lstmtraining.cpp File Reference

#include "commontraining.h"
#include "lstmtester.h"
#include "lstmtrainer.h"
#include "params.h"
#include "strngs.h"
#include "tprintf.h"
#include "unicharset_training_utils.h"

Go to the source code of this file.

Functions
	INT_PARAM_FLAG (debug_interval, 0, "How often to display the alignment.")
	STRING_PARAM_FLAG (net_spec, "", "Network specification")
	INT_PARAM_FLAG (net_mode, 192, "Controls network behavior.")
	INT_PARAM_FLAG (perfect_sample_delay, 0, "How many imperfect samples between perfect ones.")
	DOUBLE_PARAM_FLAG (target_error_rate, 0.01, "Final error rate in percent.")
	DOUBLE_PARAM_FLAG (weight_range, 0.1, "Range of initial random weights.")
	DOUBLE_PARAM_FLAG (learning_rate, 10.0e-4, "Weight factor for new deltas.")
	DOUBLE_PARAM_FLAG (momentum, 0.5, "Decay factor for repeating deltas.")
	DOUBLE_PARAM_FLAG (adam_beta, 0.999, "Decay factor for repeating deltas.")
	INT_PARAM_FLAG (max_image_MB, 6000, "Max memory to use for images.")
	STRING_PARAM_FLAG (continue_from, "", "Existing model to extend")
	STRING_PARAM_FLAG (model_output, "lstmtrain", "Basename for output models")
	STRING_PARAM_FLAG (train_listfile, "", "File listing training files in lstmf training format.")
	STRING_PARAM_FLAG (eval_listfile, "", "File listing eval files in lstmf training format.")
	BOOL_PARAM_FLAG (stop_training, false, "Just convert the training model to a runtime model.")
	BOOL_PARAM_FLAG (convert_to_int, false, "Convert the recognition model to an integer model.")
	BOOL_PARAM_FLAG (sequential_training, false, "Use the training files sequentially instead of round-robin.")
	INT_PARAM_FLAG (append_index, -1, "Index in continue_from Network at which to" " attach the new network defined by net_spec")
	BOOL_PARAM_FLAG (debug_network, false, "Get info on distribution of weight values")
	INT_PARAM_FLAG (max_iterations, 0, "If set, exit after this many iterations")
	STRING_PARAM_FLAG (traineddata, "", "Combined Dawgs/Unicharset/Recoder for language model")
	STRING_PARAM_FLAG (old_traineddata, "", "When changing the character set, this specifies the old" " character set that is to be replaced")
	BOOL_PARAM_FLAG (randomly_rotate, false, "Train OSD and randomly turn training samples upside-down")
int	main (int argc, char **argv)

Variables
const int	kNumPagesPerBatch = 100

Function Documentation

BOOL_PARAM_FLAG() [1/5]

BOOL_PARAM_FLAG	(	stop_training	,
		false	,
		"Just convert the training model to a runtime model."
	)

BOOL_PARAM_FLAG() [2/5]

BOOL_PARAM_FLAG	(	convert_to_int	,
		false	,
		"Convert the recognition model to an integer model."
	)

BOOL_PARAM_FLAG() [3/5]

BOOL_PARAM_FLAG	(	sequential_training	,
		false	,
		"Use the training files sequentially instead of round-robin."
	)

BOOL_PARAM_FLAG() [4/5]

BOOL_PARAM_FLAG	(	debug_network	,
		false	,
		"Get info on distribution of weight values"
	)

BOOL_PARAM_FLAG() [5/5]

BOOL_PARAM_FLAG	(	randomly_rotate	,
		false	,
		"Train OSD and randomly turn training samples upside-down"
	)

DOUBLE_PARAM_FLAG() [1/5]

DOUBLE_PARAM_FLAG	(	target_error_rate	,
		0.	01,
		"Final error rate in percent."
	)

DOUBLE_PARAM_FLAG() [2/5]

DOUBLE_PARAM_FLAG	(	weight_range	,
		0.	1,
		"Range of initial random weights."
	)

DOUBLE_PARAM_FLAG() [3/5]

DOUBLE_PARAM_FLAG	(	learning_rate	,
		10.0e-	4,
		"Weight factor for new deltas."
	)

DOUBLE_PARAM_FLAG() [4/5]

DOUBLE_PARAM_FLAG	(	momentum	,
		0.	5,
		"Decay factor for repeating deltas."
	)

DOUBLE_PARAM_FLAG() [5/5]

DOUBLE_PARAM_FLAG	(	adam_beta	,
		0.	999,
		"Decay factor for repeating deltas."
	)

