def render_report(model: str,
chars: int,
errors: int,
char_confusions: Counter,
scripts: Counter,
insertions: Counter,
deletions: int,
substitutions: Counter) -> str:
"""
Renders an accuracy report.
Args:
model (str): Model name.
errors (int): Number of errors on test set.
char_confusions (dict): Dictionary mapping a tuple (gt, pred) to a
number of occurrences.
scripts (dict): Dictionary counting character per script.
insertions (dict): Dictionary counting insertion operations per Unicode
script
deletions (int): Number of deletions
substitutions (dict): Dictionary counting substitution operations per
Unicode script.
Returns:
A string containing the rendered report.
"""
logger.info(f'Serializing report for {model}.')
report = {'model': model,
'chars': chars,
'errors': errors,
'accuracy': (chars-errors)/chars * 100,
'insertions': sum(insertions.values()),
'deletions': deletions,
'substitutions': sum(substitutions.values()),
'scripts': sorted([{'script': k,
'count': v,
'errors': insertions[k] + substitutions[k],
'accuracy': 100 * (v-(insertions[k] + substitutions[k]))/v} for k, v in scripts.items()],
key=lambda x: x['accuracy'],
reverse=True),
'counts': sorted([{'correct': make_printable(k[0]),
'generated': make_printable(k[1]),
'errors': v} for k, v in char_confusions.items() if k[0] != k[1]],
key=lambda x: x['errors'],
reverse=True)}
logger.debug('Initializing jinja environment.')
env = Environment(loader=PackageLoader('kraken', 'templates'),
trim_blocks=True,
lstrip_blocks=True,
autoescape=True)
logger.debug('Retrieving template.')
tmpl = env.get_template('report')
logger.debug('Rendering data.')
return tmpl.render(report=report)
def show(ctx, model_id):
"""
Retrieves model metadata from the repository.
"""
import unicodedata
from kraken import repo
from kraken.lib.util import make_printable, is_printable
desc = repo.get_description(model_id)
chars = []
combining = []
for char in sorted(desc['graphemes']):
if not is_printable(char):
combining.append(make_printable(char))
else:
chars.append(char)
message(
'name: {}\n\n{}\n\n{}\nscripts: {}\nalphabet: {} {}\nlicense: {}\nauthor: {} ({})\n{}'
.format(desc['name'], desc['summary'], desc['description'],
' '.join(desc['script']), ''.join(chars), ', '.join(combining),
desc['license'], desc['author'], desc['author-email'],
desc['url']))
ctx.exit(0)
def show(ctx, model_id):
"""
Retrieves model metadata from the repository.
"""
import unicodedata
from kraken import repo
from kraken.lib.util import make_printable, is_printable
desc = repo.get_description(model_id)
chars = []
combining = []
for char in sorted(desc['graphemes']):
if not is_printable(char):
combining.append(make_printable(char))
else:
chars.append(char)
message(
'name: {}\n\n{}\n\n{}\nscripts: {}\nalphabet: {} {}\naccuracy: {:.2f}%\nlicense: {}\nauthor(s): {}\ndate: {}'
.format(model_id, desc['summary'], desc['description'],
' '.join(desc['script']), ''.join(chars), ', '.join(combining),
desc['accuracy'], desc['license']['id'],
'; '.join(x['name'] for x in desc['creators']),
desc['publication_date']))
ctx.exit(0)
def show(ctx, model_id):
"""
Retrieves model metadata from the repository.
"""
import unicodedata
from kraken import repo
from kraken.lib.util import make_printable, is_printable
desc = repo.get_description(model_id)
chars = []
combining = []
for char in sorted(desc['graphemes']):
if not is_printable(char):
combining.append(make_printable(char))
else:
chars.append(char)
message('name: {}\n\n{}\n\n{}\nscripts: {}\nalphabet: {} {}\naccuracy: {:.2f}%\nlicense: {}\nauthor(s): {}\ndate: {}'.format(model_id,
desc['summary'],
desc['description'],
' '.join(desc['script']),
''.join(chars),
', '.join(combining),
desc['accuracy'],
desc['license']['id'],
'; '.join(x['name'] for x in desc['creators']),
desc['publication_date']))
ctx.exit(0)
def line_generator(ctx, font, maxlines, encoding, normalization, renormalize,
reorder, font_size, font_weight, language, max_length, strip,
disable_degradation, alpha, beta, distort, distortion_sigma,
legacy, output, text):
"""
Generates artificial text line training data.
"""
import errno
import numpy as np
from kraken import linegen
from kraken.lib.util import make_printable
lines: Set[str] = set()
if not text:
return
with log.progressbar(text, label='Reading texts') as bar:
for t in text:
with click.open_file(t, encoding=encoding) as fp:
logger.info('Reading {}'.format(t))
for l in fp:
lines.add(l.rstrip('\r\n'))
if normalization:
lines = set([unicodedata.normalize(normalization, line) for line in lines])
if strip:
lines = set([line.strip() for line in lines])
if max_length:
lines = set([line for line in lines if len(line) < max_length])
logger.info('Read {} lines'.format(len(lines)))
message('Read {} unique lines'.format(len(lines)))
if maxlines and maxlines < len(lines):
message('Sampling {} lines\t'.format(maxlines), nl=False)
llist = list(lines)
lines = set(llist[idx] for idx in np.random.randint(0, len(llist), maxlines))
message('\u2713', fg='green')
try:
os.makedirs(output)
except OSError as e:
if e.errno != errno.EEXIST:
raise
# calculate the alphabet and print it for verification purposes
alphabet: Set[str] = set()
for line in lines:
alphabet.update(line)
chars = []
combining = []
for char in sorted(alphabet):
k = make_printable(char)
if k != char:
combining.append(k)
else:
chars.append(k)
message('Σ (len: {})'.format(len(alphabet)))
message('Symbols: {}'.format(''.join(chars)))
if combining:
message('Combining Characters: {}'.format(', '.join(combining)))
lg = linegen.LineGenerator(font, font_size, font_weight, language)
with log.progressbar(lines, label='Writing images') as bar:
for idx, line in enumerate(bar):
logger.info(line)
try:
if renormalize:
im = lg.render_line(unicodedata.normalize(renormalize, line))
else:
im = lg.render_line(line)
except KrakenCairoSurfaceException as e:
logger.info('{}: {} {}'.format(e.message, e.width, e.height))
continue
if not disable_degradation and not legacy:
im = linegen.degrade_line(im, alpha=alpha, beta=beta)
im = linegen.distort_line(im, abs(np.random.normal(distort)), abs(np.random.normal(distortion_sigma)))
elif legacy:
im = linegen.ocropy_degrade(im)
im.save('{}/{:06d}.png'.format(output, idx))
with open('{}/{:06d}.gt.txt'.format(output, idx), 'wb') as fp:
if reorder:
fp.write(get_display(line).encode('utf-8'))
else:
fp.write(line.encode('utf-8'))
def train(ctx, pad, output, spec, append, load, freq, quit, epochs,
lag, min_delta, device, optimizer, lrate, momentum, weight_decay,
schedule, partition, normalization, normalize_whitespace, codec,
resize, reorder, training_files, evaluation_files, preload, threads,
ground_truth):
"""
Trains a model from image-text pairs.
