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51f11489
编写于
9月 20, 2017
作者:
Y
Yang yaming
提交者:
GitHub
9月 20, 2017
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差异文件
Merge pull request #3987 from pkuyym/fix-3923-c
Add modified huber loss operator
上级
cdda0cf3
bbd6e09c
变更
4
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Showing
4 changed file
with
338 addition
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0 deletion
+338
-0
paddle/operators/modified_huber_loss_op.cc
paddle/operators/modified_huber_loss_op.cc
+114
-0
paddle/operators/modified_huber_loss_op.cu
paddle/operators/modified_huber_loss_op.cu
+78
-0
paddle/operators/modified_huber_loss_op.h
paddle/operators/modified_huber_loss_op.h
+107
-0
python/paddle/v2/framework/tests/test_modified_huber_loss_op.py
.../paddle/v2/framework/tests/test_modified_huber_loss_op.py
+39
-0
未找到文件。
paddle/operators/modified_huber_loss_op.cc
0 → 100644
浏览文件 @
51f11489
/* 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/modified_huber_loss_op.h"
namespace
paddle
{
namespace
operators
{
class
ModifiedHuberLossOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
context
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
context
.
InputVar
(
"X"
),
"X must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
context
.
InputVar
(
"Y"
),
"Y must be initialized."
);
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
PADDLE_ENFORCE_EQ
(
x
->
dims
(),
y
->
dims
(),
"The shape of X and Y must be the same."
);
PADDLE_ENFORCE_EQ
(
x
->
dims
().
size
(),
2
,
"The tensor rank of X must be 2."
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
1
],
1
,
"The 2nd dimension of X must be 1."
);
context
.
Output
<
framework
::
LoDTensor
>
(
"IntermediateVal"
)
->
Resize
(
x
->
dims
());
context
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
({
x
->
dims
()[
0
],
1
});
}
};
class
ModifiedHuberLossOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ModifiedHuberLossOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input tensor of modified huber loss op."
"X is 2-D tensor with shape [batch_size, 1]."
);
AddInput
(
"Y"
,
"The target labels of modified huber loss op."
"The shape of Y is same as X. Values of Y must be 0 or 1."
);
AddOutput
(
"IntermediateVal"
,
"Variable to save intermediate result which will be reused in "
"backward processing."
)
.
AsIntermediate
();
AddOutput
(
"Out"
,
"Classification loss for X."
);
AddComment
(
R"DOC(
Modified huber loss is used in binary classification problem. The shape of
input X and target Y are both [N, 1] and so is the shape of output loss.
Since target Y is not differentiable, cacluating gradient for Y is illegal.
The formulation of modified huber loss is:
L(y, f(x)) = max(0, 1 - yf(x))^2 for yf(x) >= -1,
-4yf(x) otherwise.
Make sure the values of target label Y are in {0, 1} here. The operator will
scale values of Y to {-1, +1} when computing losses and gradients.
)DOC"
);
}
};
class
ModifiedHuberLossGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
intermediate_val
=
context
.
Input
<
Tensor
>
(
"IntermediateVal"
);
auto
*
out_grad
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x_grad
=
context
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
PADDLE_ENFORCE_NOT_NULL
(
x
,
"X must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
y
,
"Y must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
intermediate_val
,
"Intermediate value must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
out_grad
,
"Input(Out@Grad) must not be null."
);
PADDLE_ENFORCE_EQ
(
intermediate_val
->
dims
(),
x
->
dims
(),
"The shape of X and intermediate value must be the same."
);
PADDLE_ENFORCE_EQ
(
out_grad
->
dims
(),
x
->
dims
(),
"The shape of Input(Out@Grad) and X must be the same."
);
if
(
x_grad
)
x_grad
->
Resize
(
x
->
dims
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
modified_huber_loss
,
ops
::
ModifiedHuberLossOp
,
ops
::
ModifiedHuberLossOpMaker
,
modified_huber_loss_grad
,
ops
::
ModifiedHuberLossGradOp
);
REGISTER_OP_CPU_KERNEL
(
modified_huber_loss
,
ops
::
ModifiedHuberLossKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
modified_huber_loss_grad
,
ops
::
ModifiedHuberLossGradCPUKernel
<
float
>
);
paddle/operators/modified_huber_loss_op.cu
0 → 100644
浏览文件 @
51f11489
/* 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 <thrust/device_ptr.h>
#include <thrust/device_vector.h>
#include <thrust/for_each.h>
#include <thrust/tuple.h>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/modified_huber_loss_op.h"
#include "paddle/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
struct
ModifiedHuberLossBackward
{
template
<
typename
Tuple
>
HOSTDEVICE
void
operator
()(
Tuple
t
)
const
{
auto
inter_val
=
thrust
::
get
<
1
>
(
t
);
auto
y_val
=
thrust
::
get
<
2
>
(
t
);
auto
out_grad
=
thrust
::
get
<
3
>
(
t
);
if
(
inter_val
<
-
1
)
{
thrust
::
get
<
0
>
(
t
)
=
-
4
*
(
2
*
y_val
-
1
)
*
out_grad
;
}
else
if
(
inter_val
<
1
)
{
thrust
::
get
<
0
>
(
t
)
=
-
2
*
(
1
-
inter_val
)
*
(
2
*
y_val
-
1
)
*
out_grad
;
}
else
{
thrust
::
get
<
0
>
(
t
)
=
0
;
}
}
};
template
<
typename
T
>
class
ModifiedHuberLossGradGPUKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
in1
=
context
.
Input
<
Tensor
>
(
"IntermediateVal"
);
auto
*
in2
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out0
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
out0
)
{
auto
counts
=
framework
::
product
(
in1
->
dims
());
auto
y_ptr
=
thrust
::
device_pointer_cast
(
in0
->
data
<
T
>
());
auto
inter_val_ptr
=
thrust
::
device_pointer_cast
(
in1
->
data
<
T
>
());
auto
out_grad_ptr
=
thrust
::
device_pointer_cast
(
in2
->
data
<
T
>
());
thrust
::
device_ptr
<
T
>
x_grad_ptr
(
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
()));
auto
iter_begin
=
thrust
::
make_zip_iterator
(
thrust
::
make_tuple
(
x_grad_ptr
,
inter_val_ptr
,
y_ptr
,
out_grad_ptr
));
auto
iter_end
=
thrust
::
make_zip_iterator
(
thrust
::
make_tuple
(
x_grad_ptr
+
counts
,
inter_val_ptr
+
counts
,
y_ptr
+
counts
,
out_grad_ptr
+
counts
));
thrust
::
for_each
(
iter_begin
,
iter_end
,
ModifiedHuberLossBackward
());
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
modified_huber_loss
,
ops
::
ModifiedHuberLossKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
modified_huber_loss_grad
,
ops
::
ModifiedHuberLossGradGPUKernel
<
float
>
);
paddle/operators/modified_huber_loss_op.h
0 → 100644
浏览文件 @
51f11489
/* 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"
#include "paddle/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
>
struct
CheckLabelValue
{
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
PADDLE_ASSERT
(
val
==
static_cast
<
T
>
(
0
)
||
val
==
static_cast
<
T
>
(
1
));
}
};
template
<
typename
T
>
struct
ModifiedHuberLossForward
{
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
if
(
val
<
-
1
)
{
return
-
4
*
val
;
}
else
if
(
val
<
1
)
{
return
(
1
-
val
)
*
(
1
-
val
);
}
else
{
return
static_cast
<
T
>
(
0
);
}
}
};
template
<
typename
Place
,
typename
T
>
class
ModifiedHuberLossKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
in1
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
out0
=
context
.
Output
<
framework
::
LoDTensor
>
(
"IntermediateVal"
);
auto
*
out1
=
context
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out1
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
x
=
EigenVector
<
T
>::
Flatten
(
*
in0
);
auto
y
=
EigenVector
<
T
>::
Flatten
(
*
in1
);
// make sure value's of Y in {0, 1}
y
.
unaryExpr
(
CheckLabelValue
<
T
>
());
auto
inter_val
=
EigenVector
<
T
>::
Flatten
(
*
out0
);
// scale y to {-1, +1} and compute x * y
inter_val
.
device
(
place
)
=
x
*
(
2
*
y
-
static_cast
<
T
>
(
1
));
auto
loss
=
EigenVector
<
T
>::
Flatten
(
*
out1
);
loss
.
device
(
place
)
=
inter_val
.
unaryExpr
(
ModifiedHuberLossForward
<
T
>
());
}
};
// CPU backward kernel
template
<
typename
T
>
class
ModifiedHuberLossGradCPUKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
in1
=
context
.
Input
<
framework
::
LoDTensor
>
(
"IntermediateVal"
);
auto
*
in2
=
context
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out0
=
context
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
out0
)
{
const
T
*
y_ptr
=
in0
->
data
<
T
>
();
const
T
*
inter_val_ptr
=
in1
->
data
<
T
>
();
const
T
*
out_grad_ptr
=
in2
->
data
<
T
>
();
size_t
counts
=
static_cast
<
size_t
>
(
framework
::
product
(
in1
->
dims
()));
T
*
x_grad_ptr
=
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
size_t
i
=
0
;
i
<
counts
;
++
i
)
{
if
(
inter_val_ptr
[
i
]
<
-
1
)
{
x_grad_ptr
[
i
]
=
-
4
*
(
2
*
y_ptr
[
i
]
-
1
)
*
out_grad_ptr
[
i
];
}
else
if
(
inter_val_ptr
[
i
]
<
1
)
{
x_grad_ptr
[
i
]
=
-
2
*
(
1
-
inter_val_ptr
[
i
])
*
(
2
*
y_ptr
[
i
]
-
1
)
*
out_grad_ptr
[
i
];
}
else
{
x_grad_ptr
[
i
]
=
0
;
}
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_modified_huber_loss_op.py
0 → 100644
浏览文件 @
51f11489
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
modified_huber_loss_forward
(
val
):
if
val
<
-
1
:
return
-
4
*
val
elif
val
<
1
:
return
(
1
-
val
)
*
(
1
-
val
)
else
:
return
0
class
TestModifiedHuberLossOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'modified_huber_loss'
samples_num
=
32
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
-
1
,
1.
,
(
samples_num
,
1
)).
astype
(
'float32'
),
'Y'
:
np
.
random
.
choice
([
0
,
1
],
samples_num
).
reshape
((
samples_num
,
1
))
}
product_res
=
self
.
inputs
[
'X'
]
*
(
2
*
self
.
inputs
[
'Y'
]
-
1
)
loss
=
np
.
vectorize
(
modified_huber_loss_forward
)(
product_res
)
self
.
outputs
=
{
'IntermediateVal'
:
product_res
,
'Out'
:
loss
.
reshape
((
samples_num
,
1
))
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
max_relative_error
=
0.005
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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