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体验新版 GitCode,发现更多精彩内容 >>
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6735585b
编写于
9月 22, 2017
作者:
C
caoying03
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix cpu kernel with soft labels.
上级
30bfaab3
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
194 addition
and
97 deletion
+194
-97
paddle/operators/accuracy_op.cu
paddle/operators/accuracy_op.cu
+6
-2
paddle/operators/cross_entropy_op.cc
paddle/operators/cross_entropy_op.cc
+44
-28
paddle/operators/cross_entropy_op.cu
paddle/operators/cross_entropy_op.cu
+88
-40
paddle/operators/cross_entropy_op.h
paddle/operators/cross_entropy_op.h
+3
-10
paddle/operators/lookup_table_op.cu
paddle/operators/lookup_table_op.cu
+8
-3
paddle/operators/top_k_op.cu
paddle/operators/top_k_op.cu
+6
-4
python/paddle/v2/framework/tests/test_cross_entropy_op.py
python/paddle/v2/framework/tests/test_cross_entropy_op.py
+39
-10
未找到文件。
paddle/operators/accuracy_op.cu
浏览文件 @
6735585b
...
...
@@ -69,8 +69,12 @@ class AccuracyOpCUDAKernel : public framework::OpKernel {
return
;
}
AccuracyCudaKernel
<
PADDLE_CUDA_NUM_THREADS
><<<
1
,
PADDLE_CUDA_NUM_THREADS
>>>
(
num_samples
,
infer_width
,
inference_data
,
label_data
,
accuracy_data
);
AccuracyCudaKernel
<
PADDLE_CUDA_NUM_THREADS
><<<
1
,
PADDLE_CUDA_NUM_THREADS
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
num_samples
,
infer_width
,
inference_data
,
label_data
,
accuracy_data
);
}
};
...
...
paddle/operators/cross_entropy_op.cc
浏览文件 @
6735585b
...
...
@@ -23,27 +23,28 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X)
must not be
null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X)
should be not
null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Label"
),
"Input(Label) must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Y"
),
"Output(Y) must not be null."
);
"Input(Label) should be not null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Y"
),
"Output(Y) should be not 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
(
x
->
dims
().
size
(),
2
,
"Input(X)'s rank
should
be 2."
);
PADDLE_ENFORCE_EQ
(
label
->
dims
().
size
(),
2
,
"Input(Label)'s rank
must
be 2."
);
"Input(Label)'s rank
should
be 2."
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
0
],
label
->
dims
()[
0
],
"The 1st dimension of Input(X) and Input(Label)
must
"
"The 1st dimension of Input(X) and Input(Label)
should
"
"be equal."
);
if
(
ctx
.
Attr
<
bool
>
(
"soft_label"
))
{
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
1
],
label
->
dims
()[
1
],
"If Attr(soft_label) == true,
T
he 2nd dimension of "
"Input(X) and Input(Label)
must
be equal."
);
"If Attr(soft_label) == true,
t
he 2nd dimension of "
"Input(X) and Input(Label)
should
be equal."
);
}
else
{
PADDLE_ENFORCE_EQ
(
label
->
dims
()[
1
],
1
,
"If Attr(soft_label) == false,
T
he 2nd dimension of "
"Input(Label)
must
be 1."
);
"If Attr(soft_label) == false,
t
he 2nd dimension of "
"Input(Label)
should
be 1."
);
}
ctx
.
Output
<
Tensor
>
(
"Y"
)
->
Resize
({
x
->
dims
()[
0
],
1
});
...
...
@@ -57,35 +58,36 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X)
must not be
null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X)
should be not
null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Label"
),
"Input(Label)
must not be
null."
);
"Input(Label)
should be not
null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Y"
)),
"Input(Y@GRAD)
must not be
null."
);
"Input(Y@GRAD)
shoudl be not
null."
);
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
(
x
->
dims
().
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
dy
->
dims
().
size
(),
2
,
"Input(Y@Grad)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
label
->
dims
().
size
(),
2
,
"Input(Label)'s rank
must
be 2."
