def main():
global device
args = parse()
if args.gpu_id >= 0 and torch.cuda.is_available():
device = torch.device('cuda:' + str(args.gpu_id))
s_vocab = pickle.load(open(args.s_vocab, 'rb'))
t_vocab = pickle.load(open(args.t_vocab, 'rb'))
vs, es, hs = args.vocab_size, args.embed_size, args.hidden_size
if args.model_type == 'EncDec':
model = models.EncoderDecoder(
s_vocab_size=vs, t_vocab_size=vs, hidden_size=hs, embed_size=es, weight_decay=1e-5
).to(device)
elif args.model_type == 'Attn':
model = models.AttentionSeq2Seq(
s_vocab_size=vs, t_vocab_size=vs, embed_size=es, hidden_size=hs,
num_s_layers=2, bidirectional=True, weight_decay=1e-5
).to(device)
else:
sys.stderr.write('%s is not found. Model type is `EncDec` or `Attn`.' % args.model_type)
model.load_state_dict(torch.load(args.model_prefix + '.model'))
translate(args.src, model, s_vocab, t_vocab, args.output, device, 100, reverse=args.reverse)
def command_line2():
import argparse
parser = argparse.ArgumentParser(
description="Use a RNNSearch model",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("lattice_fn")
parser.add_argument("source_sentence_fn")
parser.add_argument("training_config", help="prefix of the trained model")
parser.add_argument("trained_model", help="prefix of the trained model")
parser.add_argument("--gpu",
type=int,
help="specify gpu number to use, if any")
parser.add_argument("--skip_in_src", type=int, default=0)
args = parser.parse_args()
config_training_fn = args.training_config # args.model_prefix + ".train.config"
log.info("loading model config from %s" % config_training_fn)
config_training = json.load(open(config_training_fn))
voc_fn = config_training["voc"]
log.info("loading voc from %s" % voc_fn)
src_voc, tgt_voc = json.load(open(voc_fn))
src_indexer = Indexer.make_from_serializable(src_voc)
tgt_indexer = Indexer.make_from_serializable(tgt_voc)
tgt_voc = None
src_voc = None
# Vi = len(src_voc) + 1 # + UNK
# Vo = len(tgt_voc) + 1 # + UNK
Vi = len(src_indexer) # + UNK
Vo = len(tgt_indexer) # + UNK
print config_training
Ei = config_training["command_line"]["Ei"]
Hi = config_training["command_line"]["Hi"]
Eo = config_training["command_line"]["Eo"]
Ho = config_training["command_line"]["Ho"]
Ha = config_training["command_line"]["Ha"]
Hl = config_training["command_line"]["Hl"]
eos_idx = Vo
encdec = models.EncoderDecoder(Vi, Ei, Hi, Vo + 1, Eo, Ho, Ha, Hl)
log.info("loading model from %s" % args.trained_model)
serializers.load_npz(args.trained_model, encdec)
if args.gpu is not None:
encdec = encdec.to_gpu(args.gpu)
src_sent_f = codecs.open(args.source_sentence_fn, encoding="utf8")
for _ in xrange(args.skip_in_src):
src_sent_f.readline()
src_sentence = src_sent_f.readline().strip().split(" ")
log.info("translating sentence %s" % (" ".join(src_sentence)))
src_seq = src_indexer.convert(src_sentence)
log.info("src seq: %r" % src_seq)
log.info("loading lattice %s" % args.lattice_fn)
lattice_f = codecs.open(args.lattice_fn, "r", encoding="utf8")
all_edges = parse_lattice_file(lattice_f)
log.info("loaded")
lattice_map = [None] * len(all_edges)
for num_lattice, edge_list in enumerate(all_edges):
lattice_map[num_lattice] = Lattice(edge_list)
