def __init__(self, hparams_path, source_prefix, out_dir,
environment_server_address):
"""Constructor for the reformulator.
Args:
hparams_path: Path to json hparams file.
source_prefix: A prefix that is added to every question before
translation which should be used for adding tags like <en> <2en>.
Can be empty or None in which case the prefix is ignored.
out_dir: Directory where the model output will be written.
environment_server_address: Address of the environment server.
Raises:
ValueError: if model architecture is not known.
"""
self.hparams = load_hparams(hparams_path, out_dir)
assert self.hparams.num_buckets == 1, "No bucketing when in server mode."
assert not self.hparams.server_mode, (
"server_mode set to True but not "
"running as server.")
self.hparams.environment_server = environment_server_address
if self.hparams.subword_option == "spm":
self.sentpiece = sentencepiece_processor.SentencePieceProcessor()
self.sentpiece.Load(self.hparams.subword_model.encode("utf-8"))
self.source_prefix = source_prefix
# Create the model
if not self.hparams.attention:
model_creator = nmt_model.Model
elif self.hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
elif self.hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
self.trie = trie_decoder_utils.DecoderTrie(
vocab_path=self.hparams.tgt_vocab_file,
eos_token=self.hparams.eos,
subword_option=self.hparams.subword_option,
subword_model=self.hparams.get("subword_model"),
optimize_ngrams_len=self.hparams.optimize_ngrams_len)
if self.hparams.trie_path is not None and tf.gfile.Exists(
self.hparams.trie_path):
self.trie.populate_from_text_file(self.hparams.trie_path)
combined_graph = tf.Graph()
self.train_model = model_helper.create_train_model(
model_creator,
self.hparams,
graph=combined_graph,
trie=self.trie,
use_placeholders=True)
# Create different inference models for beam search, sampling and greedy
# decoding.
default_infer_mode = self.hparams.infer_mode
default_beam_width = self.hparams.beam_width
self.infer_models = {}
self.hparams.use_rl = False
self.hparams.infer_mode = "greedy"
self.hparams.beam_width = 0
self.infer_models[reformulator_pb2.ReformulatorRequest.
GREEDY] = model_helper.create_infer_model(
model_creator,
self.hparams,
graph=combined_graph)
self.hparams.infer_mode = "sample"
self.hparams.beam_width = 0
self.infer_models[reformulator_pb2.ReformulatorRequest.
SAMPLING] = model_helper.create_infer_model(
model_creator,
self.hparams,
graph=combined_graph)
self.hparams.infer_mode = "beam_search"
self.hparams.beam_width = max(1, default_beam_width)
self.infer_models[reformulator_pb2.ReformulatorRequest.
BEAM_SEARCH] = model_helper.create_infer_model(
model_creator,
self.hparams,
graph=combined_graph)
self.hparams.infer_mode = "trie_greedy"
self.hparams.beam_width = 0
self.infer_models[reformulator_pb2.ReformulatorRequest.
TRIE_GREEDY] = model_helper.create_infer_model(
model_creator,
self.hparams,
graph=combined_graph,
trie=self.trie)
self.hparams.infer_mode = default_infer_mode
self.hparams.beam_width = default_beam_width
self.hparams.infer_mode = "trie_sample"
self.hparams.beam_width = 0
self.infer_models[reformulator_pb2.ReformulatorRequest.
TRIE_SAMPLE] = model_helper.create_infer_model(
model_creator,
self.hparams,
graph=combined_graph,
trie=self.trie)
self.hparams.infer_mode = "trie_beam_search"
self.hparams.beam_width = max(1, default_beam_width)
self.infer_models[reformulator_pb2.ReformulatorRequest.
