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059b2784
编写于
7月 30, 2018
作者:
T
tensor-tang
提交者:
GitHub
7月 30, 2018
浏览文件
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差异文件
Merge pull request #12408 from tensor-tang/refine/im2col
Refine CPU im2col padding with 1
上级
b0cf1fe3
d8d2dbcf
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
365 addition
and
124 deletion
+365
-124
paddle/fluid/operators/math/im2col.cc
paddle/fluid/operators/math/im2col.cc
+10
-52
paddle/fluid/operators/math/im2col_cfo_cpu.h
paddle/fluid/operators/math/im2col_cfo_cpu.h
+252
-0
paddle/fluid/operators/math/im2col_test.cc
paddle/fluid/operators/math/im2col_test.cc
+103
-72
未找到文件。
paddle/fluid/operators/math/im2col.cc
浏览文件 @
059b2784
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h"
#include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -35,61 +36,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
->
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
->
dims
()[
1
];
int
filter_width
=
col
->
dims
()[
2
];
int
output_height
=
col
->
dims
()[
3
];
int
output_width
=
col
->
dims
()[
4
];
int
channels_col
=
im_channels
*
filter_height
*
filter_width
;
const
T
*
im_data
=
im
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
// TODO(TJ): change me to template
// further optimaze:
// 1. padding != 1
// 2. could also support stride_h != 1
if
(
stride
[
0
]
==
1
&&
stride
[
1
]
==
1
&&
dilation
[
0
]
==
1
&&
dilation
[
1
]
==
1
&&
padding
[
0
]
==
0
&&
padding
[
1
]
==
0
)
{
int
col_matrix_width
=
output_width
*
output_height
;
size_t
copy_size
=
sizeof
(
T
)
*
output_width
;
for
(
int
oh
=
0
;
oh
<
output_height
;
++
oh
)
{
const
T
*
im_data_start
=
im_data
+
oh
*
im_width
;
T
*
dst_data
=
col_data
+
oh
*
output_width
;
for
(
int
ic
=
0
;
ic
<
im_channels
;
++
ic
)
{
const
T
*
src_data
=
im_data_start
+
ic
*
im_height
*
im_width
;
for
(
int
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
for
(
int
kw
=
0
;
kw
<
filter_width
;
++
kw
)
{
std
::
memcpy
(
dst_data
,
src_data
+
kw
,
copy_size
);
dst_data
=
dst_data
+
col_matrix_width
;
}
src_data
=
src_data
+
im_width
;
}
}
}
return
;
}
for
(
int
c
=
0
;
c
<
channels_col
;
++
c
)
{
int
w_offset
=
c
%
filter_width
;
int
h_offset
=
(
c
/
filter_width
)
%
filter_height
;
int
c_im
=
c
/
(
filter_width
*
filter_height
);
for
(
int
h
=
0
;
h
<
output_height
;
++
h
)
{
int
im_row_idx
=
h
*
stride
[
0
]
-
padding
[
0
]
+
h_offset
*
dilation
[
0
];
for
(
int
w
=
0
;
w
<
output_width
;
++
w
)
{
int
im_col_idx
=
w
*
stride
[
1
]
-
padding
[
1
]
+
w_offset
*
dilation
[
1
];
int
col_idx
=
(
c
*
output_height
+
h
)
*
output_width
+
w
;
int
im_idx
=
(
im_row_idx
+
c_im
*
im_height
)
*
im_width
+
im_col_idx
;
col_data
[
col_idx
]
=
(
im_row_idx
<
0
||
im_row_idx
>=
im_height
||
im_col_idx
<
0
||
im_col_idx
>=
im_width
)
?
static_cast
<
T
>
(
0
)
:
im_data
[
im_idx
];
}
dilation
[
1
]
==
1
)
{
if
(
padding
[
0
]
==
0
&&
padding
[
1
]
==
0
)
{
im2col_sh1sw1dh1dw1ph0pw0
<
T
>
(
im
,
col
);
return
;
}
else
if
(
padding
[
0
]
==
1
&&
padding
[
1
]
==
1
)
{
im2col_sh1sw1dh1dw1ph1pw1
<
T
>
(
im
,
col
);
return
;
}
// TODO(TJ): complete padding >=2
}
im2col_common
<
T
>
(
im
,
dilation
,
stride
,
padding
,
col
);
}
};
...
