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体验新版 GitCode,发现更多精彩内容 >>
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83578cfa
编写于
9月 29, 2021
作者:
Z
zhulei
提交者:
GitHub
9月 29, 2021
浏览文件
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差异文件
[npu] add box coder (#36171)
* [npu] add box coder * [npu] add box coder
上级
2b8fd704
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
631 addition
and
1 deletion
+631
-1
paddle/fluid/operators/detection/CMakeLists.txt
paddle/fluid/operators/detection/CMakeLists.txt
+6
-1
paddle/fluid/operators/detection/box_coder_op_npu.cc
paddle/fluid/operators/detection/box_coder_op_npu.cc
+373
-0
python/paddle/fluid/tests/unittests/npu/test_box_coder_op_npu.py
...paddle/fluid/tests/unittests/npu/test_box_coder_op_npu.py
+252
-0
未找到文件。
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @
83578cfa
...
...
@@ -15,8 +15,13 @@ function(detection_library TARGET_NAME)
PARENT_SCOPE
)
endfunction
()
if
(
WITH_ASCEND_CL
)
detection_library
(
box_coder_op SRCS box_coder_op.cc box_coder_op.cu box_coder_op_npu.cc
)
else
()
detection_library
(
box_coder_op SRCS box_coder_op.cc box_coder_op.cu
)
endif
()
detection_library
(
bipartite_match_op SRCS bipartite_match_op.cc
)
detection_library
(
box_coder_op SRCS box_coder_op.cc box_coder_op.cu
)
detection_library
(
mine_hard_examples_op SRCS mine_hard_examples_op.cc
)
detection_library
(
prior_box_op SRCS prior_box_op.cc prior_box_op.cu
)
detection_library
(
density_prior_box_op SRCS density_prior_box_op.cc density_prior_box_op.cu
)
...
...
paddle/fluid/operators/detection/box_coder_op_npu.cc
0 → 100644
浏览文件 @
83578cfa
/* Copyright (c) 2021 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. */
#include "paddle/fluid/operators/detection/box_coder_op.h"
#include "paddle/fluid/operators/npu_op_runner.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
struct
BoxCoderFunction
{
public:
explicit
BoxCoderFunction
(
const
framework
::
ExecutionContext
&
ctx
)
:
ctx
(
ctx
)
{
place
=
ctx
.
GetPlace
();
stream
=
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>()
.
stream
();
}
Tensor
Adds
(
const
Tensor
&
x
,
float
scalar
)
{
Tensor
y
;
y
.
mutable_data
<
T
>
(
x
.
dims
(),
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Adds"
,
{
x
},
{
y
},
{{
"value"
,
scalar
}});
runner
.
Run
(
stream
);
return
y
;
}
Tensor
Muls
(
const
Tensor
&
x
,
float
scalar
)
{
Tensor
y
;
y
.
mutable_data
<
T
>
(
x
.
dims
(),
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Muls"
,
{
x
},
{
y
},
{{
"value"
,
scalar
}});
runner
.
Run
(
stream
);
return
y
;
}
Tensor
Mul
(
const
Tensor
&
x
,
const
Tensor
&
y
)
{
Tensor
z
;
z
.
mutable_data
<
T
>
(
x
.
dims
(),
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Mul"
,
{
x
,
y
},
{
z
},
{});
runner
.
Run
(
stream
);
return
z
;
}
Tensor
SubWithBroadCast
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
framework
::
DDim
&
shape
)
{
Tensor
z
;
z
.
mutable_data
<
T
>
(
shape
,
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Sub"
,
{
x
,
y
},
{
z
},
{});
runner
.
Run
(
stream
);
return
z
;
}
void
DivWithBroadCastVoid
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
framework
::
DDim
&
shape
,
Tensor
*
z
)
{
z
->
mutable_data
<
T
>
(
shape
,
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Div"
,
{
x
,
y
},
{
*
z
},
{});
runner
.
Run
(
stream
);
}
Tensor
DivWithBroadCast
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
framework
::
DDim
&
shape
)
{
Tensor
z
;
DivWithBroadCastVoid
(
x
,
y
,
shape
,
&
z
);
return
z
;
}
void
MulWithBroadCastVoid
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
framework
::
DDim
&
shape
,
Tensor
*
z
)
{
z
->
mutable_data
<
T
>
(
shape
,
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Mul"
,
{
x
,
y
},
{
*
z
},
{});
runner
.
