def parse_options(self, ckpt_path, hparams_path, vocab_prefix, outdir,
batch_size):
FLAGS = None
# TBD remove argument parsing, and just have it return all default values.
nmt_parser = argparse.ArgumentParser()
add_arguments(nmt_parser)
FLAGS, unparsed = nmt_parser.parse_known_args()
# Some of these flags are never used and are just set for consistency
FLAGS.num_workers = 1
FLAGS.iterations = 1
FLAGS.infer_batch_size = batch_size
FLAGS.num_inter_threads = 1
FLAGS.num_intra_threads = 1
FLAGS.run = "accuracy" # Needs to be set to accuracy to generate output
# Pass in inference specific flags
FLAGS.ckpt = ckpt_path
FLAGS.src = 'en'
FLAGS.tgt = 'de'
FLAGS.hparams_path = hparams_path
FLAGS.out_dir = outdir
FLAGS.vocab_prefix = vocab_prefix
return FLAGS
def nmt_train():
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
nmt.FLAGS, unparsed = nmt_parser.parse_known_args(['--'+k+'='+str(v) for k,v in hparams.items()])
nmt.summary_callback = custom_summary
# Run TF with modified arguments
tf.app.run(main=nmt.main, argv=[os.getcwd() + '\nmt\nmt\nmt.py'] + unparsed)
def nmt_train():
# Modified autorun from nmt.py (bottom of the file)
# We want to use original argument parser (for validation, etc)
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
# But we have to hack settings from our config in there instead of commandline options
nmt.FLAGS, unparsed = nmt_parser.parse_known_args(['--'+k+'='+str(v) for k,v in hparams.items()])
# And now we can run TF with modified arguments
tf.app.run(main=nmt.main, argv=[os.getcwd() + '\nmt\nmt\nmt.py'] + unparsed)
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 nmt_train():
nmt_parser = ap.ArgumentParser()
nmt.add_arguments(nmt_parser)
# Get settingds from configuration file
nmt.FLAGS, unparsed = nmt_parser.parse_known_args(['--' + key + '=' + str(value) for key, value in hparams.items()])
# Custom summary callback hook
nmt.summary_callback = custom_summary
# Run tensorflow with modified arguments
tf.app.run(main=nmt.main, argv=[os.getcwd() + '\nmt\nmt\nmt.py'] + unparsed)
def testTrain(self):
"""Test the training loop is functional with basic hparams."""
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
FLAGS, unparsed = nmt_parser.parse_known_args()
_update_flags(FLAGS, "nmt_train_test")
default_hparams = nmt.create_hparams(FLAGS)
train_fn = train.train
nmt.run_main(FLAGS, default_hparams, train_fn, None)
def do_start_inference(out_dir, hparams):
# Silence all outputs
#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
global current_stdout
current_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
# Modified autorun from nmt.py (bottom of the file)
# We want to use original argument parser (for validation, etc)
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
# But we have to hack settings from our config in there instead of commandline options
flags, unparsed = nmt_parser.parse_known_args(
['--' + k + '=' + str(v) for k, v in hparams.items()])
# And now we can run TF with modified arguments
#tf.app.run(main=nmt.main, argv=[os.getcwd() + '\nmt\nmt\nmt.py'] + unparsed)
# Add output (model) folder to flags
flags.out_dir = out_dir
# Make hparams
hparams = nmt.create_hparams(flags)
## Train / Decode
if not tf.gfile.Exists(flags.out_dir):
nmt.utils.print_out("# Model folder (out_dir) doesn't exist")
sys.exit()
# Load hparams from model folder
hparams = nmt.create_or_load_hparams(flags.out_dir,
hparams,
flags.hparams_path,
save_hparams=True)
# Choose checkpoint (provided with hparams or last one)
if not flags.ckpt:
flags.ckpt = tf.train.latest_checkpoint(flags.out_dir)
# Create model
if not hparams.attention:
model_creator = nmt.inference.nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = nmt.inference.attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = nmt.inference.gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = nmt.inference.model_helper.create_infer_model(
model_creator, hparams, None)
return (infer_model, flags, hparams)
def setup_inference_parameters(out_dir, hparams):
# Print output on stdout while temporarily sending other output to /dev/null
global current_stdout
current_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
nmt_parser = ap.ArgumentParser()
nmt.add_arguments(nmt_parser)
# Get settingds from configuration file
flags, unparsed = nmt_parser.parse_known_args(
['--' + key + '=' + str(value) for key, value in hparams.items()])
# Add output (model) folder to flags
flags.out_dir = out_dir
## Exit if model folder doesn't exist
if not tf.gfile.Exists(flags.out_dir):
nmt.utils.print_out("# Model folder (out_dir) doesn't exist")