"""
if not load and append:
raise click.BadOptionUsage('append', 'append option requires loading an existing model')
if resize != 'fail' and not load:
raise click.BadOptionUsage('resize', 'resize option requires loading an existing model')
import re
import torch
import shutil
import numpy as np
from torch.utils.data import DataLoader
from kraken.lib import models, vgsl, train
from kraken.lib.util import make_printable
from kraken.lib.train import EarlyStopping, EpochStopping, TrainStopper, TrainScheduler, add_1cycle
from kraken.lib.codec import PytorchCodec
from kraken.lib.dataset import GroundTruthDataset, generate_input_transforms
logger.info('Building ground truth set from {} line images'.format(len(ground_truth) + len(training_files)))
completed_epochs = 0
# load model if given. if a new model has to be created we need to do that
# after data set initialization, otherwise to output size is still unknown.
nn = None
#hyper_fields = ['freq', 'quit', 'epochs', 'lag', 'min_delta', 'optimizer', 'lrate', 'momentum', 'weight_decay', 'schedule', 'partition', 'normalization', 'normalize_whitespace', 'reorder', 'preload', 'completed_epochs', 'output']
if load:
logger.info('Loading existing model from {} '.format(load))
message('Loading existing model from {}'.format(load), nl=False)
nn = vgsl.TorchVGSLModel.load_model(load)
#if nn.user_metadata and load_hyper_parameters:
# for param in hyper_fields:
# if param in nn.user_metadata:
# logger.info('Setting \'{}\' to \'{}\''.format(param, nn.user_metadata[param]))
# message('Setting \'{}\' to \'{}\''.format(param, nn.user_metadata[param]))
# locals()[param] = nn.user_metadata[param]
message('\u2713', fg='green', nl=False)
# preparse input sizes from vgsl string to seed ground truth data set
# sizes and dimension ordering.
if not nn:
spec = spec.strip()
if spec[0] != '[' or spec[-1] != ']':
raise click.BadOptionUsage('spec', 'VGSL spec {} not bracketed'.format(spec))
blocks = spec[1:-1].split(' ')
m = re.match(r'(\d+),(\d+),(\d+),(\d+)', blocks[0])
if not m:
raise click.BadOptionUsage('spec', 'Invalid input spec {}'.format(blocks[0]))
batch, height, width, channels = [int(x) for x in m.groups()]
else:
batch, channels, height, width = nn.input
try:
transforms = generate_input_transforms(batch, height, width, channels, pad)
except KrakenInputException as e:
raise click.BadOptionUsage('spec', str(e))
# disable automatic partition when given evaluation set explicitly
if evaluation_files:
partition = 1
ground_truth = list(ground_truth)
# merge training_files into ground_truth list
if training_files:
ground_truth.extend(training_files)
if len(ground_truth) == 0:
raise click.UsageError('No training data was provided to the train command. Use `-t` or the `ground_truth` argument.')
np.random.shuffle(ground_truth)
if len(ground_truth) > 2500 and not preload:
logger.info('Disabling preloading for large (>2500) training data set. Enable by setting --preload parameter')
preload = False
# implicit preloading enabled for small data sets
if preload is None:
preload = True
tr_im = ground_truth[:int(len(ground_truth) * partition)]
if evaluation_files:
logger.debug('Using {} lines from explicit eval set'.format(len(evaluation_files)))
te_im = evaluation_files
else:
te_im = ground_truth[int(len(ground_truth) * partition):]
logger.debug('Taking {} lines from training for evaluation'.format(len(te_im)))
# set multiprocessing tensor sharing strategy
if 'file_system' in torch.multiprocessing.get_all_sharing_strategies():
logger.debug('Setting multiprocessing tensor sharing strategy to file_system')
torch.multiprocessing.set_sharing_strategy('file_system')
gt_set = GroundTruthDataset(normalization=normalization,
whitespace_normalization=normalize_whitespace,
reorder=reorder,
im_transforms=transforms,
preload=preload)
with log.progressbar(tr_im, label='Building training set') as bar:
for im in bar:
logger.debug('Adding line {} to training set'.format(im))
try:
gt_set.add(im)
except FileNotFoundError as e:
logger.warning('{}: {}. Skipping.'.format(e.strerror, e.filename))
except KrakenInputException as e:
logger.warning(str(e))
val_set = GroundTruthDataset(normalization=normalization,
whitespace_normalization=normalize_whitespace,
reorder=reorder,
im_transforms=transforms,
preload=preload)
with log.progressbar(te_im, label='Building validation set') as bar:
for im in bar:
logger.debug('Adding line {} to validation set'.format(im))
try:
val_set.add(im)
except FileNotFoundError as e:
logger.warning('{}: {}. Skipping.'.format(e.strerror, e.filename))
except KrakenInputException as e:
logger.warning(str(e))
logger.info('Training set {} lines, validation set {} lines, alphabet {} symbols'.format(len(gt_set._images), len(val_set._images), len(gt_set.alphabet)))
alpha_diff_only_train = set(gt_set.alphabet).difference(set(val_set.alphabet))
alpha_diff_only_val = set(val_set.alphabet).difference(set(gt_set.alphabet))
if alpha_diff_only_train:
logger.warning('alphabet mismatch: chars in training set only: {} (not included in accuracy test during training)'.format(alpha_diff_only_train))
if alpha_diff_only_val:
logger.warning('alphabet mismatch: chars in validation set only: {} (not trained)'.format(alpha_diff_only_val))
logger.info('grapheme\tcount')
for k, v in sorted(gt_set.alphabet.items(), key=lambda x: x[1], reverse=True):
char = make_printable(k)
if char == k:
char = '\t' + char
logger.info(u'{}\t{}'.format(char, v))
logger.debug('Encoding training set')
# use model codec when given
if append:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
gt_set.encode(codec)
message('Slicing and dicing model ', nl=False)
# now we can create a new model
spec = '[{} O1c{}]'.format(spec[1:-1], gt_set.codec.max_label()+1)
logger.info('Appending {} to existing model {} after {}'.format(spec, nn.spec, append))
nn.append(append, spec)
nn.add_codec(gt_set.codec)
message('\u2713', fg='green')
logger.info('Assembled model spec: {}'.format(nn.spec))
elif load:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
# prefer explicitly given codec over network codec if mode is 'both'
codec = codec if (codec and resize == 'both') else nn.codec
try:
gt_set.encode(codec)
except KrakenEncodeException as e:
message('Network codec not compatible with training set')
alpha_diff = set(gt_set.alphabet).difference(set(codec.c2l.keys()))
if resize == 'fail':
logger.error('Training data and model codec alphabets mismatch: {}'.format(alpha_diff))
ctx.exit(code=1)
elif resize == 'add':
message('Adding missing labels to network ', nl=False)
logger.info('Resizing codec to include {} new code points'.format(len(alpha_diff)))
codec.c2l.update({k: [v] for v, k in enumerate(alpha_diff, start=codec.max_label()+1)})
nn.add_codec(PytorchCodec(codec.c2l))
logger.info('Resizing last layer in network to {} outputs'.format(codec.max_label()+1))
nn.resize_output(codec.max_label()+1)
gt_set.encode(nn.codec)
message('\u2713', fg='green')
elif resize == 'both':
message('Fitting network exactly to training set ', nl=False)
logger.info('Resizing network or given codec to {} code sequences'.format(len(gt_set.alphabet)))
gt_set.encode(None)
ncodec, del_labels = codec.merge(gt_set.codec)
logger.info('Deleting {} output classes from network ({} retained)'.format(len(del_labels), len(codec)-len(del_labels)))
gt_set.encode(ncodec)
nn.resize_output(ncodec.max_label()+1, del_labels)
message('\u2713', fg='green')
else:
raise click.BadOptionUsage('resize', 'Invalid resize value {}'.format(resize))
else:
gt_set.encode(codec)
logger.info('Creating new model {} with {} outputs'.format(spec, gt_set.codec.max_label()+1))
spec = '[{} O1c{}]'.format(spec[1:-1], gt_set.codec.max_label()+1)
nn = vgsl.TorchVGSLModel(spec)
# initialize weights
message('Initializing model ', nl=False)
nn.init_weights()
nn.add_codec(gt_set.codec)