);
"Input(Label)'s rank
should
be 2."
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
0
],
label
->
dims
()[
0
],
"The 1st dimension of Input(X) and Input(Label)
must
"
"The 1st dimension of Input(X) and Input(Label)
should
"
"be equal."
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
0
],
dy
->
dims
()[
0
],
"The 1st dimension of Input(X) and Input(Y@Grad)
must
"
"The 1st dimension of Input(X) and Input(Y@Grad)
should
"
"be equal."
);
PADDLE_ENFORCE_EQ
(
dy
->
dims
()[
1
],
1
,
"The 2nd dimension of Input(Y@Grad)
must
be 1."
);
"The 2nd dimension of Input(Y@Grad)
should
be 1."
);
if
(
ctx
.
Attr
<
bool
>
(
"soft_label"
))
{
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
1
],
label
->
dims
()[
1
],
"
If Attr(soft_label) == true, T
he 2nd dimension of "
"Input(X) and Input(Label)
must
be equal."
);
"
When Attr(soft_label) == true, t
he 2nd dimension of "
"Input(X) and Input(Label)
should
be equal."
);
}
else
{
PADDLE_ENFORCE_EQ
(
label
->
dims
()[
1
],
1
,
"
If Attr(soft_label) == false, T
he 2nd dimension of "
"Input(Label)
must
be 1."
);
"
When Attr(soft_label) == false, t
he 2nd dimension of "
"Input(Label)
should
be 1."
);
}
auto
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
...
...
@@ -98,12 +100,26 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
CrossEntropyOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The first input of CrossEntropyOp"
);
AddInput
(
"Label"
,
"The second input of CrossEntropyOp"
);
AddOutput
(
"Y"
,
"The output of CrossEntropyOp"
);
AddAttr
<
bool
>
(
"soft_label"
,
"Is soft label. Default zero."
)
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
"where N is the batch size and D is the number of classes. "
"This input is a probability computed by the previous operator, "
"which is almost always the result of a softmax operator."
);
AddInput
(
"Label"
,
"(Tensor, default Tensor<int>), the ground truth which is "
"a 1-D or 2-D tensor. "
"When soft_label is set to 0, `Label` is a Tensor<int> with shape "
"[N x 1]. "
"When soft_label is set to 1, `Label` is a Tensor<float/double> "
"with shape [N x K]."
);
AddOutput
(
"Y"
,
"(Tensor, default Tensor<float>), a 1-D tensor "
"with shape [N x 1]. The cross entropy loss."
);
AddAttr
<
bool
>
(
"soft_label"
,
"(bool, default false), a flag to indicate whether to interpretate "
"the given labels as soft labels."
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
CrossEntropy Operator.
...
...
paddle/operators/cross_entropy_op.cu
浏览文件 @
6735585b
...
...
@@ -32,37 +32,71 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
}
}
template
<
typename
T
>
__device__
__forceinline__
T
sum_single_warp
(
T
val
)
{
val
+=
__shfl_down
(
val
,
16
);
val
+=
__shfl_down
(
val
,
8
);
val
+=
__shfl_down
(
val
,
4
);
val
+=
__shfl_down
(
val
,
2
);
val
+=
__shfl_down
(
val
,
1
);
return
val
;
}
// This kernel is called when the class number is less than or equal to 512.
template
<
typename
T
>
__global__
void
SoftCrossEntropyKernel1
(
T
*
Y
,
const
T
*
X
,
const
T
*
label
,
const
int
class_num
)
{
int
tid
=
threadIdx
.
x
;
extern
__shared__
T
d_sum
[];
d_sum
[
tid
]
=
0
;
int
cur_idx
=
tid
;
int
next_idx
=
blockIdx
.
x
*
class_num
+
tid
;
while
(
cur_idx
<
class_num
)
{
d_sum
[
tid
]
+=
TolerableValue
<
T
>
()(
std
::
log
(
X
[
next_idx
]))
*
label
[
next_idx
];
next_idx
+=
blockDim
.
x
;
cur_idx
+=
blockDim
.
