修复mxbase变量未定义问题

official/cv/yolov5/infer/mxbase/CMakeLists.txt

@@ -9,7 +9,7 @@ add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
 set(TARGET_LIBRARY yolov5postprocessor)
 set(TARGET_MAIN yolov5)
 
-set(ACL_LIB_PATH $ENV{ASCEND_HOME}/nnrt/latest/acllib)
+set(ACL_LIB_PATH $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
 
 include_directories(${CMAKE_CURRENT_BINARY_DIR})
 
@@ -19,6 +19,7 @@ include_directories($ENV{MX_SDK_HOME}/opensource/include/opencv4)
 include_directories($ENV{MX_SDK_HOME}/opensource/include/gstreamer-1.0)
 include_directories($ENV{MX_SDK_HOME}/opensource/include/glib-2.0)
 include_directories($ENV{MX_SDK_HOME}/opensource/lib/glib-2.0/include)
+include_directories($ENV{MX_SDK_HOME}/ascend-toolkit/latest/include)
 
 link_directories($ENV{MX_SDK_HOME}/lib)
 link_directories($ENV{MX_SDK_HOME}/opensource/lib/)

official/cv/yolov5/infer/mxbase/src/PostProcess/Yolov5MindSporePost.cpp

@@ -38,6 +38,14 @@ namespace {
     auto uint8Deleter = [] (uint8_t* p) { };
 }  // namespace
 
+namespace localParameter {
+    const uint32_t VECTOR_FIRST_INDEX = 0;
+    const uint32_t VECTOR_SECOND_INDEX = 1;
+    const uint32_t VECTOR_THIRD_INDEX = 2;
+    const uint32_t VECTOR_FOURTH_INDEX = 3;
+    const uint32_t VECTOR_FIFTH_INDEX = 4;
+}
+
 namespace MxBase {
     Yolov5PostProcess& Yolov5PostProcess::operator=(const Yolov5PostProcess &other) {
         if (this == &other) {
@@ -95,13 +103,14 @@ namespace MxBase {
         if (yoloVersion_ == YOLOV5_VERSION) {
             for (size_t i = 0; i < tensors.size(); i++) {
                 auto shape = tensors[i].GetShape();
-                if (shape.size() < VECTOR_FIFTH_INDEX) {
-                    LogError << "dimensions of tensor [" << i << "] is less than " << VECTOR_FIFTH_INDEX << ".";
+                if (shape.size() < localParameter::VECTOR_FIFTH_INDEX) {
+                    LogError << "dimensions of tensor [" << i << "] is less than " <<
+                                localParameter::VECTOR_FIFTH_INDEX << ".";
                     return false;
                 }
                 uint32_t channelNumber = 1;
-                int startIndex = modelType_ ? VECTOR_SECOND_INDEX : VECTOR_FOURTH_INDEX;
-                int endIndex = modelType_ ? (shape.size() - VECTOR_THIRD_INDEX) : shape.size();
+                int startIndex = modelType_ ? localParameter::VECTOR_SECOND_INDEX : localParameter::VECTOR_FOURTH_INDEX;
+                int endIndex = modelType_ ? (shape.size() - localParameter::VECTOR_THIRD_INDEX) : shape.size();
                 for (int j = startIndex; j < endIndex; j++) {
                     channelNumber *= shape[j];
                 }

official/cv/yolov5/infer/mxbase/src/PostProcess/Yolov5MindSporePost.h

@@ -70,7 +70,7 @@ class Yolov5PostProcess : public ObjectPostProcessBase {
                       const std::map<std::string, std::shared_ptr<void>> &configParamMap = {}) override;
 
  protected:
-    bool IsValidTensors(const std::vector<TensorBase> &tensors) const override;
+    bool IsValidTensors(const std::vector<TensorBase> &tensors) const;
 
     void ObjectDetectionOutput(const std::vector<TensorBase> &tensors,
                                std::vector<std::vector<ObjectInfo>> *objectInfos,

official/cv/yolov5/infer/sdk/run.sh

@@ -56,7 +56,7 @@ if [[ $ann_file  == "" ]];then
    exit 1
 fi
 
-python3.7 main.py --pipeline_path=$pipeline_path \
+python3 main.py --pipeline_path=$pipeline_path \
                   --dataset_path=$dataset_path \
                   --ann_file=$ann_file \
                   --result_files=$result_files

research/cv/HRNetW48_seg/infer/mxbase/src/hrnet.cpp

@@ -24,6 +24,14 @@
 #include "MxBase/DvppWrapper/DvppWrapper.h"
 #include "MxBase/Log/Log.h"
 
