Python2/PyMuPDF: mupdf-source/thirdparty/tesseract/src/training/unicharset/lstmtrainer.cpp comparison

comparison mupdf-source/thirdparty/tesseract/src/training/unicharset/lstmtrainer.cpp @ 2:b50eed0cc0ef upstream

ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4. The directory name has changed: no version number in the expanded directory now.

author	Franz Glasner <fzglas.hg@dom66.de>
date	Mon, 15 Sep 2025 11:43:07 +0200
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-:1d09e1dec1d9
+:b50eed0cc0ef
+///////////////////////////////////////////////////////////////////////
+// File:        lstmtrainer.cpp
+// Description: Top-level line trainer class for LSTM-based networks.
+// Author:      Ray Smith
+//
+// (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.
+///////////////////////////////////////////////////////////////////////
+#define _USE_MATH_DEFINES // needed to get definition of M_SQRT1_2
+// Include automatically generated configuration file if running autoconf.
+#ifdef HAVE_CONFIG_H
+#  include "config_auto.h"
+#endif
+#include <cmath>
+#include <iomanip>             // for std::setprecision
+#include <locale>              // for std::locale::classic
+#include <string>
+#include "lstmtrainer.h"
+#include <allheaders.h>
+#include "boxread.h"
+#include "ctc.h"
+#include "imagedata.h"
+#include "input.h"
+#include "networkbuilder.h"
+#include "ratngs.h"
+#include "recodebeam.h"
+#include "tprintf.h"
+namespace tesseract {
+// Min actual error rate increase to constitute divergence.
+const double kMinDivergenceRate = 50.0;
+// Min iterations since last best before acting on a stall.
+const int kMinStallIterations = 10000;
+// Fraction of current char error rate that sub_trainer_ has to be ahead
+// before we declare the sub_trainer_ a success and switch to it.
+const double kSubTrainerMarginFraction = 3.0 / 128;
+// Factor to reduce learning rate on divergence.
+const double kLearningRateDecay = M_SQRT1_2;
+// LR adjustment iterations.
+const int kNumAdjustmentIterations = 100;
+// How often to add data to the error_graph_.
+const int kErrorGraphInterval = 1000;
+// Number of training images to train between calls to MaintainCheckpoints.
+const int kNumPagesPerBatch = 100;
+// Min percent error rate to consider start-up phase over.
+const int kMinStartedErrorRate = 75;
+// Error rate at which to transition to stage 1.
+const double kStageTransitionThreshold = 10.0;
+// Confidence beyond which the truth is more likely wrong than the recognizer.
+const double kHighConfidence = 0.9375; // 15/16.
+// Fraction of weight sign-changing total to constitute a definite improvement.
+const double kImprovementFraction = 15.0 / 16.0;
+// Fraction of last written best to make it worth writing another.
+const double kBestCheckpointFraction = 31.0 / 32.0;
+#ifndef GRAPHICS_DISABLED
+// Scale factor for display of target activations of CTC.
+const int kTargetXScale = 5;
+const int kTargetYScale = 100;
+#endif // !GRAPHICS_DISABLED
+LSTMTrainer::LSTMTrainer()
+: randomly_rotate_(false), training_data_(0), sub_trainer_(nullptr) {
+EmptyConstructor();
+debug_interval_ = 0;
+}
+LSTMTrainer::LSTMTrainer(const std::string &model_base, const std::string &checkpoint_name,
+int debug_interval, int64_t max_memory)
+: randomly_rotate_(false),
+training_data_(max_memory),
+sub_trainer_(nullptr) {
+EmptyConstructor();
+debug_interval_ = debug_interval;
+model_base_ = model_base;
+checkpoint_name_ = checkpoint_name;
+}
+LSTMTrainer::~LSTMTrainer() {
+#ifndef GRAPHICS_DISABLED
+delete align_win_;
+delete target_win_;
+delete ctc_win_;
+delete recon_win_;
+#endif
+}
+// Tries to deserialize a trainer from the given file and silently returns
+// false in case of failure.
+bool LSTMTrainer::TryLoadingCheckpoint(const char *filename,
+const char *old_traineddata) {
+std::vector<char> data;
+if (!LoadDataFromFile(filename, &data)) {
+return false;
+}
+tprintf("Loaded file %s, unpacking...\n", filename);
+if (!ReadTrainingDump(data, *this)) {
+return false;
+}
+if (IsIntMode()) {
+tprintf("Error, %s is an integer (fast) model, cannot continue training\n",
+filename);
+return false;
+}
+if (((old_traineddata == nullptr || *old_traineddata == '\0') &&
+network_->NumOutputs() == recoder_.code_range()) ||
+filename == old_traineddata) {
+return true; // Normal checkpoint load complete.
+}
+tprintf("Code range changed from %d to %d!\n", network_->NumOutputs(),
+recoder_.code_range());
+if (old_traineddata == nullptr || *old_traineddata == '\0') {
+tprintf("Must supply the old traineddata for code conversion!\n");
+return false;
+}
+TessdataManager old_mgr;
+ASSERT_HOST(old_mgr.Init(old_traineddata));
+TFile fp;
+if (!old_mgr.GetComponent(TESSDATA_LSTM_UNICHARSET, &fp)) {
+return false;
+}
+UNICHARSET old_chset;
+if (!old_chset.load_from_file(&fp, false)) {
+return false;
+}
+if (!old_mgr.GetComponent(TESSDATA_LSTM_RECODER, &fp)) {
+return false;
+}
+UnicharCompress old_recoder;
+if (!old_recoder.DeSerialize(&fp)) {
+return false;
+}
+std::vector<int> code_map = MapRecoder(old_chset, old_recoder);
+// Set the null_char_ to the new value.
+int old_null_char = null_char_;
+SetNullChar();
+// Map the softmax(s) in the network.
+network_->RemapOutputs(old_recoder.code_range(), code_map);
+tprintf("Previous null char=%d mapped to %d\n", old_null_char, null_char_);
+return true;
+}
+// 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 LSTMTrainer::InitNetwork(const char *network_spec, int append_index,
+int net_flags, float weight_range,
+float learning_rate, float momentum,
+float adam_beta) {
+mgr_.SetVersionString(mgr_.VersionString() + ":" + network_spec);
+adam_beta_ = adam_beta;
+learning_rate_ = learning_rate;
+momentum_ = momentum;
+SetNullChar();
+if (!NetworkBuilder::InitNetwork(recoder_.code_range(), network_spec,
+append_index, net_flags, weight_range,
+&randomizer_, &network_)) {
+return false;
+}
+network_str_ += network_spec;
+tprintf("Built network:%s from request %s\n", network_->spec().c_str(),
+network_spec);
+tprintf(
+"Training parameters:\n  Debug interval = %d,"
+" weights = %g, learning rate = %g, momentum=%g\n",
+debug_interval_, weight_range, learning_rate_, momentum_);
+tprintf("null char=%d\n", null_char_);
+return true;
+}
+// Resets all the iteration counters for fine tuning or traininng a head,
+// where we want the error reporting to reset.
