Merge pull request #2271 from stweil/refactor

Refactor class Network

src/lstm/convolve.h

@@ -4,7 +4,6 @@
 //              and pulls in random data to fill out-of-input inputs.
 //              Output is therefore same size as its input, but deeper.
 // Author:      Ray Smith
-// Created:     Tue Mar 18 16:45:34 PST 2014
 //
 // (C) Copyright 2014, Google Inc.
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -61,6 +60,11 @@ class Convolve : public Network {
                 NetworkScratch* scratch,
                 NetworkIO* back_deltas) override;
 
+ private:
+  void DebugWeights() override {
+    tprintf("Must override Network::DebugWeights for type %d\n", type_);
+  }
+
  protected:
   // Serialized data.
   int32_t half_x_;
@@ -69,5 +73,4 @@ class Convolve : public Network {
 
 }  // namespace tesseract.
 
-
 #endif  // TESSERACT_LSTM_SUBSAMPLE_H_

src/lstm/input.h

@@ -2,7 +2,6 @@
 // File:        input.h
 // Description: Input layer class for neural network implementations.
 // Author:      Ray Smith
-// Created:     Thu Mar 13 08:56:26 PDT 2014
 //
 // (C) Copyright 2014, Google Inc.
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -93,6 +92,10 @@ class Input : public Network {
                               TRand* randomizer, NetworkIO* input);
 
  private:
+  void DebugWeights() override {
+    tprintf("Must override Network::DebugWeights for type %d\n", type_);
+  }
+
   // Input shape determines how images are dealt with.
   StaticShape shape_;
   // Cached total network x scale factor for scaling bounding boxes.

src/lstm/network.cpp

@@ -2,7 +2,6 @@
 // File:        network.cpp
 // Description: Base class for neural network implementations.
 // Author:      Ray Smith
-// Created:     Wed May 01 17:25:06 PST 2013
 //
 // (C) Copyright 2013, Google Inc.
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -53,10 +52,11 @@ const int kMaxWinSize = 2000;
 const int kXWinFrameSize = 30;
 const int kYWinFrameSize = 80;
 
-// String names corresponding to the NetworkType enum. Keep in sync.
+// String names corresponding to the NetworkType enum.
+// Keep in sync with NetworkType.
 // Names used in Serialization to allow re-ordering/addition/deletion of
 // layer types in NetworkType without invalidating existing network files.
-char const* const Network::kTypeNames[NT_COUNT] = {
+static char const* const kTypeNames[NT_COUNT] = {
     "Invalid",     "Input",
     "Convolve",    "Maxpool",
     "Parallel",    "Replicated",
@@ -165,81 +165,87 @@ bool Network::Serialize(TFile* fp) const {
   return true;
 }
 
-// Reads from the given file. Returns false in case of error.
-// Should be overridden by subclasses, but NOT called by their DeSerialize.
-bool Network::DeSerialize(TFile* fp) {
+static NetworkType getNetworkType(TFile* fp) {
   int8_t data;
-  if (!fp->DeSerialize(&data)) return false;
+  if (!fp->DeSerialize(&data)) return NT_NONE;
   if (data == NT_NONE) {
     STRING type_name;
-    if (!type_name.DeSerialize(fp)) return false;
+    if (!type_name.DeSerialize(fp)) return NT_NONE;
     for (data = 0; data < NT_COUNT && type_name != kTypeNames[data]; ++data) {
     }
     if (data == NT_COUNT) {
       tprintf("Invalid network layer type:%s\n", type_name.string());
-      return false;
+      return NT_NONE;
     }
   }
-  type_ = static_cast<NetworkType>(data);
-  if (!fp->DeSerialize(&data)) return false;
-  training_ = data == TS_ENABLED ? TS_ENABLED : TS_DISABLED;
-  if (!fp->DeSerialize(&data)) return false;
-  needs_to_backprop_ = data != 0;
-  if (!fp->DeSerialize(&network_flags_)) return false;
-  if (!fp->DeSerialize(&ni_)) return false;
-  if (!fp->DeSerialize(&no_)) return false;
-  if (!fp->DeSerialize(&num_weights_)) return false;
-  if (!name_.DeSerialize(fp)) return false;
-  return true;
+  return static_cast<NetworkType>(data);
 }
 
