lstmrecognizer.h

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// File:        lstmrecognizer.h
// Description: Top-level line recognizer class for LSTM-based networks.
// Author:      Ray Smith
// Created:     Thu May 02 08:57:06 PST 2013
//
// (C) Copyright 2013, Google Inc.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#ifndef TESSERACT_LSTM_LSTMRECOGNIZER_H_
#define TESSERACT_LSTM_LSTMRECOGNIZER_H_

#include "ccutil.h"
#include "helpers.h"
#include "imagedata.h"
#include "matrix.h"
#include "network.h"
#include "networkscratch.h"
#include "recodebeam.h"
#include "series.h"
#include "strngs.h"
#include "unicharcompress.h"

class BLOB_CHOICE_IT;
struct Pix;
class ROW_RES;
class ScrollView;
class TBOX;
class WERD_RES;

namespace tesseract {

class Dict;
class ImageData;

// Enum indicating training mode control flags.
enum TrainingFlags {
  TF_INT_MODE = 1,
  TF_COMPRESS_UNICHARSET = 64,
};

// Top-level line recognizer class for LSTM-based networks.
// Note that a sub-class, LSTMTrainer is used for training.
class LSTMRecognizer {
 public:
  LSTMRecognizer();
  ~LSTMRecognizer();

  int NumOutputs() const {
    return network_->NumOutputs();
  }
  int training_iteration() const {
    return training_iteration_;
  }
  int sample_iteration() const {
    return sample_iteration_;
  }
  double learning_rate() const {
    return learning_rate_;
  }
  LossType OutputLossType() const {
    if (network_ == nullptr) return LT_NONE;
    StaticShape shape;
    shape = network_->OutputShape(shape);
    return shape.loss_type();
  }
  bool SimpleTextOutput() const { return OutputLossType() == LT_SOFTMAX; }
  bool IsIntMode() const { return (training_flags_ & TF_INT_MODE) != 0; }
  // True if recoder_ is active to re-encode text to a smaller space.
  bool IsRecoding() const {
    return (training_flags_ & TF_COMPRESS_UNICHARSET) != 0;
  }
  // Returns true if the network is a TensorFlow network.
  bool IsTensorFlow() const { return network_->type() == NT_TENSORFLOW; }
  // Returns a vector of layer ids that can be passed to other layer functions
  // to access a specific layer.
  GenericVector<STRING> EnumerateLayers() const {
    ASSERT_HOST(network_ != NULL && network_->type() == NT_SERIES);
    Series* series = static_cast<Series*>(network_);
    GenericVector<STRING> layers;
    series->EnumerateLayers(NULL, &layers);
    return layers;
  }
  // Returns a specific layer from its id (from EnumerateLayers).
  Network* GetLayer(const STRING& id) const {
    ASSERT_HOST(network_ != NULL && network_->type() == NT_SERIES);
    ASSERT_HOST(id.length() > 1 && id[0] == ':');
    Series* series = static_cast<Series*>(network_);
    return series->GetLayer(&id[1]);
  }
  // Returns the learning rate of the layer from its id.
  float GetLayerLearningRate(const STRING& id) const {
    ASSERT_HOST(network_ != NULL && network_->type() == NT_SERIES);
    if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) {
      ASSERT_HOST(id.length() > 1 && id[0] == ':');
      Series* series = static_cast<Series*>(network_);
      return series->LayerLearningRate(&id[1]);
    } else {
      return learning_rate_;
    }
  }
  // Multiplies the all the learning rate(s) by the given factor.
  void ScaleLearningRate(double factor) {
    ASSERT_HOST(network_ != NULL && network_->type() == NT_SERIES);
    learning_rate_ *= factor;
    if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) {
      GenericVector<STRING> layers = EnumerateLayers();
      for (int i = 0; i < layers.size(); ++i) {
        ScaleLayerLearningRate(layers[i], factor);
      }
    }
  }
  // Multiplies the learning rate of the layer with id, by the given factor.
  void ScaleLayerLearningRate(const STRING& id, double factor) {
    ASSERT_HOST(network_ != NULL && network_->type() == NT_SERIES);
    ASSERT_HOST(id.length() > 1 && id[0] == ':');
    Series* series = static_cast<Series*>(network_);
    series->ScaleLayerLearningRate(&id[1], factor);
  }