Run
(
stream
);
}
Tensor
MulWithBroadCast
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
framework
::
DDim
&
shape
)
{
Tensor
z
;
MulWithBroadCastVoid
(
x
,
y
,
shape
,
&
z
);
return
z
;
}
void
AddWithBroadCastVoid
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
framework
::
DDim
&
shape
,
Tensor
*
z
)
{
z
->
mutable_data
<
T
>
(
shape
,
place
);
const
auto
&
runner
=
NpuOpRunner
(
"AddV2"
,
{
x
,
y
},
{
*
z
},
{});
runner
.
Run
(
stream
);
}
Tensor
AddWithBroadCast
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
framework
::
DDim
&
shape
)
{
Tensor
z
;
AddWithBroadCastVoid
(
x
,
y
,
shape
,
&
z
);
return
z
;
}
Tensor
Abs
(
const
Tensor
&
x
)
{
Tensor
y
;
y
.
mutable_data
<
T
>
(
x
.
dims
(),
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Abs"
,
{
x
},
{
y
},
{});
runner
.
Run
(
stream
);
return
y
;
}
Tensor
Log
(
const
Tensor
&
x
)
{
Tensor
t_x_m1
=
Adds
(
x
,
-
1
);
Tensor
y
;
y
.
mutable_data
<
T
>
(
x
.
dims
(),
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Log1p"
,
{
t_x_m1
},
{
y
},
{});
runner
.
Run
(
stream
);
return
y
;
}
Tensor
Exp
(
const
Tensor
&
x
)
{
Tensor
y
;
y
.
mutable_data
<
T
>
(
x
.
dims
(),
place
);
const
auto
&
runner
=
NpuOpRunner
(
"Exp"
,
{
x
},
{
y
},
{});
runner
.
Run
(
stream
);
return
y
;
}
Tensor
Dot
(
const
Tensor
&
x
,
const
Tensor
&
y
)
{
auto
dim_x
=
x
.
dims
();
auto
dim_y
=
y
.
dims
();
PADDLE_ENFORCE_EQ
(
dim_x
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"x should be a 2-dim tensor, but got %d-dim."
,
dim_x
.
size
()));
PADDLE_ENFORCE_EQ
(
dim_y
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"y should be a 2-dim tensor, but got %d-dim."
,
dim_y
.
size
()));
PADDLE_ENFORCE_EQ
(
dim_x
[
1
],
dim_y
[
0
],
platform
::
errors
::
InvalidArgument
(
"Expect dim_x[1] == dim_y[0], but "
"got dim_x[1] = %d, dim_y[0] = %d."
,
dim_x
[
1
],
dim_y
[
0
]));
Tensor
z
;
z
.
mutable_data
<
T
>
({
dim_x
[
0
],
dim_y
[
1
]},
place
);
const
auto
&
runner
=
NpuOpRunner
(
"MatMul"
,
{
x
,
y
},
{
z
},
{{
"transpose_x1"
,
false
},
{
"transpose_x2"
,
false
}});
runner
.
Run
(
stream
);
return
z
;
}
void
ConcatVoid
(
const
std
::
vector
<
Tensor
>&
inputs
,
const
framework
::
DDim
&
shape_out
,
int
axis
,
Tensor
*
output
)
{
output
->
mutable_data
<
T
>
(
shape_out
,
place
);
std
::
vector
<
std
::
string
>
names
;
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
names
.
push_back
(
"x"
+
std
::
to_string
(
i
));
}
NpuOpRunner
runner
{
"ConcatD"
,
{
inputs
},
{
*
output
},
{{
"concat_dim"
,
axis
},
{
"N"
,
static_cast
<
int
>
(
inputs
.
size
())}}};
runner
.
AddInputNames
(
names
);
runner
.
Run
(
stream
);
}
Tensor
Concat
(
const
std
::
vector
<
Tensor
>&
inputs
,
const
framework
::
DDim
&
shape_out
,
int
axis
)
{
Tensor
output
;
ConcatVoid
(
inputs
,
shape_out
,
axis
,
&
output
);
return
output
;
}
Tensor
Slice
(
const
Tensor
&
x
,
const
std
::
vector
<
int
>&
offsets
,
const
std
::
vector
<
int
>&
size
,
const
framework
::
DDim
&
shape
)
{
Tensor
y
;
y
.
mutable_data
<
T
>
(
shape
,
place
);
const
auto
&
runner
=
NpuOpRunner
(
"SliceD"
,
{
x
},
{
y
},
{{
"offsets"
,
offsets
},
{
"size"
,
size
}});
runner
.
