Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
Paddle
提交
e254e7c6
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
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
浏览文件
下载
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
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录