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体验新版 GitCode,发现更多精彩内容 >>
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ff37e48e
编写于
10月 08, 2022
作者:
C
cifar10
提交者:
GitHub
10月 08, 2022
浏览文件
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电子邮件补丁
差异文件
[MLU] add fluid MLUOps prior_box (#46585)
上级
146d70ca
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
386 addition
and
1 deletion
+386
-1
paddle/fluid/operators/detection/CMakeLists.txt
paddle/fluid/operators/detection/CMakeLists.txt
+1
-1
paddle/fluid/operators/detection/prior_box_op_mlu.cc
paddle/fluid/operators/detection/prior_box_op_mlu.cc
+104
-0
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+49
-0
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+23
-0
python/paddle/fluid/tests/unittests/mlu/test_prior_box_op_mlu.py
...paddle/fluid/tests/unittests/mlu/test_prior_box_op_mlu.py
+209
-0
未找到文件。
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @
ff37e48e
...
...
@@ -46,7 +46,7 @@ if(WITH_XPU)
elseif
(
WITH_MLU
)
detection_library
(
iou_similarity_op SRCS iou_similarity_op.cc
iou_similarity_op_mlu.cc
)
detection_library
(
prior_box_op SRCS prior_box_op.cc
)
detection_library
(
prior_box_op SRCS prior_box_op.cc
prior_box_op_mlu.cc
)
detection_library
(
yolo_box_op SRCS yolo_box_op.cc yolo_box_op_mlu.cc
)
elseif
(
WITH_ASCEND_CL
)
detection_library
(
iou_similarity_op SRCS iou_similarity_op.cc
...
...
paddle/fluid/operators/detection/prior_box_op_mlu.cc
0 → 100644
浏览文件 @
ff37e48e
/* Copyright (c) 2022 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/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/detection/prior_box_op.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
PriorBoxMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
phi
::
DenseTensor
>
(
"Input"
);
auto
*
image
=
ctx
.
Input
<
phi
::
DenseTensor
>
(
"Image"
);
auto
*
boxes
=
ctx
.
Output
<
phi
::
DenseTensor
>
(
"Boxes"
);
auto
*
variances
=
ctx
.
Output
<
phi
::
DenseTensor
>
(
"Variances"
);
float
step_w
=
ctx
.
Attr
<
float
>
(
"step_w"
);
float
step_h
=
ctx
.
Attr
<
float
>
(
"step_h"
);
float
offset
=
ctx
.
Attr
<
float
>
(
"offset"
);
bool
clip
=
ctx
.
Attr
<
bool
>
(
"clip"
);
bool
min_max_aspect_ratios_order
=
ctx
.
Attr
<
bool
>
(
"min_max_aspect_ratios_order"
);
int
im_width
=
image
->
dims
()[
3
];
int
im_height
=
image
->
dims
()[
2
];
int
width
=
input
->
dims
()[
3
];
int
height
=
input
->
dims
()[
2
];
auto
aspect_ratios
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"aspect_ratios"
);
bool
flip
=
ctx
.
Attr
<
bool
>
(
"flip"
);
std
::
vector
<
float
>
new_aspect_ratios
;
ExpandAspectRatios
(
aspect_ratios
,
flip
,
&
new_aspect_ratios
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
MLUDeviceContext
>();
phi
::
DenseTensor
ratios
;
paddle
::
framework
::
TensorFromVector
(
new_aspect_ratios
,
dev_ctx
,
&
ratios
);
MLUOpTensorDesc
new_aspect_ratios_desc
(
ratios
);
auto
min_sizes
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"min_sizes"
);
phi
::
DenseTensor
min
;
paddle
::
framework
::
TensorFromVector
(
min_sizes
,
dev_ctx
,
&
min
);
MLUOpTensorDesc
min_sizes_desc
(
min
);
auto
max_sizes
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"max_sizes"
);
phi
::
DenseTensor
max
;
paddle
::
framework
::
TensorFromVector
(
max_sizes
,
dev_ctx
,
&
max
);
MLUOpTensorDesc
max_sizes_desc
(
max
);
auto
variances_attr
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"variances"
);
phi
::
DenseTensor
var_tensor
;
paddle
::
framework
::
TensorFromVector
(
variances_attr
,
dev_ctx
,
&
var_tensor
);
MLUOpTensorDesc
variances_attr_desc
(
var_tensor
);
auto
place
=
ctx
.
GetPlace
();
boxes
->
mutable_data
<
T
>
(
place
);
variances
->
mutable_data
<
T
>
(
place
);
MLUOpTensorDesc
var_desc
(
*
variances
);
MLUOpTensorDesc
output_desc
(
*
boxes
);
MLUOP
::
OpPriorBox
(
ctx
,
min_sizes_desc
.
get
(),
GetBasePtr
(
&
min
),
new_aspect_ratios_desc
.
get
(),
GetBasePtr
(
&
ratios
),
variances_attr_desc
.
get
(),
GetBasePtr
(
&
var_tensor
),
max_sizes_desc
.
get
(),
GetBasePtr
(
&
max
),
height
,
width
,
im_height
,
im_width
,
step_h
,
step_w
,
offset
,
clip
,
min_max_aspect_ratios_order
,
output_desc
.
get
(),
GetBasePtr
(
boxes
),
var_desc
.
get
(),
GetBasePtr
(
variances
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
prior_box
,
ops
::
PriorBoxMLUKernel
<
float
>
);
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
ff37e48e
...
