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58bfaea8
编写于
1月 31, 2018
作者:
G
gaoyuan
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
update according to the code review
上级
72eccb23
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
66 addition
and
66 deletion
+66
-66
paddle/operators/box_coder_op.cc
paddle/operators/box_coder_op.cc
+31
-18
paddle/operators/box_coder_op.cu
paddle/operators/box_coder_op.cu
+2
-2
paddle/operators/box_coder_op.h
paddle/operators/box_coder_op.h
+33
-46
未找到文件。
paddle/operators/box_coder_op.cc
浏览文件 @
58bfaea8
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 201
8
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
...
...
@@ -31,23 +31,21 @@ class BoxCoderOp : public framework::OperatorWithKernel {
auto
prior_box_var_dims
=
ctx
->
GetInputDim
(
"PriorBoxVar"
);
auto
target_box_dims
=
ctx
->
GetInputDim
(
"TargetBox"
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
.
size
(),
2UL
,
"The shape of PriorBox is [N, 4]"
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
[
1
],
4UL
,
"The shape of PriorBox is [N, 4]"
);
PADDLE_ENFORCE_EQ
(
prior_box_var_dims
.
size
(),
2UL
,
"The shape of PriorBoxVar is [N, 4]"
);
PADDLE_ENFORCE_EQ
(
prior_box_var_dims
[
1
],
4UL
,
"The shape of PriorBoxVar is [N, 4]"
);
PADDLE_ENFORCE_EQ
(
target_box_dims
.
size
(),
2UL
,
"The shape of TargetBox is [M, 4]"
);
PADDLE_ENFORCE_EQ
(
target_box_dims
[
1
],
4UL
,
PADDLE_ENFORCE_EQ
(
prior_box_dims
.
size
(),
2
,
"The rank of Input of PriorBoxVar must be 2"
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
[
1
],
4
,
"The shape of PriorBox is [N, 4]"
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
,
prior_box_var_dims
);
PADDLE_ENFORCE_EQ
(
target_box_dims
.
size
(),
2
,
"The rank of Input of TargetBox must be 2"
);
PADDLE_ENFORCE_EQ
(
target_box_dims
[
1
],
4
,
"The shape of TargetBox is [M, 4]"
);
GetBoxCodeType
(
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"code_type"
));
ctx
->
SetOutputDim
(
"OutputBox"
,
framework
::
make_ddim
({
target_box_dims
[
0
],
target_box_dims
[
1
]}));
ctx
->
SetOutputDim
(
"OutputBox"
,
framework
::
make_ddim
({
target_box_dims
[
0
],
prior_box_dims
[
0
],
4
}));
ctx
->
ShareLoD
(
"TargetBox"
,
/*->*/
"OutputBox"
);
}
};
...
...
@@ -58,7 +56,7 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"PriorBox"
,
"(Tensor, default Tensor<float>) "
"Box list PriorBox is a 2-D Tensor with shape [M, 4] holds
N
boxes, "
"Box list PriorBox is a 2-D Tensor with shape [M, 4] holds
M
boxes, "
"each box is represented as [xmin, ymin, xmax, ymax], "
"[xmin, ymin] is the left top coordinate of the anchor box, "
"if the input is image feature map, they are close to the origin "
...
...
@@ -66,7 +64,7 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
"coordinate of the anchor box."
);
AddInput
(
"PriorBoxVar"
,
"(Tensor, default Tensor<float>) "
"PriorBoxVar is a 2-D Tensor with shape [M, 4] holds
N
group "
"PriorBoxVar is a 2-D Tensor with shape [M, 4] holds
M
group "
"of variance."
);
AddInput
(
"TargetBox"
,
...
...
@@ -85,14 +83,29 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
.
InEnum
({
"encode_center_size"
,
"decode_center_size"
});
AddOutput
(
"OutputBox"
,
"(
Tensor, default Tensor<float>)
"
"(
LoDTensor or Tensor)
"
"(Tensor) The output of box_coder_op, a tensor with shape [N, M, 4] "
"representing the result of N target boxes encoded/decoded with "
"M Prior boxes and variances."
);
AddComment
(
R"DOC(
Bounding Box Coder Operator.
Encode/Decode the priorbox information with the target bounding box.
Encode/Decode the target bounding box with the priorbox information.
The Encoding schema described below:
ox = (tx - px) / pw / pxv
oy = (ty - py) / ph / pyv
ow = log(abs(tw / pw)) / pwv
oh = log(abs(th / ph)) / phv
The Decoding schema described below:
ox = (pw * pxv * tx * + px) - tw / 2
oy = (ph * pyv * ty * + py) - th / 2
ow = exp(pwv * tw) * pw + tw / 2
oh = exp(phv * th) * ph + th / 2
where tx, ty, tw, th denote the target box's center coordinates, width and
height respectively. Similarly, px, py, pw, ph denote the priorbox's(anchor)
center coordinates, width and height. pxv, pyv, pwv, phv denote the variance
of the priorbox and ox, oy, ow, oh denote the encoded/decoded coordinates,
width and height.
