def inference(ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers=1,
jobid=0,
scope=None):
"""Perform translation."""
if hparams.inference_indices:
assert num_workers == 1
if not hparams.attention:
model_creator = nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
if num_workers == 1:
single_worker_inference(infer_model, ckpt, inference_input_file,
inference_output_file, hparams)
else:
multi_worker_inference(infer_model,
ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers=num_workers,
jobid=jobid)
def infer(ckpt, inference_input_file, inference_output_file, hparams):
"""
Perform translation.
"""
model_creator = gnmt_model.GNMTModel
infer_model = model_helper.create_infer_model(model_creator, hparams)
# Read data
infer_data = utils.load_data(inference_input_file)
config_proto = tf.ConfigProto()
config_proto.gpu_options.allow_growth = True
with tf.Session(
graph=infer_model.graph, config=config_proto) as sess:
loaded_infer_model = model_helper.load_model(
infer_model.model, ckpt, sess, "infer")
sess.run(
infer_model.iterator.initializer,
feed_dict={
infer_model.src_placeholder: infer_data,
infer_model.batch_size_placeholder: hparams.infer_batch_size
})
# Decode
utils.log("Start decoding")
loaded_infer_model.decode_and_evaluate(
"infer",
sess,
inference_output_file,
ref_file=None,
beam_width=hparams.beam_width,
tgt_eos=hparams.eos,
num_translations_per_input=hparams.num_translations_per_input)
def evaluate(hparams, ckpt):
if hparams.model_architecture == "rnn-model": model_creator = model.RNN
else: raise ValueError("Unknown model architecture. Only simple_rnn is supported so far.")
if hparams.val_target_path:
eval_model = model_helper.create_eval_model(model_creator, hparams, tf.contrib.learn.ModeKeys.EVAL)
eval_sess = tf.Session(config=utils.get_config_proto(), graph=eval_model.graph)
with eval_model.graph.as_default():
loaded_eval_model = model_helper.load_model(eval_model.model, eval_sess, "evaluation", ckpt)
iterator_feed_dict={
eval_model.input_file_placeholder: hparams.eval_input_path,
eval_model.output_file_placeholder: hparams.eval_target_path
}
eval_loss = eval(loaded_eval_model, eval_sess, eval_model.iterator, iterator_feed_dict)
print("Eval loss: %.3f"%eval_loss)
print("Starting predictions:")
prediction_model = model_helper.create_infer_model(model_creator, hparams, tf.contrib.learn.ModeKeys.INFER)
prediction_sess = tf.Session(config=utils.get_config_proto(), graph=prediction_model.graph)
with prediction_model.graph.as_default():
loaded_prediction_model = model_helper.load_model(prediction_model.model, prediction_sess, "prediction", ckpt)
iterator_feed_dict = {
prediction_model.input_file_placeholder: hparams.val_input_path,
}
predictions=predict(loaded_prediction_model, prediction_sess, prediction_model.iterator, iterator_feed_dict)
np.savetxt(os.path.join(hparams.eval_output_folder, "classes.txt"), predictions["classes"])
np.savetxt(os.path.join(hparams.eval_output_folder, "probabilities.txt"), predictions["probabilities"])
def infer(hparams, ckpt_dir, scope=None, target_session=""):
output_dir = os.path.join(hparams.base_dir, "infer")
if not misc.check_file_existence(output_dir):
tf.gfile.MakeDirs(output_dir)
model_creator = get_model_creator(hparams.model_type)
infer_model = helper.create_infer_model(model_creator, hparams, scope)
infer_sess, loaded_infer_model, global_step = start_sess_and_load_model(
infer_model, ckpt_dir)
tf.logging.info("Restore model from global step %d" % global_step)
# Summary
summary_name = "infer_summary"
summary_path = os.path.join(output_dir, summary_name)
if not tf.gfile.Exists(summary_path):
tf.gfile.MakeDirs(summary_path)
summary_writer = tf.summary.FileWriter(
summary_path, infer_model.graph)
infer_results = []
tf.logging.info("Ready to infer")
start_time = time.time()
step = 0
while True:
try:
tf.logging.info("Start infer step:%d" % step)
results = loaded_infer_model.infer(infer_sess)
summary_writer.add_summary(results.summary, global_step)
infer_results.append(results.detected_images)
step += 1
except tf.errors.OutOfRangeError:
tf.logging.info("Finish infer <time:%d>" % (start_time-time.time()))
break
for result in infer_results:
plt.show(result)
def inference(ckpt_path,
inference_input_file,
inference_output_file,
hparams,
num_workers=1,
jobid=0,
scope=None):
"""Perform translation."""
if hparams.inference_indices:
assert num_workers == 1
model_creator = get_model_creator(hparams)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
sess, loaded_infer_model = start_sess_and_load_model(
infer_model, ckpt_path)
translation = ''
if num_workers == 1:
translation = single_worker_inference(sess, infer_model,
loaded_infer_model,
inference_input_file,
inference_output_file, hparams)
else:
multi_worker_inference(sess,
infer_model,
loaded_infer_model,
inference_input_file,
inference_output_file,
hparams,
num_workers=num_workers,
jobid=jobid)
sess.close()
return translation
def inference(ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers=1,
jobid=0,
scope=None):
"""Perform translation."""
