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体验新版 GitCode,发现更多精彩内容 >>
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990818f7
编写于
11月 30, 2017
作者:
Y
Yang yaming
提交者:
GitHub
11月 30, 2017
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Merge pull request #6031 from pkuyym/fix-6016
Refine doc for smooth l1 loss op.
上级
00eceea0
a5236265
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
36 addition
and
26 deletion
+36
-26
paddle/operators/smooth_l1_loss_op.cc
paddle/operators/smooth_l1_loss_op.cc
+36
-26
未找到文件。
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @
990818f7
...
...
@@ -22,22 +22,20 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"
X must be initialized
."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"
Y must be initialized
."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"
Input(X) should not be null
."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"
Input(Y) should not be null
."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
y_dims
=
ctx
->
GetInputDim
(
"Y"
);
PADDLE_ENFORCE_EQ
(
x_dims
,
y_dims
,
"The shape of X and Y must be the same."
);
PADDLE_ENFORCE_EQ
(
x_dims
,
y_dims
);
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
2
,
"The tensor rank of
X must be at least
2."
);
"The tensor rank of
Input(X) should not be less than
2."
);
if
(
ctx
->
HasInput
(
"InsideWeight"
))
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"OutsideWeight"
),
"If weights are provided, must specify both "
"inside and outside weights."
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"InsideWeight"
),
x_dims
,
"The shape of InsideWeight must be same as X."
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"OutsideWeight"
),
x_dims
,
"The shape of OutsideWeight must be same as X."
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"InsideWeight"
),
x_dims
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"OutsideWeight"
),
x_dims
);
}
ctx
->
SetOutputDim
(
"Diff"
,
x_dims
);
...
...
@@ -53,25 +51,29 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
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]
"
);
"
(Tensor, default Tensor<float>) A tensor with rank at least 2.
"
"The
input value of smooth l1 loss op
with shape "
"[batch_size,
dim1, ..., dimN].
"
);
AddInput
(
"Y"
,
"
The target tensor of smooth l1 loss op
"
"
with the
same shape as X."
);
"
(Tensor, default Tensor<float>) A tensor with rank at least 2.
"
"
The target value of smooth l1 loss op with
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 "
"(Tensor, default Tensor<float>) A tensor with rank at least 2. "
"This input is optional and should have same shape with X. "
"If provided, the result of (X - Y) will be multiplied "
"by this tensor element by element."
)
.
AsDispensable
();
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."
)
"(Tensor, default Tensor<float>) A tensor with rank at least 2. "
"This input is optional and should have same shape with X. "
"If provided, the out smooth l1 loss will be multiplied by this "
"tensor element by element."
)
.
AsDispensable
();
AddOutput
(
"Diff"
,
"Intermediate variable to cache InsideWeight
*(X-
Y)."
)
AddOutput
(
"Diff"
,
"Intermediate variable to cache InsideWeight
* (X -
Y)."
)
.
AsIntermediate
();
AddOutput
(
"Out"
,
"Smooth l1 loss."
);
AddOutput
(
"Out"
,
"(Tensor, default Tensor<float>) A tensor with rank be 2. "
"The output smooth l1 loss with shape [batch_size, 1]."
);
AddAttr
<
AttrType
>
(
"sigma"
,
"Hyper parameter of smooth l1 loss op."
"A float scalar with default value 3.0."
)
...
...
@@ -79,15 +81,23 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
Smooth L1 Loss Operator.
This operator computes the smooth l1 loss for
input and target
.
The operator takes the first dimension of
input as the
batch size.
This operator computes the smooth l1 loss for
X and Y
.
The operator takes the first dimension of
X and Y as
batch size.
For each instance, it computes the smooth l1 loss element by element first
and then sums all the losses. So the resulting output shape
is [batch_size, 1].
and then sums all the losses. So the shape of Out is [batch_size, 1].
The equation is:
loss = $$0.5 * (\sigma * (x-y))^2$$ if $$|x - y| < 1 /({\sigma}^2)$$
$$\frac{|x - y| - 0.5}{{\sigma}^2}$$ otherwise
$$
Out_{\sigma}(X, Y)_i = \begin{cases}
0.5 * (\sigma * (X_i - Y_i)) ^ 2
\quad |X_i - Y_i| \lt \frac{1} {{\sigma} ^ 2} \\
\frac{|X_i - Y_i| - 0.5}{{\sigma}^2},
\quad otherwise
\end{cases}
$$
In the above equation, $Out_{\sigma}(X, Y)_i$, $X_i$ and $Y_i$ represent the ith
element of Out, X and Y.
)DOC"
);
}
...
...
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