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e254e7c6
编写于
2月 16, 2022
作者:
T
TTerror
提交者:
GitHub
2月 16, 2022
浏览文件
操作
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电子邮件补丁
差异文件
optimize prior_box for kunlun, *test=kunlun (#39477)
上级
f138371c
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
182 addition
and
177 deletion
+182
-177
paddle/fluid/operators/detection/prior_box_op_xpu.cc
paddle/fluid/operators/detection/prior_box_op_xpu.cc
+7
-10
python/paddle/fluid/tests/unittests/xpu/test_prior_box_op_xpu.py
...paddle/fluid/tests/unittests/xpu/test_prior_box_op_xpu.py
+175
-167
未找到文件。
paddle/fluid/operators/detection/prior_box_op_xpu.cc
浏览文件 @
e254e7c6
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/detection/prior_box_op.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -81,21 +82,17 @@ class PriorBoxOpXPUKernel : public framework::OpKernel<T> {
dev_ctx
.
x_context
(),
boxes_data
,
aspect_ratios_param
,
min_sizes_param
,
max_sizes_param
,
feature_height
,
feature_width
,
img_height
,
img_width
,
offset
,
step_height
,
step_width
,
clip
,
min_max_aspect_ratios_order
);
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU gen_prior_box kernel return wrong value[%d %s]"
,
ret
,
XPUAPIErrorMsg
[
ret
]));
PADDLE_ENFORCE_XDNN_SUCCESS
(
ret
,
"gen_prior_box"
);
int
box_num
=
feature_height
*
feature_width
*
num_priors
;
int
vlen
=
variances
.
size
();
std
::
vector
<
K
>
var_cpu
(
vlen
*
box_num
);
for
(
int
i
=
0
;
i
<
box_num
;
++
i
)
{
ret
=
xpu_memcpy
(
vars_data
+
i
*
vlen
,
variances
.
data
(),
vlen
*
sizeof
(
K
),
XPUMemcpyKind
::
XPU_HOST_TO_DEVICE
);
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU xpu_memcpy return wrong "
"value[%d %s] in prior_box."
,
ret
,
XPUAPIErrorMsg
[
ret
]));
std
::
copy
(
variances
.
begin
(),
variances
.
end
(),
var_cpu
.
begin
()
+
i
*
vlen
);
}
ret
=
xpu_memcpy
(
vars_data
,
var_cpu
.
data
(),
var_cpu
.
size
()
*
sizeof
(
K
),
XPUMemcpyKind
::
XPU_HOST_TO_DEVICE
);
PADDLE_ENFORCE_XPU_SUCCESS
(
ret
);
}
};
...
...
python/paddle/fluid/tests/unittests/xpu/test_prior_box_op_xpu.py
浏览文件 @
e254e7c6
...
...
@@ -14,188 +14,196 @@
from
__future__
import
print_function
import
unittest
import
math
import
numpy
as
np
import
sys
import
unittest
sys
.
path
.
append
(
".."
)
import
math
import
paddle
from
op_test
import
OpTest
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
paddle
.
enable_static
()
class
TestPriorBoxOp
(
XPUOpTest
):
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
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
def
test_check_grad
(
self
):
pass
def
setUp
(
self
):
self
.
op_type
=
"prior_box"
self
.
use_xpu
=
True
self
.
set_data
()
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
=
False
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
.
float
).
flatten
()
self
.
variances
=
[
0.1
,
0.1
,
0.2
,
0.2
]
self
.
variances
=
np
.
array
(
self
.
variances
,
dtype
=
np
.
float
).
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
class
XPUTestPriorBoxOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'prior_box'
self
.
use_dynamic_create_class
=
False
class
TestPriorBoxOp
(
XPUOpTest
):
def
setUp
(
self
):
self
.
op_type
=
"prior_box"
self
.
use_xpu
=
True
self
.
dtype
=
self
.
in_type
self
.
set_data
()
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
test_check_output
(
self
):
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
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
=
False
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
.
float
).
flatten
()
self
.
variances
=
[
0.1
,
0.1
,
0.2
,
0.2
]
self
.
variances
=
np
.
array
(
self
.
variances
,
dtype
=
np
.
float
).
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
(
self
.
dtype
)
self
.
input
=
np
.
random
.
random
(
(
self
.
batch_size
,
self
.
input_channels
,
self
.
layer_w
,
self
.
layer_h
)).
astype
(
self
.
dtype
)
def
init_test_output
(
self
):
out_dim
=
(
self
.
layer_h
,
self
.
layer_w
,
self
.
num_priors
,
4
)
out_boxes
=
np
.
zeros
(
out_dim
).
astype
(
self
.
dtype
)
out_var
=
np
.
zeros
(
out_dim
).
astype
(
self
.
dtype
)
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
# 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
TestPriorBoxOpWithoutMaxSize
(
TestPriorBoxOp
):
def
set_max_sizes
(
self
):
self
.
max_sizes
=
[]
class
TestPriorBoxOpWithSpecifiedOutOrder
(
TestPriorBoxOp
):
def
set_min_max_aspect_ratios_order
(
self
):
self
.
min_max_aspect_ratios_order
=
True
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
(
self
.
dtype
)
self
.
out_var
=
out_var
.
astype
(
self
.
dtype
)
class
TestPriorBoxOpWithoutMaxSize
(
TestPriorBoxOp
):
def
set_max_sizes
(
self
):
self
.
max_sizes
=
[]
class
TestPriorBoxOpWithSpecifiedOutOrder
(
TestPriorBoxOp
):
def
set_min_max_aspect_ratios_order
(
self
):
self
.
min_max_aspect_ratios_order
=
True
support_types
=
get_xpu_op_support_types
(
'prior_box'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestPriorBoxOp
,
stype
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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