+void LSTMTrainer::InitIterations() {
+sample_iteration_ = 0;
+training_iteration_ = 0;
+learning_iteration_ = 0;
+prev_sample_iteration_ = 0;
+best_error_rate_ = 100.0;
+best_iteration_ = 0;
+worst_error_rate_ = 0.0;
+worst_iteration_ = 0;
+stall_iteration_ = kMinStallIterations;
+best_error_history_.clear();
+best_error_iterations_.clear();
+improvement_steps_ = kMinStallIterations;
+perfect_delay_ = 0;
+last_perfect_training_iteration_ = 0;
+for (int i = 0; i < ET_COUNT; ++i) {
+best_error_rates_[i] = 100.0;
+worst_error_rates_[i] = 0.0;
+error_buffers_[i].clear();
+error_buffers_[i].resize(kRollingBufferSize_);
+error_rates_[i] = 100.0;
+}
+error_rate_of_last_saved_best_ = kMinStartedErrorRate;
+}
+// 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 LSTMTrainer::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, std::string &results) {
+sample_iteration_ = iteration;
+NetworkIO fwd_outputs, targets;
+Trainability result =
+PrepareForBackward(trainingdata, &fwd_outputs, &targets);
+if (result == UNENCODABLE || result == HI_PRECISION_ERR || dict_ == nullptr) {
+return result;
+}
+// Encode/decode the truth to get the normalization.
+std::vector<int> truth_labels, ocr_labels, xcoords;
+ASSERT_HOST(EncodeString(trainingdata->transcription(), &truth_labels));
+// NO-dict error.
+RecodeBeamSearch base_search(recoder_, null_char_, SimpleTextOutput(),
+nullptr);
+base_search.Decode(fwd_outputs, 1.0, 0.0, RecodeBeamSearch::kMinCertainty,
+nullptr);
+base_search.ExtractBestPathAsLabels(&ocr_labels, &xcoords);
+std::string truth_text = DecodeLabels(truth_labels);
+std::string ocr_text = DecodeLabels(ocr_labels);
+double baseline_error = ComputeWordError(&truth_text, &ocr_text);
+results += "0,0=" + std::to_string(baseline_error);
+RecodeBeamSearch search(recoder_, null_char_, SimpleTextOutput(), dict_);
+for (double r = min_dict_ratio; r < max_dict_ratio; r += dict_ratio_step) {
+for (double c = min_cert_offset; c < max_cert_offset;
+c += cert_offset_step) {
+search.Decode(fwd_outputs, r, c, RecodeBeamSearch::kMinCertainty,
+nullptr);
+search.ExtractBestPathAsLabels(&ocr_labels, &xcoords);
+truth_text = DecodeLabels(truth_labels);
+ocr_text = DecodeLabels(ocr_labels);
+// This is destructive on both strings.
+double word_error = ComputeWordError(&truth_text, &ocr_text);
+if ((r == min_dict_ratio && c == min_cert_offset) ||
+!std::isfinite(word_error)) {
+std::string t = DecodeLabels(truth_labels);
+std::string o = DecodeLabels(ocr_labels);
+tprintf("r=%g, c=%g, truth=%s, ocr=%s, wderr=%g, truth[0]=%d\n", r, c,
+t.c_str(), o.c_str(), word_error, truth_labels[0]);
+}
+results += " " + std::to_string(r);
+results += "," + std::to_string(c);
+results += "=" + std::to_string(word_error);
+}
+}
+return result;
+}
+// Provides output on the distribution of weight values.
+void LSTMTrainer::DebugNetwork() {
+network_->DebugWeights();
+}
+// 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 LSTMTrainer::LoadAllTrainingData(const std::vector<std::string> &filenames,
+CachingStrategy cache_strategy,
+bool randomly_rotate) {
+randomly_rotate_ = randomly_rotate;
+training_data_.Clear();
+return training_data_.LoadDocuments(filenames, cache_strategy,
+LoadDataFromFile);
+}
+// Keeps track of best and locally worst char error_rate and launches tests
+// using tester, when a new min or max is reached.
+// Writes checkpoints at appropriate times and builds and returns a log message
+// to indicate progress. Returns false if nothing interesting happened.
+bool LSTMTrainer::MaintainCheckpoints(const TestCallback &tester,
+std::stringstream &log_msg) {
+PrepareLogMsg(log_msg);
+double error_rate = CharError();
+int iteration = learning_iteration();
+if (iteration >= stall_iteration_ &&
+error_rate > best_error_rate_ * (1.0 + kSubTrainerMarginFraction) &&
+best_error_rate_ < kMinStartedErrorRate && !best_trainer_.empty()) {
+// It hasn't got any better in a long while, and is a margin worse than the
+// best, so go back to the best model and try a different learning rate.
+StartSubtrainer(log_msg);
+}
+SubTrainerResult sub_trainer_result = STR_NONE;
+if (sub_trainer_ != nullptr) {
+sub_trainer_result = UpdateSubtrainer(log_msg);
+if (sub_trainer_result == STR_REPLACED) {
+// Reset the inputs, as we have overwritten *this.
+error_rate = CharError();
+iteration = learning_iteration();
+PrepareLogMsg(log_msg);
+}
+}
+bool result = true; // Something interesting happened.
+std::vector<char> rec_model_data;
+if (error_rate < best_error_rate_) {
+SaveRecognitionDump(&rec_model_data);
+log_msg << " New best BCER = " << error_rate;
+log_msg << UpdateErrorGraph(iteration, error_rate, rec_model_data, tester);
+// If sub_trainer_ is not nullptr, either *this beat it to a new best, or it
+// just overwrote *this. In either case, we have finished with it.
+sub_trainer_.reset();
+stall_iteration_ = learning_iteration() + kMinStallIterations;
+if (TransitionTrainingStage(kStageTransitionThreshold)) {
+log_msg << " Transitioned to stage " << CurrentTrainingStage();
+}
+SaveTrainingDump(NO_BEST_TRAINER, *this, &best_trainer_);
+if (error_rate < error_rate_of_last_saved_best_ * kBestCheckpointFraction) {
+std::string best_model_name = DumpFilename();
+if (!SaveDataToFile(best_trainer_, best_model_name.c_str())) {
+log_msg << " failed to write best model:";
+} else {
+log_msg << " wrote best model:";
+error_rate_of_last_saved_best_ = best_error_rate_;
+}
+log_msg << best_model_name;
+}
+} else if (error_rate > worst_error_rate_) {
+SaveRecognitionDump(&rec_model_data);
+log_msg << " New worst BCER = " << error_rate;
+log_msg << UpdateErrorGraph(iteration, error_rate, rec_model_data, tester);
+if (worst_error_rate_ > best_error_rate_ + kMinDivergenceRate &&
+best_error_rate_ < kMinStartedErrorRate && !best_trainer_.empty()) {
+// Error rate has ballooned. Go back to the best model.
+log_msg << "\nDivergence! ";
+// Copy best_trainer_ before reading it, as it will get overwritten.
+std::vector<char> revert_data(best_trainer_);
+if (ReadTrainingDump(revert_data, *this)) {
+LogIterations("Reverted to", log_msg);
+ReduceLearningRates(this, log_msg);
+} else {
+LogIterations("Failed to Revert at", log_msg);
+}
+// If it fails again, we will wait twice as long before reverting again.
