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PaddleDetection
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d5a7c098
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d5a7c098
编写于
4月 16, 2019
作者:
H
Hongyu Liu
提交者:
GitHub
4月 16, 2019
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差异文件
Merge pull request #16798 from phlrain/softmax_cross_support_high_rank
softmax cross entropy support high rank
上级
99aea999
d7228416
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
238 addition
and
36 deletion
+238
-36
paddle/fluid/operators/softmax_with_cross_entropy_op.cc
paddle/fluid/operators/softmax_with_cross_entropy_op.cc
+46
-17
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
+30
-10
paddle/fluid/operators/softmax_with_cross_entropy_op.h
paddle/fluid/operators/softmax_with_cross_entropy_op.h
+23
-9
python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
...uid/tests/unittests/test_softmax_with_cross_entropy_op.py
+139
-0
未找到文件。
paddle/fluid/operators/softmax_with_cross_entropy_op.cc
浏览文件 @
d5a7c098
...
@@ -106,24 +106,36 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
...
@@ -106,24 +106,36 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
auto
logits_dims
=
ctx
->
GetInputDim
(
"Logits"
);
auto
logits_dims
=
ctx
->
GetInputDim
(
"Logits"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
int
rank
=
logits_dims
.
size
();
PADDLE_ENFORCE_EQ
(
PADDLE_ENFORCE_EQ
(
logits_dims
.
size
(),
2UL
,
rank
,
labels_dims
.
size
(),
"The input of softmax_with_cross_entropy should be a 2-D tensor."
);
"Input(logits) and Input(Label) shall have the same rank."
);
PADDLE_ENFORCE_EQ
(
labels_dims
.
size
(),
2UL
,
bool
check
=
ctx
->
IsRuntime
()
||
(
framework
::
product
(
logits_dims
)
>
0
&&
"The labels should be a 2-D tensor."
);
framework
::
product
(
labels_dims
)
>
0
);
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
logits_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
labels_dims
,
0
,
rank
-
1
),
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension."
);
}
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"soft_label"
))
{
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"soft_label"
))
{
PADDLE_ENFORCE_EQ
(
logits_dims
[
1
],
labels_dims
[
1
],
if
(
check
)
{
"If Attr(soft_label) == true, the 2nd dimension of "
PADDLE_ENFORCE_EQ
(
logits_dims
[
rank
-
1
],
labels_dims
[
rank
-
1
],
"Input(X) and Input(Label) should be equal."
);
"If Attr(soft_label) == true, the last dimension of "
"Input(X) and Input(Label) should be equal."
);
}
}
else
{
}
else
{
PADDLE_ENFORCE_EQ
(
labels_dims
[
1
],
1UL
,
PADDLE_ENFORCE_EQ
(
labels_dims
[
rank
-
1
],
1UL
,
"If Attr(soft
_label) == false, the 2nd
dimension of "
"If Attr(soft
Label) == false, the last
dimension of "
"Input(Label) should be 1."
);
"Input(Label) should be 1."
);
}
}
ctx
->
SetOutputDim
(
"Softmax"
,
logits_dims
);
ctx
->
SetOutputDim
(
"Softmax"
,
logits_dims
);
ctx
->
SetOutputDim
(
"Loss"
,
{
logits_dims
[
0
],
1
});
auto
loss_dims
=
logits_dims
;
loss_dims
[
rank
-
1
]
=
1
;
ctx
->
SetOutputDim
(
"Loss"
,
loss_dims
);
ctx
->
ShareLoD
(
"Logits"
,
/*->*/
"Softmax"
);
ctx
->
ShareLoD
(
"Logits"
,
/*->*/
"Softmax"
);
ctx
->
ShareLoD
(
"Logits"
,
/*->*/
"Loss"
);
ctx
->
ShareLoD
(
"Logits"
,
/*->*/
"Loss"
);
...
@@ -152,16 +164,33 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
...
@@ -152,16 +164,33 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
auto
softmax_dims
=
ctx
->
GetInputDim
(
"Softmax"
);
auto
softmax_dims
=
ctx
->
GetInputDim
(
"Softmax"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
PADDLE_ENFORCE_EQ
(
labels_dims
.
size
(),
2UL
,
"The labels should be a 2-D tensor."
);
int
rank
=
softmax_dims
.
size
();
PADDLE_ENFORCE_EQ
(
rank
,
labels_dims
.
size
(),
"Input(logits) and Input(Label) shall have the same rank."