if hparams.inference_indices:
assert num_workers == 1
model_creator = AttentionModel
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
if num_workers == 1:
single_worker_inference(infer_model, ckpt, inference_input_file,
inference_output_file, hparams)
else:
multi_worker_inference(infer_model,
ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers=num_workers,
jobid=jobid)
def run_sample_decode_pungan_prepare(hparams, scope=None, target_session=""):
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
if not hparams.attention: # choose this model
model_creator = nmt_model.Model
else: # Attention
if (hparams.encoder_type == "gnmt"
or hparams.attention_architecture in ["gnmt", "gnmt_v2"]):
model_creator = gnmt_model.GNMTModel
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
else:
raise ValueError("Unknown attention architecture %s" %
hparams.attention_architecture)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
wsd_src_file = "%s" % (hparams.sample_prefix)
wsd_src_data = inference.load_data(wsd_src_file)
model_dir = hparams.out_dir
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
eval_result = run_sample_decode_pungan(infer_model, infer_sess, model_dir,
hparams, wsd_src_data)
print('eval_result', eval_result)
print('eval_result.len', len(eval_result))
'''
for i in range(0,256):
if i%32 == 0:
print('\n')
print('eval_result[',i,']',eval_result[i])
'''
print('wsd_src_data', wsd_src_data)
print('wsd_src_data.len', len(wsd_src_data))
#len=16 wsd_src_data [u'problem%1:10:00::', u'problem%1:26:00::', u'drive%1:04:00::', u'drive%1:04:03::', u'identity%1:07:00::', u'identity%1:24:01::', u'point%1:10:01::', u'point%1:06:00::', u'tension%1:26:01::', u'tension%1:26:03::', u'log%2:32:00::', u'log%2:35:00::', u'fan%1:06:00::', u'fan%1:18:00::', u'file%2:32:00::', u'file%2:35:00::']
eval_result_new = []
for block in range(len(eval_result) / (2 * hparams.sample_size)):
src_word1, src_word2 = wsd_src_data[2 *
block], wsd_src_data[2 * block + 1]
for sent_id in range(block * hparams.sample_size,
(block + 1) * hparams.sample_size):
tgt_sent = src_word1.decode().encode(
'utf-8') + ' ' + eval_result[sent_id]
eval_result_new.append(tgt_sent)
return wsd_src_data, eval_result_new
def infer_fn(hparams, identity, scope=None, extra_args=None, target_session=""):
"""main entry point for inference and evaluation."""
# create infer and eval models
infer_model = model_helper.create_infer_model(
diag_model.Model, hparams, scope, extra_args=extra_args)
eval_model = model_helper.create_eval_model(diag_model.Model, hparams, scope)
config_proto = utils.get_config_proto(
log_device_placement=hparams.log_device_placement,
allow_soft_placement=True)
# create the eval session
eval_sess = tf.Session(
target=target_session, config=config_proto, graph=eval_model.graph)
secondary_fn_tmp(hparams, identity, hparams.out_dir, infer_model, eval_model,
eval_sess, "infer", single_worker_inference)
def inference(ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers=1):
"""Perform inference."""
model_creator = model.Model
infer_model = model_helper.create_infer_model(model_creator, hparams)
single_worker_inference(
infer_model,
ckpt,
inference_input_file,
inference_output_file,
hparams)
def _createTestInferCheckpoint(self, hparams, out_dir):
if not hparams.attention:
model_creator = nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = model_helper.create_infer_model(model_creator, hparams)
with self.test_session(graph=infer_model.graph) as sess:
loaded_model, global_step = model_helper.create_or_load_model(
infer_model.model, out_dir, sess, "infer_name")
ckpt = loaded_model.saver.save(sess,
os.path.join(
out_dir, "translate.ckpt"),
global_step=global_step)
return ckpt
def inference(ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers=1,
jobid=0,
scope=None):
"""Perform translation."""
if hparams.inference_indices:
assert num_workers == 1
if not hparams.attention:
model_creator = nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
#emb_matrix = model_helper._create_or_load_embed("embedding_encoder", hparams.src_vocab_file, hparams.src_embed_file,
#hparams.src_vocab_size, hparams.batch_size, tf.float32)
#emb_matrix =infer_model.model.embedding_encoder
#print ("emb_matrix",emb_matrix)
if num_workers == 1:
single_worker_inference(
#emb_matrix,
infer_model,
ckpt,
inference_input_file,
inference_output_file,
hparams,
model_creator)
else:
multi_worker_inference(infer_model,
ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers=num_workers,
jobid=jobid)
def _createTestInferCheckpoint(self, hparams, name):
# Prepare
hparams.vocab_prefix = ("nmt/testdata/test_infer_vocab")
hparams.src_vocab_file = hparams.vocab_prefix + "." + hparams.src
hparams.tgt_vocab_file = hparams.vocab_prefix + "." + hparams.tgt
out_dir = os.path.join(tf.test.get_temp_dir(), name)
os.makedirs(out_dir)
hparams.out_dir = out_dir
# Create check point
model_creator = inference.get_model_creator(hparams)
infer_model = model_helper.create_infer_model(model_creator, hparams)
with self.test_session(graph=infer_model.graph) as sess:
loaded_model, global_step = model_helper.create_or_load_model(
infer_model.model, out_dir, sess, "infer_name")
ckpt_path = loaded_model.saver.save(sess,
os.path.join(
out_dir, "translate.ckpt"),
global_step=global_step)
return ckpt_path
def decode(ckpt_path, inference_input_file, inference_output_file, hparams):
model_creator = get_model_creator(hparams)
infer_model = model_helper.create_infer_model(model_creator, hparams, None)
sess, loaded_infer_model = start_sess_and_load_model(
infer_model, ckpt_path)
# Read data
infer_data = load_data(inference_input_file, hparams)
print("infer_data:", infer_data)
for i in range(len(infer_data)):
sess.run(infer_model.iterator.initializer,
feed_dict={
infer_model.src_placeholder: [infer_data[i]],
infer_model.batch_size_placeholder: 1
})
if hparams.task == "joint":
outputs, intent_pred, src_seq_length, attention_summary = \
loaded_infer_model.decode(sess)
elif hparams.task == "intent":
intent_pred, attention_summary = loaded_infer_model.decode(sess)
if hparams.infer_mode == "beam_search":