)DOC"
);
}
};
...
...
paddle/operators/box_coder_op.cu
浏览文件 @
58bfaea8
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 201
8
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
...
...
@@ -108,7 +108,7 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
auto
*
output_box
=
context
.
Output
<
Tensor
>
(
"OutputBox"
);
if
(
target_box
->
lod
().
size
())
{
PADDLE_ENFORCE_EQ
(
target_box
->
lod
().
size
(),
1
UL
,
PADDLE_ENFORCE_EQ
(
target_box
->
lod
().
size
(),
1
,
"Only support 1 level of LoD."
);
}
auto
row
=
target_box
->
dims
()[
0
];
...
...
paddle/operators/box_coder_op.h
浏览文件 @
58bfaea8
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 201
8
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
...
...
@@ -35,67 +35,52 @@ class BoxCoderKernel : public framework::OpKernel<T> {
public:
void
EncodeCenterSize
(
const
Tensor
&
target_box
,
const
Tensor
&
prior_box
,
const
Tensor
&
prior_box_var
,
T
*
output
)
const
{
PADDLE_ENFORCE_EQ
(
target_box
.
dims
().
size
(),
2
,
"The rank of target_box must be 2."
);
PADDLE_ENFORCE_EQ
(
prior_box
.
dims
().
size
(),
2
,
"The rank of prior_box must be 2."
);
PADDLE_ENFORCE_EQ
(
prior_box_var
.
dims
().
size
(),
2
,
"The rank of prior_box_var must be 2."
);
PADDLE_ENFORCE_EQ
(
prior_box
.
dims
()[
0
],
prior_box_var
.
dims
()[
0
],
"The dims of prior_box must equal to prior_box_var."
);
int64_t
row
=
target_box
.
dims
()[
0
];
int64_t
col
=
prior_box
.
dims
()[
0
];
int64_t
len
=
prior_box
.
dims
()[
1
];
auto
*
target_box_data
=
target_box
.
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
.
data
<
T
>
();
auto
*
prior_box_var_data
=
prior_box_var
.
data
<
T
>
();
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
T
prior_box_width
=
prior_box_data
[
j
*
4
+
2
]
-
prior_box_data
[
j
*
4
];
T
prior_box_width
=
prior_box_data
[
j
*
len
+
2
]
-
prior_box_data
[
j
*
len
];
T
prior_box_height
=
prior_box_data
[
j
*
4
+
3
]
-
prior_box_data
[
j
*
4
+
1
];
prior_box_data
[
j
*
len
+
3
]
-
prior_box_data
[
j
*
len
+
1
];
T
prior_box_center_x
=
(
prior_box_data
[
j
*
4
+
2
]
+
prior_box_data
[
j
*
4
])
/
2
;
(
prior_box_data
[
j
*
len
+
2
]
+
prior_box_data
[
j
*
len
])
/
2
;
T
prior_box_center_y
=
(
prior_box_data
[
j
*
4
+
3
]
+
prior_box_data
[
j
*
4
+
1
])
/
2
;
(
prior_box_data
[
j
*
len
+
3
]
+
prior_box_data
[
j
*
len
+
1
])
/
2
;
T
target_box_center_x
=
(
target_box_data
[
i
*
4
+
2
]
+
target_box_data
[
i
*
4
])
/
2
;
(
target_box_data
[
i
*
len
+
2
]
+
target_box_data
[
i
*
len
])
/
2
;
T
target_box_center_y
=
(
target_box_data
[
i
*
4
+
3
]
+
target_box_data
[
i
*
4
+
1
])
/
2
;
(
target_box_data
[
i
*
len
+
3
]
+
target_box_data
[
i
*
len
+
1
])
/
2
;
T
target_box_width
=
target_box_data
[
i
*
4
+
2
]
-
target_box_data
[
i
*
4
];
target_box_data
[
i
*
len
+
2
]
-
target_box_data
[
i
*
len
];
T
target_box_height
=
target_box_data
[
i
*
4
+
3
]
-
target_box_data
[
i
*
4
+
1
];
target_box_data
[
i
*
len
+
3
]
-
target_box_data
[
i
*
len
+
1
];
size_t
offset
=
i
*
col
*
4
+
j
*
4
;
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
output
[
offset
]
=
(
target_box_center_x
-
prior_box_center_x
)
/
prior_box_width
/
prior_box_var_data
[
j
*
4
];
prior_box_width
/
prior_box_var_data
[
j
*
len
];
output
[
offset
+
1
]
=
(
target_box_center_y
-
prior_box_center_y
)
/
prior_box_height
/
prior_box_var_data
[
j
*
4
+
1
];
prior_box_height
/
prior_box_var_data
[
j
*
len
+
1
];
output
[
offset
+
2
]
=
std
::
log
(
std
::
fabs
(
target_box_width
/
prior_box_width
))
/
prior_box_var_data
[
j
*
4
+
2
];
prior_box_var_data
[
j
*
len
+
2
];
output
[
offset
+
3
]
=
std
::
log
(
std
::
fabs
(
target_box_height
/
prior_box_height
))
/
prior_box_var_data
[
j
*
4
+
3
];
prior_box_var_data
[
j
*
len
+
3
];
}
}
}
void
DecodeCenterSize
(
const
Tensor
&
target_box
,
const
Tensor
&
prior_box
,
const
Tensor
&
prior_box_var
,
T
*
output
)
const
{
PADDLE_ENFORCE_EQ
(
target_box
.
dims
().
size
(),
2
,
"The rank of target_box must be 2."