Run
(
stream
);
return
y
;
}
private:
platform
::
Place
place
;
aclrtStream
stream
;
const
framework
::
ExecutionContext
&
ctx
;
};
template
<
typename
T
>
void
Vector2Tensor
(
const
framework
::
ExecutionContext
&
ctx
,
const
std
::
vector
<
T
>&
vec
,
const
framework
::
DDim
&
ddim
,
Tensor
*
tsr
)
{
framework
::
TensorFromVector
<
T
>
(
vec
,
ctx
.
device_context
(),
tsr
);
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>().
Wait
();
tsr
->
Resize
(
ddim
);
}
template
<
typename
T
>
void
BoxCoderEnc
(
const
framework
::
ExecutionContext
&
ctx
,
const
Tensor
*
tb
,
const
Tensor
*
pb
,
const
Tensor
*
pbv
,
const
bool
norm
,
const
std
::
vector
<
float
>&
variance
,
Tensor
*
out
)
{
auto
M
=
pb
->
dims
()[
0
];
auto
N
=
tb
->
dims
()[
0
];
auto
shape_0
=
framework
::
make_ddim
({
4
,
2
});
Tensor
m_diff
;
Tensor
m_aver
;
std
::
vector
<
T
>
vec_diff
=
{
static_cast
<
T
>
(
-
1
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
-
1
),
static_cast
<
T
>
(
1
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
1
)};
std
::
vector
<
T
>
vec_aver
=
{
static_cast
<
T
>
(
0.5
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0.5
),
static_cast
<
T
>
(
0.5
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0.5
)};
Vector2Tensor
<
T
>
(
ctx
,
vec_diff
,
shape_0
,
&
m_diff
);
Vector2Tensor
<
T
>
(
ctx
,
vec_aver
,
shape_0
,
&
m_aver
);
BoxCoderFunction
<
T
>
F
(
ctx
);
Tensor
pb_xy
=
F
.
Adds
(
F
.
Dot
(
*
pb
,
m_aver
),
(
norm
?
0
:
0.5
));
Tensor
pb_wh
=
F
.
Adds
(
F
.
Dot
(
*
pb
,
m_diff
),
(
norm
?
0
:
1
));
Tensor
tb_xy
=
F
.
Dot
(
*
tb
,
m_aver
);
Tensor
tb_wh
=
F
.
Adds
(
F
.
Dot
(
*
tb
,
m_diff
),
(
norm
?
0
:
1
));
pb_xy
.
Resize
({
1
,
M
,
2
});
pb_wh
.
Resize
({
1
,
M
,
2
});
tb_xy
.
Resize
({
N
,
1
,
2
});
tb_wh
.
Resize
({
N
,
1
,
2
});
auto
shape_half
=
framework
::
make_ddim
({
N
,
M
,
2
});
auto
shape_full
=
framework
::
make_ddim
({
N
,
M
,
4
});
Tensor
out_xy_0
=
F
.
DivWithBroadCast
(
F
.
SubWithBroadCast
(
tb_xy
,
pb_xy
,
shape_half
),
pb_wh
,
shape_half
);
Tensor
out_wh_0
=
F
.
Log
(
F
.
Abs
(
F
.
DivWithBroadCast
(
tb_wh
,
pb_wh
,
shape_half
)));
Tensor
out_0
=
F
.
Concat
({
out_xy_0
,
out_wh_0
},
shape_full
,
2
);
if
(
pbv
)
{
F
.
DivWithBroadCastVoid
(
out_0
,
*
pbv
,
shape_full
,
out
);
}
else
{
Tensor
t_var
;
std
::
vector
<
T
>
vec_var
(
4
);
for
(
auto
i
=
0
;
i
<
4
;
i
++
)
{
vec_var
[
i
]
=
static_cast
<
T
>
(
variance
[
i
]);
}
Vector2Tensor
(
ctx
,
vec_var
,
framework
::
make_ddim
({
1
,
1
,
4
}),
&
t_var
);
F
.
