# 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 paddle.trainer.PyDataProvider2 as dp2
import collections
import swig_paddle
import numpy
import itertools
from functools import reduce
__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):
raise ValueError("input type should be dataprovider2.InputType")
self.pos = pos
# data_in_gpu is used to indicate whether to create argument on GPU
# or not in GPU mode. Now if using one thread (trainer_count=1),
# trainer uses NeuralNetwork which needs to create argument on GPU
# before calling forward function. So, set data_in_gpu to True.
# Otherwise, trainer uses MultiGradientMachine which will transfer
# data from CPU to GPU in the forward function, set data_in_gpu to
# False in this case.
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
:param dat: Output arguments object.
:type dat: The Python object, numpy.array or List.
: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
class DenseScanner(IScanner):
"""
:type __mat__: numpy.ndarray
"""
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__mat__ = None
self.__shape__ = None
self.__height__ = 0
self.__dim__ = 0
def pre_scan(self, dat):
self.__height__ += 1
if self.__shape__ is None:
self.__shape__ = numpy.array(dat).shape
if len(self.__shape__) > 3:
raise ValueError(
"The dimension of input cannot be greater than 3.")
if len(self.__shape__) == 0:
raise ValueError(
"The input should be a vector, please check your input data."
)
self.__dim__ = reduce(lambda x, y: x * y, self.__shape__)
if len(self.__shape__) == 1 and self.__dim__ != self.input_type.dim:
raise ValueError(
"The data size must be equal to it in data layer.")
else:
if self.__shape__ != numpy.array(dat).shape:
raise ValueError(
"The data shape must be same in one mini-batch.")
def finish_pre_scan(self, argument):
self.__mat__ = numpy.ndarray(
shape=(self.__height__, self.__dim__), dtype=numpy.float32)
self.__height__ = 0
def scan(self, dat):
# It's better to use NumPy array for speed.
dat = numpy.array(dat)
dat = dat.flatten()
self.__mat__[self.__height__] = dat
self.__height__ += 1
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
if self.__mat__.dtype != numpy.float32:
self.__mat__ = self.__mat__.astype(numpy.float32)
m = swig_paddle.Matrix.createDenseFromNumpy(self.__mat__, True,
self.data_in_gpu)
argument.setSlotValue(self.pos, m)
if len(self.__shape__) > 1:
# The last-two dimenstions are the frame height and width.
# For example, the layout is CHW for 3-D feature of image.
# The H and W are the frame height and width.
h, w = self.__shape__[-2:]
argument.setSlotFrameHeight(self.pos, h)
argument.setSlotFrameWidth(self.pos, w)
self.__shape__ = None
class SparseBinaryScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__rows__ = [0]
self.__cols__ = []
self.__height__ = 0
self.__value__ = []
def scan(self, dat):
self.extend_cols(dat)
self.__rows__.append(len(self.__cols__))
self.__height__ += 1
def extend_cols(self, dat):
self.__cols__.extend(dat)
def finish_scan(self, argument):
assert isinstance(argument, swig_paddle.Arguments)
m = swig_paddle.Matrix.createSparse(
self.__height__,
self.input_type.dim,
len(self.__cols__),
len(self.__value__) == 0,
False, # trans
False) # TODO supoort GPU
assert isinstance(m, swig_paddle.Matrix)
m.sparseCopyFrom(self.__rows__, self.__cols__, self.__value__)
argument.setSlotValue(self.pos, m)
class SparseFloatScanner(SparseBinaryScanner):
def __init__(self, input_type, pos):
SparseBinaryScanner.__init__(self, input_type, pos)
def extend_cols(self, dat):
self.__cols__.extend((x[0] for x in dat))
self.__value__.extend((x[1] for x in dat))
class IndexScanner(IScanner):
def __init__(self, input_type, pos):
IScanner.__init__(self, input_type, pos)
self.__ids__ = None
self.__idx__ = 0
def pre_scan(self, dat):
self.__idx__ += 1
def finish_pre_scan(self, argument):
self.__ids__ = [0] * self.__idx__
self.__idx__ = 0
def scan(self, dat):
self.__ids__[self.__idx__] = dat
self.__idx__ += 1
def finish_scan(self, argument):
ids = swig_paddle.IVector.create(self.__ids__, self.data_in_gpu)
assert isinstance(argument, swig_paddle.Arguments)
argument.setSlotIds(self.pos, ids)
class SequenceScanner(IScanner):
def __init__(self, input_type, pos, inner_scanner, setter):
IScanner.__init__(self, input_type, pos)
self.__seq__ = [0]
self.__inner_scanner__ = inner_scanner
self.__setter__ = setter
def pre_scan(self, dat):
for each in dat:
self.__inner_scanner__.pre_scan(each)
def finish_pre_scan(self, argument):
self.__inner_scanner__.finish_pre_scan(argument)
def scan(self, dat):
self.__seq__.append(self.__seq__[-1] + self.get_size(dat))
for each in dat:
self.__inner_scanner__.scan(each)
def finish_scan(self, argument):
seq = swig_paddle.IVector.create(self.__seq__, False)
self.__setter__(argument, self.pos, seq)
self.__inner_scanner__.finish_scan(argument)
def get_size(self, dat):
if isinstance(self.__inner_scanner__, SequenceScanner):
return sum(self.__inner_scanner__.get_size(item) for item in dat)
else:
return len(dat)
class DataProviderConverter(object):
def __init__(self, input_types):
self.input_types = input_types
assert isinstance(self.input_types, collections.Sequence)
for each in self.input_types:
assert isinstance(each, dp2.InputType)
def convert(self, dat, argument=None):
if argument is None:
argument = swig_paddle.Arguments.createArguments(0)
assert isinstance(argument, swig_paddle.Arguments)
argument.resize(len(self.input_types))
scanners = [
DataProviderConverter.create_scanner(i, each_type)
for i, each_type in enumerate(self.input_types)
]
for each_sample in dat:
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:
scanner.finish_scan(argument)
return argument
def __call__(self, dat, argument=None):
return self.convert(dat, argument)
@staticmethod
def create_scanner(i, each):
assert isinstance(each, dp2.InputType)
retv = None
if each.type == dp2.DataType.Dense:
retv = DenseScanner(each, i)
elif each.type == dp2.DataType.Index:
retv = IndexScanner(each, i)
elif each.type == dp2.DataType.SparseNonValue:
retv = SparseBinaryScanner(each, i)
elif each.type == dp2.DataType.SparseValue:
retv = SparseFloatScanner(each, i)
assert retv is not None
if each.seq_type == dp2.SequenceType.SUB_SEQUENCE:
retv = SequenceScanner(
each, i, retv,
lambda a, p, seq: a.setSlotSubSequenceStartPositions(p, seq))
if each.seq_type in [
dp2.SequenceType.SUB_SEQUENCE, dp2.SequenceType.SEQUENCE
]:
retv = SequenceScanner(
each, i, retv,
lambda a, p, seq: a.setSlotSequenceStartPositions(p, seq))
return retv