# wargs.val_src_suffix, wargs.val_ref_suffix,
# src_vocab, trg_vocab, False, False, 1000000)
# batch_tst_data = Input(tst_src_tlst, tst_trg_tlst, batch_size=wargs.test_batch_size, batch_sort=False)
rst = tor.token_batch_trans_file(test_input_data,
batch_tst_data=batch_tst_data)
trans, tloss, wloss, sloss, alns = rst['translation'], rst['total_loss'], \
rst['word_level_loss'], rst['sent_level_loss'], rst['total_aligns']
if wargs.search_mode == 0:
p1 = 'greedy'
elif wargs.search_mode == 1:
p1 = 'nbs'
p2 = 'gpu' if args.gpu_ids is not None else 'cpu'
outdir = 'wout_{}_{}'.format(p1, p2)
init_dir(outdir)
outprefix = '{}/{}'.format(outdir, args.input_file)
# wout_nbs_gpu_wb_wvalid/nist06_
file_out = "{}_e{}_b{}_upd{}_k{}".format(outprefix, e_idx, e_bidx, n_steps,
wargs.beam_size)
mteval_bleu = tor.write_file_eval(file_out, trans, args.input_file, alns)
bleus_record_fname = '{}/record_bleu.log'.format(outdir)
bleu_content = 'epoch [{}], batch[{}], BLEU score : {}'.format(
e_idx, e_bidx, mteval_bleu)
with io.open(bleus_record_fname, mode='a', encoding='utf-8') as f:
f.write(bleu_content + '\n')
f.close()
sfig = '{}/{}'.format(outdir, 'record_bleu.sfig')
sfig_content = ('{} {} {} {} {}').format(e_idx, e_bidx, wargs.search_mode,
def main():
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
vocab_data = {}
train_srcD_file = wargs.src_vocab_from
wlog('\nPreparing out of domain source vocabulary from {} ... '.format(
train_srcD_file))
src_vocab = extract_vocab(train_srcD_file, wargs.src_dict,
wargs.src_dict_size)
#DANN
train_srcD_file_domain = wargs.src_domain_vocab_from
wlog('\nPreparing in domain source vocabulary from {} ...'.format(
train_srcD_file_domain))
src_vocab = updata_vocab(train_srcD_file_domain, src_vocab, wargs.src_dict,
wargs.src_dict_size)
vocab_data['src'] = src_vocab
train_trgD_file = wargs.trg_vocab_from
wlog('\nPreparing out of domain target vocabulary from {} ... '.format(
train_trgD_file))
trg_vocab = extract_vocab(train_trgD_file, wargs.trg_dict,
wargs.trg_dict_size)
#DANN
train_trgD_file_domain = wargs.trg_domain_vocab_from
wlog('\nPreparing in domain target vocabulary from {} ... '.format(
train_trgD_file_domain))
trg_vocab = updata_vocab(train_trgD_file_domain, trg_vocab, wargs.trg_dict,
wargs.trg_dict_size)
vocab_data['trg'] = trg_vocab
train_src_file = wargs.train_src
train_trg_file = wargs.train_trg
if wargs.fine_tune is False:
wlog('\nPreparing out of domain training set from {} and {} ... '.
format(train_src_file, train_trg_file))
train_src_tlst, train_trg_tlst = wrap_data(
train_src_file,
train_trg_file,
vocab_data['src'],
vocab_data['trg'],
max_seq_len=wargs.max_seq_len)
else:
wlog('\nNo out of domain trainin set ...')
#DANN
train_src_file_domain = wargs.train_src_domain
train_trg_file_domain = wargs.train_trg_domain
wlog('\nPreparing in domain training set from {} and {}...'.format(
train_src_file_domain, train_trg_file_domain))
train_src_tlst_domain, train_trg_tlst_domain = wrap_data(
train_src_file_domain,
train_trg_file_domain,
vocab_data['src'],
vocab_data['trg'],
max_seq_len=wargs.max_seq_len)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
valid_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_src_suffix)
wlog('\nPreparing validation set from {} ... '.format(valid_file))
valid_src_tlst, valid_src_lens = val_wrap_data(valid_file, src_vocab)
if wargs.fine_tune is False:
wlog('Out of domain Sentence-pairs count in training data: {}'.format(
len(train_src_tlst)))
wlog('In domain Sentence-pairs count in training data: {}'.format(
len(train_src_tlst_domain)))
src_vocab_size, trg_vocab_size = vocab_data['src'].size(
), vocab_data['trg'].size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(
src_vocab_size, trg_vocab_size))
if wargs.fine_tune is False:
batch_train = Input(train_src_tlst, train_trg_tlst, wargs.batch_size)
else:
batch_train = None
batch_valid = Input(valid_src_tlst, None, 1, volatile=True)
#DANN
batch_train_domain = Input(train_src_tlst_domain, train_trg_tlst_domain,
wargs.batch_size)
tests_data = None
if wargs.tests_prefix is not None:
init_dir(wargs.dir_tests)
tests_data = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix,
wargs.val_src_suffix)
wlog('Preparing test set from {} ... '.format(test_file))
test_src_tlst, _ = val_wrap_data(test_file, src_vocab)
tests_data[prefix] = Input(test_src_tlst, None, 1, volatile=True)
sv = vocab_data['src'].idx2key
tv = vocab_data['trg'].idx2key
nmtModel = NMT(src_vocab_size, trg_vocab_size)
if wargs.pre_train is not None:
assert os.path.exists(wargs.pre_train), 'Requires pre-trained model'