sys.exit()
# Load hyper parameters (hparams) from model folder
hparams = nmt.create_hparams(flags)
hparams = nmt.create_or_load_hparams(flags.out_dir,
hparams,
flags.hparams_path,
save_hparams=True)
# Choose checkpoint (provided with hparams or last one)
if not flags.ckpt:
flags.ckpt = tf.train.latest_checkpoint(flags.out_dir)
# Create model
if not hparams.attention:
model_creator = nmt.inference.nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = nmt.inference.attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = nmt.inference.gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = nmt.inference.model_helper.create_infer_model(
model_creator, hparams, None)
return (infer_model, flags, hparams)
def do_start_inference(out_dir, hparams):
global current_stdout
current_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
# Modified autorun from nmt.py (bottom of the file)
# We want to use original argument parser
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
flags, unparsed = nmt_parser.parse_known_args(['--'+k+'='+str(v) for k,v in hparams.items()])
# Add output (model) folder to flags
flags.out_dir = out_dir
# Make hparams
hparams = nmt.create_hparams(flags)
if not tf.gfile.Exists(flags.out_dir):
nmt.utils.print_out("# Model folder (out_dir) doesn't exist")
sys.exit()
# Load hparams from model folder
hparams = nmt.create_or_load_hparams(flags.out_dir, hparams, flags.hparams_path, save_hparams=True)
if not flags.ckpt:
flags.ckpt = tf.train.latest_checkpoint(flags.out_dir)
if not hparams.attention:
model_creator = nmt.inference.nmt_model.Model
elif hparams.attention_architecture == "standard":
model_creator = nmt.inference.attention_model.AttentionModel
elif hparams.attention_architecture in ["gnmt", "gnmt_v2"]:
model_creator = nmt.inference.gnmt_model.GNMTModel
else:
raise ValueError("Unknown model architecture")
infer_model = nmt.inference.model_helper.create_infer_model(model_creator, hparams, None)
return (infer_model, flags, hparams)
def testInference(self):
"""Test inference is function with basic hparams."""
nmt_parser = argparse.ArgumentParser()
nmt.add_arguments(nmt_parser)
FLAGS, unparsed = nmt_parser.parse_known_args()
_update_flags(FLAGS, "nmt_train_infer")
# Train one step so we have a checkpoint.
FLAGS.num_train_steps = 1
default_hparams = nmt.create_hparams(FLAGS)
train_fn = train.train
nmt.run_main(FLAGS, default_hparams, train_fn, None)
# Update FLAGS for inference.
FLAGS.inference_input_file = ("nmt/testdata/" "iwslt15.tst2013.100.en")
FLAGS.inference_output_file = os.path.join(FLAGS.out_dir, "output")
FLAGS.inference_ref_file = ("nmt/testdata/" "iwslt15.tst2013.100.vi")
default_hparams = nmt.create_hparams(FLAGS)
inference_fn = inference.inference
nmt.run_main(FLAGS, default_hparams, None, inference_fn)
import sys import os import argparse from setup.settings import hparams sys.path.append(os.path.realpath(os.path.dirname(__file__))) sys.path.append(os.path.realpath(os.path.dirname(__file__)) + "/nmt") from nmt import nmt import tensorflow as tf # Modified autorun from nmt.py (bottom of the file) # We want to use original argument parser (for validation, etc) nmt_parser = argparse.ArgumentParser() nmt.add_arguments(nmt_parser) # But we have to hack settings from our config in there instead of commandline options nmt.FLAGS, unparsed = nmt_parser.parse_known_args(['--'+k+'='+str(v) for k,v in hparams.items()]) # And now we can run TF with modified arguments tf.app.run(main=nmt.main, argv=[os.getcwd() + '\nmt\nmt\nmt.py'] + unparsed)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
nmt.add_arguments(parser)
nmt.FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=nmt.main, argv=[sys.argv[0]] + unparsed)
import sys
sys.path.insert(0, '../') # This is required to import common
# If importing common doesn't go well, check the Python interpreter's current working directory.
# This has to be 'chat' folder.
# import os
# print(os.getcwd()) # print current working directory
#!flask/bin/python
from infer_web import app
from infer_web import controller
import argparse
import nmt.nmt as nmt
parser = argparse.ArgumentParser()
nmt.add_arguments(parser)
FLAGS, unparsed = parser.parse_known_args()
if not FLAGS.out_dir:
# FLAGS.out_dir = "/Users/ryuji/prg/aplac/chat/generated/4_2316/model" # MacBookAir13
FLAGS.out_dir = "/home/apps/prg/aplac/chat/generated/4_2316/model" # AWS EC2
controller.init(FLAGS)
#Do not add debug=True when VSCode is used. Otherwise breakpoint doesn't hit.
#app.run(debug=True)
if __name__ == '__main__':
app.run()