...
paddle/fluid/operators/math/im2col_cfo_cpu.h
0 → 100644
浏览文件 @
059b2784
/* 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. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/tensor.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
/**
* The most common im2col algorithm.
* Support dilation, stride and padding.
*/
template
<
typename
T
>
inline
void
im2col_common
(
const
framework
::
Tensor
&
im
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
col
)
{
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
->
dims
()[
1
];
int
filter_width
=
col
->
dims
()[
2
];
int
output_height
=
col
->
dims
()[
3
];
int
output_width
=
col
->
dims
()[
4
];
int
channels_col
=
im_channels
*
filter_height
*
filter_width
;
const
T
*
im_data
=
im
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
for
(
int
c
=
0
;
c
<
channels_col
;
++
c
)
{
int
w_offset
=
c
%
filter_width
;
int
h_offset
=
(
c
/
filter_width
)
%
filter_height
;
int
c_im
=
c
/
(
filter_width
*
filter_height
);
for
(
int
h
=
0
;
h
<
output_height
;
++
h
)
{
int
im_row_idx
=
h
*
stride
[
0
]
-
padding
[
0
]
+
h_offset
*
dilation
[
0
];
for
(
int
w
=
0
;
w
<
output_width
;
++
w
)
{
int
im_col_idx
=
w
*
stride
[
1
]
-
padding
[
1
]
+
w_offset
*
dilation
[
1
];
int
col_idx
=
(
c
*
output_height
+
h
)
*
output_width
+
w
;
int
im_idx
=
(
im_row_idx
+
c_im
*
im_height
)
*
im_width
+
im_col_idx
;
col_data
[
col_idx
]
=
(
im_row_idx
<
0
||
im_row_idx
>=
im_height
||
im_col_idx
<
0
||
im_col_idx
>=
im_width
)
?
static_cast
<
T
>
(
0
)
:
im_data
[
im_idx
];
}
}
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 0
*/
template
<
typename
T
>
inline
void
im2col_sh1sw1dh1dw1ph0pw0
(
const
framework
::
Tensor
&
im
,
framework
::
Tensor
*
col
)
{
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
->
dims
()[
1
];
int
filter_width
=
col
->
dims
()[
2
];
int
output_height
=
col
->
dims
()[
3
];
int
output_width
=
col
->
dims
()[
4
];
const
T
*
im_data
=
im
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
int
col_matrix_width
=
output_width
*
output_height
;
int
im_size
=
im_height
*
im_width
;
size_t
copy_size
=
sizeof
(
T
)
*
output_width
;
const
T
*
im_data_oh
=
im_data
;
T
*
dst_data_oh
=
col_data
;
for
(
int
oh
=
0
;
oh
<
output_height
;
++
oh
)
{
const
T
*
src_data_ic
=
im_data_oh
;
T
*
dst_data
=
dst_data_oh
;
for
(
int
ic
=
0
;
ic
<
im_channels
;
++
ic
)
{
const
T
*
src_data
=
src_data_ic
;
for
(
int
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
for
(
int
kw
=
0
;
kw
<
filter_width
;
++
kw
)
{
std
::
memcpy
(
dst_data
,
src_data
+
kw
,
copy_size
);
dst_data
=
dst_data
+
col_matrix_width
;
}
src_data
=
src_data
+
im_width
;
}
src_data_ic
=
src_data_ic
+
im_size
;
}
im_data_oh
=
im_data_oh
+
im_width
;
dst_data_oh
=
dst_data_oh
+
output_width
;
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 1
* and filter_width == 1 have a special implementation
*/
template
<
typename
T
>
inline
void
im2col_sh1sw1dh1dw1ph1pw1
(
const
framework
::
Tensor
&
im
,
framework
::
Tensor
*
col
)
{
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
->
dims
()[
1
];
int
filter_width