...
@@ -5458,5 +5458,54 @@ MLURNNDesc::~MLURNNDesc() {
scores
));
}
/* static */
void
MLUOP
::
OpPriorBox
(
const
ExecutionContext
&
ctx
,
const
mluOpTensorDescriptor_t
min_sizes_desc
,
const
void
*
min_sizes
,
const
mluOpTensorDescriptor_t
aspect_ratios_desc
,
const
void
*
aspect_ratios
,
const
mluOpTensorDescriptor_t
variances_desc
,
const
void
*
variances
,
const
mluOpTensorDescriptor_t
max_sizes_desc
,
const
void
*
max_sizes
,
const
int
height
,
const
int
width
,
const
int
im_height
,
const
int
im_width
,
const
float
step_h
,
const
float
step_w
,
const
float
offset
,
const
bool
clip
,
const
bool
min_max_aspect_ratios_order
,
const
mluOpTensorDescriptor_t
output_desc
,
void
*
output
,
const
mluOpTensorDescriptor_t
var_desc
,
void
*
var
)
{
mluOpHandle_t
handle
=
GetMLUOpHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
mluOpPriorBox
(
handle
,
min_sizes_desc
,
min_sizes
,
aspect_ratios_desc
,
aspect_ratios
,
variances_desc
,
variances
,
max_sizes_desc
,
max_sizes
,
height
,
width
,
im_height
,
im_width
,
step_h
,
step_w
,
offset
,
clip
,
min_max_aspect_ratios_order
,
output_desc
,
output
,
var_desc
,
var
));
}
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
ff37e48e
...
...
@@ -2312,6 +2312,29 @@ class MLUOP {
void
*
boxes
,
const
mluOpTensorDescriptor_t
scores_desc
,
void
*
scores
);
static
void
OpPriorBox
(
const
ExecutionContext
&
ctx
,
const
mluOpTensorDescriptor_t
min_sizes_desc
,
const
void
*
min_sizes
,
const
mluOpTensorDescriptor_t
aspect_ratios_desc
,
const
void
*
aspect_ratios
,
const
mluOpTensorDescriptor_t
variances_desc
,
const
void
*
variances
,
const
mluOpTensorDescriptor_t
max_sizes_desc
,
const
void
*
max_sizes
,
const
int
height
,
const
int
width
,
const
int
im_height
,
const
int
im_width
,
const
float
step_h
,
const
float
step_w
,
const
float
offset
,
const
bool
clip
,
const
bool
min_max_aspect_ratios_order
,
const
mluOpTensorDescriptor_t
output_desc
,
void
*
output
,
const
mluOpTensorDescriptor_t
var_desc
,
void
*
var
);
};
const
std
::
map
<
const
std
::
string
,
std
::
pair
<
std
::
vector
<
int
>
,
std
::
vector
<
int
>>>
TransPermMap
=
{
...
...
python/paddle/fluid/tests/unittests/mlu/test_prior_box_op_mlu.py
0 → 100644
浏览文件 @
ff37e48e
# Copyright (c) 2022 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
sys
sys
.
path
.
append
(
'..'
)
import
numpy
as
np
from
op_test
import
OpTest
import
paddle.fluid
as
fluid
import
paddle
import
math
paddle
.
enable_static
()
class
TestMLUPriorBox
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"prior_box"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
set_data
()
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
MLUPlace
(
0
)
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
set_data
(
self
):
self
.
init_test_params
()
self
.
init_test_input
()
self
.
init_test_output
()
self
.
inputs
=
{
'Input'
:
self
.
input
,
'Image'
:
self
.
image
}
self
.
attrs
=
{
'min_sizes'
:
self
.
min_sizes
,
'aspect_ratios'
:
self
.
aspect_ratios
,
'variances'
:
self
.
variances
,
'flip'
:
self
.
flip
,
'clip'
:
self
.
clip
,
'min_max_aspect_ratios_order'
:
self
.
min_max_aspect_ratios_order
,
'step_w'
:
self
.
step_w
,
'step_h'
:
self
.
step_h
,
'offset'
:
self
.
offset
}
if
len
(
self
.
max_sizes
)
>
0
:
self
.
attrs
[
'max_sizes'
]
=
self
.
max_sizes
self
.
outputs
=
{
'Boxes'
:
self
.
out_boxes
,
'Variances'
:
self
.
out_var
}
def
set_max_sizes
(
self
):
max_sizes
=
[
5
,
10
]
self
.
max_sizes
=
np
.
array
(
max_sizes
).
astype
(
'float32'
).
tolist
()
def
set_min_max_aspect_ratios_order
(
self
):
self
.
min_max_aspect_ratios_order
=
True
def
init_test_params
(
self
):
self
.
