def get_models():
args = parse_args()
state_en2fr = prototype_state()
if hasattr(args, 'state_en2fr'):
with open(args.state_en2fr) as src:
state_en2fr.update(cPickle.load(src))
state_en2fr.update(eval("dict({})".format(args.changes)))
state_fr2en = prototype_state()
if hasattr(args, 'state_fr2en') and args.state_fr2en is not None:
with open(args.state_fr2en) as src:
state_fr2en.update(cPickle.load(src))
state_fr2en.update(eval("dict({})".format(args.changes)))
rng = numpy.random.RandomState(state_en2fr['seed'])
enc_dec_en_2_fr = RNNEncoderDecoder(state_en2fr, rng, skip_init=True)
enc_dec_en_2_fr.build()
lm_model_en_2_fr = enc_dec_en_2_fr.create_lm_model()
lm_model_en_2_fr.load(args.model_path_en2fr)
indx_word_src = cPickle.load(open(state_en2fr['word_indx'], 'rb'))
indx_word_trgt = cPickle.load(open(state_en2fr['word_indx_trgt'], 'rb'))
if hasattr(args, 'state_fr2en') and args.state_fr2en is not None:
rng = numpy.random.RandomState(state_fr2en['seed'])
enc_dec_fr_2_en = RNNEncoderDecoder(state_fr2en, rng, skip_init=True)
enc_dec_fr_2_en.build()
lm_model_fr_2_en = enc_dec_fr_2_en.create_lm_model()
lm_model_fr_2_en.load(args.model_path_fr2en)
return [lm_model_en_2_fr, enc_dec_en_2_fr, indx_word_src, indx_word_trgt, state_en2fr, \
lm_model_fr_2_en, enc_dec_fr_2_en, state_fr2en]
else:
return [lm_model_en_2_fr, enc_dec_en_2_fr, indx_word_src, indx_word_trgt, state_en2fr, \
None, None, None]
def get_models():
args = parse_args()
state_en2fr = prototype_state()
if hasattr(args, 'state_en2fr'):
with open(args.state_en2fr) as src:
state_en2fr.update(cPickle.load(src))
state_en2fr.update(eval("dict({})".format(args.changes)))
state_fr2en = prototype_state()
if hasattr(args, 'state_fr2en') and args.state_fr2en is not None:
with open(args.state_fr2en) as src:
state_fr2en.update(cPickle.load(src))
state_fr2en.update(eval("dict({})".format(args.changes)))
rng = numpy.random.RandomState(state_en2fr['seed'])
enc_dec_en_2_fr = RNNEncoderDecoder(state_en2fr, rng, skip_init=True)
enc_dec_en_2_fr.build()
lm_model_en_2_fr = enc_dec_en_2_fr.create_lm_model()
lm_model_en_2_fr.load(args.model_path_en2fr)
indx_word_src = cPickle.load(open(state_en2fr['word_indx'], 'rb'))
indx_word_trgt = cPickle.load(open(state_en2fr['word_indx_trgt'], 'rb'))
if hasattr(args, 'state_fr2en') and args.state_fr2en is not None:
rng = numpy.random.RandomState(state_fr2en['seed'])
enc_dec_fr_2_en = RNNEncoderDecoder(state_fr2en, rng, skip_init=True)
enc_dec_fr_2_en.build()
lm_model_fr_2_en = enc_dec_fr_2_en.create_lm_model()
lm_model_fr_2_en.load(args.model_path_fr2en)
return [lm_model_en_2_fr, enc_dec_en_2_fr, indx_word_src, indx_word_trgt, state_en2fr, \
lm_model_fr_2_en, enc_dec_fr_2_en, state_fr2en]
else:
return [lm_model_en_2_fr, enc_dec_en_2_fr, indx_word_src, indx_word_trgt, state_en2fr,\
None, None, None]
def __init__(self, args):
self.args = args
self.state = prototype_state()
with open(self.args.state) as src:
self.state.update(cPickle.load(src))
self.state.update(eval("dict({})".format(self.args.changes)))
logging.basicConfig(level=getattr(logging, self.state['level']),
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(self.state['seed'])
enc_dec = RNNEncoderDecoder(self.state, rng, skip_init=True)
enc_dec.build()
self.lm_model = enc_dec.create_lm_model()
self.lm_model.load(self.args.model_path)
self.indx_word = cPickle.load(open(self.state['word_indx'], 'rb'))
self.sampler = None
self.beam_search = None
if self.args.beam_search:
self.beam_search = BeamSearch(enc_dec)
self.beam_search.compile()
else:
self.sampler = enc_dec.create_sampler(many_samples=True)
self.idict_src = cPickle.load(open(self.state['indx_word'], 'r'))
def __init__(self, args):
self.args = args
self.state = prototype_state()
with open(self.args.state) as src:
self.state.update(cPickle.load(src))
self.state.update(eval("dict({})".format(self.args.changes)))
logging.basicConfig(level=getattr(logging, self.state['level']),
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(self.state['seed'])
