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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 2021-09-11 18:21:45 +08:00
parent 308b5e43db
commit 36fe418d77
3 changed files with 30 additions and 25 deletions
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1
.jenkins/check/config/filter_cpplint.txt
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@ -18,6 +18,7 @@
# Modelzoo # Modelzoo
"mindspore/model_zoo/official/cv/yolov4_tiny/infer/mxbase/src/Yolov4TinyDetection.h" "runtime/references" "mindspore/model_zoo/official/cv/yolov4_tiny/infer/mxbase/src/Yolov4TinyDetection.h" "runtime/references"
"mindspore/model_zoo/official/cv/yolov4_tiny/infer/mxbase/src/PostProcess/Yolov4TinyMindsporePost.h" "runtime/references" "mindspore/model_zoo/official/cv/yolov4_tiny/infer/mxbase/src/PostProcess/Yolov4TinyMindsporePost.h" "runtime/references"
"mindspore/model_zoo/official/cv/resnet/infer/ResNet18/mxbase/Resnet18ClassifyOpencv.h" "runtime/references"
# MindData # MindData
"mindspore/mindspore/ccsrc/minddata/mindrecord/include/shard_page.h" "runtime/string" "mindspore/mindspore/ccsrc/minddata/mindrecord/include/shard_page.h" "runtime/string"

48
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/Resnet18ClassifyOpencv.cpp
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@ -18,7 +18,11 @@
#include "MxBase/DeviceManager/DeviceManager.h" #include "MxBase/DeviceManager/DeviceManager.h"
#include "MxBase/Log/Log.h" #include "MxBase/Log/Log.h"
using namespace MxBase; using MxBase::DeviceManager;
using MxBase::TensorBase;
using MxBase::MemoryData;
using MxBase::ClassInfo;
namespace { namespace {
const uint32_t YUV_BYTE_NU = 3; const uint32_t YUV_BYTE_NU = 3;
const uint32_t YUV_BYTE_DE = 2; const uint32_t YUV_BYTE_DE = 2;
@ -27,7 +31,7 @@ namespace {
APP_ERROR Resnet18ClassifyOpencv::Init(const InitParam &initParam) { APP_ERROR Resnet18ClassifyOpencv::Init(const InitParam &initParam) {
deviceId_ = initParam.deviceId; deviceId_ = initParam.deviceId;
APP_ERROR ret = MxBase::DeviceManager::GetInstance()->InitDevices(); APP_ERROR ret = DeviceManager::GetInstance()->InitDevices();
if (ret != APP_ERR_OK) { if (ret != APP_ERR_OK) {
LogError << "Init devices failed, ret=" << ret << "."; LogError << "Init devices failed, ret=" << ret << ".";
return ret; return ret;
@ -76,36 +80,36 @@ APP_ERROR Resnet18ClassifyOpencv::DeInit() {
dvppWrapper_->DeInit(); dvppWrapper_->DeInit();
model_->DeInit(); model_->DeInit();
post_->DeInit(); post_->DeInit();
MxBase::DeviceManager::GetInstance()->DestroyDevices(); DeviceManager::GetInstance()->DestroyDevices();
return APP_ERR_OK; return APP_ERR_OK;
} }
APP_ERROR Resnet18ClassifyOpencv::ConvertImageToTensorBase(std::string &imgPath, APP_ERROR Resnet18ClassifyOpencv::ConvertImageToTensorBase(const std::string &imgPath,
MxBase::TensorBase &tensorBase) { TensorBase &tensorBase) {
static constexpr uint32_t resizeHeight = 304; static constexpr uint32_t resizeHeight = 304;
static constexpr uint32_t resizeWidth = 304; static constexpr uint32_t resizeWidth = 304;
cv::Mat imageMat = cv::imread(imgPath, cv::IMREAD_COLOR); cv::Mat imageMat = cv::imread(imgPath, cv::IMREAD_COLOR);
cv::resize(imageMat, imageMat, cv::Size(resizeWidth, resizeHeight)); cv::resize(imageMat, imageMat, cv::Size(resizeWidth, resizeHeight));
const uint32_t dataSize = imageMat.cols * imageMat.rows * XRGB_WIDTH_NU; const uint32_t dataSize = imageMat.cols * imageMat.rows * MxBase::XRGB_WIDTH_NU;