  // Converts the network to int if not already.
  void ConvertToInt() {
    if ((training_flags_ & TF_INT_MODE) == 0) {
      network_->ConvertToInt();
      training_flags_ |= TF_INT_MODE;
    }
  }

  // Provides access to the UNICHARSET that this classifier works with.
  const UNICHARSET& GetUnicharset() const { return ccutil_.unicharset; }
  // Provides access to the UnicharCompress that this classifier works with.
  const UnicharCompress& GetRecoder() const { return recoder_; }
  // Provides access to the Dict that this classifier works with.
  const Dict* GetDict() const { return dict_; }
  // Sets the sample iteration to the given value. The sample_iteration_
  // determines the seed for the random number generator. The training
  // iteration is incremented only by a successful training iteration.
  void SetIteration(int iteration) {
    sample_iteration_ = iteration;
  }
  // Accessors for textline image normalization.
  int NumInputs() const {
    return network_->NumInputs();
  }
  int null_char() const { return null_char_; }

  // Loads a model from mgr, including the dictionary only if lang is not null.
  bool Load(const char* lang, TessdataManager* mgr);

  // Writes to the given file. Returns false in case of error.
  // If mgr contains a unicharset and recoder, then they are not encoded to fp.
  bool Serialize(const TessdataManager* mgr, TFile* fp) const;
  // Reads from the given file. Returns false in case of error.
  // If mgr contains a unicharset and recoder, then they are taken from there,
  // otherwise, they are part of the serialization in fp.
  bool DeSerialize(const TessdataManager* mgr, TFile* fp);
  // Loads the charsets from mgr.
  bool LoadCharsets(const TessdataManager* mgr);
  // Loads the Recoder.
  bool LoadRecoder(TFile* fp);
  // Loads the dictionary if possible from the traineddata file.
  // Prints a warning message, and returns false but otherwise fails silently
  // and continues to work without it if loading fails.
  // Note that dictionary load is independent from DeSerialize, but dependent
  // on the unicharset matching. This enables training to deserialize a model
  // from checkpoint or restore without having to go back and reload the
  // dictionary.
  bool LoadDictionary(const char* lang, TessdataManager* mgr);

  // Recognizes the line image, contained within image_data, returning the
  // recognized tesseract WERD_RES for the words.
  // If invert, tries inverted as well if the normal interpretation doesn't
  // produce a good enough result. The line_box is used for computing the
  // box_word in the output words. worst_dict_cert is the worst certainty that
  // will be used in a dictionary word.
  void RecognizeLine(const ImageData& image_data, bool invert, bool debug,
                     double worst_dict_cert, const TBOX& line_box,
                     PointerVector<WERD_RES>* words);

  // Helper computes min and mean best results in the output.
  void OutputStats(const NetworkIO& outputs,
                   float* min_output, float* mean_output, float* sd);
  // Recognizes the image_data, returning the labels,
  // scores, and corresponding pairs of start, end x-coords in coords.
  // Returned in scale_factor is the reduction factor
  // between the image and the output coords, for computing bounding boxes.
  // If re_invert is true, the input is inverted back to its original
  // photometric interpretation if inversion is attempted but fails to
  // improve the results. This ensures that outputs contains the correct
  // forward outputs for the best photometric interpretation.
  // inputs is filled with the used inputs to the network.
  bool RecognizeLine(const ImageData& image_data, bool invert, bool debug,
                     bool re_invert, bool upside_down, float* scale_factor,
                     NetworkIO* inputs, NetworkIO* outputs);

  // Converts an array of labels to utf-8, whether or not the labels are
  // augmented with character boundaries.
  STRING DecodeLabels(const GenericVector<int>& labels);