+namespace localParameter {
+    const uint32_t VECTOR_FIRST_INDEX = 0;
+    const uint32_t VECTOR_SECOND_INDEX = 1;
+    const uint32_t VECTOR_THIRD_INDEX = 2;
+    const uint32_t VECTOR_FOURTH_INDEX = 3;
+    const uint32_t VECTOR_FIFTH_INDEX = 4;
+}
+
 APP_ERROR HRNetW48Seg::Init(const InitParam& initParam) {
     deviceId_ = initParam.deviceId;
     APP_ERROR ret = MxBase::DeviceManager::GetInstance()->InitDevices();
@@ -151,9 +159,9 @@ APP_ERROR HRNetW48Seg::PostProcess(std::vector<MxBase::TensorBase>& inputs,
     }
     uint32_t imgHeight = resizedInfo.heightOriginal;
     uint32_t imgWidth = resizedInfo.widthOriginal;
-    uint32_t outputModelWidth = tensor.GetShape()[MxBase::VECTOR_FOURTH_INDEX];
-    uint32_t outputModelHeight = tensor.GetShape()[MxBase::VECTOR_THIRD_INDEX];
-    uint32_t outputModelChannel = tensor.GetShape()[MxBase::VECTOR_SECOND_INDEX];
+    uint32_t outputModelWidth = tensor.GetShape()[localParameter::VECTOR_FOURTH_INDEX];
+    uint32_t outputModelHeight = tensor.GetShape()[localParameter::VECTOR_THIRD_INDEX];
+    uint32_t outputModelChannel = tensor.GetShape()[localParameter::VECTOR_SECOND_INDEX];
     auto data = reinterpret_cast<float(*)[outputModelHeight][outputModelWidth]>(tensor.GetBuffer());
     std::vector<cv::Mat> tensorChannels = {};
     for (size_t c = 0; c < outputModelChannel; ++c) {

research/cv/HRNetW48_seg/infer/sdk/do_infer.sh

@@ -48,7 +48,7 @@ mkdir ./inferResults
 
 echo "Inference results will be stored in ./inferResults/."
 
-python3.7 main.py --pipeline="../data/config/hrnetw48seg.pipeline" \
+python3 main.py --pipeline="../data/config/hrnetw48seg.pipeline" \
                   --data_path=$PATH1 \
                   --data_lst=$PATH2 \
                   --infer_result_path="./inferResults/"

research/cv/SE_ResNeXt50/infer/mxbase/src/SEResNeXtClassifyOpencv.cpp

@@ -19,6 +19,14 @@
 #include "MxBase/Log/Log.h"
 #include "SEResNeXtClassifyOpencv.h"
 
+namespace localParameter {
+    const uint32_t VECTOR_FIRST_INDEX = 0;
+    const uint32_t VECTOR_SECOND_INDEX = 0;
+    const uint32_t VECTOR_THIRD_INDEX = 0;
+    const uint32_t VECTOR_FOURTH_INDEX = 0;
+    const uint32_t VECTOR_FIFTH_INDEX = 0;
+}
+
 APP_ERROR SEResNeXtClassifyOpencv::Init(const InitParam &initParam) {
     deviceId_ = initParam.deviceId;
     APP_ERROR ret = MxBase::DeviceManager::GetInstance()->InitDevices();
@@ -110,10 +118,10 @@ APP_ERROR SEResNeXtClassifyOpencv::Crop(const cv::Mat &srcImageMat, cv::Mat &dst
 
 APP_ERROR SEResNeXtClassifyOpencv::Inference(const std::vector<MxBase::TensorBase> &inputs,
                                       std::vector<MxBase::TensorBase> &outputs) {
-    uint32_t first = inputs[0].GetShape()[MxBase::VECTOR_FIRST_INDEX];
-    uint32_t second = inputs[0].GetShape()[MxBase::VECTOR_SECOND_INDEX];
-    uint32_t third = inputs[0].GetShape()[MxBase::VECTOR_THIRD_INDEX];
-    uint32_t fourth = inputs[0].GetShape()[MxBase::VECTOR_FOURTH_INDEX];
+    uint32_t first = inputs[0].GetShape()[localParameter::VECTOR_FIRST_INDEX];
+    uint32_t second = inputs[0].GetShape()[localParameter::VECTOR_SECOND_INDEX];
+    uint32_t third = inputs[0].GetShape()[localParameter::VECTOR_THIRD_INDEX];
+    uint32_t fourth = inputs[0].GetShape()[localParameter::VECTOR_FOURTH_INDEX];
     std::cout << "++ inputs: " << inputs.size() << " " << first << " "
     << second << " " << third << " " << fourth << std::endl;
 