INT_PARAM_FLAG() [1/6]

INT_PARAM_FLAG	(	debug_interval	,
		0	,
		"How often to display the alignment."
	)

INT_PARAM_FLAG() [2/6]

INT_PARAM_FLAG	(	net_mode	,
		192	,
		"Controls network behavior."
	)

INT_PARAM_FLAG() [3/6]

INT_PARAM_FLAG	(	perfect_sample_delay	,
		0	,
		"How many imperfect samples between perfect ones."
	)

INT_PARAM_FLAG() [4/6]

INT_PARAM_FLAG	(	max_image_MB	,
		6000	,
		"Max memory to use for images."
	)

INT_PARAM_FLAG() [5/6]

INT_PARAM_FLAG	(	append_index	,
		-	1,
		"Index in continue_from Network at which to" " attach the new network defined by net_spec"
	)

INT_PARAM_FLAG() [6/6]

INT_PARAM_FLAG	(	max_iterations	,
		0	,
		"If	set,
		exit after this many iterations"
	)

main()

int main	(	int	argc,
		char **	argv
	)

This program reads in a text file consisting of feature samples from a training page in the following format:

   FontName UTF8-char-str xmin ymin xmax ymax page-number
    NumberOfFeatureTypes(N)
      FeatureTypeName1 NumberOfFeatures(M)
         Feature1
         ...
         FeatureM
      FeatureTypeName2 NumberOfFeatures(M)
         Feature1
         ...
         FeatureM
      ...
      FeatureTypeNameN NumberOfFeatures(M)
         Feature1
         ...
         FeatureM
   FontName CharName ...

The result of this program is a binary inttemp file used by the OCR engine.

Parameters

argc	number of command line arguments
argv	array of command line arguments

Returns

none

Note

Exceptions: none

History: Fri Aug 18 08:56:17 1989, DSJ, Created.

History: Mon May 18 1998, Christy Russson, Revistion started.

Definition at line 73 of file lstmtraining.cpp.

                                {
  ParseArguments(&argc, &argv);
  // Purify the model name in case it is based on the network string.
  if (FLAGS_model_output.empty()) {
    tprintf("Must provide a --model_output!\n");
    return 1;
  }
  if (FLAGS_traineddata.empty()) {
    tprintf("Must provide a --traineddata see training wiki\n");
    return 1;
  }
  STRING model_output = FLAGS_model_output.c_str();
  for (int i = 0; i < model_output.length(); ++i) {
    if (model_output[i] == '[' || model_output[i] == ']')
      model_output[i] = '-';
    if (model_output[i] == '(' || model_output[i] == ')')
      model_output[i] = '_';
  }
  // Setup the trainer.
  STRING checkpoint_file = FLAGS_model_output.c_str();
  checkpoint_file += "_checkpoint";
  STRING checkpoint_bak = checkpoint_file + ".bak";
  tesseract::LSTMTrainer trainer(
      nullptr, nullptr, nullptr, nullptr, FLAGS_model_output.c_str(),
      checkpoint_file.c_str(), FLAGS_debug_interval,
      static_cast<inT64>(FLAGS_max_image_MB) * 1048576);
  trainer.InitCharSet(FLAGS_traineddata.c_str());

  // Reading something from an existing model doesn't require many flags,
  // so do it now and exit.
  if (FLAGS_stop_training || FLAGS_debug_network) {
    if (!trainer.TryLoadingCheckpoint(FLAGS_continue_from.c_str(), nullptr)) {
      tprintf("Failed to read continue from: %s\n",
              FLAGS_continue_from.c_str());
      return 1;
    }
    if (FLAGS_debug_network) {
      trainer.DebugNetwork();
    } else {
      if (FLAGS_convert_to_int) trainer.ConvertToInt();
      if (!trainer.SaveTraineddata(FLAGS_model_output.c_str())) {
        tprintf("Failed to write recognition model : %s\n",
                FLAGS_model_output.c_str());
      }
    }
    return 0;
  }

  // Get the list of files to process.
  if (FLAGS_train_listfile.empty()) {
    tprintf("Must supply a list of training filenames! --train_listfile\n");
    return 1;
  }
  GenericVector<STRING> filenames;
  if (!tesseract::LoadFileLinesToStrings(FLAGS_train_listfile.c_str(),
                                         &filenames)) {
    tprintf("Failed to load list of training filenames from %s\n",
            FLAGS_train_listfile.c_str());
    return 1;
  }