=
col
->
dims
()[
2
];
int
output_height
=
col
->
dims
()[
3
];
int
output_width
=
col
->
dims
()[
4
];
constexpr
int
plh
=
1
;
constexpr
int
prh
=
1
;
constexpr
int
plw
=
1
;
constexpr
int
prw
=
1
;
const
T
*
im_data
=
im
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
int
im_size
=
im_height
*
im_width
;
int
col_matrix_width
=
output_width
*
output_height
;
int
col_block_fh
=
filter_width
*
col_matrix_width
;
// fw*oh*ow
int
col_block_ic
=
filter_height
*
col_block_fh
;
// fh*fw*oh*ow
// fill height padding
{
size_t
copy_size
=
sizeof
(
T
)
*
output_width
;
T
*
col_start_l
=
col_data
;
T
*
col_start_r
=
col_data
+
(
filter_height
-
1
)
*
col_block_fh
+
col_matrix_width
-
output_width
;
for
(
int
ic
=
0
;
ic
<
im_channels
;
++
ic
)
{
T
*
dst_data_l
=
col_start_l
;
T
*
dst_data_r
=
col_start_r
;
for
(
int
kw
=
0
;
kw
<
filter_width
;
++
kw
)
{
std
::
memset
(
dst_data_l
,
0
,
copy_size
);
std
::
memset
(
dst_data_r
,
0
,
copy_size
);
dst_data_l
=
dst_data_l
+
col_matrix_width
;
dst_data_r
=
dst_data_r
+
col_matrix_width
;
}
col_start_l
=
col_start_l
+
col_block_ic
;
col_start_r
=
col_start_r
+
col_block_ic
;
}
}
auto
pad
=
static_cast
<
T
>
(
0
);
if
(
filter_width
==
1
)
{
// fill width padding
T
*
dst_data_ic
=
col_data
;
for
(
int
ic
=
0
;
ic
<
im_channels
;
++
ic
)
{
T
*
dst_data_kh
=
dst_data_ic
;
for
(
int
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
T
*
dst_data
=
dst_data_kh
;
for
(
int
oh
=
0
;
oh
<
output_height
;
++
oh
)
{
*
dst_data
=
pad
;
dst_data
=
dst_data
+
output_width
-
1
;
*
dst_data
=
pad
;
++
dst_data
;
}
dst_data_kh
=
dst_data_kh
+
col_block_fh
;
}
dst_data_ic
=
dst_data_ic
+
col_block_ic
;
}
// fill core
size_t
copy_size
=
sizeof
(
T
)
*
(
output_width
-
plw
-
prw
);
for
(
int
oh
=
0
;
oh
<
output_height
;
++
oh
)
{
const
T
*
im_data_start
=
im_data
+
(
oh
-
plh
>
0
?
oh
-
plh
:
0
)
*
im_width
;
T
*
dst_data
=
col_data
+
oh
*
output_width
;
for
(
int
ic
=
0
;
ic
<
im_channels
;
++
ic
)
{
const
T
*
src_data
=
im_data_start
+
ic
*
im_size
;
for
(
int
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
if
((
oh
<
plh
&&
kh
<
plh
)
||
(
oh
>
(
output_height
-
prh
-
1
)
&&
kh
>
(
filter_height
-
prh
-
1
)))
{
dst_data
=
dst_data
+
col_matrix_width
;
continue
;
}
std
::
memcpy
(
dst_data
+
plw
,
src_data
,
copy_size
);
dst_data
=
dst_data
+
col_matrix_width
;
src_data
=
src_data
+
im_width
;
}
}
}
return
;
}
// filter_width != 1
// fill width padding
T
*
dst_data_ic
=
col_data
;
for
(
int
ic
=
0
;
ic
<
im_channels
;
++
ic
)
{
T
*
dst_data_kh
=
dst_data_ic
;
for
(
int
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
for
(
T
*
dst_data
:
{
dst_data_kh
,
dst_data_kh
+
(
filter_width
-
prw
)
*
col_matrix_width
+
output_width
-
1
})
{
// TODO(TJ): from plh, saving repeated assignment
for
(
int
oh
=
0
;
oh
<
output_height
;
++
oh
)
{
*
dst_data
=
pad
;
dst_data
=
dst_data
+
output_width
;
}
}
dst_data_kh
=
dst_data_kh
+
col_block_fh
;
}
dst_data_ic
=
dst_data_ic
+
col_block_ic
;
}
// TODO(TJ): use array like: size_t copy_size[kw]={sizeof(T) *
// (output_width-1)}
// length of copy_size is equal kw.