enc_dec = RNNEncoderDecoder(self.state, rng, skip_init=True)
enc_dec.build()
self.lm_model = enc_dec.create_lm_model()
self.lm_model.load(self.args.model_path)
self.indx_word = cPickle.load(open(self.state['word_indx'], 'rb'))
self.sampler = None
self.beam_search = None
if self.args.beam_search:
self.beam_search = BeamSearch(enc_dec)
self.beam_search.compile()
else:
self.sampler = enc_dec.create_sampler(many_samples=True)
self.idict_src = cPickle.load(open(self.state['indx_word'], 'r'))
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")
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state,
rng,
skip_init=True,
compute_alignment=True)
enc_dec.build()
lm_model = enc_dec.create_lm_model()
lm_model.load(args.model_path)
alignment_fun = enc_dec.create_probs_computer(return_alignment=True)
word_indx_src = cPickle.load(open(state['word_indx'], 'rb'))
word_indx_trg = cPickle.load(open(state['word_indx_trgt'], 'rb'))
source_file = args.source
target_file = args.target
output_file = args.output
comput_alignment(source_file, target_file, output_file, alignment_fun,
word_indx_src, word_indx_trg, state)
def __init__(self):
# para setting
self.arg_state = 'search_state.pkl'
self.arg_changes = ""
self.arg_model_path = 'search_model.npz'
self.arg_beam_search = True
self.arg_ignore_unk = False
self.arg_normalize = False
self.state = prototype_state()
with open(self.arg_state) as src:
self.state.update(cPickle.load(src))
self.state.update(eval("dict({})".format(self.arg_changes)))
logging.basicConfig(
level=getattr(logging, self.state['level']),
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(self.state['seed'])
self.enc_dec = RNNEncoderDecoder(self.state, rng, skip_init=True)
self.enc_dec.build()
self.lm_model = self.enc_dec.create_lm_model()
self.lm_model.load(self.arg_model_path)
self.indx_word = cPickle.load(open(self.state['word_indx'], 'rb'))
self.beam_search = None
self.beam_search = BeamSearch(self.enc_dec)
self.beam_search.compile()
self.idict_src = cPickle.load(open(self.state['indx_word'], 'r'))
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()
"""
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()
'''
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")
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)
def main():
args = parse_args()
state = prototype_state()
with open(args.state) as src:
state.update(cPickle.load(src))
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)
scoreMaker = ScoreMaker(enc_dec)
ScoreMaker.compile()
indx_word_src = cPickle.load(open(state['word_indx'],'rb'))
indx_word_trg = cPickle.load(open(state['word_indx_trgt'],'rb'))
idict_src = cPickle.load(open(state['indx_word'],'r'))
idict_trg = cPickle.load(open(state['indx_word_target'],'r'))
fsrc = open(args.source, 'r')
ftrg = open(args.target, 'r')
for srcline, trgline in zip(fsrc, ftrg):
src_seqin = srcline.strip()
trg_seqin = trgline.strip()
src_seq, src_parsed_in = parse_input(state,
indx_word_src,
src_seqin,
idx2word=idict_src)
trg_seq, trg_parsed_in = parse_input(state,
indx_word_trg,
trg_seqin,
idx2word=idict_trg)
print "Parsed Input:", src_parsed_in
ScoreMaker.score(lm_model, src_seq, trg_seq, idict_src, idict_trg)
fsrc.close()
ftrg.close()
def main():
args = parse_args()
state = prototype_state()
with open(args.state) as src:
state.update(cPickle.load(src))
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)
ScoreMaker = ScoreMaker(enc_dec)
ScoreMaker.compile()
indx_word_src = cPickle.load(open(state['word_indx'], 'rb'))
indx_word_trg = cPickle.load(open(state['word_indx_trgt'], 'rb'))
idict_src = cPickle.load(open(state['indx_word'], 'r'))
idict_trg = cPickle.load(open(state['indx_word_target'], 'r'))
fsrc = open(args.source, 'r')
ftrg = open(args.target, 'r')
for srcline, trgline in zip(fsrc, ftrg):
src_seqin = srcline.strip()
trg_seqin = trgline.strip()
src_seq, src_parsed_in = parse_input(state,
indx_word_src,
src_seqin,
idx2word=idict_src)
trg_seq, trg_parsed_in = parse_input(state,
indx_word_trg,
trg_seqin,
idx2word=idict_trg)
