def train(ctx, lineheight, pad, hiddensize, output, load, savefreq, report,
ntrain, lrate, momentum, partition, normalization, reorder,
ground_truth):
"""
Trains a model from image-text pairs.
"""
st_time = time.time()
if ctx.meta['verbose'] > 0:
click.echo(
u'[{:2.4f}] Building ground truth set from {} line images'.format(
time.time() - st_time, len(ground_truth)))
else:
spin('Building ground truth set')
gt_set = GroundTruthContainer()
for line in ground_truth:
gt_set.add(line, normalization=normalization, reorder=reorder)
if ctx.meta['verbose'] > 2:
click.echo(u'[{:2.4f}] Adding {}'.format(time.time() - st_time,
line))
else:
spin('Building ground truth set')
gt_set.repartition(partition)
if ctx.meta['verbose'] < 3:
click.echo('')
if ctx.meta['verbose'] > 0:
click.echo(
u'[{:2.4f}] Training set {} lines, test set {} lines, alphabet {} symbols'
.format(time.time() - st_time, len(gt_set.training_set),
len(gt_set.test_set), len(gt_set.training_alphabet)))
if ctx.meta['verbose'] > 1:
click.echo(u'[{:2.4f}] grapheme\tcount'.format(time.time() - st_time))
for k, v in sorted(gt_set.training_alphabet.iteritems(),
key=lambda x: x[1],
reverse=True):
if unicodedata.combining(k) or k.isspace():
k = unicodedata.name(k)
else:
k = '\t' + k
click.echo(u'[{:2.4f}] {}\t{}'.format(time.time() - st_time, k, v))
if not ctx.meta['verbose']:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
if load:
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Loading existing model from {} '.format(
time.time() - st_time, load))
else:
spin('Loading model')
rnn = tlstm.TlstmSeqRecognizer(load)
if not ctx.meta['verbose'] > 0:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
else:
if ctx.meta['verbose'] > 0:
click.echo(
u'[{:2.4f}] Creating new model with line height {}, {} hidden units, and {} outputs'
.format(time.time() - st_time, lineheight, hiddensize, codec))
else:
spin('Initializing model')
newcodec = Codec()
code2char, char2code = {}, {}
for code, char in enumerate(
[126] +
[ord(c) for c in sorted(list(gt_set.training_alphabet.keys()))]):
code2char[code] = chr(char)
char2code[chr(char)] = code
newcodec.code2char = code2char
newcodec.char2code = char2code
rnn = tlstm.TlstmSeqRecognizer.init_model(lineheight, hiddensize,
len(newcodec.code2char),
newcodec)
if not ctx.meta['verbose']:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
if ctx.meta['verbose'] > 0:
click.echo(
u'[{:2.4f}] Setting learning rate ({}) and momentum ({}) '.format(
time.time() - st_time, lrate, momentum))
rnn.setLearningRate(lrate, momentum)
for trial in range(ntrain):
line, s = gt_set.sample()
res = rnn.trainString(line, s)
if ctx.meta['verbose'] > 2:
click.echo(u'[{0:2.4f}] TRU: {1}\n[{0:2.4f}] OUT: {2}'.format(
time.time() - st_time, s, res))
else:
spin('Training')
if trial and not trial % savefreq:
rnn.save_model('{}_{}'.format(output, trial))
if ctx.meta['verbose'] < 3:
click.echo('')
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Saving to {}_{}'.format(
time.time() - st_time, output, trial))
if trial and not trial % report:
c, e = compute_error(rnn, gt_set.test_set)
if ctx.meta['verbose'] < 3:
click.echo('')
click.echo(u'[{:2.4f}] Accuracy report ({}) {:0.4f} {} {}'.format(
time.time() - st_time, trial, (c - e) / c, c, e))
def train(ctx, lineheight, pad, hiddensize, output, load, savefreq, report,
ntrain, lrate, momentum, partition, normalization, reorder,
ground_truth):
"""
Trains a model from image-text pairs.