DivWithBroadCastVoid
(
out_0
,
t_var
,
shape_full
,
out
);
}
}
template
<
typename
T
>
void
BoxCoderDec
(
const
framework
::
ExecutionContext
&
ctx
,
const
Tensor
*
tb
,
const
Tensor
*
pb
,
const
Tensor
*
pbv
,
const
bool
norm
,
const
std
::
vector
<
float
>&
variance
,
int
axis
,
Tensor
*
out
)
{
auto
shape_0
=
framework
::
make_ddim
({
4
,
2
});
Tensor
m_diff
;
Tensor
m_aver
;
std
::
vector
<
T
>
vec_diff
=
{
static_cast
<
T
>
(
-
1
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
-
1
),
static_cast
<
T
>
(
1
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
1
)};
std
::
vector
<
T
>
vec_aver
=
{
static_cast
<
T
>
(
0.5
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0.5
),
static_cast
<
T
>
(
0.5
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0
),
static_cast
<
T
>
(
0.5
)};
Vector2Tensor
<
T
>
(
ctx
,
vec_diff
,
shape_0
,
&
m_diff
);
Vector2Tensor
<
T
>
(
ctx
,
vec_aver
,
shape_0
,
&
m_aver
);
BoxCoderFunction
<
T
>
F
(
ctx
);
Tensor
pb_xy
=
F
.
Adds
(
F
.
Dot
(
*
pb
,
m_aver
),
(
norm
?
0
:
0.5
));
Tensor
pb_wh
=
F
.
Adds
(
F
.
Dot
(
*
pb
,
m_diff
),
(
norm
?
0
:
1
));
auto
pb_resize_shape
=
axis
==
0
?
framework
::
make_ddim
({
1
,
pb
->
dims
()[
0
],
2
})
:
framework
::
make_ddim
({
pb
->
dims
()[
0
],
1
,
2
});
pb_xy
.
Resize
(
pb_resize_shape
);
pb_wh
.
Resize
(
pb_resize_shape
);
auto
tbox_slice_shape
=
framework
::
make_ddim
({
tb
->
dims
()[
0
],
tb
->
dims
()[
1
],
2
});
std
::
vector
<
int
>
tbox_slice_size
=
{
static_cast
<
int
>
(
tb
->
dims
()[
0
]),
static_cast
<
int
>
(
tb
->
dims
()[
1
]),
2
};
Tensor
tbox01
=
F
.
Slice
(
*
tb
,
{
0
,
0
,
0
},
tbox_slice_size
,
tbox_slice_shape
);
Tensor
tbox23
=
F
.
Slice
(
*
tb
,
{
0
,
0
,
2
},
tbox_slice_size
,
tbox_slice_shape
);
Tensor
tb_xy
;
Tensor
tb_wh
;
if
(
pbv
)
{
auto
pbvt_slice_shape
=
framework
::
make_ddim
({
pbv
->
dims
()[
0
],
2
});
auto
pbvt_resize_shape
=
axis
==
0
?
framework
::
make_ddim
({
1
,
pbv
->
dims
()[
0
],
2
})
:
framework
::
make_ddim
({
pbv
->
dims
()[
0
],
1
,
2
});
std
::
vector
<
int
>
pbvt_slice_size
=
{
static_cast
<
int
>
(
pbv
->
dims
()[
0
]),
2
};
Tensor
pbv_t01
=
F
.
Slice
(
*
pbv
,
{
0
,
0
},
pbvt_slice_size
,
pbvt_slice_shape
);
Tensor
pbv_t23
=
F
.
Slice
(
*
pbv
,
{
0
,
2
},
pbvt_slice_size
,
pbvt_slice_shape
);
pbv_t01
.
Resize
(
pbvt_resize_shape
);
pbv_t23
.
Resize
(
pbvt_resize_shape
);
F
.
AddWithBroadCastVoid
(
F
.
MulWithBroadCast
(
tbox01
,
F
.
Mul
(
pb_wh
,
pbv_t01
),
tbox_slice_shape
),
pb_xy
,
tbox_slice_shape
,
&
tb_xy
);
F
.
MulWithBroadCastVoid
(
F
.
Exp
(
F
.
MulWithBroadCast
(
pbv_t23
,
tbox23
,
tbox_slice_shape
)),
pb_wh
,
tbox_slice_shape
,
&
tb_wh
);
}
else
if
(
variance
.
empty
())
{
F
.
AddWithBroadCastVoid
(
F
.