+stall_iteration_ = iteration + 2 * (iteration - learning_iteration());
+// Re-save the best trainer with the new learning rates and stall
+// iteration.
+SaveTrainingDump(NO_BEST_TRAINER, *this, &best_trainer_);
+}
+} else {
+// Something interesting happened only if the sub_trainer_ was trained.
+result = sub_trainer_result != STR_NONE;
+}
+if (checkpoint_name_.length() > 0) {
+// Write a current checkpoint.
+std::vector<char> checkpoint;
+if (!SaveTrainingDump(FULL, *this, &checkpoint) ||
+!SaveDataToFile(checkpoint, checkpoint_name_.c_str())) {
+log_msg << " failed to write checkpoint.";
+} else {
+log_msg << " wrote checkpoint.";
+}
+}
+return result;
+}
+// Builds a string containing a progress message with current error rates.
+void LSTMTrainer::PrepareLogMsg(std::stringstream &log_msg) const {
+LogIterations("At", log_msg);
+log_msg << std::fixed << std::setprecision(3)
+<< ", mean rms=" << error_rates_[ET_RMS]
+<< "%, delta=" << error_rates_[ET_DELTA]
+<< "%, BCER train=" << error_rates_[ET_CHAR_ERROR]
+<< "%, BWER train=" << error_rates_[ET_WORD_RECERR]
+<< "%, skip ratio=" << error_rates_[ET_SKIP_RATIO] << "%,";
+}
+// Appends <intro_str> iteration learning_iteration()/training_iteration()/
+// sample_iteration() to the log_msg.
+void LSTMTrainer::LogIterations(const char *intro_str,
+std::stringstream &log_msg) const {
+log_msg << intro_str
+<< " iteration " << learning_iteration()
+<< "/" << training_iteration()
+<< "/" << sample_iteration();
+}
+// Returns true and increments the training_stage_ if the error rate has just
+// passed through the given threshold for the first time.
+bool LSTMTrainer::TransitionTrainingStage(float error_threshold) {
+if (best_error_rate_ < error_threshold &&
+training_stage_ + 1 < num_training_stages_) {
+++training_stage_;
+return true;
+}
+return false;
+}
+// Writes to the given file. Returns false in case of error.
+bool LSTMTrainer::Serialize(SerializeAmount serialize_amount,
+const TessdataManager *mgr, TFile *fp) const {
+if (!LSTMRecognizer::Serialize(mgr, fp)) {
+return false;
+}
+if (!fp->Serialize(&learning_iteration_)) {
+return false;
+}
+if (!fp->Serialize(&prev_sample_iteration_)) {
+return false;
+}
+if (!fp->Serialize(&perfect_delay_)) {
+return false;
+}
+if (!fp->Serialize(&last_perfect_training_iteration_)) {
+return false;
+}
+for (const auto &error_buffer : error_buffers_) {
+if (!fp->Serialize(error_buffer)) {
+return false;
+}
+}
+if (!fp->Serialize(&error_rates_[0], countof(error_rates_))) {
+return false;
+}
+if (!fp->Serialize(&training_stage_)) {
+return false;
+}
+uint8_t amount = serialize_amount;
+if (!fp->Serialize(&amount)) {
+return false;
+}
+if (serialize_amount == LIGHT) {
+return true; // We are done.
+}
+if (!fp->Serialize(&best_error_rate_)) {
+return false;
+}
+if (!fp->Serialize(&best_error_rates_[0], countof(best_error_rates_))) {
+return false;
+}
+if (!fp->Serialize(&best_iteration_)) {
+return false;
+}
+if (!fp->Serialize(&worst_error_rate_)) {
+return false;
+}
+if (!fp->Serialize(&worst_error_rates_[0], countof(worst_error_rates_))) {
+return false;
+}
+if (!fp->Serialize(&worst_iteration_)) {
+return false;
+}
+if (!fp->Serialize(&stall_iteration_)) {
+return false;
+}
+if (!fp->Serialize(best_model_data_)) {
+return false;
+}
+if (!fp->Serialize(worst_model_data_)) {
+return false;
+}
+if (serialize_amount != NO_BEST_TRAINER && !fp->Serialize(best_trainer_)) {
+return false;
+}
+std::vector<char> sub_data;
+if (sub_trainer_ != nullptr &&
+!SaveTrainingDump(LIGHT, *sub_trainer_, &sub_data)) {
+return false;
+}
+if (!fp->Serialize(sub_data)) {
+return false;
+}
+if (!fp->Serialize(best_error_history_)) {
+return false;
+}
+if (!fp->Serialize(best_error_iterations_)) {
+return false;
+}
+return fp->Serialize(&improvement_steps_);
+}
+// Reads from the given file. Returns false in case of error.
+// NOTE: It is assumed that the trainer is never read cross-endian.
+bool LSTMTrainer::DeSerialize(const TessdataManager *mgr, TFile *fp) {
+if (!LSTMRecognizer::DeSerialize(mgr, fp)) {
+return false;
+}
+if (!fp->DeSerialize(&learning_iteration_)) {
+// Special case. If we successfully decoded the recognizer, but fail here
+// then it means we were just given a recognizer, so issue a warning and
+// allow it.
+tprintf("Warning: LSTMTrainer deserialized an LSTMRecognizer!\n");
+learning_iteration_ = 0;
+network_->SetEnableTraining(TS_ENABLED);
+return true;
+}
+if (!fp->DeSerialize(&prev_sample_iteration_)) {
+return false;
+}
+if (!fp->DeSerialize(&perfect_delay_)) {
+return false;
+}
+if (!fp->DeSerialize(&last_perfect_training_iteration_)) {
+return false;
+}
+for (auto &error_buffer : error_buffers_) {
+if (!fp->DeSerialize(error_buffer)) {
+return false;
+}
+}
+if (!fp->DeSerialize(&error_rates_[0], countof(error_rates_))) {
+return false;
+}
+if (!fp->DeSerialize(&training_stage_)) {
+return false;
+}
+uint8_t amount;
+if (!fp->DeSerialize(&amount)) {
+return false;
+}
+if (amount == LIGHT) {
+return true; // Don't read the rest.
