def __init__(self, bot):
logging.basicConfig(level=logging.INFO)
out_dir = '<path_to_model>'
self.ping_replace = re.compile(r"<@![0-9]{2,}>", re.IGNORECASE)
self.bot = bot
nmt_parser = argparse.ArgumentParser()
add_arguments(nmt_parser)
flags, unparsed = nmt_parser.parse_known_args()
default_hparams = create_hparams(flags)
self.hparams = create_or_load_hparams(out_dir,
default_hparams,
flags.hparams_path,
save_hparams=False)
ckpt = tf.train.latest_checkpoint(out_dir)
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.infer_model = model_helper.create_infer_model(
model_creator, self.hparams, None)
self.session = tf.InteractiveSession(graph=self.infer_model.graph,
config=utils.get_config_proto())
self.loaded_infer_model = model_helper.load_model(
self.infer_model.model, ckpt, self.session, "infer")
def nmt_main(self, flags, default_hparams, scope=None):
out_dir = flags.out_dir
if not tf.gfile.Exists(out_dir):
tf.gfile.MakeDirs(out_dir)
self.hparams = nmt.create_or_load_hparams(out_dir,
default_hparams,
flags.hparams_path,
save_hparams=False)
self.ckpt = tf.train.latest_checkpoint(out_dir)
if not self.ckpt:
print('Train is needed')
sys.exit()
hparams = self.hparams
if not hparams.attention:
model_creator = 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")
self.infer_model = model_helper.create_infer_model(
model_creator, hparams, scope)
self.sess = tf.Session(graph=self.infer_model.graph,
config=utils.get_config_proto())
with self.infer_model.graph.as_default():
self.loaded_infer_model = model_helper.load_model(
self.infer_model.model, self.ckpt, self.sess, 'infer')
def predicate(ckpt,
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)
with tf.Session(graph=infer_model.graph,
config=utils.get_config_proto()) as sess:
loaded_infer_model = model_helper.load_model(
infer_model.model, ckpt, sess, "infer")
while True:
input_data = input("translate>")
res = translate_and_return(hparams,
infer_model,
[input_data.lower()],
loaded_infer_model,
sess)
print("result: %s" % res.decode("utf-8"))
def __init__(self):
hparams = load_hparams('/tmp/nmt_model')
ckpt = tf.train.latest_checkpoint('/tmp/nmt_model')
self.model = create_infer_model(Model, hparams)
self.sess = tf.Session(graph=self.model.graph,
config=get_config_proto())
with self.model.graph.as_default():
self.loaded_infer_model = load_model(self.model.model, ckpt,
self.sess, "infer")
def chpt_to_dict_arrays():
"""
Convert a checkpoint into a dictionary of numpy arrays
for later use in TensorRT NMT sample.
git clone https://github.com/tensorflow/nmt.git
"""
sys.path.append("./nmt")
from nmt.nmt import add_arguments, create_hparams
from nmt import attention_model
from nmt import model_helper
from nmt.nmt import create_or_load_hparams
from nmt import utils
from nmt import model as nmt_model
nmt_parser = argparse.ArgumentParser()
add_arguments(nmt_parser)
FLAGS, unparsed = nmt_parser.parse_known_args()
default_hparams = create_hparams(FLAGS)
hparams = create_or_load_hparams(FLAGS.out_dir,
default_hparams,
FLAGS.hparams_path,
save_hparams=False)
print(hparams)
model_creator = None
if not hparams.attention:
model_creator = nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
else:
raise ValueError("Unknown model architecture")
infer_model = model_helper.create_infer_model(model_creator,
hparams,
scope=None)
params = {}
print("\nFound the following trainable variables:")
with tf.Session(graph=infer_model.graph,
config=utils.misc_utils.get_config_proto()) as sess:
loaded_infer_model = model_helper.load_model(infer_model.model,
FLAGS.ckpt, sess, "infer")
variables = tf.trainable_variables()
for v in variables:
params[v.name] = v.eval(session=sess)
print("{0} {1}".format(v.name, params[v.name].shape))
params["forget_bias"] = hparams.forget_bias
return params
def single_worker_inference(infer_model,
ckpt,
inference_input_file,
inference_output_file,
hparams):
"""Inference with a single worker."""
output_infer = inference_output_file
# Read data
infer_data = load_data(inference_input_file, hparams)
infer_data = ["Lúc đấy tôi nghĩ chuyện này sẽ khó khăn gian khổ đây ."]
