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48e0f432
编写于
6月 12, 2017
作者:
H
hedaoyuan
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差异文件
Add ImageExpandFunction.
上级
1b8d2e65
变更
2
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2 changed file
with
248 addition
and
0 deletion
+248
-0
paddle/function/GemmConvOp.h
paddle/function/GemmConvOp.h
+84
-0
paddle/function/ImageExpandOp.cpp
paddle/function/ImageExpandOp.cpp
+164
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未找到文件。
paddle/function/GemmConvOp.h
0 → 100644
浏览文件 @
48e0f432
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
// #include "ConvOp.h"
namespace
paddle
{
/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */
enum
ColFormat
{
kCFO
=
0
,
kOCF
=
1
};
/*
* \brief Converts the image data of four dimensions(NCHW) into a colData.
* Then you can reshape colData to a convolution matrix for
* convolution calculation based on matrix multiplication.
*
* \param imData Image data of NCHW format.
* The format of imData is:
* [input_channels, input_height, input_width].
* \param colData colData data.
* If the template argument Format is kCFO,
* the format of colData is:
* [input_channels,
* filter_height,
* filter_width,
* output_height,
* output_width]
* If the template argument Format is kOCF,
* the format of colData is:
* [output_height,
* output_width,
* input_channels,
* filter_height,
* filter_width]
*/
template
<
ColFormat
Format
,
DeviceType
Device
,
class
T
>
class
Im2ColFunctor
{
public:
void
operator
()(
const
T
*
imData
,
int
inputChannels
,
int
inputHeight
,
int
inputWidth
,
int
filterHeight
,
int
filterWidth
,
int
strideHeight
,
int
strideWidth
,
int
paddingHeight
,
int
paddingWidth
,
int
outputHeight
,
int
outputWidth
,
T
*
colData
);
};
template
<
ColFormat
Format
,
DeviceType
Device
,
class
T
>
class
Col2ImFunctor
{
public:
void
operator
()(
const
T
*
colData
,
int
inputChannels
,
int
inputHeight
,
int
inputWidth
,
int
filterHeight
,
int
filterWidth
,
int
strideHeight
,
int
strideWidth
,
int
paddingHeight
,
int
paddingWidth
,
int
outputHeight
,
int
outputWidth
,
T
*
imData
);
};
}
// namespace paddle
paddle/function/ImageExpandOp.cpp
0 → 100644
浏览文件 @
48e0f432
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#include "Function.h"
#include "GemmConvOp.h"
namespace
paddle
{
/*
* imData = [input_channels, input_height, input_width]
* colData = [output_height, output_width,
* input_channels, filter_height, filter_width]
*/
template
<
class
T
>
class
Im2ColFunctor
<
kOCF
,
DEVICE_TYPE_CPU
,
T
>
{
public:
void
operator
()(
const
T
*
imData
,
int
inputChannels
,
int
inputHeight
,
int
inputWidth
,
int
filterHeight
,
int
filterWidth
,
int
strideHeight
,
int
strideWidth
,
int
paddingHeight
,
int
paddingWidth
,
int
outputHeight
,
int
outputWidth
,
T
*
colData
)
{
for
(
int
outputH
=
0
;
outputH
<
outputHeight
;
++
outputH
)
{
for
(
int
outputW
=
0
;
outputW
<
outputWidth
;
++
outputW
)
{
for
(
int
channel
=
0
;
channel
<
inputChannels
;
++
channel
)
{
for
(
int
filterH
=
0
;
filterH
<
filterHeight
;
++
filterH
)
{
for
(
int
filterW
=
0
;
filterW
<
filterWidth
;
++
filterW
)
{
int
imRowOffset
=
outputH
*
strideHeight
+
filterH
-
paddingHeight
;
int
imColOffset
=
outputW
*
strideWidth
+
filterW
-
paddingWidth
;
int
colDataOffset
=
(((
outputH
*
outputWidth
+
outputW
)
*
inputChannels
+
channel
)
*
filterHeight
+
filterH
)
*
filterWidth
+
filterW
;
if
(
imRowOffset
<
0
||
imRowOffset
>=
inputHeight
||
imColOffset
<
0
||
imColOffset
>=
inputWidth
)
{
colData
[
colDataOffset
]
=
T
(
0
);
}
else
{
int
imDataOffset
=
(
channel
*
inputHeight
+
imRowOffset
)
*
inputWidth
+
imColOffset
;
colData
[
colDataOffset
]
=
imData
[
imDataOffset
];
}
}
}
}
}
}
}
};
/*
* \brief Converts the image data of four dimensions(NCHW) into
* a sequence data of three dimensions(NST). Where N is batch size,
* S is the length of the sequence after each image is expanded,
* T is the size of each time step in the sequence.