# initialize codec
message('\u2713', fg='green')
# half the number of data loading processes if device isn't cuda and we haven't enabled preloading
if device == 'cpu' and not preload:
loader_threads = threads // 2
else:
loader_threads = threads
train_loader = DataLoader(gt_set, batch_size=1, shuffle=True, num_workers=loader_threads, pin_memory=True)
threads -= loader_threads
# don't encode validation set as the alphabets may not match causing encoding failures
val_set.training_set = list(zip(val_set._images, val_set._gt))
logger.debug('Constructing {} optimizer (lr: {}, momentum: {})'.format(optimizer, lrate, momentum))
# set mode to trainindg
nn.train()
# set number of OpenMP threads
logger.debug('Set OpenMP threads to {}'.format(threads))
nn.set_num_threads(threads)
logger.debug('Moving model to device {}'.format(device))
optim = getattr(torch.optim, optimizer)(nn.nn.parameters(), lr=0)
if 'accuracy' not in nn.user_metadata:
nn.user_metadata['accuracy'] = []
tr_it = TrainScheduler(optim)
if schedule == '1cycle':
add_1cycle(tr_it, int(len(gt_set) * epochs), lrate, momentum, momentum - 0.10, weight_decay)
else:
# constant learning rate scheduler
tr_it.add_phase(1, (lrate, lrate), (momentum, momentum), weight_decay, train.annealing_const)
if quit == 'early':
st_it = EarlyStopping(min_delta, lag)
elif quit == 'dumb':
st_it = EpochStopping(epochs - completed_epochs)
else:
raise click.BadOptionUsage('quit', 'Invalid training interruption scheme {}'.format(quit))
#for param in hyper_fields:
# logger.debug('Setting \'{}\' to \'{}\' in model metadata'.format(param, locals()[param]))
# nn.user_metadata[param] = locals()[param]
trainer = train.KrakenTrainer(model=nn,
optimizer=optim,
device=device,
filename_prefix=output,
event_frequency=freq,
train_set=train_loader,
val_set=val_set,
stopper=st_it)
trainer.add_lr_scheduler(tr_it)
with log.progressbar(label='stage {}/{}'.format(1, trainer.stopper.epochs if trainer.stopper.epochs > 0 else '∞'),
length=trainer.event_it, show_pos=True) as bar:
def _draw_progressbar():
bar.update(1)
def _print_eval(epoch, accuracy, chars, error):
message('Accuracy report ({}) {:0.4f} {} {}'.format(epoch, accuracy, chars, error))
# reset progress bar
bar.label = 'stage {}/{}'.format(epoch+1, trainer.stopper.epochs if trainer.stopper.epochs > 0 else '∞')
bar.pos = 0
bar.finished = False
trainer.run(_print_eval, _draw_progressbar)
if quit == 'early':
message('Moving best model {0}_{1}.mlmodel ({2}) to {0}_best.mlmodel'.format(output, trainer.stopper.best_epoch, trainer.stopper.best_loss))
logger.info('Moving best model {0}_{1}.mlmodel ({2}) to {0}_best.mlmodel'.format(output, trainer.stopper.best_epoch, trainer.stopper.best_loss))
shutil.copy('{}_{}.mlmodel'.format(output, trainer.stopper.best_epoch), '{}_best.mlmodel'.format(output))
def train(ctx, pad, output, spec, append, load, freq, quit, epochs, lag,
min_delta, device, optimizer, lrate, momentum, weight_decay,
schedule, partition, normalization, normalize_whitespace, codec,
resize, reorder, training_files, evaluation_files, preload, threads,
ground_truth):
"""
Trains a model from image-text pairs.
"""
if not load and append:
raise click.BadOptionUsage(
'append', 'append option requires loading an existing model')
if resize != 'fail' and not load:
raise click.BadOptionUsage(
'resize', 'resize option requires loading an existing model')
import re
import torch
import shutil
import numpy as np
from torch.utils.data import DataLoader
from kraken.lib import models, vgsl, train
from kraken.lib.util import make_printable
from kraken.lib.train import EarlyStopping, EpochStopping, TrainStopper, TrainScheduler, add_1cycle
from kraken.lib.codec import PytorchCodec
from kraken.lib.dataset import GroundTruthDataset, generate_input_transforms
logger.info('Building ground truth set from {} line images'.format(
len(ground_truth) + len(training_files)))
completed_epochs = 0
# load model if given. if a new model has to be created we need to do that
# after data set initialization, otherwise to output size is still unknown.
nn = None
#hyper_fields = ['freq', 'quit', 'epochs', 'lag', 'min_delta', 'optimizer', 'lrate', 'momentum', 'weight_decay', 'schedule', 'partition', 'normalization', 'normalize_whitespace', 'reorder', 'preload', 'completed_epochs', 'output']
if load:
logger.info('Loading existing model from {} '.format(load))
message('Loading existing model from {}'.format(load), nl=False)
nn = vgsl.TorchVGSLModel.load_model(load)
#if nn.user_metadata and load_hyper_parameters:
# for param in hyper_fields:
# if param in nn.user_metadata:
# logger.info('Setting \'{}\' to \'{}\''.format(param, nn.user_metadata[param]))
# message('Setting \'{}\' to \'{}\''.format(param, nn.user_metadata[param]))
# locals()[param] = nn.user_metadata[param]
message('\u2713', fg='green', nl=False)
# preparse input sizes from vgsl string to seed ground truth data set
# sizes and dimension ordering.
if not nn:
spec = spec.strip()
if spec[0] != '[' or spec[-1] != ']':
raise click.BadOptionUsage(
'spec', 'VGSL spec {} not bracketed'.format(spec))
blocks = spec[1:-1].split(' ')
m = re.match(r'(\d+),(\d+),(\d+),(\d+)', blocks[0])
if not m:
raise click.BadOptionUsage(
'spec', 'Invalid input spec {}'.format(blocks[0]))
batch, height, width, channels = [int(x) for x in m.groups()]
else:
batch, channels, height, width = nn.input
try:
transforms = generate_input_transforms(batch, height, width, channels,
pad)
except KrakenInputException as e:
raise click.BadOptionUsage('spec', str(e))
# disable automatic partition when given evaluation set explicitly
if evaluation_files:
partition = 1
ground_truth = list(ground_truth)
# merge training_files into ground_truth list
if training_files:
ground_truth.extend(training_files)
if len(ground_truth) == 0:
raise click.UsageError(
'No training data was provided to the train command. Use `-t` or the `ground_truth` argument.'
)
np.random.shuffle(ground_truth)
if len(ground_truth) > 2500 and not preload:
logger.info(
'Disabling preloading for large (>2500) training data set. Enable by setting --preload parameter'
)
preload = False
# implicit preloading enabled for small data sets
if preload is None:
preload = True
tr_im = ground_truth[:int(len(ground_truth) * partition)]
if evaluation_files:
logger.debug('Using {} lines from explicit eval set'.format(
len(evaluation_files)))
te_im = evaluation_files
else:
te_im = ground_truth[int(len(ground_truth) * partition):]
logger.debug('Taking {} lines from training for evaluation'.format(
len(te_im)))
# set multiprocessing tensor sharing strategy
if 'file_system' in torch.multiprocessing.get_all_sharing_strategies():
logger.debug(
'Setting multiprocessing tensor sharing strategy to file_system')
torch.multiprocessing.set_sharing_strategy('file_system')
gt_set = GroundTruthDataset(normalization=normalization,
whitespace_normalization=normalize_whitespace,
reorder=reorder,
im_transforms=transforms,
preload=preload)
with log.progressbar(tr_im, label='Building training set') as bar:
for im in bar:
logger.debug('Adding line {} to training set'.format(im))
try:
gt_set.add(im)
except FileNotFoundError as e:
logger.warning('{}: {}. Skipping.'.format(
e.strerror, e.filename))
except KrakenInputException as e:
logger.warning(str(e))
val_set = GroundTruthDataset(normalization=normalization,
whitespace_normalization=normalize_whitespace,
reorder=reorder,
im_transforms=transforms,
preload=preload)
with log.progressbar(te_im, label='Building validation set') as bar:
for im in bar:
logger.debug('Adding line {} to validation set'.format(im))
try:
val_set.add(im)
except FileNotFoundError as e:
logger.warning('{}: {}. Skipping.'.format(
e.strerror, e.filename))
except KrakenInputException as e:
logger.warning(str(e))
logger.info(
'Training set {} lines, validation set {} lines, alphabet {} symbols'.
