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体验新版 GitCode,发现更多精彩内容 >>
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提交
eaa3fd45
编写于
2月 09, 2022
作者:
S
sneaxiy
提交者:
GitHub
2月 09, 2022
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add more int type support for softmax_with_cross_entropy (#39409)
上级
8d87b3bc
变更
6
展开全部
隐藏空白更改
内联
并排
Showing
6 changed file
with
307 addition
and
154 deletion
+307
-154
paddle/fluid/operators/math/cross_entropy.cc
paddle/fluid/operators/math/cross_entropy.cc
+67
-36
paddle/fluid/operators/math/cross_entropy.cu
paddle/fluid/operators/math/cross_entropy.cu
+48
-12
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
+99
-85
paddle/fluid/operators/softmax_with_cross_entropy_op.h
paddle/fluid/operators/softmax_with_cross_entropy_op.h
+64
-19
python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
...uid/tests/unittests/test_softmax_with_cross_entropy_op.py
+27
-1
python/paddle/nn/functional/loss.py
python/paddle/nn/functional/loss.py
+2
-1
未找到文件。
paddle/fluid/operators/math/cross_entropy.cc
浏览文件 @
eaa3fd45
...
...
@@ -29,6 +29,65 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
>
struct
HardLabelCrossEntropyCPUFunctorImpl
{
HardLabelCrossEntropyCPUFunctorImpl
(
framework
::
Tensor
*
out
,
const
framework
::
Tensor
*
prob
,
const
framework
::
Tensor
*
labels
,
const
int
ignore_index
,
const
int
axis_dim
)
:
out_
(
out
),
prob_
(
prob
),
labels_
(
labels
),
ignore_index_
(
ignore_index
),
axis_dim_
(
axis_dim
)
{}
template
<
typename
U
>
void
apply
()
const
{
const
int
batch_size
=
prob_
->
dims
()[
0
];
const
int
num_classes
=
prob_
->
dims
()[
1
];
const
int
num_remain
=
num_classes
/
axis_dim_
;
const
T
*
prob_data
=
prob_
->
template
data
<
T
>();
T
*
loss_data
=
out_
->
template
data
<
T
>();
const
auto
*
label_data
=
labels_
->
template
data
<
U
>();
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
int
j
=
0
;
j
<
num_remain
;
j
++
)
{
int
lbl
=
static_cast
<
int
>
(
label_data
[
i
*
num_remain
+
j
]);
if
(
lbl
!=
ignore_index_
)
{
PADDLE_ENFORCE_GE
(
lbl
,
0
,
platform
::
errors
::
OutOfRange
(
"label value should >= 0 when label "
"value(%f) not equal to ignore_index(%f)"
,
lbl
,
ignore_index_
));
PADDLE_ENFORCE_LT
(
lbl
,
axis_dim_
,
platform
::
errors
::
OutOfRange
(
"label value should less than the shape of axis dimension "
"when label value(%f) not equal to ignore_index(%f), But "
"received label value as %ld and shape of axis dimension "
"is %d"
,
lbl
,
ignore_index_
,
lbl
,
axis_dim_
));
}
int
index
=
i
*
num_classes
+
lbl
*
num_remain
+
j
;
int
loss_idx
=
i
*
num_remain
+
j
;
loss_data
[
loss_idx
]
=
lbl
==
ignore_index_
?
0
:
-
math
::
TolerableValue
<
T
>
()(
std
::
log
(
prob_data
[
index
]));
}
}
}
private:
framework
::
Tensor
*
out_
;
const
framework
::
Tensor
*
prob_
;
const
framework
::
Tensor
*
labels_
;
const
int
ignore_index_
;
const
int
axis_dim_
;
};
template
<
typename
T
>
class
CrossEntropyFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
public:
...
...
