE}/mnist
cd -
rm -rf mindspore-lite-*-converter-linux-x64 || exit 1
mkdir -p build
# generate code
${WORKSPACE}/codegen --modelPath=${WORKSPACE}/mnist.ms \
--moduleName=${MODULE_NAME} \
--isWeightFile=true \
--debugMode=true
rm codegen
if [ ! -d "${PROJECT_DIR}" ]; then
echo "folder ${PROJECT_DIR} does not exist"
return 1
if [ ! -e ${BASEPATH}/build/${MINDSPORE_FILE} ]; then
wget -c -O ${BASEPATH}/build/${MINDSPORE_FILE} --no-check-certificate ${MINDSPORE_LITE_DOWNLOAD_URL}
fi
cd ${PROJECT_DIR} || exit 1
tar xzvf ${BASEPATH}/build/${MINDSPORE_FILE} -C ${BASEPATH}/build/ || exit 1
rm ${BASEPATH}/build/${MINDSPORE_FILE} || exit 1
CODEGEN_PATH=${BASEPATH}/build/${MINDSPORE_FILE_NAME}/tools/codegen
HEADER_PATH=${BASEPATH}/build/${MINDSPORE_FILE_NAME}/inference
# 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
mkdir -p ${BASEPATH}/build/src && cd ${BASEPATH}/build/src || exit 1
OP_HEADER_PATH=${CODEGEN_PATH}/operator_library/include
OP_LIB=${CODEGEN_PATH}/operator_library/lib/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}
echo "Header Path: ${HEADER_PATH}"
cmake -DCMAKE_BUILD_TYPE=Debug \
-DOP_LIB=${OP_LIB} \
-DOP_HEADER_PATH=${OP_HEADER_PATH} \
-DHEADER_PATH=${HEADER_PATH} \
${BASEPATH}/src
make
# 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}
mkdir -p ${BASEPATH}/build/benchmark && cd ${BASEPATH}/build/benchmark || exit 1
cmake -DMODEL_LIB="${BASEPATH}/build/src/libnet.a" \
-DHEADER_PATH=${HEADER_PATH} \
${BASEPATH}/benchmark
make
echo "net file: ${PROJECT_DIR}/src/${MODULE_NAME}.net"
echo "net file: ${BASEPATH}/src/mnist.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
echo -e "run benchmark failed: ${MODULE_NAME}"
exit 1
fi
./benchmark ${INPUT_BIN} ${BASEPATH}/src/net.net

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mindspore/lite/micro/example/mnist/mnist_input.bin
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mindspore/lite/micro/example/mnist/src/CMakeLists.txt
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cmake_minimum_required(VERSION 3.14)
project(net)
if(NOT DEFINED OP_LIB)
message(FATAL_ERROR "OP_LIB not set")
endif()
if(NOT DEFINED OP_HEADER_PATH)
message(FATAL_ERROR "OP_HEADER_PATH not set")
endif()
get_filename_component(OP_LIB ${OP_LIB} ABSOLUTE BASE_DIR ${CMAKE_CURRENT_BINARY_DIR})
get_filename_component(OP_HEADER_PATH ${OP_HEADER_PATH} ABSOLUTE BASE_DIR ${CMAKE_CURRENT_BINARY_DIR})
message("operator lib path: ${OP_LIB}")
message("operator header path: ${OP_HEADER_PATH}")
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../include)
include_directories(${OP_HEADER_PATH})
include_directories(${HEADER_PATH})
include(net.cmake)
option(MICRO_BUILD_ARM64 "build android arm64" OFF)
option(MICRO_BUILD_ARM32A "build android arm32" OFF)
if(MICRO_BUILD_ARM64 OR MICRO_BUILD_ARM32A)
add_compile_definitions(ENABLE_NEON)
add_compile_definitions(ENABLE_ARM)
endif()
if(MICRO_BUILD_ARM64)
add_compile_definitions(ENABLE_ARM64)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -march=armv8.2-a+dotprod")
endif()
if(MICRO_BUILD_ARM32A)
add_compile_definitions(ENABLE_ARM32)
add_definitions(-mfloat-abi=softfp -mfpu=neon)
endif()
set(CMAKE_C_FLAGS "${CMAKE_ENABLE_C99} ${CMAKE_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++17")
if("${CMAKE_BUILD_TYPE}" STREQUAL "Debug")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DDebug -g")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DDebug -g")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fvisibility=default")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=default")
else()
set(CMAKE_C_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes \
-Wno-deprecated-declarations -Wno-missing-braces ${CMAKE_C_FLAGS}")
set(CMAKE_CXX_FLAGS "-fPIC -fPIE -D_FORTIFY_SOURCE=2 -O2 -Wall -Werror -fstack-protector-strong -Wno-attributes \
-Wno-deprecated-declarations -Wno-missing-braces -Wno-overloaded-virtual ${CMAKE_CXX_FLAGS}")
endif()
function(create_library)
add_custom_command(TARGET net
POST_BUILD
COMMAND rm -rf tmp
COMMAND mkdir tmp
COMMAND cd tmp && ar -x ${OP_LIB}
COMMAND echo "raw static library ${library_name} size:"
COMMAND ls -lh ${library_name}
COMMAND mv ${library_name} ./tmp && cd tmp && ar -x ${library_name}
COMMENT "unzip raw static library ${library_name}"
)
foreach(object_file ${OP_SRC})
add_custom_command(TARGET net POST_BUILD COMMAND mv ./tmp/${object_file} .)