research/cv/SE_ResNeXt50/infer/sdk/run.sh

@@ -22,12 +22,12 @@ set -e
 info() { echo -e "\033[1;34m[INFO ][MxStream] $1\033[1;37m" ; }
 warn() { echo >&2 -e "\033[1;31m[WARN ][MxStream] $1\033[1;37m" ; }
 
-export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:${LD_LIBRARY_PATH}
+export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/${ASCEND_VERSION}/latest/acllib/lib64:${LD_LIBRARY_PATH}
 export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
 export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
 
 #to set PYTHONPATH, import the StreamManagerApi.py
 export PYTHONPATH=$PYTHONPATH:${MX_SDK_HOME}/python
 
-python main.py $image_path  $result_dir
+python3 main.py $image_path  $result_dir
 exit 0

research/cv/squeezenet1_1/infer/convert/convert_om.sh

@@ -18,7 +18,7 @@ model_path=$1
 output_model_name=$2
 aipp_cfg=$3
 
-/usr/local/Ascend/atc/bin/atc \
+atc \
 --model=$model_path \
 --framework=1 \
 --output=$output_model_name \

research/cv/squeezenet1_1/infer/mxbase/Squeezenet1_1ClassifyOpencv.cpp

@@ -19,6 +19,14 @@
 #include "MxBase/Log/Log.h"
 #include "Squeezenet1_1ClassifyOpencv.h"
 
+namespace localParameter {
+    const uint32_t VECTOR_FIRST_INDEX = 0;
+    const uint32_t VECTOR_SECOND_INDEX = 1;
+    const uint32_t VECTOR_THIRD_INDEX = 2;
+    const uint32_t VECTOR_FOURTH_INDEX = 3;
+    const uint32_t VECTOR_FIFTH_INDEX = 4;
+}
+
 APP_ERROR Squeezenet1_1ClassifyOpencv::Init(const InitParam &initParam) {
     deviceId_ = initParam.deviceId;
     APP_ERROR ret = MxBase::DeviceManager::GetInstance()->InitDevices();
@@ -110,10 +118,10 @@ APP_ERROR Squeezenet1_1ClassifyOpencv::Crop(const cv::Mat &srcImageMat, cv::Mat
 
 APP_ERROR Squeezenet1_1ClassifyOpencv::Inference(const std::vector<MxBase::TensorBase> &inputs,
                                       std::vector<MxBase::TensorBase> &outputs) {
-    uint32_t first = inputs[0].GetShape()[MxBase::VECTOR_FIRST_INDEX];
-    uint32_t second = inputs[0].GetShape()[MxBase::VECTOR_SECOND_INDEX];
-    uint32_t third = inputs[0].GetShape()[MxBase::VECTOR_THIRD_INDEX];
-    uint32_t fourth = inputs[0].GetShape()[MxBase::VECTOR_FOURTH_INDEX];
+    uint32_t first = inputs[0].GetShape()[localParameter::VECTOR_FIRST_INDEX];
+    uint32_t second = inputs[0].GetShape()[localParameter::VECTOR_SECOND_INDEX];
+    uint32_t third = inputs[0].GetShape()[localParameter::VECTOR_THIRD_INDEX];
+    uint32_t fourth = inputs[0].GetShape()[localParameter::VECTOR_FOURTH_INDEX];
     std::cout << "++ inputs: " << inputs.size() << " " << first << " "
     << second << " " << third << " " << fourth << std::endl;
 

research/cv/squeezenet1_1/infer/sdk/run.sh

@@ -22,12 +22,12 @@ set -e
 info() { echo -e "\033[1;34m[INFO ][MxStream] $1\033[1;37m" ; }
 warn() { echo >&2 -e "\033[1;31m[WARN ][MxStream] $1\033[1;37m" ; }
 