 // Reads from the given file. Returns nullptr in case of error.
 // Determines the type of the serialized class and calls its DeSerialize
 // on a new object of the appropriate type, which is returned.
 Network* Network::CreateFromFile(TFile* fp) {
-  Network stub;
-  if (!stub.DeSerialize(fp)) return nullptr;
+  NetworkType type;          // Type of the derived network class.
+  TrainingState training;    // Are we currently training?
+  bool needs_to_backprop;    // This network needs to output back_deltas.
+  int32_t network_flags;     // Behavior control flags in NetworkFlags.
+  int32_t ni;                // Number of input values.
+  int32_t no;                // Number of output values.
+  int32_t num_weights;       // Number of weights in this and sub-network.
+  STRING name;               // A unique name for this layer.
+  int8_t data;
   Network* network = nullptr;
-  switch (stub.type_) {
+  type = getNetworkType(fp);
+  if (!fp->DeSerialize(&data)) return nullptr;
+  training = data == TS_ENABLED ? TS_ENABLED : TS_DISABLED;
+  if (!fp->DeSerialize(&data)) return nullptr;
+  needs_to_backprop = data != 0;
+  if (!fp->DeSerialize(&network_flags)) return nullptr;
+  if (!fp->DeSerialize(&ni)) return nullptr;
+  if (!fp->DeSerialize(&no)) return nullptr;
+  if (!fp->DeSerialize(&num_weights)) return nullptr;
+  if (!name.DeSerialize(fp)) return nullptr;
+
+  switch (type) {
     case NT_CONVOLVE:
-      network = new Convolve(stub.name_, stub.ni_, 0, 0);
+      network = new Convolve(name, ni, 0, 0);
       break;
     case NT_INPUT:
-      network = new Input(stub.name_, stub.ni_, stub.no_);
+      network = new Input(name, ni, no);
       break;
     case NT_LSTM:
     case NT_LSTM_SOFTMAX:
     case NT_LSTM_SOFTMAX_ENCODED:
     case NT_LSTM_SUMMARY:
       network =
-          new LSTM(stub.name_, stub.ni_, stub.no_, stub.no_, false, stub.type_);
+          new LSTM(name, ni, no, no, false, type);
       break;
     case NT_MAXPOOL:
-      network = new Maxpool(stub.name_, stub.ni_, 0, 0);
+      network = new Maxpool(name, ni, 0, 0);
       break;
     // All variants of Parallel.
     case NT_PARALLEL:
     case NT_REPLICATED:
     case NT_PAR_RL_LSTM:
     case NT_PAR_UD_LSTM:
     case NT_PAR_2D_LSTM:
-      network = new Parallel(stub.name_, stub.type_);
+      network = new Parallel(name, type);
       break;
     case NT_RECONFIG:
-      network = new Reconfig(stub.name_, stub.ni_, 0, 0);
+      network = new Reconfig(name, ni, 0, 0);
       break;
     // All variants of reversed.
     case NT_XREVERSED:
     case NT_YREVERSED:
     case NT_XYTRANSPOSE:
-      network = new Reversed(stub.name_, stub.type_);
+      network = new Reversed(name, type);
       break;
     case NT_SERIES:
-      network = new Series(stub.name_);
+      network = new Series(name);
       break;
     case NT_TENSORFLOW:
 #ifdef INCLUDE_TENSORFLOW
-      network = new TFNetwork(stub.name_);
+      network = new TFNetwork(name);
 #else
       tprintf("TensorFlow not compiled in! -DINCLUDE_TENSORFLOW\n");
 #endif
@@ -253,16 +259,16 @@ Network* Network::CreateFromFile(TFile* fp) {
     case NT_LOGISTIC:
     case NT_POSCLIP:
     case NT_SYMCLIP:
-      network = new FullyConnected(stub.name_, stub.ni_, stub.no_, stub.type_);
+      network = new FullyConnected(name, ni, no, type);
       break;
     default:
       break;
   }
   if (network) {
-    network->training_ = stub.training_;
-    network->needs_to_backprop_ = stub.needs_to_backprop_;
-    network->network_flags_ = stub.network_flags_;
-    network->num_weights_ = stub.num_weights_;
+    network->training_ = training;
+    network->needs_to_backprop_ = needs_to_backprop;
+    network->network_flags_ = network_flags;
+    network->num_weights_ = num_weights;
     if (!network->DeSerialize(fp)) {
       delete network;
       network = nullptr;

