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 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)
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)
'amount': .1,
'up_to': preprocessing['test_size']
}, # copy 1/10th but up to 'test_size'
'tst2013.from': {
'amount': .1,
'up_to': preprocessing['test_size']
},
'train.to': {
'amount': 1,
'up_to': -1
},
'tst2012.to': {
'amount': .1,
'up_to': preprocessing['test_size']
},
'tst2013.to': {
'amount': .1,
'up_to': preprocessing['test_size']
},
}
print("->", os.getcwd())
nmt_parser = argparse.ArgumentParser()
a, b = nmt_parser.parse_known_args(
['--' + k + '=' + str(v) for k, v in hparams.items()])
print("a : ", a)
print("b : ", b)
print([os.getcwd() + '\nmt\nmt\nmt.py'] + b)
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)