-export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:${LD_LIBRARY_PATH}
+export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/${ASCEND_VERSION}/latest/acllib/lib64:${LD_LIBRARY_PATH}
 export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
 export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
 
 #to set PYTHONPATH, import the StreamManagerApi.py
 export PYTHONPATH=$PYTHONPATH:${MX_SDK_HOME}/python
 
-python3.7 main_squeezenet.py $image_path  $result_dir
+python3 main_squeezenet.py $image_path  $result_dir
 exit 0

research/cv/ssd_ghostnet/infer/mxbase/C++/CMakeLists.txt

@@ -34,7 +34,7 @@ endif()
 # Set up ACLLIB header files and dynamic link libraries
 set(ACL_INC_DIR $ENV{ASCEND_HOME}/${ASCEND_VERSION}/${ARCH_PATTERN}/acllib/include)
 set(ACL_LIB_DIR $ENV{ASCEND_HOME}/${ASCEND_VERSION}/${ARCH_PATTERN}/acllib/lib64)
-set(ACL_LIB_PATH $ENV{ASCEND_HOME}/nnrt/latest/acllib)
+set(ACL_LIB_PATH $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib)
 
 # Set the header file and dynamic link library of MXBase
 set(MXBASE_ROOT_DIR $ENV{MX_SDK_HOME})

research/cv/ssd_ghostnet/infer/mxbase/C++/PostProcess/SsdGhost_MindsporePost.cpp

@@ -25,6 +25,15 @@ namespace {
     const int RIGHTBOTY = 2;
     const int RIGHTBOTX = 3;
 }
+
+namespace localParameter {
+    const uint32_t VECTOR_FIRST_INDEX = 0;
+    const uint32_t VECTOR_SECOND_INDEX = 1;
+    const uint32_t VECTOR_THIRD_INDEX = 2;
+    const uint32_t VECTOR_FOURTH_INDEX = 3;
+    const uint32_t VECTOR_FIFTH_INDEX = 4;
+}
+
 namespace MxBase {
     float SsdGhostPostProcess::CalcIou(DetectBox boxA, DetectBox boxB, IOUMethod method) {
         float left = std::max(boxA.x - boxA.width / 2.f, boxB.x - boxB.width / 2.f);
@@ -119,39 +128,43 @@ namespace MxBase {
 
     bool SsdGhostPostProcess::IsValidTensors(const std::vector<TensorBase> &tensors) const {
         auto shape = tensors[0].GetShape();
-        if (tensors.size() < VECTOR_THIRD_INDEX) {
+        if (tensors.size() < localParameter::VECTOR_THIRD_INDEX) {
             LogError << "number of tensors (" << tensors.size() << ") " << "is less than required ("
-                     << VECTOR_THIRD_INDEX << ")";
+                     << localParameter::VECTOR_THIRD_INDEX << ")";
             return false;
         }
-        if (shape.size() != VECTOR_FOURTH_INDEX) {
+        if (shape.size() != localParameter::VECTOR_FOURTH_INDEX) {
             LogError << "number of tensor[0] dimensions (" << shape.size() << ") " << "is not equal to ("
-                     << VECTOR_FOURTH_INDEX << ")";
+                     << localParameter::VECTOR_FOURTH_INDEX << ")";
             return false;
         }
-        if (shape[VECTOR_SECOND_INDEX] != (uint32_t)objectNum_) {
-            LogError << "dimension of tensor[0][1] (" << shape[VECTOR_SECOND_INDEX] << ") " << "is not equal to ("
+        if (shape[localParameter::VECTOR_SECOND_INDEX] != (uint32_t)objectNum_) {
+            LogError << "dimension of tensor[0][1] (" << shape[localParameter::VECTOR_SECOND_INDEX]
+                     << ") " << "is not equal to ("
                      << objectNum_ << ")";
             return false;
         }
-        if (shape[VECTOR_THIRD_INDEX] != BOX_DIM) {
-            LogError << "dimension of tensor[0][2] (" << shape[VECTOR_THIRD_INDEX] << ") " << "is not equal to ("
+        if (shape[localParameter::VECTOR_THIRD_INDEX] != BOX_DIM) {
+            LogError << "dimension of tensor[0][2] (" << shape[localParameter::VECTOR_THIRD_INDEX]
+                     << ") " << "is not equal to ("
                      << BOX_DIM << ")";
             return false;
         }
         shape = tensors[1].GetShape();
-        if (shape.size() != VECTOR_FOURTH_INDEX) {
+        if (shape.size() != localParameter::VECTOR_FOURTH_INDEX) {
             LogError << "number of tensor[1] dimensions (" << shape.size() << ") " << "is not equal to ("
-                     << VECTOR_FOURTH_INDEX << ")";
+                     << localParameter::VECTOR_FOURTH_INDEX << ")";
             return false;
         }
-        if (shape[VECTOR_SECOND_INDEX] != (uint32_t)objectNum_) {
-            LogError << "dimension of tensor[1][1] (" << shape[VECTOR_SECOND_INDEX] << ") " << "is not equal to ("
+        if (shape[localParameter::VECTOR_SECOND_INDEX] != (uint32_t)objectNum_) {
+            LogError << "dimension of tensor[1][1] (" << shape[localParameter::VECTOR_SECOND_INDEX] << ") "
+                     << "is not equal to ("
                      << objectNum_ << ")";
             return false;
         }
-        if (shape[VECTOR_THIRD_INDEX] != (uint32_t)classNum_) {
-            LogError << "dimension of tensor[1][2] (" << shape[VECTOR_THIRD_INDEX] << ") " << "is not equal to ("
+        if (shape[localParameter::VECTOR_THIRD_INDEX] != (uint32_t)classNum_) {
+            LogError << "dimension of tensor[1][2] (" << shape[localParameter::VECTOR_THIRD_INDEX] << ") "
+                     << "is not equal to ("
                      << classNum_ << ")";
             return false;
         }

