def decode(model, opts, test_batcher, i2c, i2p):
"""
Decode the input
"""
logging.basicConfig(filename=opts.log, level=logging.INFO)
if opts.use_cuda:
#print "Find GPU enable, using GPU to compute..."
model.cuda()
torch.cuda.manual_seed(opts.seed)
# else:
# print "Find GPU unable, using CPU to compute..."
result_file = opts.result_file
result_writer = codecs.open(result_file, 'w', 'utf-8')
t = trange(opts.data_size, desc='DECODE')
for iter in t:
input, target, pos, target_length, input_length = test_batcher.next()
input_tensor = Variable(torch.LongTensor(input))
target_tensor = Variable(torch.LongTensor(target))
pos_tensor = Variable(torch.LongTensor(pos))
if opts.use_cuda:
input_tensor = input_tensor.cuda()
target_tensor = target_tensor.cuda()
pos_tensor = pos_tensor.cuda()
encoder_state, encoder_output, pos_feature, _ = model.encode_once(
input_tensor, pos_tensor)
start_decode = Variable(torch.LongTensor([2])).cuda().unsqueeze(1)
beam = Beam(model, opts.beam_size, opts.max_target_len, encoder_state,
encoder_output, pos_feature, start_decode, input)
hyper = beam.run()
raw_input = ""
result = ""
raw_pos = ""
for word in hyper.word_list:
result += i2c[word]
for word in input[0]:
raw_input += i2c[word]
for word in pos[0]:
if word == 0:
break
raw_pos += i2p[word] + ";"
raw_pos = raw_pos[:-1]
result_writer.write(raw_input + '\t' + result + '\t' + raw_pos + '\n')
result_writer.close()
os.system(('../evaluation/evalm.py --gold %s --guess %s --task 1') %
(opts.test, opts.result_file))
log_result('temp.txt', opts)
