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体验新版 GitCode,发现更多精彩内容 >>
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2de7f3cf
编写于
4月 15, 2019
作者:
H
Hongyu Liu
提交者:
GitHub
4月 15, 2019
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差异文件
Merge pull request #16799 from phlrain/sigmoid_corss_entropy_support_high_rank
supprt high rank
上级
a67fbffd
a3e52381
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
127 addition
and
26 deletion
+127
-26
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
...e/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
+34
-26
python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
...ts/unittests/test_sigmoid_cross_entropy_with_logits_op.py
+93
-0
未找到文件。
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
浏览文件 @
2de7f3cf
...
...
@@ -34,15 +34,22 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
labels_dims
.
size
(),
2
,
"Input(Label)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
labels_dims
[
0
],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
labels_dims
[
1
],
"The 2nd dimension of Input(X) and Input(Label) should "
"be equal."
);
int
rank
=
x_dims
.
size
();
PADDLE_ENFORCE_EQ
(
rank
,
labels_dims
.
size
(),
"Input(X) and Input(Label) shall have the same rank."
);
bool
check
=
true
;
if
((
!
ctx
->
IsRuntime
())
&&
(
framework
::
product
(
x_dims
)
<=
0
||
framework
::
product
(
labels_dims
)
<=
0
))
{
check
=
false
;
}
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
),
framework
::
slice_ddim
(
labels_dims
,
0
,
rank
),
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension."
);
}
ctx
->
ShareDim
(
"X"
,
/*->*/
"Out"
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
...
...
@@ -65,23 +72,24 @@ class SigmoidCrossEntropyWithLogitsGradOp
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
dout_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
labels_dims
.
size
(),
2
,
"Input(Label)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
dout_dims
.
size
(),
2
,
"Input(Out@Grad)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
labels_dims
[
0
],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
labels_dims
[
1
],
"The 2nd dimension of Input(X) and Input(Label) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
dout_dims
[
0
],
"The 1st dimension of Input(X) and Input(Out@Grad) "
"should be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
dout_dims
[
1
],
"The 2nd dimension of Input(X) and Input(Out@Grad) "
"should be equal."
);
int
rank
=
x_dims
.
size
();
bool
check
=
true
;
if
((
!
ctx
->
IsRuntime
())
&&
(
framework
::
product
(
x_dims
)
<=
0
||
framework
::
product
(
labels_dims
)
<=
0
))
{
check
=
false
;
}
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
),
framework
::
slice_ddim
(
labels_dims
,
0
,
rank
),
"Input(X) and Input(Label) shall have the same shape."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
),
framework
::
slice_ddim
(
dout_dims
,
0
,
rank
),
"Input(X) and Input(Out@Grad) shall have the same shape."
);
}
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
x_dims
);
}
...
...
python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
浏览文件 @
2de7f3cf
...
...
@@ -149,5 +149,98 @@ class TestSigmoidCrossEntropyWithNorm(OpTest):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestSigmoidCrossEntropyWithLogitsOp5
(
OpTest
):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def
setUp
(
self
):
self
.
op_type
=
"sigmoid_cross_entropy_with_logits"
batch_size
=
[
10
,
10
]
num_classes
=
20
self
.
inputs
=
{
'X'
:
logit
(
np
.
random
.
uniform
(
0
,
1
,
tuple
(
batch_size
+
[
num_classes
]))
.
astype
(
"float32"
)),
'Label'
:
np
.
random
.
uniform
(
0
,
1
,
tuple
(
batch_size
+
[
num_classes
]))
.
astype
(
"float32"
)
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X
=
expit
(
self
.
inputs
[
'X'
])
term1
=
self
.
inputs
[
'Label'
]
*
np
.
log
(
sigmoid_X
)
term2
=
(
1
-
self
.
inputs
[
'Label'
])
*
np
.
log
(
1
-
sigmoid_X
)
self
.
outputs
=
{
'Out'
:
-
term1
-
term2
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestSigmoidCrossEntropyWithNorm2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sigmoid_cross_entropy_with_logits"
batch_size
=
[
10
,
10
]
num_classes
=
20
ignore_index
=
-
1
self
.
inputs
=
{
'X'
:
logit
(
np
.
random
.
uniform
(
0
,
1
,
tuple
(
batch_size
+
[
num_classes
]))
.
astype
(
"float32"
)),
'Label'
:
np
.
random
.
randint
(
-
1
,
2
,
tuple
(
batch_size
+
[
num_classes
]))
.
astype
(
"float32"
)
}
self
.
attrs
=
{
'ignore_index'
:
ignore_index
,
'normalize'
:
True
}
sigmoid_X
=
expit
(
self
.
inputs
[
'X'
])
term1
=
self
.
inputs
[
'Label'
]
*
np
.
log
(
sigmoid_X
)
term2
=
(
1
-
self
.
inputs
[
'Label'
])
*
np
.
log
(
1
-
sigmoid_X
)
out
=
-
term1
-
term2
out
[
np
.
where
(
self
.
inputs
[
'Label'
]
==
ignore_index
)]
=
0
if
self
.
attrs
[
'normalize'
]:
out
=
out
/
float
(
np
.
where
(
self
.
inputs
[
'Label'
]
!=
ignore_index
)[
0
].
size
)
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestSigmoidCrossEntropyWithLogitsOp6
(
OpTest
):
"""Test sigmoid_cross_entropy_with_logit_op with binary label
"""
def
setUp
(
self
):
self
.
op_type
=
"sigmoid_cross_entropy_with_logits"
batch_size
=
[
10
,
10
]
num_classes
=
20
self
.
inputs
=
{
'X'
:
logit
(
np
.
random
.
uniform
(
0
,
1
,
tuple
(
batch_size
+
[
num_classes
]))
.
astype
(
"float32"
)),
'Label'
:
np
.
random
.
randint
(
0
,
2
,
tuple
(
batch_size
+
[
num_classes
]))
.
astype
(
"float32"
)
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X
=
expit
(
self
.
inputs
[
'X'
])
term1
=
self
.
inputs
[
'Label'
]
*
np
.
log
(
sigmoid_X
)
term2
=
(
1
-
self
.
inputs
[
'Label'
])
*
np
.
log
(
1
-
sigmoid_X
)
self
.
outputs
=
{
'Out'
:
-
term1
-
term2
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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