format(len(gt_set._images), len(val_set._images),
len(gt_set.alphabet)))
alpha_diff = set(gt_set.alphabet).symmetric_difference(
set(val_set.alphabet))
if alpha_diff:
logger.warn('alphabet mismatch {}'.format(alpha_diff))
logger.info('grapheme\tcount')
for k, v in sorted(gt_set.alphabet.items(),
key=lambda x: x[1],
reverse=True):
char = make_printable(k)
if char == k:
char = '\t' + char
logger.info(u'{}\t{}'.format(char, v))
logger.debug('Encoding training set')
# use model codec when given
if append:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
gt_set.encode(codec)
message('Slicing and dicing model ', nl=False)
# now we can create a new model
spec = '[{} O1c{}]'.format(spec[1:-1], gt_set.codec.max_label() + 1)
logger.info('Appending {} to existing model {} after {}'.format(
spec, nn.spec, append))
nn.append(append, spec)
nn.add_codec(gt_set.codec)
message('\u2713', fg='green')
logger.info('Assembled model spec: {}'.format(nn.spec))
elif load:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
# prefer explicitly given codec over network codec if mode is 'both'
codec = codec if (codec and resize == 'both') else nn.codec
try:
gt_set.encode(codec)
except KrakenEncodeException as e:
message('Network codec not compatible with training set')
alpha_diff = set(gt_set.alphabet).difference(set(codec.c2l.keys()))
if resize == 'fail':
logger.error(
'Training data and model codec alphabets mismatch: {}'.
format(alpha_diff))
ctx.exit(code=1)
elif resize == 'add':
message('Adding missing labels to network ', nl=False)
logger.info(
'Resizing codec to include {} new code points'.format(
len(alpha_diff)))
codec.c2l.update({
k: [v]
for v, k in enumerate(alpha_diff,
start=codec.max_label() + 1)
})
nn.add_codec(PytorchCodec(codec.c2l))
logger.info(
'Resizing last layer in network to {} outputs'.format(
codec.max_label() + 1))
nn.resize_output(codec.max_label() + 1)
gt_set.encode(nn.codec)
message('\u2713', fg='green')
elif resize == 'both':
message('Fitting network exactly to training set ', nl=False)
logger.info(
'Resizing network or given codec to {} code sequences'.
format(len(gt_set.alphabet)))
gt_set.encode(None)
ncodec, del_labels = codec.merge(gt_set.codec)
logger.info(
'Deleting {} output classes from network ({} retained)'.
format(len(del_labels),
len(codec) - len(del_labels)))
gt_set.encode(ncodec)
nn.resize_output(ncodec.max_label() + 1, del_labels)
message('\u2713', fg='green')
else:
raise click.BadOptionUsage(
'resize', 'Invalid resize value {}'.format(resize))
else:
gt_set.encode(codec)
logger.info('Creating new model {} with {} outputs'.format(
spec,
gt_set.codec.max_label() + 1))
spec = '[{} O1c{}]'.format(spec[1:-1], gt_set.codec.max_label() + 1)
nn = vgsl.TorchVGSLModel(spec)
# initialize weights
message('Initializing model ', nl=False)
nn.init_weights()
nn.add_codec(gt_set.codec)
# initialize codec
message('\u2713', fg='green')
# half the number of data loading processes if device isn't cuda and we haven't enabled preloading
if device == 'cpu' and not preload:
loader_threads = threads // 2
else:
loader_threads = threads
train_loader = DataLoader(gt_set,
batch_size=1,
shuffle=True,
num_workers=loader_threads,
pin_memory=True)
threads -= loader_threads
# don't encode validation set as the alphabets may not match causing encoding failures
val_set.training_set = list(zip(val_set._images, val_set._gt))
logger.debug('Constructing {} optimizer (lr: {}, momentum: {})'.format(
optimizer, lrate, momentum))
# set mode to trainindg
nn.train()
# set number of OpenMP threads
logger.debug('Set OpenMP threads to {}'.format(threads))
nn.set_num_threads(threads)
logger.debug('Moving model to device {}'.format(device))
optim = getattr(torch.optim, optimizer)(nn.nn.parameters(), lr=0)
if 'accuracy' not in nn.user_metadata:
nn.user_metadata['accuracy'] = []
tr_it = TrainScheduler(optim)
if schedule == '1cycle':
add_1cycle(tr_it, int(len(gt_set) * epochs), lrate, momentum,
momentum - 0.10, weight_decay)
else:
# constant learning rate scheduler
tr_it.add_phase(1, (lrate, lrate), (momentum, momentum), weight_decay,
train.annealing_const)
if quit == 'early':
st_it = EarlyStopping(min_delta, lag)
elif quit == 'dumb':
st_it = EpochStopping(epochs - completed_epochs)
else:
raise click.BadOptionUsage(
'quit', 'Invalid training interruption scheme {}'.format(quit))
#for param in hyper_fields:
# logger.debug('Setting \'{}\' to \'{}\' in model metadata'.format(param, locals()[param]))
# nn.user_metadata[param] = locals()[param]
trainer = train.KrakenTrainer(model=nn,
optimizer=optim,
device=device,
filename_prefix=output,
event_frequency=freq,
train_set=train_loader,
val_set=val_set,
stopper=st_it)
trainer.add_lr_scheduler(tr_it)
with log.progressbar(label='stage {}/{}'.format(
1, trainer.stopper.epochs if trainer.stopper.epochs > 0 else '∞'),
length=trainer.event_it,
show_pos=True) as bar:
def _draw_progressbar():
bar.update(1)
def _print_eval(epoch, accuracy, chars, error):
message('Accuracy report ({}) {:0.4f} {} {}'.format(
epoch, accuracy, chars, error))
# reset progress bar
bar.label = 'stage {}/{}'.format(
epoch + 1,
trainer.stopper.epochs if trainer.stopper.epochs > 0 else '∞')
bar.pos = 0
bar.finished = False
trainer.run(_print_eval, _draw_progressbar)
if quit == 'early':
message('Moving best model {0}_{1}.mlmodel ({2}) to {0}_best.mlmodel'.
format(output, trainer.stopper.best_epoch,
trainer.stopper.best_loss))
logger.info(
'Moving best model {0}_{1}.mlmodel ({2}) to {0}_best.mlmodel'.
format(output, trainer.stopper.best_epoch,
trainer.stopper.best_loss))
shutil.copy('{}_{}.mlmodel'.format(output, trainer.stopper.best_epoch),
'{}_best.mlmodel'.format(output))
def line_generator(ctx, font, maxlines, encoding, normalization, renormalize,
reorder, font_size, font_weight, language, max_length,
strip, disable_degradation, alpha, beta, distort,
distortion_sigma, legacy, output, text):
"""
Generates artificial text line training data.