# get the top translation.
outputs = outputs[0]
utils.print_out(" src: %s" % infer_data[i])
if hparams.task == "joint":
translation = nmt_utils.get_translation(
outputs,
src_seq_length,
sent_id=0,
tgt_eos=hparams.eos,
subword_option=hparams.subword_option)
utils.print_out(b" intent (pred): %s" % intent_pred[0])
utils.print_out(b" slot (pred): %s\n" % translation)
elif hparams.task == "intent":
utils.print_out(b" intent (pred): %s\n" % intent_pred[0])
def modelInit():
out_dir = 'tmp/nmt_attention_model'
print("loading parameters")
hparams = utils.load_hparams(out_dir)
ckpt = tf.train.latest_checkpoint(out_dir)
if not hparams.attention:
model_creator = nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = model_helper.create_infer_model(model_creator,
hparams,
scope=None)
return infer_model, ckpt, hparams
def _load_model(self, checkpoint_path, default_hparams_path,
model_hparams_path, source_vocab_path, target_vocab_path):
hparams = self._create_hparams(default_hparams_path,
model_hparams_path, source_vocab_path,
target_vocab_path)
model_creator = gnmt_model.GNMTModel
infer_model = model_helper.create_infer_model(model_creator,
hparams,
scope=None)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=GPU_MEM_FRAC)
sess = tf.Session(graph=infer_model.graph,
config=tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True))
with infer_model.graph.as_default():
nmt_model = model_helper.load_model(infer_model.model,
checkpoint_path, sess, "infer")
return sess, nmt_model, infer_model, hparams
def eval_fn(hparams, scope=None, target_session=""):
"""Evaluate a translation model."""
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
avg_ckpts = hparams.avg_ckpts
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
if not hparams.attention:
model_creator = nmt_model.Model
else: # Attention
if (hparams.encoder_type == "gnmt"
or hparams.attention_architecture in ["gnmt", "gnmt_v2"]):
model_creator = gnmt_model.GNMTModel
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
else:
raise ValueError("Unknown attention architecture %s" %
hparams.attention_architecture)
eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
summary_name = "train_log"
model_dir = hparams.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("# log_file=%s" % log_file, log_f)
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
# First evaluation
ckpt_size = len(
tf.train.get_checkpoint_state(model_dir).all_model_checkpoint_paths)
for ckpt_index in range(ckpt_size):
train.run_full_eval(model_dir,
infer_model,
infer_sess,
eval_model,
eval_sess,
hparams,
None,
sample_src_data,
sample_tgt_data,
avg_ckpts,
ckpt_index=ckpt_index)
def train(hparams, scope=None, target_session="", compute_ppl=0):
"""Train a translation model."""
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
avg_ckpts = hparams.avg_ckpts
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
if not hparams.attention: # choose this model
model_creator = nmt_model.Model
else: # Attention
if (hparams.encoder_type == "gnmt"
or hparams.attention_architecture in ["gnmt", "gnmt_v2"]):
model_creator = gnmt_model.GNMTModel
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
else:
raise ValueError("Unknown attention architecture %s" %
hparams.attention_architecture)
train_model = model_helper.create_train_model(model_creator, hparams,
scope)
eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
wsd_src_file = "%s" % (hparams.sample_prefix)
wsd_src_data = inference.load_data(wsd_src_file)
summary_name = "train_log"
model_dir = hparams.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("# log_file=%s" % log_file, log_f)
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
train_sess = tf.Session(target=target_session,
config=config_proto,
graph=train_model.graph)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
# First evaluation
'''
run_full_eval(
model_dir, infer_model, infer_sess,
eval_model, eval_sess, hparams,
summary_writer, sample_src_data,
sample_tgt_data, avg_ckpts)
'''
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
stats, info, start_train_time = before_train(loaded_train_model,
train_model, train_sess,
global_step, hparams, log_f)
end_step = global_step + 100
while global_step < end_step: # num_train_steps
### Run a step ###
start_time = time.time()
try:
# then forward inference result to WSD, get reward
step_result = loaded_train_model.train(train_sess)
# forward reward to placeholder of loaded_train_model, and write a new train function where loss = loss*reward
hparams.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
hparams.epoch_step = 0
utils.print_out(
"# Finished an epoch, step %d. Perform external evaluation" %
global_step)
# run_sample_decode(infer_model, infer_sess, model_dir, hparams,
# summary_writer, sample_src_data, sample_tgt_data)
# only for pretrain
# run_external_eval(infer_model, infer_sess, model_dir, hparams,
# summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
train_sess.run(train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Process step_result, accumulate stats, and write summary
global_step, info["learning_rate"], step_summary = update_stats(
stats, start_time, step_result, hparams)
summary_writer.add_summary(step_summary, global_step)
if compute_ppl:
run_internal_eval(eval_model, eval_sess, model_dir, hparams,
summary_writer)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
is_overflow = process_stats(stats, info, global_step,
steps_per_stats, log_f)
print_step_info(" ", global_step, info,
_get_best_results(hparams), log_f)
if is_overflow:
break
# Reset statistics
stats = init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.print_out("# Save eval, global step %d" % global_step)
utils.add_summary(summary_writer, global_step, "train_ppl",
info["train_ppl"])
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_internal_eval(eval_model, eval_sess, model_dir, hparams,
summary_writer)
if global_step - last_external_eval_step >= steps_per_external_eval:
last_external_eval_step = global_step
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
'''
def run_sample_decode_pungan_prepare(hparams, scope=None, target_session=""):
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
if not hparams.attention: # choose this model
model_creator = nmt_model.Model
else: # Attention
if (hparams.encoder_type == "gnmt"
or hparams.attention_architecture in ["gnmt", "gnmt_v2"]):
model_creator = gnmt_model.GNMTModel
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
else:
raise ValueError("Unknown attention architecture %s" %
hparams.attention_architecture)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
def dealt(input, output):
with open(input) as f:
with open(output, 'w') as fw:
for line in f:
l = line.strip().split()
l.reverse()
sent = ' '.join(l)
fw.write(sent + '\n')
wsd_src_file = "%s" % (hparams.sample_prefix)
wsd_src_file_new = wsd_src_file + '.new'
dealt(wsd_src_file, wsd_src_file_new)
wsd_src_data = inference.load_data(wsd_src_file_new)
model_dir = hparams.out_dir
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
print('len wsd_src_data', len(wsd_src_data))
eval_result = []
for i in range(len(wsd_src_data) / 32):
eval_result += run_sample_decode_pungan(
infer_model, infer_sess, model_dir, hparams,
wsd_src_data[i * 32:(i + 1) * 32])
print('eval_result')
print(eval_result)
print(len(eval_result))
backward_step1_in = []
with open(PUNGAN_ROOT_PATH +