MulWithBroadCast
(
tbox01
,
pb_wh
,
tbox_slice_shape
),
pb_xy
,
tbox_slice_shape
,
&
tb_xy
);
F
.
MulWithBroadCastVoid
(
F
.
Exp
(
tbox23
),
pb_wh
,
tbox_slice_shape
,
&
tb_wh
);
}
else
{
Tensor
t_var01
,
t_var23
;
auto
t_var_shape
=
framework
::
make_ddim
({
1
,
1
,
2
});
std
::
vector
<
T
>
vec_var01
=
{
static_cast
<
T
>
(
variance
[
0
]),
static_cast
<
T
>
(
variance
[
1
])};
std
::
vector
<
T
>
vec_var23
=
{
static_cast
<
T
>
(
variance
[
2
]),
static_cast
<
T
>
(
variance
[
3
])};
Vector2Tensor
(
ctx
,
vec_var01
,
t_var_shape
,
&
t_var01
);
Vector2Tensor
(
ctx
,
vec_var23
,
t_var_shape
,
&
t_var23
);
F
.
AddWithBroadCastVoid
(
F
.
MulWithBroadCast
(
tbox01
,
F
.
MulWithBroadCast
(
pb_wh
,
t_var01
,
pb_resize_shape
),
tbox_slice_shape
),
pb_xy
,
tbox_slice_shape
,
&
tb_xy
);
F
.
MulWithBroadCastVoid
(
F
.
Exp
(
F
.
MulWithBroadCast
(
t_var23
,
tbox23
,
tbox_slice_shape
)),
pb_wh
,
tbox_slice_shape
,
&
tb_wh
);
}
Tensor
obox01
=
F
.
AddWithBroadCast
(
tb_xy
,
F
.
Muls
(
tb_wh
,
-
0.5
),
tbox_slice_shape
);
Tensor
obox23
=
F
.
Adds
(
F
.
AddWithBroadCast
(
tb_xy
,
F
.
Muls
(
tb_wh
,
0.5
),
tbox_slice_shape
),
(
norm
?
0
:
-
1
));
F
.
ConcatVoid
({
obox01
,
obox23
},
out
->
dims
(),
2
,
out
);
}
template
<
typename
T
>
class
BoxCoderNPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
prior_box
=
ctx
.
Input
<
Tensor
>
(
"PriorBox"
);
auto
*
prior_box_var
=
ctx
.
Input
<
Tensor
>
(
"PriorBoxVar"
);
auto
*
target_box
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"TargetBox"
);
auto
*
output_box
=
ctx
.
Output
<
Tensor
>
(
"OutputBox"
);
std
::
vector
<
float
>
variance
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"variance"
);
const
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
if
(
prior_box_var
)
{
PADDLE_ENFORCE_EQ
(
variance
.
empty
(),
true
,
platform
::
errors
::
InvalidArgument
(
"Input 'PriorBoxVar' and attribute 'variance'"
" of BoxCoder operator should not be used at the "
"same time."
));
}
if
(
!
(
variance
.
empty
()))
{
PADDLE_ENFORCE_EQ
(
static_cast
<
int
>
(
variance
.
size
()),
4
,
platform
::
errors
::
InvalidArgument
(
"Size of attribute 'variance' in BoxCoder operator"
" should be 4. But received size is %d"
,
variance
.
size
()));
}
if
(
target_box
->
lod
().
size
())
{
PADDLE_ENFORCE_EQ
(
target_box
->
lod
().
size
(),
1
,
platform
::
errors
::
InvalidArgument
(
"Input 'TargetBox' of BoxCoder operator only"
" supports LoD with one level."
));
}
auto
code_type
=
GetBoxCodeType
(
ctx
.
Attr
<
std
::
string
>
(
"code_type"
));
bool
normalized
=
ctx
.