endforeach()
add_custom_command(TARGET net
POST_BUILD
COMMAND ar cr ${library_name} *.o
COMMAND ranlib ${library_name}
COMMAND echo "new static library ${library_name} size:"
COMMAND ls -lh ${library_name}
COMMAND rm -rf tmp && rm -rf *.o
COMMENT "generate specified static library ${library_name}"
)
endfunction(create_library)
string(CONCAT library_name "lib" net ".a")
create_library()

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mindspore/lite/micro/example/mnist/src/microtensor.h
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/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MSMICRO_TENSOR_H
#define MSMICRO_TENSOR_H
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include <stdbool.h>
#include <stdint.h>
#define MICRO_INFO(content, args...) \
{ printf("[INFO] %s|%d: " #content "\r\n", __func__, __LINE__, ##args); }
#define MICRO_ERROR(content, args...) \
{ printf("[ERROR] %s|%d: " #content "\r\n", __func__, __LINE__, ##args); }
enum STATUS {
RET_OK = 0,
RET_ERROR = 1,
};
enum DataType {
DataType_DT_FLOAT = 0,
DataType_DT_FLOAT16 = 1,
DataType_DT_INT8 = 2,
DataType_DT_INT32 = 3,
DataType_DT_UINT8 = 4,
DataType_DT_INT16 = 5,
DataType_DT_UINT32 = 8,
DataType_DT_INT64 = 9,
DataType_DT_UINT16 = 10,
DataType_DT_UNDEFINED = 16,
DataType_MIN = DataType_DT_FLOAT,
DataType_MAX = DataType_DT_UNDEFINED
};
enum Format {
Format_NCHW = 0,
Format_NHWC = 1,
Format_HWKC = 2,
Format_HWCK = 3,
Format_KCHW = 4,
Format_CKHW = 5,
Format_KHWC = 6,
Format_CHWK = 7,
Format_NC4HW4 = 100,
Format_NUM_OF_FORMAT = 101,
Format_MIN = Format_NCHW,
Format_MAX = Format_NUM_OF_FORMAT
};
typedef struct {
enum DataType type;
enum Format format;
int ndim;
int *dim;
void *data;
} MicroTensor;
typedef struct {
int num;
MicroTensor *tensor;
} MicroTensorList;
typedef struct {
float in_scale;
float out_scale;
int in_zero_point;
int out_zero_point;
} GraphQuantArgs;
#endif // MSMICRO_TENSOR_H

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mindspore/lite/micro/example/mnist/src/net.c
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/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "microtensor.h"
#include "net_weight.h"
#include "net.h"
static const unsigned char *net_I0 = 0;
int net_SetInputs(const void **inputs, int num) {
if (inputs == NULL) {
return RET_ERROR;
}
if (num !=1) {
return RET_ERROR;
}
net_I0 = inputs[0];
return RET_OK;
}
const MicroTensorList* net_GetOutputs() {
static MicroTensor net_O[1] ;
static int dim0[] = {1, 10, };
net_O[0].ndim = 2;
net_O[0].dim = dim0;
net_O[0].type = DataType_DT_FLOAT;
net_O[0].format = Format_NHWC;
net_O[0].data =net_B+56;
static MicroTensorList net_TensorArray;
net_TensorArray.num = 1;
net_TensorArray.tensor = &net_O[0];
return &net_TensorArray;
}
int CopyOutputsData(void **outputs, int num) {
if (outputs == NULL) {
return RET_ERROR;
}
if (num != 1) {
return RET_ERROR;
}
memcpy(outputs[0], net_B+56, 40);
outputs[0] = net_B;
return RET_OK;
}
int net_GetBufferSize() {
return 40032;
}
int net_SetBuffer( void *buffer) {
if (buffer == NULL) {
return RET_ERROR;
}
net_B = buffer;
return RET_OK;
}
void net_FreeResource() {
net_B= NULL;
net_I0 = NULL;
void *allocated[] = {net_W14, net_W15, net_W16, net_W17, net_W18, net_W19, };
for (int i = 0; i < 6; ++i) {
free(allocated[i]);
allocated[i] = NULL;
}
}
void net_Inference() {
const int g_thread_num = 1;
{
DoQuantizeFp32ToInt8((float *)(net_I0), (int8_t *)(net_B+0), 0.007874015718698501587, 0, 784, false);
}
{
memset((int16_t *)(net_B+10928), 0, 2048);
memset((int16_t *)(net_B+12976), 0, 256);
memset((int *)(net_B+13232), 0, 6144);
memset((uint8_t *)(net_B+19376), 0, 8112);
memset((int16_t *)(net_B+27488), 0, 12544);
static QuantArg conv_param__quant_arg_in[1] = {{0.007874015718698501587, 0}};