print "Parsed Input:", src_parsed_in
ScoreMaker.score(lm_model, src_seq, trg_seq, idict_src, idict_trg)
fsrc.close()
ftrg.close()
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)))
state['sort_k_batches'] = 1 # which means don't sort
state['shuffle'] = False
state['use_infinite_loop'] = False
state['force_enc_repr_cpu'] = False
logging.basicConfig(
level=getattr(logging, state['level']),
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state,
rng,
skip_init=True,
compute_alignment=True)
enc_dec.build()
lm_model = enc_dec.create_lm_model()
lm_model.load(args.model_path)
indx_word_src = cPickle.load(open(state['word_indx'], 'rb'))
indx_word_trgt = cPickle.load(open(state['word_indx_trgt'], 'rb'))
if args.mode == "batch":
data_given = args.src or args.trg
txt = data_given and not (args.src.endswith(".h5")
and args.trg.endswith(".h5"))
if data_given and not txt:
state['source'] = [args.src]
state['target'] = [args.trg]
if not data_given and not txt:
logger.info("Using the training data")
if txt:
data_iter = BatchBiTxtIterator(state,
args.src,
indx_word_src,
args.trg,
indx_word_trgt,
state['bs'],
raise_unk=not args.allow_unk)
data_iter.start()
else:
data_iter = get_batch_iterator(state)
data_iter.start(0)
score_file = open(args.scores, "w") if args.scores else sys.stdout
scorer = enc_dec.create_scorer(batch=True)
count = 0
n_samples = 0
logger.info('Scoring phrases')
for i, batch in enumerate(data_iter):
if batch == None:
continue
if args.n_batches >= 0 and i == args.n_batches:
break
if args.y_noise:
y = batch['y']
random_words = numpy.random.randint(0, 100,
y.shape).astype("int64")
change_mask = numpy.random.binomial(1, args.y_noise,
y.shape).astype("int64")
y = change_mask * random_words + (1 - change_mask) * y
batch['y'] = y
st = time.time()
[scores] = scorer(batch['x'], batch['y'], batch['x_mask'],
batch['y_mask'])
if args.print_probs:
scores = numpy.exp(scores)
up_time = time.time() - st
for s in scores:
print >> score_file, "{:.5e}".format(float(s))
n_samples += batch['x'].shape[1]
count += 1
if count % 100 == 0:
score_file.flush()
logger.debug("Scores flushed")
logger.debug(
"{} batches, {} samples, {} per sample; example scores: {}".
format(count, n_samples, up_time / scores.shape[0],
scores[:5]))
logger.info("Done")
score_file.flush()
elif args.mode == "interact":
scorer = enc_dec.create_scorer()
while True:
try:
compute_probs = enc_dec.create_probs_computer()
src_line = raw_input('Source sequence: ')
trgt_line = raw_input('Target sequence: ')
src_seq = parse_input(state,
indx_word_src,
src_line,
raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_source'],
null_sym=state['null_sym_source'])
trgt_seq = parse_input(state,
indx_word_trgt,
trgt_line,
raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_target'],
null_sym=state['null_sym_target'])
print "Binarized source: ", src_seq
print "Binarized target: ", trgt_seq
probs = compute_probs(src_seq, trgt_seq)
print "Probs: {}, cost: {}".format(
probs, -numpy.sum(numpy.log(probs)))
except Exception:
traceback.print_exc()
elif args.mode == "txt":
assert args.src and args.trg
scorer = enc_dec.create_scorer()
src_file = open(args.src, "r")
trg_file = open(args.trg, "r")
compute_probs = enc_dec.create_probs_computer(return_alignment=True)
try:
numpy.set_printoptions(precision=3, linewidth=150, suppress=True)
i = 0
while True:
src_line = next(src_file).strip()
trgt_line = next(trg_file).strip()
src_seq, src_words = parse_input(
state,
indx_word_src,
src_line,
raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_source'],
null_sym=state['null_sym_source'])
trgt_seq, trgt_words = parse_input(
state,
indx_word_trgt,
trgt_line,
raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_target'],
null_sym=state['null_sym_target'])
probs, alignment = compute_probs(src_seq, trgt_seq)
if args.verbose:
print "Probs: ", probs.flatten()
if alignment.ndim == 3:
print "Alignment:".ljust(20), src_line, "<eos>"
for i, word in enumerate(trgt_words):
print "{}{}".format(word.ljust(20), alignment[i, :,