'/Pun_Generation/data/1backward/backward_step1.in') as f:
for line in f:
backward_step1_in.append(line.strip())
def wsd_input_format(wsd_src_data, eval_result):
'''
test_data[0] {'target_word': u'art#n', 'target_sense': None, 'id': 'senseval2.d000.s000.t000', 'context': ['the', '<target>', 'of', 'change_ringing', 'be', 'peculiar', 'to', 'the', 'english', ',', 'and', ',', 'like', 'most', 'english', 'peculiarity', ',', 'unintelligible', 'to', 'the', 'rest', 'of', 'the', 'world', '.'], 'poss': ['DET', 'NOUN', 'ADP', 'NOUN', 'VERB', 'ADJ', 'PRT', 'DET', 'NOUN', '.', 'CONJ', '.', 'ADP', 'ADJ', 'ADJ', 'NOUN', '.', 'ADJ', 'PRT', 'DET', 'NOUN', 'ADP', 'DET', 'NOUN', '.']}
'''
wsd_input = []
senses_input = []
for i in range(len(eval_result)):
block = i / 32
src_word1, src_word2 = backward_step1_in[
2 * block], backward_step1_in[2 * block + 1]
tgt_sent = wsd_src_data[i].decode().encode(
'utf-8') + ' ' + eval_result[i]
tgt_word = src_word1
synset = wn.lemma_from_key(tgt_word).synset()
s = synset.name()
target_word = '#'.join(s.split('.')[:2])
context = tgt_sent.split(' ')
for j in range(len(context)):
if context[j] == tgt_word:
context[j] = '<target>'
poss_list = ['.' for _ in range(len(context))]
tmp_dict = {
'target_word': target_word,
'target_sense': None,
'id': None,
'context': context,
'poss': poss_list
}
wsd_input.append(tmp_dict)
senses_input.append((src_word1, src_word2))
return wsd_input, senses_input
wsd_input, senses_input = wsd_input_format(wsd_src_data, eval_result)
print('wsd_input', wsd_input)
print("len of wsd_input", len(wsd_input))
return wsd_input, senses_input, wsd_src_data, eval_result
def train(hparams, scope=None, target_session=""):
"""Train a translation model."""
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
avg_ckpts = hparams.avg_ckpts
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
# Create model
model_creator = get_model_creator(hparams)
train_model = model_helper.create_train_model(model_creator, hparams,
scope)
eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
summary_name = "train_log"
model_dir = hparams.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("# log_file=%s" % log_file, log_f)
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
train_sess = tf.Session(target=target_session,
config=config_proto,
graph=train_model.graph)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
# First evaluation
run_full_eval(model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data,
avg_ckpts)
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
stats, info, start_train_time = before_train(loaded_train_model,
train_model, train_sess,
global_step, hparams, log_f)
while global_step < num_train_steps:
### Run a step ###
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
hparams.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
hparams.epoch_step = 0
utils.print_out(
"# Finished an epoch, step %d. Perform external evaluation" %
global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
train_sess.run(train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Process step_result, accumulate stats, and write summary
global_step, info["learning_rate"], step_summary = update_stats(
stats, start_time, step_result)
summary_writer.add_summary(step_summary, global_step)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
is_overflow = process_stats(stats, info, global_step,
steps_per_stats, log_f)
print_step_info(" ", global_step, info, get_best_results(hparams),
log_f)
if is_overflow:
break
# Reset statistics
stats = init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.print_out("# Save eval, global step %d" % global_step)
add_info_summaries(summary_writer, global_step, info)
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_internal_eval(eval_model, eval_sess, model_dir, hparams,
summary_writer)
if global_step - last_external_eval_step >= steps_per_external_eval:
last_external_eval_step = global_step
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
(result_summary, _, final_eval_metrics) = (run_full_eval(
model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
summary_writer, sample_src_data, sample_tgt_data, avg_ckpts))
print_step_info("# Final, ", global_step, info, result_summary, log_f)
utils.print_time("# Done training!", start_train_time)
summary_writer.close()
utils.print_out("# Start evaluating saved best models.")
for metric in hparams.metrics:
best_model_dir = getattr(hparams, "best_" + metric + "_dir")
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
print_step_info("# Best %s, " % metric, best_global_step, info,
result_summary, log_f)
summary_writer.close()
if avg_ckpts:
best_model_dir = getattr(hparams, "avg_best_" + metric + "_dir")
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
print_step_info("# Averaged Best %s, " % metric, best_global_step,
info, result_summary, log_f)
summary_writer.close()
return final_eval_metrics, global_step
def train(hparams, scope=None, target_session=""):
"""Train a translation model."""
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
if not hparams.attention:
model_creator = nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
train_model = model_helper.create_train_model(model_creator, hparams, scope)
eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
infer_model = model_helper.create_infer_model(model_creator, hparams, scope)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
summary_name = "train_log"
model_dir = hparams.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("# log_file=%s" % log_file, log_f)
avg_step_time = 0.0
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement)
train_sess = tf.Session(
target=target_session, config=config_proto, graph=train_model.graph)
eval_sess = tf.Session(
target=target_session, config=config_proto, graph=eval_model.graph)
infer_sess = tf.Session(
target=target_session, config=config_proto, graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(
os.path.join(out_dir, summary_name), train_model.graph)
# First evaluation
run_full_eval(
model_dir, infer_model, infer_sess,
eval_model, eval_sess, hparams,
summary_writer, sample_src_data,
sample_tgt_data)
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
step_time, checkpoint_loss, checkpoint_predict_count = 0.0, 0.0, 0.0
checkpoint_total_count = 0.0
speed, train_ppl = 0.0, 0.0
start_train_time = time.time()
utils.print_out(
"# Start step %d, lr %g, %s" %
(global_step, loaded_train_model.learning_rate.eval(session=train_sess),
time.ctime()),
log_f)
# Initialize all of the iterators
skip_count = hparams.batch_size * hparams.epoch_step
utils.print_out("# Init train iterator, skipping %d elements" % skip_count)
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: skip_count})
while global_step < num_train_steps:
### Run a step ###
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
(_, step_loss, step_predict_count, step_summary, global_step,
step_word_count, batch_size) = step_result
hparams.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
hparams.epoch_step = 0
utils.print_out(
"# Finished an epoch, step %d. Perform external evaluation" %
global_step)
run_sample_decode(infer_model, infer_sess,
model_dir, hparams, summary_writer, sample_src_data,
sample_tgt_data)
dev_scores, test_scores, _ = run_external_eval(
infer_model, infer_sess, model_dir,
hparams, summary_writer)
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Write step summary.