"""
import errno
import numpy as np
from kraken import linegen
from kraken.lib.util import make_printable
lines: Set[str] = set()
if not text:
return
with log.progressbar(text, label='Reading texts') as bar:
for t in text:
with click.open_file(t, encoding=encoding) as fp:
logger.info('Reading {}'.format(t))
for l in fp:
lines.add(l.rstrip('\r\n'))
if normalization:
lines = set(
[unicodedata.normalize(normalization, line) for line in lines])
if strip:
lines = set([line.strip() for line in lines])
if max_length:
lines = set([line for line in lines if len(line) < max_length])
logger.info('Read {} lines'.format(len(lines)))
message('Read {} unique lines'.format(len(lines)))
if maxlines and maxlines < len(lines):
message('Sampling {} lines\t'.format(maxlines), nl=False)
llist = list(lines)
lines = set(llist[idx]
for idx in np.random.randint(0, len(llist), maxlines))
message('\u2713', fg='green')
try:
os.makedirs(output)
except OSError as e:
if e.errno != errno.EEXIST:
raise
# calculate the alphabet and print it for verification purposes
alphabet: Set[str] = set()
for line in lines:
alphabet.update(line)
chars = []
combining = []
for char in sorted(alphabet):
k = make_printable(char)
if k != char:
combining.append(k)
else:
chars.append(k)
message('Σ (len: {})'.format(len(alphabet)))
message('Symbols: {}'.format(''.join(chars)))
if combining:
message('Combining Characters: {}'.format(', '.join(combining)))
lg = linegen.LineGenerator(font, font_size, font_weight, language)
with log.progressbar(lines, label='Writing images') as bar:
for idx, line in enumerate(bar):
logger.info(line)
try:
if renormalize:
im = lg.render_line(
unicodedata.normalize(renormalize, line))
else:
im = lg.render_line(line)
except KrakenCairoSurfaceException as e:
logger.info('{}: {} {}'.format(e.message, e.width, e.height))
continue
if not disable_degradation and not legacy:
im = linegen.degrade_line(im, alpha=alpha, beta=beta)
im = linegen.distort_line(
im, abs(np.random.normal(distort)),
abs(np.random.normal(distortion_sigma)))
elif legacy:
im = linegen.ocropy_degrade(im)
im.save('{}/{:06d}.png'.format(output, idx))
with open('{}/{:06d}.gt.txt'.format(output, idx), 'wb') as fp:
if reorder:
fp.write(get_display(line).encode('utf-8'))
else:
fp.write(line.encode('utf-8'))
def recognition_train_gen(
cls,
hyper_params: Dict = default_specs.RECOGNITION_HYPER_PARAMS,
progress_callback: Callable[[str, int], Callable[
[None], None]] = lambda string, length: lambda: None,
message: Callable[[str], None] = lambda *args, **kwargs: None,
output: str = 'model',
spec: str = default_specs.RECOGNITION_SPEC,
append: Optional[int] = None,
load: Optional[str] = None,
device: str = 'cpu',
reorder: bool = True,
training_data: Sequence[Dict] = None,
evaluation_data: Sequence[Dict] = None,
preload: Optional[bool] = None,
threads: int = 1,
load_hyper_parameters: bool = False,
repolygonize: bool = False,
force_binarization: bool = False,
format_type: str = 'path',
codec: Optional[Dict] = None,
resize: str = 'fail',
augment: bool = False):
"""
This is an ugly constructor that takes all the arguments from the command
line driver, finagles the datasets, models, and hyperparameters correctly
and returns a KrakenTrainer object.
Setup parameters (load, training_data, evaluation_data, ....) are named,
model hyperparameters (everything in
kraken.lib.default_specs.RECOGNITION_HYPER_PARAMS) are in in the
`hyper_params` argument.
Args:
hyper_params (dict): Hyperparameter dictionary containing all fields
from
kraken.lib.default_specs.RECOGNITION_HYPER_PARAMS
progress_callback (Callable): Callback for progress reports on various
computationally expensive processes. A
human readable string and the process
length is supplied. The callback has to
return another function which will be
executed after each step.
message (Callable): Messaging printing method for above log but below
warning level output, i.e. infos that should
generally be shown to users.
**kwargs: Setup parameters, i.e. CLI parameters of the train() command.
Returns:
A KrakenTrainer object.
"""
# load model if given. if a new model has to be created we need to do that
# after data set initialization, otherwise to output size is still unknown.
nn = None
if load:
logger.info(f'Loading existing model from {load} ')
message(f'Loading existing model from {load} ', nl=False)
nn = vgsl.TorchVGSLModel.load_model(load)
if load_hyper_parameters:
hyper_params.update(nn.hyper_params)
nn.hyper_params = hyper_params
message('\u2713', fg='green', nl=False)
DatasetClass = GroundTruthDataset
valid_norm = True
if format_type and format_type != 'path':
logger.info(
f'Parsing {len(training_data)} XML files for training data')
if repolygonize:
message('Repolygonizing data')
training_data = preparse_xml_data(training_data, format_type,
repolygonize)
evaluation_data = preparse_xml_data(evaluation_data, format_type,
repolygonize)
DatasetClass = PolygonGTDataset
valid_norm = False
elif format_type == 'path':
if force_binarization:
logger.warning(
'Forced binarization enabled in `path` mode. Will be ignored.'
)
force_binarization = False
if repolygonize:
logger.warning(
'Repolygonization enabled in `path` mode. Will be ignored.'
)
training_data = [{'image': im} for im in training_data]
if evaluation_data:
evaluation_data = [{'image': im} for im in evaluation_data]
valid_norm = True
# format_type is None. Determine training type from length of training data entry
else:
if len(training_data[0]) >= 4:
DatasetClass = PolygonGTDataset
valid_norm = False
else:
if force_binarization:
logger.warning(
'Forced binarization enabled with box lines. Will be ignored.'
)
force_binarization = False
if repolygonize:
logger.warning(
'Repolygonization enabled with box lines. Will be ignored.'
)
# preparse input sizes from vgsl string to seed ground truth data set
# sizes and dimension ordering.
if not nn:
spec = spec.strip()
if spec[0] != '[' or spec[-1] != ']':
raise click.BadOptionUsage(
'spec', 'VGSL spec {} not bracketed'.format(spec))
blocks = spec[1:-1].split(' ')
m = re.match(r'(\d+),(\d+),(\d+),(\d+)', blocks[0])
if not m:
raise click.BadOptionUsage('spec',
f'Invalid input spec {blocks[0]}')
batch, height, width, channels = [int(x) for x in m.groups()]
else:
batch, channels, height, width = nn.input
try:
transforms = generate_input_transforms(batch, height, width,
channels,
hyper_params['pad'],
valid_norm,
force_binarization)
except KrakenInputException as e:
raise click.BadOptionUsage('spec', str(e))
if len(training_data) > 2500 and not preload:
logger.info(
'Disabling preloading for large (>2500) training data set. Enable by setting --preload parameter'
)
preload = False
# implicit preloading enabled for small data sets
if preload is None:
preload = True
# set multiprocessing tensor sharing strategy
if 'file_system' in torch.multiprocessing.get_all_sharing_strategies():
logger.debug(
'Setting multiprocessing tensor sharing strategy to file_system'
)
torch.multiprocessing.set_sharing_strategy('file_system')
gt_set = DatasetClass(
normalization=hyper_params['normalization'],
whitespace_normalization=hyper_params['normalize_whitespace'],
reorder=reorder,
im_transforms=transforms,
preload=preload,
augmentation=hyper_params['augment'])
bar = progress_callback('Building training set', len(training_data))
for im in training_data:
logger.debug(f'Adding line {im} to training set')
try:
gt_set.add(**im)
bar()
except FileNotFoundError as e:
logger.warning(f'{e.strerror}: {e.filename}. Skipping.')
except KrakenInputException as e:
logger.warning(str(e))
val_set = DatasetClass(
normalization=hyper_params['normalization'],
whitespace_normalization=hyper_params['normalize_whitespace'],
reorder=reorder,
im_transforms=transforms,
preload=preload)
bar = progress_callback('Building validation set',
len(evaluation_data))
for im in evaluation_data:
logger.debug(f'Adding line {im} to validation set')
try:
val_set.add(**im)
bar()
except FileNotFoundError as e:
logger.warning(f'{e.strerror}: {e.filename}. Skipping.')
except KrakenInputException as e:
logger.warning(str(e))
if len(gt_set._images) == 0:
logger.error(
'No valid training data was provided to the train command. Please add valid XML or line data.'
