class Trainer(object):
def __init__(self, args):
super(Trainer, self).__init__()
self.config = getattr(configurations, args.proto)()
self.config['fixed_var_list'] = args.fixed_var_list
self.num_preload = args.num_preload
self.logger = ut.get_logger(self.config['log_file'])
self.lr = self.config['lr']
self.max_epochs = self.config['max_epochs']
self.save_freq = self.config['save_freq']
self.cpkt_path = None
self.validate_freq = None
self.train_perps = []
self.saver = None
self.train_m = None
self.dev_m = None
self.data_manager = DataManager(self.config)
self.validator = Validator(self.config, self.data_manager)
self.validate_freq = ut.get_validation_frequency(
self.data_manager.length_files[ac.TRAINING],
self.config['validate_freq'], self.config['batch_size'])
self.logger.info('Evaluate every {} batches'.format(
self.validate_freq))
_, self.src_ivocab = self.data_manager.init_vocab(
self.data_manager.src_lang)
_, self.trg_ivocab = self.data_manager.init_vocab(
self.data_manager.trg_lang)
# For logging
self.log_freq = 100 # log train stat every this-many batches
self.log_train_loss = 0. # total train loss every log_freq batches
self.log_train_weights = 0.
self.num_batches_done = 0 # number of batches done for the whole training
self.epoch_batches_done = 0 # number of batches done for this epoch
self.epoch_loss = 0. # total train loss for whole epoch
self.epoch_weights = 0. # total train weights (# target words) for whole epoch
self.epoch_time = 0. # total exec time for whole epoch, sounds like that tabloid
def get_model(self, mode):
reuse = mode != ac.TRAINING
d = self.config['init_range']
initializer = tf.random_uniform_initializer(-d, d, seed=ac.SEED)
with tf.variable_scope(self.config['model_name'],
reuse=reuse,
initializer=initializer):
return Model(self.config, mode)
def reload_and_get_cpkt_saver(self, config, sess):
cpkt_path = join(config['save_to'],
'{}.cpkt'.format(config['model_name']))
saver = tf.train.Saver(var_list=tf.trainable_variables(),
max_to_keep=config['n_best'] + 1)
# TF some version >= 0.11, no longer .cpkt so check for meta file instead
if exists(cpkt_path + '.meta') and config['reload']:
self.logger.info('Reload model from {}'.format(cpkt_path))
saver.restore(sess, cpkt_path)
self.cpkt_path = cpkt_path
self.saver = saver
def train(self):
tf.reset_default_graph()
with tf.Graph().as_default():
tf.set_random_seed(ac.SEED)
self.train_m = self.get_model(ac.TRAINING)
self.dev_m = self.get_model(ac.VALIDATING)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
self.reload_and_get_cpkt_saver(self.config, sess)
self.logger.info('Set learning rate to {}'.format(self.lr))
sess.run(tf.assign(self.train_m.lr, self.lr))
for e in xrange(self.max_epochs):
for b, batch_data in self.data_manager.get_batch(
mode=ac.TRAINING, num_batches=self.num_preload):
self.run_log_save(sess, b, e, batch_data)
self.maybe_validate(sess)
self.report_epoch(e)
self.logger.info('It is finally done, mate!')
self.logger.info('Train perplexities:')
self.logger.info(', '.join(map(str, self.train_perps)))
numpy.save(join(self.config['save_to'], 'train_perps.npy'),
self.train_perps)
self.logger.info('Save final checkpoint')
self.saver.save(sess, self.cpkt_path)
# Evaluate on test
if exists(self.data_manager.ids_files[ac.TESTING]):
self.logger.info('Evaluate on test')
best_bleu = numpy.max(self.validator.best_bleus)
best_cpkt_path = self.validator.get_cpkt_path(best_bleu)
self.logger.info(
'Restore best cpkt from {}'.format(best_cpkt_path))
self.saver.restore(sess, best_cpkt_path)
self.validator.evaluate(sess, self.dev_m, mode=ac.TESTING)
def report_epoch(self, e):
self.logger.info('Finish epoch {}'.format(e + 1))
self.logger.info(' It takes {}'.format(
ut.format_seconds(self.epoch_time)))
self.logger.info(' Avergage # words/second {}'.format(
self.epoch_weights / self.epoch_time))
self.logger.info(' Average seconds/batch {}'.format(
self.epoch_time / self.epoch_batches_done))
train_perp = self.epoch_loss / self.epoch_weights
self.epoch_batches_done = 0
self.epoch_time = 0.
self.epoch_loss = 0.
self.epoch_weights = 0.