summary_writer.add_summary(step_summary, global_step)
# update statistics
step_time += (time.time() - start_time)
checkpoint_loss += (step_loss * batch_size)
checkpoint_predict_count += step_predict_count
checkpoint_total_count += float(step_word_count)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
# Print statistics for the previous epoch.
avg_step_time = step_time / steps_per_stats
train_ppl = utils.safe_exp(checkpoint_loss / checkpoint_predict_count)
speed = checkpoint_total_count / (1000 * step_time)
utils.print_out(
" global step %d lr %g "
"step-time %.2fs wps %.2fK ppl %.2f %s" %
(global_step,
loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, _get_best_results(hparams)),
log_f)
if math.isnan(train_ppl):
break
# Reset timer and loss.
step_time, checkpoint_loss, checkpoint_predict_count = 0.0, 0.0, 0.0
checkpoint_total_count = 0.0
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.print_out("# Save eval, global step %d" % global_step)
utils.add_summary(summary_writer, global_step, "train_ppl", train_ppl)
# Save checkpoint
loaded_train_model.saver.save(
train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess,
model_dir, hparams, summary_writer, sample_src_data,
sample_tgt_data)
dev_ppl, test_ppl = run_internal_eval(
eval_model, eval_sess, model_dir, hparams, summary_writer)
if global_step - last_external_eval_step >= steps_per_external_eval:
last_external_eval_step = global_step
# Save checkpoint
loaded_train_model.saver.save(
train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
run_sample_decode(infer_model, infer_sess,
model_dir, hparams, summary_writer, sample_src_data,
sample_tgt_data)
dev_scores, test_scores, _ = run_external_eval(
infer_model, infer_sess, model_dir,
hparams, summary_writer)
# Done training
loaded_train_model.saver.save(
train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
result_summary, _, dev_scores, test_scores, dev_ppl, test_ppl = run_full_eval(
model_dir, infer_model, infer_sess,
eval_model, eval_sess, hparams,
summary_writer, sample_src_data,
sample_tgt_data)
utils.print_out(
"# Final, step %d lr %g "
"step-time %.2f wps %.2fK ppl %.2f, %s, %s" %
(global_step, loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, result_summary, time.ctime()),
log_f)
utils.print_time("# Done training!", start_train_time)
summary_writer.close()
utils.print_out("# Start evaluating saved best models.")
for metric in hparams.metrics:
best_model_dir = getattr(hparams, "best_" + metric + "_dir")
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _, _, _, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
summary_writer, sample_src_data, sample_tgt_data)
utils.print_out("# Best %s, step %d "
"step-time %.2f wps %.2fK, %s, %s" %
(metric, best_global_step, avg_step_time, speed,
result_summary, time.ctime()), log_f)
summary_writer.close()
return (dev_scores, test_scores, dev_ppl, test_ppl, global_step)
def train(flags):
"""Train the policy gradient model. """
out_dir = flags.out_dir
num_train_steps = flags.num_train_steps
steps_per_infer = flags.steps_per_infer
# Create model for train, infer mode
model_creator = get_model_creator(flags)
train_model = model_helper.create_train_model(flags, model_creator)
infer_model = model_helper.create_infer_model(flags, model_creator)
# TODO. set for distributed training and multi gpu
config_proto = tf.ConfigProto(allow_soft_placement=True)
config_proto.gpu_options.allow_growth = True
# Session for train, infer
train_sess = tf.Session(
config=config_proto, graph=train_model.graph)
infer_sess = tf.Session(
config=config_proto, graph=infer_model.graph)
# Load the train model if there's the file in the directory
# otherwise, initialize vars in the train model
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
out_dir, train_model.model, train_sess)
# Summary
train_summary = "train_log"
infer_summary = "infer_log"
# Summary writer for train, infer
train_summary_writer = tf.summary.FileWriter(
os.path.join(out_dir, train_summary), train_model.graph)
infer_summary_writer = tf.summary.FileWriter(
os.path.join(out_dir, infer_summary))
# First evaluation
run_infer(infer_model, out_dir, infer_sess)
# Initialize step var
last_infer_steps = global_step
# Training loop
while global_step < num_train_steps:
output_tuple = loaded_train_model.train(train_sess)
global_step = output_tuple.global_step
train_summary = output_tuple.train_summary
# Update train summary
train_summary_writer.add_summary(train_summary, global_step)
print('current global_step: {}'.format(global_step))
# Evaluate the model for steps_per_infer
if global_step - last_infer_steps >= steps_per_infer:
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "rl.ckpt"), global_step)
last_infer_steps = global_step
output_tuple = run_infer(infer_model, out_dir, infer_sess)
infer_summary = output_tuple.infer_summary
# Update infer summary
infer_summary_writer.add_summary(infer_summary, global_step)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "rl.ckpt"), global_step)
print('Train done')
def train(hps, scope=None, target_session=""):
"""Train a translation model."""