layer_w
=
32
self
.
layer_h
=
32
self
.
image_w
=
40
self
.
image_h
=
40
self
.
step_w
=
float
(
self
.
image_w
)
/
float
(
self
.
layer_w
)
self
.
step_h
=
float
(
self
.
image_h
)
/
float
(
self
.
layer_h
)
self
.
input_channels
=
2
self
.
image_channels
=
3
self
.
batch_size
=
10
self
.
min_sizes
=
[
2
,
4
]
self
.
min_sizes
=
np
.
array
(
self
.
min_sizes
).
astype
(
'float32'
).
tolist
()
self
.
set_max_sizes
()
self
.
aspect_ratios
=
[
2.0
,
3.0
]
self
.
flip
=
True
self
.
set_min_max_aspect_ratios_order
()
self
.
real_aspect_ratios
=
[
1
,
2.0
,
1.0
/
2.0
,
3.0
,
1.0
/
3.0
]
self
.
aspect_ratios
=
np
.
array
(
self
.
aspect_ratios
,
dtype
=
np
.
float64
).
flatten
()
self
.
variances
=
[
0.1
,
0.1
,
0.2
,
0.2
]
self
.
variances
=
np
.
array
(
self
.
variances
,
dtype
=
np
.
float64
).
flatten
()
self
.
clip
=
True
self
.
num_priors
=
len
(
self
.
real_aspect_ratios
)
*
len
(
self
.
min_sizes
)
if
len
(
self
.
max_sizes
)
>
0
:
self
.
num_priors
+=
len
(
self
.
max_sizes
)
self
.
offset
=
0.5
def
init_test_input
(
self
):
self
.
image
=
np
.
random
.
random
(
(
self
.
batch_size
,
self
.
image_channels
,
self
.
image_w
,
self
.
image_h
)).
astype
(
'float32'
)
self
.
input
=
np
.
random
.
random
(
(
self
.
batch_size
,
self
.
input_channels
,
self
.
layer_w
,
self
.
layer_h
)).
astype
(
'float32'
)
def
init_test_output
(
self
):
out_dim
=
(
self
.
layer_h
,
self
.
layer_w
,
self
.
num_priors
,
4
)
out_boxes
=
np
.
zeros
(
out_dim
).
astype
(
'float32'
)
out_var
=
np
.
zeros
(
out_dim
).
astype
(
'float32'
)
idx
=
0
for
h
in
range
(
self
.
layer_h
):
for
w
in
range
(
self
.
layer_w
):
c_x
=
(
w
+
self
.
offset
)
*
self
.
step_w
c_y
=
(
h
+
self
.
offset
)
*
self
.
step_h
idx
=
0
for
s
in
range
(
len
(
self
.
min_sizes
)):
min_size
=
self
.
min_sizes
[
s
]
if
not
self
.
min_max_aspect_ratios_order
:
# rest of priors
for
r
in
range
(
len
(
self
.
real_aspect_ratios
)):
ar
=
self
.
real_aspect_ratios
[
r
]
c_w
=
min_size
*
math
.
sqrt
(
ar
)
/
2
c_h
=
(
min_size
/
math
.
sqrt
(
ar
))
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
if
len
(
self
.
max_sizes
)
>
0
:
max_size
=
self
.
max_sizes
[
s
]
# second prior: aspect_ratio = 1,
c_w
=
c_h
=
math
.
sqrt
(
min_size
*
max_size
)
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
else
:
c_w
=
c_h
=
min_size
/
2.
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
if
len
(
self
.
max_sizes
)
>
0
:
max_size
=
self
.
max_sizes
[
s
]
# second prior: aspect_ratio = 1,
c_w
=
c_h
=
math
.
sqrt
(
min_size
*
max_size
)
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
# rest of priors
for
r
in
range
(
len
(
self
.
real_aspect_ratios
)):
ar
=
self
.
real_aspect_ratios
[
r
]
if
abs
(
ar
-
1.
)
<
1e-6
:
continue
c_w
=
min_size
*
math
.
sqrt
(
ar
)
/
2
c_h
=
(
min_size
/
math
.
sqrt
(
ar
))
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
# clip the prior's coordidate such that it is within[0, 1]
if
self
.
clip
:
out_boxes
=
np
.
clip
(
out_boxes
,
0.0
,
1.0
)
# set the variance.
out_var
=
np
.
tile
(
self
.
variances
,
(
self
.
layer_h
,
self
.
layer_w
,
self
.
num_priors
,
1
))
self
.
out_boxes
=
out_boxes
.
astype
(
'float32'
)
self
.
out_var
=
out_var
.
astype
(
'float32'
)
class
TestMLUPriorBoxWithoutMaxSize
(
TestMLUPriorBox
):
def
set_max_sizes
(
self
):
self
.
max_sizes
=
[]
class
TestMLUPriorBoxWithoutSpecifiedOutOrder
(
TestMLUPriorBox
):
def
set_min_max_aspect_ratios_order
(
self
):
self
.
min_max_aspect_ratios_order
=
False
if
__name__
==
'__main__'
:
unittest
.
main
()
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