train_perp = numpy.exp(train_perp) if train_perp < 300 else float(
'inf')
self.train_perps.append(train_perp)
self.logger.info(' train perplexity: {}'.format(train_perp))
def sample_input(self, batch_data):
# TODO: more on sampling
src_sent = batch_data[0][0]
src_length = batch_data[1][0]
trg_sent = batch_data[2][0]
target = batch_data[3][0]
weight = batch_data[4][0]
src_sent = map(self.src_ivocab.get, src_sent)
src_sent = u' '.join(src_sent)
trg_sent = map(self.trg_ivocab.get, trg_sent)
trg_sent = u' '.join(trg_sent)
target = map(self.trg_ivocab.get, target)
target = u' '.join(target)
weight = ' '.join(map(str, weight))
self.logger.info('Sample input data:')
self.logger.info(u'Src: {}'.format(src_sent))
self.logger.info(u'Src len: {}'.format(src_length))
self.logger.info(u'Trg: {}'.format(trg_sent))
self.logger.info(u'Tar: {}'.format(target))
self.logger.info(u'W: {}'.format(weight))
def run_log_save(self, sess, b, e, batch_data):
start = time.time()
src_inputs, src_seq_lengths, trg_inputs, trg_targets, target_weights = batch_data
feed = {
self.train_m.src_inputs: src_inputs,
self.train_m.src_seq_lengths: src_seq_lengths,
self.train_m.trg_inputs: trg_inputs,
self.train_m.trg_targets: trg_targets,
self.train_m.target_weights: target_weights
}
loss, _ = sess.run([self.train_m.loss, self.train_m.train_op], feed)
num_trg_words = numpy.sum(target_weights)
self.num_batches_done += 1
self.epoch_batches_done += 1
self.epoch_loss += loss
self.epoch_weights += num_trg_words
self.log_train_loss += loss
self.log_train_weights += num_trg_words
self.epoch_time += time.time() - start
if self.num_batches_done % (10 * self.log_freq) == 0:
self.sample_input(batch_data)
if self.num_batches_done % self.log_freq == 0:
acc_speed_word = self.epoch_weights / self.epoch_time
acc_speed_time = self.epoch_time / self.epoch_batches_done
avg_word_perp = self.log_train_loss / self.log_train_weights
avg_word_perp = numpy.exp(
avg_word_perp) if avg_word_perp < 300 else float('inf')
self.log_train_loss = 0.
self.log_train_weights = 0.
self.logger.info('Batch {}, epoch {}/{}:'.format(
b, e + 1, self.max_epochs))
self.logger.info(
' avg word perp: {0:.2f}'.format(avg_word_perp))
self.logger.info(' acc trg words/s: {}'.format(
int(acc_speed_word)))
self.logger.info(
' acc sec/batch: {0:.2f}'.format(acc_speed_time))
if self.num_batches_done % self.save_freq == 0:
start = time.time()
self.saver.save(sess, self.cpkt_path)
self.logger.info('Save model to {}, takes {}'.format(
self.cpkt_path, ut.format_seconds(time.time() - start)))
def maybe_validate(self, sess):
if self.num_batches_done % self.validate_freq == 0:
self.validator.validate_and_save(sess, self.dev_m, self.saver)
def _call_me_maybe(self):
pass # NO
class Translator(object):
def __init__(self, args):
super(Translator, self).__init__()
self.config = getattr(configurations, args.proto)()
self.reverse = self.config['reverse']
self.logger = ut.get_logger(self.config['log_file'])
self.input_file = args.input_file
self.model_file = args.model_file
self.plot_align = args.plot_align
self.unk_repl = args.unk_repl
if self.input_file is None or self.model_file is None or not os.path.exists(self.input_file) or not os.path.exists(self.model_file + '.meta'):
raise ValueError('Input file or model file does not exist')
self.data_manager = DataManager(self.config)
_, self.src_ivocab = self.data_manager.init_vocab(self.data_manager.src_lang)
_, self.trg_ivocab = self.data_manager.init_vocab(self.data_manager.trg_lang)
self.translate()
def ids_to_trans(self, trans_ids, trans_alignments, no_unk_src_toks):
words = []
word_ids = []
# Could have done better but this is clearer to me
if not self.unk_repl:
for idx, word_idx in enumerate(trans_ids):
if word_idx == ac.EOS_ID:
break
words.append(self.trg_ivocab[word_idx])
word_ids.append(word_idx)
else:
for idx, word_idx in enumerate(trans_ids):
if word_idx == ac.EOS_ID:
break
if word_idx == ac.UNK_ID:
# Replace UNK with higest attention source words
alignment = trans_alignments[idx]
highest_att_src_tok_pos = numpy.argmax(alignment)
words.append(no_unk_src_toks[highest_att_src_tok_pos])
else:
words.append(self.trg_ivocab[word_idx])
word_ids.append(word_idx)
return u' '.join(words), word_ids
def get_model(self, mode):
reuse = mode != ac.TRAINING
d = self.config['init_range']
initializer = tf.random_uniform_initializer(-d, d)
with tf.variable_scope(self.config['model_name'], reuse=reuse, initializer=initializer):
return Model(self.config, mode)
def get_trans(self, probs, scores, symbols, parents, alignments, no_unk_src_toks):
sorted_rows = numpy.argsort(scores[:, -1])[::-1]
best_trans_alignments = []
best_trans = None
best_tran_ids = None
beam_trans = []
for i, r in enumerate(sorted_rows):
row_idx = r
col_idx = scores.shape[1] - 1
trans_ids = []
trans_alignments = []
while True:
if col_idx < 0:
break
trans_ids.append(symbols[row_idx, col_idx])
align = alignments[row_idx, col_idx, :]
trans_alignments.append(align)
if i == 0:
best_trans_alignments.append(align if not self.reverse else align[::-1])
row_idx = parents[row_idx, col_idx]
col_idx -= 1
trans_ids = trans_ids[::-1]
trans_alignments = trans_alignments[::-1]
trans_out, trans_out_ids = self.ids_to_trans(trans_ids, trans_alignments, no_unk_src_toks)
beam_trans.append(u'{} {:.2f} {:.2f}'.format(trans_out, scores[r, -1], probs[r, -1]))
if i == 0: # highest prob trans
best_trans = trans_out
best_tran_ids = trans_out_ids
return best_trans, best_tran_ids, u'\n'.join(beam_trans), best_trans_alignments[::-1]
def plot_head_map(self, mma, target_labels, target_ids, source_labels, source_ids, filename):
"""https://github.com/EdinburghNLP/nematus/blob/master/utils/plot_heatmap.py
Change the font in family param below. If the system font is not used, delete matplotlib
font cache https://github.com/matplotlib/matplotlib/issues/3590
"""
fig, ax = plt.subplots()
heatmap = ax.pcolor(mma, cmap=plt.cm.Blues)
# put the major ticks at the middle of each cell
ax.set_xticks(numpy.arange(mma.shape[1]) + 0.5, minor=False)
ax.set_yticks(numpy.arange(mma.shape[0]) + 0.5, minor=False)
# without this I get some extra columns rows
# http://stackoverflow.com/questions/31601351/why-does-this-matplotlib-heatmap-have-an-extra-blank-column
ax.set_xlim(0, int(mma.shape[1]))
ax.set_ylim(0, int(mma.shape[0]))
# want a more natural, table-like display
ax.invert_yaxis()
ax.xaxis.tick_top()
# source words -> column labels
ax.set_xticklabels(source_labels, minor=False, family='Source Code Pro')
for xtick, idx in zip(ax.get_xticklabels(), source_ids):
if idx == ac.UNK_ID:
xtick.set_color('b')
# target words -> row labels
ax.set_yticklabels(target_labels, minor=False, family='Source Code Pro')
for ytick, idx in zip(ax.get_yticklabels(), target_ids):
if idx == ac.UNK_ID:
ytick.set_color('b')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig(filename)
plt.close('all')
def translate(self):
with tf.Graph().as_default():
train_model = self.get_model(ac.TRAINING)
model = self.get_model(ac.VALIDATING)
with tf.Session() as sess:
self.logger.info('Restore model from {}'.format(self.model_file))
saver = tf.train.Saver(var_list=tf.trainable_variables())
saver.restore(sess, self.model_file)
best_trans_file = self.input_file + '.best_trans'
beam_trans_file = self.input_file + '.beam_trans'
open(best_trans_file, 'w').close()
open(beam_trans_file, 'w').close()
ftrans = open(best_trans_file, 'w', 'utf-8')
btrans = open(beam_trans_file, 'w', 'utf-8')
self.logger.info('Start translating {}'.format(self.input_file))
start = time.time()
count = 0
for (src_input, src_seq_len, no_unk_src_toks) in self.data_manager.get_trans_input(self.input_file):
feed = {
model.src_inputs: src_input,
model.src_seq_lengths: src_seq_len
}
probs, scores, symbols, parents, alignments = sess.run([model.probs, model.scores, model.symbols, model.parents, model.alignments], feed_dict=feed)
alignments = numpy.transpose(alignments, axes=(1, 0, 2))
probs = numpy.transpose(numpy.array(probs))
scores = numpy.transpose(numpy.array(scores))
symbols = numpy.transpose(numpy.array(symbols))
parents = numpy.transpose(numpy.array(parents))
best_trans, best_trans_ids, beam_trans, best_trans_alignments = self.get_trans(probs, scores, symbols, parents, alignments, no_unk_src_toks)
ftrans.write(best_trans + '\n')