);
bool
check
=
true
;
if
((
!
ctx
->
IsRuntime
())
&&
(
framework
::
product
(
softmax_dims
)
<=
0
||
framework
::
product
(
labels_dims
)
<=
0
))
{
check
=
false
;
}
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
softmax_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
labels_dims
,
0
,
rank
-
1
),
"Input(Softmax) and Input(Label) shall have the same shape "
"except the last dimension."
);
}
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"soft_label"
))
{
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"soft_label"
))
{
PADDLE_ENFORCE_EQ
(
softmax_dims
[
1
],
labels_dims
[
1
],
if
(
check
)
{
"When Attr(soft_label) == true, the 2nd dimension of "
PADDLE_ENFORCE_EQ
(
softmax_dims
[
rank
-
1
],
labels_dims
[
rank
-
1
],
"Input(X) and Input(Label) should be equal."
);
"If Attr(soft_label) == true, the last dimension of "
"Input( Softmax) and Input(Label) should be equal."
);
}
}
else
{
}
else
{
PADDLE_ENFORCE_EQ
(
labels_dims
[
1
],
1UL
,
PADDLE_ENFORCE_EQ
(
labels_dims
[
rank
-
1
],
1UL
,
"
When Attr(soft_label) == false, the 2nd
dimension of "
"
If Attr(softLabel) == false, the last
dimension of "
"Input(Label) should be 1."
);
"Input(Label) should be 1."
);
}
}
...
...
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
浏览文件 @
d5a7c098
...
@@ -400,9 +400,15 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
...
@@ -400,9 +400,15 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
auto
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
auto
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
auto
ignore_index
=
context
.
Attr
<
int
>
(
"ignore_index"
);
auto
ignore_index
=
context
.
Attr
<
int
>
(
"ignore_index"
);
int
rank
=
logits
->
dims
().
size
();
if
(
soft_label
)
{
if
(
soft_label
)
{
int
batch_size
=
logits
->
dims
()[
0
];
int
batch_size
=
1
;
int
feature_size
=
logits
->
dims
()[
1
];
for
(
int
i
=
0
;
i
<
rank
-
1
;
++
i
)
{
batch_size
*=
logits
->
dims
()[
i
];
}
int
feature_size
=
logits
->
dims
()[
rank
-
1
];
auto
*
logits_data
=
logits
->
data
<
T
>
();
auto
*
logits_data
=
logits
->
data
<
T
>
();
auto
*
labels_data
=
labels
->
data
<
T
>
();
auto
*
labels_data
=
labels
->
data
<
T
>
();
SoftmaxWithCrossEntropyFusedKernel
(
SoftmaxWithCrossEntropyFusedKernel
(
...
@@ -410,14 +416,23 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
...
@@ -410,14 +416,23 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
feature_size
,
context
.
cuda_device_context
().
stream
());
feature_size
,
context
.
cuda_device_context
().
stream
());
}
else
{
}
else
{
if
(
!
context
.
Attr
<
bool
>
(
"numeric_stable_mode"
))
{
if
(
!
context
.
Attr
<
bool
>
(
"numeric_stable_mode"
))
{
math
::
SoftmaxCUDNNFunctor
<
T
>
()(
context
.
cuda_device_context
(),
logits
,
// reshape to 2d
softmax
);
Tensor
logits_2d
=
framework
::
ReshapeToMatrix
(
*
logits
,
rank
-
1
);
Tensor
softmax_2d
=
framework
::
ReshapeToMatrix
(
*
softmax
,
rank
-
1
);
Tensor
loss_2d
=
framework
::
ReshapeToMatrix
(
*
loss
,
rank
-
1
);
Tensor
labels_2d
=
framework
::
ReshapeToMatrix
(
*
labels
,
rank
-
1
);
math
::
SoftmaxCUDNNFunctor
<
T
>
()(
context
.
cuda_device_context
(),
&
logits_2d
,
&
softmax_2d
);
math
::
CrossEntropyFunctor
<
platform
::
CUDADeviceContext
,
T
>
()(
math
::
CrossEntropyFunctor
<
platform
::
CUDADeviceContext
,
T
>
()(
context
.
cuda_device_context
(),
loss
,
softmax
,
labels
,
false
,
context
.
cuda_device_context
(),
&
loss_2d
,
&
softmax_2d
,
&
labels_2d
,
ignore_index
);
false
,
ignore_index
);
}
else
{
}
else
{
int
batch_size
=
logits
->
dims
()[
0
];
int
batch_size
=
1
;
int
feature_size
=
logits
->
dims
()[
1
];
for
(
int
i
=
0
;
i
<
rank
-
1
;
++
i
)
{
batch_size
*=
logits
->
dims
()[
i
];
}
int
feature_size
=
logits
->
dims
()[
rank
-
1
];
auto
*
logits_data
=
logits
->
data
<
T
>
();
auto
*
logits_data
=
logits
->
data
<
T
>
();
auto
*
labels_data
=
labels
->
data
<
int64_t
>
();
auto
*
labels_data
=
labels
->
data
<
int64_t
>
();
HardLabelSoftmaxWithCrossEntropy
<
T
>
(
HardLabelSoftmaxWithCrossEntropy
<
T
>
(
...