+}
+if (!fp->DeSerialize(&best_error_rate_)) {
+return false;
+}
+if (!fp->DeSerialize(&best_error_rates_[0], countof(best_error_rates_))) {
+return false;
+}
+if (!fp->DeSerialize(&best_iteration_)) {
+return false;
+}
+if (!fp->DeSerialize(&worst_error_rate_)) {
+return false;
+}
+if (!fp->DeSerialize(&worst_error_rates_[0], countof(worst_error_rates_))) {
+return false;
+}
+if (!fp->DeSerialize(&worst_iteration_)) {
+return false;
+}
+if (!fp->DeSerialize(&stall_iteration_)) {
+return false;
+}
+if (!fp->DeSerialize(best_model_data_)) {
+return false;
+}
+if (!fp->DeSerialize(worst_model_data_)) {
+return false;
+}
+if (amount != NO_BEST_TRAINER && !fp->DeSerialize(best_trainer_)) {
+return false;
+}
+std::vector<char> sub_data;
+if (!fp->DeSerialize(sub_data)) {
+return false;
+}
+if (sub_data.empty()) {
+sub_trainer_ = nullptr;
+} else {
+sub_trainer_ = std::make_unique<LSTMTrainer>();
+if (!ReadTrainingDump(sub_data, *sub_trainer_)) {
+return false;
+}
+}
+if (!fp->DeSerialize(best_error_history_)) {
+return false;
+}
+if (!fp->DeSerialize(best_error_iterations_)) {
+return false;
+}
+return fp->DeSerialize(&improvement_steps_);
+}
+// 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 LSTMTrainer::StartSubtrainer(std::stringstream &log_msg) {
+sub_trainer_ = std::make_unique<LSTMTrainer>();
+if (!ReadTrainingDump(best_trainer_, *sub_trainer_)) {
+log_msg << " Failed to revert to previous best for trial!";
+sub_trainer_.reset();
+} else {
+log_msg << " Trial sub_trainer_ from iteration "
+<< sub_trainer_->training_iteration();
+// Reduce learning rate so it doesn't diverge this time.
+sub_trainer_->ReduceLearningRates(this, log_msg);
+// If it fails again, we will wait twice as long before reverting again.
+int stall_offset =
+learning_iteration() - sub_trainer_->learning_iteration();
+stall_iteration_ = learning_iteration() + 2 * stall_offset;
+sub_trainer_->stall_iteration_ = stall_iteration_;
+// Re-save the best trainer with the new learning rates and stall iteration.
+SaveTrainingDump(NO_BEST_TRAINER, *sub_trainer_, &best_trainer_);
+}
+}
+// 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 LSTMTrainer::UpdateSubtrainer(std::stringstream &log_msg) {
+double training_error = CharError();
+double sub_error = sub_trainer_->CharError();
+double sub_margin = (training_error - sub_error) / sub_error;
+if (sub_margin >= kSubTrainerMarginFraction) {
+log_msg << " sub_trainer=" << sub_error
+<< " margin=" << 100.0 * sub_margin << "\n";
+// Catch up to current iteration.
+int end_iteration = training_iteration();
+while (sub_trainer_->training_iteration() < end_iteration &&
+sub_margin >= kSubTrainerMarginFraction) {
+int target_iteration =
+sub_trainer_->training_iteration() + kNumPagesPerBatch;
+while (sub_trainer_->training_iteration() < target_iteration) {
+sub_trainer_->TrainOnLine(this, false);
+}
+std::stringstream batch_log("Sub:");
+batch_log.imbue(std::locale::classic());
+sub_trainer_->PrepareLogMsg(batch_log);
+batch_log << "\n";
+tprintf("UpdateSubtrainer:%s", batch_log.str().c_str());
+log_msg << batch_log.str();
+sub_error = sub_trainer_->CharError();
+sub_margin = (training_error - sub_error) / sub_error;
+}
+if (sub_error < best_error_rate_ &&
+sub_margin >= kSubTrainerMarginFraction) {
+// The sub_trainer_ has won the race to a new best. Switch to it.
+std::vector<char> updated_trainer;
+SaveTrainingDump(LIGHT, *sub_trainer_, &updated_trainer);
+ReadTrainingDump(updated_trainer, *this);
+log_msg << " Sub trainer wins at iteration "
+<< training_iteration() << "\n";
+return STR_REPLACED;
+}
+return STR_UPDATED;
+}
+return STR_NONE;
+}
+// Reduces network learning rates, either for everything, or for layers
+// independently, according to NF_LAYER_SPECIFIC_LR.
+void LSTMTrainer::ReduceLearningRates(LSTMTrainer *samples_trainer,
+std::stringstream &log_msg) {
+if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) {
+int num_reduced = ReduceLayerLearningRates(
+kLearningRateDecay, kNumAdjustmentIterations, samples_trainer);
+log_msg << "\nReduced learning rate on layers: " << num_reduced;
+} else {
+ScaleLearningRate(kLearningRateDecay);
+log_msg << "\nReduced learning rate to :" << learning_rate_;
+}
+log_msg << "\n";
+}
+// 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 LSTMTrainer::ReduceLayerLearningRates(TFloat factor, int num_samples,
+LSTMTrainer *samples_trainer) {
+enum WhichWay {
+LR_DOWN, // Learning rate will go down by factor.
+LR_SAME, // Learning rate will stay the same.
+LR_COUNT // Size of arrays.
+};
+std::vector<std::string> layers = EnumerateLayers();
+int num_layers = layers.size();
+std::vector<int> num_weights(num_layers);
+std::vector<TFloat> bad_sums[LR_COUNT];
+std::vector<TFloat> ok_sums[LR_COUNT];
+for (int i = 0; i < LR_COUNT; ++i) {
+bad_sums[i].resize(num_layers, 0.0);
+ok_sums[i].resize(num_layers, 0.0);
+}
+auto momentum_factor = 1 / (1 - momentum_);
+std::vector<char> orig_trainer;
+samples_trainer->SaveTrainingDump(LIGHT, *this, &orig_trainer);
+for (int i = 0; i < num_layers; ++i) {
+Network *layer = GetLayer(layers[i]);
+num_weights[i] = layer->IsTraining() ? layer->num_weights() : 0;
+}
+int iteration = sample_iteration();
+for (int s = 0; s < num_samples; ++s) {
+// Which way will we modify the learning rate?
+for (int ww = 0; ww < LR_COUNT; ++ww) {
+// Transfer momentum to learning rate and adjust by the ww factor.
+auto ww_factor = momentum_factor;
+if (ww == LR_DOWN) {
+ww_factor *= factor;
+}
+// Make a copy of *this, so we can mess about without damaging anything.
+LSTMTrainer copy_trainer;
+samples_trainer->ReadTrainingDump(orig_trainer, copy_trainer);
+// Clear the updates, doing nothing else.
+copy_trainer.network_->Update(0.0, 0.0, 0.0, 0);
+// Adjust the learning rate in each layer.
+for (int i = 0; i < num_layers; ++i) {
+if (num_weights[i] == 0) {
+continue;
+}
+copy_trainer.ScaleLayerLearningRate(layers[i], ww_factor);
+}
+copy_trainer.SetIteration(iteration);
+// Train on the sample, but keep the update in updates_ instead of
+// applying to the weights.
+const ImageData *trainingdata =
+copy_trainer.TrainOnLine(samples_trainer, true);
+if (trainingdata == nullptr) {
+continue;
+}
+// We'll now use this trainer again for each layer.
+std::vector<char> updated_trainer;
+samples_trainer->SaveTrainingDump(LIGHT, copy_trainer, &updated_trainer);
+for (int i = 0; i < num_layers; ++i) {
+if (num_weights[i] == 0) {
+continue;
+}
+LSTMTrainer layer_trainer;
+samples_trainer->ReadTrainingDump(updated_trainer, layer_trainer);
+Network *layer = layer_trainer.GetLayer(layers[i]);
+// Update the weights in just the layer, using Adam if enabled.
+layer->Update(0.0, momentum_, adam_beta_,
+layer_trainer.training_iteration_ + 1);
+// Zero the updates matrix again.