for
(
int
oh
=
0
;
oh
<
output_height
;
++
oh
)
{
const
T
*
im_data_start
=
im_data
+
(
oh
-
plh
>
0
?
oh
-
plh
:
0
)
*
im_width
;
T
*
dst_data
=
col_data
+
oh
*
output_width
;
for
(
int
ic
=
0
;
ic
<
im_channels
;
++
ic
)
{
const
T
*
src_data
=
im_data_start
+
ic
*
im_size
;
for
(
int
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
if
((
oh
<
plh
&&
kh
<
plh
)
||
(
oh
>
(
output_height
-
prh
-
1
)
&&
kh
>
(
filter_height
-
prh
-
1
)))
{
dst_data
=
dst_data
+
filter_width
*
col_matrix_width
;
continue
;
}
// TODO(TJ): reuse plw-kw outside this for
// try to unify
for
(
int
kw
=
0
;
kw
<
plw
;
++
kw
)
{
std
::
memcpy
(
dst_data
+
(
plw
-
kw
),
src_data
,
sizeof
(
T
)
*
(
output_width
-
(
plw
-
kw
)));
dst_data
=
dst_data
+
col_matrix_width
;
}
for
(
int
kw
=
plw
;
kw
<
filter_width
-
prw
;
++
kw
)
{
std
::
memcpy
(
dst_data
,
src_data
+
(
kw
-
plw
),
sizeof
(
T
)
*
output_width
);
dst_data
=
dst_data
+
col_matrix_width
;
}
int
i
=
1
;
for
(
int
kw
=
filter_width
-
prw
;
kw
<
filter_width
;
++
kw
,
++
i
)
{
std
::
memcpy
(
dst_data
,
src_data
+
(
kw
-
plw
),
sizeof
(
T
)
*
(
output_width
-
i
));
dst_data
=
dst_data
+
col_matrix_width
;
}
src_data
=
src_data
+
im_width
;
}
}
}
}
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/im2col_test.cc
浏览文件 @
059b2784
...
...
@@ -14,7 +14,9 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h"
#include <gtest/gtest.h>
#include <sys/time.h>
#include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
template
<
typename
DeviceContext
,
typename
Place
>
void
testIm2col
()
{
...
...
@@ -160,82 +162,111 @@ void testIm2col() {
delete
context
;
}
void
testIm2colCPU
(
int
ic
,
int
ih
,
int
iw
,
int
fh
,
int
fw
,
int
ph
,
int
pw
)
{
paddle
::
framework
::
Tensor
input
;
paddle
::
framework
::
Tensor
output
;
paddle
::
framework
::
Tensor
ref_output
;
std
::
vector
<
int
>
padding
({
ph
,
pw
});
std
::
vector
<
int
>
stride
({
1
,
1
});
// stride_y, stride_x
std
::
vector
<
int
>
dilation
({
1
,
1
});
// dilation_y, dilation_x
int
output_height
=
(
ih
-
fh
+
padding
[
0
]
*
2
)
/
stride
[
0
]
+
1
;
int
output_width
=
(
iw
-
fw
+
padding
[
1
]
*
2
)
/
stride
[
1
]
+
1
;
float
*
input_ptr
=
input
.
mutable_data
<
float
>
({
ic
,
ih
,
iw
},
paddle
::
platform
::
CPUPlace
());
for
(
int
i
=
0
;
i
<
input
.
numel
();
++
i
)
{
input_ptr
[
i
]
=
static_cast
<
float
>
(
i
+
1
);
}
paddle
::
platform
::
CPUPlace
place
;
paddle
::
platform
::
CPUDeviceContext
context
(
place
);
output
.
mutable_data
<
float
>
({
ic
,
fh
,
fw
,
output_height
,
output_width
},
place
);
ref_output
.
