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【修改说明】修改CI门禁报错 

【模型】ResNet18
【验证结果】昇腾910、310芯片验证通过
【修改人】yumingchuan
【评审人】chenshushu
This commit is contained in:
Atlas_ymc 2021-09-06 23:29:11 +08:00
parent 6492760c84
commit 308b5e43db
6 changed files with 123 additions and 144 deletions
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2
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/CMakeLists.txt
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@ -40,7 +40,7 @@ link_directories(${OPENSOURCE_DIR}/lib)
link_directories(${MXBASE_LIB_DIR})
link_directories(${MXBASE_POST_LIB_DIR})
add_executable(${TARGET} main_opencv.cpp Resnet50ClassifyOpencv.cpp)
add_executable(${TARGET} main.cpp Resnet18ClassifyOpencv.cpp)
target_link_libraries(${TARGET} glog cpprest mxbase resnet50postprocess opencv_world stdc++fs)

65
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/Resnet50ClassifyOpencv.cpp → model_zoo/official/cv/resnet/infer/ResNet18/mxbase/Resnet18ClassifyOpencv.cpp
View File

@ -14,7 +14,7 @@
* limitations under the License.
*/
#include "Resnet50ClassifyOpencv.h"
#include "Resnet18ClassifyOpencv.h"
#include "MxBase/DeviceManager/DeviceManager.h"
#include "MxBase/Log/Log.h"
@ -25,8 +25,7 @@ namespace {
const uint32_t VPC_H_ALIGN = 2;
}
APP_ERROR Resnet50ClassifyOpencv::Init(const InitParam &initParam)
{
APP_ERROR Resnet18ClassifyOpencv::Init(const InitParam &initParam) {
deviceId_ = initParam.deviceId;
APP_ERROR ret = MxBase::DeviceManager::GetInstance()->InitDevices();
if (ret != APP_ERR_OK) {
@ -73,8 +72,7 @@ APP_ERROR Resnet50ClassifyOpencv::Init(const InitParam &initParam)
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::DeInit()
{
APP_ERROR Resnet18ClassifyOpencv::DeInit() {
dvppWrapper_->DeInit();
model_->DeInit();
post_->DeInit();
@ -82,25 +80,16 @@ APP_ERROR Resnet50ClassifyOpencv::DeInit()
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::ReadImage(const std::string &imgPath, cv::Mat &imageMat)
{
imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::ResizeImage(const cv::Mat &srcImageMat, cv::Mat &dstImageMat)
{
APP_ERROR Resnet18ClassifyOpencv::ConvertImageToTensorBase(std::string &imgPath,
MxBase::TensorBase &tensorBase) {
static constexpr uint32_t resizeHeight = 304;
static constexpr uint32_t resizeWidth = 304;
cv::resize(srcImageMat, dstImageMat, cv::Size(resizeWidth, resizeHeight));
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase)
{
cv::Mat imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
cv::resize(imageMat, imageMat, cv::Size(resizeWidth, resizeHeight));
const uint32_t dataSize = imageMat.cols * imageMat.rows * XRGB_WIDTH_NU;
LogInfo << "image size after crop" << imageMat.cols << " " << imageMat.rows;
LogInfo << "image size after resize" << imageMat.cols << " " << imageMat.rows;
MemoryData memoryDataDst(dataSize, MemoryData::MEMORY_DEVICE, deviceId_);
MemoryData memoryDataSrc(imageMat.data, dataSize, MemoryData::MEMORY_HOST_MALLOC);
@ -115,9 +104,8 @@ APP_ERROR Resnet50ClassifyOpencv::CVMatToTensorBase(const cv::Mat &imageMat, MxB
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::Inference(const std::vector<MxBase::TensorBase> &inputs,
std::vector<MxBase::TensorBase> &outputs)
{
