tesseract/lstm/lstmtrainer.h

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///////////////////////////////////////////////////////////////////////
// File: lstmtrainer.h
// Description: Top-level line trainer class for LSTM-based networks.
// Author: Ray Smith
// Created: Fri May 03 09:07:06 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_LSTMTRAINER_H_
#define TESSERACT_LSTM_LSTMTRAINER_H_
#include "imagedata.h"
#include "lstmrecognizer.h"
#include "rect.h"
#include "tesscallback.h"
namespace tesseract {
class LSTM;
class LSTMTrainer;
class Parallel;
class Reversed;
class Softmax;
class Series;
// Enum for the types of errors that are counted.
enum ErrorTypes {
ET_RMS, // RMS activation error.
ET_DELTA, // Number of big errors in deltas.
ET_WORD_RECERR, // Output text string word recall error.
ET_CHAR_ERROR, // Output text string total char error.
ET_SKIP_RATIO, // Fraction of samples skipped.
ET_COUNT // For array sizing.
};
// Enum for the trainability_ flags.
enum Trainability {
TRAINABLE, // Non-zero delta error.
PERFECT, // Zero delta error.
UNENCODABLE, // Not trainable due to coding/alignment trouble.
HI_PRECISION_ERR, // Hi confidence disagreement.
NOT_BOXED, // Early in training and has no character boxes.
};
// Enum to define the amount of data to get serialized.
enum SerializeAmount {
LIGHT, // Minimal data for remote training.
NO_BEST_TRAINER, // Save an empty vector in place of best_trainer_.
FULL, // All data including best_trainer_.
};
// Enum to indicate how the sub_trainer_ training went.
enum SubTrainerResult {
STR_NONE, // Did nothing as not good enough.
STR_UPDATED, // Subtrainer was updated, but didn't replace *this.
STR_REPLACED // Subtrainer replaced *this.
};
class LSTMTrainer;
// Function to restore the trainer state from a given checkpoint.
// Returns false on failure.
typedef TessResultCallback2<bool, const GenericVector<char>&, LSTMTrainer*>*
CheckPointReader;
// Function to save a checkpoint of the current trainer state.
// Returns false on failure. SerializeAmount determines the amount of the
// trainer to serialize, typically used for saving the best state.
typedef TessResultCallback3<bool, SerializeAmount, const LSTMTrainer*,
GenericVector<char>*>* CheckPointWriter;
// Function to compute and record error rates on some external test set(s).
// Args are: iteration, mean errors, model, training stage.
// Returns a STRING containing logging information about the tests.
typedef TessResultCallback4<STRING, int, const double*, const TessdataManager&,
int>* TestCallback;
// Trainer class for LSTM networks. Most of the effort is in creating the
// ideal target outputs from the transcription. A box file is used if it is
// available, otherwise estimates of the char widths from the unicharset are
// used to guide a DP search for the best fit to the transcription.
class LSTMTrainer : public LSTMRecognizer {
public:
LSTMTrainer();
// Callbacks may be null, in which case defaults are used.
LSTMTrainer(FileReader file_reader, FileWriter file_writer,
CheckPointReader checkpoint_reader,
CheckPointWriter checkpoint_writer,
const char* model_base, const char* checkpoint_name,
int debug_interval, inT64 max_memory);
virtual ~LSTMTrainer();
// Tries to deserialize a trainer from the given file and silently returns
// false in case of failure. If old_traineddata is not null, then it is
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// assumed that the character set is to be re-mapped from old_traineddata to
// the new, with consequent change in weight matrices etc.
bool TryLoadingCheckpoint(const char* filename, const char* old_traineddata);
// Initializes the character set encode/decode mechanism directly from a
// previously setup traineddata containing dawgs, UNICHARSET and
// UnicharCompress. Note: Call before InitNetwork!
void InitCharSet(const string& traineddata_path) {
ASSERT_HOST(mgr_.Init(traineddata_path.c_str()));
InitCharSet();
}
void InitCharSet(const TessdataManager& mgr) {
mgr_ = mgr;
InitCharSet();
}
// Initializes the trainer with a network_spec in the network description
// net_flags control network behavior according to the NetworkFlags enum.
