diff --git a/python/paddle/v2/data_converter.py b/python/paddle/v2/data_converter.py
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+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import collections
+import py_paddle.swig_paddle
+import numpy
+
+__all__ = ['DataConverter']
+
+
+class IDataConverter(object):
+    def __init__(self, input_type, pos):
+        """
+        :param input_type: data type
+        :type input_type: dp2.InputType
+        :param pos: which input, start from 0
+        :type pos: int
+        """
+        self.input_type = input_type
+        assert isinstance(self.input_type, dp2.InputType)
+        self.pos = pos
+
+    def convert(self, data, argument):
+        """
+        Conv data to paddle format.
+        :param data: input data
+        :param argument: paddle format
+        """
+        pass
+
+
+class DenseConvert(IDataConverter):
+    def __init__(self, input_type, pos):
+        IDataConverter.__init__(self, input_type, pos)
+
+    def convert(self, data, argument):
+        """
+        :param data: input data
+        :type data: list | numpy array
+        :param argument: the type which paddle is acceptable
+        :type argument: swig_paddle.Arguments
+        """
+        assert isinstance(argument, swig_paddle.Arguments)
+        if data.dtype != numpy.float32:
+            data = data.astype(numpy.float32)
+        m = swig_paddle.Matrix.createDenseFromNumpy(data, True, False)
+        argument.setSlotValue(self.pos, m)
+
+
+class SparseBinaryConvert(IDataConverter):
+    def __init__(self, input_type, pos):
+        IDataConverter.__init__(self, input_type, pos)
+        self.__rows__ = [0]
+        self.__cols__ = []
+        self.__height__ = 0
+        self.__nnz__ = 0
+        self.__value__ = []
+
+    def fill_csr(self, data):
+        self.__height__ = len(data)
+        for x in data:
+            self.__rows__.append(self.__rows__[-1] + len(x))
+        self__cols__ = data.flatten()
+
+    def convert(self, data, argument):
+        assert isinstance(argument, swig_paddle.Arguments)
+
+        fill_csr(data)
+        m = swig_paddle.Matrix.createSparse(self.__height__,
+                                            self.input_type.dim,
+                                            len(self.__cols__),
+                                            len(self.__value__) == 0)
+        assert isinstance(m, swig_paddle.Matrix)
+        m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__)
+        argument.setSlotValue(self.pos, m)
+
+
+class SparseFloatConvert(SparseBinaryConvert):
+    def __init__(self, input_type, pos):
+        SparseBinaryConvert.__init__(self, input_type, pos)
+
+    def fill_csr(self, data):
+        self.__height__ = len(data)
+        for x in data:
+            self.__rows__.append(self.__rows__[-1] + len(x))
+        self.__cols__.extend((x[0] for x in data))
+        self.__value__.extend((x[1] for x in data))
+
+
+class IndexConvert(IDataConverter):
+    def __init__(self, input_type, pos):
+        IDataConverter.__init__(self, input_type, pos)
+        self.__ids__ = []
+
+    def convert(self, data, argument):
+        assert isinstance(argument, swig_paddle.Arguments)
+        self.__ids__ = data.flatten()
+        ids = swig_paddle.IVector.create(self.__ids__)
+        argument.setSlotIds(self.pos, ids)
+
+
+class SequenceConvert(IDataConverter):
+    def __init__(self, input_type, pos, inner_convert, setter):
+        """
+        :param input_type: the type of input data
+        :type input_type: dp2.InputType
+        :param pos: the position of this input
+        :type pos: int
+        :param inner_convert: DataConvert type
+        :type inner_convert: DenseConvert|SparseBinaryConvert|
+                             SparseFloatConvert|IndexConvert
+        :param setter:
+        :type setter:
+        """
+        IDataConverter.__init__(self, input_type, pos)
+        self.__seq__ = [0]
+        self.__inner_convert__ = inner_convert
+        self.__setter__ = setter
+
+    def fill_seq(self, data):
+        for each in data:
