Created by: OleNet

报错信息如下：

I1227 20:44:49.066433 30847 Util.cpp:158] commandline:  --use_gpu=0
I1227 20:44:49.066570 30847 Util.cpp:132] Calling runInitFunctions
I1227 20:44:49.066891 30847 Util.cpp:146] Call runInitFunctions done.
  File "<string>", line 13
    paddle version:
                 ^
SyntaxError: invalid syntax
[INFO 2016-12-27 20:44:49,263 networks.py:1466] The input order is [source_language_word, sent_id]
[INFO 2016-12-27 20:44:49,263 networks.py:1472] The output order is [__beam_search_predict__]
I1227 20:44:49.476338 30847 GradientMachine.cpp:124] Loading parameters from /home/liujiaxiang/paddle_predit/exp1/thirdparty/pass-00017
Traceback (most recent call last):
  File "model.py", line 57, in <module>
    result = model.predict('头疼怎么办')
  File "model.py", line 51, in predict
    result = self.network.forwardTest([inArg])
  File "/home/liujiaxiang/paddle_internal_release_tools/idl/paddle/output/python27-gcc482/lib/python2.7/site-packages/py_paddle/util.py", line 146, in forwardTest
    self.forward(inArgs, outArgs, swig_paddle.PASS_TEST)
  File "/home/liujiaxiang/paddle_internal_release_tools/idl/paddle/output/python27-gcc482/lib/python2.7/site-packages/py_paddle/swig_paddle.py", line 1342, in forward
    return _swig_paddle.GradientMachine_forward(self, *args)
TypeError: in method 'GradientMachine_forward', argument 2 of type 'Arguments const &'

我的使用方式如下：

class Model():
    def __init__(self, pass_path=None, trg_lang_dict=None):
        if not pass_path:
            pass_path = PASS_PATH
        self.trg_dict = dict()
        for line_count, line in enumerate(open(trg_lang_dict, "r")):
            self.trg_dict[line.strip()] = line_count
        self.network = self.init_network(pass_path)

    def init_network(self, pass_path):
        swig_paddle.initPaddle("--use_gpu=0")
        conf = parse_config(pass_path+"/trainer_config.conf", "is_predict=1")
        network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
        assert isinstance(network, swig_paddle.GradientMachine)  # For code hint.
        network.loadParameters(pass_path)
        return network

    def predict(self, sentence):
        ids = dataprovider._get_ids(sentence, self.trg_dict)
        self.converter = DataProviderConverter([integer_value_sequence(50000)])
        inArg = self.converter.convert([[ids]])
        result = self.network.forwardTest([inArg])
        return result