"""
st_time = time.time()
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Building ground truth set from {} line images'.format(time.time() - st_time, len(ground_truth)))
else:
spin('Building ground truth set')
gt_set = GroundTruthContainer()
for line in ground_truth:
gt_set.add(line, normalization=normalization, reorder=reorder)
if ctx.meta['verbose'] > 2:
click.echo(u'[{:2.4f}] Adding {}'.format(time.time() - st_time, line))
else:
spin('Building ground truth set')
gt_set.repartition(partition)
if ctx.meta['verbose'] < 3:
click.echo('')
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Training set {} lines, test set {} lines, alphabet {} symbols'.format(time.time() - st_time, len(gt_set.training_set), len(gt_set.test_set), len(gt_set.alphabet)))
if not ctx.meta['verbose']:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
if load:
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Loading existing model from {} '.format(time.time() - st_time, load))
else:
spin('Loading model')
rnn = models.ClstmSeqRecognizer(load)
if not ctx.meta['verbose'] > 0:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
else:
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Creating new model with line height {}, {} hidden units, and {} outputs'.format(time.time() - st_time, lineheight, hiddensize, codec))
else:
spin('Initializing model')
rnn = models.ClstmSeqRecognizer.init_model(lineheight, hiddensize, gt_set.alphabet)
if not ctx.meta['verbose']:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Setting learning rate ({}) and momentum ({}) '.format(time.time() - st_time, lrate, momentum))
rnn.setLearningRate(lrate, momentum)
for trial in xrange(ntrain):
line, s = gt_set.sample()
res = rnn.trainString(line, s)
if ctx.meta['verbose'] > 2:
click.echo(u'[{0:2.4f}] TRU: {1}\n[{0:2.4f}] OUT: {2}'.format(time.time() - st_time, s, res))
else:
spin('Training')
if trial and not trial % savefreq:
rnn.save_model('{}_{}'.format(output, trial))
if ctx.meta['verbose'] < 3:
click.echo('')
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Saving to {}_{}'.format(time.time() - st_time, output, trial))
if trial and not trial % report:
c, e = compute_error(rnn, gt_set.test_set)
if ctx.meta['verbose'] < 3:
click.echo('')
click.echo(u'[{:2.4f}] Accuracy report ({}) {:0.4f} {} {}'.format(time.time() - st_time, trial, (c-e)/c, c, e))
def train(ctx, lineheight, pad, hiddensize, output, load, savefreq, report,
ntrain, lrate, momentum, partition, normalization, reorder,
ground_truth):
"""
Trains a model from image-text pairs.
"""
if load is None:
message(u'Training from scratch net yet supported.')
ctx.exit(1)
logger.info(u'Building ground truth set from {} line images'.format(
len(ground_truth)))
spin(u'Building ground truth set')
gt_set = GroundTruthContainer()
for line in ground_truth:
gt_set.add(line, normalization=normalization, reorder=reorder)
logger.debug(u'Adding {}'.format(line))
spin(u'Building ground truth set')
gt_set.repartition(partition)
logger.info(
u'Training set {} lines, test set {} lines, alphabet {} symbols'.
format(len(gt_set.training_set), len(gt_set.test_set),
len(gt_set.training_alphabet)))
logger.debug(u'grapheme\tcount')
for k, v in sorted(gt_set.training_alphabet.items(),
key=lambda x: x[1],
reverse=True):
if unicodedata.combining(k) or k.isspace():
k = unicodedata.name(k)
else:
k = '\t' + k
logger.debug(u'{}\t{}'.format(k, v))
message(u'\b\u2713', fg='green', nl=False)
message('\033[?25h\n', nl=False)
if load:
logger.info(u'Loading existing model from {} '.format(load))
spin('Loading model')
rnn = models.ClstmSeqRecognizer(load)
message(u'\b\u2713', fg='green', nl=False)
message('\033[?25h\n', nl=False)
else:
ctx.exit(1)
logger.info(u'Setting learning rate ({}) and momentum ({}) '.format(
lrate, momentum))
rnn.setLearningRate(lrate, momentum)
for trial in xrange(ntrain):
line, s = gt_set.sample()
res = rnn.trainString(line, s)
logger.debug(u'TRU: {1}\n[{0:2.4f}] OUT: {2}'.format(s, res))
spin('Training')
if trial and not trial % savefreq:
rnn.save_model('{}_{}'.format(output, trial))
logger.info(u'Saving to {}_{}'.format(output, trial))
if trial and not trial % report:
c, e = compute_error(rnn, gt_set.test_set)
logger.info(u'Accuracy report ({}) {:0.4f} {} {}'.format(
trial, (c - e) / c, c, e))