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体验新版 GitCode,发现更多精彩内容 >>
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1e510d99
编写于
2月 28, 2018
作者:
W
wanghaoshuang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add ceil_mode option for pool2d and pool3d
上级
69643b5e
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
154 addition
and
29 deletion
+154
-29
paddle/fluid/operators/pool_op.cc
paddle/fluid/operators/pool_op.cc
+43
-10
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+3
-1
python/paddle/fluid/tests/unittests/test_pool2d_op.py
python/paddle/fluid/tests/unittests/test_pool2d_op.py
+51
-8
python/paddle/fluid/tests/unittests/test_pool3d_op.py
python/paddle/fluid/tests/unittests/test_pool3d_op.py
+57
-10
未找到文件。
paddle/fluid/operators/pool_op.cc
浏览文件 @
1e510d99
...
...
@@ -17,8 +17,15 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
int
PoolOutputSize
(
int
input_size
,
int
filter_size
,
int
padding
,
int
stride
)
{
int
output_size
=
(
input_size
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
int
PoolOutputSize
(
int
input_size
,
int
filter_size
,
int
padding
,
int
stride
,
bool
ceil_mode
)
{
int
output_size
;
if
(
!
ceil_mode
)
{
output_size
=
(
input_size
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
}
else
{
output_size
=
(
input_size
-
filter_size
+
2
*
padding
+
stride
-
1
)
/
stride
+
1
;
}
PADDLE_ENFORCE
(
output_size
>
0
,
"Due to the settings of padding(%d), filter_size(%d) and "
"stride(%d), the output size is less than 0, please check "
...
...
@@ -38,6 +45,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
std
::
vector
<
int
>
ksize
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
ceil_mode
=
ctx
->
Attrs
().
Get
<
bool
>
(
"ceil_mode"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
||
in_x_dims
.
size
()
==
5
,
"Pooling intput should be 4-D or 5-D tensor."
);
...
...
@@ -59,8 +67,8 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
std
::
vector
<
int64_t
>
output_shape
({
in_x_dims
[
0
],
in_x_dims
[
1
]});
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
output_shape
.
push_back
(
PoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]
));
output_shape
.
push_back
(
PoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
],
ceil_mode
));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
ctx
->
ShareLoD
(
"X"
,
"Out"
);
...
...
@@ -167,6 +175,13 @@ Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
"use_cudnn"
,
"(bool, default false) Only used in cudnn kernel, need install cudnn"
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"ceil_mode"
,
"(bool, default false) Wether to use the ceil function to calculate "
"output height and width."
"True is the default. If it is set to False, the floor function will"
"be used"
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"data_format"
,
"(string, default NCHW) Only used in "
...
...
@@ -187,16 +202,21 @@ Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Example:
Input:
X shape: $(N, C, H_{in}, W_{in})$
Output:
Out shape: $(N, C, H_{out}, W_{out})$
Where
$$
For ceil_mode = false:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
For ceil_mode = true:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
$$
)DOC"
);
}
...
...
@@ -251,6 +271,13 @@ Pool3dOpMaker::Pool3dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
"use_cudnn"
,
"(bool, default false) Only used in cudnn kernel, need install cudnn"
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"ceil_mode"
,
"(bool, default false) Wether to use the ceil function to calculate "
"output height and width."
"True is the default. If it is set to False, the floor function will"
"be used"
)
.
SetDefault
(
false
);
AddAttr
<
std
::
string
>
(
"data_format"
,
"(string, default NCHW) Only used in "
...
...
@@ -267,8 +294,8 @@ The pooling3d operation calculates the output based on
the input, pooling_type, ksize, strides, and paddings parameters.
Input(X) and output(Out) are in NCDHW format, where N is batch
size, C is the number of channels, and D, H and W are the depth, height and
width of the feature, respectively. Parameters(ksize, strides, paddings)
are three elements. These three elements represent depth, height and
width of the feature, respectively. Parameters(ksize, strides, paddings)
are three elements. These three elements represent depth, height and
width, respectively. The input(X) size and output(Out) size may be different.
Example:
...
...
@@ -276,12 +303,18 @@ Example:
X shape: $(N, C, D_{in}, H_{in}, W_{in})$
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
Where
For ceil_mode = false:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
For ceil_mode = true:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
)DOC"
);
}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
1e510d99
...
...
@@ -1437,6 +1437,7 @@ def pool2d(input,
pool_padding
=
0
,
global_pooling
=
False
,
use_cudnn
=
True
,
ceil_mode
=
False
,
name
=
None
):
"""
This function adds the operator for pooling in 2 dimensions, using the
...
...
