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1e4f55ae
编写于
4月 28, 2018
作者:
B
Bin Li
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
optimize general convolution
上级
ecea6d5b
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
164 addition
and
77 deletion
+164
-77
mace/kernels/conv_2d.h
mace/kernels/conv_2d.h
+164
-77
未找到文件。
mace/kernels/conv_2d.h
浏览文件 @
1e4f55ae
...
...
@@ -98,33 +98,168 @@ struct Conv2dFunctor<DeviceType::CPU, float> : Conv2dFunctorBase {
const
int
dilation_h
,
const
int
dilation_w
,
float
*
output
)
{
const
index_t
in_image_size
=
in_height
*
in_width
;
const
index_t
out_image_size
=
out_height
*
out_width
;
const
index_t
in_batch_size
=
in_channels
*
in_image_size
;
const
index_t
out_batch_size
=
out_channels
*
out_image_size
;
const
index_t
filter_size
=
filter_height
*
filter_width
;
const
index_t
in_tile_size
=
3
*
stride_w
+
(
filter_width
-
1
)
*
dilation_w
+
1
;
#pragma omp parallel for collapse(2)
for
(
index_t
b
=
0
;
b
<
batch
;
++
b
)
{
for
(
index_t
m
=
0
;
m
<
out_channels
;
++
m
)
{
for
(
index_t
h
=
0
;
h
<
out_height
;
++
h
)
{
for
(
index_t
w
=
0
;
w
<
out_width
;
++
w
)
{
index_t
out_offset
=
((
b
*
out_channels
+
m
)
*
out_height
+
h
)
*
out_width
+
w
;
for
(
index_t
c
=
0
;
c
<
in_channels
;
++
c
)
{
for
(
index_t
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
for
(
index_t
kw
=
0
;
kw
<
filter_width
;
++
kw
)
{
index_t
ih
=
h
*
stride_h
+
kh
*
dilation_h
;
index_t
iw
=
w
*
stride_w
+
kw
*
dilation_w
;
index_t
in_offset
=
((
b
*
in_channels
+
c
)
*
in_height
+
ih
)
*
in_width
+
iw
;
index_t
filter_offset
=
(((
m
*
in_channels
)
+
c
)
*
filter_height
+
kh
)
*
filter_width
+
kw
;
output
[
out_offset
]
+=
input
[
in_offset
]
*
filter
[
filter_offset
];
for
(
index_t
m
=
0
;
m
<
out_channels
;
m
+=
4
)
{
if
(
m
+
3
<
out_channels
)
{
float
*
out_ptr0_base
=
output
+
b
*
out_batch_size
+
m
*
out_image_size
;
float
*
out_ptr1_base
=
output
+
b
*
out_batch_size
+
(
m
+
1
)
*
out_image_size
;
float
*
out_ptr2_base
=
output
+
b
*
out_batch_size
+
(
m
+
2
)
*
out_image_size
;
float
*
out_ptr3_base
=
output
+
b
*
out_batch_size
+
(
m
+
3
)
*
out_image_size
;
for
(
index_t
c
=
0
;
c
<
in_channels
;
++
c
)
{
const
float
*
in_ptr_base
=
input
+
b
*
in_batch_size
+
c
*
in_image_size
;
const
float
*
filter_ptr0
=
filter
+
m
*
in_channels
*
filter_size
+
c
*
filter_size
;
const
float
*
filter_ptr1
=
filter
+
(
m
+
1
)
*
in_channels
*
filter_size
+
c
*
filter_size
;
const
float
*
filter_ptr2
=
filter
+
(
m
+
2
)
*
in_channels
*
filter_size
+
c
*
filter_size
;
const
float
*
filter_ptr3
=
filter
+
(
m
+
3
)
*
in_channels
*
filter_size
+
c
*
filter_size
;
for
(
index_t
h
=
0
;
h
<
out_height
;
++
h
)
{
for
(
index_t
w
=
0
;
w
+
3
<
out_width
;
w
+=
4
)
{
// input offset
index_t
ih
=
h
*
stride_h
;
index_t
iw
=
w
*
stride_w
;
index_t
in_offset
=
ih
*
in_width
+
iw
;
// output (4 outch x 1 height x 4 width): vo_outch_height
float
vo0
[
4
],
vo1
[
4
],
vo2
[
4
],
vo3
[
4
];
// load output
index_t
out_offset
=
h
*
out_width
+
w
;
for
(
index_t
ow
=
0
;
ow
<
4
;
++
ow
)
{
vo0
[
ow
]
=
out_ptr0_base
[
out_offset
+
ow
];
vo1
[
ow
]
=
out_ptr1_base
[
out_offset
+
ow
];
vo2
[
ow
]
=
out_ptr2_base
[
out_offset
+
ow
];
vo3
[
ow
]
=
out_ptr3_base
[
out_offset
+
ow
];
}
}
}
}
}
}
}
// calc by row
for
(
index_t
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
for
(
index_t
kw
=
0
;
kw
<
filter_width
;
++
kw
)
{
// outch 0
vo0
[
0
]
+=
in_ptr_base
[
in_offset
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
vo0
[
1
]
+=
in_ptr_base
[
in_offset
+
stride_w