0])
print "Generated by:"
for i, word in enumerate(trgt_words):
j = numpy.argmax(alignment[i, :, 0])
print "{} <--- {}".format(
word, src_words[j]
if j < len(src_words) else "<eos>")
i += 1
if i % 100 == 0:
sys.stdout.flush()
logger.debug(i)
print -numpy.sum(numpy.log(probs))
except StopIteration:
pass
else:
raise Exception("Unknown mode {}".format(args.mode))
def main():
args = parse_args()
state = prototype_state()
with open(args.state) as src:
state.update(cPickle.load(src))
if args.config:
state.update(eval(open(args.config).read()))
if args.weights: state['weights'] = args.weights
if args.lm_file: state['lm_file'] = args.lm_file
if args.lm_vocab: state['lm_vocab'] = args.lm_vocab
if args.pt_file: state['phrase_table'] = args.pt_file
if args.lm_ngram: state['lm_ngram'] = args.lm_ngram
logging.basicConfig(
level=getattr(logging, state['level']),
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state,
rng,
skip_init=True,
compute_alignment=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'))
idict_src = cPickle.load(open(state['indx_word'], 'r'))
trg_idx2word = cPickle.load(open(state['indx_word_target'], 'r'))
trg_word2idx = cPickle.load(open(state['word_indx_trgt'], 'r'))
#0:UNK_tm_value 1:rnn_weight 2:lm_weight 3:tm_weight 4:word_penalty_weight
fea_weights = map(float, state['weights'].split(','))
beam_search = BeamSearch(enc_dec, trg_idx2word, trg_word2idx, indx_word)
beam_search.compile()
beam_search.init_features(state, fea_weights)
#beam_search.init_lm(state['lm_vocab'], state['lm_file'], ngram=int(state['lm_ngram']), weight=fea_weights[2])
#beam_search.init_tm(state['phrase_table'], weights=fea_weights[3:])
fsrc = open(args.source, 'r')
ftrans = open(args.trans, 'w')
start_time = time.time()
n_samples = args.beam_size
total_cost = 0.0
logging.debug("Beam size: {}".format(n_samples))
for i, line in enumerate(fsrc):
seqin = line.strip()
seq, parsed_in = parse_input(state,
indx_word,
seqin,
idx2word=idict_src)
if args.verbose:
print >> sys.stderr, "Parsed Input:", parsed_in
trans, costs, trans_ids, aligns, lm_costs, tm_costs, unk_nums, rnn_costs = sample(
lm_model,
seqin,
seq,
n_samples,
beam_search=beam_search,
ignore_unk=args.ignore_unk,
normalize=args.normalize)
#for (i, t) in enumerate(trans):
# costs[i] = costs[i] / len(t)
best = numpy.argmin(costs)
align_str = []
for (idx, _a) in enumerate(aligns[best]):
align_str.append("[%s]" % ' '.join(map(str, _a)))
if args.nbest:
nbest_trans = trans
nbest_costs = costs
nbest_lm_costs = lm_costs
nbest_tm_costs = tm_costs
nbest_unk_nums = unk_nums
nbest_rnn_costs = rnn_costs
nbest_trans = numpy.array(nbest_trans)[numpy.argsort(nbest_costs)]
nbest_lm_costs = numpy.array(nbest_lm_costs)[numpy.argsort(
nbest_costs)]
nbest_tm_costs = numpy.array(nbest_tm_costs)[numpy.argsort(
nbest_costs)]
nbest_unk_nums = numpy.array(nbest_unk_nums)[numpy.argsort(
nbest_costs)]
nbest_rnn_costs = numpy.array(nbest_rnn_costs)[numpy.argsort(
nbest_costs)]
nbest_costs = numpy.array(sorted(nbest_costs))
for (t, lm, tm, c, u, r) in zip(nbest_trans, nbest_lm_costs,
nbest_tm_costs, nbest_costs,
nbest_unk_nums, nbest_rnn_costs):
sum_lm = numpy.sum(lm)
sum_unk = numpy.sum(u)
sum_tm = numpy.sum(tm)
rnn_cost = numpy.sum(r)
sum_wp = len(t.split(' ')) + 1
#rnn_cost = c - sum_lm * beam_search.weight_lm - sum_tm * beam_search.weight_tm - sum_wp * beam_search.weight_wp
pure_tm = sum_tm + sum_unk * beam_search.unk_tm_value
#rnn_cost = sum_rnn / beam_search.weight_rnn
#print >> ftrans, "%s ||| %f %f %f %f %f ||| 0" % (t, c, rnn_cost, sum_lm, sum_tm, sum_wp)
#print >> ftrans, "%s ||| %f %f %f %f %f ||| 0" % (t, sum_unk * beam_search.weight_tm, -rnn_cost, -sum_lm, -pure_tm, -sum_wp)
print >> ftrans, "%s ||| %f %f %f %f ||| 0" % (
t, -rnn_cost, -sum_lm, -sum_tm, -sum_wp)
if args.verbose:
print >>sys.stderr, "%s ||| %f %f %f %f %f %f %f ||| 0" % (t, sum_unk * beam_search.unk_tm_value * beam_search.weight_tm,\
-rnn_cost * beam_search.weight_rnn, \
-sum_lm * beam_search.weight_lm, \
-pure_tm * beam_search.weight_tm, \