@@ -443,8 +458,13 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
...
@@ -443,8 +458,13 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
context
.
device_context
(),
logit_grad
);
context
.
device_context
(),
logit_grad
);
T
*
logit_grad_data
=
logit_grad
->
data
<
T
>
();
T
*
logit_grad_data
=
logit_grad
->
data
<
T
>
();
const
int
batch_size
=
logit_grad
->
dims
()[
0
];
int
rank
=
logit_grad
->
dims
().
size
();
const
int
class_num
=
logit_grad
->
dims
()[
1
];
int
batch_size
=
1
;
for
(
int
i
=
0
;
i
<
rank
-
1
;
++
i
)
{
batch_size
*=
logit_grad
->
dims
()[
i
];
}
const
int
class_num
=
logit_grad
->
dims
()[
rank
-
1
];
int
block
=
512
;
int
block
=
512
;
auto
stream
=
context
.
cuda_device_context
().
stream
();
auto
stream
=
context
.
cuda_device_context
().
stream
();
auto
ignore_index
=
context
.
Attr
<
int
>
(
"ignore_index"
);
auto
ignore_index
=
context
.
Attr
<
int
>
(
"ignore_index"
);
...
...
paddle/fluid/operators/softmax_with_cross_entropy_op.h
浏览文件 @
d5a7c098
...
@@ -40,15 +40,22 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
...
@@ -40,15 +40,22 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
axis_dim
=
logits
->
dims
()[
logits
->
dims
().
size
()
-
1
];
// reshape to 2D tensor
int
rank
=
logits
->
dims
().
size
();
Tensor
logits_2d
=
framework
::
ReshapeToMatrix
(
*
logits
,
rank
-
1
);
Tensor
labels_2d
=
framework
::
ReshapeToMatrix
(
*
labels
,
rank
-
1
);
Tensor
loss_2d
=
framework
::
ReshapeToMatrix
(
*
loss
,
rank
-
1
);
Tensor
softmax_2d
=
framework
::
ReshapeToMatrix
(
*
softmax
,
rank
-
1
);
int
axis_dim
=
logits
->
dims
()[
rank
-
1
];
auto
&
dev_ctx
=
auto
&
dev_ctx
=
context
.
template
device_context
<
platform
::
CPUDeviceContext
>();
context
.
template
device_context
<
platform
::
CPUDeviceContext
>();
math
::
SoftmaxFunctor
<
platform
::
CPUDeviceContext
,
T
,
false
>
()(
math
::
SoftmaxFunctor
<
platform
::
CPUDeviceContext
,
T
,
false
>
()(
dev_ctx
,
axis_dim
,
logits
,
softmax
);
dev_ctx
,
axis_dim
,
&
logits_2d
,
&
softmax_2d
);
math
::
CrossEntropyFunctor
<
platform
::
CPUDeviceContext
,
T
>
()(
math
::
CrossEntropyFunctor
<
platform
::
CPUDeviceContext
,
T
>
()(
dev_ctx
,
loss
,
softmax
,
labels
,
context
.
Attr
<
bool
>
(
"soft_label"
)
,
dev_ctx
,
&
loss_2d
,
&
softmax_2d
,
&
labels_2d
,
context
.
Attr
<
int
>
(
"ignore_index"
));
context
.
Attr
<
bool
>
(
"soft_label"
),
context
.
Attr
<
int
>
(
"ignore_index"
));
}
}
};
};
...
@@ -63,13 +70,19 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
...
@@ -63,13 +70,19 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Logits"
));
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Logits"
));
logit_grad
->
ShareDataWith
(
*
context
.
Input
<
Tensor
>
(
"Softmax"
));
logit_grad
->
ShareDataWith
(
*
context
.
