fix size_t and dims

app/Graph/build.cpp

@@ -40,7 +40,7 @@ void build_graph(Tensor input, Tensor output) {
           tensor.get_shape()[0],
           std::vector<float>(tensor.get_shape()[1], 0.0f));
       int q = 0;
-      for (int i = 0; i < Values_vector.size(); i++) {
+      for (size_t i = 0; i < Values_vector.size(); i++) {
         Values_vector_2d[q][i - (q * tensor.get_shape()[1])] = Values_vector[i];
         if ((i + 1) % tensor.get_shape()[1] == 0) {
           q++;
@@ -50,17 +50,17 @@ void build_graph(Tensor input, Tensor output) {
           tensor.get_shape()[1],
           std::vector<float>(tensor.get_shape()[0], 0.0f));
 
-      for (int i = 0; i < tensor.get_shape()[0]; ++i) {
-        for (int j = 0; j < tensor.get_shape()[1]; ++j) {
+      for (size_t i = 0; i < tensor.get_shape()[0]; ++i) {
+        for (size_t j = 0; j < tensor.get_shape()[1]; ++j) {
           Values_vector_2d_2[j][i] = Values_vector_2d[i][j];
         }
       }
       std::vector<float> Values_vector_1d(
           tensor.get_shape()[0] * tensor.get_shape()[1], 0.0f);
       int index_1d = 0;
 
-      for (int j = 0; j < tensor.get_shape()[1]; ++j) {
-        for (int k = 0; k < tensor.get_shape()[0]; ++k) {
+      for (size_t j = 0; j < tensor.get_shape()[1]; ++j) {
+        for (size_t k = 0; k < tensor.get_shape()[0]; ++k) {
           Values_vector_1d[index_1d++] = Values_vector_2d_2[j][k];
         }
       }

app/Graph/build.hpp

@@ -1,8 +1,9 @@
 #include <iostream>
+#include <opencv2/opencv.hpp>
 #include <stdexcept>
 #include <variant>
 #include <vector>
-#include <opencv2/opencv.hpp>
+
 #include "Weights_Reader/reader_weights.hpp"
 #include "graph/graph.hpp"
 #include "layers/ConvLayer.hpp"

app/Graph/graph_build.cpp

@@ -1,5 +1,5 @@
-#include "build.hpp"
 #include "build.cpp"
+#include "build.hpp"
 
 using namespace itlab_2023;
 
@@ -13,15 +13,15 @@ int main() {
   cv::cvtColor(image, image, cv::COLOR_BGR2GRAY);
   cv::resize(image, resized_image, cv::Size(28, 28));
   std::vector<cv::Mat> channels;
- 
+
   cv::split(resized_image, channels);
 
   int count_pic = 1;
   std::vector<float> res(count_pic * 28 * 28);
 
   for (int i = 0; i < 28; ++i) {
     for (int j = 0; j < 28; ++j) {
-      res[i * 28 + j] = channels[0].at<uchar>(i,j);
+      res[i * 28 + j] = channels[0].at<uchar>(i, j);
     }
   }
   Shape sh({static_cast<size_t>(count_pic), 28, 28, 1});

include/Weights_Reader/reader_weights.hpp

@@ -10,7 +10,6 @@ using namespace itlab_2023;
 json read_json(const std::string& filename);
 void extract_values_without_bias(const json& j, std::vector<float>& values);
 void extract_values_from_json(const json& j, std::vector<float>& values);
-void parse_json_shape(const json& j, std::vector<size_t>& shape,
-                      size_t dim);
+void parse_json_shape(const json& j, std::vector<size_t>& shape, size_t dim);
 Tensor create_tensor_from_json(const json& j, Type type);
 void extract_bias_from_json(const json& j, std::vector<float>& bias);

src/Weights_Reader/reader_weights.cpp

@@ -55,7 +55,8 @@ void extract_values_without_bias(const json& j, std::vector<float>& values) {
   }
 }
 
-void parse_json_shape(const json& j, std::vector<size_t>& shape, size_t dim = 0) {
+void parse_json_shape(const json& j, std::vector<size_t>& shape,
+    size_t dim) {
   if (dim == 0) {
     if (j.is_array()) {
       if (j.empty()) {
@@ -98,7 +99,7 @@ Tensor create_tensor_from_json(const json& j, Type type) {
     extract_values_without_bias(j, vals);
     std::cout << "Extracted values size: " << vals.size() << std::endl;
 
-    parse_json_shape(j, shape);
+    parse_json_shape(j, shape, 0);
     std::cout << "Parsed shape: ";
     size_t expected_size = 1;
     for (const auto& dim : shape) {