mutable_data
<
float
>
({
ic
,
fh
,
fw
,
output_height
,
output_width
},
place
);
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
paddle
::
platform
::
CPUDeviceContext
,
float
>
im2col
;
im2col
(
context
,
input
,
dilation
,
stride
,
padding
,
&
output
);
auto
ref_im2col
=
[
&
](
const
paddle
::
framework
::
Tensor
&
im
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
paddle
::
framework
::
Tensor
*
col
)
{
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
->
dims
()[
1
];
int
filter_width
=
col
->
dims
()[
2
];
int
output_height
=
col
->
dims
()[
3
];
int
output_width
=
col
->
dims
()[
4
];
int
channels_col
=
im_channels
*
filter_height
*
filter_width
;
const
float
*
im_data
=
im
.
data
<
float
>
();
float
*
col_data
=
col
->
data
<
float
>
();
for
(
int
c
=
0
;
c
<
channels_col
;
++
c
)
{
int
w_offset
=
c
%
filter_width
;
int
h_offset
=
(
c
/
filter_width
)
%
filter_height
;
int
c_im
=
c
/
(
filter_width
*
filter_height
);
for
(
int
h
=
0
;
h
<
output_height
;
++
h
)
{
int
im_row_idx
=
h
*
stride
[
0
]
-
padding
[
0
]
+
h_offset
*
dilation
[
0
];
for
(
int
w
=
0
;
w
<
output_width
;
++
w
)
{
int
im_col_idx
=
w
*
stride
[
1
]
-
padding
[
1
]
+
w_offset
*
dilation
[
1
];
int
col_idx
=
(
c
*
output_height
+
h
)
*
output_width
+
w
;
int
im_idx
=
(
im_row_idx
+
c_im
*
im_height
)
*
im_width
+
im_col_idx
;
col_data
[
col_idx
]
=
(
im_row_idx
<
0
||
im_row_idx
>=
im_height
||
im_col_idx
<
0
||
im_col_idx
>=
im_width
)
?
0.
f
:
im_data
[
im_idx
];
}
}
}
};
ref_im2col
(
input
,
dilation
,
stride
,
padding
,
&
ref_output
);
float
*
out_cfo_ptr
=
output
.
data
<
float
>
();
float
*
out_ref_ptr
=
ref_output
.
data
<
float
>
();
for
(
int
i
=
0
;
i
<
output
.
numel
();
++
i
)
{
EXPECT_EQ
(
out_cfo_ptr
[
i
],
out_ref_ptr
[
i
]);
}
}
TEST
(
math
,
im2col
)
{
testIm2col
<
paddle
::
platform
::
CPUDeviceContext
,
paddle
::
platform
::
CPUPlace
>
();
testIm2colCPU
(
/*ic*/
3
,
/*ih*/
5
,
/*iw*/
5
,
/*fh*/
3
,
/*fw*/
2
,
/*ph*/
0
,
/*pw*/
0
);
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
5
,
/*iw*/
4
,
/*fh*/
3
,
/*fw*/
3
,
/*ph*/
1
,
/*pw*/
1
);
#ifdef PADDLE_WITH_CUDA
testIm2col
<
paddle
::
platform
::
CUDADeviceContext
,
paddle
::
platform
::
CUDAPlace
>
();
#endif
}
#define PREPARE_IM2COL_CPU \
paddle::platform::CPUPlace place; \
paddle::platform::CPUDeviceContext context(place); \
paddle::framework::Tensor input; \
paddle::framework::Tensor out; \
paddle::framework::Tensor ref; \
std::vector<int> padding({ph, pw}); \
std::vector<int> stride({1, 1}); \
std::vector<int> dilation({1, 1}); \
float* input_ptr = input.mutable_data<float>({ic, ih, iw}, place); \
for (int i = 0; i < input.numel(); ++i) { \
input_ptr[i] = static_cast<float>(i + 1); \
} \
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1; \
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1; \
out.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
ref.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
paddle::operators::math::Im2ColFunctor< \
paddle::operators::math::ColFormat::kCFO, \
paddle::platform::CPUDeviceContext, float> \
im2col
void
testIm2colCPU
(
int
ic
,
int
ih
,
int
iw
,
int
fh
,
int
fw
,
int
ph
,
int
pw
)
{
PREPARE_IM2COL_CPU
;
im2col
(
context
,
input
,
dilation
,
stride
,
padding
,
&
out
);
paddle
::
operators
::
math
::
im2col_common
<
float
>
(
input
,
dilation
,
stride
,
padding
,
&
ref
);
float
*
ref_data
=
ref
.