log_device_placement = hps.log_device_placement
out_dir = hps.out_dir
num_train_steps = hps.num_train_steps
steps_per_stats = hps.steps_per_stats
steps_per_external_eval = hps.steps_per_external_eval
steps_per_eval = 100 * steps_per_stats
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
if hps.attention_architecture == "baseline":
model_creator = AttentionModel
else:
model_creator = AttentionHistoryModel
train_model = model_helper.create_train_model(model_creator, hps, scope)
eval_model = model_helper.create_eval_model(model_creator, hps, scope)
infer_model = model_helper.create_infer_model(model_creator, hps, scope)
# Preload data for sample decoding.
article_filenames = []
abstract_filenames = []
art_dir = hps.data_dir + '/article'
abs_dir = hps.data_dir + '/abstract'
for file in os.listdir(art_dir):
if file.startswith(hps.dev_prefix):
article_filenames.append(art_dir + "/" + file)
for file in os.listdir(abs_dir):
if file.startswith(hps.dev_prefix):
abstract_filenames.append(abs_dir + "/" + file)
# if random_decode:
# """if this is a random sampling process during training"""
decode_id = random.randint(0, len(article_filenames) - 1)
single_article_file = article_filenames[decode_id]
single_abstract_file = abstract_filenames[decode_id]
dev_src_file = single_article_file
dev_tgt_file = single_abstract_file
sample_src_data = inference_base_model.load_data(dev_src_file)
sample_tgt_data = inference_base_model.load_data(dev_tgt_file)
summary_name = "train_log"
model_dir = hps.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("# log_file=%s" % log_file, log_f)
avg_step_time = 0.0
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement)
train_sess = tf.Session(target=target_session,
config=config_proto,
graph=train_model.graph)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
# First evaluation
# run_full_eval(
# model_dir, infer_model, infer_sess,
# eval_model, eval_sess, hps,
# summary_writer,sample_src_data,sample_tgt_data)
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
stats = init_stats()
speed, train_ppl = 0.0, 0.0
start_train_time = time.time()
utils.print_out(
"# Start step %d, lr %g, %s" %
(global_step, loaded_train_model.learning_rate.eval(
session=train_sess), time.ctime()), log_f)
# Initialize all of the iterators
skip_count = hps.batch_size * hps.epoch_step
utils.print_out("# Init train iterator, skipping %d elements" % skip_count)
train_sess.run(train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: skip_count})
epoch_step = 0
while global_step < num_train_steps:
### Run a step ###
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
epoch_step = 0
utils.print_out(
"# Finished an epoch, step %d. Perform external evaluation" %
global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hps,
summary_writer, sample_src_data, sample_tgt_data)
dev_scores, test_scores, _ = run_external_eval(
infer_model, infer_sess, model_dir, hps, summary_writer)
train_sess.run(train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Write step summary and accumulate statistics
global_step = update_stats(stats, summary_writer, start_time,
step_result)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
is_overflow = check_stats(stats, global_step, steps_per_stats, hps,
log_f)
if is_overflow:
break
# Reset statistics
stats = init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.print_out("# Save eval, global step %d" % global_step)
utils.add_summary(summary_writer, global_step, "train_ppl",
train_ppl)
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"summarized.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess, model_dir, hps,
summary_writer, sample_src_data, sample_tgt_data)
dev_ppl, test_ppl = run_internal_eval(eval_model, eval_sess,
model_dir, hps,
summary_writer)
if global_step - last_external_eval_step >= steps_per_external_eval:
last_external_eval_step = global_step
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"summarized.ckpt"),
global_step=global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hps,
summary_writer, sample_src_data, sample_tgt_data)
dev_scores, test_scores, _ = run_external_eval(
infer_model, infer_sess, model_dir, hps, summary_writer)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "summarized.ckpt"),
global_step=global_step)
result_summary, _, dev_scores, test_scores, dev_ppl, test_ppl = run_full_eval(
model_dir, infer_model, infer_sess, eval_model, eval_sess, hps,
summary_writer, sample_src_data, sample_tgt_data)
utils.print_out(
"# Final, step %d lr %g "
"step-time %.2f wps %.2fK ppl %.2f, %s, %s" %
(global_step,
loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, result_summary, time.ctime()), log_f)
utils.print_time("# Done training!", start_train_time)
summary_writer.close()
utils.print_out("# Start evaluating saved best models.")
for metric in hps.metrics:
best_model_dir = getattr(hps, "best_" + metric + "_dir")
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _, _, _, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hps, summary_writer, sample_src_data, sample_tgt_data)
utils.print_out(
"# Best %s, step %d "
"step-time %.2f wps %.2fK, %s, %s" %
(metric, best_global_step, avg_step_time, speed, result_summary,
time.ctime()), log_f)
summary_writer.close()
return (dev_scores, test_scores, dev_ppl, test_ppl, global_step)
def train(hparams, scope=None):
model_dir = hparams.out_dir
avg_ckpts = hparams.avg_ckpts
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
summary_name = "summary"
model_creator = gnmt_model.GNMTModel
train_model = model_helper.create_train_model(model_creator, hparams)
eval_model = model_helper.create_eval_model(model_creator, hparams)
infer_model = model_helper.create_infer_model(model_creator, hparams)
config_proto = tf.ConfigProto()
config_proto.gpu_options.allow_growth = True
train_sess = tf.Session(graph=train_model.graph, config=config_proto)
eval_sess = tf.Session(graph=eval_model.graph, config=config_proto)
infer_sess = tf.Session(graph=infer_model.graph, config=config_proto)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(
os.path.join(model_dir, summary_name), train_model.graph)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
sample_src_data = utils.load_data(dev_src_file)
sample_tgt_data = utils.load_data(dev_tgt_file)
# First evaluation
result_summary, _, _ = run_full_eval(model_dir, infer_model, infer_sess,
eval_model, eval_sess, hparams,
summary_writer, sample_src_data,
sample_tgt_data, avg_ckpts)
utils.log('First evaluation: {}'.format(result_summary))
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
stats = init_stats()
info = {
"train_ppl": 0.0,
"speed": 0.0,
"avg_step_time": 0.0,
"avg_grad_norm": 0.0,
"learning_rate":
loaded_train_model.learning_rate.eval(session=train_sess)
}
utils.log("Start step %d, lr %g" % (global_step, info["learning_rate"]))
# Initialize all of the iterators
train_sess.run(train_model.iterator.initializer)
epoch = 1
while True:
### Run a step ###
start_time = time.time()
try:
step_result = loaded_train_model.train(train_sess)
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
utils.log(
"Finished epoch %d, step %d. Perform external evaluation" %
(epoch, global_step))
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
train_sess.run(train_model.iterator.initializer)
if epoch < hparams.epochs:
epoch += 1
continue
else:
break
# Process step_result, accumulate stats, and write summary
global_step, info["learning_rate"], step_summary = update_stats(
stats, start_time, step_result)
summary_writer.add_summary(step_summary, global_step)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
is_overflow = process_stats(stats, info, global_step,
steps_per_stats)
print_step_info(" ", global_step, info,
"BLEU %.2f" % (hparams.best_bleu, ))
if is_overflow:
break
# Reset statistics
stats = init_stats()
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.log("Save eval, global step %d" % (global_step, ))
utils.add_summary(summary_writer, global_step, "train_ppl",
info["train_ppl"])
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(model_dir,
"translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_internal_eval(eval_model, eval_sess, model_dir, hparams,
summary_writer)
if global_step - last_external_eval_step >= steps_per_external_eval:
last_external_eval_step = global_step
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(model_dir,
"translate.ckpt"),
global_step=global_step)
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
run_external_eval(infer_model, infer_sess, model_dir, hparams,
summary_writer)
if avg_ckpts:
run_avg_external_eval(infer_model, infer_sess, model_dir,
hparams, summary_writer, global_step)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(model_dir, "translate.ckpt"),
global_step=global_step)
(result_summary, _, final_eval_metrics) = run_full_eval(
model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
summary_writer, sample_src_data, sample_tgt_data, avg_ckpts)
print_step_info("Final, ", global_step, info, result_summary)
utils.log("Done training!")