+            self.__seq__.append(self.__seq__[-1] + self.get_size(each))
+
+    def convert(self, data, argument):
+        fill_seq(data)
+        seq = swig_paddle.IVector.create(self.__seq__, False)
+        self.__setter__(argument, self.pos, seq)
+
+        dat = []
+        for each in data:
+            dat.append(each)
+        self.__inner_scanner__.convert(dat, argument)
+
+    def get_size(self, data):
+        if isinstance(self.__inner_scanner__, SequenceConvert):
+            return sum(self.__inner_scanner__.get_size(item) for item in dat)
+        else:
+            return len(data)
+
+
+class DataConverter(object):
+    def __init__(self, input_mapper):
+        """
+        Usege:
+
+        .. code-block:: python
+            inputs = [('image', dense_vector), ('label', integer_value)]
+            cvt = DataConverter(inputs)
+            arg = cvt.convert(minibatch_data, {'image':0, 'label':1})
+
+        :param input_mapper: list of (input_name, input_type)
+        :type input_mapper: list
+        """
+        assert isinstance(self.input_types, collections.Sequence)
+        self.input_names = []
+        self.input_types = []
+        for each in self.input_types:
+            self.input_names.append(each[0])
+            self.input_types.append(each[1])
+            assert isinstance(each[1], dp2.InputType)
+
+    def convert(self, data, input_dict=None, argument=None):
+        """
+        Convert minibatch data to Paddle's argument. The data is numpy array
+        or list.
+
+        :param data: input samples, for example, [column0, column1, ...] or
+                     (column0, column1, ...) each column is one minibatch
+                     feature. Note, if only one column featrue, data also
+                     shuld be a list or tupe, [column0] or (column0).
+        :type data: list|tuple
+        :param input_dict: a dictionary to specify the correspondence
+                           of data_layer and input data. If None,
+                           the feature order in argument and data is the same.
+        :type input_dict: dict, like {string:integer, string, integer, ...}|None
+        :param argument: converted data will be saved in this argument. If None,
+                         it will create a swig_paddle.Arguments firstly.
+        :param type: swig_paddle.Arguments|None
+        """
+        if argument is None:
+            argument = swig_paddle.Arguments.createArguments(0)
+        assert isinstance(argument, swig_paddle.Arguments)
+        argument.resize(len(self.input_types))
+
+        converts = [
+            DataConverter.create_scanner(i, each_type)
+            for i, each_type in enumerate(self.input_types)
+        ]
+
+        for i, cvt in enumerate(converts):
+            if input_dict is not None:
+                dat = data[input_dict[self.input_names[i]]]
+            else:
+                dat = data[i]
+            cvt.convert(dat, argument)
+
+        return argument
+
+    def __call__(self, dat, argument=None):
+        return self.convert(dat, argument)
+
+    @staticmethod
+    def create_scanner(pos, each):
+        assert isinstance(each, dp2.InputType)
+        retv = None
+        if each.type == dp2.DataType.Dense:
+            retv = DenseConvert(each, pos)
+        elif each.type == dp2.DataType.Index:
+            retv = IndexConvert(each, pos)
+        elif each.type == dp2.DataType.SparseNonValue:
+            retv = SparseBinaryConvert(each, pos)
+        elif each.type == dp2.DataType.SparseValue:
+            retv = SparseFloatConvert(each, pos)
+        assert retv is not None
+
+        if each.seq_type == dp2.SequenceType.SUB_SEQUENCE:
+            retv = SequenceConvert(
+                each, pos, retv,
+                lambda arg, pos, seq: arg.setSlotSubSequenceStartPositions(pos, seq)
+            )
+
+        if each.seq_type in [
+                dp2.SequenceType.SUB_SEQUENCE, dp2.SequenceType.SEQUENCE
+        ]:
+            retv = SequenceConvert(
+                each, pos, retv,
+                lambda arg, pos, seq: arg.setSlotSequenceStartPositions(pos, seq)
+            )
+        return retv