static QuantArg conv_param__quant_arg_w[12] = {{0.003238174133002758026, -6}, {0.003890725085511803627, -8}, {0.003394871251657605171, -7}, {0.001685356837697327137, -127}, {0.004322394262999296188, 1}, {0.002274985425174236298, -56}, {0.003617759561166167259, 17}, {0.004447745624929666519, 23}, {0.004683905746787786484, 26}, {0.004021023400127887726, 24}, {0.005650237202644348145, 11}, {0.001966834301128983498, -84}};
static QuantArg conv_param__quant_arg_out[1] = {{0.01778890006244182587, 0}};
static double conv_param__real_multiplier[12] = {0.001433333970799530351, 0.001722176774828924938, 0.00150269379968211614, 0.0007460003866156953226, 0.001913249346122961134, 0.001006991503636309139, 0.001601352314486244018, 0.001968734305210294733, 0.002073267527210802957, 0.00177985160945266568, 0.002501001060249878095, 0.0008705926067589928779};
static int conv_param__left_shift[12] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
static int conv_param__right_shift[12] = {-9, -9, -9, -10, -9, -9, -9, -8, -8, -9, -8, -10};
static int conv_param__quant_multiplier[12] = {1575967367, 1893553389, 1652229306, 1640472199, 2103639903, 1107198867, 1760705490, 1082323130, 1139790877, 1956967540, 1374939873, 1914453388};
static int conv_param__out_act_min[1] = {0};
static int conv_param__out_act_max[1] = {127};
const ConvQuantArg conv_param__conv_quant_arg = {(RoundingMode)(1), 2, conv_param__quant_arg_in, conv_param__quant_arg_w, conv_param__quant_arg_out, conv_param__real_multiplier, conv_param__left_shift, conv_param__right_shift, conv_param__quant_multiplier, conv_param__out_act_min, conv_param__out_act_max, 1, 12, 1, 2};
int thread_num = MSMIN(g_thread_num, 26);
const ConvParameter conv_param_ = {{ "", 35, g_thread_num}, conv_param__conv_quant_arg, 3, 3, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 28, 28, 1, 1, 26, 26, 12, thread_num, 0, 0, (PadMode)(2), (ActType)(1), 0, 0, 0};
PackInputToC8Int8((int8_t *)(net_B+0), (int16_t *)(net_B+27488), &conv_param_);
Conv3x3Int8((int16_t *)(net_B+27488), net_W10, net_W11, (int8_t *)(net_B+784), (int16_t *)(net_B+10928), (int16_t *)(net_B+12976), (int *)(net_B+13232), (uint8_t *)(net_B+19376), 0, &conv_param_);
PackNC4HW4ToNHWCInt8((uint8_t *)(net_B+19376), (int8_t *)(net_B+784), 1, 676, 12);
}
{
static QuantArg pooling_parameter_quant_in = {0.01778890006244182587, 0};
static QuantArg pooling_parameter_quant_out = {0.01778890006244182587, 0};
static QuantArg *pooling_parameter_quant[2] = { &pooling_parameter_quant_in, &pooling_parameter_quant_out};
const PoolingParameter pooling_parameter = {{ "", 92, g_thread_num}, (PoolMode)(1), (RoundMode)(2), (PadMode)(2), (ActType)(0), 0, false, 2, 2, 2, 2, 26, 26, 1, 12, 13, 13, 1, 12, 0, 0, 0, 0, 0, pooling_parameter_quant, false};
MaxPoolingInt8((int8_t *)(net_B+784), (int8_t *)(net_B+8896), (PoolingParameter *)&pooling_parameter, 0);
}
{
memset((int16_t *)(net_B+10928), 0, 4096);
memset((int16_t *)(net_B+15024), 0, 256);
memset((int *)(net_B+15280), 0, 6144);
memset((uint8_t *)(net_B+21424), 0, 1452);
memset((int16_t *)(net_B+22876), 0, 5408);
static QuantArg conv_param__quant_arg_in[1] = {{0.01778890006244182587, 0}};
static QuantArg conv_param__quant_arg_w[12] = {{0.005374609492719173431, 33}, {0.005837683100253343582, 22}, {0.004709810949862003326, -15}, {0.003726204857230186462, 27}, {0.00318551529198884964, -8}, {0.003453079145401716232, 50}, {0.004045850131660699844, -9}, {0.003903790842741727829, 30}, {0.004003710579127073288, -10}, {0.00560879148542881012, 27}, {0.005486610345542430878, -23}, {0.003554018214344978333, 4}};
static QuantArg conv_param__quant_arg_out[1] = {{0.07183934003114700317, 0}};