@@ -36,13 +95,12 @@ class CrossEntropyFunctor<platform::CPUDeviceContext, T> {
const
framework
::
Tensor
*
prob
,
const
framework
::
Tensor
*
labels
,
const
bool
softLabel
,
const
int
ignore_index
,
const
int
axis_dim
)
{
const
int
batch_size
=
prob
->
dims
()[
0
];
const
int
num_classes
=
prob
->
dims
()[
1
];
const
int
num_remain
=
num_classes
/
axis_dim
;
Eigen
::
DSizes
<
int
,
3
>
batch_axis_remain
(
batch_size
,
axis_dim
,
num_remain
);
if
(
softLabel
)
{
const
int
batch_size
=
prob
->
dims
()[
0
];
const
int
num_classes
=
prob
->
dims
()[
1
];
const
int
num_remain
=
num_classes
/
axis_dim
;
Eigen
::
DSizes
<
int
,
3
>
batch_axis_remain
(
batch_size
,
axis_dim
,
num_remain
);
auto
in
=
EigenMatrix
<
T
>::
From
(
*
prob
);
auto
lbl
=
EigenMatrix
<
T
>::
From
(
*
labels
);
auto
loss
=
EigenMatrix
<
T
>::
From
(
*
out
);
...
...
@@ -52,36 +110,9 @@ class CrossEntropyFunctor<platform::CPUDeviceContext, T> {
.
reshape
(
batch_axis_remain
)
.
sum
(
Eigen
::
DSizes
<
int
,
1
>
(
1
)));
}
else
{
const
T
*
prob_data
=
prob
->
data
<
T
>
();
T
*
loss_data
=
out
->
data
<
T
>
();
const
int64_t
*
label_data
=
labels
->
data
<
int64_t
>
();
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
int
j
=
0
;
j
<
num_remain
;
j
++
)
{
int
lbl
=
label_data
[
i
*
num_remain
+
j
];
if
(
lbl
!=
ignore_index
)
{
PADDLE_ENFORCE_GE
(
lbl
,
0
,
platform
::
errors
::
OutOfRange
(
"label value should >= 0 when label "
"value(%f) not equal to ignore_index(%f)"
,
lbl
,
ignore_index
));
PADDLE_ENFORCE_LT
(
lbl
,
axis_dim
,
platform
::
errors
::
OutOfRange
(
"label value should less than the shape of axis dimension "
"when label value(%f) not equal to ignore_index(%f), But "
"received label value as %ld and shape of axis dimension "
"is %d"
,
lbl
,
ignore_index
,
lbl
,
axis_dim
));
}
int
index
=
i
*
num_classes
+
lbl
*
num_remain
+
j
;
int
loss_idx
=
i
*
num_remain
+
j
;
loss_data
[
loss_idx
]
=
lbl
==
ignore_index
?
0
:
-
math
::
TolerableValue
<
T
>
()(
std
::
log
(
prob_data
[
index
]));
}
}
HardLabelCrossEntropyCPUFunctorImpl
<
T
>
functor_impl
(
out
,
prob
,
labels
,
ignore_index
,
axis_dim
);
framework
::
VisitIntDataType
(
labels
->
type
(),
functor_impl
);
}
}
};
...
...
paddle/fluid/operators/math/cross_entropy.cu
浏览文件 @
eaa3fd45
...
...
@@ -21,18 +21,19 @@ namespace paddle {
namespace
operators
{
namespace
math
{
template
<
typename
T
>
__global__
void
CrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
int64_t
*
label
,
template
<
typename
T
,
typename
LabelT
>
__global__
void
CrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
LabelT
*
label
,
const
int
N
,
const
int
D
,
const
int
ignore_index
)
{
CUDA_KERNEL_LOOP
(
i
,
N
)
{
PADDLE_ENFORCE
(
label
[
i
]
>=
0
&&
label
[
i
]
<
D
||
label
[
i
]
==
ignore_index
,
auto
lbl
=
static_cast
<
int64_t
>
(
label
[
i
]);
PADDLE_ENFORCE
(
lbl
>=
0
&&
lbl
<
D
||
lbl
==
ignore_index
,
"The value of label[%d] expected >= 0 and < %ld, or == %ld, "
"but got %ld. Please check input value."
