本文整理匯總了Python中onmt.model_builder方法的典型用法代碼示例。如果您正苦於以下問題：Python onmt.model_builder方法的具體用法？Python onmt.model_builder怎麽用？Python onmt.model_builder使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類onmt的用法示例。
在下文中一共展示了onmt.model_builder方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: main
# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import model_builder [as 別名]
def main():
dummy_parser = argparse.ArgumentParser(description='train.py')
onmt.opts.model_opts(dummy_parser)
dummy_opt = dummy_parser.parse_known_args([])[0]
opt = parser.parse_args()
opt.cuda = opt.gpu > -1
if opt.cuda:
torch.cuda.set_device(opt.gpu)
# Add in default model arguments, possibly added since training.
checkpoint = torch.load(opt.model,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
src_dict, tgt_dict = None, None
# the vocab object is a list of tuple (name, torchtext.Vocab)
# we iterate over this list and associate vocabularies based on the name
for vocab in checkpoint['vocab']:
if vocab[0] == 'src':
src_dict = vocab[1]
if vocab[0] == 'tgt':
tgt_dict = vocab[1]
assert src_dict is not None and tgt_dict is not None
fields = onmt.inputters.load_fields_from_vocab(checkpoint['vocab'])
model_opt = checkpoint['opt']
for arg in dummy_opt.__dict__:
if arg not in model_opt:
model_opt.__dict__[arg] = dummy_opt.__dict__[arg]
model = onmt.model_builder.build_base_model(
model_opt, fields, use_gpu(opt), checkpoint)
encoder = model.encoder
decoder = model.decoder
encoder_embeddings = encoder.embeddings.word_lut.weight.data.tolist()
decoder_embeddings = decoder.embeddings.word_lut.weight.data.tolist()
logger.info("Writing source embeddings")
write_embeddings(opt.output_dir + "/src_embeddings.txt", src_dict,
encoder_embeddings)
logger.info("Writing target embeddings")
write_embeddings(opt.output_dir + "/tgt_embeddings.txt", tgt_dict,
decoder_embeddings)
logger.info('... done.')
logger.info('Converting model...') 示例2: main
# 需要導入模塊: import onmt [as 別名]
# 或者: from onmt import model_builder [as 別名]
def main():
dummy_parser = argparse.ArgumentParser(description='train.py')
onmt.opts.model_opts(dummy_parser)
dummy_opt = dummy_parser.parse_known_args([])[0]
opt = parser.parse_args()
opt.cuda = opt.gpu > -1
if opt.cuda:
torch.cuda.set_device(opt.gpu)
# Add in default model arguments, possibly added since training.
checkpoint = torch.load(opt.model,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
vocab = checkpoint['vocab']
if inputters.old_style_vocab(vocab):
fields = onmt.inputters.load_old_vocab(vocab)
else:
fields = vocab
src_dict = fields['src'].base_field.vocab # assumes src is text
tgt_dict = fields['tgt'].base_field.vocab
model_opt = checkpoint['opt']
for arg in dummy_opt.__dict__:
if arg not in model_opt:
model_opt.__dict__[arg] = dummy_opt.__dict__[arg]
model = onmt.model_builder.build_base_model(
model_opt, fields, use_gpu(opt), checkpoint)
encoder = model.encoder
decoder = model.decoder
encoder_embeddings = encoder.embeddings.word_lut.weight.data.tolist()
decoder_embeddings = decoder.embeddings.word_lut.weight.data.tolist()
logger.info("Writing source embeddings")
write_embeddings(opt.output_dir + "/src_embeddings.txt", src_dict,
encoder_embeddings)
logger.info("Writing target embeddings")
write_embeddings(opt.output_dir + "/tgt_embeddings.txt", tgt_dict,
decoder_embeddings)
logger.info('... done.')
logger.info('Converting model...')