Fixed broken commit in mnist learn example: KasperskyLab#188

Author: DavidIkov

examples/mnist-learn/inference.h

@@ -26,10 +26,10 @@
 #include <knp/framework/projection/wta.h>
 #include <knp/framework/tags/name.h>
 
-#include <map>
-#include <memory>
 #include <filesystem>
 #include <fstream>
+#include <map>
+#include <memory>
 #include <string>
 #include <utility>
 #include <vector>
@@ -42,11 +42,12 @@
 /**
  * @brief Run inference on a neural network and record spike activity.
  *
- * @details This function executes the inference process on a trained neural network, processing input data through the network 
- * and recording spike messages for analysis. It configures the network with appropriate input and output channels, sets up 
- * WTA mechanisms, initializes logging for spike monitoring, and executes the simulation.
+ * @details This function executes the inference process on a trained neural network, processing input data through the
+ * network and recording spike messages for analysis. It configures the network with appropriate input and output
+ * channels, sets up WTA mechanisms, initializes logging for spike monitoring, and executes the simulation.
  *
  * @tparam Neuron neuron type template parameter for neuron model specification.
+ *
  * @param backend shared pointer to the computational backend for execution.
  * @param network annotated network structure containing the network and its annotations.
  * @param model_desc model description containing configuration parameters and paths.
@@ -87,7 +88,7 @@ std::vector<knp::core::messaging::SpikeMessage> infer_network(
     // Connect rasterized image projections to the input channel.
     for (auto image_proj_uid : network.data_.projections_from_raster_)
         model.add_input_channel(input_image_channel_uid, image_proj_uid);
-    
+
     // Initialize model executor with backend and channel mappings.
     // Online Help link: https://click.kaspersky.com/?hl=en-US&version=2.0&pid=KNP&link=online_help&helpid=251296
     knp::framework::ModelExecutor model_executor(model, backend, std::move(channel_map));
@@ -143,8 +144,7 @@ std::vector<knp::core::messaging::SpikeMessage> infer_network(
                     for (const auto& message : messages)
                     {
                         const auto name_iter = pop_names.find(message.header_.sender_uid_);
-                        const std::string sender_name =
-                            name_iter == pop_names.end() ? "UNKNOWN" : name_iter->second;
+                        const std::string sender_name = name_iter == pop_names.end() ? "UNKNOWN" : name_iter->second;
 
                         // Log each neuron index from the spike message.
                         for (const auto neuron_index : message.neuron_indexes_)
@@ -179,7 +179,7 @@ std::vector<knp::core::messaging::SpikeMessage> infer_network(
             if (step % 20 == 0) std::cout << "Inference step: " << step << std::endl;
             return step != dataset.get_steps_amount_for_inference();
         });
-    
+
     // Retrieve final spike results from output channel.
     auto spikes = out_channel.update();
 
@@ -193,12 +193,12 @@ std::vector<knp::core::messaging::SpikeMessage> infer_network(
 
 /**
  * @brief Run inference on a model and record spike activity.
- * 
- * @details This function provides a high-level interface for running inference on a trained model, handling backend loading and 
- * delegation to the lower-level inference function.
- * 
+ *
+ * @details This function provides a high-level interface for running inference on a trained model, handling backend
+ * loading and delegation to the lower-level inference function.
+ *
  * @tparam Neuron neuron type template parameter for neuron model specification.
- * 
+ *
  * @param model_desc model description containing configuration parameters and paths.
  * @param dataset dataset with inference data.
  * @param network annotated network structure containing the network and its annotations.

examples/mnist-learn/main.cpp

@@ -19,44 +19,31 @@
  * limitations under the License.
  */
 
-#include <knp/framework/network_validator.h>
-
 #include <iostream>
 
 #include "dataset.h"
 #include "evaluate_results.h"
 #include "inference.h"
+#include "network_validation.h"
 #include "parse_arguments.h"
 #include "save_network.h"
 #include "training.h"
 
 
-/**
- * @brief Execute complete model pipeline for specified neuron type.
- *
- * @details This template function orchestrates the entire machine learning pipeline for neural networks, including
- * dataset processing, network construction, training, inference, and evaluation. It serves as the core execution
- * engine for both AltAI and BLIFAT neuron models.
- *
- * @tparam Neuron neuron type for neuron model specification.
- *
- * @param model_desc model description containing configuration parameters and paths.
- */
+// Execute complete model pipeline for specified neuron type.
+// This template function orchestrates the entire machine learning pipeline for neural networks, including dataset
+// processing, network construction, training, inference, and evaluation. It serves as the core execution engine
+// for both AltAI and BLIFAT neuron models.
 template <typename Neuron>
 void run_model(const ModelDescription& model_desc)
 {
     Dataset dataset = process_dataset(model_desc);
 
     AnnotatedNetwork network = construct_network<Neuron>(model_desc);
 
-    knp::framework::NetworkValidator validator;
-    validator.add_validator(knp::framework::network_validators::Connectivity());
-    bool validation_result = validator.run_validators(network.network_);
-    if (!validation_result)
-    {
-        throw std::runtime_error("Network validation failed.");
-    }
+    validate_network(network.network_);
 
+    // Create backend loader for training and inference.
     // Online Help link: https://click.kaspersky.com/?hl=en-US&version=2.0&pid=KNP&link=online_help&helpid=243548
     knp::framework::BackendLoader backend_loader;
 
@@ -74,17 +61,10 @@ void run_model(const ModelDescription& model_desc)
 }
 
 
-/**
- * @brief Main application entry point.
- *
- * @details This function serves as the primary execution point for the MNIST neural network learning application.
- * It handles command-line argument parsing, configuration validation, user interaction, and routes execution to
- * the appropriate neuron model.
- *
- * @param argc argument count.
- * @param argv arguments values.
- * @return ret code.
- */
+// Main application entry point.
+// This function serves as the primary execution point for the MNIST neural network learning application.
+// It handles command-line argument parsing, configuration validation, user interaction, and routes execution to
+// the appropriate neuron model.
 int main(int argc, char** argv)
 {
     // Parse command-line arguments and validate configuration.
@@ -103,7 +83,6 @@ int main(int argc, char** argv)
     {
         case SupportedModelType::BLIFAT:
         {
-            // cppcheck-suppress throwInEntryPoint
             run_model<knp::neuron_traits::BLIFATNeuron>(model_desc);
             break;
         }