  // Displays the forward results in a window with the characters and
  // boundaries as determined by the labels and label_coords.
  void DisplayForward(const NetworkIO& inputs,
                      const GenericVector<int>& labels,
                      const GenericVector<int>& label_coords,
                      const char* window_name,
                      ScrollView** window);
  // Converts the network output to a sequence of labels. Outputs labels, scores
  // and start xcoords of each char, and each null_char_, with an additional
  // final xcoord for the end of the output.
  // The conversion method is determined by internal state.
  void LabelsFromOutputs(const NetworkIO& outputs, GenericVector<int>* labels,
                         GenericVector<int>* xcoords);

 protected:
  // Sets the random seed from the sample_iteration_;
  void SetRandomSeed() {
    inT64 seed = static_cast<inT64>(sample_iteration_) * 0x10000001;
    randomizer_.set_seed(seed);
    randomizer_.IntRand();
  }

  // Displays the labels and cuts at the corresponding xcoords.
  // Size of labels should match xcoords.
  void DisplayLSTMOutput(const GenericVector<int>& labels,
                         const GenericVector<int>& xcoords,
                         int height, ScrollView* window);

  // Prints debug output detailing the activation path that is implied by the
  // xcoords.
  void DebugActivationPath(const NetworkIO& outputs,
                           const GenericVector<int>& labels,
                           const GenericVector<int>& xcoords);

  // Prints debug output detailing activations and 2nd choice over a range
  // of positions.
  void DebugActivationRange(const NetworkIO& outputs, const char* label,
                            int best_choice, int x_start, int x_end);

  // As LabelsViaCTC except that this function constructs the best path that
  // contains only legal sequences of subcodes for recoder_.
  void LabelsViaReEncode(const NetworkIO& output, GenericVector<int>* labels,
                         GenericVector<int>* xcoords);
  // Converts the network output to a sequence of labels, with scores, using
  // the simple character model (each position is a char, and the null_char_ is
  // mainly intended for tail padding.)
  void LabelsViaSimpleText(const NetworkIO& output,
                           GenericVector<int>* labels,
                           GenericVector<int>* xcoords);

  // Returns a string corresponding to the label starting at start. Sets *end
  // to the next start and if non-null, *decoded to the unichar id.
  const char* DecodeLabel(const GenericVector<int>& labels, int start, int* end,
                          int* decoded);

  // Returns a string corresponding to a given single label id, falling back to
  // a default of ".." for part of a multi-label unichar-id.
  const char* DecodeSingleLabel(int label);

 protected:
  // The network hierarchy.
  Network* network_;
  // The unicharset. Only the unicharset element is serialized.
  // Has to be a CCUtil, so Dict can point to it.
  CCUtil ccutil_;
  // For backward compatibility, recoder_ is serialized iff
  // training_flags_ & TF_COMPRESS_UNICHARSET.
  // Further encode/decode ccutil_.unicharset's ids to simplify the unicharset.
  UnicharCompress recoder_;

  // ==Training parameters that are serialized to provide a record of them.==
  STRING network_str_;
  // Flags used to determine the training method of the network.
  // See enum TrainingFlags above.
  inT32 training_flags_;
  // Number of actual backward training steps used.
  inT32 training_iteration_;
  // Index into training sample set. sample_iteration >= training_iteration_.
  inT32 sample_iteration_;
  // Index in softmax of null character. May take the value UNICHAR_BROKEN or
  // ccutil_.unicharset.size().
  inT32 null_char_;
  // Learning rate and momentum multipliers of deltas in backprop.
  float learning_rate_;
  float momentum_;
  // Smoothing factor for 2nd moment of gradients.
  float adam_beta_;

  // === NOT SERIALIZED.
  TRand randomizer_;
  NetworkScratch scratch_space_;
  // Language model (optional) to use with the beam search.
  Dict* dict_;
  // Beam search held between uses to optimize memory allocation/use.
  RecodeBeamSearch* search_;

  // == Debugging parameters.==
  // Recognition debug display window.
  ScrollView* debug_win_;
};

}  // namespace tesseract.

#endif  // TESSERACT_LSTM_LSTMRECOGNIZER_H_