)
return None
logger.info(
f'Training set {len(gt_set._images)} lines, validation set {len(val_set._images)} lines, alphabet {len(gt_set.alphabet)} symbols'
)
alpha_diff_only_train = set(gt_set.alphabet).difference(
set(val_set.alphabet))
alpha_diff_only_val = set(val_set.alphabet).difference(
set(gt_set.alphabet))
if alpha_diff_only_train:
logger.warning(
f'alphabet mismatch: chars in training set only: {alpha_diff_only_train} (not included in accuracy test during training)'
)
if alpha_diff_only_val:
logger.warning(
f'alphabet mismatch: chars in validation set only: {alpha_diff_only_val} (not trained)'
)
logger.info('grapheme\tcount')
for k, v in sorted(gt_set.alphabet.items(),
key=lambda x: x[1],
reverse=True):
char = make_printable(k)
if char == k:
char = '\t' + char
logger.info(f'{char}\t{v}')
logger.debug('Encoding training set')
# use model codec when given
if append:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
gt_set.encode(codec)
message('Slicing and dicing model ', nl=False)
# now we can create a new model
spec = '[{} O1c{}]'.format(spec[1:-1],
gt_set.codec.max_label() + 1)
logger.info(
f'Appending {spec} to existing model {nn.spec} after {append}')
nn.append(append, spec)
nn.add_codec(gt_set.codec)
message('\u2713', fg='green')
logger.info(f'Assembled model spec: {nn.spec}')
elif load:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
# prefer explicitly given codec over network codec if mode is 'both'
codec = codec if (codec and resize == 'both') else nn.codec
try:
gt_set.encode(codec)
except KrakenEncodeException:
message('Network codec not compatible with training set')
alpha_diff = set(gt_set.alphabet).difference(
set(codec.c2l.keys()))
if resize == 'fail':
logger.error(
f'Training data and model codec alphabets mismatch: {alpha_diff}'
)
return None
elif resize == 'add':
message('Adding missing labels to network ', nl=False)
logger.info(
f'Resizing codec to include {len(alpha_diff)} new code points'
)
codec.c2l.update({
k: [v]
for v, k in enumerate(alpha_diff,
start=codec.max_label() + 1)
})
nn.add_codec(PytorchCodec(codec.c2l))
logger.info(
f'Resizing last layer in network to {codec.max_label()+1} outputs'
)
nn.resize_output(codec.max_label() + 1)
gt_set.encode(nn.codec)
message('\u2713', fg='green')
elif resize == 'both':
message('Fitting network exactly to training set ',
nl=False)
logger.info(
f'Resizing network or given codec to {gt_set.alphabet} code sequences'
)
gt_set.encode(None)
ncodec, del_labels = codec.merge(gt_set.codec)
logger.info(
f'Deleting {len(del_labels)} output classes from network ({len(codec)-len(del_labels)} retained)'
)
gt_set.encode(ncodec)
nn.resize_output(ncodec.max_label() + 1, del_labels)
message('\u2713', fg='green')
else:
logger.error(f'invalid resize parameter value {resize}')
return None
else:
gt_set.encode(codec)
logger.info(
f'Creating new model {spec} with {gt_set.codec.max_label()+1} outputs'
)
spec = '[{} O1c{}]'.format(spec[1:-1],
gt_set.codec.max_label() + 1)
nn = vgsl.TorchVGSLModel(spec)
# initialize weights
message('Initializing model ', nl=False)
nn.init_weights()
nn.add_codec(gt_set.codec)
# initialize codec
message('\u2713', fg='green')
if nn.one_channel_mode and gt_set.im_mode != nn.one_channel_mode:
logger.warning(
f'Neural network has been trained on mode {nn.one_channel_mode} images, training set contains mode {gt_set.im_mode} data. Consider setting `force_binarization`'
)
if format_type != 'path' and nn.seg_type == 'bbox':
logger.warning(
'Neural network has been trained on bounding box image information but training set is polygonal.'
)
# half the number of data loading processes if device isn't cuda and we haven't enabled preloading
if device == 'cpu' and not preload:
loader_threads = threads // 2
else:
loader_threads = threads
train_loader = InfiniteDataLoader(
gt_set,
batch_size=hyper_params['batch_size'],
shuffle=True,
num_workers=loader_threads,
pin_memory=True,
collate_fn=collate_sequences)
threads = max(threads - loader_threads, 1)
# don't encode validation set as the alphabets may not match causing encoding failures
val_set.no_encode()
val_loader = DataLoader(val_set,
batch_size=hyper_params['batch_size'],
num_workers=loader_threads,
pin_memory=True,
collate_fn=collate_sequences)
logger.debug('Constructing {} optimizer (lr: {}, momentum: {})'.format(
hyper_params['optimizer'], hyper_params['lrate'],
hyper_params['momentum']))
# set model type metadata field
nn.model_type = 'recognition'
# set mode to trainindg
nn.train()
# set number of OpenMP threads
logger.debug(f'Set OpenMP threads to {threads}')
nn.set_num_threads(threads)
optim = getattr(torch.optim,
hyper_params['optimizer'])(nn.nn.parameters(), lr=0)
if 'seg_type' not in nn.user_metadata:
nn.user_metadata[
'seg_type'] = 'baselines' if format_type != 'path' else 'bbox'
tr_it = TrainScheduler(optim)
if hyper_params['schedule'] == '1cycle':
add_1cycle(tr_it, int(len(gt_set) * hyper_params['epochs']),
hyper_params['lrate'], hyper_params['momentum'],
hyper_params['momentum'] - 0.10,
hyper_params['weight_decay'])
elif hyper_params['schedule'] == 'exponential':
add_exponential_decay(tr_it,
int(len(gt_set) * hyper_params['epochs']),
len(gt_set), hyper_params['lrate'], 0.95,
hyper_params['momentum'],
hyper_params['weight_decay'])
else:
# constant learning rate scheduler
tr_it.add_phase(1, 2 * (hyper_params['lrate'], ),
2 * (hyper_params['momentum'], ),
hyper_params['weight_decay'], annealing_const)
if hyper_params['quit'] == 'early':
st_it = EarlyStopping(hyper_params['min_delta'],
hyper_params['lag'])
elif hyper_params['quit'] == 'dumb':
st_it = EpochStopping(hyper_params['epochs'] -
hyper_params['completed_epochs'])
else:
logger.error(f'Invalid training interruption scheme {quit}')
return None
trainer = cls(model=nn,
optimizer=optim,
device=device,
filename_prefix=output,
event_frequency=hyper_params['freq'],
train_set=train_loader,
val_set=val_loader,
stopper=st_it)
trainer.add_lr_scheduler(tr_it)
return trainer
def build_binary_dataset(
files: Optional[List[Union[str, pathlib.Path]]] = None,
output_file: Union[str, pathlib.Path] = None,
format_type: str = 'xml',
num_workers: int = 0,
ignore_splits: bool = False,
random_split: Optional[Tuple[float, float, float]] = None,
force_type: Optional[str] = None,
recordbatch_size: int = 100,
callback: Callable[[int, int],
None] = lambda chunk, lines: None) -> None:
"""
Parses XML files and dumps the baseline-style line images and text into a
binary dataset.
Args:
files: List of XML input files.
output_file: Path to the output file.
format_type: One of `xml`, `alto`, `page`, or `path`.
num_workers: Number of workers for parallelized extraction of line
images. Set to `0` to disable parallelism.
ignore_splits: Switch to disable serialization of the explicit
train/validation/test splits contained in the source
files.
random_split: Serializes a random split into the dataset with the
proportions (train, val, test).
force_type: Forces a dataset type. Can be `kraken_recognition_baseline`
or `kraken_recognition_bbox`.
recordbatch_size: Minimum number of records per RecordBatch written to
the output file. Larger batches require more
transient memory but slightly improve reading
performance.
callback: Function called everytime a new recordbatch is flushed into
the Arrow IPC file.
"""
logger.info('Parsing XML files')
extract_fn = _extract_line
if format_type == 'xml':
parse_fn = parse_xml
elif format_type == 'alto':
parse_fn = parse_alto
elif format_type == 'page':
parse_fn = parse_page
elif format_type == 'path':
if not ignore_splits:
logger.warning(
'ignore_splits is False and format_type is path. Will not serialize splits.'