@@ -1473,7 +1474,8 @@ def pool2d(input,
"global_pooling"
:
global_pooling
,
"strides"
:
pool_stride
,
"paddings"
:
pool_padding
,
"use_cudnn"
:
use_cudnn
"use_cudnn"
:
use_cudnn
,
"ceil_mode"
:
ceil_mode
})
return
pool_out
...
...
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @
1e510d99
...
...
@@ -19,12 +19,21 @@ import paddle.fluid.core as core
from
op_test
import
OpTest
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
):
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
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
xrange
(
H_out
):
for
j
in
xrange
(
W_out
):
...
...
@@ -38,12 +47,21 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
return
out
def
avg_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
def
avg_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
):
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
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
xrange
(
H_out
):
for
j
in
xrange
(
W_out
):
...
...
@@ -65,12 +83,13 @@ class TestPool2d_Op(OpTest):
self
.
init_global_pool
()
self
.
init_op_type
()
self
.
init_pool_type
()
self
.
init_ceil_mode
()
if
self
.
global_pool
:
self
.
paddings
=
[
0
for
_
in
range
(
len
(
self
.
paddings
))]
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
=
self
.
pool2D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
).
astype
(
"float32"
)
self
.
paddings
,
self
.
global_pool
,
self
.
ceil_mode
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
input
}
self
.
attrs
=
{
...
...
@@ -80,6 +99,7 @@ class TestPool2d_Op(OpTest):
'pooling_type'
:
self
.
pool_type
,
'global_pooling'
:
self
.
global_pool
,
'use_cudnn'
:
self
.
use_cudnn
,
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
# TODO(dzhwinter) : should be fix latter
}
...
...
@@ -116,6 +136,9 @@ class TestPool2d_Op(OpTest):
def
init_global_pool
(
self
):
self
.
global_pool
=
True
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
False
class
TestCase1
(
TestPool2d_Op
):
def
init_test_case
(
self
):
...
...
@@ -217,5 +240,25 @@ class TestCUDNNCase6(TestCase5):
self
.
op_type
=
"pool2d"
class
TestCeilModeCase1
(
TestCUDNNCase1
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
class
TestCeilModeCase2
(
TestCUDNNCase2
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
class
TestCeilModeCase3
(
TestCase1
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
class
TestCeilModeCase4
(
TestCase2
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_pool3d_op.py
浏览文件 @
1e510d99
...
...
@@ -19,13 +19,24 @@ import paddle.fluid.core as core
from
op_test
import
OpTest
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
D
,
H
,
W
]
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
/
strides
[
2
]
+
1
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
/
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
/
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
]
+
strides
[
2
]
-
1
)
/
strides
[
2
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
/
strides
[
2
]
+
1
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
xrange
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
...
...
@@ -42,13 +53,24 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
return
out
def
avg_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
):
def
avg_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
D
,
H
,
W
]
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
/
strides
[
2
]
+
1
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
]
+
strides
[
0
]
-
1
)
/
strides
[
0
]
+
1
if
ceil_mode
else
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
]
+
strides
[
1
]
-
1
)
/
strides
[
1
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
]
+
strides
[
2
]
-
1
)
/
strides
[
2
]
+
1
if
ceil_mode
else
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
/
strides
[
2
]
+
1
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
xrange
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
...
...
@@ -73,13 +95,14 @@ class TestPool3d_Op(OpTest):
self
.
init_global_pool
()
self
.
init_op_type
()
self
.
init_pool_type
()
self
.
init_ceil_mode
()
if
self
.
global_pool
:
self
.
paddings
=
[
0
for
_
in
range
(
len
(
self
.
paddings
))]
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
=
self
.
pool3D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
).
astype
(
"float32"
)
self
.
paddings
,
self
.
global_pool
,
self
.
ceil_mode
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
input
}
self
.
attrs
=
{
...
...
@@ -89,6 +112,7 @@ class TestPool3d_Op(OpTest):
'pooling_type'
:
self
.
pool_type
,
'global_pooling'
:
self
.
global_pool
,
'use_cudnn'
:
self
.
use_cudnn
,
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
# TODO(dzhwinter) : should be fix latter
}
...
...
@@ -125,6 +149,9 @@ class TestPool3d_Op(OpTest):
def
init_global_pool
(
self
):
self
.
global_pool
=
True
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
False
class
TestCase1
(
TestPool3d_Op
):
def
init_test_case
(
self
):
...
...
@@ -227,5 +254,25 @@ class TestCUDNNCase6(TestCase5):
self
.
op_type
=
"pool3d"
class
TestCeilModeCase1
(
TestCUDNNCase1
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
class
TestCeilModeCase2
(
TestCUDNNCase2
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
class
TestCeilModeCase3
(
TestCase1
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
class
TestCeilModeCase4
(
TestCase2
):
def
init_ceil_mode
(
self
):
self
.
ceil_mode
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
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