-sum_tm * beam_search.weight_tm, \
-sum_wp * beam_search.weight_wp, c)
print >> ftrans, ''
#nbest_str = ' ||| '.join("%s | %f" % (t, c) for (t, c) in zip(nbest_trans, nbest_costs))
#out_str += "\t" + nbest_str
else:
out_str = trans[best]
if args.alignment:
out_str += "\t" + ' '.join(align_str)
if args.show_unk:
best_ids = trans_ids[best]
unk_ids = []
for (i, idx) in enumerate(best_ids):
if idx == beam_search.unk_id:
unk_ids.append(i)
out_str += "\t" + ' '.join(map(str, unk_ids))
print >> ftrans, out_str
if args.verbose:
print "[Translation]%s\t[Align]%s" % (trans[best],
' '.join(align_str))
total_cost += costs[best]
if (i + 1) % 100 == 0:
ftrans.flush()
logger.debug("Current speed is {} per sentence".format(
(time.time() - start_time) / (i + 1)))
print "Total cost of the translations: {}".format(total_cost)
print "Total used time: {}".format(time.time() - start_time)
fsrc.close()
ftrans.close()
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")
rng = numpy.random.RandomState(state['seed'])
###########################################################
# by He Wei
#enc_dec = RNNEncoderDecoder(state, rng, skip_init=True)
enc_dec = RNNEncoderDecoder(state,
rng,
skip_init=True,
compute_alignment=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'))
if args.source and args.trans:
# Actually only beam search is currently supported here
#assert beam_search
#assert args.beam_size
fsrc = open(args.source, 'r')
ftrans = open(args.trans, 'w')
start_time = time.time()
#n_samples = args.beam_size
total_cost = 0.0
#logging.debug("Beam size: {}".format(n_samples))
for i, line in enumerate(fsrc):
seqin = line.strip()
seq, parsed_in = parse_input(state,
indx_word,
seqin,
idx2word=idict_src)
if args.verbose:
print "Parsed Input:", parsed_in
if args.beam_search:
trans, costs, _, aligns = sample(lm_model,
seq,
args.beam_size,
sampler=sampler,
beam_search=beam_search,
ignore_unk=args.ignore_unk,
normalize=args.normalize)
else:
trans, costs, _, aligns = sample(lm_model,
seq,
1,
sampler=sampler,
beam_search=beam_search,
ignore_unk=args.ignore_unk,
normalize=args.normalize)
best = numpy.argmin(costs)
out_str = trans[best]
align_str = []
if args.beam_search and args.alignment:
for (idx, _a) in enumerate(aligns[best]):
align_str.append("[%s]" % ' '.join(map(str, _a)))
#align_str.append("[%d-%d:%f,%d-%d:%f]" % (idx, _a[0], _a[1], idx, _a[2], _a[3]))
out_str += "\t" + ' '.join(align_str)
if args.beam_search and args.nbest:
nbest_trans = trans
nbest_costs = costs
nbest_trans = numpy.array(nbest_trans)[numpy.argsort(
nbest_costs)]
nbest_costs = numpy.array(sorted(nbest_costs))
nbest_str = ' ||| '.join(
"%s | %f" % (t, c)
for (t, c) in zip(nbest_trans, nbest_costs))
out_str += "\t" + nbest_str
print >> ftrans, out_str
if args.verbose:
print "[Translation]%s\t[Align]%s" % (trans[best],
' '.join(align_str))
total_cost += costs[best]
if (i + 1) % 100 == 0:
ftrans.flush()
logger.debug("Current speed is {} per sentence".format(
(time.time() - start_time) / (i + 1)))
print "Total cost of the translations: {}".format(total_cost)
print "Total used time: {}".format(time.time() - start_time)
fsrc.close()
ftrans.close()
else:
while True:
try:
seqin = raw_input('Input Sequence: ')
n_samples = int(raw_input('How many samples? '))
alpha = None
if not args.beam_search:
alpha = float(raw_input('Inverse Temperature? '))
seq, parsed_in = parse_input(state,
indx_word,
seqin,
idx2word=idict_src)
print "Parsed Input:", parsed_in
except Exception:
print "Exception while parsing your input:"
traceback.print_exc()
continue
sample(lm_model,
seq,
n_samples,
sampler=sampler,
beam_search=beam_search,
ignore_unk=args.ignore_unk,
normalize=args.normalize,
alpha=alpha,
verbose=True)
def main():
args = parse_args()
state = prototype_state()
with open(args.state) as src:
state.update(cPickle.load(src))
if args.config:
state.update(eval(open(args.config).read()))
if args.weights: state['weights'] = args.weights
if args.lm_file: state['lm_file'] = args.lm_file
if args.lm_vocab: state['lm_vocab'] = args.lm_vocab