LogInfo << "image size after resize" << imageMat.cols << " " << imageMat.rows; LogInfo << "image size after resize" << imageMat.cols << " " << imageMat.rows;
MemoryData memoryDataDst(dataSize, MemoryData::MEMORY_DEVICE, deviceId_); MemoryData memoryDataDst(dataSize, MemoryData::MEMORY_DEVICE, deviceId_);
MemoryData memoryDataSrc(imageMat.data, dataSize, MemoryData::MEMORY_HOST_MALLOC); MemoryData memoryDataSrc(imageMat.data, dataSize, MemoryData::MEMORY_HOST_MALLOC);
APP_ERROR ret = MemoryHelper::MxbsMallocAndCopy(memoryDataDst, memoryDataSrc); APP_ERROR ret = MxBase::MemoryHelper::MxbsMallocAndCopy(memoryDataDst, memoryDataSrc);
if (ret != APP_ERR_OK) { if (ret != APP_ERR_OK) {
LogError << GetError(ret) << "Memory malloc failed."; LogError << GetError(ret) << "Memory malloc failed.";
return ret; return ret;
} }
std::vector<uint32_t> shape = {imageMat.rows * XRGB_WIDTH_NU, static_cast<uint32_t>(imageMat.cols)}; std::vector<uint32_t> shape = {imageMat.rows * MxBase::XRGB_WIDTH_NU, static_cast<uint32_t>(imageMat.cols)};
tensorBase = TensorBase(memoryDataDst, false, shape, TENSOR_DTYPE_UINT8); tensorBase = TensorBase(memoryDataDst, false, shape, MxBase::TENSOR_DTYPE_UINT8);
return APP_ERR_OK; return APP_ERR_OK;
} }
APP_ERROR Resnet18ClassifyOpencv::Inference(std::vector<MxBase::TensorBase> &inputs, APP_ERROR Resnet18ClassifyOpencv::Inference(std::vector<TensorBase> &inputs,
std::vector<MxBase::TensorBase> &outputs) { std::vector<TensorBase> &outputs) {
auto dtypes = model_->GetOutputDataType(); auto dtypes = model_->GetOutputDataType();
for (size_t i = 0; i < modelDesc_.outputTensors.size(); ++i) { for (size_t i = 0; i < modelDesc_.outputTensors.size(); ++i) {
std::vector<uint32_t> shape = {}; std::vector<uint32_t> shape = {};
@ -120,8 +124,8 @@ APP_ERROR Resnet18ClassifyOpencv::Inference(std::vector<MxBase::TensorBase> &inp
} }
outputs.push_back(tensor); outputs.push_back(tensor);
} }
DynamicInfo dynamicInfo = {}; MxBase::DynamicInfo dynamicInfo = {};
dynamicInfo.dynamicType = DynamicType::STATIC_BATCH; dynamicInfo.dynamicType = MxBase::DynamicType::STATIC_BATCH;
auto startTime = std::chrono::high_resolution_clock::now(); auto startTime = std::chrono::high_resolution_clock::now();
APP_ERROR ret = model_->ModelInference(inputs, outputs, dynamicInfo); APP_ERROR ret = model_->ModelInference(inputs, outputs, dynamicInfo);
auto endTime = std::chrono::high_resolution_clock::now(); auto endTime = std::chrono::high_resolution_clock::now();
@ -134,8 +138,8 @@ APP_ERROR Resnet18ClassifyOpencv::Inference(std::vector<MxBase::TensorBase> &inp
return APP_ERR_OK; return APP_ERR_OK;
} }
APP_ERROR Resnet18ClassifyOpencv::PostProcess(std::vector<MxBase::TensorBase> &inputs, APP_ERROR Resnet18ClassifyOpencv::PostProcess(std::vector<TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos) { std::vector<std::vector<ClassInfo>> &clsInfos) {
APP_ERROR ret = post_->Process(inputs, clsInfos); APP_ERROR ret = post_->Process(inputs, clsInfos);
if (ret != APP_ERR_OK) { if (ret != APP_ERR_OK) {
LogError << "Process failed, ret=" << ret << "."; LogError << "Process failed, ret=" << ret << ".";
@ -144,8 +148,8 @@ APP_ERROR Resnet18ClassifyOpencv::PostProcess(std::vector<MxBase::TensorBase> &i
return APP_ERR_OK; return APP_ERR_OK;
} }