research/cv/ssd_ghostnet/infer/mxbase/C++/PostProcess/SsdGhost_MindsporePost.h

@@ -52,7 +52,7 @@ class SsdGhostPostProcess:public ObjectPostProcessBase {
                         const std::vector<ResizedImageInfo> &resizedImageInfos = {},
                         const std::map<std::string, std::shared_ptr<void>> &configParamMap = {}) override;
 
-    bool IsValidTensors(const std::vector<TensorBase> &tensors) const override;
+    bool IsValidTensors(const std::vector<TensorBase> &tensors) const;
 
  private:
     void ObjectDetectionOutput(const std::vector<TensorBase> &tensors,

research/cv/ssd_ghostnet/infer/sdk/conf/ssd_ghost.pipeline

@@ -31,7 +31,7 @@
         "mxpi_tensorinfer0": {
             "props": {
                 "dataSource": "mxpi_imageresize0",
-                "modelPath": "../../ssd_ghostnet.om"
+                "modelPath": "../ssd_ghostnet.om"
             },
             "factory": "mxpi_tensorinfer",
             "next": "mxpi_objectpostprocessor0"
@@ -41,7 +41,7 @@
                 "dataSource": "mxpi_tensorinfer0",
                 "postProcessConfigPath": "./ssd_ghost_fpn_ms_on_coco_postprocess.cfg",
                 "labelPath": "./coco.names",
-                "postProcessLibPath": "../mxManufacture/lib/libssdghostprocessor.so"
+                "postProcessLibPath": "libssdghostprocessor.so"
             },
             "factory": "mxpi_objectpostprocessor",
             "next": "mxpi_dataserialize0"

research/cv/ssd_ghostnet/infer/sdk/perf/run_map_test.sh

@@ -14,15 +14,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-PY=/usr/bin/python3.7
-
 export PYTHONPATH=${PYTHONPATH}:.
 
 annotations_json=$1
 det_result_json=$2
 output_path_name=$3
 
-${PY} generate_map_report.py \
+python3 generate_map_report.py \
 --annotations_json=${annotations_json} \
 --det_result_json=${det_result_json} \
 --output_path_name=${output_path_name} \

research/cv/ssd_ghostnet/infer/sdk/run.sh

@@ -20,7 +20,7 @@ set -e
 info() { echo -e "\033[1;34m[INFO ][MxStream] $1\033[1;37m" ; }
 warn() { echo >&2 -e "\033[1;31m[WARN ][MxStream] $1\033[1;37m" ; }
 
-export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:${LD_LIBRARY_PATH}
+export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/${ASCEND_VERSION}/latest/acllib/lib64:${LD_LIBRARY_PATH}
 export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
 export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
 