)
parse_fn = parse_path
extract_fn = _extract_path_line
else:
raise ValueError(
f'invalid format {format_type} for parse_(xml,alto,page,path)')
if force_type and force_type not in [
'kraken_recognition_baseline', 'kraken_recognition_bbox'
]:
raise ValueError(f'force_type set to invalid value {force_type}')
docs = []
for doc in files:
try:
data = parse_fn(doc)
except KrakenInputException:
logger.warning(f'Invalid input file {doc}')
continue
try:
with open(data['image'], 'rb') as fp:
Image.open(fp)
except FileNotFoundError as e:
logger.warning(f'Could not open file {e.filename} in {doc}')
continue
docs.append(data)
logger.info(f'Parsed {len(docs)} files.')
logger.info('Assembling dataset alphabet.')
alphabet = Counter()
num_lines = 0
for doc in docs:
for line in doc['lines']:
num_lines += 1
alphabet.update(line['text'])
callback(0, num_lines)
for k, v in sorted(alphabet.items(), key=lambda x: x[1], reverse=True):
char = make_printable(k)
if char == k:
char = '\t' + char
logger.info(f'{char}\t{v}')
if force_type:
ds_type = force_type
else:
ds_type = 'kraken_recognition_baseline' if format_type != 'path' else 'kraken_recognition_bbox'
metadata = {
'lines': {
'type': ds_type,
'alphabet': alphabet,
'text_type': 'raw',
'image_type': 'raw',
'splits': ['train', 'eval', 'test'],
'im_mode': '1',
'counts': Counter({
'all': 0,
'train': 0,
'validation': 0,
'test': 0
}),
}
}
ty = pa.struct([('text', pa.string()), ('im', pa.binary())])
schema = pa.schema([('lines', ty), ('train', pa.bool_()),
('validation', pa.bool_()), ('test', pa.bool_())])
def _make_record_batch(line_cache):
ar = pa.array(line_cache, type=ty)
if random_split:
indices = np.random.choice(4,
len(line_cache),
p=(0.0, ) + random_split)
else:
indices = np.zeros(len(line_cache))
tr_ind = np.zeros(len(line_cache), dtype=bool)
tr_ind[indices == 1] = True
val_ind = np.zeros(len(line_cache), dtype=bool)
val_ind[indices == 2] = True
test_ind = np.zeros(len(line_cache), dtype=bool)
test_ind[indices == 3] = True
train_mask = pa.array(tr_ind)
val_mask = pa.array(val_ind)
test_mask = pa.array(test_ind)
rbatch = pa.RecordBatch.from_arrays(
[ar, train_mask, val_mask, test_mask], schema=schema)
return rbatch, (len(line_cache), int(sum(indices == 1)),
int(sum(indices == 2)), int(sum(indices == 3)))
line_cache = []
logger.info('Writing lines to temporary file.')
with tempfile.TemporaryDirectory() as tmp_output_dir:
tmp_file = tmp_output_dir + '/dataset.arrow'
with pa.OSFile(tmp_file, 'wb') as sink:
with pa.ipc.new_file(sink, schema) as writer:
if num_workers and num_workers > 1:
logger.info(
f'Spinning up processing pool with {num_workers} workers.'
)
with Pool(num_workers) as pool:
for page_lines, im_mode in pool.imap_unordered(
extract_fn, docs):
if page_lines:
line_cache.extend(page_lines)
# comparison RGB(A) > L > 1
if im_mode > metadata['lines']['im_mode']:
metadata['lines']['im_mode'] = im_mode
if len(line_cache) >= recordbatch_size:
logger.info(
f'Flushing {len(line_cache)} lines into {tmp_file}.'
)
rbatch, counts = _make_record_batch(line_cache)
metadata['lines']['counts'].update({
'all':
counts[0],
'train':
counts[1],
'validation':
counts[2],
'test':
counts[3]
})
writer.write(rbatch)
callback(len(line_cache), num_lines)
line_cache = []
else:
for page_lines, im_mode in map(extract_fn, docs):
if page_lines:
line_cache.extend(page_lines)
# comparison RGB(A) > L > 1
if im_mode > metadata['lines']['im_mode']:
metadata['lines']['im_mode'] = im_mode
if len(line_cache) >= recordbatch_size:
logger.info(
f'Flushing {len(line_cache)} lines into {tmp_file}.'
)
rbatch, counts = _make_record_batch(line_cache)
metadata['lines']['counts'].update({
'all':
counts[0],
'train':
counts[1],
'validation':
counts[2],
'test':
counts[3]
})
writer.write(rbatch)
callback(len(line_cache), num_lines)
line_cache = []
if line_cache:
logger.info(
f'Flushing last {len(line_cache)} lines into {tmp_file}.'
)
rbatch, counts = _make_record_batch(line_cache)
metadata['lines']['counts'].update({
'all': counts[0],
'train': counts[1],
'validation': counts[2],
'test': counts[3]
})
writer.write(rbatch)
callback(len(line_cache), num_lines)
logger.info('Dataset metadata')
logger.info(f"type: {metadata['lines']['type']}\n"
f"text_type: {metadata['lines']['text_type']}\n"
f"image_type: {metadata['lines']['image_type']}\n"
f"splits: {metadata['lines']['splits']}\n"
f"im_mode: {metadata['lines']['im_mode']}\n"
f"lines: {metadata['lines']['counts']}\n")
with pa.memory_map(tmp_file, 'rb') as source:
logger.info(
f'Rewriting output ({output_file}) to update metadata.')
ds = pa.ipc.open_file(source).read_all()
metadata['lines']['counts'] = dict(metadata['lines']['counts'])
metadata['lines'] = json.dumps(metadata['lines'])
schema = schema.with_metadata(metadata)
with pa.OSFile(output_file, 'wb') as sink:
with pa.ipc.new_file(sink, schema) as writer:
writer.write(ds)
def train(ctx, pad, output, spec, append, load, savefreq, report, quit, epochs,
lag, min_delta, device, optimizer, lrate, momentum, weight_decay,
schedule, partition, normalization, codec, resize, reorder,
training_files, evaluation_files, preload, threads, ground_truth):
"""
Trains a model from image-text pairs.
"""
if not load and append:
raise click.BadOptionUsage(
'append', 'append option requires loading an existing model')
if resize != 'fail' and not load:
raise click.BadOptionUsage(
'resize', 'resize option requires loading an existing model')
import re
import torch
import shutil
import numpy as np
from torch.utils.data import DataLoader
from kraken.lib import models, vgsl, train
from kraken.lib.util import make_printable
from kraken.lib.train import EarlyStopping, EpochStopping, TrainStopper, TrainScheduler, add_1cycle
from kraken.lib.codec import PytorchCodec
from kraken.lib.dataset import GroundTruthDataset, compute_error, generate_input_transforms
logger.info('Building ground truth set from {} line images'.format(
len(ground_truth) + len(training_files)))
# load model if given. if a new model has to be created we need to do that
# after data set initialization, otherwise to output size is still unknown.
nn = None
if load:
logger.info('Loading existing model from {} '.format(load))
message('Loading model {}'.format(load), nl=False)
nn = vgsl.TorchVGSLModel.load_model(load)
message('\u2713', fg='green', nl=False)
# preparse input sizes from vgsl string to seed ground truth data set
# sizes and dimension ordering.
if not nn:
spec = spec.strip()
if spec[0] != '[' or spec[-1] != ']':
raise click.BadOptionUsage(
'spec', 'VGSL spec {} not bracketed'.format(spec))
blocks = spec[1:-1].split(' ')
m = re.match(r'(\d+),(\d+),(\d+),(\d+)', blocks[0])
if not m:
raise click.BadOptionUsage(
'spec', 'Invalid input spec {}'.format(blocks[0]))
batch, height, width, channels = [int(x) for x in m.groups()]
else:
batch, channels, height, width = nn.input
try:
transforms = generate_input_transforms(batch, height, width, channels,
pad)
except KrakenInputException as e:
raise click.BadOptionUsage('spec', str(e))
# disable automatic partition when given evaluation set explicitly
if evaluation_files:
partition = 1
ground_truth = list(ground_truth)
# merge training_files into ground_truth list
if training_files:
ground_truth.extend(training_files)
if len(ground_truth) == 0:
raise click.UsageError(
'No training data was provided to the train command. Use `-t` or the `ground_truth` argument.'
