def sample_targets(input_phrase, model, n_samples, reverse_score, normalize):
[lm_model, enc_dec, indx_word_src, indx_word_trgt, state, \
lm_model_fr_2_en, enc_dec_fr_2_en, state_fr2en] = model
beam_search = BeamSearch(enc_dec)
beam_search.compile()
sampler = enc_dec.create_sampler(many_samples=True)
#sample_func can take argument : normalize (bool)
trans, scores, trans_bin = cached_sample_func(lm_model,
input_phrase,
n_samples,
sampler=sampler,
beam_search=beam_search)
#Reordering scores-trans
#Warning : selection of phrases to rescore is hard-coded
trans = [tra for (sco, tra) in sorted(zip(scores, trans))][0:10]
trans_bin = [tra_bin
for (sco, tra_bin) in sorted(zip(scores, trans_bin))][0:10]
scores = sorted(scores)[0:10]
#Reverse scoring of selected phrases
if reverse_score:
reverse_scorer = enc_dec_fr_2_en.create_scorer(batch=True)
source_phrases_to_reverse_score = []
target_phrases_to_reverse_score = []
for tra_bin in trans_bin:
source_phrases_to_reverse_score.append(input_phrase)
target_phrases_to_reverse_score.append(tra_bin)
state_fr2en['seqlen'] = 1000
x, x_mask, y, y_mask = create_padded_batch(
state_fr2en, [numpy.asarray(target_phrases_to_reverse_score)],
[numpy.asarray(source_phrases_to_reverse_score)])
reverse_scores = -reverse_scorer(
numpy.atleast_2d(x), numpy.atleast_2d(y), numpy.atleast_2d(x_mask),
numpy.atleast_2d(y_mask))[0]
for index in xrange(len(scores)):
scores[index] = (scores[index] + reverse_scores[index]) / 2.
else:
for index in xrange(len(scores)):
scores[index] = scores[index]
trans = trans[numpy.argmin(scores)]
score = numpy.min(scores)
if normalize == False:
final_score = score
else:
final_score = score / numpy.log(len(input_phrase) + 1)
return trans, final_score
def sample_targets(input_phrase, model, n_samples, reverse_score, normalize):
[lm_model, enc_dec, indx_word_src, indx_word_trgt, state, \
lm_model_fr_2_en, enc_dec_fr_2_en, state_fr2en] = model
beam_search = BeamSearch(enc_dec)
beam_search.compile()
sampler = enc_dec.create_sampler(many_samples=True)
#sample_func can take argument : normalize (bool)
trans, scores, trans_bin = cached_sample_func(lm_model, input_phrase, n_samples,
sampler=sampler, beam_search=beam_search)
#Reordering scores-trans
#Warning : selection of phrases to rescore is hard-coded
trans = [tra for (sco, tra) in sorted(zip(scores, trans))][0:10]
trans_bin = [tra_bin for (sco, tra_bin) in sorted(zip(scores, trans_bin))][0:10]
scores = sorted(scores)[0:10]
#Reverse scoring of selected phrases
if reverse_score:
reverse_scorer = enc_dec_fr_2_en.create_scorer(batch=True)
source_phrases_to_reverse_score = []
target_phrases_to_reverse_score = []
for tra_bin in trans_bin:
source_phrases_to_reverse_score.append(input_phrase)
target_phrases_to_reverse_score.append(tra_bin)
state_fr2en['seqlen'] = 1000
x, x_mask, y, y_mask = create_padded_batch(
state_fr2en,
[numpy.asarray(target_phrases_to_reverse_score)],
[numpy.asarray(source_phrases_to_reverse_score)])
reverse_scores = - reverse_scorer(numpy.atleast_2d(x), numpy.atleast_2d(y),
numpy.atleast_2d(x_mask),
numpy.atleast_2d(y_mask))[0]
for index in xrange(len(scores)):
scores[index] = (scores[index] + reverse_scores[index]) / 2.