btrans.write(beam_trans + '\n\n')
if self.plot_align:
src_input = numpy.reshape(src_input, [-1])
if self.reverse:
src_input = src_input[::-1]
no_unk_src_toks = no_unk_src_toks[::-1]
trans_toks = best_trans.split()
best_trans_alignments = numpy.array(best_trans_alignments)[:len(trans_toks)]
filename = '{}_{}.png'.format(self.input_file, count)
self.plot_head_map(best_trans_alignments, trans_toks, best_trans_ids, no_unk_src_toks, src_input, filename)
count += 1
if count % 100 == 0:
self.logger.info(' Translating line {}, average {} seconds/sent'.format(count, (time.time() - start) / count))
ftrans.close()
btrans.close()
self.logger.info('Done translating {}, it takes {} minutes'.format(self.input_file, float(time.time() - start) / 60.0))
class Translator(object):
def __init__(self, args):
super(Translator, self).__init__()
self.config = getattr(configurations, args.proto)()
self.reverse = self.config['reverse']
self.unk_repl = self.config['unk_repl']
self.logger = ut.get_logger(self.config['log_file'])
self.input_file = args.input_file
self.model_file = args.model_file
self.plot_align = args.plot_align
if self.input_file is None or self.model_file is None or not os.path.exists(
self.input_file) or not os.path.exists(self.model_file +
'.meta'):
raise ValueError('Input file or model file does not exist')
self.data_manager = DataManager(self.config)
_, self.src_ivocab = self.data_manager.init_vocab(
self.data_manager.src_lang)
_, self.trg_ivocab = self.data_manager.init_vocab(
self.data_manager.trg_lang)
self.translate()
def get_model(self, mode):
reuse = mode != ac.TRAINING
d = self.config['init_range']
initializer = tf.random_uniform_initializer(-d, d)
with tf.variable_scope(self.config['model_name'],
reuse=reuse,
initializer=initializer):
return Model(self.config, mode)
def translate(self):
with tf.Graph().as_default():
train_model = self.get_model(ac.TRAINING)
model = self.get_model(ac.VALIDATING)
with tf.Session() as sess:
self.logger.info('Restore model from {}'.format(
self.model_file))
saver = tf.train.Saver(var_list=tf.trainable_variables())
saver.restore(sess, self.model_file)
best_trans_file = self.input_file + '.best_trans'
beam_trans_file = self.input_file + '.beam_trans'
open(best_trans_file, 'w').close()
open(beam_trans_file, 'w').close()
ftrans = open(best_trans_file, 'w', 'utf-8')
btrans = open(beam_trans_file, 'w', 'utf-8')
self.logger.info('Start translating {}'.format(
self.input_file))
start = time.time()
count = 0
for (src_input, src_seq_len,
no_unk_src_toks) in self.data_manager.get_trans_input(
self.input_file):
feed = {
model.src_inputs: src_input,
model.src_seq_lengths: src_seq_len
}
probs, scores, symbols, parents, alignments = sess.run(
[
model.probs, model.scores, model.symbols,
model.parents, model.alignments
],
feed_dict=feed)
alignments = numpy.transpose(alignments, axes=(1, 0, 2))
probs = numpy.transpose(numpy.array(probs))
scores = numpy.transpose(numpy.array(scores))
symbols = numpy.transpose(numpy.array(symbols))
parents = numpy.transpose(numpy.array(parents))
best_trans, best_trans_ids, beam_trans, best_trans_alignments = ut.get_trans(
probs,
scores,
symbols,
parents,
alignments,
no_unk_src_toks,
self.trg_ivocab,
reverse=self.reverse,
unk_repl=self.unk_repl)
best_trans_wo_eos = best_trans.split()[:-1]
best_trans_wo_eos = u' '.join(best_trans_wo_eos)
ftrans.write(best_trans_wo_eos + '\n')
btrans.write(beam_trans + '\n\n')
if self.plot_align:
src_input = numpy.reshape(src_input, [-1])
if self.reverse:
src_input = src_input[::-1]
no_unk_src_toks = no_unk_src_toks[::-1]
trans_toks = best_trans.split()
best_trans_alignments = numpy.array(
best_trans_alignments)[:len(trans_toks)]
filename = '{}_{}.png'.format(self.input_file, count)
ut.plot_head_map(best_trans_alignments, trans_toks,
best_tran_ids, no_unk_src_toks,
src_input, filename)
count += 1
if count % 100 == 0:
self.logger.info(
' Translating line {}, average {} seconds/sent'.
format(count, (time.time() - start) / count))
ftrans.close()
btrans.close()
self.logger.info(
'Done translating {}, it takes {} minutes'.format(
self.input_file,
float(time.time() - start) / 60.0))