static double conv_param__real_multiplier[12] = {0.001330863973520378732, 0.001445530533608141606, 0.001166246148374064893, 0.0009226850783705293785, 0.0007887991893445710223, 0.0008550534992628172192, 0.001001835847923064193, 0.0009666590447744700769, 0.0009914011740411567478, 0.001388852288199173826, 0.00135859773990280961, 0.0008800481219728497088};
static int conv_param__left_shift[12] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
static int conv_param__right_shift[12] = {-9, -9, -9, -10, -10, -10, -9, -10, -9, -9, -9, -10};
static int conv_param__quant_multiplier[12] = {1463300414, 1589377630, 1282301201, 2029005945, 1734587761, 1880282530, 1101530164, 2125705720, 1090057119, 1527059240, 1493794012, 1935246286};
static int conv_param__out_act_min[1] = {0};
static int conv_param__out_act_max[1] = {127};
const ConvQuantArg conv_param__conv_quant_arg = {(RoundingMode)(1), 2, conv_param__quant_arg_in, conv_param__quant_arg_w, conv_param__quant_arg_out, conv_param__real_multiplier, conv_param__left_shift, conv_param__right_shift, conv_param__quant_multiplier, conv_param__out_act_min, conv_param__out_act_max, 1, 12, 1, 2};
int thread_num = MSMIN(g_thread_num, 11);
const ConvParameter conv_param_ = {{ "", 35, g_thread_num}, conv_param__conv_quant_arg, 3, 3, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 13, 13, 12, 1, 11, 11, 12, thread_num, 0, 0, (PadMode)(2), (ActType)(1), 0, 0, 0};
PackInputToC8Int8((int8_t *)(net_B+8896), (int16_t *)(net_B+22876), &conv_param_);
Conv3x3Int8((int16_t *)(net_B+22876), net_W12, net_W13, (int8_t *)(net_B+0), (int16_t *)(net_B+10928), (int16_t *)(net_B+15024), (int *)(net_B+15280), (uint8_t *)(net_B+21424), 0, &conv_param_);
PackNC4HW4ToNHWCInt8((uint8_t *)(net_B+21424), (int8_t *)(net_B+0), 1, 121, 12);
}
{
static QuantArg pooling_parameter_quant_in = {0.07136065512895584106, 0};
static QuantArg pooling_parameter_quant_out = {0.07136065512895584106, 0};
static QuantArg *pooling_parameter_quant[2] = { &pooling_parameter_quant_in, &pooling_parameter_quant_out};
const PoolingParameter pooling_parameter = {{ "", 92, g_thread_num}, (PoolMode)(1), (RoundMode)(2), (PadMode)(2), (ActType)(0), 0, false, 2, 2, 2, 2, 11, 11, 1, 12, 5, 5, 1, 12, 0, 0, 0, 0, 0, pooling_parameter_quant, false};
MaxPoolingInt8((int8_t *)(net_B+0), (int8_t *)(net_B+1456), (PoolingParameter *)&pooling_parameter, 0);
}
{
const ReshapeQuantArg reshape_quant_arg = {{0.07136065512895584106, 0}, {0.07136065512895584106, 0}, -128, 127};
Int8Reshape((int8_t *)(net_B+1456), (int8_t *)(net_B+0), 300, reshape_quant_arg);
}
{
int32_t tmp_weight_zp = 1;
RowMajor2Row16x4MajorInt8((int8_t *)(net_B+0)+0, (int8_t *)(net_B+10928), 1, 300);
CalcInputSums((int8_t *)(net_B+0)+0, 1, 300, tmp_weight_zp, (int *)(net_B+12144), RowMajor);
const float filter_scale[20] = {0.003479549195617437363, 0.004490676335990428925, 0.004529818892478942871, 0.002983231563121080399, 0.003455155529081821442, 0.003223794745281338692, 0.003272445406764745712, 0.003801185870543122292, 0.003679843153804540634, 0.003040234791114926338, 0.003704284550622105598, 0.003355232765898108482, 0.002904496388509869576, 0.003024494973942637444, 0.002794801956042647362, 0.004355110693722963333, 0.003499472280964255333, 0.004184196703135967255, 0.003057289868593215942, 0.003264668164774775505};
const int filter_zp[20] = {1, 12, 3, 2, -10, -5, -11, 5, 12, 22, 16, 1, -5, 15, 13, 5, -10, -5, -6, 0};
const int left_shift[20] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
const int right_shift[20] = {-10, -9, -9, -10, -10, -10, -10, -9, -9, -10, -9, -10, -10, -10, -10, -9, -10, -9, -10, -10};
const int multiplier[20] = {2108215049, 1360422072, 1372280070, 1807502393, 2093435146, 1953256619, 1982733521, 1151545365, 1114785262, 1842040025, 1122189669, 2032893316, 1759797843, 1832503464, 1693335354, 1319353429, 2120286176, 1267576078, 1852373503, 1978021333};