APP_ERROR Resnet18ClassifyOpencv::Inference(std::vector<MxBase::TensorBase> &inputs,
std::vector<MxBase::TensorBase> &outputs) {
auto dtypes = model_->GetOutputDataType();
for (size_t i = 0; i < modelDesc_.outputTensors.size(); ++i) {
std::vector<uint32_t> shape = {};
@ -137,7 +125,7 @@ APP_ERROR Resnet50ClassifyOpencv::Inference(const std::vector<MxBase::TensorBase
auto startTime = std::chrono::high_resolution_clock::now();
APP_ERROR ret = model_->ModelInference(inputs, outputs, dynamicInfo);
auto endTime = std::chrono::high_resolution_clock::now();
double costMs = std::chrono::duration<double, std::milli>(endTime - startTime).count();// save time
double costMs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
inferCostTimeMilliSec += costMs;
if (ret != APP_ERR_OK) {
LogError << "ModelInference failed, ret=" << ret << ".";
@ -146,9 +134,8 @@ APP_ERROR Resnet50ClassifyOpencv::Inference(const std::vector<MxBase::TensorBase
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::PostProcess(const std::vector<MxBase::TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos)
{
APP_ERROR Resnet18ClassifyOpencv::PostProcess(std::vector<MxBase::TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos) {
APP_ERROR ret = post_->Process(inputs, clsInfos);
if (ret != APP_ERR_OK) {
LogError << "Process failed, ret=" << ret << ".";
@ -157,8 +144,8 @@ APP_ERROR Resnet50ClassifyOpencv::PostProcess(const std::vector<MxBase::TensorBa
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::SaveResult(const std::string &imgPath, const std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos)
{
APP_ERROR Resnet18ClassifyOpencv::SaveResult(std::string &imgPath,
std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos) {
LogInfo << "image path" << imgPath;
std::string fileName = imgPath.substr(imgPath.find_last_of("/") + 1);
size_t dot = fileName.find_last_of(".");
@ -187,24 +174,14 @@ APP_ERROR Resnet50ClassifyOpencv::SaveResult(const std::string &imgPath, const s
return APP_ERR_OK;
}
APP_ERROR Resnet50ClassifyOpencv::Process(const std::string &imgPath)
{
cv::Mat imageMat;
APP_ERROR ret = ReadImage(imgPath, imageMat);
if (ret != APP_ERR_OK) {
LogError << "ReadImage failed, ret=" << ret << ".";
return ret;
}
ResizeImage(imageMat, imageMat);
APP_ERROR Resnet18ClassifyOpencv::Process(std::string &imgPath) {
MxBase::TensorBase tensorBase;
std::vector<MxBase::TensorBase> inputs = {};
std::vector<MxBase::TensorBase> outputs = {};
TensorBase tensorBase;
ret = CVMatToTensorBase(imageMat, tensorBase);
APP_ERROR ret = ConvertImageToTensorBase(imgPath, tensorBase);
if (ret != APP_ERR_OK) {
LogError << "CVMatToTensorBase failed, ret=" << ret << ".";
LogError << "Convert image to TensorBase failed, ret=" << ret << ".";
return ret;
}
@ -213,7 +190,7 @@ APP_ERROR Resnet50ClassifyOpencv::Process(const std::string &imgPath)
auto startTime = std::chrono::high_resolution_clock::now();
ret = Inference(inputs, outputs);
auto endTime = std::chrono::high_resolution_clock::now();
double costMs = std::chrono::duration<double, std::milli>(endTime - startTime).count();// save time
double costMs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
inferCostTimeMilliSec += costMs;
if (ret != APP_ERR_OK) {
LogError << "Inference failed, ret=" << ret << ".";