*
* \param inputs[0] Image data of NCHW format.
* \param outputs[0] Sequence data of NST format.
*/
template
<
DeviceType
Device
>
class
ImageExpandFunction
:
public
FunctionBase
{
public:
void
init
(
const
FuncConfig
&
config
)
override
{
// function arguments
strides_
=
config
.
get
<
std
::
vector
<
size_t
>>
(
"strides"
);
paddings_
=
config
.
get
<
std
::
vector
<
size_t
>>
(
"paddings"
);
blocks_
=
config
.
get
<
std
::
vector
<
size_t
>>
(
"blocks"
);
// number of inputs and outputs
numInputs_
=
1
;
numOutputs_
=
1
;
}
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
numInputs_
,
inputs
.
size
());
CHECK_EQ
(
numOutputs_
,
outputs
.
size
());
const
TensorShape
&
input
=
inputs
[
0
].
shape
();
const
TensorShape
&
output
=
outputs
[
0
].
shape
();
// input argument should be 4-dimensional.
CHECK_EQ
(
input
.
ndims
(),
(
size_t
)
4
);
// output argument should be 3-dimensional.
CHECK_EQ
(
output
.
ndims
(),
(
size_t
)
3
);
// The batchSize of the input needs to be equal to
// the batchSize of the output.
CHECK_EQ
(
input
[
0
],
output
[
0
]);
size_t
batchSize
=
input
[
0
];
size_t
inputChannels
=
input
[
1
];
size_t
inputHeight
=
input
[
2
];
size_t
inputWidth
=
input
[
3
];
size_t
seqLength
=
output
[
1
];
size_t
stepSize
=
output
[
2
];
size_t
outputHeight
=
1
+
(
inputHeight
+
2
*
paddingH
()
-
blockH
()
+
strideH
()
-
1
)
/
strideH
();
size_t
outputWidth
=
1
+
(
inputWidth
+
2
*
paddingW
()
-
blockW
()
+
strideW
()
-
1
)
/
strideW
();
CHECK_EQ
(
seqLength
,
outputHeight
*
outputWidth
);
CHECK_EQ
(
stepSize
,
inputChannels
*
blockH
()
*
blockH
());
real
*
inputData
=
inputs
[
0
].
data
<
real
>
();
real
*
outputData
=
outputs
[
0
].
data
<
real
>
();
Im2ColFunctor
<
kOCF
,
Device
,
real
>
im2col
;
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
im2col
(
inputData
,
inputChannels
,
inputHeight
,
inputWidth
,
blockH
(),
blockW
(),
strideH
(),
strideW
(),
paddingH
(),
paddingW
(),
outputHeight
,
outputWidth
,
outputData
);
inputData
+=
inputChannels
*
inputHeight
*
inputWidth
;
outputData
+=
seqLength
*
stepSize
;
}
}
protected:
std
::
vector
<
size_t
>
strides_
;
std
::
vector
<
size_t
>
paddings_
;
std
::
vector
<
size_t
>
blocks_
;
inline
int
strideH
()
const
{
return
strides_
[
0
];
}
inline
int
strideW
()
const
{
return
strides_
[
1
];
}
inline
int
paddingH
()
const
{
return
paddings_
[
0
];
}
inline
int
paddingW
()
const
{
return
paddings_
[
1
];
}
inline
int
blockH
()
const
{
return
blocks_
[
0
];
}
inline
int
blockW
()
const
{
return
blocks_
[
1
];
}
};
}
// namespace paddle
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