const MatmulQuantParameter matmul_quant_parameter = {{0.07136065512895584106, 0}, {0, 0}, {0.258998185396194458, 0}, -128, 127, filter_scale, filter_zp, left_shift, right_shift, multiplier};
int32_t *cur_left = matmul_quant_parameter.left_shift_ + 0;
int32_t *cur_right = matmul_quant_parameter.right_shift_ + 0;
int32_t *cur_mul = matmul_quant_parameter.quant_multiplier_ + 0;
int32_t *cur_zp = matmul_quant_parameter.filter_zp_ + 0;
MatmulInt8Opt((int8_t *)(net_B+10928), net_W15+0 + 0, (int8_t *)(net_B+304)+0+0, 1, 20, 304, (int *)(net_B+12144), net_W16+0, -128, 127, 0, cur_mul, cur_left, cur_right, 20, true, cur_zp);
}
{
int32_t tmp_weight_zp = 1;
RowMajor2Row16x4MajorInt8((int8_t *)(net_B+304)+0, (int8_t *)(net_B+10928), 1, 20);
CalcInputSums((int8_t *)(net_B+304)+0, 1, 20, tmp_weight_zp, (int *)(net_B+11056), RowMajor);
const float filter_scale[10] = {0.004678330849856138229, 0.005127115640789270401, 0.00471437256783246994, 0.004531511571258306503, 0.005476122256368398666, 0.004348111804574728012, 0.004803542047739028931, 0.006081215571612119675, 0.004532597027719020844, 0.004762654658406972885};
const int filter_zp[10] = {7, -2, 9, 2, -6, 21, 16, 10, -19, 8};
const int left_shift[10] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
const int right_shift[10] = {-8, -8, -8, -8, -8, -8, -8, -8, -8, -8};
const int multiplier[10] = {1242805482, 1362025788, 1252380041, 1203802750, 1454739904, 1155082292, 1276068015, 1615483838, 1204091115, 1265206260};
const MatmulQuantParameter matmul_quant_parameter = {{0.258998185396194458, 0}, {0, 0}, {0.5359870791435241699, 0}, -128, 127, filter_scale, filter_zp, left_shift, right_shift, multiplier};
int32_t *cur_left = matmul_quant_parameter.left_shift_ + 0;
int32_t *cur_right = matmul_quant_parameter.right_shift_ + 0;
int32_t *cur_mul = matmul_quant_parameter.quant_multiplier_ + 0;
int32_t *cur_zp = matmul_quant_parameter.filter_zp_ + 0;
MatmulInt8Opt((int8_t *)(net_B+10928), net_W18+0 + 0, (int8_t *)(net_B+0)+0+0, 1, 10, 32, (int *)(net_B+11056), net_W19+0, -128, 127, 0, cur_mul, cur_left, cur_right, 10, true, cur_zp);
}
{
DoDequantizeInt8ToFp32((int8_t *)(net_B+0), (float *)(net_B+16), 0.5359870791435241699, 0, 10);
}
{
const SoftmaxParameter softmax_parameter = {{ "", 138, g_thread_num}, 1, {1, 10}, 10, 2};
memset((float *)(net_B+10928), 0, 4);
Softmax((float *)(net_B+16), (float *)(net_B+56), (float *)(net_B+10928), &softmax_parameter);
}
}

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mindspore/lite/micro/example/mnist/src/net.cmake
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include_directories(${CMAKE_CURRENT_SOURCE_DIR}/../include/)
set(OP_SRC
common_func.c.o
common_func_int8.c.o
conv3x3_int8.c.o
conv_int8.c.o
exp_fp32.c.o
fixed_point.c.o
matmul_int8.c.o
matmul_int8_wrapper.c.o
pack_int8.c.o
pooling_int8.c.o
quant_dtype_cast_int8.c.o
reshape_int8.c.o
softmax_fp32.c.o
net_weight.c.o
net.c.o
session.cc.o
tensor.cc.o
)
file(GLOB NET_SRC
${CMAKE_CURRENT_SOURCE_DIR}/*.cc
${CMAKE_CURRENT_SOURCE_DIR}/*.c
)
add_library(net STATIC ${NET_SRC})

67
mindspore/lite/micro/example/mnist/src/net.h
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/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "microtensor.h"
#ifdef __cplusplus
extern "C" {
#endif
/**
* set input tensors
* @param inputs, the input data ptr's array of the model, the tensors' count of input may be greater than one.
* @param num, the input data's number of the model.
**/
int net_SetInputs(const void **inputs, int num);
/**
* get output tensor of the model
**/
const MicroTensorList *net_GetOutputs();
int CopyOutputsData(void **outputs, int num);
/**
* @param weight_buffer, the address of the weight binary file
* @param weight_size, the size of the model file in bytes
**/
int net_Init(void *weight_buffer, int weight_size);
/**
* get the memory space size of the inference.
