Implement function to find subgraph and change LayerType assignment (#209)

Author: NeiroYT

app/Converters/requirements.txt

@@ -1,4 +1,4 @@
-tensorflow==2.19.0
+tensorflow>=2.19.0
 onnx>=1.15.0
 ultralytics>=8.0.0
 numpy>=1.21.0

app/Graph/build.cpp

@@ -73,14 +73,12 @@ void build_graph(it_lab_ai::Tensor& input, it_lab_ai::Tensor& output,
       it_lab_ai::Tensor tmp_bias = it_lab_ai::make_tensor(tensor.get_bias());
       auto conv_layer = std::make_shared<it_lab_ai::ConvolutionalLayer>(
           1, pads, 1, tmp_values, tmp_bias, impl2);
-      conv_layer->setName(it_lab_ai::kConvolution);
       layers.push_back(conv_layer);
       layerpostop.push_back(false);
       if (comments) std::cout << "ConvLayer added to layers." << std::endl;
     }
     if (layer_type.find("relu") != std::string::npos) {
       auto ew_layer = std::make_shared<it_lab_ai::EWLayer>("relu");
-      ew_layer->setName(it_lab_ai::kElementWise);
       layers.push_back(ew_layer);
       layerpostop.push_back(true);
       if (comments)
@@ -101,7 +99,6 @@ void build_graph(it_lab_ai::Tensor& input, it_lab_ai::Tensor& output,
       //
       tensor = tmp_tensor;
       auto fc_layer = std::make_shared<it_lab_ai::FCLayer>(tensor, tmp_bias);
-      fc_layer->setName(it_lab_ai::kFullyConnected);
       layers.push_back(fc_layer);
       layerpostop.push_back(false);
       if (comments) std::cout << "DenseLayer added to layers." << std::endl;
@@ -120,7 +117,6 @@ void build_graph(it_lab_ai::Tensor& input, it_lab_ai::Tensor& output,
                   << std::endl;
       auto pool_layer =
           std::make_shared<it_lab_ai::PoolingLayer>(shape, pooltype, impl1);
-      pool_layer->setName(it_lab_ai::kPooling);
       layers.push_back(pool_layer);
       layerpostop.push_back(false);
       if (comments) std::cout << "PoolingLayer added to layers." << std::endl;
@@ -129,15 +125,13 @@ void build_graph(it_lab_ai::Tensor& input, it_lab_ai::Tensor& output,
     if (layer_type.find("Flatten") != std::string::npos) {
       auto flatten_layer = std::make_shared<it_lab_ai::FlattenLayer>(
           std::vector<size_t>({0, 3, 2, 1}));
-      flatten_layer->setName(it_lab_ai::kFlatten);
       layers.push_back(flatten_layer);
       layerpostop.push_back(false);
       if (comments) std::cout << "FlattenLayer added to layers." << std::endl;
     }
 
     if (layer_type.find("Dropout") != std::string::npos) {
       auto dropout_layer = std::make_shared<it_lab_ai::DropOutLayer>(0.0);
-      dropout_layer->setName(it_lab_ai::kDropout);
       layers.push_back(dropout_layer);
       layerpostop.push_back(false);
       if (comments)
@@ -151,7 +145,6 @@ void build_graph(it_lab_ai::Tensor& input, it_lab_ai::Tensor& output,
     std::cout << "number of layers - " << layers.size() + 1 << std::endl;
   it_lab_ai::Graph graph(static_cast<int>(layers.size()));
   it_lab_ai::InputLayer a1(it_lab_ai::kNchw, it_lab_ai::kNchw);
-  a1.setName(it_lab_ai::kInput);
 
   if (comments) std::cout << "InputLayer created." << std::endl;
 

include/CMakeLists.txt

@@ -1,6 +1,9 @@
 file(GLOB_RECURSE graph_headers graph/*.h graph/*.hpp)
 set(GRAPH_HEADERS "${graph_headers}" PARENT_SCOPE)
 