65
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/Resnet18ClassifyOpencv.h Normal file
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@ -0,0 +1,65 @@
/*
* Copyright (c) 2021. Huawei Technologies Co., Ltd. All rights reserved.
*
* 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 MXBASE_RESNET18CLASSIFYOPENCV_H
#define MXBASE_RESNET18CLASSIFYOPENCV_H
#include <opencv2/opencv.hpp>
#include <map>
#include <memory>
#include <string>
#include <vector>
#include "MxBase/DvppWrapper/DvppWrapper.h"
#include "MxBase/ModelInfer/ModelInferenceProcessor.h"
#include "ClassPostProcessors/Resnet50PostProcess.h"
#include "MxBase/Tensor/TensorContext/TensorContext.h"
struct InitParam {
uint32_t deviceId;
std::string labelPath;
uint32_t classNum;
uint32_t topk;
bool softmax;
bool checkTensor;
std::string modelPath;
};
class Resnet18ClassifyOpencv {
public:
APP_ERROR Init(const InitParam &initParam);
APP_ERROR DeInit();
APP_ERROR ConvertImageToTensorBase(std::string &imgPath, MxBase::TensorBase &tensorBase);
APP_ERROR Inference(std::vector<MxBase::TensorBase> &inputs,
std::vector<MxBase::TensorBase> &outputs);
APP_ERROR PostProcess(std::vector<MxBase::TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos);
APP_ERROR Process(std::string &imgPath);
// get infer time
double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
private:
APP_ERROR SaveResult(std::string &imgPath,
std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos);
std::shared_ptr<MxBase::DvppWrapper> dvppWrapper_;
std::shared_ptr<MxBase::ModelInferenceProcessor> model_;
std::shared_ptr<MxBase::Resnet50PostProcess> post_;
MxBase::ModelDesc modelDesc_;
uint32_t deviceId_ = 0;
// infer time
double inferCostTimeMilliSec = 0.0;
};
#endif

64
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/Resnet50ClassifyOpencv.h
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@ -1,64 +0,0 @@
/*
* Copyright (c) 2021. Huawei Technologies Co., Ltd. All rights reserved.
*
* 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 MXBASE_RESNET50CLASSIFYOPENCV_H
#define MXBASE_RESNET50CLASSIFYOPENCV_H
#include <opencv2/opencv.hpp>
#include "MxBase/DvppWrapper/DvppWrapper.h"
#include "MxBase/ModelInfer/ModelInferenceProcessor.h"
#include "ClassPostProcessors/Resnet50PostProcess.h"
#include "MxBase/Tensor/TensorContext/TensorContext.h"
struct InitParam {
uint32_t deviceId;
std::string labelPath;
uint32_t classNum;
uint32_t topk;
bool softmax;
bool checkTensor;
std::string modelPath;
};
class Resnet50ClassifyOpencv {
public:
APP_ERROR Init(const InitParam &initParam);
APP_ERROR DeInit();
APP_ERROR ReadImage(const std::string &imgPath, cv::Mat &imageMat);
APP_ERROR ResizeImage(const cv::Mat &srcImageMat, cv::Mat &dstImageMat);
APP_ERROR CenterCropImage(const cv::Mat &img, cv::Mat &crop_im);
APP_ERROR CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase);
APP_ERROR Inference(const std::vector<MxBase::TensorBase> &inputs, std::vector<MxBase::TensorBase> &outputs);
APP_ERROR PostProcess(const std::vector<MxBase::TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos);
APP_ERROR Process(const std::string &imgPath);
// get infer time
double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
private:
APP_ERROR SaveResult(const std::string &imgPath, const std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos);
private:
std::shared_ptr<MxBase::DvppWrapper> dvppWrapper_;
std::shared_ptr<MxBase::ModelInferenceProcessor> model_;
std::shared_ptr<MxBase::Resnet50PostProcess> post_;
MxBase::ModelDesc modelDesc_;
uint32_t deviceId_ = 0;
// infer time
double inferCostTimeMilliSec = 0.0;
};
#endif

6
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/build.sh
View File

@ -33,7 +33,7 @@ function check_env()
fi
}
function build_resnet50()
function build_resnet18()
{
cd $path_cur
rm -rf build
@ -43,11 +43,11 @@ function build_resnet50()
make
ret=$?
if [ ${ret} -ne 0 ]; then
echo "Failed to build resnet50."
echo "Failed to build resnet18."
exit ${ret}
fi
make install
}
check_env
build_resnet50
build_resnet18

65
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/main_opencv.cpp → model_zoo/official/cv/resnet/infer/ResNet18/mxbase/main.cpp
View File