+layer->Update(0.0, 0.0, 0.0, 0);
+// Train again on the same sample, again holding back the updates.
+layer_trainer.TrainOnLine(trainingdata, true);
+// Count the sign changes in the updates in layer vs in copy_trainer.
+float before_bad = bad_sums[ww][i];
+float before_ok = ok_sums[ww][i];
+layer->CountAlternators(*copy_trainer.GetLayer(layers[i]),
+&ok_sums[ww][i], &bad_sums[ww][i]);
+float bad_frac =
+bad_sums[ww][i] + ok_sums[ww][i] - before_bad - before_ok;
+if (bad_frac > 0.0f) {
+bad_frac = (bad_sums[ww][i] - before_bad) / bad_frac;
+}
+}
+}
+++iteration;
+}
+int num_lowered = 0;
+for (int i = 0; i < num_layers; ++i) {
+if (num_weights[i] == 0) {
+continue;
+}
+Network *layer = GetLayer(layers[i]);
+float lr = GetLayerLearningRate(layers[i]);
+TFloat total_down = bad_sums[LR_DOWN][i] + ok_sums[LR_DOWN][i];
+TFloat total_same = bad_sums[LR_SAME][i] + ok_sums[LR_SAME][i];
+TFloat frac_down = bad_sums[LR_DOWN][i] / total_down;
+TFloat frac_same = bad_sums[LR_SAME][i] / total_same;
+tprintf("Layer %d=%s: lr %g->%g%%, lr %g->%g%%", i, layer->name().c_str(),
+lr * factor, 100.0 * frac_down, lr, 100.0 * frac_same);
+if (frac_down < frac_same * kImprovementFraction) {
+tprintf(" REDUCED\n");
+ScaleLayerLearningRate(layers[i], factor);
+++num_lowered;
+} else {
+tprintf(" SAME\n");
+}
+}
+if (num_lowered == 0) {
+// Just lower everything to make sure.
+for (int i = 0; i < num_layers; ++i) {
+if (num_weights[i] > 0) {
+ScaleLayerLearningRate(layers[i], factor);
+++num_lowered;
+}
+}
+}
+return num_lowered;
+}
+// Converts the string to integer class labels, with appropriate null_char_s
+// in between if not in SimpleTextOutput mode. Returns false on failure.
+/* static */
+bool LSTMTrainer::EncodeString(const std::string &str,
+const UNICHARSET &unicharset,
+const UnicharCompress *recoder, bool simple_text,
+int null_char, std::vector<int> *labels) {
+if (str.c_str() == nullptr || str.length() <= 0) {
+tprintf("Empty truth string!\n");
+return false;
+}
+unsigned err_index;
+std::vector<int> internal_labels;
+labels->clear();
+if (!simple_text) {
+labels->push_back(null_char);
+}
+std::string cleaned = unicharset.CleanupString(str.c_str());
+if (unicharset.encode_string(cleaned.c_str(), true, &internal_labels, nullptr,
+&err_index)) {
+bool success = true;
+for (auto internal_label : internal_labels) {
+if (recoder != nullptr) {
+// Re-encode labels via recoder.
+RecodedCharID code;
+int len = recoder->EncodeUnichar(internal_label, &code);
+if (len > 0) {
+for (int j = 0; j < len; ++j) {
+labels->push_back(code(j));
+if (!simple_text) {
+labels->push_back(null_char);
+}
+}
+} else {
+success = false;
+err_index = 0;
+break;
+}
+} else {
+labels->push_back(internal_label);
+if (!simple_text) {
+labels->push_back(null_char);
+}
+}
+}
+if (success) {
+return true;
+}
+}
+tprintf("Encoding of string failed! Failure bytes:");
+while (err_index < cleaned.size()) {
+tprintf(" %x", cleaned[err_index++] & 0xff);
+}
+tprintf("\n");
+return false;
+}
+// Performs forward-backward on the given trainingdata.
+// Returns a Trainability enum to indicate the suitability of the sample.
+Trainability LSTMTrainer::TrainOnLine(const ImageData *trainingdata,
+bool batch) {
+NetworkIO fwd_outputs, targets;
+Trainability trainable =
+PrepareForBackward(trainingdata, &fwd_outputs, &targets);
+++sample_iteration_;
+if (trainable == UNENCODABLE || trainable == NOT_BOXED) {
+return trainable; // Sample was unusable.
+}
+bool debug =
+debug_interval_ > 0 && training_iteration() % debug_interval_ == 0;
+// Run backprop on the output.
+NetworkIO bp_deltas;
+if (network_->IsTraining() &&
+(trainable != PERFECT ||
+training_iteration() >
+last_perfect_training_iteration_ + perfect_delay_)) {
+network_->Backward(debug, targets, &scratch_space_, &bp_deltas);
+network_->Update(learning_rate_, batch ? -1.0f : momentum_, adam_beta_,
+training_iteration_ + 1);
+}
+#ifndef GRAPHICS_DISABLED
+if (debug_interval_ == 1 && debug_win_ != nullptr) {
+debug_win_->AwaitEvent(SVET_CLICK);
+}
+#endif // !GRAPHICS_DISABLED
+// Roll the memory of past means.
+RollErrorBuffers();
+return trainable;
+}
+// Prepares the ground truth, runs forward, and prepares the targets.
+// Returns a Trainability enum to indicate the suitability of the sample.
+Trainability LSTMTrainer::PrepareForBackward(const ImageData *trainingdata,
+NetworkIO *fwd_outputs,
+NetworkIO *targets) {
+if (trainingdata == nullptr) {
+tprintf("Null trainingdata.\n");
+return UNENCODABLE;
+}
+// Ensure repeatability of random elements even across checkpoints.
+bool debug =
+debug_interval_ > 0 && training_iteration() % debug_interval_ == 0;
+std::vector<int> truth_labels;
+if (!EncodeString(trainingdata->transcription(), &truth_labels)) {
+tprintf("Can't encode transcription: '%s' in language '%s'\n",
+trainingdata->transcription().c_str(),
+trainingdata->language().c_str());
+return UNENCODABLE;
+}
+bool upside_down = false;
+if (randomly_rotate_) {
+// This ensures consistent training results.
+SetRandomSeed();
+upside_down = randomizer_.SignedRand(1.0) > 0.0;
+if (upside_down) {
+// Modify the truth labels to match the rotation:
+// Apart from space and null, increment the label. This changes the
+// script-id to the same script-id but upside-down.
+// The labels need to be reversed in order, as the first is now the last.
