ctc.h

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// File:        ctc.h
// Description: Slightly improved standard CTC to compute the targets.
// Author:      Ray Smith
// Created:     Wed Jul 13 15:17:06 PDT 2016
//
// (C) Copyright 2016, 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_CTC_H_
#define TESSERACT_LSTM_CTC_H_
 
#include "export.h"
#include "network.h"
#include "networkio.h"
#include "scrollview.h"
 
namespace tesseract {
 
// Class to encapsulate CTC and simple target generation.
class TESS_COMMON_TRAINING_API CTC {
public:
  // Normalizes the probabilities such that no target has a prob below min_prob,
  // and, provided that the initial total is at least min_total_prob, then all
  // probs will sum to 1, otherwise to sum/min_total_prob. The maximum output
  // probability is thus 1 - (num_classes-1)*min_prob.
  static void NormalizeProbs(NetworkIO *probs) {
    NormalizeProbs(probs->mutable_float_array());
  }
 
  // Builds a target using CTC. Slightly improved as follows:
  // Includes normalizations and clipping for stability.
  // labels should be pre-padded with nulls wherever desired, but they don't
  // have to be between all labels. Allows for multi-label codes with no
  // nulls between.
  // labels can be longer than the time sequence, but the total number of
  // essential labels (non-null plus nulls between equal labels) must not exceed
  // the number of timesteps in outputs.
  // outputs is the output of the network, and should have already been
  // normalized with NormalizeProbs.
  // On return targets is filled with the computed targets.
  // Returns false if there is insufficient time for the labels.
  static bool ComputeCTCTargets(const std::vector<int> &truth_labels, int null_char,
                                const GENERIC_2D_ARRAY<float> &outputs, NetworkIO *targets);
 
private:
  // Constructor is private as the instance only holds information specific to
  // the current labels, outputs etc, and is built by the static function.
  CTC(const std::vector<int> &labels, int null_char, const GENERIC_2D_ARRAY<float> &outputs);
 
  // Computes vectors of min and max label index for each timestep, based on
  // whether skippability of nulls makes it possible to complete a valid path.
  bool ComputeLabelLimits();
  // Computes targets based purely on the labels by spreading the labels evenly
  // over the available timesteps.
  void ComputeSimpleTargets(GENERIC_2D_ARRAY<float> *targets) const;
  // Computes mean positions and half widths of the simple targets by spreading
  // the labels even over the available timesteps.
  void ComputeWidthsAndMeans(std::vector<float> *half_widths, std::vector<int> *means) const;
  // Calculates and returns a suitable fraction of the simple targets to add
  // to the network outputs.
  float CalculateBiasFraction();
  // Runs the forward CTC pass, filling in log_probs.
  void Forward(GENERIC_2D_ARRAY<double> *log_probs) const;
  // Runs the backward CTC pass, filling in log_probs.
  void Backward(GENERIC_2D_ARRAY<double> *log_probs) const;
  // Normalizes and brings probs out of log space with a softmax over time.
  void NormalizeSequence(GENERIC_2D_ARRAY<double> *probs) const;
  // For each timestep computes the max prob for each class over all
  // instances of the class in the labels_, and sets the targets to
  // the max observed prob.
  void LabelsToClasses(const GENERIC_2D_ARRAY<double> &probs, NetworkIO *targets) const;
  // Normalizes the probabilities such that no target has a prob below min_prob,
  // and, provided that the initial total is at least min_total_prob, then all
  // probs will sum to 1, otherwise to sum/min_total_prob. The maximum output
  // probability is thus 1 - (num_classes-1)*min_prob.
  static void NormalizeProbs(GENERIC_2D_ARRAY<float> *probs);
  // Returns true if the label at index is a needed null.
  bool NeededNull(int index) const;
  // Returns exp(clipped(x)), clipping x to a reasonable range to prevent over/
  // underflow.
  static double ClippedExp(double x) {
    if (x < -kMaxExpArg_) {
      return exp(-kMaxExpArg_);
    }
    if (x > kMaxExpArg_) {
      return exp(kMaxExpArg_);
    }
    return exp(x);
  }
 
  // Minimum probability limit for softmax input to ctc_loss.
  static const float kMinProb_;
  // Maximum absolute argument to exp().
  static const double kMaxExpArg_;
  // Minimum probability for total prob in time normalization.
  static const double kMinTotalTimeProb_;
  // Minimum probability for total prob in final normalization.
  static const double kMinTotalFinalProb_;
 
  // The truth label indices that are to be matched to outputs_.
  const std::vector<int> &labels_;
  // The network outputs.
  GENERIC_2D_ARRAY<float> outputs_;
  // The null or "blank" label.
  int null_char_;
  // Number of timesteps in outputs_.
  int num_timesteps_;
  // Number of classes in outputs_.
  int num_classes_;
  // Number of labels in labels_.
  int num_labels_;
  // Min and max valid label indices for each timestep.
  std::vector<int> min_labels_;
  std::vector<int> max_labels_;
};
 
} // namespace tesseract
 
#endif // TESSERACT_LSTM_CTC_H_