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
vo0
[
2
]
+=
in_ptr_base
[
in_offset
+
2
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
vo0
[
3
]
+=
in_ptr_base
[
in_offset
+
3
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
// outch 1
vo1
[
0
]
+=
in_ptr_base
[
in_offset
+
kw
*
dilation_w
]
*
filter_ptr1
[
kw
];
vo1
[
1
]
+=
in_ptr_base
[
in_offset
+
stride_w
+
kw
*
dilation_w
]
*
filter_ptr1
[
kw
];
vo1
[
2
]
+=
in_ptr_base
[
in_offset
+
2
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr1
[
kw
];
vo1
[
3
]
+=
in_ptr_base
[
in_offset
+
3
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr1
[
kw
];
// outch 2
vo2
[
0
]
+=
in_ptr_base
[
in_offset
+
kw
*
dilation_w
]
*
filter_ptr2
[
kw
];
vo2
[
1
]
+=
in_ptr_base
[
in_offset
+
stride_w
+
kw
*
dilation_w
]
*
filter_ptr2
[
kw
];
vo2
[
2
]
+=
in_ptr_base
[
in_offset
+
2
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr2
[
kw
];
vo2
[
3
]
+=
in_ptr_base
[
in_offset
+
3
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr2
[
kw
];
// outch 3
vo3
[
0
]
+=
in_ptr_base
[
in_offset
+
kw
*
dilation_w
]
*
filter_ptr3
[
kw
];
vo3
[
1
]
+=
in_ptr_base
[
in_offset
+
stride_w
+
kw
*
dilation_w
]
*
filter_ptr3
[
kw
];
vo3
[
2
]
+=
in_ptr_base
[
in_offset
+
2
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr3
[
kw
];
vo3
[
3
]
+=
in_ptr_base
[
in_offset
+
3
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr3
[
kw
];
}
// kw
in_offset
+=
dilation_h
*
in_width
;
filter_ptr0
+=
filter_width
;
filter_ptr1
+=
filter_width
;
filter_ptr2
+=
filter_width
;
filter_ptr3
+=
filter_width
;
}
// kh
for
(
index_t
ow
=
0
;
ow
<
4
;
++
ow
)
{
out_ptr0_base
[
out_offset
+
ow
]
=
vo0
[
ow
];
out_ptr1_base
[
out_offset
+
ow
]
=
vo1
[
ow
];
out_ptr2_base
[
out_offset
+
ow
]
=
vo2
[
ow
];
out_ptr3_base
[
out_offset
+
ow
]
=
vo3
[
ow
];
}
filter_ptr0
-=
filter_size
;
filter_ptr1
-=
filter_size
;
filter_ptr2
-=
filter_size
;
filter_ptr3
-=
filter_size
;
}
// w
}
// h
}
// c
}
else
{
for
(
index_t
mm
=
m
;
mm
<
out_channels
;
++
mm
)
{
float
*
out_ptr0_base
=
output
+
b
*
out_batch_size
+
mm
*
out_image_size
;
for
(
index_t
c
=
0
;
c
<
in_channels
;
++
c
)
{
const
float
*
in_ptr_base
=
input
+
b
*
in_batch_size
+
c
*
in_image_size
;
const
float
*
filter_ptr0
=
filter
+
mm
*
in_channels
*
filter_size
+
c
*
filter_size
;
for
(
index_t
h
=
0
;
h
<
out_height
;
++
h
)
{
for
(
index_t
w
=
0
;
w
+
3
<
out_width
;
w
+=
4
)
{
// input offset
index_t
ih
=
h
*
stride_h
;
index_t
iw
=
w
*
stride_w
;
index_t
in_offset
=
ih
*
in_width
+
iw
;
// output (1 outch x 1 height x 4 width): vo_outch_height
float
vo0
[
4
];
// load output
index_t
out_offset
=
h
*
out_width
+
w
;
for
(
index_t
ow
=
0
;
ow
<
4
;
++
ow
)
{
vo0
[
ow
]
=
out_ptr0_base
[
out_offset
+
ow
];
}
// calc by row
for
(
index_t
kh
=
0
;
kh
<
filter_height
;
++
kh
)
{
for
(
index_t
kw
=
0
;
kw
<
filter_width
;
++
kw
)
{
// outch 0
vo0
[
0
]
+=
in_ptr_base
[
in_offset
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
vo0
[
1
]
+=
in_ptr_base
[
in_offset
+
stride_w
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
vo0
[
2
]
+=
in_ptr_base
[
in_offset
+
2
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
vo0
[
3
]
+=
in_ptr_base
[
in_offset
+
3
*
stride_w
+
kw
*
dilation_w
]
*
filter_ptr0
[
kw
];
}
// kw
in_offset
+=
dilation_h
*
in_width
;
filter_ptr0
+=
filter_width
;
}
// kh
for
(
index_t
ow
=
0
;
ow
<
4
;
++
ow
)
{
out_ptr0_base
[
out_offset
+
ow
]
=
vo0
[
ow
];
}
filter_ptr0
-=
filter_size
;
}
// w
}
// h
}
// c
}
// mm
}
// if
}
// m
}
// b
}
void
operator
()(
const
Tensor
*
input
,
...
