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体验新版 GitCode,发现更多精彩内容 >>
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b56cbd30
编写于
10月 11, 2017
作者:
Y
Yibing Liu
提交者:
GitHub
10月 11, 2017
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Merge pull request #4285 from kuke/margin_rank_loss_op_dev
Add margin rank loss operator
上级
2002c78e
989e19ca
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4
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4 changed file
with
285 addition
and
0 deletion
+285
-0
paddle/operators/margin_rank_loss_op.cc
paddle/operators/margin_rank_loss_op.cc
+124
-0
paddle/operators/margin_rank_loss_op.cu
paddle/operators/margin_rank_loss_op.cu
+24
-0
paddle/operators/margin_rank_loss_op.h
paddle/operators/margin_rank_loss_op.h
+98
-0
python/paddle/v2/framework/tests/test_margin_rank_loss_op.py
python/paddle/v2/framework/tests/test_margin_rank_loss_op.py
+39
-0
未找到文件。
paddle/operators/margin_rank_loss_op.cc
0 → 100644
浏览文件 @
b56cbd30
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/margin_rank_loss_op.h"
namespace
paddle
{
namespace
operators
{
class
MarginRankLossOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
// input check
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X1"
),
"Input(X1) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X2"
),
"Input(X2) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) shouldn't be null."
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
x1_dims
=
ctx
->
GetInputDim
(
"X1"
);
auto
x2_dims
=
ctx
->
GetInputDim
(
"X2"
);
PADDLE_ENFORCE
(
(
label_dims
==
x1_dims
)
&&
(
x1_dims
==
x2_dims
)
&&
(
label_dims
.
size
()
==
2
)
&&
(
label_dims
[
1
]
==
1
),
"All inputs must be 2-D tensor with shape [batch_size x 1]."
);
ctx
->
SetOutputDim
(
"Activated"
,
label_dims
);
ctx
->
SetOutputDim
(
"Out"
,
label_dims
);
}
};
template
<
typename
T
>
class
MarginRankLossOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
MarginRankLossOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X1"
,
"(2-D tensor with shape [batch_size x 1]) The score for "
"one item X1 to be ranked, from pairwise ranking model."
);
AddInput
(
"X2"
,
"(2-D tensor with shape [batch_size x 1]) The score for "
"another item X2 to be ranked, from pairwise ranking model."
);
AddInput
(
"Label"
,
"(2-D tensor with shape [batch_size x 1]) "
"The label indicating X1 ranked higher than X2 or not, "
"can only be +1 or -1."
);
AddAttr
<
T
>
(
"margin"
,
"(scalar, default 0) Margin for MarginRankLossOp."
)
.
SetDefault
(
static_cast
<
T
>
(
0
));
AddOutput
(
"Activated"
,
"(2-D tensor with shape [batch_size x 1]) Intermediate tensor "
"to indicate whether each element of Output(Out) is activated."
)
.
AsIntermediate
();
AddOutput
(
"Out"
,
"(2-D tensor with shape [batch_size x 1]) "
"The output loss of MarginRankLoss operator."
);
AddComment
(
R"DOC(
MarginRankLoss operator measures the loss given a pair of training sample
{`X1`, `X2`} and the `Label` with attribute `margin`, where `Label = +1`
indicating X1 is ranked higher than `X2`, otherwise `Label = -1`. The loss
turns out
loss(X1, X2, Label) = max(0, -Label * (X1 - X2) + margin).
The attribute `margin` involved here helps make the predictions more robust.
Denote the item ranked higher as the positive sample, otherwise the negative
sample. If the score of the two samples satisfies
positive sample - negative sample < margin,
the pair of samples will contribute to the final loss, which will backpropogate
and train the ranking model to enlarge the difference of the two score.
For batch input with size `batch_size`, `X1`, `X2` and `Label`
all have the same shape [batch_size x 1].
)DOC"
);
}
};
class
MarginRankLossGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X1"
),
"Input(X1) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X2"
),
"Input(X2) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Activated"
),
"Intermediate(Activated) shouldn't be null."