+for (auto truth_label : truth_labels) {
+if (truth_label != UNICHAR_SPACE && truth_label != null_char_) {
+++truth_label;
+}
+}
+std::reverse(truth_labels.begin(), truth_labels.end());
+}
+}
+unsigned w = 0;
+while (w < truth_labels.size() &&
+(truth_labels[w] == UNICHAR_SPACE || truth_labels[w] == null_char_)) {
+++w;
+}
+if (w == truth_labels.size()) {
+tprintf("Blank transcription: %s\n", trainingdata->transcription().c_str());
+return UNENCODABLE;
+}
+float image_scale;
+NetworkIO inputs;
+bool invert = trainingdata->boxes().empty();
+if (!RecognizeLine(*trainingdata, invert ? 0.5f : 0.0f, debug, invert, upside_down,
+&image_scale, &inputs, fwd_outputs)) {
+tprintf("Image %s not trainable\n", trainingdata->imagefilename().c_str());
+return UNENCODABLE;
+}
+targets->Resize(*fwd_outputs, network_->NumOutputs());
+LossType loss_type = OutputLossType();
+if (loss_type == LT_SOFTMAX) {
+if (!ComputeTextTargets(*fwd_outputs, truth_labels, targets)) {
+tprintf("Compute simple targets failed for %s!\n",
+trainingdata->imagefilename().c_str());
+return UNENCODABLE;
+}
+} else if (loss_type == LT_CTC) {
+if (!ComputeCTCTargets(truth_labels, fwd_outputs, targets)) {
+tprintf("Compute CTC targets failed for %s!\n",
+trainingdata->imagefilename().c_str());
+return UNENCODABLE;
+}
+} else {
+tprintf("Logistic outputs not implemented yet!\n");
+return UNENCODABLE;
+}
+std::vector<int> ocr_labels;
+std::vector<int> xcoords;
+LabelsFromOutputs(*fwd_outputs, &ocr_labels, &xcoords);
+// CTC does not produce correct target labels to begin with.
+if (loss_type != LT_CTC) {
+LabelsFromOutputs(*targets, &truth_labels, &xcoords);
+}
+if (!DebugLSTMTraining(inputs, *trainingdata, *fwd_outputs, truth_labels,
+*targets)) {
+tprintf("Input width was %d\n", inputs.Width());
+return UNENCODABLE;
+}
+std::string ocr_text = DecodeLabels(ocr_labels);
+std::string truth_text = DecodeLabels(truth_labels);
+targets->SubtractAllFromFloat(*fwd_outputs);
+if (debug_interval_ != 0) {
+if (truth_text != ocr_text) {
+tprintf("Iteration %d: BEST OCR TEXT : %s\n", training_iteration(),
+ocr_text.c_str());
+}
+}
+double char_error = ComputeCharError(truth_labels, ocr_labels);
+double word_error = ComputeWordError(&truth_text, &ocr_text);
+double delta_error = ComputeErrorRates(*targets, char_error, word_error);
+if (debug_interval_ != 0) {
+tprintf("File %s line %d %s:\n", trainingdata->imagefilename().c_str(),
+trainingdata->page_number(), delta_error == 0.0 ? "(Perfect)" : "");
+}
+if (delta_error == 0.0) {
+return PERFECT;
+}
+if (targets->AnySuspiciousTruth(kHighConfidence)) {
+return HI_PRECISION_ERR;
+}
+return TRAINABLE;
+}
+// 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 LSTMTrainer::SaveTrainingDump(SerializeAmount serialize_amount,
+const LSTMTrainer &trainer,
+std::vector<char> *data) const {
+TFile fp;
+fp.OpenWrite(data);
+return trainer.Serialize(serialize_amount, &mgr_, &fp);
+}
+// Restores the model to *this.
+bool LSTMTrainer::ReadLocalTrainingDump(const TessdataManager *mgr,
+const char *data, int size) {
+if (size == 0) {
+tprintf("Warning: data size is 0 in LSTMTrainer::ReadLocalTrainingDump\n");
+return false;
+}
+TFile fp;
+fp.Open(data, size);
+return DeSerialize(mgr, &fp);
+}
+// Writes the full recognition traineddata to the given filename.
+bool LSTMTrainer::SaveTraineddata(const char *filename) {
+std::vector<char> recognizer_data;
+SaveRecognitionDump(&recognizer_data);
+mgr_.OverwriteEntry(TESSDATA_LSTM, &recognizer_data[0],
+recognizer_data.size());
+return mgr_.SaveFile(filename, SaveDataToFile);
+}
+// Writes the recognizer to memory, so that it can be used for testing later.
+void LSTMTrainer::SaveRecognitionDump(std::vector<char> *data) const {
+TFile fp;
+fp.OpenWrite(data);
+network_->SetEnableTraining(TS_TEMP_DISABLE);
+ASSERT_HOST(LSTMRecognizer::Serialize(&mgr_, &fp));
+network_->SetEnableTraining(TS_RE_ENABLE);
+}
+// Returns a suitable filename for a training dump, based on the model_base_,
+// best_error_rate_, best_iteration_ and training_iteration_.
+std::string LSTMTrainer::DumpFilename() const {
+std::stringstream filename;
+filename.imbue(std::locale::classic());
+filename << model_base_ << std::fixed << std::setprecision(3)
+<< "_" << best_error_rate_
+<< "_" << best_iteration_
+<< "_" << training_iteration_
+<< ".checkpoint";
+return filename.str();
+}
+// Fills the whole error buffer of the given type with the given value.
+void LSTMTrainer::FillErrorBuffer(double new_error, ErrorTypes type) {
+for (int i = 0; i < kRollingBufferSize_; ++i) {
+error_buffers_[type][i] = new_error;
+}
+error_rates_[type] = 100.0 * new_error;
+}
+// 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> LSTMTrainer::MapRecoder(
+const UNICHARSET &old_chset, const UnicharCompress &old_recoder) const {
+int num_new_codes = recoder_.code_range();
+int num_new_unichars = GetUnicharset().size();
+std::vector<int> code_map(num_new_codes, -1);
+for (int c = 0; c < num_new_codes; ++c) {
+int old_code = -1;
+// Find all new unichar_ids that recode to something that includes c.
+// The <= is to include the null char, which may be beyond the unicharset.
+for (int uid = 0; uid <= num_new_unichars; ++uid) {
+RecodedCharID codes;
+int length = recoder_.EncodeUnichar(uid, &codes);
+int code_index = 0;
+while (code_index < length && codes(code_index) != c) {
+++code_index;
+}
+if (code_index == length) {
+continue;
+}
+// The old unicharset must have the same unichar.
+int old_uid =
+uid < num_new_unichars
+? old_chset.unichar_to_id(GetUnicharset().id_to_unichar(uid))
+: old_chset.size() - 1;
+if (old_uid == INVALID_UNICHAR_ID) {
+continue;
+}
+// The encoding of old_uid at the same code_index is the old code.
+RecodedCharID old_codes;
+if (code_index < old_recoder.EncodeUnichar(old_uid, &old_codes)) {
+old_code = old_codes(code_index);
+break;
+}
+}
+code_map[c] = old_code;
+}
+return code_map;
+}
+// Private version of InitCharSet above finishes the job after initializing
+// the mgr_ data member.
+void LSTMTrainer::InitCharSet() {
+EmptyConstructor();
+training_flags_ = TF_COMPRESS_UNICHARSET;
+// Initialize the unicharset and recoder.
+if (!LoadCharsets(&mgr_)) {
+ASSERT_HOST(
+"Must provide a traineddata containing lstm_unicharset and"
+" lstm_recoder!\n" != nullptr);
+}
+SetNullChar();
+}
+// Helper computes and sets the null_char_.