...
@@ -286,63 +421,15 @@ struct Conv2dFunctor<DeviceType::CPU, float> : Conv2dFunctorBase {
if
(
extra_input_width
!=
padded_input_width
)
{
pad_right
+=
(
extra_input_width
-
padded_input_width
);
}
}
else
if
(
use_neon_3x3_s2
)
{
extra_output_height
=
height
;
extra_input_height
=
std
::
max
(
padded_input_height
,
(
extra_output_height
-
1
)
*
2
+
3
);
extra_output_width
=
RoundUp
<
index_t
>
(
width
,
4
);
extra_input_width
=
std
::
max
(
padded_input_width
,
(
extra_output_width
-
1
)
*
2
+
3
);
if
(
extra_input_height
!=
padded_input_height
)
{
pad_bottom
+=
(
extra_input_height
-
padded_input_height
);
}
if
(
extra_input_width
!=
padded_input_width
)
{
pad_right
+=
(
extra_input_width
-
padded_input_width
);
}
}
else
if
(
use_neon_5x5_s1
)
{
extra_output_height
=
height
;
extra_input_height
=
std
::
max
(
padded_input_height
,
extra_output_height
+
4
);
extra_output_width
=
RoundUp
<
index_t
>
(
width
,
4
);
extra_input_width
=
std
::
max
(
padded_input_width
,
extra_output_width
+
4
);
if
(
extra_input_height
!=
padded_input_height
)
{
pad_bottom
+=
(
extra_input_height
-
padded_input_height
);
}
if
(
extra_input_width
!=
padded_input_width
)
{
pad_right
+=
(
extra_input_width
-
padded_input_width
);
}
}
else
if
(
use_neon_7x7_s1
)
{
extra_output_height
=
height
;
extra_input_height
=
std
::
max
(
padded_input_height
,
extra_output_height
+
6
);
extra_output_width
=
RoundUp
<
index_t
>
(
width
,
4
);
extra_input_width
=
std
::
max
(
padded_input_width
,
extra_output_width
+
6
);
if
(
extra_input_height
!=
padded_input_height
)
{
pad_bottom
+=
(
extra_input_height
-
padded_input_height
);
}
if
(
extra_input_width
!=
padded_input_width
)
{
pad_right
+=
(
extra_input_width
-
padded_input_width
);
}
}
else
if
(
use_neon_7x7_s2
)
{
extra_output_height
=
height
;
extra_input_height
=
std
::
max
(
padded_input_height
,
(
extra_output_height
-
1
)
*
2
+
7
);
extra_output_width
=
RoundUp
<
index_t
>
(
width
,
4
);
extra_input_width
=
std
::
max
(
padded_input_width
,
(
extra_output_width
-
1
)
*
2
+
7
);
if
(
extra_input_height
!=
padded_input_height
)
{
pad_bottom
+=
(
extra_input_height
-
padded_input_height
);
}
if
(
extra_input_width
!=
padded_input_width
)
{
pad_right
+=
(
extra_input_width
-
padded_input_width
);
}
}
else
if
(
use_neon_7x7_s3
)
{
}
else
{
extra_output_height
=
height
;
extra_input_height
=
std
::
max
(
padded_input_height
,
(
extra_output_height
-
1
)
*
3
+
7
);
std
::
max
(
padded_input_height
,
(
extra_output_height
-
1
)
*
stride_h
+
(
filter_h
-
1
)
*
dilation_h
+
1
);
extra_output_width
=
RoundUp
<
index_t
>
(
width
,
4
);
extra_input_width
=
std
::
max
(
padded_input_width
,
(
extra_output_width
-
1
)
*
3
+
7
);
std
::
max
(
padded_input_width
,
(
extra_output_width
-
1
)
*
stride_w
+
(
filter_w
-
1
)
*
dilation_w
+
1
);
if
(
extra_input_height
!=
padded_input_height
)
{
pad_bottom
+=
(
extra_input_height
-
padded_input_height
);
}
...
...
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