modify again based on retest info

model_zoo/official/cv/dpn/infer/mxbase/src/DPN.cpp

@@ -20,7 +20,6 @@
 #include <iostream>
 #include <opencv2/dnn.hpp>
 
-
 using namespace MxBase;
 using namespace cv::dnn;
 namespace {
@@ -105,21 +104,18 @@ APP_ERROR DPN::ResizeImage(const cv::Mat &srcImageMat, cv::Mat &dstImageMat)
 APP_ERROR DPN::CVMatToTensorBase(const cv::Mat &imageMat, MxBase::TensorBase &tensorBase)
 {
     uint32_t dataSize=1;
-    
-    for (size_t i = 0; i < modelDesc_.inputTensors.size(); ++i) {
+    for (size_t i=0; i<modelDesc_.inputTensors.size(); ++i) {
         std::vector<uint32_t> shape = {};
         for (size_t j = 0; j < modelDesc_.inputTensors[i].tensorDims.size(); ++j) {
             shape.push_back((uint32_t)modelDesc_.inputTensors[i].tensorDims[j]);
         }
-        for(uint32_t i = 0; i < shape.size(); ++i){
+        for(uint32_t i=0; i<shape.size(); ++i){
             dataSize *= shape[i];
-        }
-        std::cout<< std::endl;
-               
+        } 
     }
 
     // mat NCHW to NHWC
-    size_t N=32, H=224, W=224, C=3;
+    size_t N=32,H=224,W=224,C=3;
     unsigned char *mat_data = new unsigned char[dataSize];
     uint32_t idx=0;
     for(size_t n=0; n<N; n++){
@@ -190,7 +186,8 @@ APP_ERROR DPN::PostProcess(const std::vector<MxBase::TensorBase> &inputs,
     return APP_ERR_OK;
 }
 
-APP_ERROR DPN::SaveResult(const std::vector<std::string> &batchImgPaths, const std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos)
+APP_ERROR DPN::SaveResult(const std::vector<std::string> &batchImgPaths, 
+                            const std::vector<std::vector<MxBase::ClassInfo>> &batchClsInfos)
 {
     uint32_t batchIndex = 0;
     for(auto &imgPath: batchImgPaths){

model_zoo/official/cv/dpn/infer/mxbase/src/DPN.h

@@ -61,5 +61,4 @@ class DPN {
         double inferCostTimeMilliSec = 0.0;
 };
 
-
 #endif

model_zoo/official/cv/dpn/infer/mxbase/src/main.cpp

@@ -43,7 +43,6 @@ APP_ERROR ScanImages(const std::string &path, std::vector<std::string> &imgFiles
     return APP_ERR_OK;
 }
 
-
 int main(int argc, char* argv[])
 {
     if (argc <= 1) {
@@ -75,7 +74,7 @@ int main(int argc, char* argv[])
     auto startTime = std::chrono::high_resolution_clock::now();
     int inferImgsCount = 0;
     LogInfo << "Number of total images load from input data path: " << imgFilePaths.size();
-    for(uint32_t i=0; i<=imgFilePaths.size()-BATCH_SIZE; i+=BATCH_SIZE){
+    for(uint32_t i = 0; i <= imgFilePaths.size() - BATCH_SIZE; i += BATCH_SIZE){
         std::vector<std::string>batchImgFilePaths(imgFilePaths.begin()+i, imgFilePaths.begin()+(i+BATCH_SIZE));
         ret = dpn->Process(batchImgFilePaths);
         if (ret != APP_ERR_OK) {

model_zoo/official/cv/dpn/modelarts/train_start.py

@@ -160,7 +160,6 @@ def filter_weight_by_list(origin_dict, param_filter):
                 break
 
 def dpn_train(config_args, ma_config):
-    # create dataset
     ma_config["training_data"] = config_args.data_path + "/train"
     ma_config["image_size"] = [config_args.image_size_height, config_args.image_size_width]
     train_dataset = classification_dataset(ma_config["training_data"], 
@@ -269,13 +268,12 @@ def dpn_train(config_args, ma_config):
     return 0
 
 def dpn_export(config_args, ma_config):
-    # define net
     backbone = config_args.backbone
     num_classes = config_args.num_classes
     net = dpns[backbone](num_classes=num_classes)
 
     # load checkpoint
-    prob_ckpt_list = os.path.join(ma_config["checkpoint_path"] , "dpn*.ckpt")
+    prob_ckpt_list = os.path.join(ma_config["checkpoint_path"], "dpn*.ckpt")
     ckpt_list = glob.glob(prob_ckpt_list)
     if not ckpt_list:
         print('Freezing model failed!')
@@ -299,18 +297,17 @@ def dpn_export(config_args, ma_config):
 
 
 def main():
-    # parser arguments
     config_args = _parse_args()
     # create local path
     if not os.path.exists(config_args.data_path):
-            os.makedirs(config_args.data_path, exist_ok=True)
+        os.makedirs(config_args.data_path, exist_ok=True)
     if not os.path.exists(config_args.output_path):
-            os.makedirs(config_args.output_path, exist_ok=True)
+        os.makedirs(config_args.output_path, exist_ok=True)
     ma_config = {}
     # init context
     ma_config["checkpoint_path"] = os.path.join(config_args.output_path, config_args.checkpoint_dir)
     if not os.path.exists(ma_config["checkpoint_path"]):
-            os.makedirs(ma_config["checkpoint_path"], exist_ok=True)
+        os.makedirs(ma_config["checkpoint_path"], exist_ok=True)
     ma_config["device_id"] = get_device_id()
     context.set_context(mode=context.GRAPH_MODE,
                         device_target=config_args.device_target, save_graphs=False, device_id=ma_config["device_id"])