// There isn't really much difference between them - only where the effects
// are implemented.
// For other args see NetworkBuilder::InitNetwork.
// Note: Be sure to call InitCharSet before InitNetwork!
bool InitNetwork(const STRING& network_spec, int append_index, int net_flags,
float weight_range, float learning_rate, float momentum,
float adam_beta);
// Initializes a trainer from a serialized TFNetworkModel proto.
// Returns the global step of TensorFlow graph or 0 if failed.
// Building a compatible TF graph: See tfnetwork.proto.
int InitTensorFlowNetwork(const std::string& tf_proto);
// Resets all the iteration counters for fine tuning or training a head,
// where we want the error reporting to reset.
void InitIterations();
// Accessors.
double ActivationError() const {
return error_rates_[ET_DELTA];
}
double CharError() const { return error_rates_[ET_CHAR_ERROR]; }
const double* error_rates() const {
return error_rates_;
}
double best_error_rate() const {
return best_error_rate_;
}
int best_iteration() const {
return best_iteration_;
}
int learning_iteration() const { return learning_iteration_; }
int improvement_steps() const { return improvement_steps_; }
void set_perfect_delay(int delay) { perfect_delay_ = delay; }
const GenericVector<char>& best_trainer() const { return best_trainer_; }
// Returns the error that was just calculated by PrepareForBackward.
double NewSingleError(ErrorTypes type) const {
return error_buffers_[type][training_iteration() % kRollingBufferSize_];
}
// Returns the error that was just calculated by TrainOnLine. Since
// TrainOnLine rolls the error buffers, this is one further back than
// NewSingleError.
double LastSingleError(ErrorTypes type) const {
return error_buffers_[type]
[(training_iteration() + kRollingBufferSize_ - 1) %
kRollingBufferSize_];
}
const DocumentCache& training_data() const {
return training_data_;
}
DocumentCache* mutable_training_data() { return &training_data_; }
// If the training sample is usable, grid searches for the optimal
// dict_ratio/cert_offset, and returns the results in a string of space-
// separated triplets of ratio,offset=worderr.
Trainability GridSearchDictParams(
const ImageData* trainingdata, int iteration, double min_dict_ratio,
double dict_ratio_step, double max_dict_ratio, double min_cert_offset,
double cert_offset_step, double max_cert_offset, STRING* results);
// Provides output on the distribution of weight values.
void DebugNetwork();
// Loads a set of lstmf files that were created using the lstm.train config to
// tesseract into memory ready for training. Returns false if nothing was
// loaded.
bool LoadAllTrainingData(const GenericVector<STRING>& filenames,
CachingStrategy cache_strategy);
// Keeps track of best and locally worst error rate, using internally computed
// values. See MaintainCheckpointsSpecific for more detail.
bool MaintainCheckpoints(TestCallback tester, STRING* log_msg);
// Keeps track of best and locally worst error_rate (whatever it is) and
// launches tests using rec_model, when a new min or max is reached.
// Writes checkpoints using train_model at appropriate times and builds and
// returns a log message to indicate progress. Returns false if nothing
// interesting happened.
bool MaintainCheckpointsSpecific(int iteration,
const GenericVector<char>* train_model,
const GenericVector<char>* rec_model,
TestCallback tester, STRING* log_msg);
// Builds a string containing a progress message with current error rates.
void PrepareLogMsg(STRING* log_msg) const;
// Appends <intro_str> iteration learning_iteration()/training_iteration()/
// sample_iteration() to the log_msg.
void LogIterations(const char* intro_str, STRING* log_msg) const;
// TODO(rays) Add curriculum learning.