);
PADDLE_ENFORCE_EQ
(
prior_box
.
dims
().
size
(),
2
,
"The rank of prior_box must be 2."
);
PADDLE_ENFORCE_EQ
(
prior_box_var
.
dims
().
size
(),
2
,
"The rank of prior_box_var must be 2."
);
PADDLE_ENFORCE_EQ
(
prior_box
.
dims
()[
0
],
prior_box_var
.
dims
()[
0
],
"The dims of prior_box must equal to prior_box_var."
);
int64_t
row
=
target_box
.
dims
()[
0
];
int64_t
col
=
prior_box
.
dims
()[
0
];
int64_t
len
=
prior_box
.
dims
()[
1
];
auto
*
target_box_data
=
target_box
.
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
.
data
<
T
>
();
...
...
@@ -103,29 +88,30 @@ class BoxCoderKernel : public framework::OpKernel<T> {
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
T
prior_box_width
=
prior_box_data
[
j
*
4
+
2
]
-
prior_box_data
[
j
*
4
];
T
prior_box_width
=
prior_box_data
[
j
*
len
+
2
]
-
prior_box_data
[
j
*
len
];
T
prior_box_height
=
prior_box_data
[
j
*
4
+
3
]
-
prior_box_data
[
j
*
4
+
1
];
prior_box_data
[
j
*
len
+
3
]
-
prior_box_data
[
j
*
len
+
1
];
T
prior_box_center_x
=
(
prior_box_data
[
j
*
4
+
2
]
+
prior_box_data
[
j
*
4
])
/
2
;
(
prior_box_data
[
j
*
len
+
2
]
+
prior_box_data
[
j
*
len
])
/
2
;
T
prior_box_center_y
=
(
prior_box_data
[
j
*
4
+
3
]
+
prior_box_data
[
j
*
4
+
1
])
/
2
;
(
prior_box_data
[
j
*
len
+
3
]
+
prior_box_data
[
j
*
len
+
1
])
/
2
;
T
target_box_center_x
=
prior_box_var_data
[
j
*
4
]
*
target_box_data
[
i
*
4
]
*
prior_box_width
+
T
target_box_center_x
=
prior_box_var_data
[
j
*
len
]
*
target_box_data
[
i
*
len
]
*
prior_box_width
+
prior_box_center_x
;
T
target_box_center_y
=
prior_box_var_data
[
j
*
4
+
1
]
*
target_box_data
[
i
*
4
+
1
]
*
T
target_box_center_y
=
prior_box_var_data
[
j
*
len
+
1
]
*
target_box_data
[
i
*
len
+
1
]
*
prior_box_height
+
prior_box_center_y
;
T
target_box_width
=
std
::
exp
(
prior_box_var_data
[
j
*
4
+
2
]
*
target_box_data
[
i
*
4
+
2
])
*
T
target_box_width
=
std
::
exp
(
prior_box_var_data
[
j
*
len
+
2
]
*
target_box_data
[
i
*
len
+
2
])
*
prior_box_width
;
T
target_box_height
=
std
::
exp
(
prior_box_var_data
[
j
*
4
+
3
]
*
target_box_data
[
i
*
4
+
3
])
*
T
target_box_height
=
std
::
exp
(
prior_box_var_data
[
j
*
len
+
3
]
*
target_box_data
[
i
*
len
+
3
])
*
prior_box_height
;
size_t
offset
=
i
*
col
*
4
+
j
*
4
;
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
output
[
offset
]
=
target_box_center_x
-
target_box_width
/
2
;
output
[
offset
+
1
]
=
target_box_center_y
-
target_box_height
/
2
;
output
[
offset
+
2
]
=
target_box_center_x
+
target_box_width
/
2
;
...
...
@@ -146,8 +132,9 @@ class BoxCoderKernel : public framework::OpKernel<T> {
}
auto
row
=
target_box
->
dims
()[
0
];
auto
col
=
prior_box
->
dims
()[
0
];
auto
len
=
prior_box
->
dims
()[
1
];
output_box
->
mutable_data
<
T
>
({
row
,
col
,
4
},
context
.
GetPlace
());
output_box
->
mutable_data
<
T
>
({
row
,
col
,
len
},
context
.
GetPlace
());
auto
code_type
=
GetBoxCodeType
(
context
.
Attr
<
std
::
string
>
(
"code_type"
));
T
*
output
=
output_box
->
data
<
T
>
();
...
...
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