def init_model(word2id, opt):
model = Seq2SeqLSTMAttention(
emb_dim=opt.word_vec_size,
vocab_size=opt.vocab_size,
src_hidden_dim=opt.rnn_size,
trg_hidden_dim=opt.rnn_size,
ctx_hidden_dim=opt.rnn_size,
attention_mode='dot',
batch_size=opt.batch_size,
bidirectional=opt.bidirectional,
pad_token_src = word2id[pykp.io.PAD_WORD],
pad_token_trg = word2id[pykp.io.PAD_WORD],
nlayers_src=opt.enc_layers,
nlayers_trg=opt.dec_layers,
dropout=opt.dropout,
teacher_forcing_ratio=opt.teacher_forcing_ratio,
scheduled_sampling=opt.scheduled_sampling,
scheduled_sampling_batches=opt.scheduled_sampling_batches
)
logging.info('====================== Model Parameters =========================')
if opt.train_from:
logging.info("loading previous checkpoint from %s" % opt.train_from)
if torch.cuda.is_available():
model.load_state_dict(torch.load(open(opt.train_from, 'rb')))
else:
model.load_state_dict(torch.load(
open(opt.train_from, 'rb'), map_location=lambda storage, loc: storage
))
utils.tally_parameters(model)
return model
def init_model(word2id, opt):
model = Seq2SeqLSTMAttention(
emb_dim=opt.word_vec_size,
vocab_size=opt.vocab_size,
src_hidden_dim=opt.rnn_size,
trg_hidden_dim=opt.rnn_size,
ctx_hidden_dim=opt.rnn_size,
attention_mode='dot',
batch_size=opt.batch_size,
bidirectional=opt.bidirectional,
pad_token_src=word2id[pykp.io.PAD_WORD],
pad_token_trg=word2id[pykp.io.PAD_WORD],
nlayers_src=opt.enc_layers,
nlayers_trg=opt.dec_layers,
dropout=opt.dropout,
teacher_forcing_ratio=opt.teacher_forcing_ratio,
scheduled_sampling=opt.scheduled_sampling,
scheduled_sampling_batches=opt.scheduled_sampling_batches)
logging.info(
'====================== Model Parameters =========================')
if opt.train_from:
logging.info("loading previous checkpoint from %s" % opt.train_from)
if torch.cuda.is_available():
model.load_state_dict(torch.load(open(opt.train_from, 'rb')))
else:
model.load_state_dict(
torch.load(open(opt.train_from, 'rb'),
map_location=lambda storage, loc: storage))
utils.tally_parameters(model)
return model
def init_model(opt):
logging.info(
'====================== Model Parameters =========================')
if opt.cascading_model:
model = Seq2SeqLSTMAttentionCascading(opt)
else:
if opt.copy_attention:
logging.info('Train a Seq2Seq model with Copy Mechanism')
else:
logging.info('Train a normal Seq2Seq model')
if opt.encoder_type == 'bert':
model = Seq2SeqBERT(opt)
elif opt.encoder_type == 'bert_low':
model = Seq2SeqBERTLow(opt)
elif opt.encoder_type == 'transformer':
model = Seq2SeqTransformer(opt)
else:
model = Seq2SeqLSTMAttention(opt)
if opt.train_from:
logging.info("loading previous checkpoint from %s" % opt.train_from)
# train_from_model_dir = opt.train_from[:opt.train_from.rfind('model/') + 6]
# load the saved the meta-model and override the current one
# model = torch.load(
# open(os.path.join(opt.model_path, opt.exp + '.initial.model'), 'rb')
# )
if torch.cuda.is_available() and opt.useGpu:
checkpoint = torch.load(open(opt.train_from, 'rb'))
else:
checkpoint = torch.load(open(opt.train_from, 'rb'),
map_location=lambda storage, loc: storage)
# some compatible problems, keys are started with 'module.'
# checkpoint = dict([(k[7:], v) if k.startswith('module.') else (k, v) for k, v in checkpoint.items()])
model.load_state_dict(checkpoint)
else:
# dump the meta-model
torch.save(
model.state_dict(),
open(
os.path.join(opt.train_from[:opt.train_from.find('.epoch=')],
'initial.model'), 'wb'))
utils.tally_parameters(model)
return model
def main():
# Load checkpoint if we resume from a previous training.
if opt.train_from:
print('Loading checkpoint from %s' % opt.train_from)
checkpoint = torch.load(opt.train_from,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