,
i
,
D
,
ignore_index
,
l
abel
[
i
]
);
Y
[
i
]
=
ignore_index
==
l
abel
[
i
]
i
,
D
,
ignore_index
,
l
bl
);
Y
[
i
]
=
ignore_index
==
l
bl
?
static_cast
<
T
>
(
0
)
:
-
math
::
TolerableValue
<
T
>
()(
real_log
(
X
[
i
*
D
+
l
abel
[
i
]
]));
:
-
math
::
TolerableValue
<
T
>
()(
real_log
(
X
[
i
*
D
+
l
bl
]));
}
}
...
...
@@ -54,6 +55,43 @@ __global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
}
}
template
<
typename
T
>
struct
HardLabelCrossEntropyCUDAFunctorImpl
{
public:
HardLabelCrossEntropyCUDAFunctorImpl
(
T
*
loss_data
,
const
T
*
prob_data
,
const
void
*
label_data
,
const
int
batch_size
,
const
int
class_num
,
const
int
ignore_index
,
const
int
block_size
,
gpuStream_t
stream
)
:
loss_data_
(
loss_data
),
prob_data_
(
prob_data
),
label_data_
(
label_data
),
batch_size_
(
batch_size
),
class_num_
(
class_num
),
ignore_index_
(
ignore_index
),
block_size_
(
block_size
),
stream_
(
stream
)
{}
template
<
typename
U
>
void
apply
()
const
{
int
grid_size
=
(
batch_size_
+
block_size_
-
1
)
/
block_size_
;
CrossEntropyKernel
<
T
,
U
><<<
grid_size
,
block_size_
,
0
,
stream_
>>>
(
loss_data_
,
prob_data_
,
static_cast
<
const
U
*>
(
label_data_
),
batch_size_
,
class_num_
,
ignore_index_
);
}
private:
T
*
loss_data_
;
const
T
*
prob_data_
;
const
void
*
label_data_
;
const
int
batch_size_
;
const
int
class_num_
;
const
int
ignore_index_
;
const
int
block_size_
;
gpuStream_t
stream_
;
};
template
<
typename
T
>
class
CrossEntropyFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
public:
...
...
@@ -81,12 +119,10 @@ class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
SoftCrossEntropyKernel
<
T
><<<
batch_size
,
block
,
0
,
ctx
.
stream
()
>>>
(
loss_data
,
prob_data
,
label_data
,
class_num
);
}
else
{
const
int64_t
*
label_data
=
labels
->
data
<
int64_t
>
();
int
block
=
kMaxBlockDim
;
int
grid
=
(
batch_size
+
block
-
1
)
/
block
;
CrossEntropyKernel
<
T
><<<
grid
,
block
,
0
,
ctx
.
stream
()
>>>
(
loss_data
,
prob_data
,
label_data
,
batch_size
,
class_num
,
ignore_index
);
HardLabelCrossEntropyCUDAFunctorImpl
<
T
>
functor
(
loss_data
,
prob_data
,
labels
->
data
(),
batch_size
,
class_num
,
ignore_index
,
kMaxBlockDim
,
ctx
.
stream
());
framework
::
VisitDataType
(
labels
->
type
(),
functor
);
}
}
};
...
...
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
浏览文件 @
eaa3fd45
此差异已折叠。
点击以展开。
paddle/fluid/operators/softmax_with_cross_entropy_op.h
浏览文件 @
eaa3fd45
...
...
