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体验新版 GitCode,发现更多精彩内容 >>
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60eaf967
编写于
12月 29, 2018
作者:
X
xiaoli.liu@intel.com
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Clean unittest code.
test=develop
上级
157e79e8
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
65 addition
and
201 deletion
+65
-201
python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py
...addle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py
+45
-171
python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py
python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py
+16
-29
python/paddle/fluid/tests/unittests/test_pool2d_op.py
python/paddle/fluid/tests/unittests/test_pool2d_op.py
+4
-1
未找到文件。
python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py
浏览文件 @
60eaf967
...
...
@@ -20,217 +20,91 @@ import numpy as np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
from
test_pool2d_op
import
TestPool2D_Op
,
avg_pool2D_forward_naive
,
max_pool2D_forward_naive
def
adaptive_start_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
floor
(
index
*
input_size
/
output_size
))
def
adaptive_end_index
(
index
,
input_size
,
output_size
):
return
int
(
np
.
ceil
((
index
+
1
)
*
input_size
/
output_size
))
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
W_out
):
if
adaptive
:
r_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
0
])
r_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
0
])
c_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
1
])
c_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
1
])
else
:
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
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
//
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
//
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
W_out
):
if
adaptive
:
r_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
0
])
r_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
0
])
c_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
1
])
c_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
1
])
else
:
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
]
field_size
=
((
r_end
-
r_start
)
*
(
c_end
-
c_start
))
\
if
(
exclusive
or
adaptive
)
else
(
ksize
[
0
]
*
ksize
[
1
])
out
[:,
:,
i
,
j
]
=
np
.
sum
(
x_masked
,
axis
=
(
2
,
3
))
/
field_size
return
out
class
TestPool2D_Op
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"pool2d"
self
.
use_cudnn
=
False
class
TestPool2dMKLDNNInt8_Op
(
TestPool2D_Op
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
self
.
dtype
=
np
.
int8
self
.
init_test_case
()
self
.
init_global_pool
()
self
.
init_pool_type
()
self
.
init_ceil_mode
()
self
.
init_exclusive
()
self
.
init_adaptive
()
if
self
.
global_pool
:
self
.
paddings
=
[
0
for
_
in
range
(
len
(
self
.
paddings
))]
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
output
=
self
.
pool2D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
,
self
.
ceil_mode
,
self
.
exclusive
,
self
.
adaptive
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
input
)}
self
.
attrs
=
{
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'pooling_type'
:
self
.
pool_type
,
'global_pooling'
:
self
.
global_pool
,
'use_cudnn'
:
self
.
use_cudnn
,
'use_mkldnn'
:
self
.
use_mkldnn
,
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
,
# TODO(dzhwinter) : should be fix latter
'exclusive'
:
self
.
exclusive
,
'adaptive'
:
self
.
adaptive
}
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
core
.
CPUPlace
(),
atol
=
1e-5
)
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
def
init_data_type
(
self
):
self
.
dtype
=
np
.
int8
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
True
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
False
def
setUp
(
self
):
TestPool2D_Op
.
setUp
(
self
)
assert
self
.
dtype
in
[
np
.
int8
,
np
.
uint8
],
'Dtype should be int8 or uint8'
def
init_exclusive
(
self
):
self
.
exclusive
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
core
.
CPUPlace
(),
atol
=
1e-5
)
def
init_adaptive
(
self
):
self
.
adaptive
=
False
def
test_check_grad
(
self
):
pass
class
TestCase1
(
TestPool2D
_Op
):
class
TestCase1
Avg
(
TestPool2dMKLDNNInt8
_Op
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
self
.
dtype
=
np
.
int8
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase2
(
TestPool2D
_Op
):
class
TestCase2
Avg
(
TestPool2dMKLDNNInt8
_Op
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
self
.
dtype
=
np
.
uint8
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
def
init_global_pool
(
self
):
self
.
global_pool
=
False
class
TestCase3
(
TestPool2D_Op
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
self
.
dtype
=
np
.
int8
class
TestCase0Max
(
TestPool2dMKLDNNInt8_Op
):
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
class
TestCase4
(
TestCase1
):
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
self
.
dtype
=
np
.
uint8
class
TestCase1Max
(
TestCase1Avg
):
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
class
TestCase2Max
(
TestCase2Avg
):
def
init_pool_type
(
self
):
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
def
create_test_s8_u8_class
(
parent
):
class
TestS8Case
(
parent
):
def
init_data_type
(
self
):
self
.
dtype
=
np
.
int8
class
TestU8Case
(
parent
):
def
init_data_type
(
self
):
self
.
dtype
=
np
.
uint8
cls_name_s8
=
"{0}_{1}"
.
format
(
parent
.
__name__
,
"mkldnn_s8"
)
cls_name_u8
=
"{0}_{1}"
.
format
(
parent
.
__name__
,
"mkldnn_u8"
)
TestS8Case
.
__name__
=
cls_name_s8
TestU8Case
.
__name__
=
cls_name_u8
globals
()[
cls_name_s8
]
=
TestS8Case
globals
()[
cls_name_u8
]
=
TestU8Case
create_test_s8_u8_class
(
TestPool2dMKLDNNInt8_Op
)
create_test_s8_u8_class
(
TestCase1Avg
)
create_test_s8_u8_class
(
TestCase2Avg
)
create_test_s8_u8_class
(
TestCase0Max
)
create_test_s8_u8_class
(
TestCase1Max
)
create_test_s8_u8_class
(
TestCase2Max
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py
浏览文件 @
60eaf967
...
...
@@ -18,35 +18,22 @@ import unittest
from
test_pool2d_op
import
TestPool2D_Op
,
TestCase1
,
TestCase2
,
TestCase3
,
TestCase4
,
TestCase5
class
TestMKLDNNCase1
(
TestPool2D_Op
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
class
TestMKLDNNCase2
(
TestCase1
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
class
TestMKLDNNCase3
(
TestCase2
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
class
TestMKLDNNCase4
(
TestCase3
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
class
TestMKLDNNCase5
(
TestCase4
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
class
TestMKLDNNCase6
(
TestCase5
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
def
create_test_mkldnn_class
(
parent
):
class
TestMKLDNNCase
(
parent
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
cls_name
=
"{0}_{1}"
.
format
(
parent
.
__name__
,
"MKLDNNOp"
)
TestMKLDNNCase
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestMKLDNNCase
create_test_mkldnn_class
(
TestPool2D_Op
)
create_test_mkldnn_class
(
TestCase1
)
create_test_mkldnn_class
(
TestCase2
)
create_test_mkldnn_class
(
TestCase3
)
create_test_mkldnn_class
(
TestCase4
)
create_test_mkldnn_class
(
TestCase5
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @
60eaf967
...
...
@@ -115,7 +115,7 @@ class TestPool2D_Op(OpTest):
self
.
op_type
=
"pool2d"
self
.
use_cudnn
=
False
self
.
use_mkldnn
=
False
self
.
dtype
=
np
.
float32
self
.
init_data_type
()
self
.
init_test_case
()
self
.
init_global_pool
()
self
.
init_kernel_type
()
...
...
@@ -177,6 +177,9 @@ class TestPool2D_Op(OpTest):
def
init_kernel_type
(
self
):
pass
def
init_data_type
(
self
):
self
.
dtype
=
np
.
float32
def
init_pool_type
(
self
):
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
...
...
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