def load_model(model_file, cuda):
# Load model
model = torch.load(model_file, map_location=lambda storage, loc: storage)
if cuda:
model.cuda()
model.rnn.flatten_parameters()
# Send extra argument with model parameters to forward function
model.rnn.forward = lambda input, hidden: lstm.forward(model.rnn, input, hidden)
model_original = copy.deepcopy(model.state_dict())
model.load_state_dict(model_original)
return model
]
gold = pandas.read_csv(args.input + '.gold',
sep='\t',
header=None,
names=['verb_pos', 'correct', 'wrong', 'nattr'])
# Load model
print('Loading models...')
import lstm
print('\nmodel: ' + args.model + '\n')
model = torch.load(
args.model,
map_location=lambda storage, loc: storage) # requires GPU model
model.rnn.flatten_parameters()
# hack the forward function to send an extra argument containing the model parameters
model.rnn.forward = lambda input, hidden: lstm.forward(model.rnn, input, hidden
)
model_orig_state = copy.deepcopy(model.state_dict())
log_p_targets_correct = np.zeros((len(sentences), 1))
log_p_targets_wrong = np.zeros((len(sentences), 1))
model.load_state_dict(model_orig_state)
stime = time.time()
output_fn = args.output + '.abl'
if args.lang == 'en':
init_sentence = " ".join([
"In service , the aircraft was operated by a crew of five and could accommodate either 30 paratroopers , 32 <unk> and 28 sitting casualties , or 50 fully equipped troops . <eos>",
"He even speculated that technical classes might some day be held \" for the better training of workmen in their several crafts and industries . <eos>",
"After the War of the Holy League in 1537 against the Ottoman Empire , a truce between Venice and the Ottomans was created in 1539 . <eos>",