+file(GLOB_RECURSE graphT_headers graph_transformations/*.h graph_transformations/*.hpp)
+set(GRAPHT_HEADERS "${graphT_headers}" PARENT_SCOPE)
+
 file(GLOB_RECURSE layers_headers layers/*.h layers/*.hpp)
 set(LAYERS_HEADERS "${layers_headers}" PARENT_SCOPE)
 

include/graph/graph.hpp

@@ -23,17 +23,17 @@ struct BranchState {
 
 class Graph {
   int BiggestSize_;
-  int V_;
-  std::vector<Layer*> layers_;
-  std::vector<int> arrayV_;
-  std::vector<int> arrayE_;
+  int V_;                       // amount of ids
+  std::vector<Layer*> layers_;  // layers vector with some ids
+  std::vector<int> arrayV_;     // vertices (id -> vertex number)
+  std::vector<int> arrayE_;     // edges (vertex number -> id)
   std::vector<Tensor> inten_;
   std::vector<Tensor> outten_;
   Tensor* outtenres_;
   int start_;
   int end_;
   std::list<BranchState> branch_list_;
-  std::vector<std::vector<int>> in_edges_;
+  std::vector<std::vector<int>> in_edges_;  // next -> prev
   std::vector<std::vector<std::pair<int, int>>> split_distribution_;
   int count_used_split_distribution_;
 #ifdef ENABLE_STATISTIC_TENSORS
@@ -67,6 +67,35 @@ class Graph {
     in_edges_.clear();
   }
 
+  int getVertexValue(size_t layerID) const {
+    if (layerID >= arrayV_.size()) {
+      throw std::invalid_argument("ArrayV does not contain this ID.");
+    }
+    return arrayV_[layerID];
+  }
+
+  int getEdgeValue(size_t pos) const {
+    if (pos >= arrayE_.size()) {
+      throw std::invalid_argument("ArrayE does not contain this.");
+    }
+    return arrayE_[pos];
+  }
+
+  size_t getInputsSize(size_t layerID) const {
+    if (layerID >= in_edges_.size()) {
+      throw std::invalid_argument("Input edges array do not contain this ID.");
+    }
+    return in_edges_[layerID].size();
+  }
+
+  int getLayersCount() const { return V_; }
+  const Layer& getLayerFromID(size_t layerID) const {
+    if (layerID >= layers_.size()) {
+      throw std::invalid_argument("Layers do not contain this ID.");
+    }
+    return *layers_[layerID];
+  }
+
   void setInput(Layer& lay, Tensor& vec) {
     lay.setID(0);
     layers_.push_back(&lay);
@@ -76,6 +105,29 @@ class Graph {
     V_++;
     in_edges_.resize(1);
   }
+
+  void addSingleLayer(Layer& lay) {
+    bool layer_exists = false;
+    for (const auto* layer : layers_) {
+      if (layer == &lay) {
+        layer_exists = true;
+        break;
+      }
+    }
+
+    if (!layer_exists) {
+      lay.setID(V_);
+      layers_.push_back(&lay);
+      arrayV_.push_back(static_cast<int>(arrayE_.size()));
+
+      if (V_ >= static_cast<int>(in_edges_.size())) {
+        in_edges_.resize(V_ + 1);
+      }
+
+      V_++;
+    }
+  }
+
   void makeConnection(const Layer& layPrev, Layer& layNext) {
     bool layer_exists = false;
     for (const auto* layer : layers_) {

include/graph_transformations/graph_transformations.hpp

@@ -0,0 +1,16 @@
+#include <algorithm>
+#include <vector>
+
+#include "graph/graph.hpp"
+#include "layers/Layer.hpp"
+
+namespace it_lab_ai {
+std::vector<std::vector<int>> find_subgraphs(const Graph& graph,
+                                             const Graph& subgraph);
+bool has_edge(const Graph& graph, int id_from, int id_to);
+bool is_root(const Graph& graph, int id);
+bool is_leaf(const Graph& graph, int id);
+bool run_search(const Graph& graph, const Graph& subgraph,
+                std::vector<int>& assignments,
+                std::vector<std::vector<int>>& results);
+}  // namespace it_lab_ai

include/layers/BinaryOpLayer.hpp

@@ -14,10 +14,9 @@ class BinaryOpLayer : public Layer {
  public:
   enum class Operation : uint8_t { kMul, kAdd, kSub, kDiv };
 