TRIE_BEAM_SEARCH] = model_helper.create_infer_model(
model_creator,
self.hparams,
graph=combined_graph,
trie=self.trie)
self.hparams.use_rl = True
self.sess = tf.Session(graph=combined_graph,
config=misc_utils.get_config_proto())
with combined_graph.as_default():
# p1 = "C:/Users/aanamika/Documents/QuestionGeneration/active-qa-master/tmp/active-qa/translate.ckpt-1460356"
p1 = 'C:/Users/aanamika/Documents/QuestionGeneration/active-qa-master/tmp/active-qa/temp/translate.ckpt-1460356'
print('p1:', p1)
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.tables_initializer())
_, global_step = model_helper.create_or_load_model(
self.train_model.model, p1, self.sess, "train")
# self.train_model.model, out_dir, self.sess, "train")
self.last_save_step = global_step
self.summary_writer = tf.summary.FileWriter(
os.path.join(out_dir, "train_log"), self.train_model.graph)
self.checkpoint_path = os.path.join(out_dir, "translate.ckpt")
self.trie_save_path = os.path.join(out_dir, "trie")
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
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)
combined_graph = tf.Graph()
train_model = model_helper.create_train_model(model_creator,
hparams,
scope,
graph=combined_graph)
eval_model = model_helper.create_eval_model(model_creator,
hparams,
scope,
graph=combined_graph)
infer_model = model_helper.create_infer_model(model_creator,
hparams,
scope,
graph=combined_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)
dev_ctx_file = None
if hparams.ctx is not None:
dev_ctx_file = "%s.%s" % (hparams.dev_prefix, hparams.ctx)
sample_src_data = inference.load_data(dev_src_file)
sample_tgt_data = inference.load_data(dev_tgt_file)
sample_ctx_data = None
if dev_ctx_file is not None:
sample_ctx_data = inference.load_data(dev_ctx_file)
sample_annot_data = None
if hparams.dev_annotations is not None:
sample_annot_data = inference.load_data(hparams.dev_annotations)
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)
sess = tf.Session(target=target_session,
config=config_proto,
graph=combined_graph)
with train_model.graph.as_default():
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
loaded_train_model, global_step = model_helper.create_or_load_model(
train_model.model, model_dir, sess, "train")
# Summary writer
summary_writer = tf.summary.FileWriter(os.path.join(out_dir, summary_name),
train_model.graph)
# First evaluation
run_full_eval(infer_model, sess, eval_model, sess, hparams, summary_writer,
sample_src_data, sample_ctx_data, sample_tgt_data,
sample_annot_data)
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, 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(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, sess, hparams, summary_writer,
sample_src_data, sample_ctx_data,
sample_tgt_data, sample_annot_data)
run_external_eval(infer_model, sess, hparams, summary_writer)
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)
utils.add_summary(summary_writer, global_step, "train_ppl",
info["train_ppl"])
# Save checkpoint
loaded_train_model.saver.save(sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
# Evaluate on dev/test
run_sample_decode(infer_model, sess, hparams, summary_writer,
sample_src_data, sample_ctx_data,
sample_tgt_data, sample_annot_data)
dev_ppl, test_ppl = None, None
# only evaluate perplexity when supervised learning
if not hparams.use_rl:
dev_ppl, test_ppl = run_internal_eval(eval_model, sess,
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(sess,
os.path.join(out_dir,
"translate.ckpt"),
global_step=global_step)
run_sample_decode(infer_model, sess, hparams, summary_writer,
sample_src_data, sample_ctx_data,
sample_tgt_data, sample_annot_data)
run_external_eval(infer_model, sess, hparams, summary_writer)
# Done training
loaded_train_model.saver.save(sess,
os.path.join(out_dir, "translate.ckpt"),
global_step=global_step)
(result_summary, _, final_eval_metrics) = (run_full_eval(
infer_model, sess, eval_model, sess, hparams, summary_writer,
sample_src_data, sample_ctx_data, sample_tgt_data, sample_annot_data))
print_step_info("# Final, ", global_step, info, result_summary, log_f)
utils.print_time("# Done training!", start_train_time)
summary_writer.close()
return final_eval_metrics, global_step