**/
int net_GetBufferSize();
/**
* set the memory space for the inference
**/
int net_SetBuffer(void *buffer);
/**
* free the memory of packed weights, and set the membuf buffer and input address to NULL
**/
void net_FreeResource();
/**
* net inference function
**/
void net_Inference();
#ifdef __cplusplus
}
#endif

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103
mindspore/lite/micro/example/mnist/src/net_weight.c
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/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "net_weight.h"
unsigned char * net_B = 0 ;
int16_t net_W10[1536];
int32_t net_W11[12];
int16_t net_W12[3072];
int32_t net_W13[12];
int32_t *net_W14 = NULL;
int8_t *net_W15 = NULL;
int32_t *net_W16 = NULL;
int32_t *net_W17 = NULL;
int8_t *net_W18 = NULL;
int32_t *net_W19 = NULL;
int net_Init(void *weight_buffer, int weight_size) {
if (weight_buffer == NULL) {
return RET_ERROR;
}
int g_thread_num = 1;
struct ModelParameter {
void *addr;
size_t size;
size_t offset;
};
int8_t *net_W6 = (weight_buffer + 9312);
int32_t *net_W7 = (weight_buffer + 15312);
int8_t *net_W8 = (weight_buffer + 15392);
int32_t *net_W9 = (weight_buffer + 15592);
struct ModelParameter model_params[] = {
{net_W10, 3072, 0},
{net_W11, 48, 3072},
{net_W12, 6144, 3120},
{net_W13, 48, 9264},
};
for(int i = 0; i < 4; ++i) {
if (model_params[i].offset + model_params[i].size > weight_size) {
return RET_ERROR;
}
memcpy(model_params[i].addr, (weight_buffer + model_params[i].offset), model_params[i].size);
}
{
net_W14 = malloc(80);
if (net_W14 == NULL) {
return RET_ERROR;
}
memset(net_W14, 0, 80);
memcpy(net_W14, net_W7, 80);
net_W16 = malloc(80);
if (net_W16 == NULL) {
return RET_ERROR;
}
memset(net_W16, 0, 80);
net_W15 = malloc(6080);
if (net_W15 == NULL) {
return RET_ERROR;
}
memset(net_W15, 0, 6080);
const int init_filter_zp[20] = {1, 12, 3, 2, -10, -5, -11, 5, 12, 22, 16, 1, -5, 15, 13, 5, -10, -5, -6, 0};
InitInt8MatrixB(net_W6, net_W16, net_W15, 1, 300, 20, 20, 304, 0, init_filter_zp, net_W14, true, true);
}
{
net_W17 = malloc(48);
if (net_W17 == NULL) {
return RET_ERROR;
}
memset(net_W17, 0, 48);
memcpy(net_W17, net_W9, 48);
net_W19 = malloc(48);
if (net_W19 == NULL) {
return RET_ERROR;
}
memset(net_W19, 0, 48);
net_W18 = malloc(384);
if (net_W18 == NULL) {
return RET_ERROR;
}
memset(net_W18, 0, 384);
const int init_filter_zp[10] = {7, -2, 9, 2, -6, 21, 16, 10, -19, 8};
InitInt8MatrixB(net_W8, net_W19, net_W18, 1, 20, 10, 12, 32, 0, init_filter_zp, net_W17, true, true);
}
return RET_OK;
}

43
mindspore/lite/micro/example/mnist/src/net_weight.h
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/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "nnacl/common_func.h"
#include "nnacl/errorcode.h"
#include "nnacl/fp32/softmax_fp32.h"
#include "nnacl/int8/common_func_int8.h"
#include "nnacl/int8/conv3x3_int8.h"
#include "nnacl/int8/conv_int8.h"
#include "nnacl/int8/matmul_int8.h"
#include "nnacl/int8/pooling_int8.h"
#include "nnacl/int8/quant_dtype_cast_int8.h"
#include "nnacl/int8/reshape_int8.h"
#include "wrapper/int8/matmul_int8_wrapper.h"
#include <stdlib.h>
#include <string.h>
#include "microtensor.h"
extern unsigned char *net_B;
extern int16_t net_W10[];
extern int32_t net_W11[];
extern int16_t net_W12[];
extern int32_t net_W13[];
extern int32_t *net_W14;
extern int8_t *net_W15;
extern int32_t *net_W16;
extern int32_t *net_W17;
extern int8_t *net_W18;
extern int32_t *net_W19;

157
mindspore/lite/micro/example/mnist/src/session.cc
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/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "session.h"
#include "net.h"
namespace mindspore {
namespace lite {
int LiteSession::CompileGraph(lite::Model *model) {
inputs_.resize(1);
inputs_[0] = new (std::nothrow) MTensor("graph_input-0", kNumberTypeFloat32, {1, 28, 28, 1, });
MS_ERROR_IF_NULL(inputs_[0]);
outputs_.resize(1);
outputs_[0] = new (std::nothrow) MTensor("Softmax-7", kNumberTypeFloat32, {1, 10, });
MS_ERROR_IF_NULL(outputs_[0]);
for (const auto &output: outputs_) {
output_tensor_map_[output->tensor_name()] = output;
}
return RET_OK;
}
int LiteSession::RunGraph(const KernelCallBack &before, const KernelCallBack &after) {
const void *inputs_data[inputs_.size()];
for (size_t i = 0; i < inputs_.size(); ++i) {
inputs_data[i] = inputs_[i]->MutableData();
}
net_SetInputs(inputs_data, inputs_.size());
net_Inference();
void *outputs_data[outputs_.size()];
for (size_t i = 0; i < outputs_.size(); ++i) {