-  BinaryOpLayer() = default;
-  explicit BinaryOpLayer(Operation op) : op_(op) {}
+  BinaryOpLayer() : Layer(kBinaryOp), op_(Operation::kMul) {}
+  explicit BinaryOpLayer(Operation op) : Layer(kBinaryOp), op_(op) {}
 
-  static std::string get_name() { return "Binary Operation Layer"; }
   void run(const std::vector<Tensor>& input,
            std::vector<Tensor>& output) override;
   static bool is_scalar_tensor(const Tensor& t);
@@ -30,7 +29,7 @@ class BinaryOpLayer : public Layer {
 #endif
 
  private:
-  Operation op_ = Operation::kMul;
+  Operation op_;
 
   template <typename ValueType>
   void run_with_scalar_impl(const Tensor& input, ValueType scalar,

include/layers/ConcatLayer.hpp

@@ -11,13 +11,11 @@ namespace it_lab_ai {
 
 class ConcatLayer : public Layer {
  public:
-  explicit ConcatLayer(int64_t axis = 0) : axis_(axis) {}
+  explicit ConcatLayer(int64_t axis = 0) : Layer(kConcat), axis_(axis) {}
 
   void run(const std::vector<Tensor>& input,
            std::vector<Tensor>& output) override;
 
-  static std::string get_name() { return "ConcatLayer"; }
-
 #ifdef ENABLE_STATISTIC_WEIGHTS
   Tensor get_weights() override { return Tensor(); }
 #endif

include/layers/ConvLayer.hpp

@@ -18,10 +18,16 @@ class ConvolutionalLayer : public Layer {
   ImplType implType_;
 
  public:
-  ConvolutionalLayer() = default;
+  ConvolutionalLayer() : Layer(kConvolution) {
+    stride_ = 0;
+    pads_ = 0;
+    dilations_ = 0;
+    implType_ = kDefault;
+  }
   ConvolutionalLayer(size_t step, size_t pads, size_t dilations,
                      const Tensor& kernel, const Tensor& bias = Tensor(),
-                     ImplType implType = kDefault) {
+                     ImplType implType = kDefault)
+      : Layer(kConvolution) {
     stride_ = step;
     pads_ = pads;
     dilations_ = dilations;

include/layers/DropOutLayer.hpp

@@ -10,9 +10,9 @@ class DropOutLayer : public Layer {
   double drop_rate_;
 
  public:
-  DropOutLayer() = default;
-  DropOutLayer(double drop_rate) { drop_rate_ = drop_rate; }
-  static std::string get_name() { return "DropOut layer"; }
+  DropOutLayer(double drop_rate = 0.0) : Layer(kDropout) {
+    drop_rate_ = drop_rate;
+  }
   void run(const std::vector<Tensor>& input,
            std::vector<Tensor>& output) override;
 #ifdef ENABLE_STATISTIC_WEIGHTS

include/layers/EWLayer.hpp

@@ -19,11 +19,13 @@ T relu(const T& value) {
 
 class EWLayer : public Layer {
  public:
-  EWLayer() = default;
+  EWLayer() : Layer(kElementWise), func_("none"), alpha_(0.0F), beta_(0.0F) {}
   EWLayer(std::string function, float alpha = 0.0F, float beta = 0.0F)
-      : func_(std::move(function)), alpha_(alpha), beta_(beta) {}
+      : Layer(kElementWise),
+        func_(std::move(function)),
+        alpha_(alpha),
+        beta_(beta) {}
 
-  static std::string get_name() { return "Element-wise layer"; }
   void run(const std::vector<Tensor>& input,
            std::vector<Tensor>& output) override;
 #ifdef ENABLE_STATISTIC_WEIGHTS