Input
<
Tensor
>
(
"Softmax"
));
const
int
class_num
=
logit_grad
->
dims
()[
1
];
int
rank
=
logit_grad
->
dims
().
size
();
auto
out_grad_mat
=
EigenMatrix
<
T
>::
From
(
*
out_grad
);
const
int
class_num
=
logit_grad
->
dims
()[
rank
-
1
];
auto
logit_grad_mat
=
EigenMatrix
<
T
>::
From
(
*
logit_grad
);
// reshape to 2d
Tensor
logit_grad_2d
=
framework
::
ReshapeToMatrix
(
*
logit_grad
,
rank
-
1
);
Tensor
out_grad_2d
=
framework
::
ReshapeToMatrix
(
*
out_grad
,
rank
-
1
);
auto
out_grad_mat
=
EigenMatrix
<
T
>::
From
(
out_grad_2d
);
auto
logit_grad_mat
=
EigenMatrix
<
T
>::
From
(
logit_grad_2d
);
auto
&
place
=
*
context
.
template
device_context
<
platform
::
CPUDeviceContext
>()
auto
&
place
=
*
context
.
template
device_context
<
platform
::
CPUDeviceContext
>()
.
eigen_device
();
.
eigen_device
();
if
(
context
.
Attr
<
bool
>
(
"soft_label"
))
{
if
(
context
.
Attr
<
bool
>
(
"soft_label"
))
{
auto
lbl_mat
=
EigenMatrix
<
T
>::
From
(
*
labels
);
Tensor
labels_2d
=
framework
::
ReshapeToMatrix
(
*
labels
,
rank
-
1
);
auto
lbl_mat
=
EigenMatrix
<
T
>::
From
(
labels_2d
);
logit_grad_mat
.
device
(
place
)
=
logit_grad_mat
.
device
(
place
)
=
out_grad_mat
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
1
,
class_num
))
*
out_grad_mat
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
1
,
class_num
))
*
(
logit_grad_mat
-
lbl_mat
);
(
logit_grad_mat
-
lbl_mat
);
...
@@ -78,7 +91,8 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
...
@@ -78,7 +91,8 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
logit_grad_mat
*
logit_grad_mat
*
out_grad_mat
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
1
,
class_num
));
out_grad_mat
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
1
,
class_num
));
const
int
batch_size
=
logit_grad
->
dims
()[
0
];
const
int
batch_size
=
logit_grad_2d
.
dims
()[
0
];
const
int64_t
*
label_data
=
labels
->
data
<
int64_t
>
();
const
int64_t
*
label_data
=
labels
->
data
<
int64_t
>
();
T
*
logit_grad_data
=
logit_grad
->
data
<
T
>
();
T
*
logit_grad_data
=
logit_grad
->
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
->
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
->
data
<
T
>
();
...
...
python/paddle/fluid/tests/unittests/test_softmax_with_cross_entropy_op.py
浏览文件 @
d5a7c098
...
@@ -195,5 +195,144 @@ class TestSoftmaxWithCrossEntropyOp3NoCudnn(TestSoftmaxWithCrossEntropyOp3):
...
@@ -195,5 +195,144 @@ class TestSoftmaxWithCrossEntropyOp3NoCudnn(TestSoftmaxWithCrossEntropyOp3):
self
.
numeric_stable_mode
=
True
self
.
numeric_stable_mode
=
True
class
TestSoftmaxWithCrossEntropyOp5
(
OpTest
):
"""
Test softmax with cross entropy operator with ignore_index.
"""
def
initParams
(
self
):
self
.
numeric_stable_mode
=
False
def
setUp
(
self
):
self
.
initParams
()
self
.
op_type
=
"softmax_with_cross_entropy"
batch_size
=
[
6
,
10
]
class_num
=
47
logits
=
np
.
random
.
uniform
(
0.1
,
1.0
,
tuple
(
batch_size
+
[
class_num
])).
astype
(
"float64"
)
softmax
=
np
.
apply_along_axis
(
stable_softmax
,
2
,
logits
)
labels
=
np
.
random
.
randint
(
0
,
class_num
,
tuple
(
batch_size
+
[
1
]),
dtype
=
"int64"
)
ignore_index
=
7
softmax_2d
=
np
.
reshape
(
softmax
,
[
-
1
,
class_num
])
labels_2d
=
np
.
reshape
(
labels
,
[
-
1
,
1
])
cross_entropy
=
np
.
asmatrix
(
[[
-
np
.
log
(
softmax_2d
[
i
][
labels_2d
[
i
][
0
]])]
if
labels_2d
[
i
]
!=
ignore_index
else
[
0
]
for
i
in
range
(
softmax_2d
.
shape
[
0
])],
dtype
=
"float64"
)
cross_entropy
=
np
.
reshape
(
cross_entropy
,
batch_size
)
output_shape
=
tuple
(
batch_size
+
[
1
])
output_res
=
cross_entropy
.
astype
(
"float64"
)
output_res
=
np
.