@@ -32,5 +32,5 @@ img_path=$2
 res_path=$3
 stream_name=$4
 
-python3.7 infer_by_sdk.py --pipeline_path ${pipeline_path} --img_path ${img_path} --res_path ${res_path} --stream_name ${stream_name}
+python3 infer_by_sdk.py --pipeline_path ${pipeline_path} --img_path ${img_path} --res_path ${res_path} --stream_name ${stream_name}
 exit 0
\ No newline at end of file

research/cv/wdsr/infer/convert/convert_om.sh

@@ -19,7 +19,7 @@ aipp_cfg_path=$2
 output_model_name=$3
 
 
-/usr/local/Ascend/atc/bin/atc \
+atc \
 --model=$model_path \
 --input_format=NCHW \
 --framework=1 \

research/cv/wdsr/infer/mxbase/WdsrSuperresolution.cpp

@@ -23,6 +23,13 @@
 #include "MxBase/DvppWrapper/DvppWrapper.h"
 #include "MxBase/Log/Log.h"
 
+namespace localParameter {
+    const uint32_t VECTOR_FIRST_INDEX = 0;
+    const uint32_t VECTOR_SECOND_INDEX = 1;
+    const uint32_t VECTOR_THIRD_INDEX = 2;
+    const uint32_t VECTOR_FOURTH_INDEX = 3;
+    const uint32_t VECTOR_FIFTH_INDEX = 4;
+}
 
 APP_ERROR WdsrSuperresolution::Init(const InitParam &initParam) {
     deviceId_ = initParam.deviceId;
@@ -43,9 +50,9 @@ APP_ERROR WdsrSuperresolution::Init(const InitParam &initParam) {
         return ret;
     }
 
-    uint32_t outputModelHeight = modelDesc_.outputTensors[0].tensorDims[MxBase::VECTOR_THIRD_INDEX];
-    uint32_t inputModelHeight = modelDesc_.inputTensors[0].tensorDims[MxBase::VECTOR_SECOND_INDEX];
-    uint32_t inputModelWidth = modelDesc_.inputTensors[0].tensorDims[MxBase::VECTOR_THIRD_INDEX];
+    uint32_t outputModelHeight = modelDesc_.outputTensors[0].tensorDims[localParameter::VECTOR_THIRD_INDEX];
+    uint32_t inputModelHeight = modelDesc_.inputTensors[0].tensorDims[localParameter::VECTOR_SECOND_INDEX];
+    uint32_t inputModelWidth = modelDesc_.inputTensors[0].tensorDims[localParameter::VECTOR_THIRD_INDEX];
 
     scale_ = outputModelHeight/inputModelHeight;
     maxEdge_ = inputModelWidth > inputModelHeight ? inputModelWidth:inputModelHeight;
@@ -122,9 +129,9 @@ APP_ERROR WdsrSuperresolution::PostProcess(std::vector<MxBase::TensorBase> *inpu
             LogError << GetError(ret) << "Tensor deploy to host failed.";
             return ret;
         }
-        uint32_t outputModelChannel = tensor.GetShape()[MxBase::VECTOR_SECOND_INDEX];
-        uint32_t outputModelHeight = tensor.GetShape()[MxBase::VECTOR_THIRD_INDEX];
-        uint32_t outputModelWidth = tensor.GetShape()[MxBase::VECTOR_FOURTH_INDEX];
+        uint32_t outputModelChannel = tensor.GetShape()[localParameter::VECTOR_SECOND_INDEX];
+        uint32_t outputModelHeight = tensor.GetShape()[localParameter::VECTOR_THIRD_INDEX];
+        uint32_t outputModelWidth = tensor.GetShape()[localParameter::VECTOR_FOURTH_INDEX];
         LogInfo << "Channel:" << outputModelChannel << " Height:"
                 << outputModelHeight << " Width:" <<outputModelWidth;
 

research/cv/wdsr/infer/sdk/run.sh

@@ -54,7 +54,7 @@ echo "enter $CUR_PATH"
 info() { echo -e "\033[1;34m[INFO ][MxStream] $1\033[1;37m" ; }
 warn() { echo >&2 -e "\033[1;31m[WARN ][MxStream] $1\033[1;37m" ; }
 
-export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:${LD_LIBRARY_PATH}
+export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/${ASCEND_VERSION}/latest/acllib/lib64:${LD_LIBRARY_PATH}
 
 #to set PYTHONPATH, import the StreamManagerApi.py
 export PYTHONPATH=$PYTHONPATH:${MX_SDK_HOME}/python