def load_test_model(model_path, opt, dummy_opt):
checkpoint = torch.load(model_path,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
for arg in dummy_opt:
if arg not in model_opt:
model_opt.__dict__[arg] = dummy_opt[arg]
for attribute in ["share_embeddings", "stateful"]:
if not hasattr(model_opt, attribute):
model_opt.__dict__[attribute] = False
# TODO: fix this
if model_opt.stateful and not opt.sample:
raise ValueError(
'Beam search generator does not work with stateful models yet')
mappings = read_pickle('{}/vocab.pkl'.format(model_opt.mappings))
# mappings = read_pickle('{0}/{1}/vocab.pkl'.format(model_opt.mappings, model_opt.model))
mappings = make_model_mappings(model_opt.model, mappings)
model = make_base_model(model_opt, mappings, use_gpu(opt), checkpoint)
model.eval()
model.generator.eval()
return mappings, model, model_opt
def load_test_model(model_path, opt, dummy_opt):
checkpoint = torch.load(model_path,
map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
for arg in dummy_opt:
if arg not in model_opt:
model_opt.__dict__[arg] = dummy_opt[arg]
mappings = read_pickle('{}/vocab.pkl'.format(model_opt.mappings))
mappings = make_model_mappings(model_opt.model, mappings)
model = make_base_model(model_opt, mappings, use_gpu(opt), checkpoint)
model.eval()
model.generator.eval()
return mappings, model, model_opt
def load_test_model(model_path, opt, dummy_opt):
if model_path is not None:
print('Load model from {}.'.format(model_path))
checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)
model_opt = checkpoint['opt']
for arg in dummy_opt:
if arg not in model_opt:
model_opt.__dict__[arg] = dummy_opt[arg]
else:
print('Build model from scratch.')
checkpoint = None
model_opt = opt
mappings = read_pickle('{}/vocab.pkl'.format(model_opt.mappings))
# mappings = read_pickle('{0}/{1}/vocab.pkl'.format(model_opt.mappings, model_opt.model))
mappings = make_model_mappings(model_opt.model, mappings)
model, critic = make_base_model(model_opt, mappings, use_gpu(opt), checkpoint)
model.eval()
critic.eval()
return mappings, model, model_opt, critic
def get_vocabs(vocab_path, vocab_type):
mappings = read_pickle(vocab_path)
mapping_key = "{}_vocab".format(vocab_type)
vocab = mappings[mapping_key]
print('{0} vocab size: {1}'.format(vocab_type, len(vocab)))
return vocab