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cdda0cf3
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 #3913 from pkuyym/fix-3789
Complete smooth_l1_loss_op.
上级
63bc2ff8
4e3ba65f
变更
4
隐藏空白更改
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并排
Showing
4 changed file
with
428 addition
and
0 deletion
+428
-0
paddle/operators/smooth_l1_loss_op.cc
paddle/operators/smooth_l1_loss_op.cc
+135
-0
paddle/operators/smooth_l1_loss_op.cu
paddle/operators/smooth_l1_loss_op.cu
+24
-0
paddle/operators/smooth_l1_loss_op.h
paddle/operators/smooth_l1_loss_op.h
+182
-0
python/paddle/v2/framework/tests/test_smooth_l1_loss_op.py
python/paddle/v2/framework/tests/test_smooth_l1_loss_op.py
+87
-0
未找到文件。
paddle/operators/smooth_l1_loss_op.cc
0 → 100644
浏览文件 @
cdda0cf3
/* 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/smooth_l1_loss_op.h"
namespace
paddle
{
namespace
operators
{
class
SmoothL1LossOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
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
(),
"The shape of X and Y must be the same."
);
PADDLE_ENFORCE_GE
(
x
->
dims
().
size
(),
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"
);
PADDLE_ENFORCE_NOT_NULL
(
outside_weight
,
"If weights are provided, must specify both "
"inside and outside weights."
);
PADDLE_ENFORCE_EQ
(
inside_weight
->
dims
(),
x
->
dims
(),
"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"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
diff
->
Resize
(
x
->
dims
());
// loss is a two-rank tensor
out
->
Resize
({
x
->
dims
()[
0
],
1
});
}
};
template
<
typename
AttrType
>
class
SmoothL1LossOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SmoothL1LossOpMaker
(
framework
::
OpProto
*
proto
,
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]"
);
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"
,
"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 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
)DOC"
);
}
};
class
SmoothL1LossGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
auto
in_dims
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_GE
(
out_dims
.
size
(),
2
,
"The tensor rank of Input(Out@Grad) should be 2."
);
PADDLE_ENFORCE_EQ
(
out_dims
[
0
],
in_dims
[
0
],
"The 1st dimension of Input(Out@Grad) must be "
"same as input."
);
PADDLE_ENFORCE_EQ
(
out_dims
[
1
],
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
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
smooth_l1_loss
,
ops
::
SmoothL1LossOp
,
ops
::
SmoothL1LossOpMaker
<
float
>
,
smooth_l1_loss_grad
,
ops
::
SmoothL1LossGradOp
);
REGISTER_OP_CPU_KERNEL
(
smooth_l1_loss
,
ops
::
SmoothL1LossKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
smooth_l1_loss_grad
,
ops
::
SmoothL1LossGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/smooth_l1_loss_op.cu
0 → 100644
浏览文件 @
cdda0cf3
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/smooth_l1_loss_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
smooth_l1_loss
,
ops
::
SmoothL1LossKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
smooth_l1_loss_grad
,
ops
::
SmoothL1LossGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/smooth_l1_loss_op.h
0 → 100644
浏览文件 @
cdda0cf3
/* 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
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
>
struct
SmoothL1LossForward
{
HOSTDEVICE
SmoothL1LossForward
(
const
T
&
sigma2
)
:
sigma2
(
sigma2
)
{}
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
T
abs_val
=
std
::
abs
(
val
);
if
(
abs_val
<
1.0
/
sigma2
)
{
return
0.5
*
val
*
val
*
sigma2
;
}
else
{
return
abs_val
-
0.5
/
sigma2
;
}
}
T
sigma2
;
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
SmoothL1LossKernel
:
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
*
in2
=
context
.
Input
<
Tensor
>
(
"InsideWeight"
);
auto
*
in3
=
context
.
Input
<
Tensor
>
(
"OutsideWeight"
);
auto
*
out0
=
context
.
Output
<
Tensor
>
(
"Diff"
);
auto
*
out1
=
context
.
Output
<
Tensor
>
(
"Out"
);
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out1
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
sigma
=
static_cast
<
T
>
(
context
.
Attr
<
AttrType
>
(
"sigma"
));
T
sigma2
=
sigma
*
sigma
;
bool
has_weight
=
(
in2
!=
nullptr
)
&&
(
in3
!=
nullptr
);
auto
x
=
EigenVector
<
T
>::
Flatten
(
*
in0
);
auto
y
=
EigenVector
<
T
>::
Flatten
(
*
in1
);
auto
diff
=
EigenVector
<
T
>::
Flatten
(
*
out0
);
diff
.
device
(
place
)
=
x
-
y
;
// multiply inside weight
if
(
has_weight
)
{
auto
inside_weight
=
EigenVector
<
T
>::
Flatten
(
*
in2
);
// cache diff, reused in bp
diff
.
device
(
place
)
=
diff
*
inside_weight
;
}
auto
in_counts
=
in0
->
numel
();
Tensor
ptensor_errors
;
ptensor_errors
.
mutable_data
<
T
>
({
static_cast
<
int
>
(
in_counts
)},
context
.
GetPlace
());
auto
errors
=
EigenVector
<
T
>::
Flatten
(
ptensor_errors
);
// apply smooth l1 forward
errors
.
device
(
place
)
=
diff
.
unaryExpr
(
SmoothL1LossForward
<
T
>
(
sigma2
));
// multiply outside weight
if
(
has_weight
)
{
auto
outside_weight
=
EigenVector
<
T
>::
Flatten
(
*
in3
);
errors
.
device
(
place
)
=
errors
*
outside_weight
;
}
auto
loss
=
EigenVector
<
T
>::
Flatten
(
*
out1
);
// first dimension of 'X' is the number of samples
auto
mat_dims
=
framework
::
make_ddim
({
static_cast
<
int
>
(
in0
->
dims
()[
0
]),
static_cast
<
int
>
(
in_counts
/
in0
->
dims
()[
0
])});
auto
errors_mat_view
=
EigenMatrix
<
T
>::
From
(
ptensor_errors
,
mat_dims
);
loss
.
device
(
place
)
=
errors_mat_view
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
1
}}));
}
};
template
<
typename
T
>
struct
SmoothL1LossBackward
{
HOSTDEVICE
SmoothL1LossBackward
(
const
T
&
sigma2
)
:
sigma2
(
sigma2
)
{}
HOSTDEVICE
T
operator
()(
const
T
&
val
)
const
{
T
abs_val
=
std
::
abs
(
val
);
if
(
abs_val
<
1.0
/
sigma2
)
{
return
sigma2
*
val
;
}
else
{
return
(
0
<
val
)
-
(
val
<
0
);
}
}
T
sigma2
;
};
template
<
typename
Place
,
typename
T
,
typename
AttrType
=
T
>
class
SmoothL1LossGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"InsideWeight"
);
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
.
Attr
<
AttrType
>
(
"sigma"
));
T
sigma2
=
sigma
*
sigma
;
bool
has_weight
=
(
in0
!=
nullptr
)
&&
(
in1
!=
nullptr
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
in_dims
=
in2
->
dims
();
auto
counts
=
in2
->
numel
();
auto
cols
=
counts
/
in_dims
[
0
];
auto
mat_dims
=
framework
::
make_ddim
(
{
static_cast
<
int
>
(
in_dims
[
0
]),
static_cast
<
int
>
(
cols
)});
Tensor
ptensor_diff
;
ptensor_diff
.
mutable_data
<
T
>
({
static_cast
<
int
>
(
counts
)},
context
.
GetPlace
());
auto
diff
=
EigenVector
<
T
>::
Flatten
(
ptensor_diff
);
// apply smooth l1 backwoard
diff
.
device
(
place
)
=
EigenVector
<
T
>::
Flatten
(
*
in2
).
unaryExpr
(
SmoothL1LossBackward
<
T
>
(
sigma2
));
// compute weights
Tensor
ptensor_weights
;
ptensor_weights
.
mutable_data
<
T
>
(
mat_dims
,
context
.
GetPlace
());
auto
weights
=
EigenMatrix
<
T
>::
From
(
ptensor_weights
);
// initialize to 1.0
weights
.
device
(
place
)
=
weights
.
constant
(
static_cast
<
T
>
(
1.0
));
if
(
has_weight
)
{
auto
inside_weight
=
EigenMatrix
<
T
>::
From
(
*
in0
,
mat_dims
);
auto
outside_weight
=
EigenMatrix
<
T
>::
From
(
*
in1
,
mat_dims
);
weights
.
device
(
place
)
=
inside_weight
*
outside_weight
;
}
// compute gradients
auto
out_grad
=
EigenMatrix
<
T
>::
From
(
*
og
);
auto
diff_mat_view
=
EigenMatrix
<
T
>::
From
(
ptensor_diff
,
mat_dims
);
auto
gradients
=
out_grad
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
1
,
static_cast
<
int
>
(
cols
)}}))
*
weights
*
diff_mat_view
;
auto
*
out0
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
out1
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
if
(
out0
)
{
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_grad
=
EigenMatrix
<
T
>::
From
(
*
out0
,
mat_dims
);
x_grad
.
device
(
place
)
=
gradients
;
}
if
(
out1
)
{
out1
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
y_grad
=
EigenMatrix
<
T
>::
From
(
*
out1
,
mat_dims
);
y_grad
.
device
(
place
)
=
-
1
*
gradients
;
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_smooth_l1_loss_op.py
0 → 100644
浏览文件 @
cdda0cf3
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
smooth_l1_loss_forward
(
val
,
sigma2
):
abs_val
=
abs
(
val
)
if
abs_val
<
1.0
/
sigma2
:
return
0.5
*
val
*
val
*
sigma2
else
:
return
abs_val
-
0.5
/
sigma2
class
TestSmoothL1LossOp1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"smooth_l1_loss"
dims
=
(
5
,
10
)
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
)
}
sigma
=
3.0
self
.
attrs
=
{
'sigma'
:
sigma
}
sigma2
=
sigma
*
sigma
diff
=
self
.
inputs
[
'X'
]
-
self
.
inputs
[
'Y'
]
loss
=
np
.
vectorize
(
smooth_l1_loss_forward
)(
diff
,
sigma2
).
sum
(
1
)
loss
=
loss
.
reshape
((
dims
[
0
],
1
))
self
.
outputs
=
{
'Diff'
:
diff
,
'Out'
:
loss
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.02
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
(
'Y'
))
class
TestSmoothL1LossOp2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"smooth_l1_loss"
dims
=
(
5
,
10
)
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
),
'InsideWeight'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
),
'OutsideWeight'
:
np
.
random
.
random
(
dims
).
astype
(
"float32"
)
}
sigma
=
3.0
self
.
attrs
=
{
'sigma'
:
sigma
}
sigma2
=
sigma
*
sigma
diff
=
self
.
inputs
[
'X'
]
-
self
.
inputs
[
'Y'
]
diff
=
diff
*
self
.
inputs
[
'InsideWeight'
]
loss
=
np
.
vectorize
(
smooth_l1_loss_forward
)(
diff
,
sigma2
)
loss
=
loss
*
self
.
inputs
[
'OutsideWeight'
]
loss
=
loss
.
sum
(
1
).
reshape
((
dims
[
0
],
1
))
self
.
outputs
=
{
'Diff'
:
diff
,
'Out'
:
loss
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.03
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'X'
,
'InsideWeight'
,
'OutsideWeight'
]))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.03
,
no_grad_set
=
set
([
'Y'
,
'InsideWeight'
,
'OutsideWeight'
]))
if
__name__
==
'__main__'
:
unittest
.
main
()
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