summary_writer.close()
utils.log("Start evaluating saved best models.")
best_model_dir = hparams.best_bleu_dir
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
print_step_info("Best BLEU, ", best_global_step, info, result_summary)
summary_writer.close()
if avg_ckpts:
best_model_dir = hparams.avg_best_bleu_dir
summary_writer = tf.summary.FileWriter(
os.path.join(best_model_dir, summary_name), infer_model.graph)
result_summary, best_global_step, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
print_step_info("Averaged Best BLEU, ", best_global_step, info,
result_summary)
summary_writer.close()
return final_eval_metrics, global_step
def train(hparams, scope=None, target_session=""):
"""Train a translation model."""
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
steps_per_external_eval = hparams.steps_per_external_eval
steps_per_eval = 10 * steps_per_stats
if not steps_per_external_eval:
steps_per_external_eval = 5 * steps_per_eval
if not hparams.attention:
model_creator = nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
train_model = model_helper.create_train_model(model_creator, hparams,
scope)
eval_model = model_helper.create_eval_model(model_creator, hparams, scope)
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
# Preload data for sample decoding.
dev_src_file = "%s.%s" % (hparams.dev_prefix, hparams.src)
dev_tgt_file = "%s.%s" % (hparams.dev_prefix, hparams.tgt)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
summary_name = "train_log"
model_dir = hparams.out_dir
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="w")
utils.print_out("# log_file=%s" % log_file, log_f)
avg_step_time = 0.0
# TensorFlow model
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement)
train_sess = tf.Session(target=target_session,
config=config_proto,
graph=train_model.graph)
eval_sess = tf.Session(target=target_session,
config=config_proto,
graph=eval_model.graph)
infer_sess = tf.Session(target=target_session,
config=config_proto,
graph=infer_model.graph)
with train_model.graph.as_default():
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, train_sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
# First evaluation
run_full_eval(model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
last_stats_step = global_step
last_eval_step = global_step
last_external_eval_step = global_step
# This is the training loop.
step_time, checkpoint_loss, checkpoint_predict_count = 0.0, 0.0, 0.0
checkpoint_total_count = 0.0
speed, train_ppl = 0.0, 0.0
start_train_time = time.time()
utils.print_out(
"# Start step %d, lr %g, %s" %
(global_step, loaded_train_model.learning_rate.eval(
session=train_sess), time.ctime()), log_f)
# Initialize all of the iterators
skip_count = hparams.batch_size * hparams.epoch_step
utils.print_out("# Init train iterator, skipping %d elements" % skip_count)
if hparams.curriculum == 'none':
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: skip_count})
else:
if hparams.curriculum == 'predictive_gain':
exp3s = Exp3S(hparams.num_curriculum_buckets, 0.001, 0, 0.05)
elif hparams.curriculum == 'look_back_and_forward':
curriculum_point = 0
handle = train_model.iterator.handle
for i in range(hparams.num_curriculum_buckets):
train_sess.run(
train_model.iterator.initializer[i].initializer,
feed_dict={train_model.skip_count_placeholder: skip_count})
iterator_handles = [
train_sess.run(
train_model.iterator.initializer[i].string_handle(),
feed_dict={train_model.skip_count_placeholder: skip_count})
for i in range(hparams.num_curriculum_buckets)
]
utils.print_out("Starting training")
while global_step < num_train_steps:
### Run a step ###
start_time = time.time()
try:
if hparams.curriculum != 'none':
if hparams.curriculum == 'predictive_gain':
lesson = exp3s.draw_task()
elif hparams.curriculum == 'look_back_and_forward':
if curriculum_point == hparams.num_curriculum_buckets:
lesson = np.random.randint(
low=0, high=hparams.num_curriculum_buckets)
else:
lesson = curriculum_point if np.random.random_sample(
) < 0.8 else np.random.randint(
low=0, high=hparams.num_curriculum_buckets)
step_result = loaded_train_model.train(
hparams,
train_sess,
handle=handle,
iterator_handle=iterator_handles[lesson],
use_fed_source_placeholder=loaded_train_model.
