提交 d8c9608f 编写于 作者: Y Yu Yang 提交者: GitHub

Merge pull request #1618 from reyoung/feature/speed_up_converter

Speed up dense converter.
......@@ -16,11 +16,25 @@ import paddle.trainer.PyDataProvider2 as dp2
import collections
import swig_paddle
import numpy
import itertools
__all__ = ['DataProviderConverter']
class IScanner(object):
"""
The scanner will scan Python object two passes, then convert it to Paddle's
argument.
In the first pass, `pre_scan` will be invoked by every data instance, and
then invoke `finish_pre_scan` to arguments. And the second pass do the same
thing except the functions changed to `scan`, `finish_scan`.
During the first pass, a scanner may count the shape of input matrix and
allocate memory for this argument. Then fill the data into this argument
in second pass.
"""
def __init__(self, input_type, pos):
self.input_type = input_type
if not isinstance(self.input_type, dp2.InputType):
......@@ -36,10 +50,40 @@ class IScanner(object):
self.data_in_gpu = swig_paddle.isUsingGpu(
) and swig_paddle.getTrainerCount() == 1
def pre_scan(self, dat):
"""
First pass scan method. During this method, the scanner could count the
data number, and get the total memory size this batch would use.
:param dat: The python object.
"""
pass
def finish_pre_scan(self, argument):
"""
Finish first scan pass. Allocate the memory.
:param argument: Output arguments object.
:type argument: swig_paddle.Arguments
:return:
"""
pass
def scan(self, dat):
"""
Second pass scan method. Copy the data to arguments.
:param dat: The python object.
"""
pass
def finish_scan(self, argument):
"""
Finish second pass. Finalize the resources, etc.
:param argument: Output arguments object.
:type argument: swig_paddle.Arguments
"""
pass
......@@ -51,12 +95,19 @@ class DenseScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__mat__ = None
self.__height__ = 0
def pre_scan(self, dat):
self.__height__ += 1
def finish_pre_scan(self, argument):
self.__mat__ = numpy.ndarray(
shape=(self.__height__, self.input_type.dim), dtype=numpy.float32)
self.__height__ = 0
def scan(self, dat):
if self.__mat__ is None:
self.__mat__ = numpy.array([dat], dtype='float32')
else:
self.__mat__ = numpy.append(self.__mat__, [dat], axis=0)
self.__mat__[self.__height__] = dat
self.__height__ += 1
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
......@@ -163,7 +214,14 @@ class DataProviderConverter(object):
]
for each_sample in dat:
for each_step, scanner in zip(each_sample, scanners):
for each_step, scanner in itertools.izip(each_sample, scanners):
scanner.pre_scan(each_step)
for scanner in scanners:
scanner.finish_pre_scan(argument)
for each_sample in dat:
for each_step, scanner in itertools.izip(each_sample, scanners):
scanner.scan(each_step)
for scanner in scanners:
......
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