  // Checkpoints always take priority if they are available.
  if (trainer.TryLoadingCheckpoint(checkpoint_file.string(), nullptr) ||
      trainer.TryLoadingCheckpoint(checkpoint_bak.string(), nullptr)) {
    tprintf("Successfully restored trainer from %s\n",
            checkpoint_file.string());
  } else {
    if (!FLAGS_continue_from.empty()) {
      // Load a past model file to improve upon.
      if (!trainer.TryLoadingCheckpoint(FLAGS_continue_from.c_str(),
                                        FLAGS_append_index >= 0
                                            ? FLAGS_continue_from.c_str()
                                            : FLAGS_old_traineddata.c_str())) {
        tprintf("Failed to continue from: %s\n", FLAGS_continue_from.c_str());
        return 1;
      }
      tprintf("Continuing from %s\n", FLAGS_continue_from.c_str());
      trainer.InitIterations();
    }
    if (FLAGS_continue_from.empty() || FLAGS_append_index >= 0) {
      if (FLAGS_append_index >= 0) {
        tprintf("Appending a new network to an old one!!");
        if (FLAGS_continue_from.empty()) {
          tprintf("Must set --continue_from for appending!\n");
          return 1;
        }
      }
      // We are initializing from scratch.
      if (!trainer.InitNetwork(FLAGS_net_spec.c_str(), FLAGS_append_index,
                               FLAGS_net_mode, FLAGS_weight_range,
                               FLAGS_learning_rate, FLAGS_momentum,
                               FLAGS_adam_beta)) {
        tprintf("Failed to create network from spec: %s\n",
                FLAGS_net_spec.c_str());
        return 1;
      }
      trainer.set_perfect_delay(FLAGS_perfect_sample_delay);
    }
  }
  if (!trainer.LoadAllTrainingData(filenames,
                                   FLAGS_sequential_training
                                       ? tesseract::CS_SEQUENTIAL
                                       : tesseract::CS_ROUND_ROBIN,
                                   FLAGS_randomly_rotate)) {
    tprintf("Load of images failed!!\n");
    return 1;
  }

  tesseract::LSTMTester tester(static_cast<inT64>(FLAGS_max_image_MB) *
                               1048576);
  tesseract::TestCallback tester_callback = nullptr;
  if (!FLAGS_eval_listfile.empty()) {
    if (!tester.LoadAllEvalData(FLAGS_eval_listfile.c_str())) {
      tprintf("Failed to load eval data from: %s\n",
              FLAGS_eval_listfile.c_str());
      return 1;
    }
    tester_callback =
        NewPermanentTessCallback(&tester, &tesseract::LSTMTester::RunEvalAsync);
  }
  do {
    // Train a few.
    int iteration = trainer.training_iteration();
    for (int target_iteration = iteration + kNumPagesPerBatch;
         iteration < target_iteration;
         iteration = trainer.training_iteration()) {
      trainer.TrainOnLine(&trainer, false);
    }
    STRING log_str;
    trainer.MaintainCheckpoints(tester_callback, &log_str);
    tprintf("%s\n", log_str.string());
  } while (trainer.best_error_rate() > FLAGS_target_error_rate &&
           (trainer.training_iteration() < FLAGS_max_iterations ||
            FLAGS_max_iterations == 0));
  delete tester_callback;
  tprintf("Finished! Error rate = %g\n", trainer.best_error_rate());
  return 0;
} /* main */

STRING_PARAM_FLAG() [1/7]

STRING_PARAM_FLAG	(	net_spec	,
		""	,
		"Network specification"
	)

STRING_PARAM_FLAG() [2/7]

STRING_PARAM_FLAG	(	continue_from	,
		""	,
		"Existing model to extend"
	)

STRING_PARAM_FLAG() [3/7]

STRING_PARAM_FLAG	(	model_output	,
		"lstmtrain"	,
		"Basename for output models"
	)

STRING_PARAM_FLAG() [4/7]

STRING_PARAM_FLAG	(	train_listfile	,
		""	,
		"File listing training files in lstmf training format."
	)

STRING_PARAM_FLAG() [5/7]

STRING_PARAM_FLAG	(	eval_listfile	,
		""	,
		"File listing eval files in lstmf training format."
	)

STRING_PARAM_FLAG() [6/7]

STRING_PARAM_FLAG	(	traineddata	,
		""	,
		"Combined Dawgs/Unicharset/Recoder for language model"
	)

STRING_PARAM_FLAG() [7/7]

STRING_PARAM_FLAG	(	old_traineddata	,
		""	,
		"When changing the character	set,
		this specifies the old" " character set that is to be replaced"
	)

Variable Documentation

kNumPagesPerBatch

const int kNumPagesPerBatch = 100

Definition at line 67 of file lstmtraining.cpp.