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
booniebears
Paddle
提交
4e3ba65f
P
Paddle
项目概览
booniebears
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
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
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
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 >>
提交
4e3ba65f
编写于
9月 20, 2017
作者:
Y
yangyaming
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Refine doc.
上级
12596a16
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
46 addition
and
35 deletion
+46
-35
paddle/operators/smooth_l1_loss_op.cc
paddle/operators/smooth_l1_loss_op.cc
+37
-26
paddle/operators/smooth_l1_loss_op.h
paddle/operators/smooth_l1_loss_op.h
+2
-2
python/paddle/v2/framework/tests/test_smooth_l1_loss_op.py
python/paddle/v2/framework/tests/test_smooth_l1_loss_op.py
+7
-7
未找到文件。
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @
4e3ba65f
...
...
@@ -23,19 +23,15 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input of SmoothL1LossOp must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Target of SmoothL1LossOp must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"X must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Y must be initialized."
);
auto
*
x
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Y"
);
PADDLE_ENFORCE_EQ
(
x
->
dims
(),
y
->
dims
(),
"Dimensions of SmoothL1LossOp's input and target "
"must be same."
);
"The shape of X and Y must be the same."
);
PADDLE_ENFORCE_GE
(
x
->
dims
().
size
(),
2
,
"Tensor rank of SmoothL1LossOp's input must be "
"at least 2."
);
"The tensor rank of X must be at least 2."
);
auto
*
inside_weight
=
ctx
.
Input
<
framework
::
Tensor
>
(
"InsideWeight"
);
if
(
inside_weight
)
{
auto
*
outside_weight
=
ctx
.
Input
<
framework
::
Tensor
>
(
"OutsideWeight"
);
...
...
@@ -43,10 +39,9 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
"If weights are provided, must specify both "
"inside and outside weights."
);
PADDLE_ENFORCE_EQ
(
inside_weight
->
dims
(),
x
->
dims
(),
"Dimensions of inside weight must be same with input."
);
PADDLE_ENFORCE_EQ
(
outside_weight
->
dims
(),
x
->
dims
(),
"Dimensions of outside weight must be same with input."
);
"The shape of InsideWeight must be same as X."
);
PADDLE_ENFORCE_EQ
(
outside_weight
->
dims
(),
x
->
dims
(),
"The shape of OutsideWeight must be same as X."
);
}
auto
*
diff
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Diff"
);
...
...
@@ -63,21 +58,37 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
SmoothL1LossOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of SmoothL1LossOp."
);
AddInput
(
"Y"
,
"Target of SmoothL1LossOp."
);
AddInput
(
"InsideWeight"
,
"Optional input to scale (X-Y)."
);
AddInput
(
"OutsideWeight"
,
"Optinal input to scale smooth l1 loss."
);
AddOutput
(
"Diff"
,
"Intermediate variable to cache Win*(X-Y)."
)
AddInput
(
"X"
,
"The input tensor of smooth l1 loss op."
"The rank should be greater or equal to 2 with shape "
"[batch_size, value_dim1, value_dim2, ..., value_dimN]"
);
AddInput
(
"Y"
,
"The target tensor of smooth l1 loss op "
"with the same shape as X."
);
AddInput
(
"InsideWeight"
,
"Optional input tensor of smooth l1 loss op with the same shape "
"as X. If provided, the result of (X - Y) will be multiplied "
"by this tensor element by element."
);
AddInput
(
"OutsideWeight"
,
"Optinal input of smooth l1 loss op with the same shape as X."
"If provided, the output smooth l1 loss will be multiplied by "
"this tensor element by element."
);
AddOutput
(
"Diff"
,
"Intermediate variable to cache InsideWeight*(X-Y)."
)
.
AsIntermediate
();
AddOutput
(
"Out"
,
"Final smooth l1 loss of inputs."
);
AddAttr
<
AttrType
>
(
"sigma"
,
"Hyper parameter, default value is 3.0 ."
)
AddOutput
(
"Out"
,
"Smooth l1 loss."
);
AddAttr
<
AttrType
>
(
"sigma"
,
"Hyper parameter of smooth l1 loss op."
"A float scalar with default value 3.0."
)
.
