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5c057f95
编写于
12月 03, 2017
作者:
S
sweetsky0901
浏览文件
操作
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下载
电子邮件补丁
差异文件
add spp op only can test ok
上级
d89061c3
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
294 addition
and
0 deletion
+294
-0
paddle/operators/spp_op.cc
paddle/operators/spp_op.cc
+98
-0
paddle/operators/spp_op.h
paddle/operators/spp_op.h
+148
-0
python/paddle/v2/fluid/tests/test_spp_op.py
python/paddle/v2/fluid/tests/test_spp_op.py
+48
-0
未找到文件。
paddle/operators/spp_op.cc
0 → 100644
浏览文件 @
5c057f95
/* 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.
Indicesou 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 "paddle/operators/spp_op.h"
namespace
paddle
{
namespace
operators
{
class
SppOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SppOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(Tensor) The input tensor of spp operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature."
);
AddOutput
(
"Out"
,
"(Tensor) The output tensor of spp operator."
"N * M."
"M = C * H * W"
);
AddAttr
<
int
>
(
"pyramid_height"
,
">= 1"
);
AddComment
(
R"DOC(
"Input shape: $(N, C_{in}, H_{in}, W_{in})$
Output shape: $(H_{out}, W_{out})$
Where
$$
H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\
W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1]
$$
)DOC"
);
}
};
int
OutputSize
(
int
pyramid_level
,
int
input_size
)
{
int
bins
=
std
::
pow
(
2
,
pyramid_level
);
int
ksize
=
std
::
ceil
(
input_size
/
static_cast
<
double
>
(
bins
));
int
padding
=
(
ksize
*
bins
-
input_size
+
1
)
/
2
;
int
output_size
=
(
input_size
-
ksize
+
2
*
padding
)
/
ksize
+
1
;
// output_size = bins
return
output_size
;
}
class
SppOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SppOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SppOp should not be null."
);
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
int
pyramid_height
=
ctx
->
Attrs
().
Get
<
int
>
(
"pyramid_height"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
,
"Spping intput must be of 4-dimensional."
);
int
outlen
=
0
;
for
(
int
p
=
0
;
p
<
pyramid_height
;
++
p
)
{
int
outh
=
OutputSize
(
p
,
in_x_dims
[
2
]);
int
outw
=
OutputSize
(
p
,
in_x_dims
[
3
]);
int
p_level_outlen
=
outh
*
outw
*
in_x_dims
[
1
];
outlen
+=
p_level_outlen
;
}
std
::
vector
<
int64_t
>
output_shape
({
in_x_dims
[
0
],
outlen
});
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
};
class
SppOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input(X@GRAD) should not be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
spp
,
ops
::
SppOp
,
ops
::
SppOpMaker
,
spp_grad
,
ops
::
SppOpGrad
);
REGISTER_OP_CPU_KERNEL
(
spp
,
ops
::
SppKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
SppKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
spp_grad
,
ops
::
SppGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
SppGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/spp_op.h
0 → 100644
浏览文件 @
5c057f95
/* 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.
Indicesou 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 "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/pooling.h"
#include "paddle/operators/strided_memcpy.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
Place
,
typename
T
>
class
SppKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
in_x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
int
pyramid_height
=
context
.
template
Attr
<
int
>(
"pyramid_height"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
out_stride
=
framework
::
stride
(
out
->
dims
());
int
input_h
=
in_x
->
dims
()[
2
];
int
input_w
=
in_x
->
dims
()[
3
];
size_t
output_offset
=
0
;
for
(
int
p
=
0
;
p
<
pyramid_height
;
++
p
)
{
int
bins
=
std
::
pow
(
2
,
p
);
int
ksize_h
=
std
::
ceil
(
input_h
/
static_cast
<
double
>
(
bins
));
int
ksize_w
=
std
::
ceil
(
input_w
/
static_cast
<
double
>
(
bins
));
int
padding_h
=
(
ksize_h
*
bins
-
input_h
+
1
)
/
2
;
int
padding_w
=
(
ksize_w
*
bins
-
input_w
+
1
)
/
2
;
std
::
vector
<
int
>
ksize
({
ksize_h
,
ksize_w
});
std
::
vector
<
int
>
strides
({
ksize_h
,
ksize_w
});
std
::
vector
<
int
>
paddings
({
padding_h
,
padding_w
});
// pooling output shape
std
::
vector
<
int64_t
>
output_shape_vec
({
in_x
->
dims
()[
0
],
in_x
->
dims
()[
1
]});
output_shape_vec
.