// Returns true and increments the training_stage_ if the error rate has just
// passed through the given threshold for the first time.
bool TransitionTrainingStage(float error_threshold);
// Returns the current training stage.
int CurrentTrainingStage() const { return training_stage_; }
// Writes to the given file. Returns false in case of error.
virtual bool Serialize(SerializeAmount serialize_amount,
const TessdataManager* mgr, TFile* fp) const;
// Reads from the given file. Returns false in case of error.
virtual bool DeSerialize(const TessdataManager* mgr, TFile* fp);
// De-serializes the saved best_trainer_ into sub_trainer_, and adjusts the
// learning rates (by scaling reduction, or layer specific, according to
// NF_LAYER_SPECIFIC_LR).
void StartSubtrainer(STRING* log_msg);
// While the sub_trainer_ is behind the current training iteration and its
// training error is at least kSubTrainerMarginFraction better than the
// current training error, trains the sub_trainer_, and returns STR_UPDATED if
// it did anything. If it catches up, and has a better error rate than the
// current best, as well as a margin over the current error rate, then the
// trainer in *this is replaced with sub_trainer_, and STR_REPLACED is
// returned. STR_NONE is returned if the subtrainer wasn't good enough to
// receive any training iterations.
SubTrainerResult UpdateSubtrainer(STRING* log_msg);
// Reduces network learning rates, either for everything, or for layers
// independently, according to NF_LAYER_SPECIFIC_LR.
void ReduceLearningRates(LSTMTrainer* samples_trainer, STRING* log_msg);
// Considers reducing the learning rate independently for each layer down by
// factor(<1), or leaving it the same, by double-training the given number of
// samples and minimizing the amount of changing of sign of weight updates.
// Even if it looks like all weights should remain the same, an adjustment
// will be made to guarantee a different result when reverting to an old best.
// Returns the number of layer learning rates that were reduced.
int ReduceLayerLearningRates(double factor, int num_samples,
LSTMTrainer* samples_trainer);
// Converts the string to integer class labels, with appropriate null_char_s
// in between if not in SimpleTextOutput mode. Returns false on failure.
bool EncodeString(const STRING& str, GenericVector<int>* labels) const {
return EncodeString(str, GetUnicharset(), IsRecoding() ? &recoder_ : NULL,
SimpleTextOutput(), null_char_, labels);
}
// Static version operates on supplied unicharset, encoder, simple_text.
static bool EncodeString(const STRING& str, const UNICHARSET& unicharset,
const UnicharCompress* recoder, bool simple_text,
int null_char, GenericVector<int>* labels);
// Performs forward-backward on the given trainingdata.
// Returns the sample that was used or NULL if the next sample was deemed
// unusable. samples_trainer could be this or an alternative trainer that
// holds the training samples.
const ImageData* TrainOnLine(LSTMTrainer* samples_trainer, bool batch) {
int sample_index = sample_iteration();
const ImageData* image =
samples_trainer->training_data_.GetPageBySerial(sample_index);
if (image != NULL) {
Trainability trainable = TrainOnLine(image, batch);
if (trainable == UNENCODABLE || trainable == NOT_BOXED) {
return NULL; // Sample was unusable.
}
} else {
++sample_iteration_;
}
return image;
}
Trainability TrainOnLine(const ImageData* trainingdata, bool batch);
// Prepares the ground truth, runs forward, and prepares the targets.
// Returns a Trainability enum to indicate the suitability of the sample.