@@ -24,6 +24,48 @@ namespace operators {
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
typename
Visitor
>
struct
SoftmaxWithCrossEntropyFunctor
{
public:
SoftmaxWithCrossEntropyFunctor
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
labels
,
const
bool
soft_label
,
const
Visitor
&
visitor
)
:
context_
(
context
),
labels_
(
labels
),
soft_label_
(
soft_label
),
visitor_
(
visitor
)
{}
template
<
typename
U
>
void
apply
()
const
{
visitor_
.
template
Apply
<
U
>(
context_
,
labels_
,
soft_label_
);
}
private:
const
framework
::
ExecutionContext
&
context_
;
const
framework
::
Tensor
&
labels_
;
const
bool
soft_label_
;
const
Visitor
&
visitor_
;
};
template
<
typename
T
,
typename
Visitor
>
static
void
RunSoftmaxWithCrossEntropyFunctor
(
const
framework
::
ExecutionContext
&
context
,
const
Visitor
&
visitor
)
{
const
auto
*
labels
=
context
.
Input
<
framework
::
Tensor
>
(
"Label"
);
const
bool
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
SoftmaxWithCrossEntropyFunctor
<
T
,
Visitor
>
functor
(
context
,
*
labels
,
soft_label
,
visitor
);
auto
dtype
=
labels
->
type
();
if
(
soft_label
)
{
PADDLE_ENFORCE_EQ
(
dtype
,
framework
::
DataTypeTrait
<
T
>::
DataType
(),
platform
::
errors
::
InvalidArgument
(
"The Input(Label) should be with the "
"same data type as Input(Logits)."
));
functor
.
template
apply
<
T
>();
}
else
{
framework
::
VisitIntDataType
(
dtype
,
functor
);
}
}
template
<
typename
T
>
class
SoftmaxWithCrossEntropyKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -32,14 +74,14 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
platform
::
is_cpu_place
(
context
.
GetPlace
()),
true
,
platform
::
errors
::
Unimplemented
(
"This kernel only runs on CPU."
));
const
bool
use_softmax
=
context
.
Attr
<
bool
>
(
"use_softmax"
);
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
const
bool
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
// do not with softmax op, and input is softmax
if
(
!
use_softmax
)
{
const
Tensor
*
softmax
=
context
.
Input
<
Tensor
>
(
"Logits"
);
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
softmax_out
=
context
.
Output
<
Tensor
>
(
"Softmax"
);
Tensor
*
loss
=
context
.
Output
<
Tensor
>
(
"Loss"
);
const
bool
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
const
int
rank
=
softmax
->
dims
().
size
();
const
int
axis
=
CanonicalAxis
(
context
.
Attr
<
int
>
(
"axis"
),
rank
);
int
axis_dim
=
softmax
->
dims
()[
axis
];
...
...
@@ -86,10 +128,8 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
}
const
Tensor
*
logits
=
context
.
Input
<
Tensor
>
(
"Logits"
);
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
softmax
=
context
.
Output
<
Tensor
>
(
"Softmax"
);
Tensor
*
loss
=
context
.
Output
<
Tensor
>
(
"Loss"
);
const
bool
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
const
int
rank
=
logits
->
dims
().
size
();
const
int
axis
=
CanonicalAxis
(
context
.
Attr
<
int
>
(
"axis"
),
rank
);
...
...
@@ -132,9 +172,14 @@ template <typename T>
class
SoftmaxWithCrossEntropyGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
RunSoftmaxWithCrossEntropyFunctor
<
T
>
(
context
,
*
this
);
}
template
<
typename
LabelT
>
static
void
Apply
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
labels
,
const
bool
soft_label
)
{
const
Tensor
*
out_grad
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Loss"
));
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
logit_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Logits"
));
const
Tensor
*
softmax
=
context
.
Input
<
Tensor
>
(
"Softmax"
);
...
...
@@ -143,7 +188,6 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
framework
::
TensorCopy
(
*
softmax
,
context
.
GetPlace
(),
context
.
device_context
(),
logit_grad
);
}
const
bool
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
auto
ignore_index
=
context
.
Attr
<
int
>
(
"ignore_index"
);
const
int
rank
=
logit_grad
->
dims
().
size
();
...
...