);
auto
dims
=
ctx
->
GetInputDim
(
"Label"
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X1"
),
dims
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X2"
),
dims
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
margin_rank_loss
,
ops
::
MarginRankLossOp
,
ops
::
MarginRankLossOpMaker
<
float
>
,
margin_rank_loss_grad
,
ops
::
MarginRankLossGradOp
);
REGISTER_OP_CPU_KERNEL
(
margin_rank_loss
,
ops
::
MarginRankLossKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
margin_rank_loss_grad
,
ops
::
MarginRankLossGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/margin_rank_loss_op.cu
0 → 100644
浏览文件 @
b56cbd30
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/margin_rank_loss_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
margin_rank_loss
,
ops
::
MarginRankLossKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
margin_rank_loss_grad
,
ops
::
MarginRankLossGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/margin_rank_loss_op.h
0 → 100644
浏览文件 @
b56cbd30
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
struct
ReLU
{
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
return
val
>
0
?
val
:
static_cast
<
T
>
(
0
);
}
};
template
<
typename
T
>
struct
Heaviside
{
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
return
static_cast
<
T
>
(
val
>
0
?
1
:
0
);
}
};
template
<
typename
Place
,
typename
T
>
class
MarginRankLossKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
out_t
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
act_t
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Activated"
);
auto
*
label_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Label"
);
auto
*
x1_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X1"
);
auto
*
x2_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X2"
);
out_t
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
act_t
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
margin
=
static_cast
<
T
>
(
ctx
.
Attr
<
T
>
(
"margin"
));
auto
out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
out_t
);
auto
act
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
act_t
);
auto
label
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
label_t
);
auto
x1
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x1_t
);
auto
x2
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x2_t
);
auto
&
dev
=
ctx
.
GetEigenDevice
<
Place
>
();
out
.
device
(
dev
)
=
(
-
label
*
(
x1
-
x2
)
+
margin
).
unaryExpr
(
ReLU
<
T
>
());
act
.
device
(
dev
)
=
out
.
unaryExpr
(
Heaviside
<
T
>
());
}
};
template
<
typename
Place
,
typename
T
>
class
MarginRankLossGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
d_x1_t
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X1"
));
auto
*
d_x2_t
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X2"
));
auto
*
act_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Activated"
);
auto
*
d_out_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
label_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Label"
);
auto
d_out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_out_t
);
auto
act
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
act_t
);
auto
label
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
label_t
);
auto
&
dev
=
ctx
.
GetEigenDevice
<
Place
>
();
// compute d_x1
if
(
d_x1_t
)
{
d_x1_t
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
d_x1
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_x1_t
);
d_x1
.
device
(
dev
)
=
-
d_out
*
act
*
label
;
}
// compute d_x2
if
(
d_x2_t
)
{
d_x2_t
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
d_x2
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_x2_t
);
d_x2
.
device
(
dev
)
=
d_out
*
act
*
label
;
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_margin_rank_loss_op.py
0 → 100644
浏览文件 @
b56cbd30
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestMarginRankLossOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"margin_rank_loss"
batch_size
=
5
margin
=
0.5
# labels_{i} = {-1, 1}
label
=
2
*
np
.
random
.
randint
(
0
,
2
,
size
=
(
batch_size
,
1
)).
astype
(
"float32"
)
-
1
x1
=
np
.
random
.
random
((
batch_size
,
1
)).
astype
(
"float32"
)
x2
=
np
.
random
.
random
((
batch_size
,
1
)).
astype
(
"float32"
)
# loss = max(0, -label * (x1 - x2) + margin)
loss
=
-
label
*
(
x1
-
x2
)
+
margin
loss
=
np
.
where
(
loss
>
0
,
loss
,
0
)
act
=
np
.
where
(
loss
>
0
,
1.
,
0.
)
self
.
attrs
=
{
'margin'
:
margin
}
self
.
inputs
=
{
'Label'
:
label
,
'X1'
:
x1
,
'X2'
:
x2
}
self
.
outputs
=
{
'Activated'
:
act
,
'Out'
:
loss
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X1"
,
"X2"
],
"Out"
)
def
test_check_grad_ignore_x1
(
self
):
self
.
check_grad
([
"X2"
],
"Out"
,
no_grad_set
=
set
(
'X1'
))
def
test_check_grad_ignore_x2
(
self
):
self
.
check_grad
([
"X1"
],
"Out"
,
no_grad_set
=
set
(
'X2'
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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