def main():
with open(args.model, 'rb') as f:
model = torch.load(f)
if args.cuda:
model.cuda()
with open(args.word_path, 'rb') as f:
word2id = pickle.load(f)
raw_data = seg_data(args.data)
transformed_data = transform_data_to_id(raw_data, word2id)
data = [x + [y[2]] for x, y in zip(transformed_data, raw_data)]
data = sorted(data, key=lambda x: len(x[1]))
print('test data size {:d}'.format(len(data)))
inference(model, data)
help='batch size')
parser.add_argument('--cuda', action='store_true',default=True,
help='use CUDA')
args = parser.parse_args()
with open(args.model, 'rb') as f:
model = torch.load(f)
if args.cuda:
model.cuda()
with open(args.word_path, 'rb') as f:
word2id = cPickle.load(f)
raw_data = seg_data(args.data)
transformed_data = transform_data_to_id(raw_data, word2id)
data = [x + [y[2]] for x, y in zip(transformed_data, raw_data)]
data = sorted(data, key=lambda x: len(x[1]))
print( 'test data size {:d}'.format(len(data)))
def inference():
model.eval()
predictions = []
with torch.no_grad():
for i in range(0, len(data), args.batch_size):
# for i in range(0, len(data), 3):
try:
one = data[i:i + args.batch_size]
# print(one)
query, _ = padding([x[0] for x in one], max_len=50)
help='use CUDA')
args = parser.parse_args()
with open(args.model, 'rb') as f:
model = torch.load(f)
if args.cuda:
model.cuda()
print(model)
with open(args.word_path, 'rb') as f:
word2id = pickle.load(f)
print(len(word2id))
raw_data = seg_data(args.data)
transformed_data = transform_data_to_id(raw_data, word2id)
data = [x + [y[2]] for x, y in zip(transformed_data, raw_data)]
data = sorted(data, key=lambda x: len(x[1]))
print('test data size {:d}'.format(len(data)))
raw_data_valid = seg_data(args.valid_data)
transformed_data_valid = transform_data_to_id(raw_data_valid, word2id)
dev_data = [x + [y[2]] for x, y in zip(transformed_data_valid, raw_data_valid)]
dev_data = sorted(dev_data, key=lambda x: len(x[1]))
print('valid data size {:d}'.format(len(dev_data)))
def inference():
model.eval()
predictions = []
with torch.no_grad():