+void LSTMTrainer::SetNullChar() {
+null_char_ = GetUnicharset().has_special_codes() ? UNICHAR_BROKEN
+: GetUnicharset().size();
+RecodedCharID code;
+recoder_.EncodeUnichar(null_char_, &code);
+null_char_ = code(0);
+}
+// Factored sub-constructor sets up reasonable default values.
+void LSTMTrainer::EmptyConstructor() {
+#ifndef GRAPHICS_DISABLED
+align_win_ = nullptr;
+target_win_ = nullptr;
+ctc_win_ = nullptr;
+recon_win_ = nullptr;
+#endif
+checkpoint_iteration_ = 0;
+training_stage_ = 0;
+num_training_stages_ = 2;
+InitIterations();
+}
+// 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 LSTMTrainer::DebugLSTMTraining(const NetworkIO &inputs,
+const ImageData &trainingdata,
+const NetworkIO &fwd_outputs,
+const std::vector<int> &truth_labels,
+const NetworkIO &outputs) {
+const std::string &truth_text = DecodeLabels(truth_labels);
+if (truth_text.c_str() == nullptr || truth_text.length() <= 0) {
+tprintf("Empty truth string at decode time!\n");
+return false;
+}
+if (debug_interval_ != 0) {
+// Get class labels, xcoords and string.
+std::vector<int> labels;
+std::vector<int> xcoords;
+LabelsFromOutputs(outputs, &labels, &xcoords);
+std::string text = DecodeLabels(labels);
+tprintf("Iteration %d: GROUND  TRUTH : %s\n", training_iteration(),
+truth_text.c_str());
+if (truth_text != text) {
+tprintf("Iteration %d: ALIGNED TRUTH : %s\n", training_iteration(),
+text.c_str());
+}
+if (debug_interval_ > 0 && training_iteration() % debug_interval_ == 0) {
+tprintf("TRAINING activation path for truth string %s\n",
+truth_text.c_str());
+DebugActivationPath(outputs, labels, xcoords);
+#ifndef GRAPHICS_DISABLED
+DisplayForward(inputs, labels, xcoords, "LSTMTraining", &align_win_);
+if (OutputLossType() == LT_CTC) {
+DisplayTargets(fwd_outputs, "CTC Outputs", &ctc_win_);
+DisplayTargets(outputs, "CTC Targets", &target_win_);
+}
+#endif
+}
+}
+return true;
+}
+#ifndef GRAPHICS_DISABLED
+// Displays the network targets as line a line graph.
+void LSTMTrainer::DisplayTargets(const NetworkIO &targets,
+const char *window_name, ScrollView **window) {
+int width = targets.Width();
+int num_features = targets.NumFeatures();
+Network::ClearWindow(true, window_name, width * kTargetXScale, kTargetYScale,
+window);
+for (int c = 0; c < num_features; ++c) {
+int color = c % (ScrollView::GREEN_YELLOW - 1) + 2;
+(*window)->Pen(static_cast<ScrollView::Color>(color));
+int start_t = -1;
+for (int t = 0; t < width; ++t) {
+double target = targets.f(t)[c];
+target *= kTargetYScale;
+if (target >= 1) {
+if (start_t < 0) {
+(*window)->SetCursor(t - 1, 0);
+start_t = t;
+}
+(*window)->DrawTo(t, target);
+} else if (start_t >= 0) {
+(*window)->DrawTo(t, 0);
+(*window)->DrawTo(start_t - 1, 0);
+start_t = -1;
+}
+}
+if (start_t >= 0) {
+(*window)->DrawTo(width, 0);
+(*window)->DrawTo(start_t - 1, 0);
+}
+}
+(*window)->Update();
+}
+#endif // !GRAPHICS_DISABLED
+// Builds a no-compromises target where the first positions should be the
+// truth labels and the rest is padded with the null_char_.
+bool LSTMTrainer::ComputeTextTargets(const NetworkIO &outputs,
+const std::vector<int> &truth_labels,
+NetworkIO *targets) {
+if (truth_labels.size() > targets->Width()) {
+tprintf("Error: transcription %s too long to fit into target of width %d\n",
+DecodeLabels(truth_labels).c_str(), targets->Width());
+return false;
+}
+int i = 0;
+for (auto truth_label : truth_labels) {
+targets->SetActivations(i, truth_label, 1.0);
+++i;
+}
+for (i = truth_labels.size(); i < targets->Width(); ++i) {
+targets->SetActivations(i, null_char_, 1.0);
+}
+return true;
+}
+// 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 LSTMTrainer::ComputeCTCTargets(const std::vector<int> &truth_labels,
+NetworkIO *outputs, NetworkIO *targets) {
+// Bottom-clip outputs to a minimum probability.
+CTC::NormalizeProbs(outputs);
+return CTC::ComputeCTCTargets(truth_labels, null_char_,
+outputs->float_array(), 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 LSTMTrainer::ComputeErrorRates(const NetworkIO &deltas,
+double char_error, double word_error) {
+UpdateErrorBuffer(ComputeRMSError(deltas), ET_RMS);
+// Delta error is the fraction of timesteps with >0.5 error in the top choice
+// score. If zero, then the top choice characters are guaranteed correct,
+// even when there is residue in the RMS error.
+double delta_error = ComputeWinnerError(deltas);
+UpdateErrorBuffer(delta_error, ET_DELTA);
+UpdateErrorBuffer(word_error, ET_WORD_RECERR);
+UpdateErrorBuffer(char_error, ET_CHAR_ERROR);
+// Skip ratio measures the difference between sample_iteration_ and
+// training_iteration_, which reflects the number of unusable samples,
+// usually due to unencodable truth text, or the text not fitting in the
+// space for the output.
+double skip_count = sample_iteration_ - prev_sample_iteration_;
+UpdateErrorBuffer(skip_count, ET_SKIP_RATIO);
+return delta_error;
+}
+// Computes the network activation RMS error rate.
+double LSTMTrainer::ComputeRMSError(const NetworkIO &deltas) {
+double total_error = 0.0;
+int width = deltas.Width();
+int num_classes = deltas.NumFeatures();
+for (int t = 0; t < width; ++t) {
+const float *class_errs = deltas.f(t);
+for (int c = 0; c < num_classes; ++c) {
+double error = class_errs[c];
+total_error += error * error;
+}
+}
+return sqrt(total_error / (width * num_classes));
+}
+// 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 LSTMTrainer::ComputeWinnerError(const NetworkIO &deltas) {
+int num_errors = 0;
+int width = deltas.Width();
+int num_classes = deltas.NumFeatures();
+for (int t = 0; t < width; ++t) {
+const float *class_errs = deltas.f(t);
+for (int c = 0; c < num_classes; ++c) {
+float abs_delta = std::fabs(class_errs[c]);
+// TODO(rays) Filtering cases where the delta is very large to cut out
+// GT errors doesn't work. Find a better way or get better truth.
+if (0.5 <= abs_delta) {
+++num_errors;
+}
+}
+}
+return static_cast<double>(num_errors) / width;
+}
+// Computes a very simple bag of chars char error rate.