Trainability PrepareForBackward(const ImageData* trainingdata,
NetworkIO* fwd_outputs, NetworkIO* targets);
// Writes the trainer to memory, so that the current training state can be
// restored. *this must always be the master trainer that retains the only
// copy of the training data and language model. trainer is the model that is
// actually serialized.
bool SaveTrainingDump(SerializeAmount serialize_amount,
const LSTMTrainer* trainer,
GenericVector<char>* data) const;
// Reads previously saved trainer from memory. *this must always be the
// master trainer that retains the only copy of the training data and
// language model. trainer is the model that is restored.
bool ReadTrainingDump(const GenericVector<char>& data,
LSTMTrainer* trainer) const {
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if (data.empty()) return false;
return ReadSizedTrainingDump(&data[0], data.size(), trainer);
}
bool ReadSizedTrainingDump(const char* data, int size,
LSTMTrainer* trainer) const {
return trainer->ReadLocalTrainingDump(&mgr_, data, size);
}
// Restores the model to *this.
bool ReadLocalTrainingDump(const TessdataManager* mgr, const char* data,
int size);
// Sets up the data for MaintainCheckpoints from a light ReadTrainingDump.
void SetupCheckpointInfo();
// Writes the full recognition traineddata to the given filename.
bool SaveTraineddata(const STRING& filename);
// Writes the recognizer to memory, so that it can be used for testing later.
void SaveRecognitionDump(GenericVector<char>* data) const;
// Returns a suitable filename for a training dump, based on the model_base_,
// the iteration and the error rates.
STRING DumpFilename() const;
// Fills the whole error buffer of the given type with the given value.
void FillErrorBuffer(double new_error, ErrorTypes type);
// Helper generates a map from each current recoder_ code (ie softmax index)
// to the corresponding old_recoder code, or -1 if there isn't one.
std::vector<int> MapRecoder(const UNICHARSET& old_chset,
const UnicharCompress& old_recoder) const;
protected:
// Private version of InitCharSet above finishes the job after initializing
// the mgr_ data member.
void InitCharSet();
// Helper computes and sets the null_char_.
void SetNullChar();
// Factored sub-constructor sets up reasonable default values.
void EmptyConstructor();
// Outputs the string and periodically displays the given network inputs
// as an image in the given window, and the corresponding labels at the
// corresponding x_starts.
// Returns false if the truth string is empty.
bool DebugLSTMTraining(const NetworkIO& inputs,
const ImageData& trainingdata,
const NetworkIO& fwd_outputs,
const GenericVector<int>& truth_labels,
const NetworkIO& outputs);
// Displays the network targets as line a line graph.
void DisplayTargets(const NetworkIO& targets, const char* window_name,
ScrollView** window);
// Builds a no-compromises target where the first positions should be the
// truth labels and the rest is padded with the null_char_.
bool ComputeTextTargets(const NetworkIO& outputs,
const GenericVector<int>& truth_labels,
NetworkIO* targets);
// Builds a target using standard CTC. truth_labels should be pre-padded with
// nulls wherever desired. They don't have to be between all labels.
// outputs is input-output, as it gets clipped to minimum probability.
bool ComputeCTCTargets(const GenericVector<int>& truth_labels,
NetworkIO* outputs, NetworkIO* targets);
// Computes network errors, and stores the results in the rolling buffers,
// along with the supplied text_error.
// Returns the delta error of the current sample (not running average.)
double ComputeErrorRates(const NetworkIO& deltas, double char_error,
double word_error);
// Computes the network activation RMS error rate.
double ComputeRMSError(const NetworkIO& deltas);
// Computes network activation winner error rate. (Number of values that are
// in error by >= 0.5 divided by number of time-steps.) More closely related
// to final character error than RMS, but still directly calculable from
// just the deltas. Because of the binary nature of the targets, zero winner
// error is a sufficient but not necessary condition for zero char error.
double ComputeWinnerError(const NetworkIO& deltas);
// Computes a very simple bag of chars char error rate.
double ComputeCharError(const GenericVector<int>& truth_str,
const GenericVector<int>& ocr_str);
// Computes a very simple bag of words word recall error rate.
// NOTE that this is destructive on both input strings.
double ComputeWordError(STRING* truth_str, STRING* ocr_str);
// Updates the error buffer and corresponding mean of the given type with
// the new_error.
void UpdateErrorBuffer(double new_error, ErrorTypes type);
// Rolls error buffers and reports the current means.
void RollErrorBuffers();
// Given that error_rate is either a new min or max, updates the best/worst
// error rates, and record of progress.