Attr
<
bool
>
(
"box_normalized"
);
if
(
code_type
==
BoxCodeType
::
kEncodeCenterSize
)
{
BoxCoderEnc
<
T
>
(
ctx
,
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output_box
);
}
else
{
BoxCoderDec
<
T
>
(
ctx
,
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
axis
,
output_box
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_NPU_KERNEL
(
box_coder
,
ops
::
BoxCoderNPUKernel
<
float
>
,
ops
::
BoxCoderNPUKernel
<
plat
::
float16
>
);
python/paddle/fluid/tests/unittests/npu/test_box_coder_op_npu.py
0 → 100644
浏览文件 @
83578cfa
# Copyright (c) 2018 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
import
math
import
paddle
from
op_test
import
OpTest
paddle
.
enable_static
()
np
.
random
.
seed
(
2021
)
def
box_decoder
(
t_box
,
p_box
,
pb_v
,
output_box
,
norm
,
axis
=
0
):
pb_w
=
p_box
[:,
2
]
-
p_box
[:,
0
]
+
(
norm
==
False
)
pb_h
=
p_box
[:,
3
]
-
p_box
[:,
1
]
+
(
norm
==
False
)
pb_x
=
pb_w
*
0.5
+
p_box
[:,
0
]
pb_y
=
pb_h
*
0.5
+
p_box
[:,
1
]
shape
=
(
1
,
p_box
.
shape
[
0
])
if
axis
==
0
else
(
p_box
.
shape
[
0
],
1
)
pb_w
=
pb_w
.
reshape
(
shape
)
pb_h
=
pb_h
.
reshape
(
shape
)
pb_x
=
pb_x
.
reshape
(
shape
)
pb_y
=
pb_y
.
reshape
(
shape
)
if
pb_v
.
ndim
==
2
:
var_shape
=
(
1
,
pb_v
.
shape
[
0
],
pb_v
.
shape
[
1
])
if
axis
==
0
else
(
pb_v
.
shape
[
0
],
1
,
pb_v
.
shape
[
1
])
pb_v
=
pb_v
.
reshape
(
var_shape
)
if
pb_v
.
ndim
==
1
:
tb_x
=
pb_v
[
0
]
*
t_box
[:,
:,
0
]
*
pb_w
+
pb_x
tb_y
=
pb_v
[
1
]
*
t_box
[:,
:,
1
]
*
pb_h
+
pb_y
tb_w
=
np
.
exp
(
pb_v
[
2
]
*
t_box
[:,
:,
2
])
*
pb_w
tb_h
=
np
.
exp
(
pb_v
[
3
]
*
t_box
[:,
:,
3
])
*
pb_h
else
:
tb_x
=
pb_v
[:,
:,
0
]
*
t_box
[:,
:,
0
]
*
pb_w
+
pb_x
tb_y
=
pb_v
[:,
:,
1
]
*
t_box
[:,
:,
1
]
*
pb_h
+
pb_y
tb_w
=
np
.
exp
(
pb_v
[:,
:,
2
]
*
t_box
[:,
:,
2
])
*
pb_w
tb_h
=
np
.
exp
(
pb_v
[:,
:,
3
]
*
t_box
[:,
:,
3
])
*
pb_h
output_box
[:,
:,
0
]
=
tb_x
-
tb_w
/
2
output_box
[:,
:,
1
]
=
tb_y
-
tb_h
/
2
output_box
[:,
:,
2
]
=
tb_x
+
tb_w
/
2
-
(
not
norm
)
output_box
[:,
:,
3
]
=
tb_y
+
tb_h
/
2
-
(
not
norm
)
def
box_encoder
(
t_box
,
p_box
,
pb_v
,
output_box
,
norm
):
pb_w
=
p_box
[:,
2
]
-
p_box
[:,
0
]
+
(
norm
==
False
)
pb_h
=
p_box
[:,
3
]
-
p_box
[:,
1
]
+
(
norm
==
False
)
pb_x
=
pb_w
*
0.5
+
p_box
[:,
0
]
pb_y
=
pb_h
*
0.5
+
p_box
[:,
1
]
shape
=
(
1
,
p_box
.
shape
[
0
])
pb_w
=
pb_w
.
reshape
(
shape
)
pb_h
=
pb_h
.
reshape
(
shape
)
pb_x
=
pb_x
.
reshape
(
shape
)
pb_y
=
pb_y
.
reshape
(
shape
)
if
pb_v
.
ndim
==
2
:
pb_v
=
pb_v
.
reshape
(
1
,
pb_v
.
shape
[
0
],
pb_v
.
shape
[
1
])
tb_x
=
((
t_box
[:,
2
]
+
t_box
[:,
0
])
/
2
).
reshape
(
t_box
.
shape
[
0
],
1
)
tb_y
=
((
t_box
[:,
3
]
+
t_box
[:,
1
])
/
2
).
reshape
(
t_box
.