data
<
float
>
();
float
*
out_data
=
out
.
data
<
float
>
();
for
(
int
i
=
0
;
i
<
out
.
numel
();
++
i
)
{
EXPECT_EQ
(
out_data
[
i
],
ref_data
[
i
]);
}
}
void
benchIm2col
(
int
ic
,
int
ih
,
int
iw
,
int
fh
,
int
fw
,
int
ph
,
int
pw
)
{
PREPARE_IM2COL_CPU
;
constexpr
int
repeat
=
100
;
auto
GetCurrentMs
=
[]()
->
double
{
struct
timeval
time
;
gettimeofday
(
&
time
,
NULL
);
return
1e+3
*
time
.
tv_sec
+
1e-3
*
time
.
tv_usec
;
};
auto
t1
=
GetCurrentMs
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
im2col
(
context
,
input
,
dilation
,
stride
,
padding
,
&
out
);
}
auto
t2
=
GetCurrentMs
();
for
(
int
i
=
0
;
i
<
repeat
;
++
i
)
{
paddle
::
operators
::
math
::
im2col_common
<
float
>
(
input
,
dilation
,
stride
,
padding
,
&
ref
);
}
auto
t3
=
GetCurrentMs
();
LOG
(
INFO
)
<<
"before: "
<<
(
t3
-
t2
)
/
repeat
<<
",after: "
<<
(
t2
-
t1
)
/
repeat
<<
",boost: "
<<
((
t3
-
t2
)
/
(
t2
-
t1
)
-
1
)
*
100
<<
"%"
;
}
TEST
(
math
,
im2col_cputest
)
{
// padding_h == padding_w
for
(
int
p
=
0
;
p
<
4
;
++
p
)
{
// width == height
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
5
,
/*iw*/
5
,
/*fh*/
4
,
/*fw*/
4
,
/*ph*/
p
,
/*pw*/
p
);
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
4
,
/*iw*/
4
,
/*fh*/
3
,
/*fw*/
3
,
/*ph*/
p
,
/*pw*/
p
);
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
4
,
/*iw*/
4
,
/*fh*/
2
,
/*fw*/
2
,
/*ph*/
p
,
/*pw*/
p
);
// height != width
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
5
,
/*iw*/
4
,
/*fh*/
2
,
/*fw*/
3
,
/*ph*/
p
,
/*pw*/
p
);
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
5
,
/*iw*/
4
,
/*fh*/
1
,
/*fw*/
3
,
/*ph*/
p
,
/*pw*/
p
);
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
4
,
/*iw*/
5
,
/*fh*/
3
,
/*fw*/
1
,
/*ph*/
p
,
/*pw*/
p
);
// filter == 1
testIm2colCPU
(
/*ic*/
3
,
/*ih*/
4
,
/*iw*/
4
,
/*fh*/
1
,
/*fw*/
1
,
/*ph*/
p
,
/*pw*/
p
);
testIm2colCPU
(
/*ic*/
3
,
/*ih*/
3
,
/*iw*/
4
,
/*fh*/
1
,
/*fw*/
1
,
/*ph*/
p
,
/*pw*/
p
);
}
// padding_h != padding_w
testIm2colCPU
(
/*ic*/
2
,
/*ih*/
4
,
/*iw*/
4
,
/*fh*/
2
,
/*fw*/
3
,
/*ph*/
1
,
/*pw*/
2
);
// benchmark
for
(
int
p
:
{
0
,
1
})
{
for
(
int
k
:
{
1
,
3
,
5
})
{
LOG
(
INFO
)
<<
"padding == "
<<
p
<<
", filter == "
<<
k
;
benchIm2col
(
/*ic*/
3
,
/*ih*/
224
,
/*iw*/
224
,
/*fh*/
k
,
/*fw*/
k
,
/*ph*/
p
,
/*pw*/
p
);
}
}
}
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