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提交
e5c167dc
编写于
10月 27, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix unit test
上级
0ab012cf
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
140 addition
and
161 deletion
+140
-161
python/paddle/v2/framework/tests/test_pool2d_cudnn_op.py
python/paddle/v2/framework/tests/test_pool2d_cudnn_op.py
+0
-144
python/paddle/v2/framework/tests/test_pool2d_op.py
python/paddle/v2/framework/tests/test_pool2d_op.py
+140
-17
未找到文件。
python/paddle/v2/framework/tests/test_pool2d_cudnn_op.py
已删除
100644 → 0
浏览文件 @
0ab012cf
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
xrange
(
H_out
):
for
j
in
xrange
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
))
return
out
def
avg_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
xrange
(
H_out
):
for
j
in
xrange
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
sum
(
x_masked
,
axis
=
(
2
,
3
))
/
(
(
r_end
-
r_start
)
*
(
c_end
-
c_start
))
return
out
class
TestPool2d_cudnn_Op
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
=
self
.
pool2D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
)
self
.
inputs
=
{
'X'
:
input
}
self
.
attrs
=
{
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'poolingType'
:
self
.
pool_type
,
'globalPooling'
:
self
.
global_pool
,
}
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
if
self
.
pool_type
!=
"max"
:
self
.
check_grad
(
set
([
'X'
]),
'Out'
,
max_relative_error
=
0.07
)
def
initTestCase
(
self
):
self
.
global_pool
=
True
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase1
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase2
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
class
TestCase3
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
True
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase4
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase5
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/framework/tests/test_pool2d_op.py
浏览文件 @
e5c167dc
...
...
@@ -46,7 +46,9 @@ def avg_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
class
TestPool2d_Op
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
init_test_case
()
self
.
init_op_type
()
self
.
init_pool_type
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
=
self
.
pool2D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
)
...
...
@@ -69,76 +71,197 @@ class TestPool2d_Op(OpTest):
if
self
.
pool_type
!=
"max"
:
self
.
check_grad
(
set
([
'X'
]),
'Out'
,
max_relative_error
=
0.07
)
def
init
TestC
ase
(
self
):
def
init
_test_c
ase
(
self
):
self
.
global_pool
=
True
self
.
op_type
=
"pool2d"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCase1
(
TestPool2d_Op
):
def
init
TestC
ase
(
self
):
def
init
_test_c
ase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCase2
(
TestPool2d_Op
):
def
init
TestC
ase
(
self
):
def
init
_test_c
ase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCase3
(
TestPool2d_Op
):
def
init
TestC
ase
(
self
):
def
init
_test_c
ase
(
self
):
self
.
global_pool
=
True
self
.
op_type
=
"pool2d"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCase4
(
TestPool2d_Op
):
def
init
TestC
ase
(
self
):
def
init
_test_c
ase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCase5
(
TestPool2d_Op
):
def
init
TestC
ase
(
self
):
def
init
_test_c
ase
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
#--------------------test pool2d_cudnn--------------------
class
TestCaseCudnn1
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
True
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCaseCudnn2
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCaseCudnn3
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
class
TestCaseCudnn4
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
True
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCaseCudnn5
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
class
TestCaseCudnn6
(
TestPool2d_Op
):
def
init_test_case
(
self
):
self
.
global_pool
=
False
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
def
init_op_type
(
self
):
self
.
op_type
=
"pool2d_cudnn"
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
if
__name__
==
'__main__'
:
unittest
.
main
()
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