Use numpy in DenseScanner.

paddle/py_paddle/dataprovider_converter.py

@@ -15,6 +15,7 @@
 import paddle.trainer.PyDataProvider2 as dp2
 import collections
 import swig_paddle
+import numpy
 
 __all__ = ['DataProviderConverter']
 
@@ -35,18 +36,18 @@ class IScanner(object):
 class DenseScanner(IScanner):
     def __init__(self, input_type, pos):
         IScanner.__init__(self, input_type, pos)
-        self.__mat__ = []
-        self.__height__ = 0
+        self.__mat__ = None
 
     def scan(self, dat):
-        self.__mat__.extend(dat)
-        self.__height__ += 1
+        if self.__mat__ is None:
+            self.__mat__ = numpy.array([dat], dtype='float32')
+        else:
+            self.__mat__ = numpy.append(self.__mat__, [dat], axis=0)
 
     def finish_scan(self, argument):
         assert isinstance(argument, swig_paddle.Arguments)
         assert isinstance(self.input_type, dp2.InputType)
-        m = swig_paddle.Matrix.createDense(self.__mat__, self.__height__,
-                                           self.input_type.dim, False)
+        m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True, False)
         argument.setSlotValue(self.pos, m)