APP_ERROR Resnet18ClassifyOpencv::SaveResult(std::string &imgPath, APP_ERROR Resnet18ClassifyOpencv::SaveResult(const std::string &imgPath,
std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos) { std::vector<std::vector<ClassInfo>> &batchClsInfos) {
LogInfo << "image path" << imgPath; LogInfo << "image path" << imgPath;
std::string fileName = imgPath.substr(imgPath.find_last_of("/") + 1); std::string fileName = imgPath.substr(imgPath.find_last_of("/") + 1);
size_t dot = fileName.find_last_of("."); size_t dot = fileName.find_last_of(".");
@ -174,10 +178,10 @@ APP_ERROR Resnet18ClassifyOpencv::SaveResult(std::string &imgPath,
return APP_ERR_OK; return APP_ERR_OK;
} }
APP_ERROR Resnet18ClassifyOpencv::Process(std::string &imgPath) { APP_ERROR Resnet18ClassifyOpencv::Process(const std::string &imgPath) {
MxBase::TensorBase tensorBase; TensorBase tensorBase;
std::vector<MxBase::TensorBase> inputs = {}; std::vector<TensorBase> inputs;
std::vector<MxBase::TensorBase> outputs = {}; std::vector<TensorBase> outputs;
APP_ERROR ret = ConvertImageToTensorBase(imgPath, tensorBase); APP_ERROR ret = ConvertImageToTensorBase(imgPath, tensorBase);
if (ret != APP_ERR_OK) { if (ret != APP_ERR_OK) {
@ -196,7 +200,7 @@ APP_ERROR Resnet18ClassifyOpencv::Process(std::string &imgPath) {
LogError << "Inference failed, ret=" << ret << "."; LogError << "Inference failed, ret=" << ret << ".";
return ret; return ret;
} }
std::vector<std::vector<MxBase::ClassInfo>> BatchClsInfos = {}; std::vector<std::vector<ClassInfo>> BatchClsInfos;
ret = PostProcess(outputs, BatchClsInfos); ret = PostProcess(outputs, BatchClsInfos);
if (ret != APP_ERR_OK) { if (ret != APP_ERR_OK) {
LogError << "PostProcess failed, ret=" << ret << "."; LogError << "PostProcess failed, ret=" << ret << ".";

6
model_zoo/official/cv/resnet/infer/ResNet18/mxbase/Resnet18ClassifyOpencv.h
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@ -41,17 +41,17 @@ class Resnet18ClassifyOpencv {
public: public:
APP_ERROR Init(const InitParam &initParam); APP_ERROR Init(const InitParam &initParam);
APP_ERROR DeInit(); APP_ERROR DeInit();
APP_ERROR ConvertImageToTensorBase(std::string &imgPath, MxBase::TensorBase &tensorBase); APP_ERROR ConvertImageToTensorBase(const std::string &imgPath, MxBase::TensorBase &tensorBase);
APP_ERROR Inference(std::vector<MxBase::TensorBase> &inputs, APP_ERROR Inference(std::vector<MxBase::TensorBase> &inputs,
std::vector<MxBase::TensorBase> &outputs); std::vector<MxBase::TensorBase> &outputs);
APP_ERROR PostProcess(std::vector<MxBase::TensorBase> &inputs, APP_ERROR PostProcess(std::vector<MxBase::TensorBase> &inputs,
std::vector<std::vector<MxBase::ClassInfo>> &clsInfos); std::vector<std::vector<MxBase::ClassInfo>> &clsInfos);
APP_ERROR Process(std::string &imgPath); APP_ERROR Process(const std::string &imgPath);
// get infer time // get infer time
double GetInferCostMilliSec() const {return inferCostTimeMilliSec;} double GetInferCostMilliSec() const {return inferCostTimeMilliSec;}
private: private:
APP_ERROR SaveResult(std::string &imgPath, APP_ERROR SaveResult(const std::string &imgPath,
std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos); std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos);
std::shared_ptr<MxBase::DvppWrapper> dvppWrapper_; std::shared_ptr<MxBase::DvppWrapper> dvppWrapper_;
std::shared_ptr<MxBase::ModelInferenceProcessor> model_; std::shared_ptr<MxBase::ModelInferenceProcessor> model_;