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tesseract/src/training/unicharset/lstmtrainer.cpp
Stefan Weil d7c0a05ffa Remove Tensorflow support 
Tensorflow was never used because of missing models.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2024-11-07 13:40:43 +01:00

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///////////////////////////////////////////////////////////////////////
// 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.
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