Implement PostOp structure

app/Graph/build.cpp

@@ -24,6 +24,7 @@ void build_graph(Tensor& input, Tensor& output, bool comments,
   ImplType impl1 = parallel ? kTBB : kDefault;
   ImplType impl2 = parallel ? kSTL : kDefault;
   std::vector<std::shared_ptr<Layer>> layers;
+  std::vector<bool> layerpostop;
 
   std::string json_file = MODEL_PATH_H5;
   json model_data = read_json(json_file);
@@ -73,12 +74,14 @@ void build_graph(Tensor& input, Tensor& output, bool comments,
           1, pads, 1, tmp_values, tmp_bias, impl2);
       conv_layer->setName(kConvolution);
       layers.push_back(conv_layer);
+      layerpostop.push_back(0);
       if (comments) std::cout << "ConvLayer added to layers." << std::endl;
     }
     if (layer_type.find("relu") != std::string::npos) {
       auto ew_layer = std::make_shared<EWLayer>("relu");
       ew_layer->setName(kElementWise);
       layers.push_back(ew_layer);
+      layerpostop.push_back(1);
       if (comments)
         std::cout << "Element wise (relu) added to layers" << std::endl;
     }
@@ -99,6 +102,7 @@ void build_graph(Tensor& input, Tensor& output, bool comments,
       auto fc_layer = std::make_shared<FCLayer>(tensor, tmp_bias);
       fc_layer->setName(kFullyConnected);
       layers.push_back(fc_layer);
+      layerpostop.push_back(0);
       if (comments) std::cout << "DenseLayer added to layers." << std::endl;
     }
 
@@ -116,6 +120,7 @@ void build_graph(Tensor& input, Tensor& output, bool comments,
       auto pool_layer = std::make_shared<PoolingLayer>(shape, pooltype, impl1);
       pool_layer->setName(kPooling);
       layers.push_back(pool_layer);
+      layerpostop.push_back(0);
       if (comments) std::cout << "PoolingLayer added to layers." << std::endl;
     }
 
@@ -124,13 +129,15 @@ void build_graph(Tensor& input, Tensor& output, bool comments,
           std::make_shared<FlattenLayer>(std::vector<size_t>({0, 3, 2, 1}));
       flatten_layer->setName(kFlatten);
       layers.push_back(flatten_layer);
+      layerpostop.push_back(0);
       if (comments) std::cout << "FlattenLayer added to layers." << std::endl;
     }
 
     if (layer_type.find("Dropout") != std::string::npos) {
       auto dropout_layer = std::make_shared<DropOutLayer>(0.0);
       dropout_layer->setName(kDropout);
       layers.push_back(dropout_layer);
+      layerpostop.push_back(0);
       if (comments)
         std::cout
             << "DropOutLayer added to layers with probability 0.4 (turned "
@@ -155,7 +162,12 @@ void build_graph(Tensor& input, Tensor& output, bool comments,
               << std::endl;
 
   for (size_t i = 0; i < layers.size() - 1; ++i) {
-    graph.makeConnection(*layers[i], *layers[i + 1]);
+    if (layerpostop[i]) {
+      layers[i - 1]->postops.layers.push_back(layers[i].get());
+      layers[i - 1]->postops.count++;
+      graph.makeConnection(*layers[i - 1], *layers[i + 1]);
+    } else if (!layerpostop[i + 1])
+      graph.makeConnection(*layers[i], *layers[i + 1]);
   }
 
   graph.setOutput(*layers.back(), output);

include/graph/graph.hpp

@@ -114,6 +114,12 @@ class Graph {
       weights_.push_back(layers_[i]->get_weights());
 #endif
       inten_ = *outten_;
+      if (layers_[i]->postops.count > 0) {
+        for (unsigned int j = 0; j < layers_[i]->postops.count; j++) {
+          layers_[i]->postops.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 PostOperations {
+  std::vector<Layer*> layers;
+  unsigned int count = 0;
+};
+
 class Layer {
  public:
   Layer() = default;
   virtual ~Layer() = default;
+  PostOperations postops;
   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.postops.layers.push_back(&a4);
+  a2.postops.count++;
+
+  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);
+}