x
;
}
__syncthreads
();
for
(
unsigned
int
stride
=
blockDim
.
x
>>
1
;
stride
>=
32
;
stride
>>=
1
)
{
if
(
tid
<
stride
)
d_sum
[
tid
]
+=
d_sum
[
tid
+
stride
];
__syncthreads
();
}
T
val
=
d_sum
[
tid
];
val
=
sum_single_warp
<
T
>
(
val
);
if
(
tid
==
0
)
Y
[
blockIdx
.
x
]
=
-
val
;
}
// This kernel is called when the class number is larger than 512.
template
<
typename
T
,
int
BlockSize
>
__global__
void
SoftCrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
T
*
label
,
const
int
N
,
const
int
D
)
{
__global__
void
SoftCrossEntropyKernel
2
(
T
*
Y
,
const
T
*
X
,
const
T
*
label
,
const
int
class_num
)
{
int
tid
=
threadIdx
.
x
;
__shared__
T
d_sum
[
BlockSize
];
int
next_idx
=
blockIdx
.
x
*
D
+
tid
;
int
next_idx
=
blockIdx
.
x
*
class_num
+
tid
;
d_sum
[
tid
]
=
0
;
int
cur_idx
=
tid
;
while
(
cur_idx
<
D
)
{
while
(
cur_idx
<
class_num
)
{
d_sum
[
tid
]
+=
TolerableValue
<
T
>
()(
std
::
log
(
X
[
next_idx
]))
*
label
[
next_idx
];
next_idx
+=
BlockSize
;
cur_idx
+=
BlockSize
;
}
__syncthreads
();
for
(
int
stride
=
BlockSize
>>
1
;
stride
>
0
;
stride
>>=
1
)
{
for
(
unsigned
int
stride
=
BlockSize
>>
1
;
stride
>=
32
;
stride
>>=
1
)
{
if
(
tid
<
stride
)
d_sum
[
tid
]
+=
d_sum
[
tid
+
stride
];
__syncthreads
();
if
(
tid
<
stride
)
{
next_idx
=
tid
+
stride
;
d_sum
[
tid
]
+=
d_sum
[
next_idx
];
}
}
__syncthreads
();
if
(
tid
==
0
)
{
Y
[
blockIdx
.
x
]
=
-
d_sum
[
0
]
;
}
T
val
=
d_sum
[
tid
];
val
=
sum_single_warp
<
T
>
(
val
)
;
if
(
tid
==
0
)
Y
[
blockIdx
.
x
]
=
-
val
;
}
// TODO(qingqing): make zero setting a
n
common function.
// TODO(qingqing): make zero setting a common function.
template
<
typename
T
>
__global__
void
zero
(
T
*
X
,
const
int
N
)
{
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
N
;
...
...
@@ -88,11 +122,9 @@ template <typename T>
__global__
void
SoftCrossEntropyGradientKernel
(
T
*
dX
,
const
T
*
dY
,
const
T
*
X
,
const
T
*
label
,
const
int
N
,
const
int
D
)
{
int
row_ids
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
col_ids
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
int
ids
=
row_ids
*
D
+
col_ids
;
int
ids
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
ids
<
N
*
D
)
{
int
row_ids
=
ids
/
D
;
dX
[
ids
]
=
-
label
[
ids
]
*
dY
[
row_ids
]
/
X
[
ids
];
}
}
...
...
@@ -112,20 +144,34 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
y_data
=
y
->
data
<
T
>
();
int
n
=
x
->
dims
()[
0
];
int
d
=
x
->
dims
()[
1
];
int
batch_size
=
x
->
dims
()[
0
];
int
class_num
=
x
->
dims
()[
1
];
int
block
=
512
;
int
grid
=
(
n
+
block
-
1
)
/
block
;
// TODO(qingqing) launch kernel on specified stream
// base on ExecutionContext.
if
(
ctx
.