if args.pt_file: state['phrase_table'] = args.pt_file
if args.lm_ngram: state['lm_ngram'] = args.lm_ngram
logging.basicConfig(level=getattr(logging, state['level']), format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state, rng, skip_init=True, compute_alignment=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'))
idict_src = cPickle.load(open(state['indx_word'],'r'))
trg_idx2word = cPickle.load(open(state['indx_word_target'],'r'))
trg_word2idx = cPickle.load(open(state['word_indx_trgt'],'r'))
#0:UNK_tm_value 1:rnn_weight 2:lm_weight 3:tm_weight 4:word_penalty_weight
fea_weights = map(float, state['weights'].split(','))
beam_search = BeamSearch(enc_dec, trg_idx2word, trg_word2idx, indx_word)
beam_search.compile()
beam_search.init_features(state, fea_weights)
#beam_search.init_lm(state['lm_vocab'], state['lm_file'], ngram=int(state['lm_ngram']), weight=fea_weights[2])
#beam_search.init_tm(state['phrase_table'], weights=fea_weights[3:])
fsrc = open(args.source, 'r')
ftrans = open(args.trans, 'w')
start_time = time.time()
n_samples = args.beam_size
total_cost = 0.0
logging.debug("Beam size: {}".format(n_samples))
for i, line in enumerate(fsrc):
seqin = line.strip()
seq, parsed_in = parse_input(state, indx_word, seqin, idx2word=idict_src)
if args.verbose:
print >> sys.stderr, "Parsed Input:", parsed_in
trans, costs, trans_ids, aligns, lm_costs, tm_costs, unk_nums, rnn_costs = sample(lm_model, seqin, seq, n_samples,
beam_search=beam_search, ignore_unk=args.ignore_unk, normalize=args.normalize)
#for (i, t) in enumerate(trans):
# costs[i] = costs[i] / len(t)
best = numpy.argmin(costs)
align_str = []
for (idx, _a) in enumerate(aligns[best]):
align_str.append("[%s]" % ' '.join(map(str, _a)))
if args.nbest:
nbest_trans = trans
nbest_costs = costs
nbest_lm_costs = lm_costs
nbest_tm_costs = tm_costs
nbest_unk_nums = unk_nums
nbest_rnn_costs = rnn_costs
nbest_trans = numpy.array(nbest_trans)[numpy.argsort(nbest_costs)]
nbest_lm_costs = numpy.array(nbest_lm_costs)[numpy.argsort(nbest_costs)]
nbest_tm_costs = numpy.array(nbest_tm_costs)[numpy.argsort(nbest_costs)]
nbest_unk_nums = numpy.array(nbest_unk_nums)[numpy.argsort(nbest_costs)]
nbest_rnn_costs = numpy.array(nbest_rnn_costs)[numpy.argsort(nbest_costs)]
nbest_costs = numpy.array(sorted(nbest_costs))
for (t, lm, tm, c, u, r) in zip(nbest_trans, nbest_lm_costs, nbest_tm_costs, nbest_costs, nbest_unk_nums, nbest_rnn_costs):
sum_lm = numpy.sum(lm)
sum_unk = numpy.sum(u)
sum_tm = numpy.sum(tm)
rnn_cost = numpy.sum(r)
sum_wp = len(t.split(' ')) + 1
#rnn_cost = c - sum_lm * beam_search.weight_lm - sum_tm * beam_search.weight_tm - sum_wp * beam_search.weight_wp
pure_tm = sum_tm + sum_unk * beam_search.unk_tm_value
#rnn_cost = sum_rnn / beam_search.weight_rnn
#print >> ftrans, "%s ||| %f %f %f %f %f ||| 0" % (t, c, rnn_cost, sum_lm, sum_tm, sum_wp)
#print >> ftrans, "%s ||| %f %f %f %f %f ||| 0" % (t, sum_unk * beam_search.weight_tm, -rnn_cost, -sum_lm, -pure_tm, -sum_wp)
print >> ftrans, "%s ||| %f %f %f %f ||| 0" % (t, -rnn_cost, -sum_lm, -sum_tm, -sum_wp)
if args.verbose:
print >>sys.stderr, "%s ||| %f %f %f %f %f %f %f ||| 0" % (t, sum_unk * beam_search.unk_tm_value * beam_search.weight_tm,\
-rnn_cost * beam_search.weight_rnn, \
-sum_lm * beam_search.weight_lm, \
-pure_tm * beam_search.weight_tm, \
-sum_tm * beam_search.weight_tm, \
-sum_wp * beam_search.weight_wp, c)
print >> ftrans, ''
#nbest_str = ' ||| '.join("%s | %f" % (t, c) for (t, c) in zip(nbest_trans, nbest_costs))
#out_str += "\t" + nbest_str
else:
out_str = trans[best]
if args.alignment:
out_str += "\t" + ' '.join(align_str)
if args.show_unk:
best_ids = trans_ids[best]
unk_ids = []
for (i, idx) in enumerate(best_ids):
if idx == beam_search.unk_id:
unk_ids.append(i)
out_str += "\t" + ' '.join(map(str, unk_ids))
print >>ftrans, out_str
if args.verbose:
print "[Translation]%s\t[Align]%s" % (trans[best], ' '.join(align_str))
total_cost += costs[best]
if (i + 1) % 100 == 0:
ftrans.flush()
logger.debug("Current speed is {} per sentence".
