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5bfcb7f8
编写于
6月 21, 2017
作者:
H
hedaoyuan
浏览文件
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电子邮件补丁
差异文件
Remove useless code.
上级
9e6ed83c
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
0 addition
and
248 deletion
+0
-248
paddle/function/GemmConvOp.h
paddle/function/GemmConvOp.h
+0
-62
paddle/function/GemmConvOpGpu.cu
paddle/function/GemmConvOpGpu.cu
+0
-186
未找到文件。
paddle/function/GemmConvOp.h
已删除
100644 → 0
浏览文件 @
9e6ed83c
/* 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
{
/*
* imData = [input_channels, input_height, input_width]
* colData = [input_channels, filter_height, filter_width,
* output_height, output_width]
*/
template
<
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
<
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/GemmConvOpGpu.cu
已删除
100644 → 0
浏览文件 @
9e6ed83c
/* 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 "ConvOp.h"
#include "GemmConvOp.h"
namespace
paddle
{
template
<
class
T
>
__global__
void
im2col
(
const
T
*
data_im
,
int
numOuts
,
int
height
,
int
width
,
int
blockH
,
int
blockW
,
int
strideH
,
int
strideW
,
int
paddingH
,
int
paddingW
,
int
height_col
,
int
width_col
,
T
*
data_col
)
{
int
index
=
(
blockIdx
.
x
*
gridDim
.
y
+
blockIdx
.
y
)
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
numOuts
)
{
int
w_out
=
index
%
width_col
;
index
/=
width_col
;
int
h_out
=
index
%
height_col
;
int
channel_in
=
index
/
height_col
;
int
channel_out
=
channel_in
*
blockH
*
blockW
;
int
h_in
=
h_out
*
strideH
;
int
w_in
=
w_out
*
strideW
;
data_col
+=
(
channel_out
*
height_col
+
h_out
)
*
width_col
+
w_out
;
for
(
int
i
=
0
;
i
<
blockH
;
++
i
)
{
for
(
int
j
=
0
;
j
<
blockW
;
++
j
)
{
int
rIdx
=
int
(
h_in
+
i
);
int
cIdx
=
int
(
w_in
+
j
);
if
((
rIdx
-
(
int
)
paddingH
)
>=
(
int
)
height
||
(
rIdx
-
(
int
)
paddingH
)
<
0
||
(
cIdx
-
(
int
)
paddingW
)
>=
(
int
)
width
||
(
cIdx
-
(
int
)
paddingW
)
<
0
)
{
*
data_col
=
0
;
}
else
{
rIdx
=
rIdx
+
channel_in
*
height
-
paddingH
;
cIdx
=
cIdx
-
paddingW
;
*
data_col
=
data_im
[
rIdx
*
width
+
cIdx
];
}
data_col
+=
height_col
*
width_col
;
}
}
}
}
template
<
class
T
>
class
Im2ColFunctor
<
DEVICE_TYPE_GPU
,
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
)
{
int
numKernels
=
inputChannels
*
outputHeight
*
outputWidth
;
int
blocks
=
(
numKernels
+
1024
-
1
)
/
1024
;
int
blockX
=
512
;
int
blockY
=
(
blocks
+
512
-
1
)
/
512
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blockX
,
blockY
);
im2col
<
T
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
imData
,
numKernels
,
inputHeight
,
inputWidth
,
filterHeight
,
filterWidth
,
strideHeight
,
strideWidth
,
paddingHeight
,
paddingWidth
,
outputHeight
,
outputWidth
,
colData
);
CHECK_SYNC
(
"Im2ColFunctor GPU failed"
);
}
};
template
<
class
T
>
__global__
void
col2im
(
size_t
n
,
const
T
*
data_col
,
size_t
height
,
size_t
width
,
size_t
channels
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
height_col
,
size_t
width_col
,
T
*
data_im
)
{
size_t
index
=
(
blockIdx
.
x
*
gridDim
.
y
+
blockIdx
.
y
)
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
n
)
{
T
val
=
0
;
int
w
=
int
(
index
%
width
);
int
h
=
int
((
index
/
width
)
%
height
);
int
c
=
int
(
index
/
(
width
*
height
));
if
((
w
-
(
int
)
paddingW
)
>=
0
&&
(
w
-
(
int
)
paddingW
)
<
(
width
-
2
*
paddingW
)
&&
(
h
-
(
int
)
paddingH
)
>=
0
&&
(
h
-
paddingH
)
<
(
height
-
2
*
paddingH
))
{
// compute the start and end of the output
int
w_col_start
=
(
w
<
(
int
)
blockW
)
?
0
:
(
w
-
int
(
blockW
))
/
(
int
)
strideW
+
1
;
int
w_col_end
=
min
((
int
)(
w
/
(
int
)
strideW
+
1
),
(
int
)(
width_col
));
int
h_col_start
=
(
h
<
(
int
)
blockH
)
?
0
:
(
h
-
(
int
)
blockH
)
/
(
int
)
strideH
+
1
;
int
h_col_end
=
min
(
int
(
h
/
strideH
+
1
),
int
(
height_col
));
for
(
int
h_col
=
h_col_start
;
h_col
<
h_col_end
;
++
h_col
)
{
for
(
int
w_col
=
w_col_start
;
w_col
<
w_col_end
;
++
w_col
)
{
// the col location: [c * width * height + h_out, w_out]
int
c_col
=
int
(
c
*
blockH
*
blockW
)
+
\
(
h
-
h_col
*
(
int
)
strideH
)
*
(
int
)
blockW
+
(
w
-
w_col
*
(
int
)
strideW
);
val
+=
data_col
[(
c_col
*
height_col
+
h_col
)
*
width_col
+
w_col
];
}
}
h
-=
paddingH
;
w
-=
paddingW
;
data_im
[
c
*
((
width
-
2
*
paddingW
)
*
(
height
-
2
*
paddingH
))
+
h
*
(
width
-
2
*
paddingW
)
+
w
]
+=
val
;
}
}
}
template
<
class
T
>
class
Col2ImFunctor
<
DEVICE_TYPE_GPU
,
T
>
{
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
)
{
size_t
numKernels
=
inputChannels
*
(
inputHeight
+
2
*
paddingHeight
)
*
(
inputWidth
+
2
*
paddingWidth
);
size_t
blocks
=
(
numKernels
+
1024
-
1
)
/
1024
;
size_t
blockX
=
512
;
size_t
blockY
=
(
blocks
+
512
-
1
)
/
512
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blockX
,
blockY
);
// To avoid involving atomic operations, we will launch one kernel per
// bottom dimension, and then in the kernel add up the top dimensions.
col2im
<
T
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
numKernels
,
colData
,
inputHeight
+
2
*
paddingHeight
,
inputWidth
+
2
*
paddingWidth
,
inputChannels
,
filterHeight
,
filterWidth
,
strideHeight
,
strideWidth
,
paddingHeight
,
paddingWidth
,
outputHeight
,
outputWidth
,
imData
);
CHECK_SYNC
(
"Col2ImFunctor GPU failed"
);
}
};
template
class
Im2ColFunctor
<
DEVICE_TYPE_GPU
,
float
>;
template
class
Im2ColFunctor
<
DEVICE_TYPE_GPU
,
double
>;
template
class
Col2ImFunctor
<
DEVICE_TYPE_GPU
,
float
>;
template
class
Col2ImFunctor
<
DEVICE_TYPE_GPU
,
double
>;
}
// namespace paddle
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