top_lattice_id = num_lattice
log.info("built lattices")
log.info("removing epsilons")
log.info("nb edges before %i" % sum(
len(edge_list) for lattice in lattice_map
for edge_list in lattice.outgoing.itervalues()))
remove_all_epsilons(lattice_map)
log.info("nb edges before %i" % sum(
len(edge_list) for lattice in lattice_map
for edge_list in lattice.outgoing.itervalues()))
if args.gpu is not None:
seq_as_batch = [
Variable(cuda.to_gpu(np.array([x], dtype=np.int32), args.gpu),
volatile="on") for x in src_seq
]
else:
seq_as_batch = [
Variable(np.array([x], dtype=np.int32), volatile="on")
for x in src_seq
]
predictor = encdec.get_predictor(seq_as_batch, [])
global_memoizer = {}
global_count_memoizer = {}
initial_node = Node(top_lattice_id)
initial_node.add_elem(PosElem(Lattice.kInitial))
current_path = initial_node
selected_seq = []
while True:
print "#node current_path", current_path.count_distincts_subnodes()
current_path.assert_is_reduced_and_consistent()
next_words_set = current_path.get_next_w(lattice_map, global_memoizer,
global_count_memoizer)
for w in next_words_set:
next_words_set[w] = sum(next_words_set[w].itervalues())
has_eos = Lattice.EOS in next_words_set
next_words_list = sorted(
list(w for w in next_words_set if w != Lattice.EOS))
print "next_words_set", next_words_set
voc_choice = tgt_indexer.convert(next_words_list)
if has_eos:
voc_choice.append(eos_idx)
chosen = predictor(voc_choice)
if chosen != eos_idx and tgt_indexer.is_unk_idx(chosen):
print "warning: unk chosen"
unk_list = []
for ix, t_idx in enumerate(voc_choice):
if tgt_indexer.is_unk_idx(t_idx):
unk_list.append((next_words_set[next_words_list[ix]],
next_words_list[ix]))
unk_list.sort(reverse=True)
print "UNK:", unk_list
selected_w = unk_list[0][1]
else:
idx_chosen = voc_choice.index(
chosen
) # TODO: better handling when several tgt candidates map to UNK
selected_w = (next_words_list + [Lattice.EOS])[idx_chosen]
# for num_word, word in enumerate(next_words_list):
# print num_word, word
# print "selected_seq", selected_seq
# i = int(raw_input("choice\n"))
# selected_w = next_words_list[i]
#
selected_seq.append(selected_w)
print "selected_seq", selected_seq
current_path.update_better(selected_w, lattice_map, global_memoizer)
current_path.reduce()
if current_path.is_empty_node():
print "DONE"
break
print "final seq:", selected_seq
def create_encdec_from_config(config_training):
voc_fn = config_training["voc"]
log.info("loading voc from %s"% voc_fn)
src_voc, tgt_voc = json.load(open(voc_fn))
src_indexer = Indexer.make_from_serializable(src_voc)
tgt_indexer = Indexer.make_from_serializable(tgt_voc)
tgt_voc = None
src_voc = None
# Vi = len(src_voc) + 1 # + UNK
# Vo = len(tgt_voc) + 1 # + UNK
Vi = len(src_indexer) # + UNK
Vo = len(tgt_indexer) # + UNK
print config_training
Ei = config_training["command_line"]["Ei"]
Hi = config_training["command_line"]["Hi"]
Eo = config_training["command_line"]["Eo"]
Ho = config_training["command_line"]["Ho"]
Ha = config_training["command_line"]["Ha"]
Hl = config_training["command_line"]["Hl"]
is_multitarget = config_training["is_multitarget"]
if is_multitarget:
print "Last state of backward encoder RNN is first state of decoder RNN."
encoder_cell_type = config_training["command_line"].get("encoder_cell_type", "gru")
decoder_cell_type = config_training["command_line"].get("decoder_cell_type", "gru")
use_bn_length = config_training["command_line"].get("use_bn_length", None)
import gzip
if "lexical_probability_dictionary" in config_training["command_line"] and config_training["command_line"]["lexical_probability_dictionary"] is not None:
log.info("opening lexical_probability_dictionary %s" % config_training["command_line"]["lexical_probability_dictionary"])
lexical_probability_dictionary_all = json.load(gzip.open(config_training["command_line"]["lexical_probability_dictionary"], "rb"))
log.info("computing lexical_probability_dictionary_indexed")
lexical_probability_dictionary_indexed = {}
for ws in lexical_probability_dictionary_all:
ws_idx = src_indexer.convert([ws])[0]
if ws_idx in lexical_probability_dictionary_indexed:
assert src_indexer.is_unk_idx(ws_idx)
else:
lexical_probability_dictionary_indexed[ws_idx] = {}
for wt in lexical_probability_dictionary_all[ws]:
wt_idx = tgt_indexer.convert([wt])[0]
if wt_idx in lexical_probability_dictionary_indexed[ws_idx]:
assert src_indexer.is_unk_idx(ws_idx) or tgt_indexer.is_unk_idx(wt_idx)
lexical_probability_dictionary_indexed[ws_idx][wt_idx] += lexical_probability_dictionary_all[ws][wt]
else:
lexical_probability_dictionary_indexed[ws_idx][wt_idx] = lexical_probability_dictionary_all[ws][wt]
lexical_probability_dictionary = lexical_probability_dictionary_indexed
else:
lexical_probability_dictionary = None
eos_idx = Vo
encdec = models.EncoderDecoder(Vi, Ei, Hi, Vo + 1, Eo, Ho, Ha, Hl, use_bn_length = use_bn_length,
encoder_cell_type = rnn_cells.create_cell_model_from_string(encoder_cell_type),
decoder_cell_type = rnn_cells.create_cell_model_from_string(decoder_cell_type),
lexical_probability_dictionary = lexical_probability_dictionary,
lex_epsilon = config_training["command_line"].get("lexicon_prob_epsilon", 0.001), is_multitarget = is_multitarget)
return encdec, eos_idx, src_indexer, tgt_indexer
return all_result, all_loss.mean().item()
if args.encode_savepath != '':
start_time = time.time()
cp = torch.load(args.load_state)
c_args = cp['args']
c_args.load_state = args.load_state
c_args.encode_savepath = args.encode_savepath
c_args.test_data = args.test_data
args = c_args
print('checkpoint arguments loaded')
print(args)
print(time.time() - start_time)
#start_time = time.time()
testdata = readdata.readfile(args.test_data, args.batch, args.max_length, 'cut', False)
model = cuda(models.EncoderDecoder(2, args.hidden_size, args.layers, args.dropout, False), args.cuda)
model.load_state_dict(cp['state_dict'])
loss = MSE
print(time.time() - start_time)
#start_time = time.time()
all_test_result, all_test_loss = eval_data(testdata, 100)
all_test_result = np.array(all_test_result)
pickle.dump(all_test_result, open(args.encode_savepath, 'wb'))
print(time.time() - start_time)
exit()
print(args)
if not os.path.exists(args.checkpoint):
os.mkdir(args.checkpoint)
if not os.path.isdir(args.checkpoint):
def command_line(arguments=None):
import argparse
parser = argparse.ArgumentParser(
description="Train a RNNSearch model",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"data_prefix",
help="prefix of the training data created by make_data.py")
parser.add_argument(
"save_prefix",
help="prefix to be added to all files created during the training")
parser.add_argument("--gpu",
type=int,
nargs="+",
default=None,
help="specify gpu number to use, if any")
#parser.add_argument("--gpulist", type = int, nargs = "+", default = None, help = "specify gpu number to use, if any")
parser.add_argument(
"--load_model",
help="load the parameters of a previously trained model")
parser.add_argument("--load_optimizer_state",
help="load previously saved optimizer states")
parser.add_argument("--Ei",
type=int,
default=620,
help="Source words embedding size.")
parser.add_argument("--Eo",
type=int,
default=620,
help="Target words embedding size.")
parser.add_argument("--Hi",
type=int,
default=1000,
help="Source encoding layer size.")
parser.add_argument("--Ho",
type=int,
default=1000,
help="Target hidden layer size.")
parser.add_argument("--Ha",
type=int,
default=1000,
help="Attention Module Hidden layer size.")
parser.add_argument("--Hl",
type=int,
default=500,
help="Maxout output size.")
parser.add_argument("--mb_size",
type=int,
default=80,
help="Minibatch size")
parser.add_argument("--nb_batch_to_sort",
type=int,
default=20,
help="Sort this many batches by size.")
parser.add_argument("--noise_on_prev_word",
default=False,
action="store_true")
parser.add_argument(
"--use_memory_optimization",
default=False,
action="store_true",
help="Experimental option that could strongly reduce memory used.")
parser.add_argument("--max_nb_iters",
type=int,
default=None,
help="maximum number of iterations")
parser.add_argument("--max_src_tgt_length",
type=int,
help="Limit length of training sentences")
parser.add_argument("--l2_gradient_clipping",
type=float,
default=1,
help="L2 gradient clipping. 0 for None")
parser.add_argument("--hard_gradient_clipping",
type=float,
nargs=2,
help="hard gradient clipping.")
parser.add_argument("--weight_decay",
type=float,
help="Weight decay value. ")
parser.add_argument("--optimizer",
choices=[
"sgd", "rmsprop", "rmspropgraves", "momentum",
"nesterov", "adam", "adagrad", "adadelta"
],
default="adam",
help="Optimizer type.")
parser.add_argument("--learning_rate",
type=float,
default=0.01,
help="Learning Rate")
parser.add_argument("--momentum",
type=float,
default=0.9,
help="Momentum term")
parser.add_argument("--report_every",
type=int,
default=200,
help="report every x iterations")
parser.add_argument("--randomized_data",
default=False,
action="store_true")
parser.add_argument("--use_accumulated_attn",
default=False,
action="store_true")
parser.add_argument("--use_deep_attn", default=False, action="store_true")
parser.add_argument("--no_shuffle_of_training_data",
default=False,
action="store_true")
parser.add_argument("--no_resume", default=False, action="store_true")
parser.add_argument("--init_orth", default=False, action="store_true")
parser.add_argument("--reverse_src", default=False, action="store_true")
parser.add_argument("--reverse_tgt", default=False, action="store_true")
parser.add_argument("--curiculum_training",
default=False,
action="store_true")
parser.add_argument("--use_bn_length", default=0, type=int)
parser.add_argument("--use_previous_prediction", default=0, type=float)
parser.add_argument("--no_report_or_save",
default=False,
action="store_true")
parser.add_argument(
"--lexical_probability_dictionary",
help=
"lexical translation probabilities in zipped JSON format. Used to implement https://arxiv.org/abs/1606.02006"
)
parser.add_argument(
"--lexicon_prob_epsilon",
default=1e-3,
type=float,
help="epsilon value for combining the lexical probabilities")
parser.add_argument(
"--encoder_cell_type",
default="lstm",
help=
"cell type of encoder. format: type,param1:val1,param2:val2,... where type is in [%s]"
% (" ".join(rnn_cells.cell_dict.keys())))
parser.add_argument(
"--decoder_cell_type",
default="lstm",
help="cell type of decoder. format same as for encoder")
parser.add_argument("--sample_every", default=200, type=int)
parser.add_argument("--save_ckpt_every", default=4000, type=int)
parser.add_argument("--use_reinf", default=False, action="store_true")
parser.add_argument("--is_multitarget", default=False, action="store_true")
parser.add_argument(
"--postprocess",
default=False,
action="store_true",
help=
"This flag indicates whether the translations should be postprocessed or not. For now it simply indicates that the BPE segmentation should be undone."
)
args = parser.parse_args(args=arguments)
output_files_dict = {}
output_files_dict["train_config"] = args.save_prefix + ".train.config"
output_files_dict[
"model_ckpt"] = args.save_prefix + ".model." + "ckpt" + ".npz"
output_files_dict[
"model_final"] = args.save_prefix + ".model." + "final" + ".npz"
output_files_dict[
"model_best"] = args.save_prefix + ".model." + "best" + ".npz"
output_files_dict[
"model_best_loss"] = args.save_prefix + ".model." + "best_loss" + ".npz"
output_files_dict[
"test_translation_output"] = args.save_prefix + ".test.out"
output_files_dict["test_src_output"] = args.save_prefix + ".test.src.out"
output_files_dict["dev_translation_output"] = args.save_prefix + ".dev.out"
output_files_dict["dev_src_output"] = args.save_prefix + ".dev.src.out"
output_files_dict[
"valid_translation_output"] = args.save_prefix + ".valid.out"
output_files_dict["valid_src_output"] = args.save_prefix + ".valid.src.out"
output_files_dict["sqlite_db"] = args.save_prefix + ".result.sqlite"
output_files_dict[
"optimizer_ckpt"] = args.save_prefix + ".optimizer." + "ckpt" + ".npz"
output_files_dict[
"optimizer_final"] = args.save_prefix + ".optimizer." + "final" + ".npz"
save_prefix_dir, save_prefix_fn = os.path.split(args.save_prefix)
ensure_path(save_prefix_dir)
already_existing_files = []
for key_info, filename in output_files_dict.iteritems(
): #, valid_data_fn]:
if os.path.exists(filename):
already_existing_files.append(filename)
if len(already_existing_files) > 0:
print "Warning: existing files are going to be replaced / updated: ", already_existing_files
#raw_input("Press Enter to Continue")
config_fn = args.data_prefix + ".data.config"
voc_fn = args.data_prefix + ".voc"
data_fn = args.data_prefix + ".data.json.gz"
log.info("loading training data from %s" % data_fn)
training_data_all = json.load(gzip.open(data_fn, "rb"))
training_data = training_data_all["train"]
log.info("loaded %i sentences as training data" % len(training_data))
if "test" in training_data_all:
test_data = training_data_all["test"]
log.info("Found test data: %i sentences" % len(test_data))
else:
test_data = None
log.info("No test data found")
if "dev" in training_data_all:
dev_data = training_data_all["dev"]
log.info("Found dev data: %i sentences" % len(dev_data))
else:
dev_data = None
log.info("No dev data found")
if "valid" in training_data_all:
valid_data = training_data_all["valid"]
log.info("Found valid data: %i sentences" % len(valid_data))
else:
valid_data = None
log.info("No valid data found")
log.info("loading voc from %s" % voc_fn)
src_voc, tgt_voc = json.load(open(voc_fn))
src_indexer = Indexer.make_from_serializable(src_voc)
tgt_indexer = Indexer.make_from_serializable(tgt_voc)
tgt_voc = None
src_voc = None
# Vi = len(src_voc) + 1 # + UNK
# Vo = len(tgt_voc) + 1 # + UNK
Vi = len(src_indexer) # + UNK
Vo = len(tgt_indexer) # + UNK
if args.lexical_probability_dictionary is not None:
log.info("opening lexical_probability_dictionary %s" %
args.lexical_probability_dictionary)
lexical_probability_dictionary_all = json.load(
gzip.open(args.lexical_probability_dictionary, "rb"))
log.info("computing lexical_probability_dictionary_indexed")
lexical_probability_dictionary_indexed = {}
for ws in lexical_probability_dictionary_all:
ws_idx = src_indexer.convert([ws])[0]
if ws_idx in lexical_probability_dictionary_indexed:
assert src_indexer.is_unk_idx(ws_idx)
else:
lexical_probability_dictionary_indexed[ws_idx] = {}
for wt in lexical_probability_dictionary_all[ws]:
wt_idx = tgt_indexer.convert([wt])[0]
if wt_idx in lexical_probability_dictionary_indexed[ws_idx]:
assert src_indexer.is_unk_idx(
ws_idx) or tgt_indexer.is_unk_idx(wt_idx)
lexical_probability_dictionary_indexed[ws_idx][
wt_idx] += lexical_probability_dictionary_all[ws][wt]
else:
lexical_probability_dictionary_indexed[ws_idx][
wt_idx] = lexical_probability_dictionary_all[ws][wt]
lexical_probability_dictionary = lexical_probability_dictionary_indexed
else:
lexical_probability_dictionary = None
if args.max_src_tgt_length is not None:
log.info("filtering sentences of length larger than %i" %
(args.max_src_tgt_length))
filtered_training_data = []
nb_filtered = 0
for src, tgt in training_data:
if len(src) <= args.max_src_tgt_length and len(
tgt) <= args.max_src_tgt_length:
filtered_training_data.append((src, tgt))
else:
nb_filtered += 1
log.info("filtered %i sentences of length larger than %i" %
(nb_filtered, args.max_src_tgt_length))
training_data = filtered_training_data
if not args.no_shuffle_of_training_data:
log.info("shuffling")
import random
random.shuffle(training_data)
log.info("done")
#
# Vi = len(src_voc) + 1 # + UNK
# Vo = len(tgt_voc) + 1 # + UNK
is_multitarget = args.is_multitarget
config_training = {
"command_line": args.__dict__,
"Vi": Vi,
"Vo": Vo,
"voc": voc_fn,
"data": data_fn,
"is_multitarget": is_multitarget
}
save_train_config_fn = output_files_dict["train_config"]
log.info("Saving training config to %s" % save_train_config_fn)
with io.open(save_train_config_fn, 'w', encoding="utf-8") as outfile:
outfile.write(unicode(json.dumps(config_training, ensure_ascii=False)))
#json.dump(config_training, open(save_train_config_fn, "w"), indent=2, separators=(',', ': '))
eos_idx = Vo
# Selecting Attention type
attn_cls = models.AttentionModule
if args.use_accumulated_attn:
raise NotImplemented
# encdec = models.EncoderDecoder(Vi, args.Ei, args.Hi, Vo + 1, args.Eo, args.Ho, args.Ha, args.Hl,
# attn_cls= models.AttentionModuleAcumulated,
# init_orth = args.init_orth)
if args.use_deep_attn:
attn_cls = models.DeepAttentionModule
# Creating encoder/decoder
encdec = models.EncoderDecoder(
Vi,
args.Ei,
args.Hi,
Vo + 1,
args.Eo,
args.Ho,
args.Ha,
args.Hl,
init_orth=args.init_orth,
use_bn_length=args.use_bn_length,
attn_cls=attn_cls,
encoder_cell_type=args.encoder_cell_type,
decoder_cell_type=args.decoder_cell_type,
lexical_probability_dictionary=lexical_probability_dictionary,
lex_epsilon=args.lexicon_prob_epsilon,
is_multitarget=is_multitarget)
if args.load_model is not None:
serializers.load_npz(args.load_model, encdec)
if args.gpu is not None:
models_list = []
models_list.append(encdec)
import copy
for i in range(len(args.gpu) - 1):
log.info(
"Creating copy #%d of model for data parallel computation." %
(i + 1))
encdec_copy = copy.deepcopy(encdec)
models_list.append(encdec_copy)
for i in range(len(args.gpu)):
models_list[i] = models_list[i].to_gpu(args.gpu[i])
assert models_list[0] == encdec
#print len(models_list)
if args.optimizer == "adadelta":
optimizer = optimizers.AdaDelta()
elif args.optimizer == "adam":
optimizer = optimizers.Adam()
elif args.optimizer == "adagrad":
optimizer = optimizers.AdaGrad(lr=args.learning_rate)
elif args.optimizer == "sgd":
optimizer = optimizers.SGD(lr=args.learning_rate)
elif args.optimizer == "momentum":
optimizer = optimizers.MomentumSGD(lr=args.learning_rate,
momentum=args.momentum)
elif args.optimizer == "nesterov":
optimizer = optimizers.NesterovAG(lr=args.learning_rate,
momentum=args.momentum)
elif args.optimizer == "rmsprop":
optimizer = optimizers.RMSprop(lr=args.learning_rate)
elif args.optimizer == "rmspropgraves":
optimizer = optimizers.RMSpropGraves(lr=args.learning_rate,
momentum=args.momentum)
else:
raise NotImplemented
with cuda.get_device(args.gpu):
optimizer.setup(encdec)
if args.l2_gradient_clipping is not None and args.l2_gradient_clipping > 0:
optimizer.add_hook(
chainer.optimizer.GradientClipping(args.l2_gradient_clipping))
if args.hard_gradient_clipping is not None and args.hard_gradient_clipping > 0:
optimizer.add_hook(
chainer.optimizer.GradientHardClipping(
*args.hard_gradient_clipping))
if args.weight_decay is not None:
optimizer.add_hook(chainer.optimizer.WeightDecay(args.weight_decay))
if args.load_optimizer_state is not None:
with cuda.get_device(args.gpu):
serializers.load_npz(args.load_optimizer_state, optimizer)
with cuda.get_device(args.gpu[0]):
# with MyTimerHook() as timer:
# try:
train_on_data(
encdec,
optimizer,
training_data,
output_files_dict,
src_indexer,
tgt_indexer,
eos_idx=eos_idx,
mb_size=args.mb_size,
nb_of_batch_to_sort=args.nb_batch_to_sort * len(args.gpu),
test_data=test_data,
dev_data=dev_data,
valid_data=valid_data,
gpu=args.gpu,
report_every=args.report_every,
randomized=args.randomized_data,
reverse_src=args.reverse_src,
reverse_tgt=args.reverse_tgt,
max_nb_iters=args.max_nb_iters,
do_not_save_data_for_resuming=args.no_resume,
noise_on_prev_word=args.noise_on_prev_word,
curiculum_training=args.curiculum_training,
use_previous_prediction=args.use_previous_prediction,
no_report_or_save=args.no_report_or_save,
use_memory_optimization=args.use_memory_optimization,
sample_every=args.sample_every,
use_reinf=args.use_reinf,
save_ckpt_every=args.save_ckpt_every,
postprocess=args.postprocess,
models_list=models_list
# lexical_probability_dictionary = lexical_probability_dictionary,
# V_tgt = Vo + 1,
# lexicon_prob_epsilon = args.lexicon_prob_epsilon
)
# finally:
# print timer
# timer.print_sorted()
# print "total time:"
# print(timer.total_time())
import sys
sys.exit(0)
import training_chainer
with cuda.get_device(args.gpu):
training_chainer.train_on_data_chainer(
encdec,
optimizer,
training_data,
output_files_dict,
src_indexer,
tgt_indexer,
eos_idx=eos_idx,
output_dir=args.save_prefix,
stop_trigger=None,
mb_size=args.mb_size,
nb_of_batch_to_sort=args.nb_batch_to_sort,
test_data=test_data,
dev_data=dev_data,
valid_data=valid_data,
gpu=args.gpu,
report_every=args.report_every,
randomized=args.randomized_data,
reverse_src=args.reverse_src,
reverse_tgt=args.reverse_tgt,
max_nb_iters=args.max_nb_iters,
do_not_save_data_for_resuming=args.no_resume,
noise_on_prev_word=args.noise_on_prev_word,
curiculum_training=args.curiculum_training,
use_previous_prediction=args.use_previous_prediction,
no_report_or_save=args.no_report_or_save,
use_memory_optimization=args.use_memory_optimization,
sample_every=args.sample_every,
use_reinf=args.use_reinf,
save_ckpt_every=args.save_ckpt_every,
postprocess=args.postprocess
# lexical_probability_dictionary = lexical_probability_dictionary,
# V_tgt = Vo + 1,
# lexicon_prob_epsilon = args.lexicon_prob_epsilon
)
def main():
global device
args = parse()
s_vocab = utils.make_vocab(args.train_src, args.vocab_size)
t_vocab = utils.make_vocab(args.train_tgt, args.vocab_size)
train_source_seqs, train_target_seqs = [], []
valid_source_seqs, valid_target_seqs = [], []
if args.gpu_id is not None and torch.cuda.is_available():
device = torch.device('cuda:' + args.gpu_id[0])
# ファイルを全てID列に変換
with open(args.train_src, encoding='utf-8') as fin:
for line in fin:
train_source_seqs.append([
s_vocab[t] if t in s_vocab else s_vocab['<UNK>']
for t in line.strip().split(' ')
])
with open(args.train_tgt, encoding='utf-8') as fin:
for line in fin:
train_target_seqs.append([
t_vocab[t] if t in t_vocab else t_vocab['<UNK>']
for t in line.strip().split(' ')
])
with open(args.valid_src, encoding='utf-8') as fin:
for line in fin:
valid_source_seqs.append([
s_vocab[t] if t in s_vocab else s_vocab['<UNK>']
for t in line.strip().split(' ')
])
with open(args.valid_tgt, encoding='utf-8') as fin:
for line in fin:
valid_target_seqs.append([
t_vocab[t] if t in t_vocab else t_vocab['<UNK>']
for t in line.strip().split(' ')
])
if args.model_type == 'EncDec':
model = models.EncoderDecoder(s_vocab_size=args.vocab_size,
t_vocab_size=args.vocab_size,
embed_size=args.embed_size,
hidden_size=args.hidden_size,
weight_decay=1e-5).to(device)
elif args.model_type == 'Attn':
model = models.AttentionSeq2Seq(s_vocab_size=args.vocab_size,
t_vocab_size=args.vocab_size,
embed_size=args.embed_size,
hidden_size=args.hidden_size,
num_s_layers=2,
bidirectional=True,
weight_decay=1e-5).to(device)
else:
sys.stderr.write('%s is not found. Model type is `EncDec` or `Attn`.' %
args.model_type)
if args.gpu_id is not None and len(args.gpu_id) > 1:
model = torch.nn.DataParallel(model, device_ids=args.gpu_id)
train_losses, valid_losses = train(train_source_seqs, train_target_seqs,
valid_source_seqs, valid_target_seqs,
model, s_vocab, t_vocab, args.epochs,
args.batch_size, device, args.reverse)
# テストデータの翻訳に必要な各データを出力
pickle.dump(s_vocab, open('s_vocab.pkl', 'wb'))
pickle.dump(t_vocab, open('t_vocab.pkl', 'wb'))
torch.save(model.state_dict(), args.model_prefix + '.model')
plt.plot(np.array([i for i in range(1,
len(train_losses) + 1)]),
train_losses,
label='train loss')
plt.plot(np.array([i for i in range(1,
len(valid_losses) + 1)]),
valid_losses,
label='valid loss')
plt.xlabel('Epochs')
plt.ylabel('loss')
plt.legend()
plt.tight_layout()
plt.savefig('loss_curve.pdf')
