!15936 fix codex

From: @yuzhenhua666
Reviewed-by: @c_34,@oacjiewen
Signed-off-by: @c_34

model_zoo/official/cv/ctpn/ascend310_infer/src/main.cc

@@ -99,11 +99,11 @@ int main(int argc, char **argv) {
         return 1;
     }
 
-    std::shared_ptr<TensorTransform> decode(new Decode());
-    std::shared_ptr<TensorTransform> resize(new Resize({576, 960}));
-    std::shared_ptr<TensorTransform> normalize(new Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375}));
-    std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
-    std::shared_ptr<TensorTransform> typeCast(new TypeCast(DataType::kNumberTypeFloat16));
+    auto decode = Decode();
+    auto resize = Resize({576, 960});
+    auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
+    auto hwc2chw = HWC2CHW();
+    auto typeCast = TypeCast(DataType::kNumberTypeFloat16);
 
     mindspore::dataset::Execute transformDecode(decode);
     mindspore::dataset::Execute transform({resize, normalize, hwc2chw});

model_zoo/official/cv/ctpn/ascend310_infer/src/utils.cc

@@ -48,8 +48,7 @@ int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outpu
     std::string homePath = "./result_Files";
     for (size_t i = 0; i < outputs.size(); ++i) {
         size_t outputSize;
-        std::shared_ptr<const void> netOutput;
-        netOutput = outputs[i].Data();
+        std::shared_ptr<const void> netOutput = outputs[i].Data();
         outputSize = outputs[i].DataSize();
         int pos = imageFile.rfind('/');
         std::string fileName(imageFile, pos + 1);
@@ -104,8 +103,7 @@ DIR *OpenDir(std::string_view dirName) {
         std::cout << "dirName is not a valid directory !" << std::endl;
         return nullptr;
     }
-    DIR *dir;
-    dir = opendir(realPath.c_str());
+    DIR *dir = opendir(realPath.c_str());
     if (dir == nullptr) {
         std::cout << "Can not open dir " << dirName << std::endl;
         return nullptr;

model_zoo/official/cv/deeptext/ascend310_infer/src/main.cc

@@ -101,7 +101,8 @@ int main(int argc, char **argv) {
     std::map<double, double> costTime_map;
     size_t size = all_files.size();
 
-    Execute transform(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg({576, 960})));
+    auto dvppDecodeResizeJpeg = DvppDecodeResizeJpeg({576, 960});
+    Execute transform(dvppDecodeResizeJpeg);
 
     for (size_t i = 0; i < size; ++i) {
         struct timeval start;
@@ -141,7 +142,7 @@ int main(int argc, char **argv) {
         infer_cnt++;
     }
 
-    average = average/infer_cnt;
+    average = average / infer_cnt;
     std::stringstream timeCost;
     timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << infer_cnt << std::endl;
     std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;

model_zoo/official/cv/deeptext/ascend310_infer/src/utils.cc

@@ -48,14 +48,13 @@ int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outpu
     std::string homePath = "./result_Files";
     for (size_t i = 0; i < outputs.size(); ++i) {
         size_t outputSize;
-        std::shared_ptr<const void> netOutput;
-        netOutput = outputs[i].Data();
+        std::shared_ptr<const void> netOutput = outputs[i].Data();
         outputSize = outputs[i].DataSize();
         int pos = imageFile.rfind('/');
         std::string fileName(imageFile, pos + 1);
         fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
         std::string outFileName = homePath + "/" + fileName;
-        FILE * outputFile = fopen(outFileName.c_str(), "wb");
+        FILE *outputFile = fopen(outFileName.c_str(), "wb");
         fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
         fclose(outputFile);
         outputFile = nullptr;
@@ -104,8 +103,7 @@ DIR *OpenDir(std::string_view dirName) {
         std::cout << "dirName is not a valid directory !" << std::endl;
         return nullptr;
     }
-    DIR *dir;
-    dir = opendir(realPath.c_str());
+    DIR *dir = opendir(realPath.c_str());
     if (dir == nullptr) {
         std::cout << "Can not open dir " << dirName << std::endl;
         return nullptr;
@@ -118,7 +116,6 @@ std::string RealPath(std::string_view path) {
     char realPathMem[PATH_MAX] = {0};
     char *realPathRet = nullptr;
     realPathRet = realpath(path.data(), realPathMem);
-
     if (realPathRet == nullptr) {
         std::cout << "File: " << path << " is not exist.";
         return "";