else:
for index in xrange(len(scores)):
scores[index] = scores[index]
trans = trans[numpy.argmin(scores)]
score = numpy.min(scores)
if normalize == False:
final_score = score
else:
final_score = score / numpy.log(len(input_phrase) + 1)
return trans, final_score
def main():
args = parse_args()
# this loads the state specified in the prototype
state = getattr(experiments.nmt, args.proto)()
# this is based on the suggestion in the README.md in this foloder
if args.state:
if args.state.endswith(".py"):
state.update(eval(open(args.state).read()))
else:
with open(args.state) as src:
state.update(cPickle.load(src))
for change in args.changes:
state.update(eval("dict({})".format(change)))
logging.basicConfig(level=getattr(logging, state['level']), format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
logger.debug("State:\n{}".format(pprint.pformat(state)))
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state, rng, args.skip_init)
enc_dec.build()
lm_model = enc_dec.create_lm_model()
# If we are going to use validation with the bleu script, we
# will need early stopping
bleu_validator = None
if state['bleu_script'] is not None and state['validation_set'] is not None\
and state['validation_set_grndtruth'] is not None:
# make beam search
beam_search = BeamSearch(enc_dec)
beam_search.compile()
bleu_validator = BleuValidator(state, lm_model, beam_search, verbose=state['output_validation_set'])
logger.debug("Load data")
train_data = get_batch_iterator(state)
logger.debug("Compile trainer")
algo = eval(state['algo'])(lm_model, state, train_data)
logger.debug("Run training")
main = MainLoop(train_data, None, None, lm_model, algo, state, None,
reset=state['reset'],
bleu_val_fn = bleu_validator,
hooks=[RandomSamplePrinter(state, lm_model, train_data)]
if state['hookFreq'] >= 0 #and state['validation_set'] is not None
else None)
if state['reload']:
main.load()
if state['loopIters'] > 0:
main.main()
def main():
args = parse_args()
state = prototype_phrase_state()
with open(args.state) as src:
state.update(cPickle.load(src))
state.update(eval("dict({})".format(args.changes)))
logging.basicConfig(
level=getattr(logging, state['level']),
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
server_address = ('', args.port)
httpd = ThreadedHTTPServer(server_address, MTReqHandler)
#httpd = BaseHTTPServer.HTTPServer(server_address, MTReqHandler)
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state, rng, skip_init=True)
enc_dec.build()
lm_model = enc_dec.create_lm_model()
lm_model.load(args.model_path)
indx_word = cPickle.load(open(state['word_indx'], 'rb'))
sampler = None
beam_search = None
if args.beam_search:
beam_search = BeamSearch(enc_dec)
beam_search.compile()
else:
sampler = enc_dec.create_sampler(many_samples=True)
idict_src = cPickle.load(open(state['indx_word'], 'r'))
tokenizer_cmd = [os.getcwd() + '/tokenizer.perl', '-l', 'en', '-q', '-']
detokenizer_cmd = [
os.getcwd() + '/detokenizer.perl', '-l', 'fr', '-q', '-'
]
sampler = Sampler(state,
lm_model,
indx_word,
idict_src,
beam_search=beam_search,
tokenizer_cmd=tokenizer_cmd,
detokenizer_cmd=detokenizer_cmd)
httpd.sampler = sampler
print 'Server starting..'
httpd.serve_forever()
'''
def main():
args = parse_args()
state = prototype_state()
with open(args.state) as src:
state.update(cPickle.load(src))
state.update(eval("dict({})".format(args.changes)))
logging.basicConfig(
level=getattr(logging, state["level"]), format="%(asctime)s: %(name)s: %(levelname)s: %(message)s"
)
server_address = ("", args.port)
httpd = BaseHTTPServer.HTTPServer(server_address, MTReqHandler)
rng = numpy.random.RandomState(state["seed"])
enc_dec = RNNEncoderDecoder(state, rng, skip_init=True)
enc_dec.build()
lm_model = enc_dec.create_lm_model()
lm_model.load(args.model_path)
indx_word = cPickle.load(open(state["word_indx"], "rb"))
sampler = None
beam_search = None
if args.beam_search:
beam_search = BeamSearch(enc_dec)
beam_search.compile()
else:
sampler = enc_dec.create_sampler(many_samples=True)
idict_src = cPickle.load(open(state["indx_word"], "r"))
tokenizer_cmd = [os.getcwd() + "/tokenizer.perl", "-l", "en", "-q", "-"]
detokenizer_cmd = [os.getcwd() + "/detokenizer.perl", "-l", "fr", "-q", "-"]
sampler = Sampler(
state,
lm_model,
indx_word,
idict_src,
beam_search=beam_search,
tokenizer_cmd=tokenizer_cmd,
detokenizer_cmd=detokenizer_cmd,
)
httpd.sampler = sampler
print "Server starting.."
httpd.serve_forever()
"""