use_fed_source,
fed_source_placeholder=loaded_train_model.fed_source)
(_, step_loss, step_predict_count, step_summary, global_step,
step_word_count, batch_size, source) = step_result
if hparams.curriculum == 'predictive_gain':
new_loss = train_sess.run(
[loaded_train_model.train_loss],
feed_dict={
handle: iterator_handles[lesson],
loaded_train_model.use_fed_source: True,
loaded_train_model.fed_source: source
})
# new_loss = loaded_train_model.train_loss.eval(
# session=train_sess,
# feed_dict={
# handle: iterator_handles[lesson],
# loaded_train_model.use_fed_source: True,
# loaded_train_model.fed_source: source
# })
# utils.print_out("lesson: %s, step loss: %s, new_loss: %s" % (lesson, step_loss, new_loss))
# utils.print_out("exp3s dist: %s" % (exp3s.pi, ))
curriculum_point_a = lesson * (
hparams.src_max_len //
hparams.num_curriculum_buckets) + 1
curriculum_point_b = (
lesson + 1) * (hparams.src_max_len //
hparams.num_curriculum_buckets) + 1
v = step_loss - new_loss
exp3s.update_w(
v,
float(curriculum_point_a + curriculum_point_b) / 2.0)
elif hparams.curriculum == 'look_back_and_forward':
utils.print_out("step loss: %s, lesson: %s" %
(step_loss, lesson))
curriculum_point_a = curriculum_point * (
hparams.src_max_len //
hparams.num_curriculum_buckets) + 1
curriculum_point_b = (curriculum_point + 1) * (
hparams.src_max_len //
hparams.num_curriculum_buckets) + 1
if step_loss < (hparams.curriculum_progress_loss *
(float(curriculum_point_a +
curriculum_point_b) / 2.0)):
curriculum_point += 1
else:
step_result = loaded_train_model.train(hparams, train_sess)
(_, step_loss, step_predict_count, step_summary, global_step,
step_word_count, batch_size) = step_result
hparams.epoch_step += 1
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
hparams.epoch_step = 0
# utils.print_out(
# "# Finished an epoch, step %d. Perform external evaluation" %
# global_step)
# run_sample_decode(infer_model, infer_sess,
# model_dir, hparams, summary_writer, sample_src_data,
# sample_tgt_data)
# dev_scores, test_scores, _ = run_external_eval(
# infer_model, infer_sess, model_dir,
# hparams, summary_writer)
if hparams.curriculum == 'none':
train_sess.run(
train_model.iterator.initializer,
feed_dict={train_model.skip_count_placeholder: 0})
else:
train_sess.run(
train_model.iterator.initializer[lesson].initializer,
feed_dict={train_model.skip_count_placeholder: 0})
continue
# Write step summary.
summary_writer.add_summary(step_summary, global_step)
# update statistics
step_time += (time.time() - start_time)
checkpoint_loss += (step_loss * batch_size)
checkpoint_predict_count += step_predict_count
checkpoint_total_count += float(step_word_count)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
if hparams.curriculum == 'predictive_gain':
utils.print_out("lesson: %s, step loss: %s, new_loss: %s" %
(lesson, step_loss, new_loss))
utils.print_out("exp3s dist: %s" % (exp3s.pi, ))
last_stats_step = global_step
# Print statistics for the previous epoch.
avg_step_time = step_time / steps_per_stats
train_ppl = utils.safe_exp(checkpoint_loss /
checkpoint_predict_count)
speed = checkpoint_total_count / (1000 * step_time)
utils.print_out(
" global step %d lr %g "
"step-time %.2fs wps %.2fK ppl %.2f %s" %
(global_step,
loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, _get_best_results(hparams)),
log_f)
if math.isnan(train_ppl):
break
# Reset timer and loss.
step_time, checkpoint_loss, checkpoint_predict_count = 0.0, 0.0, 0.0
checkpoint_total_count = 0.0
if global_step - last_eval_step >= steps_per_eval:
last_eval_step = global_step
utils.print_out("# Save eval, global step %d" % global_step)
utils.add_summary(summary_writer, global_step, "train_ppl",
train_ppl)
# Save checkpoint
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, infer_sess, model_dir, hparams,
summary_writer, sample_src_data, sample_tgt_data)
dev_ppl, test_ppl = run_internal_eval(eval_model, eval_sess,
model_dir, hparams,
summary_writer)
dev_scores, test_scores, _ = run_external_eval(
infer_model, infer_sess, model_dir, hparams, summary_writer)
# if global_step - last_external_eval_step >= steps_per_external_eval:
# last_external_eval_step = global_step
# # Save checkpoint
# loaded_train_model.saver.save(
# train_sess,
# os.path.join(out_dir, "translate.ckpt"),
# global_step=global_step)
# run_sample_decode(infer_model, infer_sess,
# model_dir, hparams, summary_writer, sample_src_data,
# sample_tgt_data)
# dev_scores, test_scores, _ = run_external_eval(
# infer_model, infer_sess, model_dir,
# hparams, summary_writer)
# Done training
loaded_train_model.saver.save(train_sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
result_summary, _, dev_scores, test_scores, dev_ppl, test_ppl = run_full_eval(
model_dir, infer_model, infer_sess, eval_model, eval_sess, hparams,
summary_writer, sample_src_data, sample_tgt_data)
utils.print_out(
"# Final, step %d lr %g "
"step-time %.2f wps %.2fK ppl %.2f, %s, %s" %
(global_step,
loaded_train_model.learning_rate.eval(session=train_sess),
avg_step_time, speed, train_ppl, result_summary, time.ctime()), log_f)
utils.print_time("# Done training!", start_train_time)
utils.print_out("# Start evaluating saved best models.")
for metric in hparams.metrics:
best_model_dir = getattr(hparams, "best_" + metric + "_dir")
result_summary, best_global_step, _, _, _, _ = run_full_eval(
best_model_dir, infer_model, infer_sess, eval_model, eval_sess,
hparams, summary_writer, sample_src_data, sample_tgt_data)
utils.print_out(
"# Best %s, step %d "
"step-time %.2f wps %.2fK, %s, %s" %
(metric, best_global_step, avg_step_time, speed, result_summary,
time.ctime()), log_f)
summary_writer.close()
return (dev_scores, test_scores, dev_ppl, test_ppl, global_step)