push_back
((
input_h
-
ksize_h
+
2
*
padding_h
)
/
ksize_h
+
1
);
output_shape_vec
.
push_back
((
input_w
-
ksize_w
+
2
*
padding_w
)
/
ksize_w
+
1
);
framework
::
DDim
output_shape
(
framework
::
make_ddim
(
output_shape_vec
));
// flatten pooling output shape
int
output_flatten_w
=
in_x
->
dims
()[
1
]
*
bins
*
bins
;
std
::
vector
<
int64_t
>
output_flatten_shape_vec
(
{
in_x
->
dims
()[
0
],
output_flatten_w
});
framework
::
DDim
output_flatten_shape
(
framework
::
make_ddim
(
output_flatten_shape_vec
));
framework
::
Tensor
out_level
;
framework
::
Tensor
out_flatten_level
;
out_level
.
mutable_data
<
T
>
(
output_shape
,
context
.
GetPlace
());
// pooling
math
::
Pool2dFunctor
<
Place
,
math
::
MaxPool
<
T
>
,
T
>
pool_forward
;
math
::
MaxPool
<
T
>
max_process
;
pool_forward
(
context
.
device_context
(),
*
in_x
,
ksize
,
strides
,
paddings
,
max_process
,
&
out_level
);
out_flatten_level
.
ShareDataWith
(
out_level
);
out_flatten_level
.
Resize
(
output_flatten_shape
);
auto
in_stride
=
framework
::
stride
(
out_flatten_level
.
dims
());
const
T
*
src_data
=
out_flatten_level
.
data
<
T
>
();
StridedMemcpy
<
T
>
(
context
.
device_context
(),
src_data
,
in_stride
,
out_flatten_level
.
dims
(),
out_stride
,
out
->
data
<
T
>
()
+
output_offset
);
output_offset
+=
out_flatten_level
.
dims
()[
1
]
*
in_stride
[
1
];
}
}
};
template
<
typename
Place
,
typename
T
>
class
SppGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
in_x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
const
framework
::
Tensor
*
out
=
context
.
Input
<
framework
::
Tensor
>
(
"Out"
);
const
framework
::
Tensor
*
out_grad
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
framework
::
Tensor
*
in_x_grad
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
&
device_ctx
=
context
.
device_context
();
math
::
SetConstant
<
Place
,
T
>
zero
;
in_x_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
zero
(
device_ctx
,
in_x_grad
,
static_cast
<
T
>
(
0
));
int
pyramid_height
=
context
.
template
Attr
<
int
>(
"pyramid_height"
);
auto
outgrad_stride
=
framework
::
stride
(
out_grad
->
dims
());
auto
out_stride
=
framework
::
stride
(
out
->
dims
());
int
input_h
=
in_x
->
dims
()[
2
];
int
input_w
=
in_x
->
dims
()[
3
];
size_t
out_offset
=
0
;
for
(
int
p
=
0
;
p
<
pyramid_height
;
++
p
)
{
int
bins
=
std
::
pow
(
2
,
p
);
int
ksize_h
=
std
::
ceil
(
input_h
/
static_cast
<
double
>
(
bins
));
int
ksize_w
=
std
::
ceil
(
input_w
/
static_cast
<
double
>
(
bins
));
int
padding_h
=
(
ksize_h
*
bins
-
input_h
+
1
)
/
2
;
int
padding_w
=
(
ksize_w
*
bins
-
input_w
+
1
)
/
2
;
std
::
vector
<
int
>
ksize
({
ksize_h
,
ksize_w
});
std
::
vector
<
int
>
strides
({
ksize_h
,
ksize_w
});
std
::
vector
<
int
>
paddings
({
padding_h
,
padding_w
});
// split outgrad and get flatten
std
::
vector
<
int64_t
>
out_shape_vec
({
in_x
->
dims
()[
0
],
in_x
->
dims
()[
1
]});
out_shape_vec
.
push_back
((
input_h
-
ksize_h
+
2
*
padding_h
)
/
ksize_h
+
1
);
out_shape_vec
.