with tf.Session(
graph=infer_model.graph, config=utils.get_config_proto()) as sess:
while True:
# infer_data = ["Lúc đấy tôi nghĩ chuyện này sẽ khó khăn gian khổ đây ."]
var = input("Input Vi Src: ")
infer_data = [var]
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.print_out("# Start decoding")
if hparams.inference_indices:
_decode_inference_indices(
loaded_infer_model,
sess,
output_infer=output_infer,
output_infer_summary_prefix=output_infer,
inference_indices=hparams.inference_indices,
tgt_eos=hparams.eos,
subword_option=hparams.subword_option)
else:
nmt_utils.decode_and_evaluate(
"infer",
loaded_infer_model,
sess,
output_infer,
ref_file=None,
metrics=hparams.metrics,
subword_option=hparams.subword_option,
beam_width=hparams.beam_width,
tgt_eos=hparams.eos,
num_translations_per_input=hparams.num_translations_per_input)
def translate(ckpt,
infer_data,
inference_output_file,
hparams,
num_workers=1,
jobid=0,
scope=None):
"""Inference with a single worker."""
output_infer = inference_output_file
"""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)
with tf.Session(
graph=infer_model.graph, config=utils.get_config_proto()) as sess:
loaded_infer_model = model_helper.load_model(
infer_model.model, ckpt, sess, "infer")
# Encode Data
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.print_out("# Start decoding")
return nmt_utils.decode_and_return(
"infer",
loaded_infer_model,
sess,
output_infer,
ref_file=None,
metrics=hparams.metrics,
subword_option=hparams.subword_option,
beam_width=hparams.beam_width,
tgt_eos=hparams.eos,
num_translations_per_input=hparams.num_translations_per_input)
def generate_reply(input_text, flags):
# Format data
tokenized_text = tokenize_text(input_text)
infer_data = [tokenized_text]
# Load hparams.
jobid = flags.jobid
default_hparams = nmt.create_hparams(flags)
hparams = nmt.create_or_load_hparams(model_dir,
default_hparams,
flags.hparams_path,
save_hparams=(jobid == 0))
# Load checkpoint
ckpt = tf.train.latest_checkpoint(model_dir)
# Inference
model_creator = attention_model.AttentionModel
# Create model
scope = None
infer_model = model_helper.create_infer_model(model_creator, hparams,
scope)
with tf.Session(graph=infer_model.graph,
config=misc_utils.get_config_proto()) as sess:
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
nmt_outputs, infer_summary = model.decode(sess)
# get text translation(reply as a chatbot)
assert nmt_outputs.shape[0] == 1
translation = nmt_utils.get_translation(
nmt_outputs,
sent_id=0,
tgt_eos=hparams.eos,
subword_option=hparams.subword_option)
return translation.decode("utf-8")
def multi_worker_inference(infer_model,
ckpt,
inference_input_file,
inference_output_file,
hparams,
num_workers,
jobid):
"""Inference using multiple workers."""
assert num_workers > 1
final_output_infer = inference_output_file
output_infer = "%s_%d" % (inference_output_file, jobid)
output_infer_done = "%s_done_%d" % (inference_output_file, jobid)
# Read data
infer_data = load_data(inference_input_file, hparams)
# Split data to multiple workers
total_load = len(infer_data)
load_per_worker = int((total_load - 1) / num_workers) + 1
start_position = jobid * load_per_worker
end_position = min(start_position + load_per_worker, total_load)
infer_data = infer_data[start_position:end_position]
with tf.Session(
graph=infer_model.graph, config=utils.get_config_proto()) as sess:
loaded_infer_model = model_helper.load_model(
infer_model.model, ckpt, sess, "infer")
sess.run(infer_model.iterator.initializer,
{
infer_model.src_placeholder: infer_data,
infer_model.batch_size_placeholder: hparams.infer_batch_size
})
# Decode
utils.print_out("# Start decoding")
nmt_utils.decode_and_evaluate(
"infer",
loaded_infer_model,
sess,
output_infer,
ref_file=None,
metrics=hparams.metrics,
subword_option=hparams.subword_option,
beam_width=hparams.beam_width,
tgt_eos=hparams.eos,
num_translations_per_input=hparams.num_translations_per_input)
# Change file name to indicate the file writing is completed.
tf.gfile.Rename(output_infer, output_infer_done, overwrite=True)
# Job 0 is responsible for the clean up.
if jobid != 0: return
# Now write all translations
with codecs.getwriter("utf-8")(
tf.gfile.GFile(final_output_infer, mode="wb")) as final_f:
for worker_id in range(num_workers):
worker_infer_done = "%s_done_%d" % (inference_output_file, worker_id)
while not tf.gfile.Exists(worker_infer_done):
utils.print_out(" waitting job %d to complete." % worker_id)
time.sleep(10)
with codecs.getreader("utf-8")(
tf.gfile.GFile(worker_infer_done, mode="rb")) as f:
for translation in f:
final_f.write("%s" % translation)
for worker_id in range(num_workers):
worker_infer_done = "%s_done_%d" % (inference_output_file, worker_id)
tf.gfile.Remove(worker_infer_done)