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体验新版 GitCode,发现更多精彩内容 >>
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d8046da0
编写于
9月 19, 2017
作者:
X
Xinghai Sun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Use soft_label attribute for cross-entropy.
上级
8e7fe8ca
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
138 addition
and
86 deletion
+138
-86
paddle/operators/cross_entropy_op.cc
paddle/operators/cross_entropy_op.cc
+70
-25
paddle/operators/cross_entropy_op.cu
paddle/operators/cross_entropy_op.cu
+6
-25
paddle/operators/cross_entropy_op.h
paddle/operators/cross_entropy_op.h
+7
-18
python/paddle/v2/framework/tests/test_cross_entropy_op.py
python/paddle/v2/framework/tests/test_cross_entropy_op.py
+55
-18
未找到文件。
paddle/operators/cross_entropy_op.cc
浏览文件 @
d8046da0
...
...
@@ -25,25 +25,32 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) of CrossEntropyOp must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Label"
),
"Input(Label) of CrossEntropyOp must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Y"
),
"Output(Y) of CrossEntropyOp must not be null."
);
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
PADDLE_ENFORCE_EQ
(
x
->
dims
().
size
(),
2
,
"X's rank must be 2."
);
PADDLE_ASSERT
(
label
->
dims
().
size
()
==
1
||
label
->
dims
().
size
()
==
2
);
if
(
label
->
dims
().
size
()
==
2
)
{
// soft cross entropy
PADDLE_ENFORCE_EQ
(
x
->
dims
(),
label
->
dims
());
"Input(Label) must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Y"
),
"Output(Y) must not be null."
);
auto
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
PADDLE_ENFORCE_EQ
(
x
->
dims
().
size
(),
2
,
"Input(X)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
label
->
dims
().
size
(),
2
,
"Input(Label)'s rank must be 2."
);
// TODO(xinghai-sun): remove this check after swtiching to bool
PADDLE_ENFORCE
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
0
||
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
0
],
label
->
dims
()[
0
],
"The 1st dimension of Input(X) and Input(Label) must "
"be equal."
);
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
1
],
label
->
dims
()[
1
],
"If Attr(soft_label) == 1, The 2nd dimension of "
"Input(X) and Input(Label) must be equal."
);
}
else
{
// normal cross entropy
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
0
],
label
->
dims
()[
0
]);
PADDLE_ENFORCE_EQ
(
label
->
dims
()[
1
],
1
,
"If Attr(soft_label) == 0, The 2nd dimension of "
"Input(Label) must be 1."
);
}
ctx
.
Output
<
LoDTensor
>
(
"Y"
)
->
Resize
({
x
->
dims
()[
0
],
1
});
}
};
...
...
@@ -54,12 +61,41 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) of CrossEntropyOp must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Label"
),
"Input(Label) must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Y"
)),
"Input(Y@GRAD) must not be null."
);
auto
dx
=
ctx
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
auto
dy
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_EQ
(
x
->
dims
().
size
(),
2
,
"Input(X)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
dy
->
dims
().
size
(),
2
,
"Input(Y@Grad)'s rank must be 2."
);
PADDLE_ENFORCE_EQ
(
label
->
dims
().
size
(),
2
,
"Input(Label)'s rank must be 2."
);
// TODO(xinghai-sun): remove this check after swtiching to bool
PADDLE_ENFORCE
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
0
||
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
0
],
label
->
dims
()[
0
],
"The 1st dimension of Input(X) and Input(Label) must "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
0
],
dy
->
dims
()[
0
],
"The 1st dimension of Input(X) and Input(Y@Grad) must "
"be equal."
);
PADDLE_ENFORCE_EQ
(
dy
->
dims
()[
1
],
1
,
"The 2nd dimension of Input(Y@Grad) must be 1."
);
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
1
],
label
->
dims
()[
1
],
"If Attr(soft_label) == 1, The 2nd dimension of "
"Input(X) and Input(Label) must be equal."
);
}
else
{
PADDLE_ENFORCE_EQ
(
label
->
dims
()[
1
],
1
,
"If Attr(soft_label) == 0, The 2nd dimension of "
"Input(Label) must be 1."
);
}
auto
dx
=
ctx
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
dx
->
Resize
(
x
->
dims
());
}
};
...
...
@@ -72,22 +108,31 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"X"
,
"The first input of CrossEntropyOp"
);
AddInput
(
"Label"
,
"The second input of CrossEntropyOp"
);
AddOutput
(
"Y"
,
"The output of CrossEntropyOp"
);
AddAttr
<
int
>
(
"soft_label"
,
"Is soft label. Default zero."
).
SetDefault
(
0
);
AddComment
(
R"DOC(
CrossEntropy Operator.
The second input (Label tensor) supports two kinds of shapes:
1) Rank(Label) = 1, Label[i] indicates the class index for sample i:
It supports both standard cross-entropy and soft-label cross-entropy loss
computation.
1) One-hot cross-entropy:
soft_label = 0, Label[i, 0] indicates the class index for sample i:
Y[i] = -log(X[i, Label[i]])
2) Rank(Label) = 2, Label[i, j] indicates the soft label of class j
for sample i:
2) Soft-label cross-entropy:
soft_label = 1, Label[i, j] indicates the soft label of class j
for sample i:
Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
Please make sure that in this case the summuation of each row of Label
equals one. If each row of Label has only one non-zero element (equals 1),
it degenerates to a standard one-hot representation.
equals one.
3) One-hot cross-entropy with vecterized Input(Label):
As a special case of 2), when each row of Input(Label) has only one
non-zero element (equals 1), soft-label cross-entropy degenerates to a
one-hot cross-entropy with one-hot label representation.
)DOC"
);
}
};
...
...
paddle/operators/cross_entropy_op.cu
浏览文件 @
d8046da0
...
...
@@ -13,27 +13,13 @@
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
HOSTDEVICE
T
tolerable_value
(
const
T
x
)
{
PADDLE_ASSERT
(
std
::
is_floating_point
<
T
>::
value
);
const
T
kApproInf
=
1e20
;
if
(
x
==
INFINITY
)
{
return
kApproInf
;
}
if
(
x
==
-
INFINITY
)
{
return
-
kApproInf
;
}
return
x
;
}
template
<
typename
T
>
__global__
void
CrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
int
*
label
,
const
int
N
,
const
int
D
)
{
...
...
@@ -53,9 +39,9 @@ __global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
i
+=
blockDim
.
x
*
gridDim
.
x
)
{
T
sum
=
static_cast
<
T
>
(
0
);
for
(
int
j
=
0
;
j
<
D
;
j
++
)
{
sum
+=
label
[
i
*
D
+
j
]
*
log
(
X
[
i
*
D
+
j
]
);
sum
+=
label
[
i
*
D
+
j
]
*
tolerable_value
(
log
(
X
[
i
*
D
+
j
])
);
}
Y
[
i
]
=
-
tolerable_value
(
sum
)
;
Y
[
i
]
=
-
sum
;
}
}
...
...
@@ -85,6 +71,7 @@ template <typename T>
__global__
void
SoftCrossEntropyGradientKernel
(
T
*
dX
,
const
T
*
dY
,
const
T
*
X
,
const
T
*
label
,
const
int
N
,
const
int
D
)
{
// TOOD(qingqing): optimize for this kernel
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
N
;
i
+=
blockDim
.
x
*
gridDim
.
x
)
{
for
(
int
j
=
0
;
j
<
D
;
++
j
)
{
...
...
@@ -115,14 +102,11 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
int
grid
=
(
n
+
block
-
1
)
/
block
;
// TODO(qingqing) launch kernel on specified stream
// base on ExecutionContext.
int
label_rank
=
label
->
dims
().
size
();
if
(
label_rank
==
2
)
{
// soft cross entropy
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
T
>
();
SoftCrossEntropyKernel
<
T
><<<
grid
,
block
>>>
(
y_data
,
x_data
,
label_data
,
n
,
d
);
}
else
{
// normal cross entropy
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
int
>
();
CrossEntropyKernel
<
T
><<<
grid
,
block
>>>
(
y_data
,
x_data
,
label_data
,
n
,
d
);
}
...
...
@@ -153,14 +137,11 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
grid
=
(
n
+
block
-
1
)
/
block
;
// TODO(qingqing): launch kernel on specified stream
// base on ExecutionContext.
int
label_rank
=
label
->
dims
().
size
();
if
(
label_rank
==
2
)
{
// soft cross entropy
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
auto
*
label_data
=
label
->
data
<
T
>
();
SoftCrossEntropyGradientKernel
<
T
><<<
grid
,
block
>>>
(
dx_data
,
dy_data
,
x_data
,
label_data
,
n
,
d
);
}
else
{
// normal cross entropy
auto
*
label_data
=
label
->
data
<
int
>
();
CrossEntropyGradientKernel
<
T
><<<
grid
,
block
>>>
(
dx_data
,
dy_data
,
x_data
,
label_data
,
n
,
d
);
...
...
paddle/operators/cross_entropy_op.h
浏览文件 @
d8046da0
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -21,21 +22,15 @@ namespace operators {
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
inline
T
tolerable_value
(
const
T
x
)
{
static_assert
(
std
::
is_floating_point
<
T
>::
value
,
"tolerable_value works only on float, "
"double and double double."
);
HOSTDEVICE
T
tolerable_value
(
const
T
x
)
{
PADDLE_ASSERT
(
std
::
is_floating_point
<
T
>::
value
);
const
T
kApproInf
=
1e20
;
if
(
x
==
INFINITY
)
{
return
kApproInf
;
}
if
(
x
==
-
INFINITY
)
{
return
-
kApproInf
;
}
return
x
;
}
...
...
@@ -55,22 +50,19 @@ class CrossEntropyOpKernel : public framework::OpKernel {
int
batch_size
=
x
->
dims
()[
0
];
int
class_num
=
x
->
dims
()[
1
];
int
label_rank
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
dims
().
size
();
if
(
label_rank
==
2
)
{
// soft cross entropy
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
T
>
();
int
index
=
0
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
T
sum
=
static_cast
<
T
>
(
0
);
for
(
int
j
=
0
;
j
<
class_num
;
++
j
)
{
sum
+=
label_data
[
index
]
*
std
::
log
(
x_data
[
index
]
);
y_data
[
i
]
=
-
tolerable_value
(
sum
)
;
sum
+=
label_data
[
index
]
*
tolerable_value
(
std
::
log
(
x_data
[
index
])
);
y_data
[
i
]
=
-
sum
;
index
++
;
}
}
}
else
{
// normal cross entropy
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
int
>
();
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
int
index
=
i
*
class_num
+
label_data
[
i
];
...
...
@@ -98,11 +90,9 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel {
int
batch_size
=
x
->
dims
()[
0
];
int
class_num
=
x
->
dims
()[
1
];
int
label_rank
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
dims
().
size
();
// TODO(qingqing): make zero setting an common function.
if
(
label_rank
==
2
)
{
// soft cross entropy
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
T
>
();
int
index
=
0
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
...
...
@@ -112,7 +102,6 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel {
}
}
}
else
{
// normal cross entropy
auto
*
label_data
=
label
->
data
<
int
>
();
memset
(
dx_data
,
0
,
sizeof
(
T
)
*
batch_size
*
class_num
);
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
...
...
python/paddle/v2/framework/tests/test_cross_entropy_op.py
浏览文件 @
d8046da0
import
unittest
import
numpy
import
numpy
as
np
from
op_test
import
OpTest
class
TestOnehotCrossEntropyOp
(
OpTest
):
class
TestCrossEntropyOp1
(
OpTest
):
"""Test standard cross-entropy, with index representation of labels.
"""
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
batch_size
=
30
class_num
=
10
X
=
numpy
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
labels
=
numpy
.
random
.
randint
(
0
,
class_num
,
batch_size
,
dtype
=
"int32"
)
cross_entropy
=
numpy
.
asmatrix
(
[[
-
numpy
.
log
(
X
[
i
][
labels
[
i
]])]
for
i
in
range
(
X
.
shape
[
0
])],
X
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
label
=
np
.
random
.
randint
(
0
,
class_num
,
(
batch_size
,
1
),
dtype
=
"int32"
)
cross_entropy
=
np
.
asmatrix
(
[[
-
np
.
log
(
X
[
i
][
label
[
i
][
0
]])]
for
i
in
range
(
X
.
shape
[
0
])],
dtype
=
"float32"
)
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
s
}
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
'soft_label'
:
0
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -26,20 +28,55 @@ class TestOnehotCrossEntropyOp(OpTest):
self
.
check_grad
([
"X"
],
"Y"
)
class
TestCrossEntropySoftLabel
(
OpTest
):
class
TestCrossEntropyOp2
(
OpTest
):
"""Test soft-label cross-entropy, with vecterized soft labels.
"""
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
batch_size
=
3
0
class_num
=
10
X
=
n
umpy
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
label
=
n
umpy
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
batch_size
=
1
0
class_num
=
5
X
=
n
p
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
label
=
n
p
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
label
/=
label
.
sum
(
axis
=
1
,
keepdims
=
True
)
cross_entropy
=
(
-
label
*
np
.
log
(
X
)).
sum
(
axis
=
1
,
keepdims
=
True
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
X
,
'Label'
:
label
}
cross_entropy
=
(
-
label
*
numpy
.
log
(
X
)).
sum
(
self
.
outputs
=
{
'Y'
:
cross_entropy
}
self
.
attrs
=
{
'soft_label'
:
1
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Y'
)
class
TestCrossEntropyOp3
(
OpTest
):
"""Test one-hot cross-entropy, with vecterized one-hot representation of
labels.
"""
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
batch_size
=
30
class_num
=
10
X
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
label_index
=
np
.
random
.
randint
(
0
,
class_num
,
(
batch_size
),
dtype
=
"int32"
)
label
=
np
.
zeros
(
X
.
shape
)
label
[
np
.
arange
(
batch_size
),
label_index
]
=
1
cross_entropy
=
np
.
asmatrix
(
[[
-
np
.
log
(
X
[
i
][
label_index
[
i
]])]
for
i
in
range
(
X
.
shape
[
0
])],
dtype
=
"float32"
)
cross_entropy2
=
(
-
label
*
np
.
log
(
X
)).
sum
(
axis
=
1
,
keepdims
=
True
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
X
,
'Label'
:
label
}
self
.
outputs
=
{
'Y'
:
cross_entropy
}
self
.
attrs
=
{
'soft_label'
:
1
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
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