src/lstm/network.h

@@ -2,7 +2,6 @@
 // File:        network.h
 // Description: Base class for neural network implementations.
 // Author:      Ray Smith
-// Created:     Wed May 01 16:38:06 PST 2013
 //
 // (C) Copyright 2013, Google Inc.
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -215,17 +214,16 @@ class Network {
   virtual void CacheXScaleFactor(int factor) {}
 
   // Provides debug output on the weights.
-  virtual void DebugWeights() {
-    tprintf("Must override Network::DebugWeights for type %d\n", type_);
-  }
+  virtual void DebugWeights() = 0;
 
   // Writes to the given file. Returns false in case of error.
   // Should be overridden by subclasses, but called by their Serialize.
   virtual bool Serialize(TFile* fp) const;
   // Reads from the given file. Returns false in case of error.
   // Should be overridden by subclasses, but NOT called by their DeSerialize.
-  virtual bool DeSerialize(TFile* fp);
+  virtual bool DeSerialize(TFile* fp) = 0;
 
+ public:
   // Updates the weights using the given learning rate, momentum and adam_beta.
   // num_samples is used in the adam computation iff use_adam_ is true.
   virtual void Update(float learning_rate, float momentum, float adam_beta,
@@ -261,9 +259,7 @@ class Network {
   // instead of all the replicated networks having to do it.
   virtual void Forward(bool debug, const NetworkIO& input,
                        const TransposedArray* input_transpose,
-                       NetworkScratch* scratch, NetworkIO* output) {
-    tprintf("Must override Network::Forward for type %d\n", type_);
-  }
+                       NetworkScratch* scratch, NetworkIO* output) = 0;
 
   // Runs backward propagation of errors on fwdX_deltas.
   // Note that fwd_deltas and back_deltas are both 2-d arrays as with Forward.
@@ -272,10 +268,7 @@ class Network {
   // return false from Backward!
   virtual bool Backward(bool debug, const NetworkIO& fwd_deltas,
                         NetworkScratch* scratch,
-                        NetworkIO* back_deltas) {
-    tprintf("Must override Network::Backward for type %d\n", type_);
-    return false;
-  }
+                        NetworkIO* back_deltas) = 0;
 
   // === Debug image display methods. ===
   // Displays the image of the matrix to the forward window.
@@ -309,12 +302,8 @@ class Network {
   ScrollView* forward_win_;   // Recognition debug display window.
   ScrollView* backward_win_;  // Training debug display window.
   TRand* randomizer_;         // Random number generator.
-
-  // Static serialized name/type_ mapping. Keep in sync with NetworkType.
-  static char const* const kTypeNames[NT_COUNT];
 };
 
-
 }  // namespace tesseract.
 
 #endif  // TESSERACT_LSTM_NETWORK_H_

src/lstm/reconfig.h

@@ -3,7 +3,6 @@
 // Description: Network layer that reconfigures the scaling vs feature
 //              depth.
 // Author:      Ray Smith
-// Created:     Wed Feb 26 15:37:42 PST 2014
 //
 // (C) Copyright 2014, Google Inc.
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -16,10 +15,10 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 ///////////////////////////////////////////////////////////////////////
+
 #ifndef TESSERACT_LSTM_RECONFIG_H_
 #define TESSERACT_LSTM_RECONFIG_H_
 
-
 #include "genericvector.h"
 #include "matrix.h"
 #include "network.h"
@@ -71,6 +70,11 @@ class Reconfig : public Network {
                 NetworkScratch* scratch,
                 NetworkIO* back_deltas) override;
 
+ private:
+  void DebugWeights() override {
+    tprintf("Must override Network::DebugWeights for type %d\n", type_);
+  }
+
  protected:
   // Non-serialized data used to store parameters between forward and back.
   StrideMap back_map_;
@@ -81,5 +85,4 @@ class Reconfig : public Network {
 
 }  // namespace tesseract.
 
-
 #endif  // TESSERACT_LSTM_SUBSAMPLE_H_