model_zoo/official/cv/resnet/ascend310_infer/src/main.cc

@@ -70,7 +70,6 @@ int main(int argc, char **argv) {
   Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
   Model model;
   Status ret = model.Build(GraphCell(graph), context);
-
   if (ret != kSuccess) {
     std::cout << "ERROR: Build failed." << std::endl;
     return 1;

model_zoo/official/cv/unet/ascend310_infer/src/main.cc

@@ -72,7 +72,7 @@ int main(int argc, char **argv) {
   Model model;
   Status ret = model.Build(GraphCell(graph), context);
   if (ret != kSuccess) {
-    std::cout << "EEEEEEEERROR Build failed." << std::endl;
+    std::cout << "ERROR Build failed." << std::endl;
     return 1;
   }
 
@@ -86,11 +86,11 @@ int main(int argc, char **argv) {
   std::map<double, double> costTime_map;
   size_t size = all_files.size();
 
-  auto decode(new Decode());
-  auto swapredblue(new SwapRedBlue());
-  auto resize(new Resize({96, 96}));
-  auto normalize(new Normalize({127.5, 127.5, 127.5}, {127.5, 127.5, 127.5}));
-  auto hwc2chw(new HWC2CHW());
+  auto decode = Decode();
+  auto swapredblue = SwapRedBlue();
+  auto resize = Resize({96, 96});
+  auto normalize = Normalize({127.5, 127.5, 127.5}, {127.5, 127.5, 127.5});
+  auto hwc2chw = HWC2CHW();
   Execute preprocess({decode, swapredblue, resize, normalize, hwc2chw});
 
   for (size_t i = 0; i < size; ++i) {

model_zoo/official/cv/unet/postprocess.py

@@ -18,7 +18,6 @@ import argparse
 import cv2
 import numpy as np
 
-from src.data_loader import create_dataset, create_cell_nuclei_dataset
 from src.config import cfg_unet
 
 class dice_coeff():
@@ -74,25 +73,6 @@ class dice_coeff():
             raise RuntimeError('Total samples num must not be 0.')
         return (self._dice_coeff_sum / float(self._samples_num), self._iou_sum / float(self._samples_num))
 
-
-def test_net(data_dir,
-             cross_valid_ind=1,
-             cfg=None):
-
-    if 'dataset' in cfg and cfg['dataset'] == "Cell_nuclei":
-        valid_dataset = create_cell_nuclei_dataset(data_dir, cfg['img_size'], 1, 1, is_train=False,
-                                                   eval_resize=cfg["eval_resize"], split=0.8)
-    else:
-        _, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False, do_crop=cfg['crop'],
-                                          img_size=cfg['img_size'])
-    labels_list = []
-
-    for data in valid_dataset:
-        labels_list.append(data[1].asnumpy())
-
-    return labels_list
-
-
 def get_args():
     parser = argparse.ArgumentParser(description='Test the UNet on images and target masks',
                                      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -105,24 +85,31 @@ def get_args():
 
 
 if __name__ == '__main__':
-
     args = get_args()
 