expand_dims
(
output_res
,
axis
=
2
)
self
.
inputs
=
{
"Logits"
:
logits
,
"Label"
:
labels
}
self
.
outputs
=
{
"Softmax"
:
softmax
.
astype
(
"float64"
),
"Loss"
:
output_res
,
}
self
.
attrs
=
{
"ignore_index"
:
ignore_index
,
"numeric_stable_mode"
:
self
.
numeric_stable_mode
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"Logits"
],
"Loss"
)
class
TestSoftmaxWithCrossEntropyOp5NoCudnn
(
TestSoftmaxWithCrossEntropyOp5
):
def
initParams
(
self
):
self
.
numeric_stable_mode
=
True
class
TestSoftmaxWithCrossEntropyOp6
(
OpTest
):
"""
Test softmax with cross entropy operator with soft labels.
"""
def
setUp
(
self
):
self
.
op_type
=
"softmax_with_cross_entropy"
batch_size
=
[
6
,
10
]
class_num
=
37
logits
=
np
.
random
.
uniform
(
0.1
,
1.0
,
tuple
(
batch_size
+
[
class_num
])).
astype
(
"float64"
)
softmax
=
np
.
apply_along_axis
(
stable_softmax
,
2
,
logits
)
labels
=
np
.
random
.
uniform
(
0.1
,
1.0
,
tuple
(
batch_size
+
[
class_num
])).
astype
(
"float64"
)
labels
/=
np
.
sum
(
labels
,
axis
=
2
,
keepdims
=
True
)
cross_entropy
=
(
-
labels
*
np
.
log
(
softmax
)).
sum
(
axis
=
2
,
keepdims
=
True
).
astype
(
"float64"
)
self
.
inputs
=
{
"Logits"
:
logits
,
"Label"
:
labels
}
self
.
outputs
=
{
"Softmax"
:
softmax
.
astype
(
"float64"
),
"Loss"
:
cross_entropy
.
astype
(
"float64"
)
}
self
.
attrs
=
{
"soft_label"
:
True
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"Logits"
],
"Loss"
)
class
TestSoftmaxWithCrossEntropyOpFp16_2
(
TestSoftmaxWithCrossEntropyOp
):
def
initParams
(
self
):
self
.
numeric_stable_mode
=
False
self
.
dtype
=
np
.
float16
def
setUp
(
self
):
self
.
initParams
()
self
.
op_type
=
"softmax_with_cross_entropy"
batch_size
=
[
64
,
10
]
class_num
=
37
# NOTE: numpy float16 have very low accuracy, use float32 for numpy check.
logits
=
np
.
random
.
uniform
(
0.1
,
1.0
,
tuple
(
batch_size
+
[
class_num
])).
astype
(
np
.
float32
)
softmax
=
np
.
apply_along_axis
(
stable_softmax
,
2
,
logits
)
labels
=
np
.
random
.
randint
(
0
,
class_num
,
tuple
(
batch_size
+
[
1
]),
dtype
=
"int64"
)
softmax_2d
=
np
.
reshape
(
softmax
,
[
-
1
,
class_num
])
labels_2d
=
np
.
reshape
(
labels
,
[
-
1
,
1
])
cross_entropy
=
np
.
asmatrix
(
[[
-
np
.
log
(
softmax_2d
[
i
][
labels_2d
[
i
][
0
]])]
for
i
in
range
(
softmax_2d
.
shape
[
0
])],
dtype
=
np
.
float32
)
cross_entropy
=
np
.
reshape
(
cross_entropy
,
batch_size
)
output_shape
=
tuple
(
batch_size
+
[
1
])
output_res
=
cross_entropy
.
astype
(
self
.
dtype
)
output_res
=
np
.
expand_dims
(
output_res
,
axis
=
2
)
self
.
inputs
=
{
"Logits"
:
logits
,
"Label"
:
labels
}
self
.
inputs
=
{
"Logits"
:
logits
.
astype
(
self
.
dtype
).
view
(
np
.
uint16
),
"Label"
:
labels
}
self
.
outputs
=
{
"Softmax"
:
softmax
.
astype
(
self
.
dtype
),
"Loss"
:
output_res
,
}
self
.
attrs
=
{
"numeric_stable_mode"
:
self
.
numeric_stable_mode
}
def
test_check_output
(
self
):
self
.
check_output
(
atol
=
1e-2
)
def
test_check_grad
(
self
):
self
.
check_grad
([
"Logits"
],
"Loss"
,
max_relative_error
=
0.1
)
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
unittest
.
main
()
unittest
.
main
()
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