SetDefault
(
3.0
);
AddComment
(
R"DOC(
Compute SmoothL1Loss for input and target.
Compute smooth l1 loss for input and target. The operator take the 1st
dimension of input as batch size. For each instance, it will compute
smooth l1 loss element by element first and sum all losses to one value.
So the output shape is [batch_size, 1].
The equation is:
loss = 0.5 * (sigma * (x
- y)) ^ 2
if abs(x - y) < 1 / sigma^2
abs(x - y) - 0.5 / sigma^2
otherwise
loss = 0.5 * (sigma * (x
-y))^2
if abs(x - y) < 1 / sigma^2
abs(x - y) - 0.5 / sigma^2 otherwise
)DOC"
);
}
...
...
@@ -98,12 +109,12 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_GE
(
out_dims
.
size
(),
2
,
"T
ensor rank of output gradient
should be 2."
);
"T
he tensor rank of Input(Out@Grad)
should be 2."
);
PADDLE_ENFORCE_EQ
(
out_dims
[
0
],
in_dims
[
0
],
"
First dimension of ouptut gradient
must be "
"same
with
input."
);
"
The 1st dimension of Input(Out@Grad)
must be "
"same
as
input."
);
PADDLE_ENFORCE_EQ
(
out_dims
[
1
],
1
,
"
Second dimension of output gradient
must be 1."
);
"
The 2nd dimension of Input(Out@Grad)
must be 1."
);
if
(
x_grad
)
x_grad
->
Resize
(
in_dims
);
if
(
y_grad
)
y_grad
->
Resize
(
in_dims
);
...
...
paddle/operators/smooth_l1_loss_op.h
浏览文件 @
4e3ba65f
...
...
@@ -59,7 +59,7 @@ class SmoothL1LossKernel : public framework::OpKernel {
out1
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
sigma
=
static_cast
<
T
>
(
context
.
op
().
Attr
<
AttrType
>
(
"sigma"
));
auto
sigma
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"sigma"
));
T
sigma2
=
sigma
*
sigma
;
bool
has_weight
=
(
in2
!=
nullptr
)
&&
(
in3
!=
nullptr
);
...
...
@@ -122,7 +122,7 @@ class SmoothL1LossGradKernel : public framework::OpKernel {
auto
*
in1
=
context
.
Input
<
Tensor
>
(
"OutsideWeight"
);
auto
*
in2
=
context
.
Input
<
Tensor
>
(
"Diff"
);
auto
*
og
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
sigma
=
static_cast
<
T
>
(
context
.
op
().
Attr
<
AttrType
>
(
"sigma"
));
auto
sigma
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"sigma"
));
T
sigma2
=
sigma
*
sigma
;
bool
has_weight
=
(
in0
!=
nullptr
)
&&
(
in1
!=
nullptr
);
...
...
python/paddle/v2/framework/tests/test_smooth_l1_loss_op.py
浏览文件 @
4e3ba65f
...
...
@@ -14,7 +14,7 @@ def smooth_l1_loss_forward(val, sigma2):
class
TestSmoothL1LossOp1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"smooth_l1_loss"
dims
=
(
6
,
10
)
dims
=
(
5
,
10
)
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
)
...
...
@@ -35,17 +35,17 @@ class TestSmoothL1LossOp1(OpTest):
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.0
2
,
no_grad_set
=
set
(
"X"
))
[
'Y'
],
'Out'
,
max_relative_error
=
0.0
3
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.0
2
,
no_grad_set
=
set
(
'Y'
))
[
'X'
],
'Out'
,
max_relative_error
=
0.0
3
,
no_grad_set
=
set
(
'Y'
))
class
TestSmoothL1LossOp2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"smooth_l1_loss"
dims
=
(
6
,
10
)
dims
=
(
5
,
10
)
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
),
...
...
@@ -66,20 +66,20 @@ class TestSmoothL1LossOp2(OpTest):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.0
2
)
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.0
3
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.0
2
,
max_relative_error
=
0.0
3
,
no_grad_set
=
set
([
'X'
,
'InsideWeight'
,
'OutsideWeight'
]))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.0
2
,
max_relative_error
=
0.0
3
,
no_grad_set
=
set
([
'Y'
,
'InsideWeight'
,
'OutsideWeight'
]))
...
...
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