+double LSTMTrainer::ComputeCharError(const std::vector<int> &truth_str,
+const std::vector<int> &ocr_str) {
+std::vector<int> label_counts(NumOutputs());
+unsigned truth_size = 0;
+for (auto ch : truth_str) {
+if (ch != null_char_) {
+++label_counts[ch];
+++truth_size;
+}
+}
+for (auto ch : ocr_str) {
+if (ch != null_char_) {
+--label_counts[ch];
+}
+}
+unsigned char_errors = 0;
+for (auto label_count : label_counts) {
+char_errors += abs(label_count);
+}
+// Limit BCER to interval [0,1] and avoid division by zero.
+if (truth_size <= char_errors) {
+return (char_errors == 0) ? 0.0 : 1.0;
+}
+return static_cast<double>(char_errors) / truth_size;
+}
+// Computes word recall error rate using a very simple bag of words algorithm.
+// NOTE that this is destructive on both input strings.
+double LSTMTrainer::ComputeWordError(std::string *truth_str,
+std::string *ocr_str) {
+using StrMap = std::unordered_map<std::string, int, std::hash<std::string>>;
+std::vector<std::string> truth_words = split(*truth_str, ' ');
+if (truth_words.empty()) {
+return 0.0;
+}
+std::vector<std::string> ocr_words = split(*ocr_str, ' ');
+StrMap word_counts;
+for (const auto &truth_word : truth_words) {
+std::string truth_word_string(truth_word.c_str());
+auto it = word_counts.find(truth_word_string);
+if (it == word_counts.end()) {
+word_counts.insert(std::make_pair(truth_word_string, 1));
+} else {
+++it->second;
+}
+}
+for (const auto &ocr_word : ocr_words) {
+std::string ocr_word_string(ocr_word.c_str());
+auto it = word_counts.find(ocr_word_string);
+if (it == word_counts.end()) {
+word_counts.insert(std::make_pair(ocr_word_string, -1));
+} else {
+--it->second;
+}
+}
+int word_recall_errs = 0;
+for (const auto &word_count : word_counts) {
+if (word_count.second > 0) {
+word_recall_errs += word_count.second;
+}
+}
+return static_cast<double>(word_recall_errs) / truth_words.size();
+}
+// Updates the error buffer and corresponding mean of the given type with
+// the new_error.
+void LSTMTrainer::UpdateErrorBuffer(double new_error, ErrorTypes type) {
+int index = training_iteration_ % kRollingBufferSize_;
+error_buffers_[type][index] = new_error;
+// Compute the mean error.
+int mean_count =
+std::min<int>(training_iteration_ + 1, error_buffers_[type].size());
+double buffer_sum = 0.0;
+for (int i = 0; i < mean_count; ++i) {
+buffer_sum += error_buffers_[type][i];
+}
+double mean = buffer_sum / mean_count;
+// Trim precision to 1/1000 of 1%.
+error_rates_[type] = IntCastRounded(100000.0 * mean) / 1000.0;
+}
+// Rolls error buffers and reports the current means.
+void LSTMTrainer::RollErrorBuffers() {
+prev_sample_iteration_ = sample_iteration_;
+if (NewSingleError(ET_DELTA) > 0.0) {
+++learning_iteration_;
+} else {
+last_perfect_training_iteration_ = training_iteration_;
+}
+++training_iteration_;
+if (debug_interval_ != 0) {
+tprintf("Mean rms=%g%%, delta=%g%%, train=%g%%(%g%%), skip ratio=%g%%\n",
+error_rates_[ET_RMS], error_rates_[ET_DELTA],
+error_rates_[ET_CHAR_ERROR], error_rates_[ET_WORD_RECERR],
+error_rates_[ET_SKIP_RATIO]);
+}
+}
+// Given that error_rate is either a new min or max, updates the best/worst
+// error rates, and record of progress.
+// Tester is an externally supplied callback function that tests on some
+// data set with a given model and records the error rates in a graph.
+std::string LSTMTrainer::UpdateErrorGraph(int iteration, double error_rate,
+const std::vector<char> &model_data,
+const TestCallback &tester) {
+if (error_rate > best_error_rate_ &&
+iteration < best_iteration_ + kErrorGraphInterval) {
+// Too soon to record a new point.
+if (tester != nullptr && !worst_model_data_.empty()) {
+mgr_.OverwriteEntry(TESSDATA_LSTM, &worst_model_data_[0],
+worst_model_data_.size());
+return tester(worst_iteration_, nullptr, mgr_, CurrentTrainingStage());
+} else {
+return "";
+}
+}
+std::string result;
+// NOTE: there are 2 asymmetries here:
+// 1. We are computing the global minimum, but the local maximum in between.
+// 2. If the tester returns an empty string, indicating that it is busy,
+//    call it repeatedly on new local maxima to test the previous min, but
+//    not the other way around, as there is little point testing the maxima
+//    between very frequent minima.
+if (error_rate < best_error_rate_) {
+// This is a new (global) minimum.
+if (tester != nullptr && !worst_model_data_.empty()) {
+mgr_.OverwriteEntry(TESSDATA_LSTM, &worst_model_data_[0],
+worst_model_data_.size());
+result = tester(worst_iteration_, worst_error_rates_, mgr_,
+CurrentTrainingStage());
+worst_model_data_.clear();
+best_model_data_ = model_data;
+}
+best_error_rate_ = error_rate;
+memcpy(best_error_rates_, error_rates_, sizeof(error_rates_));
+best_iteration_ = iteration;
+best_error_history_.push_back(error_rate);
+best_error_iterations_.push_back(iteration);
+// Compute 2% decay time.
+double two_percent_more = error_rate + 2.0;
+int i;
+for (i = best_error_history_.size() - 1;
+i >= 0 && best_error_history_[i] < two_percent_more; --i) {
+}
+int old_iteration = i >= 0 ? best_error_iterations_[i] : 0;
+improvement_steps_ = iteration - old_iteration;
+tprintf("2 Percent improvement time=%d, best error was %g @ %d\n",
+improvement_steps_, i >= 0 ? best_error_history_[i] : 100.0,
+old_iteration);
+} else if (error_rate > best_error_rate_) {
+// This is a new (local) maximum.
+if (tester != nullptr) {
+if (!best_model_data_.empty()) {
+mgr_.OverwriteEntry(TESSDATA_LSTM, &best_model_data_[0],
+best_model_data_.size());
+result = tester(best_iteration_, best_error_rates_, mgr_,
+CurrentTrainingStage());
+} else if (!worst_model_data_.empty()) {
+// Allow for multiple data points with "worst" error rate.
+mgr_.OverwriteEntry(TESSDATA_LSTM, &worst_model_data_[0],
+worst_model_data_.size());
+result = tester(worst_iteration_, worst_error_rates_, mgr_,
+CurrentTrainingStage());
+}
+if (result.length() > 0) {
+best_model_data_.clear();
+}
+worst_model_data_ = model_data;
+}
+}
+worst_error_rate_ = error_rate;
+memcpy(worst_error_rates_, error_rates_, sizeof(error_rates_));
+worst_iteration_ = iteration;
+return result;
+}
+} // namespace tesseract.

Mercurial > hgrepos > Python2 > PyMuPDF

comparison mupdf-source/thirdparty/tesseract/src/training/unicharset/lstmtrainer.cpp @ 2:b50eed0cc0ef upstream