@ -14,39 +14,38 @@
* limitations under the License.
*/
#include "Resnet50ClassifyOpencv.h"
#include "Resnet18ClassifyOpencv.h"
#include "MxBase/Log/Log.h"
#include <dirent.h>
namespace {
const uint32_t CLASS_NUM = 1001;
} // namespace
}
APP_ERROR ScanImages(const std::string &path, std::vector<std::string> &imgFiles)
{
DIR *dirPtr = opendir(path.c_str());
if (dirPtr == nullptr) {
LogError << "opendir failed. dir:" << path;
return APP_ERR_INTERNAL_ERROR;
APP_ERROR ReadFilesFromPath(const std::string &path, std::vector<std::string> *files) {
DIR *dir = NULL;
struct dirent *ptr = NULL;
if ((dir=opendir(path.c_str())) == NULL) {
LogError << "Open dir error: " << path;
return APP_ERR_COMM_OPEN_FAIL;
}
dirent *direntPtr = nullptr;
while ((direntPtr = readdir(dirPtr)) != nullptr) {
std::string fileName = direntPtr->d_name;
if (fileName == "." || fileName == "..") {
continue;
while ((ptr=readdir(dir)) != NULL) {
// d_type == 8 is file
if (ptr->d_type == 8) {
files->push_back(path + ptr->d_name);
}
imgFiles.emplace_back(path + "/" + fileName);
}
closedir(dirPtr);
closedir(dir);
// sort ascending order
sort(files->begin(), files->end());
return APP_ERR_OK;
}
int main(int argc, char* argv[])
{
int main(int argc, char* argv[]) {
if (argc <= 1) {
LogWarn << "Please input image path, such as './resnet50 image_dir'.";
LogWarn << "Please input image path, such as './resnet image_dir'.";
return APP_ERR_OK;
}
@ -58,32 +57,34 @@ int main(int argc, char* argv[])
initParam.softmax = false;
initParam.checkTensor = true;
initParam.modelPath = "../data/model/resnet18-304_304.om";
auto resnet50 = std::make_shared<Resnet50ClassifyOpencv>();
APP_ERROR ret = resnet50->Init(initParam);
auto resnet18 = std::make_shared<Resnet18ClassifyOpencv>();
APP_ERROR ret = resnet18->Init(initParam);
if (ret != APP_ERR_OK) {
LogError << "Resnet50Classify init failed, ret=" << ret << ".";
LogError << "resnet18Classify init failed, ret=" << ret << ".";
return ret;
}
std::string imgPath = argv[1];
std::vector<std::string> imgFilePaths;
ret = ScanImages(imgPath, imgFilePaths);
std::string inferPath = argv[1];
std::vector<std::string> files;
ret = ReadFilesFromPath(inferPath, &files);
if (ret != APP_ERR_OK) {
LogError << "Read files from path failed, ret=" << ret << ".";
return ret;
}
auto startTime = std::chrono::high_resolution_clock::now();
for (auto &imgFile : imgFilePaths) {
ret = resnet50->Process(imgFile);
for (uint32_t i = 0; i < files.size(); i++) {
ret = resnet18->Process(files[i]);
if (ret != APP_ERR_OK) {
LogError << "Resnet50Classify process failed, ret=" << ret << ".";
resnet50->DeInit();
LogError << "resnet18Classify process failed, ret=" << ret << ".";
resnet18->DeInit();
return ret;
}
}
auto endTime = std::chrono::high_resolution_clock::now();
resnet50->DeInit();
resnet18->DeInit();
double costMilliSecs = std::chrono::duration<double, std::milli>(endTime - startTime).count();
double fps = 1000.0 * imgFilePaths.size() / resnet50->GetInferCostMilliSec();
double fps = 1000.0 * files.size() / resnet18->GetInferCostMilliSec();
LogInfo << "[Process Delay] cost: " << costMilliSecs << " ms\tfps: " << fps << " imgs/sec";
return APP_ERR_OK;
}