Address 'performance-avoid-endl' clang-tidy remarks (#250)

Author: aobolensk

.clang-tidy

@@ -19,7 +19,6 @@ Checks: >
   -modernize-avoid-c-arrays,
   -modernize-pass-by-value,
   -performance-move-const-arg,
-  -performance-avoid-endl,
   -readability-braces-around-statements,
   -readability-identifier-length,
   -readability-magic-numbers,

app/Converters/reader_weights_sample.cpp

@@ -12,15 +12,15 @@ int main() {
     std::string layer_type = layer_data["type"];
 
     std::cout << "Layer " << layer_index << " (" << layer_type << ", "
-              << layer_name << "):" << std::endl;
+              << layer_name << "):" << '\n';
 
     try {
       it_lab_ai::Tensor tensor = it_lab_ai::create_tensor_from_json(
           layer_data, it_lab_ai::Type::kFloat);
-      // std::cout << tensor << std::endl;
+      // std::cout << tensor << '\n';
     } catch (const std::exception& e) {
       std::cerr << "Error processing layer " << layer_name << ": " << e.what()
-                << std::endl;
+                << '\n';
     }
   }
 

app/Converters/reader_weights_sample_onnx.cpp

@@ -6,10 +6,8 @@ int main() {
   std::string json_file = MODEL_PATH_GOOGLENET_ONNX;
   it_lab_ai::json model_data = it_lab_ai::read_json(json_file);
 
-  std::cout << "Model contains " << model_data.size()
-            << " layers:" << std::endl;
-  std::cout << "--------------------------------------------------"
-            << std::endl;
+  std::cout << "Model contains " << model_data.size() << " layers:" << '\n';
+  std::cout << "--------------------------------------------------" << '\n';
 
   for (const auto& layer_data : model_data) {
     int layer_index = layer_data["index"];
@@ -20,11 +18,11 @@ int main() {
     bool has_value = layer_data.contains("value");
 
     std::cout << "Layer " << layer_index << ": " << layer_name << " ("
-              << layer_type << ")" << std::endl;
+              << layer_type << ")" << '\n';
 
     if (layer_data.contains("attributes") &&
         !layer_data["attributes"].empty()) {
-      std::cout << "  Attributes:" << std::endl;
+      std::cout << "  Attributes:" << '\n';
       for (const auto& [key, value] : layer_data["attributes"].items()) {
         std::cout << "    " << key << ": ";
         if (value.is_array()) {
@@ -42,16 +40,16 @@ int main() {
         } else if (value.is_string()) {
           std::cout << value.get<std::string>();
         }
-        std::cout << std::endl;
+        std::cout << '\n';
       }
     }
 
     if (has_value) {
       try {
         float value = layer_data["value"].get<float>();
-        std::cout << "  Value: " << value << std::endl;
+        std::cout << "  Value: " << value << '\n';
       } catch (const std::exception& e) {
-        std::cerr << "  Error processing value: " << e.what() << std::endl;
+        std::cerr << "  Error processing value: " << e.what() << '\n';
       }
     }
 
@@ -60,20 +58,19 @@ int main() {
         it_lab_ai::Tensor tensor = it_lab_ai::create_tensor_from_json(
             layer_data, it_lab_ai::Type::kFloat);
 
-        std::cout << "  Weights shape: " << tensor.get_shape() << std::endl;
+        std::cout << "  Weights shape: " << tensor.get_shape() << '\n';
 
         if (!tensor.get_bias().empty()) {
-          std::cout << "  Bias size: " << tensor.get_bias().size() << std::endl;
+          std::cout << "  Bias size: " << tensor.get_bias().size() << '\n';
         }
       } catch (const std::exception& e) {
-        std::cerr << "  Error processing weights: " << e.what() << std::endl;
+        std::cerr << "  Error processing weights: " << e.what() << '\n';
       }
     } else if (!has_value) {
-      std::cout << "  No weights or value" << std::endl;
+      std::cout << "  No weights or value" << '\n';
     }
 
-    std::cout << "--------------------------------------------------"
-              << std::endl;
+    std::cout << "--------------------------------------------------" << '\n';
   }
 
   return 0;

app/Graph/acc_check.cpp

@@ -31,7 +31,7 @@ int main(int argc, char* argv[]) {
   std::string json_path = model_paths[model_name];
   std::vector<int> input_shape = get_input_shape_from_json(json_path);
 
-  std::cout << std::endl;
+  std::cout << '\n';
 
   if (model_name == "alexnet_mnist") {
     std::vector<size_t> counts = {979, 1134, 1031, 1009, 981,
@@ -101,7 +101,7 @@ int main(int argc, char* argv[]) {
     double percentage =
         (static_cast<double>(stat) / static_cast<double>(sum + 10)) * 100;
     std::cout << "Stat: " << std::fixed << std::setprecision(2) << percentage
-              << "%" << std::endl;
+              << "%" << '\n';
     return 0;
   }
   std::vector<size_t> counts;
@@ -130,8 +130,7 @@ int main(int argc, char* argv[]) {
   }
 
   if (total_images == 0) {
-    std::cerr << "No images found in dataset path: " << dataset_path
-              << std::endl;
+    std::cerr << "No images found in dataset path: " << dataset_path << '\n';
     return 1;
   }
 
@@ -157,7 +156,7 @@ int main(int argc, char* argv[]) {
         cv::Mat image = cv::imread(entry.path().string());
         if (image.empty()) {
           std::cerr << "Failed to load image: " << entry.path().string()
-                    << std::endl;
+                    << '\n';
           continue;
         }
 
@@ -237,18 +236,18 @@ int main(int argc, char* argv[]) {
   double final_accuracy_top5 =
       (static_cast<double>(correct_predictions_top5) / total_images) * 100;
 
-  std::cout << "\nFinal Results:" << std::endl;
-  std::cout << "Model: " << model_name << std::endl;
-  std::cout << "Dataset: " << dataset_path << std::endl;
-  std::cout << "Total images: " << total_images << std::endl;
+  std::cout << "\nFinal Results:" << '\n';
+  std::cout << "Model: " << model_name << '\n';
+  std::cout << "Dataset: " << dataset_path << '\n';
+  std::cout << "Total images: " << total_images << '\n';
   std::cout << "Correct predictions (Top-1): " << correct_predictions_top1
-            << std::endl;
+            << '\n';
   std::cout << "Correct predictions (Top-5): " << correct_predictions_top5
-            << std::endl;
+            << '\n';
   std::cout << "Top-1 Accuracy: " << std::fixed << std::setprecision(2)
-            << final_accuracy_top1 << "%" << std::endl;
+            << final_accuracy_top1 << "%" << '\n';
   std::cout << "Top-5 Accuracy: " << std::fixed << std::setprecision(2)
-            << final_accuracy_top5 << "%" << std::endl;
+            << final_accuracy_top5 << "%" << '\n';
 
   return 0;
 }