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1e6e5ac6
编写于
1月 15, 2018
作者:
F
fengjiayi
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python/paddle/v2/fluid/tests/test_elementwise_max_op.py
python/paddle/v2/fluid/tests/test_elementwise_max_op.py
+107
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python/paddle/v2/fluid/tests/test_elementwise_min_op.py
python/paddle/v2/fluid/tests/test_elementwise_min_op.py
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python/paddle/v2/fluid/tests/test_elementwise_max_op.py
0 → 100644
浏览文件 @
1e6e5ac6
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestElementwiseOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
# If x and y have the same value, the max() is not differentiable.
# So we generate test data by the following method
# to avoid them being too close to each other.
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
[
13
,
17
]).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.005
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMaxOp_Vector
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
random
((
32
,
)).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
32
,
)).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
(
32
,
)).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
class
TestElementwiseMaxOp_broadcast_0
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
2
,
)).
astype
(
np
.
float32
)
y
=
x
[:,
0
,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
2
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
0
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
2
,
1
,
1
))
}
class
TestElementwiseMaxOp_broadcast_1
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
1
))
}
class
TestElementwiseMaxOp_broadcast_2
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
4
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
0
,
:]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
4
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
1
,
4
))
}
class
TestElementwiseMaxOp_broadcast_3
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_max"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
,
5
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
4
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
4
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
4
,
1
))
}
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/fluid/tests/test_elementwise_min_op.py
0 → 100644
浏览文件 @
1e6e5ac6
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestElementwiseOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
# If x and y have the same value, the max() is not differentiable.
# So we generate test data by the following method
# to avoid them being too close to each other.
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
[
13
,
17
]).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.005
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.005
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMaxOp_Vector
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
random
((
32
,
)).
astype
(
"float32"
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
32
,
)).
astype
(
"float32"
)
y
=
x
+
sgn
*
np
.
random
.
uniform
(
0.1
,
1
,
(
32
,
)).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
class
TestElementwiseMaxOp_broadcast_0
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
2
,
)).
astype
(
np
.
float32
)
y
=
x
[:,
0
,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
2
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
0
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
2
,
1
,
1
))
}
class
TestElementwiseMaxOp_broadcast_1
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
1
))
}
class
TestElementwiseMaxOp_broadcast_2
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
4
,
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
0
,
:]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
4
,
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
1
,
4
))
}
class
TestElementwiseMaxOp_broadcast_3
(
TestElementwiseOp
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_min"
x
=
np
.
random
.
uniform
(
0.5
,
1
,
(
2
,
3
,
4
,
5
)).
astype
(
np
.
float32
)
sgn
=
np
.
random
.
choice
([
-
1
,
1
],
(
3
,
4
)).
astype
(
np
.
float32
)
y
=
x
[
0
,
:,
:,
0
]
+
sgn
*
\
np
.
random
.
uniform
(
1
,
2
,
(
3
,
4
)).
astype
(
np
.
float32
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
minimum
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
4
,
1
))
}
if
__name__
==
'__main__'
:
unittest
.
main
()
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