shape
[
0
],
1
)
tb_w
=
(
t_box
[:,
2
]
-
t_box
[:,
0
]).
reshape
(
t_box
.
shape
[
0
],
1
)
+
(
not
norm
)
tb_h
=
(
t_box
[:,
3
]
-
t_box
[:,
1
]).
reshape
(
t_box
.
shape
[
0
],
1
)
+
(
not
norm
)
if
pb_v
.
ndim
==
1
:
output_box
[:,
:,
0
]
=
(
tb_x
-
pb_x
)
/
pb_w
/
pb_v
[
0
]
output_box
[:,
:,
1
]
=
(
tb_y
-
pb_y
)
/
pb_h
/
pb_v
[
1
]
output_box
[:,
:,
2
]
=
np
.
log
(
np
.
fabs
(
tb_w
/
pb_w
))
/
pb_v
[
2
]
output_box
[:,
:,
3
]
=
np
.
log
(
np
.
fabs
(
tb_h
/
pb_h
))
/
pb_v
[
3
]
else
:
output_box
[:,
:,
0
]
=
(
tb_x
-
pb_x
)
/
pb_w
/
pb_v
[:,
:,
0
]
output_box
[:,
:,
1
]
=
(
tb_y
-
pb_y
)
/
pb_h
/
pb_v
[:,
:,
1
]
output_box
[:,
:,
2
]
=
np
.
log
(
np
.
fabs
(
tb_w
/
pb_w
))
/
pb_v
[:,
:,
2
]
output_box
[:,
:,
3
]
=
np
.
log
(
np
.
fabs
(
tb_h
/
pb_h
))
/
pb_v
[:,
:,
3
]
def
batch_box_coder
(
p_box
,
pb_v
,
t_box
,
lod
,
code_type
,
norm
,
axis
=
0
):
n
=
t_box
.
shape
[
0
]
m
=
p_box
.
shape
[
0
]
if
code_type
==
"decode_center_size"
:
m
=
t_box
.
shape
[
1
]
output_box
=
np
.
zeros
((
n
,
m
,
4
),
dtype
=
np
.
float32
)
cur_offset
=
0
for
i
in
range
(
len
(
lod
)):
if
(
code_type
==
"encode_center_size"
):
box_encoder
(
t_box
[
cur_offset
:(
cur_offset
+
lod
[
i
]),
:],
p_box
,
pb_v
,
output_box
[
cur_offset
:(
cur_offset
+
lod
[
i
]),
:,
:],
norm
)
elif
(
code_type
==
"decode_center_size"
):
box_decoder
(
t_box
,
p_box
,
pb_v
,
output_box
,
norm
,
axis
)
cur_offset
+=
lod
[
i
]
return
output_box
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_npu
(),
"core is not compiled with NPU"
)
class
TestBoxCoderOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"box_coder"
self
.
set_npu
()
self
.
init_dtype
()
self
.
set_init_config
()
self
.
set_inputs
()
self
.
set_attrs
()
self
.
set_outputs
()
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
self
.
place
=
paddle
.
NPUPlace
(
0
)
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
set_init_config
(
self
):
self
.
M
=
81
self
.
N
=
20
self
.
code_type
=
'decode_center_size'
self
.
box_normalized
=
False
self
.
lod
=
[[
1
,
1
,
1
,
1
,
1
]]
self
.
axis
=
0
self
.
use_variance
=
False
self
.
without_prior_box_var
=
False
self
.
atol
=
1e-5
def
set_inputs
(
self
):
self
.
inputs
=
{}
assert
(
self
.
code_type
in
[
'decode_center_size'
,
'encode_center_size'
])
assert
(
self
.
axis
in
[
0
,
1
])
if
self
.
code_type
==
'decode_center_size'
:
assert
(
not
self
.
use_variance
or
not
self
.
without_prior_box_var
)
self
.
prior_box
=
np
.
random
.
random
((
self
.
M
,
4
)).
astype
(
self
.
dtype
)
if
self
.
use_variance
:
self
.
prior_box_var
=
np
.
random
.
random
(
4
).
astype
(
self
.
dtype
)
else
:
if
self
.
without_prior_box_var
:
self
.
prior_box_var
=
np
.
ones
((
self
.
M
,
4
)).
astype
(
self
.
dtype
)
else
:
self
.
prior_box_var
=
np
.
random
.
random
(
(
self
.