@@ -166,7 +210,7 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
const
int
d
=
SizeFromAxis
(
axis
,
logit_grad
->
dims
());
Tensor
logit_grad_2d
,
labels_2d
,
out_grad_2d
;
logit_grad_2d
.
ShareDataWith
(
*
logit_grad
).
Resize
({
n
,
d
});
labels_2d
.
ShareDataWith
(
*
labels
).
Resize
({
n
,
labels
->
numel
()
/
n
});
labels_2d
.
ShareDataWith
(
labels
).
Resize
({
n
,
labels
.
numel
()
/
n
});
out_grad_2d
.
ShareDataWith
(
*
out_grad
).
Resize
({
n
,
d
/
axis_dim
});
auto
out_grad_mat
=
framework
::
EigenMatrix
<
T
>::
From
(
out_grad_2d
);
auto
logit_grad_mat
=
framework
::
EigenMatrix
<
T
>::
From
(
logit_grad_2d
);
...
...
@@ -183,23 +227,24 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
logit_grad_mat
;
}
else
{
// use_softmax step2
const
int64_t
*
label_data
=
labels
->
data
<
int64_t
>
();
T
*
logit_grad_data
=
logit_grad
->
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
->
data
<
T
>
();
const
auto
*
label_data
=
labels
.
template
data
<
LabelT
>();
T
*
logit_grad_data
=
logit_grad
->
template
data
<
T
>();
const
T
*
out_grad_data
=
out_grad
->
template
data
<
T
>();
const
int
remain
=
d
/
axis_dim
;
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
// for each sample_1_dim
for
(
int
j
=
0
;
j
<
remain
;
j
++
)
{
// for each sample_other_dims
int
idx
=
i
*
remain
+
j
;
// this sample's label_idx. for 1d case,
// remain=1 and j=0, so, idx = i
if
(
label_data
[
idx
]
==
ignore_index
)
{
auto
lbl
=
static_cast
<
int64_t
>
(
label_data
[
idx
]);
if
(
lbl
==
ignore_index
)
{
for
(
int
k
=
0
;
k
<
axis_dim
;
++
k
)
{
// for each class id's label
logit_grad_data
[
i
*
d
+
k
*
remain
+
j
]
=
0
;
}
}
else
{
// only for this sample's label_idx, the label is 1, others is 0,
// so, only compute this label_idx's class
logit_grad_data
[
i
*
d
+
l
abel_data
[
idx
]
*
remain
+
j
]
=
(
-
1
/
logit_grad_data
[
i
*
d
+
l
abel_data
[
idx
]
*
remain
+
j
])
*
logit_grad_data
[
i
*
d
+
l
bl
*
remain
+
j
]
=
(
-
1
/
logit_grad_data
[
i
*
d
+
l
bl
*
remain
+
j
])
*
out_grad_data
[
idx
];
for
(
int
k
=
0
;
k
<
axis_dim
;
++
k
)
{
// for each class id's label
if
(
k
!=
...
...
@@ -233,15 +278,16 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
logit_grad_mat
*
// element_wise multiply
out_grad_mat
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
1
,
axis_dim
));
const
int64_t
*
label_data
=
labels
->
data
<
int64_t
>
();
T
*
logit_grad_data
=
logit_grad
->
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
->
data
<
T
>
();
const
auto
*
label_data
=
labels
.
template
data
<
LabelT
>();
T
*
logit_grad_data
=
logit_grad
->
template
data
<
T
>();
const
T
*
out_grad_data
=
out_grad
->
template
data
<
T
>();
const
int
remain
=
d
/
axis_dim
;
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
// for each sample_1_dim
for
(
int
j
=
0
;
j
<
remain
;
j
++
)
{
// for each sample_other_dims
int
idx
=
i
*
remain
+
j
;
// this sample's label_idx. for 1d case,
// remain=1 and j=0, so, idx = i
if
(
label_data
[
idx
]
==
ignore_index
)
{
auto
lbl
=
static_cast
<
int64_t
>
(
label_data
[
idx
]);
if
(
lbl
==
ignore_index
)
{
for
(
int
k
=
0
;
k
<
axis_dim
;
++
k
)
{
// for each class id's label
logit_grad_data
[
i
*
d
+
k
*
remain
+
j
]
=
0
;
}
...
...
@@ -258,8 +304,7 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
// out_grad_data[idx]
// means: dy/dp * dy= ( p - y ) * dy
logit_grad_data
[
i
*
d
+
label_data
[
idx
]
*
remain
+
j
]
-=
out_grad_data
[
idx
];
logit_grad_data
[
i
*
d
+
lbl
*
remain
+
j
]
-=
out_grad_data
[
idx
];
}
}
}
...
...
python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
浏览文件 @
eaa3fd45
...
...
@@ -16,6 +16,7 @@ from __future__ import print_function
import
unittest
import
numpy
as
np
import
paddle
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
...
...
@@ -58,6 +59,9 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
self
.
shape
=
[
41
,
37
]
self
.
use_softmax
=
True
def
hard_label_dtype
(
self
):
return
"int64"
def
setUp
(
self
):
self
.
initParams
()
...
...
@@ -72,7 +76,8 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
else
:
axis_dim
=
self
.
shape
[
self
.
axis
]
self
.
shape
[
self
.
axis
]
=
1
labels
=
np
.
random
.
randint
(
0
,
axis_dim
,
self
.
shape
,
dtype
=
"int64"
)
labels
=
np
.
random
.
randint
(
0
,
axis_dim
,
self
.
shape
,
dtype
=
self
.
hard_label_dtype
())
loss
=
cross_entropy
(
softmax
,
labels
,
self
.
soft_label
,
self
.
axis
,
self
.
ignore_index
)
...
...
@@ -107,6 +112,26 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
self
.
check_grad
([
"Logits"
],
"Loss"
,
numeric_grad_delta
=
0.001
)
class
TestSoftmaxWithCrossEntropyOpInt32
(
TestSoftmaxWithCrossEntropyOp
):
def
hard_label_dtype
(
self
):
return
"int32"
class
TestSoftmaxWithCrossEntropyOpInt16
(
TestSoftmaxWithCrossEntropyOp
):
def
hard_label_dtype
(
self
):
return
"int16"
class
TestSoftmaxWithCrossEntropyOpInt8
(
TestSoftmaxWithCrossEntropyOp
):
def
hard_label_dtype
(
self
):
return
"int8"
class
TestSoftmaxWithCrossEntropyOpUInt8
(
TestSoftmaxWithCrossEntropyOp
):
def
hard_label_dtype
(
self
):
return
"uint8"
class
TestSoftmaxWithCrossEntropyOp_NotWithSoftmax_SoftLabel_1D
(
TestSoftmaxWithCrossEntropyOp
):
def
initParams
(
self
):
...
...
@@ -711,4 +736,5 @@ class TestSoftmaxWithCrossEntropyOpBoundary1(TestSoftmaxWithCrossEntropyOp):
if
__name__
==
"__main__"
:
paddle
.
enable_static
()
unittest
.
main
()
python/paddle/nn/functional/loss.py
浏览文件 @
eaa3fd45
...
...
@@ -1783,7 +1783,8 @@ def cross_entropy(input,
fluid
.
data_feeder
.
check_variable_and_dtype
(
input
,
'input'
,
[
'float32'
,
'float64'
],
'softmax_cross_entropy'
)
fluid
.
data_feeder
.
check_variable_and_dtype
(
label
,
'label'
,
[
'int32'
,
'int64'
,
'float32'
,
'float64'
],
label
,
'label'
,
[
'uint8'
,
'int8'
,
'int16'
,
'int32'
,
'int64'
,
'float32'
,
'float64'
],
'softmax_cross_entropy'
)
attrs
=
{
'soft_label'
:
soft_label
,
...
...
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