push_back
((
input_w
-
ksize_w
+
2
*
padding_w
)
/
ksize_w
+
1
);
framework
::
DDim
out_shape
(
framework
::
make_ddim
(
out_shape_vec
));
int
out_flatten_w
=
in_x
->
dims
()[
1
]
*
bins
*
bins
;
std
::
vector
<
int64_t
>
out_flatten_shape_vec
(
{
in_x
->
dims
()[
0
],
out_flatten_w
});
framework
::
DDim
out_flatten_shape
(
framework
::
make_ddim
(
out_flatten_shape_vec
));
framework
::
Tensor
out_level
;
framework
::
Tensor
outgrad_level
;
framework
::
Tensor
out_flatten_level
;
framework
::
Tensor
outgrad_flatten_level
;
out_flatten_level
.
mutable_data
<
T
>
(
out_flatten_shape
,
context
.
GetPlace
());
outgrad_flatten_level
.
mutable_data
<
T
>
(
out_flatten_shape
,
context
.
GetPlace
());
auto
flatten_stride
=
framework
::
stride
(
out_flatten_level
.
dims
());
// memcpy
StridedMemcpy
<
T
>
(
context
.
device_context
(),
out
->
data
<
T
>
()
+
out_offset
,
out_stride
,
out_flatten_level
.
dims
(),
flatten_stride
,
out_flatten_level
.
data
<
T
>
());
StridedMemcpy
<
T
>
(
context
.
device_context
(),
out_grad
->
data
<
T
>
()
+
out_offset
,
outgrad_stride
,
outgrad_flatten_level
.
dims
(),
flatten_stride
,
outgrad_flatten_level
.
data
<
T
>
());
out_offset
+=
out_flatten_level
.
dims
()[
1
]
*
out_stride
[
1
];
// flatten backward
out_level
.
ShareDataWith
(
out_flatten_level
);
out_level
.
Resize
(
out_shape
);
outgrad_level
.
ShareDataWith
(
outgrad_flatten_level
);
outgrad_level
.
Resize
(
out_shape
);
math
::
MaxPool2dGradFunctor
<
Place
,
T
>
pool2d_backward
;
pool2d_backward
(
context
.
device_context
(),
*
in_x
,
*&
out_level
,
*&
outgrad_level
,
ksize
,
strides
,
paddings
,
in_x_grad
);
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/tests/test_spp_op.py
0 → 100644
浏览文件 @
5c057f95
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
from
test_pool2d_op
import
max_pool2D_forward_naive
class
TestSppOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"spp"
self
.
init_test_case
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
nsize
,
csize
,
hsize
,
wsize
=
input
.
shape
out_level_flatten
=
[]
for
i
in
xrange
(
self
.
pyramid_height
):
bins
=
np
.
power
(
2
,
i
)
ksize
=
[
0
,
0
]
padding
=
[
0
,
0
]
ksize
[
0
]
=
np
.
ceil
(
hsize
/
bins
.
astype
(
"double"
)).
astype
(
"int32"
)
padding
[
0
]
=
((
ksize
[
0
]
*
bins
-
hsize
+
1
)
/
2
).
astype
(
"int32"
)
ksize
[
1
]
=
np
.
ceil
(
wsize
/
bins
.
astype
(
"double"
)).
astype
(
"int32"
)
padding
[
1
]
=
((
ksize
[
1
]
*
bins
-
wsize
+
1
)
/
2
).
astype
(
"int32"
)
out_level
=
max_pool2D_forward_naive
(
input
,
ksize
,
ksize
,
padding
)
out_level_flatten
.
append
(
out_level
.
reshape
(
nsize
,
bins
*
bins
*
csize
))
if
i
==
0
:
output
=
out_level_flatten
[
i
]
else
:
output
=
np
.
concatenate
((
output
,
out_level_flatten
[
i
]),
1
)
# output = np.concatenate(out_level_flatten.tolist(), 0);
self
.
inputs
=
{
'X'
:
input
.
astype
(
'float32'
),
}
self
.
attrs
=
{
'pyramid_height'
:
self
.
pyramid_height
}
self
.
outputs
=
{
'Out'
:
output
.
astype
(
'float32'
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
def
init_test_case
(
self
):
self
.
shape
=
[
1
,
1
,
2
,
2
]
self
.
pyramid_height
=
2
if
__name__
==
'__main__'
:
unittest
.
main
()
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