M
,
4
)).
astype
(
self
.
dtype
)
if
self
.
axis
==
0
:
self
.
target_box
=
np
.
random
.
random
(
(
self
.
N
,
self
.
M
,
4
)).
astype
(
self
.
dtype
)
else
:
self
.
target_box
=
np
.
random
.
random
(
(
self
.
M
,
self
.
N
,
4
)).
astype
(
self
.
dtype
)
self
.
inputs
[
'PriorBox'
]
=
self
.
prior_box
self
.
inputs
[
'TargetBox'
]
=
self
.
target_box
if
(
not
self
.
use_variance
and
not
self
.
without_prior_box_var
):
self
.
inputs
[
'PriorBoxVar'
]
=
self
.
prior_box_var
else
:
#encode_center_size
self
.
prior_box
=
np
.
random
.
random
((
self
.
M
,
4
)).
astype
(
self
.
dtype
)
if
self
.
use_variance
:
self
.
prior_box_var
=
np
.
random
.
random
(
4
).
astype
(
self
.
dtype
)
else
:
self
.
prior_box_var
=
np
.
random
.
random
(
(
self
.
M
,
4
)).
astype
(
self
.
dtype
)
self
.
target_box
=
np
.
random
.
random
((
self
.
N
,
4
)).
astype
(
self
.
dtype
)
self
.
inputs
[
'PriorBox'
]
=
self
.
prior_box
#self.inputs['PriorBoxVar'] = self.prior_box_var
self
.
inputs
[
'TargetBox'
]
=
(
self
.
target_box
,
self
.
lod
)
if
(
not
self
.
use_variance
):
self
.
inputs
[
'PriorBoxVar'
]
=
self
.
prior_box_var
def
set_attrs
(
self
):
self
.
attrs
=
{
'code_type'
:
self
.
code_type
,
'box_normalized'
:
self
.
box_normalized
}
if
self
.
use_variance
:
self
.
attrs
[
'variance'
]
=
self
.
prior_box_var
.
astype
(
np
.
float
).
flatten
()
if
self
.
axis
!=
0
:
self
.
attrs
[
'axis'
]
=
self
.
axis
def
set_outputs
(
self
):
output_box
=
batch_box_coder
(
self
.
prior_box
,
self
.
prior_box_var
,
self
.
target_box
,
self
.
lod
[
0
],
self
.
code_type
,
self
.
box_normalized
,
self
.
axis
)
self
.
outputs
=
{
'OutputBox'
:
output_box
.
astype
(
self
.
dtype
)}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
self
.
atol
)
class
TestBoxCoderOpWithoutBoxVar
(
TestBoxCoderOp
):
def
set_init_config
(
self
):
super
(
TestBoxCoderOpWithoutBoxVar
,
self
).
set_init_config
()
self
.
without_prior_box_var
=
True
self
.
lod
=
[[
0
,
1
,
2
,
3
,
4
,
5
]]
class
TestBoxCoderOpWithLoD
(
TestBoxCoderOp
):
def
set_init_config
(
self
):
super
(
TestBoxCoderOpWithLoD
,
self
).
set_init_config
()
self
.
M
=
20
self
.
N
=
50
self
.
lod
=
[[
10
,
20
,
20
]]
self
.
code_type
=
'encode_center_size'
self
.
box_normalized
=
True
class
TestBoxCoderOpWithLoDWithVariance
(
TestBoxCoderOpWithLoD
):
def
set_init_config
(
self
):
super
(
TestBoxCoderOpWithLoDWithVariance
,
self
).
set_init_config
()
self
.
use_variance
=
True
class
TestBoxCoderOpWithAxis
(
TestBoxCoderOp
):
def
set_init_config
(
self
):
super
(
TestBoxCoderOpWithAxis
,
self
).
set_init_config
()
self
.
axis
=
1
class
TestBoxCoderOpWithVariance
(
TestBoxCoderOp
):
def
set_init_config
(
self
):
super
(
TestBoxCoderOpWithVariance
,
self
).
set_init_config
()
self
.
use_variance
=
True
class
TestBoxCoderOpFP16
(
TestBoxCoderOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
def
set_init_config
(
self
):
super
(
TestBoxCoderOpFP16
,
self
).
set_init_config
()
self
.
atol
=
1e-2
if
__name__
==
'__main__'
:
unittest
.
main
()
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