outputs_data[i] = outputs_[i]->MutableData();
}
CopyOutputsData(outputs_data, outputs_.size());
return RET_OK;
}
LiteSession::~LiteSession() {
net_FreeResource();
if (runtime_buffer_ != nullptr) {
free(runtime_buffer_);
runtime_buffer_ = nullptr;
}
for (auto &input : inputs_) {
if (input == nullptr) {
continue;
}
delete input;
input = nullptr;
}
for (auto &item : output_tensor_map_) {
auto output = item.second;
if (output == nullptr) {
continue;
}
delete output;
output = nullptr;
}
}
int LiteSession::InitRuntimeBuffer() {
int buffer_size = net_GetBufferSize();
runtime_buffer_ = malloc(buffer_size);
if (runtime_buffer_ == nullptr) {
return RET_ERROR;
}
int ret = net_SetBuffer(runtime_buffer_);
if (ret != RET_OK) {
return RET_ERROR;
}
return RET_OK;
}
std::vector<tensor::MSTensor *> LiteSession::GetInputs() const {
std::vector<tensor::MSTensor *> inputs;
inputs.insert(inputs.begin(), inputs_.begin(), inputs_.end());
return inputs;
}
std::vector<tensor::MSTensor *> LiteSession::GetOutputsByNodeName(const std::string &node_name) const {
auto iter = output_node_map_.find(node_name);
if (iter == output_node_map_.end()) {
std::vector<tensor::MSTensor *> empty;
return empty;
}
return iter->second;
}
std::unordered_map<std::string, mindspore::tensor::MSTensor *> LiteSession::GetOutputs() const {
return output_tensor_map_;
}
std::vector<std::string> LiteSession::GetOutputTensorNames() const {
std::vector<std::string> output_names;
for (const auto &item : output_node_map_) {
for (const auto &output : item.second) {
output_names.emplace_back(output->tensor_name());
}
}
return output_names;
}
mindspore::tensor::MSTensor *LiteSession::GetOutputByTensorName(const std::string &tensor_name) const {
auto item = output_tensor_map_.find(tensor_name);
if (item == output_tensor_map_.end()) {
return nullptr;
}
return item->second;
}
int LiteSession::Resize(const std::vector<tensor::MSTensor *> &inputs, const std::vector<std::vector<int>> &dims) {
return RET_OK;
}
} // namespace lite
session::LiteSession *session::LiteSession::CreateSession(const lite::Context *context) {
auto *session = new (std::nothrow) lite::LiteSession();
if (session == nullptr) {
return nullptr;
}
session->InitRuntimeBuffer();
return session;
}
session::LiteSession *session::LiteSession::CreateSession(const char *net_buf, size_t size,
const lite::Context *context) {
session::LiteSession *session = CreateSession(context);
if (session == nullptr) {
return nullptr;
}
int ret = session->CompileGraph(nullptr);
if (ret != lite::RET_OK) {
return nullptr;
}
net_Init(const_cast<char *>(net_buf), size);
return session;
}
} // namespace mindspore

78
mindspore/lite/micro/example/mnist/src/session.h
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@ -0,0 +1,78 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_LITE_MICRO_LIBRARY_SOURCE_SESSION_H_
#define MINDSPORE_LITE_MICRO_LIBRARY_SOURCE_SESSION_H_
#include "include/errorcode.h"
#include "include/lite_session.h"
#include "tensor.h"
namespace mindspore {
namespace lite {
#define MS_ERROR_IF_NULL(ptr) \
do { \
if ((ptr) == nullptr) { \
return mindspore::lite::RET_ERROR; \
} \
} while (0)
class LiteSession : public session::LiteSession {
public:
LiteSession() = default;
~LiteSession() override;
void BindThread(bool if_bind) override {}
int CompileGraph(lite::Model *model) override;
std::vector<tensor::MSTensor *> GetInputs() const override;
mindspore::tensor::MSTensor *GetInputsByTensorName(const std::string &tensor_name) const override { return nullptr; }
int RunGraph(const KernelCallBack &before = nullptr, const KernelCallBack &after = nullptr) override;
std::vector<tensor::MSTensor *> GetOutputsByNodeName(const std::string &node_name) const override;
std::unordered_map<std::string, mindspore::tensor::MSTensor *> GetOutputs() const override;
std::vector<std::string> GetOutputTensorNames() const override;
mindspore::tensor::MSTensor *GetOutputByTensorName(const std::string &tensor_name) const override;
int Resize(const std::vector<tensor::MSTensor *> &inputs, const std::vector<std::vector<int>> &dims) override;
int InitRuntimeBuffer();
private:
int SetInputsData(const std::vector<MTensor *> &inputs) const;
std::vector<MTensor *> inputs_;
std::vector<MTensor *> outputs_;
std::unordered_map<std::string, mindspore::tensor::MSTensor *> output_tensor_map_;
std::unordered_map<std::string, std::vector<mindspore::tensor::MSTensor *>> output_node_map_;
void *runtime_buffer_;
};
} // namespace lite
} // namespace mindspore
