Implement mechanism for subgraph replacement

.clang-tidy

@@ -27,6 +27,7 @@ Checks: >
   -bugprone-easily-swappable-parameters,
   -bugprone-implicit-widening-of-multiplication-result,
   -bugprone-unchecked-optional-access,
+  -clang-analyzer-security.insecureAPI.rand,
 
 WarningsAsErrors: "*"
 

.github/workflows/ci.yml

@@ -14,7 +14,7 @@ jobs:
     - uses: actions/checkout@v4
     - uses: DoozyX/clang-format-lint-action@v0.15
       with:
-        source: './app ./include ./src ./test'
+        source: '.'
   build-linux:
     strategy:
       matrix:

CMakeLists.txt

@@ -2,8 +2,6 @@ cmake_minimum_required(VERSION 3.20)
 
 project(ITLabAI)
 
-set(CMAKE_BUILD_TYPE "Release" CACHE STRING "Build type" FORCE)
-
 option(ENABLE_STATISTIC_TENSORS "Enable statistic tensors" OFF)
 
 if(ENABLE_STATISTIC_TENSORS)
@@ -34,7 +32,9 @@ add_subdirectory(3rdparty)
 
 include(cmake/opencv_config.cmake)
 
-find_package(OpenMP REQUIRED)
+if (NOT WIN32)
+    find_package(OpenMP REQUIRED)
+endif()
 
 if (NOT WIN32)
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -Wextra -Werror")

app/Accuracy/accuracy_check.cpp

@@ -34,7 +34,7 @@ int main() {
   }
   Shape sh({static_cast<size_t>(count_pic), 227, 227, 3});
   Tensor t = make_tensor<float>(res, sh);
-  Graph graph;
+  Graph graph(6);
   Shape sh1({1, 5, 5, 3});
   std::vector<float> vec;
   vec.reserve(75);
@@ -43,29 +43,23 @@ int main() {
   }
   Tensor input = t;
   Tensor output = make_tensor(vec, sh1);
-  auto a1 = std::make_unique<InputLayer>(kNchw, kNchw, 1, 2);
-  Layer* a1_ptr = a1.get();
+  auto a1 = std::make_shared<InputLayer>(kNchw, kNchw, 1, 2);
   std::vector<float> kernelvec = {1, 1, 1, 1, 1, 1, 1, 1, 1};
   Shape sh2({3, 3});
   Tensor kernel = make_tensor(kernelvec, sh2);
-  auto a2 = std::make_unique<ConvolutionalLayer>(1, 0, 0, kernel);
-  Layer* a2_ptr = a2.get();
+  auto a2 = std::make_shared<ConvolutionalLayer>(1, 0, 0, kernel);
   Shape poolshape = {2, 2};
-  auto a3 = std::make_unique<EWLayer>("linear", 2.0F, 3.0F);
-  Layer* a3_ptr = a3.get();
-  auto a4 = std::make_unique<PoolingLayer>(poolshape, "average");
-  Layer* a4_ptr = a4.get();
-  auto a5 = std::make_unique<OutputLayer>();
-  Layer* a5_ptr = a5.get();
-  auto a6 = std::make_unique<FCLayer>();
-  Layer* a6_ptr = a6.get();
-  graph.setInput(a1_ptr, input);
-  graph.makeConnection(a1_ptr, a2_ptr);
-  graph.makeConnection(a2_ptr, a3_ptr);
-  graph.makeConnection(a3_ptr, a4_ptr);
-  graph.makeConnection(a4_ptr, a5_ptr);
-  graph.makeConnection(a5_ptr, a6_ptr);
-  graph.setOutput(a5_ptr, output);
+  auto a3 = std::make_shared<EWLayer>("linear", 2.0F, 3.0F);
+  auto a4 = std::make_shared<PoolingLayer>(poolshape, "average");
+  auto a5 = std::make_shared<OutputLayer>();
+  auto a6 = std::make_shared<FCLayer>();
+  graph.setInput(a1, input);
+  graph.makeConnection(a1, a2);
+  graph.makeConnection(a2, a3);
+  graph.makeConnection(a3, a4);
+  graph.makeConnection(a4, a5);
+  graph.makeConnection(a5, a6);
+  graph.setOutput(a5, output);
   graph.inference();
   std::vector<float> tmp = *output.as<float>();
   std::vector<float> tmp_output = softmax<float>(*output.as<float>());

app/AccuracyImgNet/CMakeLists.txt

@@ -0,0 +1,8 @@
+add_executable(ACCImgNet accimgnet.cpp)
+
+find_package( OpenCV REQUIRED PATHS "${OPENCV_BUILD_DIR}" )
+include_directories( ${OpenCV_INCLUDE_DIRS} )
+target_link_libraries( ACCImgNet ${OpenCV_LIBS} )
+target_link_libraries( ACCImgNet TBB_unified)
+target_link_libraries( ACCImgNet layers_lib)
+target_link_libraries( ACCImgNet gtest_main)