-    label_list = test_net(data_dir=args.data_url, cross_valid_ind=cfg_unet['cross_valid_ind'], cfg=cfg_unet)
     rst_path = args.rst_path
     metrics = dice_coeff()
 
     if 'dataset' in cfg_unet and cfg_unet['dataset'] == "Cell_nuclei":
+        img_size = tuple(cfg_unet['img_size'])
         for i, bin_name in enumerate(os.listdir('./preprocess_Result/')):
-            bin_name_softmax = bin_name.replace(".png", "") + "_0.bin"
-            bin_name_argmax = bin_name.replace(".png", "") + "_1.bin"
+            f = bin_name.replace(".png", "")
+            bin_name_softmax = f + "_0.bin"
+            bin_name_argmax = f + "_1.bin"
             file_name_sof = rst_path + bin_name_softmax
             file_name_arg = rst_path + bin_name_argmax
             softmax_out = np.fromfile(file_name_sof, np.float32).reshape(1, 96, 96, 2)
             argmax_out = np.fromfile(file_name_arg, np.float32).reshape(1, 96, 96)
-            label = label_list[i]
+            mask = cv2.imread(os.path.join(args.data_url, f, "mask.png"), cv2.IMREAD_GRAYSCALE)
+            mask = cv2.resize(mask, img_size)
+            mask = mask.astype(np.float32) / 255
+            mask = (mask > 0.5).astype(np.int)
+            mask = (np.arange(2) == mask[..., None]).astype(int)
+            mask = mask.transpose(2, 0, 1).astype(np.float32)
+            label = mask.reshape(1, 2, 96, 96)
             metrics.update((softmax_out, argmax_out), label)
     else:
+        label_list = np.load('label.npy')
         for j in range(len(os.listdir('./preprocess_Result/'))):
             file_name_sof = rst_path + "ISBI_test_bs_1_" + str(j) + "_0" + ".bin"
             file_name_arg = rst_path + "ISBI_test_bs_1_" + str(j) + "_1" + ".bin"

model_zoo/official/cv/unet/preprocess.py

@@ -27,11 +27,15 @@ def preprocess_dataset(data_dir, result_path, cross_valid_ind=1, cfg=None):
     _, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False, do_crop=cfg['crop'],
                                       img_size=cfg['img_size'])
 
+    labels_list = []
     for i, data in enumerate(valid_dataset):
         file_name = "ISBI_test_bs_1_" + str(i) + ".bin"
         file_path = result_path + file_name
         data[0].asnumpy().tofile(file_path)
 
+        labels_list.append(data[1].asnumpy())
+
+    np.save("./label.npy", labels_list)
 
 class CellNucleiDataset:
     """

model_zoo/official/cv/yolov4/ascend310_infer/src/main.cc

@@ -69,7 +69,6 @@ int main(int argc, char **argv) {
   mindspore::Graph graph;
   Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
 
-
   if (!FLAGS_precision_mode.empty()) {
     ascend310->SetPrecisionMode(FLAGS_precision_mode);
   }
@@ -83,7 +82,7 @@ int main(int argc, char **argv) {
   Model model;
   Status ret = model.Build(GraphCell(graph), context);
   if (ret != kSuccess) {
-    std::cout << "EEEEEEEERROR Build failed." << std::endl;
+    std::cout << "ERROR Build failed." << std::endl;
     return 1;
   }
 

model_zoo/official/recommend/naml/ascend310_infer/src/sample_process.cpp

@@ -350,8 +350,7 @@ std::vector<std::string> SampleProcess::GetModelExecCostTimeInfo() {
         " ms of infer_count " << infer_cnt << std::endl;
     result.emplace_back(timeCost.str());
 
-    double totalCostTime;
-    totalCostTime = totalCostTime_map_.begin()->second - totalCostTime_map_.begin()->first;
+    double totalCostTime = totalCostTime_map_.begin()->second - totalCostTime_map_.begin()->first;
     std::stringstream totalTimeCost;
     totalTimeCost << "total inference cost time: "<< totalCostTime << " ms; count " << infer_cnt << std::endl;
     result.emplace_back(totalTimeCost.str());

model_zoo/official/recommend/naml/ascend310_infer/src/utils.cpp

@@ -157,8 +157,7 @@ DIR *Utils::OpenDir(std::string dir_name) {
         return nullptr;
     }
 
-    DIR *dir;
-    dir = opendir(real_path.c_str());
+    DIR *dir = opendir(real_path.c_str());
     if (dir == nullptr) {
         std::cout << "Can not open dir " << dir_name << std::endl;
         return nullptr;