#endif // MINDSPORE_LITE_MICRO_LIBRARY_SOURCE_SESSION_H_

93
mindspore/lite/micro/example/mnist/src/tensor.cc
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@ -0,0 +1,93 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "tensor.h"
namespace mindspore {
namespace lite {
size_t DataTypeSize(const TypeId type) {
switch (type) {
case kNumberTypeFloat64:
return sizeof(double);
case kNumberTypeFloat:
case kNumberTypeFloat32:
return sizeof(float);
case kNumberTypeInt8:
return sizeof(int8_t);
case kNumberTypeUInt8:
return sizeof(uint8_t);
case kNumberTypeFloat16:
case kNumberTypeInt16:
return sizeof(int16_t);
case kNumberTypeInt32:
return sizeof(int32_t);
case kNumberTypeInt64:
return sizeof(int64_t);
case kNumberTypeUInt16:
return sizeof(uint16_t);
case kNumberTypeUInt32:
return sizeof(uint32_t);
case kNumberTypeUInt64:
return sizeof(uint64_t);
case kNumberTypeBool:
return sizeof(bool);
case kObjectTypeString:
return sizeof(char);
case kObjectTypeTensorType:
default:
return 0;
}
}
MTensor::~MTensor() {
if (data_ != nullptr) {
free(data_);
data_ = nullptr;
}
}
int MTensor::DimensionSize(const size_t index) const {
int dim_size = -1;
if (index < shape_.size()) {
dim_size = shape_[index];
}
return dim_size;
}
int MTensor::ElementsNum() const {
int elements = 1;
for (int i : shape_) {
elements *= i;
}
return elements;
}
size_t MTensor::Size() const {
size_t element_size = DataTypeSize(data_type_);
return element_size * ElementsNum();
}
void *MTensor::MutableData() {
if (data_ == nullptr) {
data_ = malloc(this->Size());
}
return data_;
}
} // namespace lite
} // namespace mindspore

71
mindspore/lite/micro/example/mnist/src/tensor.h
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@ -0,0 +1,71 @@
/**
* 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.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_LITE_MICRO_LIBRARY_SOURCE_TENSOR_H_
#define MINDSPORE_LITE_MICRO_LIBRARY_SOURCE_TENSOR_H_
#include "include/ms_tensor.h"
#include <utility>
#include <vector>
namespace mindspore {
namespace lite {
struct QuantArg {
double scale;
int32_t zeroPoint;
float var_corr{1};
float mean_corr{0};
bool inited;
std::vector<float> clusters{};
int bitNum;
int roundType;
int multiplier;
int dstDtype;
};
class MTensor : public mindspore::tensor::MSTensor {
public:
MTensor() = default;
MTensor(std::string name, enum TypeId type, std::vector<int32_t> shape)
: tensor_name_(std::move(name)), data_type_(type), shape_(std::move(shape)) {}
~MTensor() override;
TypeId data_type() const override { return data_type_; }
std::vector<int> shape() const override { return shape_; }
int DimensionSize(size_t index) const override;
int ElementsNum() const override;
size_t Size() const override;
void *MutableData() override;
std::string tensor_name() const override { return tensor_name_; }
void set_tensor_name(const std::string name) override { tensor_name_ = name; }
void set_data(void *data) override { data_ = data; }
private:
std::string tensor_name_;
TypeId data_type_;
std::vector<int> shape_;
void *data_ = nullptr;
std::vector<QuantArg> quant_params_;
};
} // namespace lite
} // namespace mindspore
#endif // MINDSPORE_LITE_MICRO_LIBRARY_SOURCE_TENSOR_H_

11
scripts/check_clang_format.sh
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@ -33,7 +33,7 @@ echo "SCRIPTS_PATH=$SCRIPTS_PATH"
# print usage message
function usage()
{
echo "Check whether the specified source files were well formated"
echo "Check whether the specified source files were well formatted"
echo "Usage:"
echo "bash $0 [-a] [-c] [-l] [-h]"
echo "e.g. $0 -a"
@ -97,8 +97,11 @@ fi
CHECK_RESULT_FILE=__code_format_check_result__
echo "0" > "$CHECK_RESULT_FILE"
# check format of files modified in the lastest commit
# check format of files modified in the latest commit
while read line; do
if [ ! -e ${line} ]; then
continue
fi
BASE_NAME=$(basename "${line}")
TEMP_FILE="__TEMP__${BASE_NAME}"
cp "${line}" "${TEMP_FILE}"
@ -107,7 +110,7 @@ while read line; do
ret=$?
rm "${TEMP_FILE}"
if [[ "${ret}" -ne 0 ]]; then
echo "File ${line} is not formated, please format it."
echo "File ${line} is not formatted, please format it."
echo "1" > "${CHECK_RESULT_FILE}"
break
fi
@ -118,6 +121,6 @@ rm "${CHECK_RESULT_FILE}"
rm "${CHECK_LIST_FILE}"
cd "${CURRENT_PATH}" || exit 1
if [[ "X${result}" == "X0" ]]; then
echo "Check PASS: specified files are well formated!"
echo "Check PASS: specified files are well formatted!"
fi
exit "${result}"

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