format((time.time() - start_time) / (i + 1)))
print "Total cost of the translations: {}".format(total_cost)
print "Total used time: {}".format(time.time() - start_time)
fsrc.close()
ftrans.close()
sen = indices_to_words(lm_model.word_indxs, trans[i])
# sentences.append(" ".join(sen))
sentences.append(sen)
for i in range(len(costs)):
if verbose:
print "{}: {}".format(costs[i], sentences[i])
return sentences, costs, alignment
model_path = "path/to/search_model.npz"
state_file = "path/to/search_state.pkl"
states = prototype_state()
with open(state_file) as src:
states.update(cPickle.load(src))
states.update(eval("dict({})".format("")))
logging.basicConfig(level=getattr(logging, states['level']), format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(states['seed'])
enc_dec = RNNEncoderDecoder(states, rng, skip_init=True)
enc_dec.build()
lm_models = enc_dec.create_lm_model()
lm_models.load(model_path)
indx_word = cPickle.load(open(states['word_indx'],'rb'))
beam_search = None
beam_search = BeamSearch(enc_dec)
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)))
state['sort_k_batches'] = 1
state['shuffle'] = False
state['use_infinite_loop'] = False
state['force_enc_repr_cpu'] = False
logging.basicConfig(level=getattr(logging, state['level']), format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
rng = numpy.random.RandomState(state['seed'])
enc_dec = RNNEncoderDecoder(state, rng, skip_init=True, compute_alignment=True)
enc_dec.build()
lm_model = enc_dec.create_lm_model()
lm_model.load(args.model_path)
indx_word_src = cPickle.load(open(state['word_indx'],'rb'))
indx_word_trgt = cPickle.load(open(state['word_indx_trgt'], 'rb'))
if args.mode == "batch":
data_given = args.src or args.trg
txt = data_given and not (args.src.endswith(".h5") and args.trg.endswith(".h5"))
if data_given and not txt:
state['source'] = [args.src]
state['target'] = [args.trg]
if not data_given and not txt:
logger.info("Using the training data")
if txt:
data_iter = BatchBiTxtIterator(state,
args.src, indx_word_src, args.trg, indx_word_trgt,
state['bs'], raise_unk=not args.allow_unk)
data_iter.start()
else:
data_iter = get_batch_iterator(state)
data_iter.start(0)
score_file = open(args.scores, "w") if args.scores else sys.stdout
scorer = enc_dec.create_scorer(batch=True)
count = 0
n_samples = 0
logger.info('Scoring phrases')
for i, batch in enumerate(data_iter):
if batch == None:
continue
if args.n_batches >= 0 and i == args.n_batches:
break
if args.y_noise:
y = batch['y']
random_words = numpy.random.randint(0, 100, y.shape).astype("int64")
change_mask = numpy.random.binomial(1, args.y_noise, y.shape).astype("int64")
y = change_mask * random_words + (1 - change_mask) * y
batch['y'] = y
st = time.time()
[scores] = scorer(batch['x'], batch['y'],
batch['x_mask'], batch['y_mask'])
if args.print_probs:
scores = numpy.exp(scores)
up_time = time.time() - st
for s in scores:
print >>score_file, "{:.5e}".format(float(s))
n_samples += batch['x'].shape[1]
count += 1
if count % 100 == 0:
score_file.flush()
logger.debug("Scores flushed")
logger.debug("{} batches, {} samples, {} per sample; example scores: {}".format(
count, n_samples, up_time/scores.shape[0], scores[:5]))
logger.info("Done")
score_file.flush()
elif args.mode == "interact":
scorer = enc_dec.create_scorer()
while True:
try:
compute_probs = enc_dec.create_probs_computer()
src_line = raw_input('Source sequence: ')
trgt_line = raw_input('Target sequence: ')
src_seq = parse_input(state, indx_word_src, src_line, raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_source'], null_sym=state['null_sym_source'])
trgt_seq = parse_input(state, indx_word_trgt, trgt_line, raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_target'], null_sym=state['null_sym_target'])
print "Binarized source: ", src_seq
print "Binarized target: ", trgt_seq
probs = compute_probs(src_seq, trgt_seq)
print "Probs: {}, cost: {}".format(probs, -numpy.sum(numpy.log(probs)))
except Exception:
traceback.print_exc()
elif args.mode == "txt":
assert args.src and args.trg
scorer = enc_dec.create_scorer()
