Implement PostOp structure

include/graph/graph.hpp

@@ -114,6 +114,12 @@ class Graph {
       weights_.push_back(layers_[i]->get_weights());
 #endif
       inten_ = *outten_;
+      if (layers_[i]->ewops.countlayers > 0) {
+        for (unsigned int j = 0; j < layers_[i]->ewops.countlayers; j++) {
+          layers_[i]->ewops.layers[j]->run(inten_, *outten_);
+        }
+        inten_ = *outten_;
+      }
 #ifdef ENABLE_STATISTIC_TIME
       auto end = std::chrono::high_resolution_clock::now();
       auto elapsed =

include/layers/Layer.hpp

@@ -25,10 +25,18 @@ enum LayerType : uint8_t {
 
 enum ImplType : uint8_t { kDefault, kTBB, kSTL };
 
+class Layer;
+
+struct EWoperations {
+  std::vector<Layer*> layers;
+  unsigned int countlayers = 0;
+};
+
 class Layer {
  public:
   Layer() = default;
   virtual ~Layer() = default;
+  EWoperations ewops;
   int getID() const { return id_; }
   void setID(int id) { id_ = id; }
   LayerType getName() const { return type_; }

test/inference/test_inference.cpp

@@ -138,3 +138,69 @@ TEST(bfs, check_end_to_end) {
   std::vector<float> res(3, 21);
   ASSERT_EQ(tmp, res);
 }
+TEST(bfs, check_struct_layer) {
+  Graph graph(5);
+  Shape sh1({1, 5, 5, 3});
+  std::vector<int> vec;
+  vec.reserve(75);
+  for (int i = 0; i < 75; ++i) {
+    vec.push_back(3);
+  }
+  Tensor input = make_tensor(vec, sh1);
+  Tensor output = make_tensor(vec, sh1);
+  InputLayer a1(kNhwc, kNchw, 1, 2);
+  a1.setName(kInput);
+  std::vector<int> kernelvec = {1, 1, 1, 1, 1, 1, 1, 1, 1};
+  Shape sh2({3, 3});
+  Tensor kernel = make_tensor(kernelvec, sh2);
+  ConvolutionalLayer a2(1, 0, 1, kernel);
+  ConvolutionalLayer a3(1, 0, 1, kernel);
+
+  // EWLayer a4("linear", 2.0F, 3.0F);
+  // a2.ewops.layers.push_back(&a4);
+  // a2.ewops.countlayers++;
+
+  a2.setName(kConvolution);
+  a3.setName(kConvolution);
+  graph.setInput(a1, input);
+  graph.makeConnection(a1, a2);
+  graph.makeConnection(a2, a3);
+  graph.setOutput(a3, output);
+  graph.inference();
+  std::vector<int> tmp = *output.as<int>();
+  std::vector<int> res = {81, 81, 81};
+  ASSERT_EQ(tmp, res);
+}
+TEST(bfs, check_struct_layer_added) {
+  Graph graph(5);
+  Shape sh1({1, 5, 5, 3});
+  std::vector<int> vec;
+  vec.reserve(75);
+  for (int i = 0; i < 75; ++i) {
+    vec.push_back(3);
+  }
+  Tensor input = make_tensor(vec, sh1);
+  Tensor output = make_tensor(vec, sh1);
+  InputLayer a1(kNhwc, kNchw, 1, 2);
+  a1.setName(kInput);
+  std::vector<int> kernelvec = {1, 1, 1, 1, 1, 1, 1, 1, 1};
+  Shape sh2({3, 3});
+  Tensor kernel = make_tensor(kernelvec, sh2);
+  ConvolutionalLayer a2(1, 0, 1, kernel);
+  ConvolutionalLayer a3(1, 0, 1, kernel);
+
+  EWLayer a4("linear", 2.0F, 3.0F);
+  a2.ewops.layers.push_back(&a4);
+  a2.ewops.countlayers++;
+
+  a2.setName(kConvolution);
+  a3.setName(kConvolution);
+  graph.setInput(a1, input);
+  graph.makeConnection(a1, a2);
+  graph.makeConnection(a2, a3);
+  graph.setOutput(a3, output);
+  graph.inference();
+  std::vector<int> tmp = *output.as<int>();
+  std::vector<int> res = {189, 189, 189};
+  ASSERT_EQ(tmp, res);
+}