_dict = _load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 4: model_dict, eid, bid, optim = _dict
elif len(_dict) == 5:
model_dict, class_dict, eid, bid, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad,
name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
else:
init_params(param, name, True)
wargs.start_epoch = eid + 1
else:
for n, p in nmtModel.named_parameters():
init_params(p, n, True)
optim = Optim(wargs.opt_mode,
wargs.learning_rate,
wargs.max_grad_norm,
learning_rate_decay=wargs.learning_rate_decay,
start_decay_from=wargs.start_decay_from,
last_valid_bleu=wargs.last_valid_bleu)
if wargs.gpu_id:
nmtModel.cuda()
wlog('Push model onto GPU[{}] ... '.format(wargs.gpu_id[0]))
else:
nmtModel.cpu()
wlog('Push model onto CPU ... ')
wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('Parameters number: {}/{}'.format(pcnt1, pcnt2))
optim.init_optimizer(nmtModel.parameters())
trainer = Trainer(nmtModel, batch_train, batch_train_domain, vocab_data,
optim, batch_valid, tests_data)
trainer.train()
def main():
#if wargs.ss_type is not None: assert wargs.model == 1, 'Only rnnsearch support schedule sample'
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
src = os.path.join(wargs.dir_data, '{}.{}'.format(wargs.train_prefix, wargs.train_src_suffix))
trg = os.path.join(wargs.dir_data, '{}.{}'.format(wargs.train_prefix, wargs.train_trg_suffix))
vocabs = {}
wlog('\nPreparing source vocabulary from {} ... '.format(src))
src_vocab = extract_vocab(src, wargs.src_vcb, wargs.n_src_vcb_plan,
wargs.max_seq_len, char=wargs.src_char)
wlog('\nPreparing target vocabulary from {} ... '.format(trg))
trg_vocab = extract_vocab(trg, wargs.trg_vcb, wargs.n_trg_vcb_plan, wargs.max_seq_len)
n_src_vcb, n_trg_vcb = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(n_src_vcb, n_trg_vcb))
vocabs['src'], vocabs['trg'] = src_vocab, trg_vocab
wlog('\nPreparing training set from {} and {} ... '.format(src, trg))
trains = {}
train_src_tlst, train_trg_tlst = wrap_data(wargs.dir_data, wargs.train_prefix,
wargs.train_src_suffix, wargs.train_trg_suffix,
src_vocab, trg_vocab, shuffle=True,
sort_k_batches=wargs.sort_k_batches,
max_seq_len=wargs.max_seq_len,
char=wargs.src_char)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
batch_train = Input(train_src_tlst, train_trg_tlst, wargs.batch_size,
batch_type=wargs.batch_type, bow=wargs.trg_bow, batch_sort=False)
wlog('Sentence-pairs count in training data: {}'.format(len(train_src_tlst)))
batch_valid = None
if wargs.val_prefix is not None:
val_src_file = os.path.join(wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix, wargs.val_src_suffix))
val_trg_file = os.path.join(wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix, wargs.val_ref_suffix))
wlog('\nPreparing validation set from {} and {} ... '.format(val_src_file, val_trg_file))
valid_src_tlst, valid_trg_tlst = wrap_data(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_src_suffix, wargs.val_ref_suffix,
src_vocab, trg_vocab, shuffle=False,
max_seq_len=wargs.dev_max_seq_len,
char=wargs.src_char)
batch_valid = Input(valid_src_tlst, valid_trg_tlst, 1, batch_sort=False)
batch_tests = None
if wargs.tests_prefix is not None:
assert isinstance(wargs.tests_prefix, list), 'Test files should be list.'
init_dir(wargs.dir_tests)
batch_tests = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix, wargs.val_src_suffix)
wlog('\nPreparing test set from {} ... '.format(test_file))
test_src_tlst, _ = wrap_tst_data(test_file, src_vocab, char=wargs.src_char)
batch_tests[prefix] = Input(test_src_tlst, None, 1, batch_sort=False)
wlog('\n## Finish to Prepare Dataset ! ##\n')
src_emb = WordEmbedding(n_src_vcb, wargs.d_src_emb, wargs.input_dropout,
wargs.position_encoding, prefix='Src')
trg_emb = WordEmbedding(n_trg_vcb, wargs.d_trg_emb, wargs.input_dropout,
wargs.position_encoding, prefix='Trg')
# share the embedding matrix - preprocess with share_vocab required.
if wargs.embs_share_weight:
if n_src_vcb != n_trg_vcb:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
src_emb.we.weight = trg_emb.we.weight
nmtModel = build_NMT(src_emb, trg_emb)
if not wargs.copy_attn:
classifier = Classifier(wargs.d_model if wargs.decoder_type == 'att' else 2 * wargs.d_enc_hid,
n_trg_vcb, trg_emb, loss_norm=wargs.loss_norm,
label_smoothing=wargs.label_smoothing,
emb_loss=wargs.emb_loss, bow_loss=wargs.bow_loss)
nmtModel.decoder.classifier = classifier
if wargs.gpu_id is not None:
wlog('push model onto GPU {} ... '.format(wargs.gpu_id), 0)
#nmtModel = nn.DataParallel(nmtModel, device_ids=wargs.gpu_id)
nmtModel.to(tc.device('cuda'))
else:
wlog('push model onto CPU ... ', 0)
nmtModel.to(tc.device('cpu'))
wlog('done.')
if wargs.pre_train is not None:
assert os.path.exists(wargs.pre_train)
from tools.utils import load_model
_dict = load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 5:
model_dict, class_dict, eid, bid, optim = _dict
elif len(_dict) == 4:
model_dict, eid, bid, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
else: init_params(param, name, init_D=wargs.param_init_D, a=float(wargs.u_gain))
wargs.start_epoch = eid + 1
else:
optim = Optim(wargs.opt_mode, wargs.learning_rate, wargs.max_grad_norm)
#for n, p in nmtModel.named_parameters():
# bias can not be initialized uniformly
#if wargs.encoder_type != 'att' and wargs.decoder_type != 'att':
# init_params(p, n, init_D=wargs.param_init_D, a=float(wargs.u_gain))
wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('parameters number: {}/{}'.format(pcnt1, pcnt2))
wlog('\n' + '*' * 30 + ' trainable parameters ' + '*' * 30)
for n, p in nmtModel.named_parameters():
if p.requires_grad: wlog('{:60} : {}'.format(n, p.size()))
optim.init_optimizer(nmtModel.parameters())
trainer = Trainer(nmtModel, batch_train, vocabs, optim, batch_valid, batch_tests)
trainer.train()
def main():
# if wargs.ss_type is not None: assert wargs.model == 1, 'Only rnnsearch support schedule sample'
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
src = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_src_suffix))
trg = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_trg_suffix))
src, trg = os.path.abspath(src), os.path.abspath(trg)
vocabs = {}
if wargs.share_vocab is False:
wlog('\nPreparing source vocabulary from {} ... '.format(src))
src_vocab = extract_vocab(src,
wargs.src_vcb,
wargs.n_src_vcb_plan,
wargs.max_seq_len,
char=wargs.src_char)
wlog('\nPreparing target vocabulary from {} ... '.format(trg))
trg_vocab = extract_vocab(trg, wargs.trg_vcb, wargs.n_trg_vcb_plan,
wargs.max_seq_len)
n_src_vcb, n_trg_vcb = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(
n_src_vcb, n_trg_vcb))
else:
wlog('\nPreparing the shared vocabulary from \n\t{}\n\t{}'.format(
src, trg))
trg_vocab = src_vocab = extract_vocab(src,
wargs.src_vcb,
wargs.n_src_vcb_plan,
wargs.max_seq_len,
share_vocab=True,
trg_file=trg)
n_src_vcb, n_trg_vcb = src_vocab.size(), trg_vocab.size()
wlog('Shared vocabulary size: |vocab|={}'.format(src_vocab.size()))
vocabs['src'], vocabs['trg'] = src_vocab, trg_vocab
wlog('\nPreparing training set from {} and {} ... '.format(src, trg))
trains = {}
train_src_tlst, train_trg_tlst = wrap_data(
wargs.dir_data,
wargs.train_prefix,
wargs.train_src_suffix,
wargs.train_trg_suffix,
src_vocab,
trg_vocab,
shuffle=True,
sort_k_batches=wargs.sort_k_batches,
max_seq_len=wargs.max_seq_len,
char=wargs.src_char)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
batch_train = Input(train_src_tlst,
train_trg_tlst,
wargs.batch_size,
batch_type=wargs.batch_type,
bow=wargs.trg_bow,
batch_sort=False,
gpu_ids=device_ids)
wlog('Sentence-pairs count in training data: {}'.format(
len(train_src_tlst)))
batch_valid = None
if wargs.val_prefix is not None:
val_src_file = os.path.join(
wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix,
wargs.val_src_suffix))
val_trg_file = os.path.join(
wargs.val_tst_dir, '{}.{}'.format(wargs.val_prefix,
wargs.val_ref_suffix))
val_src_file, val_trg_file = os.path.abspath(
val_src_file), os.path.abspath(val_trg_file)
wlog('\nPreparing validation set from {} and {} ... '.format(
val_src_file, val_trg_file))
valid_src_tlst, valid_trg_tlst = wrap_data(
wargs.val_tst_dir,
wargs.val_prefix,
wargs.val_src_suffix,
wargs.val_ref_suffix,
src_vocab,
trg_vocab,
shuffle=False,
max_seq_len=wargs.dev_max_seq_len,
char=wargs.src_char)
batch_valid = Input(valid_src_tlst,
valid_trg_tlst,
batch_size=wargs.valid_batch_size,
batch_sort=False,
gpu_ids=device_ids)
batch_tests = None
if wargs.tests_prefix is not None:
assert isinstance(wargs.tests_prefix,
list), 'Test files should be list.'
init_dir(wargs.dir_tests)
batch_tests = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix,
wargs.val_src_suffix)
test_file = os.path.abspath(test_file)
wlog('\nPreparing test set from {} ... '.format(test_file))
test_src_tlst, _ = wrap_tst_data(test_file,
src_vocab,
char=wargs.src_char)
batch_tests[prefix] = Input(test_src_tlst,
None,
batch_size=wargs.test_batch_size,
batch_sort=False,
gpu_ids=device_ids)
wlog('\n## Finish to Prepare Dataset ! ##\n')
src_emb = WordEmbedding(n_src_vcb,
wargs.d_src_emb,
wargs.input_dropout,
wargs.position_encoding,
prefix='Src')
trg_emb = WordEmbedding(n_trg_vcb,
wargs.d_trg_emb,
wargs.input_dropout,
wargs.position_encoding,
prefix='Trg')
# share the embedding matrix between the source and target
if wargs.share_vocab is True: src_emb.we.weight = trg_emb.we.weight
nmtModel = build_NMT(src_emb, trg_emb)
if device_ids is not None:
wlog('push model onto GPU {} ... '.format(device_ids[0]), 0)
nmtModel_par = nn.DataParallel(nmtModel, device_ids=device_ids)
nmtModel_par.to(device)
else:
wlog('push model onto CPU ... ', 0)
nmtModel.to(tc.device('cpu'))
wlog('done.')
if wargs.pre_train is not None:
wlog(wargs.pre_train)
assert os.path.exists(wargs.pre_train)
from tools.utils import load_model
_dict = load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 5:
# model_dict, e_idx, e_bidx, n_steps, optim = _dict['model'], _dict['epoch'], _dict['batch'], _dict['steps'], _dict['optim']
model_dict, e_idx, e_bidx, n_steps, optim = _dict
elif len(_dict) == 4:
model_dict, e_idx, e_bidx, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
# wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
# wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
# wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad, name))
else:
init_params(param,
name,
init_D=wargs.param_init_D,
a=float(wargs.u_gain))
# wargs.start_epoch = e_idx + 1
# # 不重新开始
# optim.n_current_steps = 0
else:
optim = Optim(wargs.opt_mode, wargs.learning_rate, wargs.max_grad_norm)
for n, p in nmtModel.named_parameters():
# bias can not be initialized uniformly
if 'norm' in n:
wlog('ignore layer norm init ...')
continue
if 'emb' in n:
wlog('ignore word embedding weight init ...')
continue
if 'vcb_proj' in n:
wlog('ignore vcb_proj weight init ...')
continue
init_params(p, n, init_D=wargs.param_init_D, a=float(wargs.u_gain))
# if wargs.encoder_type != 'att' and wargs.decoder_type != 'att':
# init_params(p, n, init_D=wargs.param_init_D, a=float(wargs.u_gain))
# wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('parameters number: {}/{}'.format(pcnt1, pcnt2))
# wlog('\n' + '*' * 30 + ' trainable parameters ' + '*' * 30)
# for n, p in nmtModel.named_parameters():
# if p.requires_grad: wlog('{:60} : {}'.format(n, p.size()))
opt_state = None
if wargs.pre_train:
opt_state = optim.optimizer.state_dict()
if wargs.use_reinfore_ce is False:
criterion = LabelSmoothingCriterion(
trg_emb.n_vocab, label_smoothing=wargs.label_smoothing)
else:
word2vec = tc.load(wargs.word2vec_weight)['w2v']
# criterion = Word2VecDistanceCriterion(word2vec)
criterion = CosineDistance(word2vec)
if device_ids is not None:
wlog('push criterion onto GPU {} ... '.format(device_ids[0]), 0)
criterion = criterion.to(device)
wlog('done.')
# if wargs.reinfore_type == 0 or wargs.reinfore_type == 1:
# param = list(nmtModel.parameters())
# else:
# param = list(nmtModel.parameters()) + list(criterion.parameters())
param = list(nmtModel.parameters())
optim.init_optimizer(param)
lossCompute = MultiGPULossCompute(
nmtModel.generator,
criterion,
wargs.d_model if wargs.decoder_type == 'att' else 2 * wargs.d_enc_hid,
n_trg_vcb,
trg_emb,
nmtModel.bowMapper,
loss_norm=wargs.loss_norm,
chunk_size=wargs.chunk_size,
device_ids=device_ids)
trainer = Trainer(nmtModel_par, batch_train, vocabs, optim, lossCompute,
nmtModel, batch_valid, batch_tests, writer)
trainer.train()
writer.close()
def main():
#if wargs.ss_type is not None: assert wargs.model == 1, 'Only rnnsearch support schedule sample'
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
src = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_src_suffix))
trg = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_trg_suffix))
vocabs = {}
wlog('\n[o/Subword] Preparing source vocabulary from {} ... '.format(src))
src_vocab = extract_vocab(src,
wargs.src_dict,
wargs.src_dict_size,
wargs.max_seq_len,
char=wargs.src_char)
wlog('\n[o/Subword] Preparing target vocabulary from {} ... '.format(trg))
trg_vocab = extract_vocab(trg, wargs.trg_dict, wargs.trg_dict_size,
wargs.max_seq_len)
src_vocab_size, trg_vocab_size = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(
src_vocab_size, trg_vocab_size))
vocabs['src'], vocabs['trg'] = src_vocab, trg_vocab
wlog('\nPreparing training set from {} and {} ... '.format(src, trg))
trains = {}
train_src_tlst, train_trg_tlst = wrap_data(wargs.dir_data,
wargs.train_prefix,
wargs.train_src_suffix,
wargs.train_trg_suffix,
src_vocab,
trg_vocab,
max_seq_len=wargs.max_seq_len,
char=wargs.src_char)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
batch_train = Input(train_src_tlst,
train_trg_tlst,
wargs.batch_size,
batch_sort=True)
wlog('Sentence-pairs count in training data: {}'.format(
len(train_src_tlst)))
batch_valid = None
if wargs.val_prefix is not None:
val_src_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_src_suffix)
val_trg_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_ref_suffix)
wlog('\nPreparing validation set from {} and {} ... '.format(
val_src_file, val_trg_file))
valid_src_tlst, valid_trg_tlst = wrap_data(
wargs.val_tst_dir,
wargs.val_prefix,
wargs.val_src_suffix,
wargs.val_ref_suffix,
src_vocab,
trg_vocab,
shuffle=False,
sort_data=False,
max_seq_len=wargs.dev_max_seq_len,
char=wargs.src_char)
batch_valid = Input(valid_src_tlst,
valid_trg_tlst,
1,
volatile=True,
batch_sort=False)
batch_tests = None
if wargs.tests_prefix is not None:
assert isinstance(wargs.tests_prefix,
list), 'Test files should be list.'
init_dir(wargs.dir_tests)
batch_tests = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix,
wargs.val_src_suffix)
wlog('\nPreparing test set from {} ... '.format(test_file))
test_src_tlst, _ = wrap_tst_data(test_file,
src_vocab,
char=wargs.src_char)
batch_tests[prefix] = Input(test_src_tlst,
None,
1,
volatile=True,
batch_sort=False)
wlog('\n## Finish to Prepare Dataset ! ##\n')
nmtModel = NMT(src_vocab_size, trg_vocab_size)
if wargs.pre_train is not None:
assert os.path.exists(wargs.pre_train)
_dict = _load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 4: model_dict, eid, bid, optim = _dict
elif len(_dict) == 5:
model_dict, class_dict, eid, bid, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad,
name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
else:
init_params(param, name, True)
wargs.start_epoch = eid + 1
else:
for n, p in nmtModel.named_parameters():
init_params(p, n, True)
optim = Optim(wargs.opt_mode,
wargs.learning_rate,
wargs.max_grad_norm,
learning_rate_decay=wargs.learning_rate_decay,
start_decay_from=wargs.start_decay_from,
last_valid_bleu=wargs.last_valid_bleu,
model=wargs.model)
if wargs.gpu_id is not None:
wlog('Push model onto GPU {} ... '.format(wargs.gpu_id), 0)
nmtModel.cuda()
else:
wlog('Push model onto CPU ... ', 0)
nmtModel.cpu()
wlog('done.')
wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('Parameters number: {}/{}'.format(pcnt1, pcnt2))
optim.init_optimizer(nmtModel.parameters())
trainer = Trainer(nmtModel, batch_train, vocabs, optim, batch_valid,
batch_tests)
trainer.train()
def main():
# Check if CUDA is available
if cuda.is_available():
wlog('CUDA is available, specify device by gpu_id argument (i.e. gpu_id=[3])')
else:
wlog('Warning: CUDA is not available, try CPU')
if wargs.gpu_id:
cuda.set_device(wargs.gpu_id[0])
wlog('Using GPU {}'.format(wargs.gpu_id[0]))
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
init_dir(wargs.dir_tests)
for prefix in wargs.tests_prefix:
if not prefix == wargs.val_prefix: init_dir(wargs.dir_tests + '/' + prefix)
wlog('Preparing data ... ', 0)
train_srcD_file = wargs.dir_data + 'train.10k.zh5'
wlog('\nPreparing source vocabulary from {} ... '.format(train_srcD_file))
src_vocab = extract_vocab(train_srcD_file, wargs.src_dict, wargs.src_dict_size)
train_trgD_file = wargs.dir_data + 'train.10k.en5'
wlog('\nPreparing target vocabulary from {} ... '.format(train_trgD_file))
trg_vocab = extract_vocab(train_trgD_file, wargs.trg_dict, wargs.trg_dict_size)
train_src_file = wargs.dir_data + 'train.10k.zh0'
train_trg_file = wargs.dir_data + 'train.10k.en0'
wlog('\nPreparing training set from {} and {} ... '.format(train_src_file, train_trg_file))
train_src_tlst, train_trg_tlst = wrap_data(train_src_file, train_trg_file, src_vocab, trg_vocab)
#list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...], no padding
wlog('Sentence-pairs count in training data: {}'.format(len(train_src_tlst)))
src_vocab_size, trg_vocab_size = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(src_vocab_size, trg_vocab_size))
batch_train = Input(train_src_tlst, train_trg_tlst, wargs.batch_size)
tests_data = None
if wargs.tests_prefix is not None:
tests_data = {}
for prefix in wargs.tests_prefix:
test_file = wargs.val_tst_dir + prefix + '.src'
test_src_tlst, _ = val_wrap_data(test_file, src_vocab)
# we select best model by nist03 testing data
if prefix == wargs.val_prefix:
wlog('\nPreparing model-select set from {} ... '.format(test_file))
batch_valid = Input(test_src_tlst, None, 1, volatile=True, prefix=prefix)
else:
wlog('\nPreparing test set from {} ... '.format(test_file))
tests_data[prefix] = Input(test_src_tlst, None, 1, volatile=True)
nmtModel = NMT()
classifier = Classifier(wargs.out_size, trg_vocab_size)
if wargs.pre_train:
model_dict, class_dict, eid, bid, optim = load_pytorch_model(wargs.pre_train)
if isinstance(optim, list): _, _, optim = optim
# initializing parameters of interactive attention model
for p in nmtModel.named_parameters(): p[1].data = model_dict[p[0]]
for p in classifier.named_parameters(): p[1].data = class_dict[p[0]]
#wargs.start_epoch = eid + 1
else:
for p in nmtModel.parameters(): init_params(p, uniform=True)
for p in classifier.parameters(): init_params(p, uniform=True)
optim = Optim(
wargs.opt_mode, wargs.learning_rate, wargs.max_grad_norm,
learning_rate_decay=wargs.learning_rate_decay,
start_decay_from=wargs.start_decay_from,
last_valid_bleu=wargs.last_valid_bleu
)
if wargs.gpu_id:
wlog('Push model onto GPU ... ')
nmtModel.cuda()
classifier.cuda()
else:
wlog('Push model onto CPU ... ')
nmtModel.cpu()
classifier.cuda()
nmtModel.classifier = classifier
wlog(nmtModel)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('Parameters number: {}/{}'.format(pcnt1, pcnt2))
optim.init_optimizer(nmtModel.parameters())
#tor = Translator(nmtModel, src_vocab.idx2key, trg_vocab.idx2key)
#tor.trans_tests(tests_data, pre_dict['epoch'], pre_dict['batch'])
trainer = Trainer(nmtModel, src_vocab.idx2key, trg_vocab.idx2key, optim, trg_vocab_size)
dev_src0 = wargs.dir_data + 'dev.1k.zh0'
dev_trg0 = wargs.dir_data + 'dev.1k.en0'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src0, dev_trg0))
dev_src0, dev_trg0 = wrap_data(dev_src0, dev_trg0, src_vocab, trg_vocab)
wlog(len(train_src_tlst))
# add 1000 to train
train_all_chunks = (train_src_tlst, train_trg_tlst)
dh = DataHisto(train_all_chunks)
dev_src1 = wargs.dir_data + 'dev.1k.zh1'
dev_trg1 = wargs.dir_data + 'dev.1k.en1'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src1, dev_trg1))
dev_src1, dev_trg1 = wrap_data(dev_src1, dev_trg1, src_vocab, trg_vocab)
dev_src2 = wargs.dir_data + 'dev.1k.zh2'
dev_trg2 = wargs.dir_data + 'dev.1k.en2'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src2, dev_trg2))
dev_src2, dev_trg2 = wrap_data(dev_src2, dev_trg2, src_vocab, trg_vocab)
dev_src3 = wargs.dir_data + 'dev.1k.zh3'
dev_trg3 = wargs.dir_data + 'dev.1k.en3'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src3, dev_trg3))
dev_src3, dev_trg3 = wrap_data(dev_src3, dev_trg3, src_vocab, trg_vocab)
dev_src4 = wargs.dir_data + 'dev.1k.zh4'
dev_trg4 = wargs.dir_data + 'dev.1k.en4'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src4, dev_trg4))
dev_src4, dev_trg4 = wrap_data(dev_src4, dev_trg4, src_vocab, trg_vocab)
wlog(len(dev_src4+dev_src3+dev_src2+dev_src1+dev_src0))
dev_input = Input(dev_src4+dev_src3+dev_src2+dev_src1+dev_src0, dev_trg4+dev_trg3+dev_trg2+dev_trg1+dev_trg0, wargs.batch_size)
trainer.train(dh, dev_input, 0, batch_valid, tests_data, merge=True, name='DH_{}'.format('dev'))
'''
chunk_size = 1000
rand_ids = tc.randperm(len(train_src_tlst))[:chunk_size * 1000]
rand_ids = rand_ids.split(chunk_size)
#train_chunks = [(dev_src, dev_trg)]
train_chunks = []
for k in range(len(rand_ids)):
rand_id = rand_ids[k]
chunk_src_tlst = [train_src_tlst[i] for i in rand_id]
chunk_trg_tlst = [train_trg_tlst[i] for i in rand_id]
#wlog('Sentence-pairs count in training data: {}'.format(len(src_samples_train)))
#batch_train = Input(train_src_tlst, train_trg_tlst, wargs.batch_size)
#batch_train = Input(src_samples_train, trg_samples_train, wargs.batch_size)
train_chunks.append((chunk_src_tlst, chunk_trg_tlst))
chunk_D0 = train_chunks[0]
dh = DataHisto(chunk_D0)
c0_input = Input(chunk_D0[0], chunk_D0[1], wargs.batch_size)
trainer.train(dh, c0_input, 0, batch_valid, tests_data, merge=False, name='DH_{}'.format(0))
for k in range(1, len(train_chunks)):
wlog('*' * 30, False)
wlog(' Next Data {} '.format(k), False)
wlog('*' * 30)
chunk_Dk = train_chunks[k]
ck_input = Input(chunk_Dk[0], chunk_Dk[1], wargs.batch_size)
trainer.train(dh, ck_input, k, batch_valid, tests_data, merge=True, name='DH_{}'.format(k))
dh.add_batch_data(chunk_Dk)
'''
if tests_data and wargs.final_test:
bestModel = NMT()
classifier = Classifier(wargs.out_size, trg_vocab_size)
assert os.path.exists(wargs.best_model)
model_dict = tc.load(wargs.best_model)
best_model_dict = model_dict['model']
best_model_dict = {k: v for k, v in best_model_dict.items() if 'classifier' not in k}
bestModel.load_state_dict(best_model_dict)
classifier.load_state_dict(model_dict['class'])
if wargs.gpu_id:
wlog('Push NMT model onto GPU ... ')
bestModel.cuda()
classifier.cuda()
else:
wlog('Push NMT model onto CPU ... ')
bestModel.cpu()
classifier.cpu()
bestModel.classifier = classifier
tor = Translator(bestModel, src_vocab.idx2key, trg_vocab.idx2key)
tor.trans_tests(tests_data, model_dict['epoch'], model_dict['batch'])
def main():
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
vocab_data = {}
train_srcD_file = wargs.src_vocab_from
wlog('\nPreparing source vocabulary from {} ... '.format(train_srcD_file))
src_vocab = extract_vocab(train_srcD_file, wargs.src_dict,
wargs.src_dict_size)
vocab_data['src'] = src_vocab
train_trgD_file = wargs.trg_vocab_from
wlog('\nPreparing target vocabulary from {} ... '.format(train_trgD_file))
trg_vocab = extract_vocab(train_trgD_file, wargs.trg_dict,
wargs.trg_dict_size)
vocab_data['trg'] = trg_vocab
train_src_file = wargs.train_src
train_trg_file = wargs.train_trg
wlog('\nPreparing training set from {} and {} ... '.format(
train_src_file, train_trg_file))
train_src_tlst, train_trg_tlst = wrap_data(train_src_file,
train_trg_file,
src_vocab,
trg_vocab,
max_seq_len=wargs.max_seq_len)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
'''
devs = {}
dev_src = wargs.val_tst_dir + wargs.val_prefix + '.src'
dev_trg = wargs.val_tst_dir + wargs.val_prefix + '.ref0'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src, dev_trg))
dev_src, dev_trg = wrap_data(dev_src, dev_trg, src_vocab, trg_vocab)
devs['src'], devs['trg'] = dev_src, dev_trg
'''
valid_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_src_suffix)
wlog('\nPreparing validation set from {} ... '.format(valid_file))
valid_src_tlst, valid_src_lens = val_wrap_data(valid_file, src_vocab)
wlog('Sentence-pairs count in training data: {}'.format(
len(train_src_tlst)))
src_vocab_size, trg_vocab_size = vocab_data['src'].size(
), vocab_data['trg'].size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(
src_vocab_size, trg_vocab_size))
batch_train = Input(train_src_tlst, train_trg_tlst, wargs.batch_size)
batch_valid = Input(valid_src_tlst, None, 1, volatile=True)
tests_data = None
if wargs.tests_prefix is not None:
init_dir(wargs.dir_tests)
tests_data = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix,
wargs.val_src_suffix)
wlog('Preparing test set from {} ... '.format(test_file))
test_src_tlst, _ = val_wrap_data(test_file, src_vocab)
tests_data[prefix] = Input(test_src_tlst, None, 1, volatile=True)
'''
# lookup_table on cpu to save memory
src_lookup_table = nn.Embedding(wargs.src_dict_size + 4,
wargs.src_wemb_size, padding_idx=utils.PAD).cpu()
trg_lookup_table = nn.Embedding(wargs.trg_dict_size + 4,
wargs.trg_wemb_size, padding_idx=utils.PAD).cpu()
wlog('Lookup table on CPU ... ')
wlog(src_lookup_table)
wlog(trg_lookup_table)
'''
sv = vocab_data['src'].idx2key
tv = vocab_data['trg'].idx2key
nmtModel = NMT(src_vocab_size, trg_vocab_size)
#classifier = Classifier(wargs.out_size, trg_vocab_size,
# nmtModel.decoder.trg_lookup_table if wargs.copy_trg_emb is True else None)
if wargs.pre_train:
assert os.path.exists(wargs.pre_train)
_dict = _load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 4: model_dict, eid, bid, optim = _dict
elif len(_dict) == 5:
model_dict, class_dict, eid, bid, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad,
name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
else:
init_params(param, name, True)
wargs.start_epoch = eid + 1
#tor = Translator(nmtModel, sv, tv)
#tor.trans_tests(tests_data, eid, bid)
else:
for n, p in nmtModel.named_parameters():
init_params(p, n, True)
#for n, p in classifier.named_parameters(): init_params(p, n, True)
optim = Optim(wargs.opt_mode,
wargs.learning_rate,
wargs.max_grad_norm,
learning_rate_decay=wargs.learning_rate_decay,
start_decay_from=wargs.start_decay_from,
last_valid_bleu=wargs.last_valid_bleu)
if wargs.gpu_id:
nmtModel.cuda()
#classifier.cuda()
wlog('Push model onto GPU[{}] ... '.format(wargs.gpu_id[0]))
else:
nmtModel.cpu()
#classifier.cpu()
wlog('Push model onto CPU ... ')
#nmtModel.classifier = classifier
#nmtModel.decoder.map_vocab = classifier.map_vocab
'''
nmtModel.src_lookup_table = src_lookup_table
nmtModel.trg_lookup_table = trg_lookup_table
print nmtModel.src_lookup_table.weight.data.is_cuda
nmtModel.classifier.init_weights(nmtModel.trg_lookup_table)
'''
wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('Parameters number: {}/{}'.format(pcnt1, pcnt2))
optim.init_optimizer(nmtModel.parameters())
#tor = Translator(nmtModel, sv, tv, wargs.search_mode)
#tor.trans_tests(tests_data, pre_dict['epoch'], pre_dict['batch'])
trainer = Trainer(nmtModel, batch_train, vocab_data, optim, batch_valid,
tests_data)
trainer.train()
def main():
# Check if CUDA is available
if cuda.is_available():
wlog(
'CUDA is available, specify device by gpu_id argument (i.e. gpu_id=[3])'
)
else:
wlog('Warning: CUDA is not available, try CPU')
if wargs.gpu_id:
cuda.set_device(wargs.gpu_id[0])
wlog('Using GPU {}'.format(wargs.gpu_id[0]))
init_dir(wargs.dir_model)
init_dir(wargs.dir_valid)
'''
train_srcD_file = wargs.dir_data + 'train.10k.zh5'
wlog('\nPreparing source vocabulary from {} ... '.format(train_srcD_file))
src_vocab = extract_vocab(train_srcD_file, wargs.src_dict, wargs.src_dict_size)
train_trgD_file = wargs.dir_data + 'train.10k.en5'
wlog('\nPreparing target vocabulary from {} ... '.format(train_trgD_file))
trg_vocab = extract_vocab(train_trgD_file, wargs.trg_dict, wargs.trg_dict_size)
train_src_file = wargs.dir_data + 'train.10k.zh0'
train_trg_file = wargs.dir_data + 'train.10k.en0'
wlog('\nPreparing training set from {} and {} ... '.format(train_src_file, train_trg_file))
train_src_tlst, train_trg_tlst = wrap_data(train_src_file, train_trg_file, src_vocab, trg_vocab)
#list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...], no padding
wlog('Sentence-pairs count in training data: {}'.format(len(train_src_tlst)))
src_vocab_size, trg_vocab_size = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(src_vocab_size, trg_vocab_size))
batch_train = Input(train_src_tlst, train_trg_tlst, wargs.batch_size)
'''
src = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_src_suffix))
trg = os.path.join(
wargs.dir_data, '{}.{}'.format(wargs.train_prefix,
wargs.train_trg_suffix))
vocabs = {}
wlog('\nPreparing source vocabulary from {} ... '.format(src))
src_vocab = extract_vocab(src, wargs.src_dict, wargs.src_dict_size)
wlog('\nPreparing target vocabulary from {} ... '.format(trg))
trg_vocab = extract_vocab(trg, wargs.trg_dict, wargs.trg_dict_size)
src_vocab_size, trg_vocab_size = src_vocab.size(), trg_vocab.size()
wlog('Vocabulary size: |source|={}, |target|={}'.format(
src_vocab_size, trg_vocab_size))
vocabs['src'], vocabs['trg'] = src_vocab, trg_vocab
wlog('\nPreparing training set from {} and {} ... '.format(src, trg))
trains = {}
train_src_tlst, train_trg_tlst = wrap_data(wargs.dir_data,
wargs.train_prefix,
wargs.train_src_suffix,
wargs.train_trg_suffix,
src_vocab,
trg_vocab,
max_seq_len=wargs.max_seq_len)
'''
list [torch.LongTensor (sentence), torch.LongTensor, torch.LongTensor, ...]
no padding
'''
batch_train = Input(train_src_tlst,
train_trg_tlst,
wargs.batch_size,
batch_sort=True)
wlog('Sentence-pairs count in training data: {}'.format(
len(train_src_tlst)))
batch_valid = None
if wargs.val_prefix is not None:
val_src_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_src_suffix)
val_trg_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.val_prefix,
wargs.val_ref_suffix)
wlog('\nPreparing validation set from {} and {} ... '.format(
val_src_file, val_trg_file))
valid_src_tlst, valid_trg_tlst = wrap_data(
wargs.val_tst_dir,
wargs.val_prefix,
wargs.val_src_suffix,
wargs.val_ref_suffix,
src_vocab,
trg_vocab,
shuffle=False,
sort_data=False,
max_seq_len=wargs.dev_max_seq_len)
batch_valid = Input(valid_src_tlst,
valid_trg_tlst,
1,
volatile=True,
batch_sort=False)
batch_tests = None
if wargs.tests_prefix is not None:
assert isinstance(wargs.tests_prefix,
list), 'Test files should be list.'