src_file = open(args.src, "r")
trg_file = open(args.trg, "r")
compute_probs = enc_dec.create_probs_computer(return_alignment=True)
try:
numpy.set_printoptions(precision=3, linewidth=150, suppress=True)
i = 0
while True:
src_line = next(src_file).strip()
trgt_line = next(trg_file).strip()
src_seq, src_words = parse_input(state,
indx_word_src, src_line, raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_source'], null_sym=state['null_sym_source'])
trgt_seq, trgt_words = parse_input(state,
indx_word_trgt, trgt_line, raise_unk=not args.allow_unk,
unk_sym=state['unk_sym_target'], null_sym=state['null_sym_target'])
probs, alignment = compute_probs(src_seq, trgt_seq)
if args.verbose:
print "Probs: ", probs.flatten()
if alignment.ndim == 3:
print "Alignment:".ljust(20), src_line, "<eos>"
for i, word in enumerate(trgt_words):
print "{}{}".format(word.ljust(20), alignment[i, :, 0])
print "Generated by:"
for i, word in enumerate(trgt_words):
j = numpy.argmax(alignment[i, :, 0])
print "{} <--- {}".format(word,
src_words[j] if j < len(src_words) else "<eos>")
i += 1
if i % 100 == 0:
sys.stdout.flush()
logger.debug(i)
print -numpy.sum(numpy.log(probs))
except StopIteration:
pass
else:
raise Exception("Unknown mode {}".format(args.mode))
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")
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'))
if args.source and args.trans:
# Actually only beam search is currently supported here
assert beam_search
assert args.beam_size
fsrc = open(args.source, 'r')
ftrans = open(args.trans, 'w')
start_time = time.time()
n_samples = args.beam_size
total_cost = 0.0
logging.debug("Beam size: {}".format(n_samples))
for i, line in enumerate(fsrc):
seqin = line.strip()
seq, parsed_in = parse_input(state, indx_word, seqin, idx2word=idict_src)
if args.verbose:
print "Parsed Input:", parsed_in
trans, costs, _ = sample(lm_model, seq, n_samples, sampler=sampler,
beam_search=beam_search, ignore_unk=args.ignore_unk, normalize=args.normalize)
try:
best = numpy.argmin(costs)
print >>ftrans, trans[best]
total_cost += costs[best]
except:
print >> ftrans, "FAIL"
if args.verbose:
print "Translation:", trans[best]
if (i + 1) % 100 == 0:
ftrans.flush()
logger.debug("Current speed is {} per sentence".
format((time.time() - start_time) / (i + 1)))
print "Total cost of the translations: {}".format(total_cost)
fsrc.close()
ftrans.close()
else:
while True:
try:
seqin = raw_input('Input Sequence: ')
n_samples = int(raw_input('How many samples? '))
alpha = None
if not args.beam_search:
alpha = float(raw_input('Inverse Temperature? '))
seq,parsed_in = parse_input(state, indx_word, seqin, idx2word=idict_src)
print "Parsed Input:", parsed_in
except Exception:
print "Exception while parsing your input:"
traceback.print_exc()
continue
sample(lm_model, seq, n_samples, sampler=sampler,
beam_search=beam_search,
ignore_unk=args.ignore_unk, normalize=args.normalize,
alpha=alpha, verbose=True)
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")
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'))
if args.source and args.trans:
# Actually only beam search is currently supported here
assert beam_search
assert args.beam_size
fsrc = open(args.source, 'r')
ftrans = open(args.trans, 'w')
start_time = time.time()
n_samples = args.beam_size
total_cost = 0.0
logging.debug("Beam size: {}".format(n_samples))
for i, line in enumerate(fsrc):
seqin = line.strip()
seq, parsed_in = parse_input(state, indx_word, seqin, idx2word=idict_src)
if lm_model.maintain_coverage:
trans, costs, coverages, _ = sample(lm_model, seq, n_samples, sampler=sampler,
beam_search=beam_search, ignore_unk=args.ignore_unk, normalize=args.normalize)
else:
trans, costs, _ = sample(lm_model, seq, n_samples, sampler=sampler,
beam_search=beam_search, ignore_unk=args.ignore_unk, normalize=args.normalize)
if args.verbose:
print "Parsed Input:", parsed_in
if len(trans) == 0:
trans = ['Failed']
costs = [0.0]
best = numpy.argmin(costs)
print >>ftrans, trans[best]
if args.verbose:
print "Translation:", trans[best]
if lm_model.maintain_coverage: