def gen(opt):
opt.experiment = os.path.join(root_dir, opt.experiment)
opt.load_chkpt = os.path.join(opt.experiment, opt.save_model)
opt.save_file = os.path.join(opt.experiment, opt.save_file)
opt.class_file = os.path.join(opt.data_root, opt.class_file)
# Model loading
model = make_model(opt)
chkpt = torch.load(opt.load_chkpt, map_location = lambda storage, log: storage)
model.load_state_dict(chkpt)
if opt.deviceid >= 0:
model = model.cuda()
print(model)
# ====== *********************** ================
model.eval()
# ===============================================
# decode
print('Decoding ...')
with codecs.open(opt.class_file, 'r') as f:
classes = f.readlines()
res = []
for cls in classes:
class_string = cls.strip()
pred_utterances = decode_utterance(model, class_string, opt.memory, opt.cuda, opt.nbest)
for i in range(len(pred_utterances)):
res.append((pred_utterances[i], class_string))
with open(opt.save_file, 'w') as f:
for (utterance, class_string) in res:
f.write('{}\t<=>\t{}\n'.format(utterance, class_string))
print('Decode results saved in {}'.format(opt.save_file))
def gen(opt):
opt.experiment = os.path.join(root_dir, opt.experiment)
opt.load_chkpt = os.path.join(opt.experiment, opt.save_model)
opt.save_file = os.path.join(opt.data_root, opt.save_file)
opt.class_file = os.path.join(opt.data_root, opt.class_file)
dir_name = os.path.abspath(os.path.dirname(opt.save_file))
print(dir_name)
if not os.path.exists(dir_name):
os.makedirs(dir_name)
# Model loading
model = make_model(opt)
chkpt = torch.load(opt.load_chkpt,
map_location=lambda storage, log: storage)
model.load_state_dict(chkpt)
if opt.deviceid >= 0:
model = model.cuda()
print(model)
# ====== *********************** ================
model.eval()
# ===============================================
# decode
print('Decoding ...')
classes = json.loads(open(opt.class_file, 'r').read())
res = []
for cls in classes:
triple = cls[0].strip()
class_string = cls[1].strip()
value_dict = cls[2]
pred_utterances = decode_utterance(model, class_string, opt.memory,
opt.cuda, opt.nbest)
for i in range(len(pred_utterances)):
utt = pred_utterances[i]
trp = triple
for key in value_dict:
utt = utt.replace(key, value_dict[key])
trp = trp.replace(key, value_dict[key])
res.append((utt, trp))
#res = list(set(res))
with open(opt.save_file, 'w') as f:
for (utterance, class_string) in res:
f.write('{}\t<=>\t{}\n'.format(utterance, class_string))
print('Decode results saved in {}'.format(opt.save_file))