def _load_theano(self):
"""
Loads models, sets theano shared variables and builds samplers.
This entails irrevocable binding to a specific GPU.
"""
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano import shared
from nmt import (build_sampler, gen_sample)
from theano_util import (numpy_floatX, load_params, init_theano_params)
trng = RandomStreams(1234)
use_noise = shared(numpy_floatX(0.))
fs_init = []
fs_next = []
for model, option in zip(self._models, self._options):
param_list = numpy.load(model).files
param_list = dict.fromkeys(
[key for key in param_list if not key.startswith('adam_')], 0)
params = load_params(model, param_list)
tparams = init_theano_params(params)
# always return alignment at this point
f_init, f_next = build_sampler(
tparams, option, use_noise, trng, return_alignment=True)
fs_init.append(f_init)
fs_next.append(f_next)
return trng, fs_init, fs_next, gen_sample
def _load_theano(self):
"""
Loads models, sets theano shared variables and builds samplers.
This entails irrevocable binding to a specific GPU.
"""
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano import shared
from nmt import (build_sampler, build_multi_sampler, gen_sample)
from theano_util import (numpy_floatX, load_params, init_theano_params)
trng = RandomStreams(1234)
use_noise = shared(numpy_floatX(0.))
fs_init = []
fs_next = []
for model, option in zip(self._models, self._options):
# check compatibility with multisource
if option["multisource_type"] is not None and len(
option['extra_sources']) == 0:
logging.error(
"This model is multi-source but no auxiliary source file was provided."
)
sys.exit(1)
elif option["multisource_type"] is None and len(
option['extra_sources']) != 0:
logging.warn(
"You provided an auxiliary input but this model is not multi-source. Ignoring extra input."
)
param_list = numpy.load(model).files
param_list = dict.fromkeys(
[key for key in param_list if not key.startswith('adam_')], 0)
params = load_params(model, param_list)
tparams = init_theano_params(params)
# always return alignment at this point
if option['multisource_type'] is not None:
f_init, f_next = build_multi_sampler(tparams,
option,
use_noise,
trng,
return_alignment=True)
else:
f_init, f_next = build_sampler(tparams,
option,
use_noise,
trng,
return_alignment=True)
fs_init.append(f_init)
fs_next.append(f_next)
return trng, fs_init, fs_next, gen_sample
def translate_model(queue, rqueue, pid, models, options, k, normalization_alpha, verbose, nbest, return_alignment, suppress_unk, return_hyp_graph, deviceid):
# if the --device-list argument is set
if deviceid != '':
import os
theano_flags = os.environ['THEANO_FLAGS'].split(',')
exist = False
for i in xrange(len(theano_flags)):
if theano_flags[i].strip().startswith('device'):
exist = True
theano_flags[i] = '%s=%s' % ('device', deviceid)
break
if exist == False:
theano_flags.append('%s=%s' % ('device', deviceid))
os.environ['THEANO_FLAGS'] = ','.join(theano_flags)
from theano_util import (floatX, numpy_floatX, load_params, init_theano_params)
from nmt import (build_sampler, gen_sample, init_params)
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano import shared
trng = RandomStreams(1234)
use_noise = shared(numpy_floatX(0.))
fs_init = []
fs_next = []
for model, option in zip(models, options):
# load model parameters and set theano shared variables
param_list = numpy.load(model).files
param_list = dict.fromkeys([key for key in param_list if not key.startswith('adam_')], 0)
params = load_params(model, param_list)
tparams = init_theano_params(params)
# word index
f_init, f_next = build_sampler(tparams, option, use_noise, trng, return_alignment=return_alignment)
fs_init.append(f_init)
fs_next.append(f_next)
def _translate(seq):
# sample given an input sequence and obtain scores
sample, score, word_probs, alignment, hyp_graph = gen_sample(fs_init, fs_next,
numpy.array(seq).T.reshape([len(seq[0]), len(seq), 1]),
trng=trng, k=k, maxlen=200,
stochastic=False, argmax=False, return_alignment=return_alignment,
suppress_unk=suppress_unk, return_hyp_graph=return_hyp_graph)
# normalize scores according to sequence lengths
if normalization_alpha:
adjusted_lengths = numpy.array([len(s) ** normalization_alpha for s in sample])
score = score / adjusted_lengths
if nbest:
return sample, score, word_probs, alignment, hyp_graph
else:
sidx = numpy.argmin(score)
return sample[sidx], score[sidx], word_probs[sidx], alignment[sidx], hyp_graph
while True:
req = queue.get()
if req is None:
break
idx, x = req[0], req[1]
if verbose:
sys.stderr.write('{0} - {1}\n'.format(pid,idx))
seq = _translate(x)
rqueue.put((idx, seq))
return