# I don't like reassigning attributes of opt: it's not clear.
opt.start_epoch = checkpoint['epoch'] + 1
elif opt.init_with:
print('Loading checkpoint from %s' % opt.init_with)
checkpoint = torch.load(opt.init_with,
map_location=lambda storage, loc: storage)
model_opt = opt
elif opt.eval_with:
print('Loading checkpoint from %s' % opt.eval_with)
checkpoint = torch.load(opt.eval_with,
map_location=lambda storage, loc: storage)
model_opt = checkpoint["opt"]
model_opt.eval_only = 1
else:
checkpoint = None
model_opt = opt
for k, v in vars(model_opt).items():
print("{}: {}".format(k, v))
first_dataset = next(lazily_load_dataset("train"))
data_type = first_dataset.data_type
fields = load_fields(first_dataset, data_type, checkpoint)
collect_report_features(fields)
model = build_model(model_opt, opt, fields, checkpoint)
tally_parameters(model)
check_save_model_path()
optim = build_optim(model, checkpoint)
train_model(model, fields, optim, data_type, model_opt)
if opt.tensorboard:
writer.close()
def init_model(opt):
logging.info(
'====================== Model Parameters =========================')
if opt.cascading_model:
model = Seq2SeqLSTMAttentionCascading(opt)
else:
model = Seq2SeqLSTMAttention(opt)
if opt.train_from:
logging.info("loading previous checkpoint from %s" % opt.train_from)
# load the saved the meta-model and override the current one
# model = torch.load(
# open(os.path.join(opt.model_path, opt.exp, '.initial.model'), 'wb')
# )
if torch.cuda.is_available() and opt.use_gpu:
checkpoint = torch.load(open(opt.train_from, 'rb'))
else:
checkpoint = torch.load(open(opt.train_from, 'rb'),
map_location=lambda storage, loc: storage)
# some compatible problems, keys are started with 'module.'
checkpoint = dict([(k[7:], v) if k.startswith('module.') else (k, v)
for k, v in checkpoint.items()])
model.load_state_dict(checkpoint)
else:
# dump the meta-model
meta_model_dir = os.path.join(
opt.train_from[:opt.train_from.find('.epoch=')], 'initial.model')
torch.save(model.state_dict(), open(meta_model_dir, 'wb'))
if torch.cuda.is_available() and opt.use_gpu:
model = model.cuda()
utils.tally_parameters(model)
# embedding = torch.load('embedding40004.pt')
# model.init_embedding(embedding,requires_grad=False)
return model
def init_model(opt):
logging.info(
'====================== Model Parameters =========================')
if opt.cascading_model:
model = Seq2SeqLSTMAttentionCascading(opt)
else:
model = Seq2SeqLSTMAttention(opt)
# opt.train_from ="/home/wangxingpeng/en/keyphrase/model/kp20k.ml.copy.bi-directional.test_sgd_0.01/kp20k.ml.copy.bi-directional.epoch=1.batch=3700.total_batch=3700.model"
if opt.train_from:
logging.info("loading previous checkpoint from %s" % opt.train_from)
# load the saved the meta-model and override the current one
# model = torch.load(
# open(os.path.join(opt.model_path, opt.exp, '.initial.model'), 'wb')
# )
if torch.cuda.is_available() and opt.use_gpu:
checkpoint = torch.load(open(opt.train_from, 'rb'))
else:
checkpoint = torch.load(open(opt.train_from, 'rb'),
map_location=lambda storage, loc: storage)
