mirror of
https://github.com/tesseract-ocr/tesseract.git
synced 2024-12-13 16:09:04 +08:00
8f7be2e72c
Signed-off-by: Stefan Weil <sw@weilnetz.de>
489 lines
22 KiB
C++
489 lines
22 KiB
C++
///////////////////////////////////////////////////////////////////////
|
|
// 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_t 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
|
|
// 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 std::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,
|
|
bool randomly_rotate);
|
|
|
|
// 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_ : nullptr,
|
|
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 nullptr 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 != nullptr) {
|
|
Trainability trainable = TrainOnLine(image, batch);
|
|
if (trainable == UNENCODABLE || trainable == NOT_BOXED) {
|
|
return nullptr; // 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 {
|
|
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.
|
|
bool randomly_rotate_;
|
|
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_
|