)
np.random.shuffle(ground_truth)
if len(ground_truth) > 2500 and not preload:
logger.info(
'Disabling preloading for large (>2500) training data set. Enable by setting --preload parameter'
)
preload = False
# implicit preloading enabled for small data sets
if preload is None:
preload = True
tr_im = ground_truth[:int(len(ground_truth) * partition)]
if evaluation_files:
logger.debug('Using {} lines from explicit eval set'.format(
len(evaluation_files)))
te_im = evaluation_files
else:
te_im = ground_truth[int(len(ground_truth) * partition):]
logger.debug('Taking {} lines from training for evaluation'.format(
len(te_im)))
gt_set = GroundTruthDataset(normalization=normalization,
reorder=reorder,
im_transforms=transforms,
preload=preload)
with log.progressbar(tr_im, label='Building training set') as bar:
for im in bar:
logger.debug('Adding line {} to training set'.format(im))
try:
gt_set.add(im)
except FileNotFoundError as e:
logger.warning('{}: {}. Skipping.'.format(
e.strerror, e.filename))
except KrakenInputException as e:
logger.warning(str(e))
val_set = GroundTruthDataset(normalization=normalization,
reorder=reorder,
im_transforms=transforms,
preload=preload)
with log.progressbar(te_im, label='Building validation set') as bar:
for im in bar:
logger.debug('Adding line {} to validation set'.format(im))
try:
val_set.add(im)
except FileNotFoundError as e:
logger.warning('{}: {}. Skipping.'.format(
e.strerror, e.filename))
except KrakenInputException as e:
logger.warning(str(e))
logger.info(
'Training set {} lines, validation set {} lines, alphabet {} symbols'.
format(len(gt_set._images), len(val_set._images),
len(gt_set.alphabet)))
alpha_diff = set(gt_set.alphabet).symmetric_difference(
set(val_set.alphabet))
if alpha_diff:
logger.warn('alphabet mismatch {}'.format(alpha_diff))
logger.info('grapheme\tcount')
for k, v in sorted(gt_set.alphabet.items(),
key=lambda x: x[1],
reverse=True):
char = make_printable(k)
if char == k:
char = '\t' + char
logger.info(u'{}\t{}'.format(char, v))
logger.debug('Encoding training set')
# use model codec when given
if append:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
gt_set.encode(codec)
message('Slicing and dicing model ', nl=False)
# now we can create a new model
spec = '[{} O1c{}]'.format(spec[1:-1], gt_set.codec.max_label() + 1)
logger.info('Appending {} to existing model {} after {}'.format(
spec, nn.spec, append))
nn.append(append, spec)
nn.add_codec(gt_set.codec)
message('\u2713', fg='green')
logger.info('Assembled model spec: {}'.format(nn.spec))
elif load:
# is already loaded
nn = cast(vgsl.TorchVGSLModel, nn)
# prefer explicitly given codec over network codec if mode is 'both'
codec = codec if (codec and resize == 'both') else nn.codec
try:
gt_set.encode(codec)
except KrakenEncodeException as e:
message('Network codec not compatible with training set')
alpha_diff = set(gt_set.alphabet).difference(set(codec.c2l.keys()))
if resize == 'fail':
logger.error(
'Training data and model codec alphabets mismatch: {}'.
format(alpha_diff))
ctx.exit(code=1)
elif resize == 'add':
message('Adding missing labels to network ', nl=False)
logger.info(
'Resizing codec to include {} new code points'.format(
len(alpha_diff)))
codec.c2l.update({
k: [v]
for v, k in enumerate(alpha_diff,
start=codec.max_label() + 1)
})
nn.add_codec(PytorchCodec(codec.c2l))
logger.info(
'Resizing last layer in network to {} outputs'.format(
codec.max_label() + 1))
nn.resize_output(codec.max_label() + 1)
message('\u2713', fg='green')
elif resize == 'both':
message('Fitting network exactly to training set ', nl=False)
logger.info(
'Resizing network or given codec to {} code sequences'.
format(len(gt_set.alphabet)))
gt_set.encode(None)
ncodec, del_labels = codec.merge(gt_set.codec)
logger.info(
'Deleting {} output classes from network ({} retained)'.
format(len(del_labels),
len(codec) - len(del_labels)))
gt_set.encode(ncodec)
nn.resize_output(ncodec.max_label() + 1, del_labels)
message('\u2713', fg='green')
else:
raise click.BadOptionUsage(
'resize', 'Invalid resize value {}'.format(resize))
else:
gt_set.encode(codec)
logger.info('Creating new model {} with {} outputs'.format(
spec,
gt_set.codec.max_label() + 1))
spec = '[{} O1c{}]'.format(spec[1:-1], gt_set.codec.max_label() + 1)
nn = vgsl.TorchVGSLModel(spec)
# initialize weights
message('Initializing model ', nl=False)
nn.init_weights()
nn.add_codec(gt_set.codec)
# initialize codec
message('\u2713', fg='green')
train_loader = DataLoader(gt_set,
batch_size=1,
shuffle=True,
pin_memory=True)
# don't encode validation set as the alphabets may not match causing encoding failures
val_set.training_set = list(zip(val_set._images, val_set._gt))
logger.debug('Constructing {} optimizer (lr: {}, momentum: {})'.format(
optimizer, lrate, momentum))
# set mode to trainindg
nn.train()
# set number of OpenMP threads
logger.debug('Set OpenMP threads to {}'.format(threads))
nn.set_num_threads(threads)
logger.debug('Moving model to device {}'.format(device))
rec = models.TorchSeqRecognizer(nn, train=True, device=device)
optim = getattr(torch.optim, optimizer)(nn.nn.parameters(), lr=0)
tr_it = TrainScheduler(optim)
if schedule == '1cycle':
add_1cycle(tr_it, epochs * len(gt_set), lrate, momentum,
momentum - 0.10, weight_decay)
else:
# constant learning rate scheduler
tr_it.add_phase(1, (lrate, lrate), (momentum, momentum), weight_decay,
train.annealing_const)
st_it = cast(TrainStopper, None) # type: TrainStopper
if quit == 'early':
st_it = EarlyStopping(train_loader, min_delta, lag)
elif quit == 'dumb':
st_it = EpochStopping(train_loader, epochs)
else:
raise click.BadOptionUsage(
'quit', 'Invalid training interruption scheme {}'.format(quit))
for epoch, loader in enumerate(st_it):
with log.progressbar(label='epoch {}/{}'.format(
epoch, epochs - 1 if epochs > 0 else '∞'),
length=len(loader),
show_pos=True) as bar:
acc_loss = torch.tensor(0.0).to(device, non_blocking=True)
for trial, (input, target) in enumerate(loader):
tr_it.step()
input = input.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
input = input.requires_grad_()
o = nn.nn(input)
# height should be 1 by now
if o.size(2) != 1:
raise KrakenInputException(
'Expected dimension 3 to be 1, actual {}'.format(
o.size(2)))
o = o.squeeze(2)
optim.zero_grad()
# NCW -> WNC
loss = nn.criterion(
o.permute(2, 0, 1), # type: ignore
target,
(o.size(2), ),
(target.size(1), ))
logger.info('trial {}'.format(trial))
if not torch.isinf(loss):
loss.backward()
optim.step()
else:
logger.debug('infinite loss in trial {}'.format(trial))
bar.update(1)
if not epoch % savefreq:
logger.info('Saving to {}_{}'.format(output, epoch))
try:
nn.save_model('{}_{}.mlmodel'.format(output, epoch))
except Exception as e:
logger.error('Saving model failed: {}'.format(str(e)))
if not epoch % report:
logger.debug('Starting evaluation run')
nn.eval()
chars, error = compute_error(rec, list(val_set))
nn.train()
accuracy = (chars - error) / chars
logger.info('Accuracy report ({}) {:0.4f} {} {}'.format(
epoch, accuracy, chars, error))
message('Accuracy report ({}) {:0.4f} {} {}'.format(
epoch, accuracy, chars, error))
st_it.update(accuracy)
if quit == 'early':
message('Moving best model {0}_{1}.mlmodel ({2}) to {0}_best.mlmodel'.
format(output, st_it.best_epoch, st_it.best_loss))
logger.info(
'Moving best model {0}_{1}.mlmodel ({2}) to {0}_best.mlmodel'.
format(output, st_it.best_epoch, st_it.best_loss))
shutil.copy('{}_{}.mlmodel'.format(output, st_it.best_epoch),
'{}_best.mlmodel'.format(output))