# some compatible problems, keys are started with 'module.'
checkpoint = dict([(k[7:], v) if k.startswith('module.') else (k, v)
for k, v in checkpoint.items()])
model.load_state_dict(checkpoint)
else:
# dump the meta-model
meta_model_dir = os.path.join(
opt.train_from[:opt.train_from.find('.epoch=')], 'initial.model')
torch.save(model.state_dict(), open(meta_model_dir, 'wb'))
if torch.cuda.is_available() and opt.use_gpu:
model = model.cuda()
utils.tally_parameters(model)
return model
def init_model(opt):
logging.info('====================== Model Parameters =========================')
if not opt.copy_model:
logging.info('Train a normal seq2seq model')
model = Seq2SeqLSTMAttention(
emb_dim=opt.word_vec_size,
vocab_size=opt.vocab_size,
src_hidden_dim=opt.rnn_size,
trg_hidden_dim=opt.rnn_size,
ctx_hidden_dim=opt.rnn_size,
attention_mode='dot',
batch_size=opt.batch_size,
bidirectional=opt.bidirectional,
pad_token_src = opt.word2id[pykp.io.PAD_WORD],
pad_token_trg = opt.word2id[pykp.io.PAD_WORD],
nlayers_src=opt.enc_layers,
nlayers_trg=opt.dec_layers,
dropout=opt.dropout,
must_teacher_forcing=opt.must_teacher_forcing,
teacher_forcing_ratio=opt.teacher_forcing_ratio,
scheduled_sampling=opt.scheduled_sampling,
scheduled_sampling_batches=opt.scheduled_sampling_batches,
)
else:
logging.info('Train a seq2seq model with copy mechanism')
model = Seq2SeqLSTMAttentionCopy(
emb_dim=opt.word_vec_size,
vocab_size=opt.vocab_size,
src_hidden_dim=opt.rnn_size,
trg_hidden_dim=opt.rnn_size,
ctx_hidden_dim=opt.rnn_size,
attention_mode='dot',
batch_size=opt.batch_size,
bidirectional=opt.bidirectional,
pad_token_src = opt.word2id[pykp.io.PAD_WORD],
pad_token_trg = opt.word2id[pykp.io.PAD_WORD],
nlayers_src=opt.enc_layers,
nlayers_trg=opt.dec_layers,
dropout=opt.dropout,
must_teacher_forcing=opt.must_teacher_forcing,
teacher_forcing_ratio=opt.teacher_forcing_ratio,
scheduled_sampling=opt.scheduled_sampling,
scheduled_sampling_batches=opt.scheduled_sampling_batches,
unk_word=opt.word2id[pykp.io.UNK_WORD],
)
if torch.cuda.is_available():
model = model.cuda()
if opt.train_from:
logging.info("loading previous checkpoint from %s" % opt.train_from)
if torch.cuda.is_available():
checkpoint = torch.load(open(opt.train_from, 'rb'))
else:
checkpoint = torch.load(
open(opt.train_from, 'rb'), map_location=lambda storage, loc: storage
)
print(checkpoint.keys())
# some compatible problems, keys are started with 'module.'
checkpoint = dict([(k[7:],v) if k.startswith('module.') else (k,v) for k,v in checkpoint.items()])
model.load_state_dict(checkpoint)
utils.tally_parameters(model)
return model
def init_model(opt):
logging.info(
'====================== Model Parameters =========================')
if not opt.copy_model:
logging.info('Train a normal seq2seq model')
model = Seq2SeqLSTMAttention(
emb_dim=opt.word_vec_size,
vocab_size=opt.vocab_size,
src_hidden_dim=opt.rnn_size,
trg_hidden_dim=opt.rnn_size,
ctx_hidden_dim=opt.rnn_size,
attention_mode='dot',
batch_size=opt.batch_size,
bidirectional=opt.bidirectional,
pad_token_src=opt.word2id[pykp.io.PAD_WORD],
pad_token_trg=opt.word2id[pykp.io.PAD_WORD],
nlayers_src=opt.enc_layers,
nlayers_trg=opt.dec_layers,
dropout=opt.dropout,
must_teacher_forcing=opt.must_teacher_forcing,
teacher_forcing_ratio=opt.teacher_forcing_ratio,
scheduled_sampling=opt.scheduled_sampling,
scheduled_sampling_batches=opt.scheduled_sampling_batches,
)
else:
logging.info('Train a seq2seq model with copy mechanism')
model = Seq2SeqLSTMAttentionCopy(
emb_dim=opt.word_vec_size,
vocab_size=opt.vocab_size,
src_hidden_dim=opt.rnn_size,
trg_hidden_dim=opt.rnn_size,
ctx_hidden_dim=opt.rnn_size,
attention_mode='dot',
batch_size=opt.batch_size,
bidirectional=opt.bidirectional,
pad_token_src=opt.word2id[pykp.io.PAD_WORD],
pad_token_trg=opt.word2id[pykp.io.PAD_WORD],
nlayers_src=opt.enc_layers,
nlayers_trg=opt.dec_layers,
dropout=opt.dropout,
must_teacher_forcing=opt.must_teacher_forcing,
teacher_forcing_ratio=opt.teacher_forcing_ratio,
scheduled_sampling=opt.scheduled_sampling,
scheduled_sampling_batches=opt.scheduled_sampling_batches,
unk_word=opt.word2id[pykp.io.UNK_WORD],
)
if torch.cuda.is_available():
model = model.cuda()
if opt.train_from:
logging.info("loading previous checkpoint from %s" % opt.train_from)
if torch.cuda.is_available():
checkpoint = torch.load(open(opt.train_from, 'rb'))
else:
checkpoint = torch.load(open(opt.train_from, 'rb'),
map_location=lambda storage, loc: storage)
print(checkpoint.keys())
# some compatible problems, keys are started with 'module.'
checkpoint = dict([(k[7:], v) if k.startswith('module.') else (k, v)
for k, v in checkpoint.items()])
model.load_state_dict(checkpoint)
utils.tally_parameters(model)
return model