init_dir(wargs.dir_tests)
batch_tests = {}
for prefix in wargs.tests_prefix:
init_dir(wargs.dir_tests + '/' + prefix)
test_file = '{}{}.{}'.format(wargs.val_tst_dir, prefix,
wargs.val_src_suffix)
wlog('\nPreparing test set from {} ... '.format(test_file))
test_src_tlst, _ = wrap_tst_data(test_file, src_vocab)
batch_tests[prefix] = Input(test_src_tlst,
None,
1,
volatile=True,
batch_sort=False)
wlog('\n## Finish to Prepare Dataset ! ##\n')
nmtModel = NMT(src_vocab_size, trg_vocab_size)
if wargs.pre_train is not None:
assert os.path.exists(wargs.pre_train)
_dict = _load_model(wargs.pre_train)
# initializing parameters of interactive attention model
class_dict = None
if len(_dict) == 4: model_dict, eid, bid, optim = _dict
elif len(_dict) == 5:
model_dict, class_dict, eid, bid, optim = _dict
for name, param in nmtModel.named_parameters():
if name in model_dict:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(model_dict[name])
wlog('{:7} -> grad {}\t{}'.format('Model', param.requires_grad,
name))
elif name.endswith('map_vocab.weight'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.weight'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
elif name.endswith('map_vocab.bias'):
if class_dict is not None:
param.requires_grad = not wargs.fix_pre_params
param.data.copy_(class_dict['map_vocab.bias'])
wlog('{:7} -> grad {}\t{}'.format('Model',
param.requires_grad,
name))
else:
init_params(param, name, True)
wargs.start_epoch = eid + 1
else:
for n, p in nmtModel.named_parameters():
init_params(p, n, True)
optim = Optim(wargs.opt_mode,
wargs.learning_rate,
wargs.max_grad_norm,
learning_rate_decay=wargs.learning_rate_decay,
start_decay_from=wargs.start_decay_from,
last_valid_bleu=wargs.last_valid_bleu)
optim.init_optimizer(nmtModel.parameters())
if wargs.gpu_id:
wlog('Push model onto GPU {} ... '.format(wargs.gpu_id), 0)
nmtModel.cuda()
else:
wlog('Push model onto CPU ... ', 0)
nmtModel.cpu()
wlog('done.')
wlog(nmtModel)
wlog(optim)
pcnt1 = len([p for p in nmtModel.parameters()])
pcnt2 = sum([p.nelement() for p in nmtModel.parameters()])
wlog('Parameters number: {}/{}'.format(pcnt1, pcnt2))
trainer = Trainer(nmtModel,
src_vocab.idx2key,
trg_vocab.idx2key,
optim,
trg_vocab_size,
valid_data=batch_valid,
tests_data=batch_tests)
# add 1000 to train
train_all_chunks = (train_src_tlst, train_trg_tlst)
dh = DataHisto(train_all_chunks)
'''
dev_src0 = wargs.dir_data + 'dev.1k.zh0'
dev_trg0 = wargs.dir_data + 'dev.1k.en0'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src0, dev_trg0))
dev_src0, dev_trg0 = wrap_data(dev_src0, dev_trg0, src_vocab, trg_vocab)
wlog(len(train_src_tlst))
dev_src1 = wargs.dir_data + 'dev.1k.zh1'
dev_trg1 = wargs.dir_data + 'dev.1k.en1'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src1, dev_trg1))
dev_src1, dev_trg1 = wrap_data(dev_src1, dev_trg1, src_vocab, trg_vocab)
dev_src2 = wargs.dir_data + 'dev.1k.zh2'
dev_trg2 = wargs.dir_data + 'dev.1k.en2'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src2, dev_trg2))
dev_src2, dev_trg2 = wrap_data(dev_src2, dev_trg2, src_vocab, trg_vocab)
dev_src3 = wargs.dir_data + 'dev.1k.zh3'
dev_trg3 = wargs.dir_data + 'dev.1k.en3'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src3, dev_trg3))
dev_src3, dev_trg3 = wrap_data(dev_src3, dev_trg3, src_vocab, trg_vocab)
dev_src4 = wargs.dir_data + 'dev.1k.zh4'
dev_trg4 = wargs.dir_data + 'dev.1k.en4'
wlog('\nPreparing dev set for tuning from {} and {} ... '.format(dev_src4, dev_trg4))
dev_src4, dev_trg4 = wrap_data(dev_src4, dev_trg4, src_vocab, trg_vocab)
wlog(len(dev_src4+dev_src3+dev_src2+dev_src1+dev_src0))
batch_dev = Input(dev_src4+dev_src3+dev_src2+dev_src1+dev_src0, dev_trg4+dev_trg3+dev_trg2+dev_trg1+dev_trg0, wargs.batch_size)
'''
batch_dev = None
assert wargs.dev_prefix is not None, 'Requires development to tuning.'
dev_src_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.dev_prefix,
wargs.val_src_suffix)
dev_trg_file = '{}{}.{}'.format(wargs.val_tst_dir, wargs.dev_prefix,
wargs.val_ref_suffix)
wlog('\nPreparing dev set from {} and {} ... '.format(
dev_src_file, dev_trg_file))
valid_src_tlst, valid_trg_tlst = wrap_data(
wargs.val_tst_dir,
wargs.dev_prefix,
wargs.val_src_suffix,
wargs.val_ref_suffix,
src_vocab,
trg_vocab,
shuffle=True,
sort_data=True,
max_seq_len=wargs.dev_max_seq_len)
batch_dev = Input(valid_src_tlst,
valid_trg_tlst,
wargs.batch_size,
batch_sort=True)
trainer.train(dh, batch_dev, 0, merge=True, name='DH_{}'.format('dev'))
'''