STRING UpdateErrorGraph(int iteration, double error_rate,
const GenericVector<char>& model_data,
TestCallback tester);
protected:
// Alignment display window.
ScrollView* align_win_;
// CTC target display window.
ScrollView* target_win_;
// CTC output display window.
ScrollView* ctc_win_;
// Reconstructed image window.
ScrollView* recon_win_;
// How often to display a debug image.
int debug_interval_;
// Iteration at which the last checkpoint was dumped.
int checkpoint_iteration_;
// Basename of files to save best models to.
STRING model_base_;
// Checkpoint filename.
STRING checkpoint_name_;
// Training data.
DocumentCache training_data_;
// Name to use when saving best_trainer_.
STRING best_model_name_;
// Number of available training stages.
int num_training_stages_;
// Checkpointing callbacks.
FileReader file_reader_;
FileWriter file_writer_;
// TODO(rays) These are pointers, and must be deleted. Switch to unique_ptr
// when we can commit to c++11.
CheckPointReader checkpoint_reader_;
CheckPointWriter checkpoint_writer_;
// ===Serialized data to ensure that a restart produces the same results.===
// These members are only serialized when serialize_amount != LIGHT.
// Best error rate so far.
double best_error_rate_;
// Snapshot of all error rates at best_iteration_.
double best_error_rates_[ET_COUNT];
// Iteration of best_error_rate_.
int best_iteration_;
// Worst error rate since best_error_rate_.
double worst_error_rate_;
// Snapshot of all error rates at worst_iteration_.
double worst_error_rates_[ET_COUNT];
// Iteration of worst_error_rate_.
int worst_iteration_;
// Iteration at which the process will be thought stalled.
int stall_iteration_;
// Saved recognition models for computing test error for graph points.
GenericVector<char> best_model_data_;
GenericVector<char> worst_model_data_;
// Saved trainer for reverting back to last known best.
GenericVector<char> best_trainer_;
// A subsidiary trainer running with a different learning rate until either
// *this or sub_trainer_ hits a new best.
LSTMTrainer* sub_trainer_;
// Error rate at which last best model was dumped.
float error_rate_of_last_saved_best_;
// Current stage of training.
int training_stage_;
// History of best error rate against iteration. Used for computing the
// number of steps to each 2% improvement.
GenericVector<double> best_error_history_;
GenericVector<int> best_error_iterations_;
// Number of iterations since the best_error_rate_ was 2% more than it is now.
int improvement_steps_;
// Number of iterations that yielded a non-zero delta error and thus provided
// significant learning. learning_iteration_ <= training_iteration_.
// learning_iteration_ is used to measure rate of learning progress.
int learning_iteration_;
// Saved value of sample_iteration_ before looking for the the next sample.
int prev_sample_iteration_;
// How often to include a PERFECT training sample in backprop.
// A PERFECT training sample is used if the current
// training_iteration_ > last_perfect_training_iteration_ + perfect_delay_,
// so with perfect_delay_ == 0, all samples are used, and with
// perfect_delay_ == 4, at most 1 in 5 samples will be perfect.
int perfect_delay_;
// Value of training_iteration_ at which the last PERFECT training sample
// was used in back prop.
int last_perfect_training_iteration_;
// Rolling buffers storing recent training errors are indexed by
// training_iteration % kRollingBufferSize_.
static const int kRollingBufferSize_ = 1000;
GenericVector<double> error_buffers_[ET_COUNT];
// Rounded mean percent trailing training errors in the buffers.
double error_rates_[ET_COUNT]; // RMS training error.
// Traineddata file with optional dawgs + UNICHARSET and recoder.
TessdataManager mgr_;
};
} // namespace tesseract.
#endif // TESSERACT_LSTM_LSTMTRAINER_H_