Attr
<
bool
>
(
"soft_label"
))
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
T
>
();
grid
=
d
;
SoftCrossEntropyKernel
<
T
,
512
><<<
grid
,
block
>>>
(
y_data
,
x_data
,
label_data
,
n
,
d
);
if
(
class_num
>
512
)
{
SoftCrossEntropyKernel2
<
T
,
512
><<<
batch_size
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
y_data
,
x_data
,
label_data
,
class_num
);
}
else
{
int
block_size
=
pow
(
2
,
int
(
std
::
log2
(
class_num
)));
SoftCrossEntropyKernel1
<
T
><<<
batch_size
,
block_size
,
block_size
*
sizeof
(
T
),
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
y_data
,
x_data
,
label_data
,
class_num
);
}
}
else
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
int
>
();
CrossEntropyKernel
<
T
><<<
grid
,
block
>>>
(
y_data
,
x_data
,
label_data
,
n
,
d
);
int
grid
=
(
batch_size
+
block
-
1
)
/
block
;
CrossEntropyKernel
<
T
><<<
grid
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
y_data
,
x_data
,
label_data
,
batch_size
,
class_num
);
}
}
};
...
...
@@ -148,25 +194,27 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
int
n
=
x
->
dims
()[
0
];
int
d
=
x
->
dims
()[
1
];
int
block
=
512
;
int
grid
=
(
n
*
d
+
block
-
1
)
/
block
;
zero
<
T
><<<
grid
,
block
>>>
(
dx_data
,
n
*
d
);
grid
=
(
n
+
block
-
1
)
/
block
;
// TODO(qingqing): launch kernel on specified stream
// base on ExecutionContext.
zero
<
T
><<<
grid
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
dx_data
,
n
*
d
);
if
(
ctx
.
Attr
<
bool
>
(
"soft_label"
))
{
int
block_x
=
32
;
int
block_y
=
32
;
dim3
block
(
block_x
,
block_y
);
dim3
grid
((
n
+
block_x
-
1
)
/
block_x
,
(
d
+
block_y
-
1
)
/
block_y
);
auto
*
label_data
=
label
->
data
<
T
>
();
SoftCrossEntropyGradientKernel
<
T
><<<
grid
,
block
>>>
(
dx_data
,
dy_data
,
x_data
,
label_data
,
n
,
d
);
SoftCrossEntropyGradientKernel
<
T
><<<
grid
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
dx_data
,
dy_data
,
x_data
,
label_data
,
n
,
d
);
}
else
{
auto
*
label_data
=
label
->
data
<
int
>
();
CrossEntropyGradientKernel
<
T
><<<
grid
,
block
>>>
(
dx_data
,
dy_data
,
x_data
,
label_data
,
n
,
d
);
CrossEntropyGradientKernel
<
T
><<<
grid
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
dx_data
,
dy_data
,
x_data
,
label_data
,
n
,
d
);
}
}
};
...
...
paddle/operators/cross_entropy_op.h
浏览文件 @
6735585b
...
...
@@ -31,12 +31,8 @@ struct TolerableValue {
PADDLE_ASSERT
(
std
::
is_floating_point
<
T
>::
value
);
const
T
kApproInf
=
1e20
;
if
(
x
==
INFINITY
)
{
return
kApproInf
;
}
if
(
x
==
-
INFINITY
)
{
return
-
kApproInf
;
}
if
(
x
==
INFINITY
)
return
kApproInf
;
if
(
x
==
-
INFINITY
)
return
-
kApproInf
;
return
x
;
}
};
...
...
@@ -58,11 +54,8 @@ class CrossEntropyOpKernel : public framework::OpKernel {
auto
lbl_mat
=
EigenMatrix
<
T
>::
From
(
*
labels
);
auto
loss
=
EigenMatrix
<
T
>::
From
(
*
y
);
// loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
// prob.log().unaryExpr(TolerableValue<T>());
loss
.
device
(
ctx
.
GetEigenDevice
<
platform
::
CPUPlace
>
())
=
-
((
lbl_mat
*
prob
.
log
())
-
((
lbl_mat
*
prob
.
log
()
.
unaryExpr
(
TolerableValue
<
T
>
())
)
.
sum
(
Eigen
::
DSizes
<
int
,
1
>
(
1
))
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
batch_size
,
1
)));
}
else
{
...
...
paddle/operators/lookup_table_op.cu
浏览文件 @
6735585b
...
...
@@ -77,7 +77,10 @@ class LookupTableCUDAKernel : public framework::OpKernel {
dim3
threads
(
128
,
8
);
dim3
grids
(
8
,
1
);
LookupTable
<
T
,
128
,
8
,
8
><<<
grids
,
threads
>>>
(
output
,
table
,
ids
,
N
,
K
,
D
);
LookupTable
<
T
,
128
,
8
,
8
><<<
grids
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
.
device_context
())
.
stream
()
>>>
(
output
,
table
,
ids
,
N
,
K
,
D
);
}
};
...
...
@@ -102,8 +105,10 @@ class LookupTableGradCUDAKernel : public framework::OpKernel {
dim3
threads
(
128
,
8
);
dim3
grids
(
8
,
1
);
LookupTableGrad
<
T
,
128
,
8
,
8
><<<
grids
,
threads
>>>
(
d_table
,
d_output
,
ids
,
N
,
K
,
D
);
LookupTableGrad
<
T
,
128
,
8
,
8
><<<
grids
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
.
device_context
())
.
stream
()
>>>
(
d_table
,
d_output
,
ids
,
N
,
K
,
D
);
}
};
...
...
paddle/operators/top_k_op.cu
浏览文件 @
6735585b
...
...
@@ -301,14 +301,16 @@ class TopkOpCUDAKernel : public framework::OpKernel {
// NOTE: pass lds and dim same to input width.
// NOTE: old matrix implementation of stride is different to eigen.
// TODO(typhoonzero): launch kernel on specified stream.
// TODO(typhoonzero): refine this kernel.
dim3
threads
(
256
,
1
);
dim3
grid
(
input_height
,
1
);
KeMatrixTopK
<
T
,
5
,
256
><<<
grid
,
threads
>>>
(
output_data
,
output
->
dims
()[
1
],
indices_data
,
input_data
,
input_width
,
input_width
,
int
(
k
));
KeMatrixTopK
<
T
,
5
,
256
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
output_data
,
output
->
dims
()[
1
],
indices_data
,
input_data
,
input_width
,
input_width
,
int
(
k
));
}
};
...
...
python/paddle/v2/framework/tests/test_cross_entropy_op.py
浏览文件 @
6735585b
...
...
@@ -19,7 +19,7 @@ class TestCrossEntropyOp1(OpTest):
dtype
=
"float32"
)
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
'soft_label'
:
False
}
self
.
attrs
=
{
"soft_label"
:
False
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -34,7 +34,8 @@ class TestCrossEntropyOp2(OpTest):
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
batch_size
=
13
batch_size
=
5
# this setting tests threads in more than one wrap.
class_num
=
37
X
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
...
...
@@ -43,9 +44,9 @@ class TestCrossEntropyOp2(OpTest):
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
}
self
.
outputs
=
{
'Y'
:
cross_entropy
}
self
.
attrs
=
{
'soft_label'
:
True
}
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
"soft_label"
:
True
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -61,8 +62,9 @@ class TestCrossEntropyOp3(OpTest):
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
batch_size
=
13
class_num
=
37
batch_size
=
5
# this setting tests all threads in one wrap.
class_num
=
17
X
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
label_index
=
np
.
random
.
randint
(
...
...
@@ -74,9 +76,36 @@ class TestCrossEntropyOp3(OpTest):
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'
:
True
}
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
"soft_label"
:
True
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Y"
,
max_relative_error
=
0.05
)
class
TestCrossEntropyOp4
(
OpTest
):
"""Test soft-label cross-entropy.
This unittest tests the gpu kernel for layer size excesses 512.
"""
def
setUp
(
self
):
self
.
op_type
=
"cross_entropy"
batch_size
=
2
class_num
=
517
X
=
np
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
)
label
=
np
.
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
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
"soft_label"
:
True
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
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