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dc76e4b0
编写于
12月 14, 2018
作者:
K
Kaipeng Deng
提交者:
GitHub
12月 14, 2018
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差异文件
Merge pull request #14701 from heavengate/adaptive_pool
add adaptive pool2d and pool3d
上级
0b1c7d83
f16aa394
变更
14
展开全部
隐藏空白更改
内联
并排
Showing
14 changed file
with
1014 addition
and
317 deletion
+1014
-317
paddle/fluid/API.spec
paddle/fluid/API.spec
+2
-0
paddle/fluid/operators/math/pooling.cc
paddle/fluid/operators/math/pooling.cc
+153
-62
paddle/fluid/operators/math/pooling.cu
paddle/fluid/operators/math/pooling.cu
+268
-147
paddle/fluid/operators/math/pooling.h
paddle/fluid/operators/math/pooling.h
+22
-10
paddle/fluid/operators/pool_op.cc
paddle/fluid/operators/pool_op.cc
+62
-3
paddle/fluid/operators/pool_op.h
paddle/fluid/operators/pool_op.h
+10
-6
paddle/fluid/operators/pool_with_index_op.cc
paddle/fluid/operators/pool_with_index_op.cc
+35
-3
paddle/fluid/operators/pool_with_index_op.h
paddle/fluid/operators/pool_with_index_op.h
+8
-4
paddle/fluid/operators/spp_op.h
paddle/fluid/operators/spp_op.h
+3
-3
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+200
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+23
-0
python/paddle/fluid/tests/unittests/test_pool2d_op.py
python/paddle/fluid/tests/unittests/test_pool2d_op.py
+65
-26
python/paddle/fluid/tests/unittests/test_pool3d_op.py
python/paddle/fluid/tests/unittests/test_pool3d_op.py
+86
-35
python/paddle/fluid/tests/unittests/test_pool_max_op.py
python/paddle/fluid/tests/unittests/test_pool_max_op.py
+77
-18
未找到文件。
paddle/fluid/API.spec
浏览文件 @
dc76e4b0
...
...
@@ -77,6 +77,8 @@ paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name']
paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
paddle.fluid.layers.adaptive_pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
paddle.fluid.layers.adaptive_pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None))
paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False))
paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
...
...
paddle/fluid/operators/math/pooling.cc
浏览文件 @
dc76e4b0
...
...
@@ -31,7 +31,7 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_process
,
bool
exclusive
,
framework
::
Tensor
*
output
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -51,16 +51,28 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
T
ele
=
pool_process
.
initial
();
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
...
...
@@ -68,8 +80,9 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
pool_process
.
compute
(
input_data
[
h
*
input_width
+
w
],
&
ele
);
}
}
int
pool_size
=
exclusive
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
int
pool_size
=
(
exclusive
||
adaptive
)
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
pool_process
.
finalize
(
static_cast
<
T
>
(
pool_size
),
&
ele
);
output_data
[
ph
*
output_width
+
pw
]
=
ele
;
}
...
...
@@ -94,7 +107,7 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_grad_process
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -115,18 +128,31 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
int
pool_size
=
exclusive
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
pool_size
=
(
exclusive
||
adaptive
)
?
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_height
*
ksize_width
;
float
scale
=
1.0
/
pool_size
;
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
...
...
@@ -251,7 +277,7 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_process
,
bool
exclusive
,
framework
::
Tensor
*
output
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
...
...
@@ -276,20 +302,38 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
dstart
,
dend
;
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
if
(
adaptive
)
{
dstart
=
AdaptStartIndex
(
pd
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
pd
,
input_depth
,
output_depth
);
}
else
{
dstart
=
pd
*
stride_depth
-
padding_depth
;
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
}
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
output_idx
=
(
pd
*
output_height
+
ph
)
*
output_width
+
pw
;
T
ele
=
pool_process
.
initial
();
for
(
int
d
=
dstart
;
d
<
dend
;
++
d
)
{
...
...
@@ -302,7 +346,7 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
}
}
int
pool_size
=
exclusive
(
exclusive
||
adaptive
)
?
(
dend
-
dstart
)
*
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_depth
*
ksize_height
*
ksize_width
;
pool_process
.
finalize
(
static_cast
<
T
>
(
pool_size
),
&
ele
);
...
...
@@ -330,7 +374,7 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_grad_process
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
)
{
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
...
...
@@ -356,24 +400,41 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
dstart
,
dend
;
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
if
(
adaptive
)
{
dstart
=
AdaptStartIndex
(
pd
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
pd
,
input_depth
,
output_depth
);
}
else
{
dstart
=
pd
*
stride_depth
-
padding_depth
;
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
}
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
pool_size
=
exclusive
(
exclusive
||
adaptive
)
?
(
dend
-
dstart
)
*
(
hend
-
hstart
)
*
(
wend
-
wstart
)
:
ksize_depth
*
ksize_height
*
ksize_width
;
float
scale
=
1.0
/
pool_size
;
...
...
@@ -517,8 +578,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -538,16 +599,28 @@ class MaxPool2dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
T1
*
output_data
=
output
->
mutable_data
<
T1
>
(
context
.
GetPlace
());
T2
*
mask_data
=
mask
->
mutable_data
<
T2
>
(
context
.
GetPlace
());
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
T1
ele
=
static_cast
<
T1
>
(
-
FLT_MAX
);
int
index
=
-
1
;
...
...
@@ -584,7 +657,7 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUDeviceContext, T1, T2> {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input_grad
->
dims
()[
0
];
const
int
input_height
=
input_grad
->
dims
()[
2
];
...
...
@@ -637,8 +710,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
...
...
@@ -663,20 +736,38 @@ class MaxPool3dWithIndexFunctor<platform::CPUDeviceContext, T1, T2> {
T1
*
output_data
=
output
->
mutable_data
<
T1
>
(
context
.
GetPlace
());
T2
*
mask_data
=
mask
->
mutable_data
<
T2
>
(
context
.
GetPlace
());
int
dstart
,
dend
;
int
hstart
,
hend
;
int
wstart
,
wend
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
if
(
adaptive
)
{
dstart
=
AdaptStartIndex
(
pd
,
input_depth
,
output_depth
);
dend
=
AdaptEndIndex
(
pd
,
input_depth
,
output_depth
);
}
else
{
dstart
=
pd
*
stride_depth
-
padding_depth
;
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
}
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
if
(
adaptive
)
{
hstart
=
AdaptStartIndex
(
ph
,
input_height
,
output_height
);
hend
=
AdaptEndIndex
(
ph
,
input_height
,
output_height
);
}
else
{
hstart
=
ph
*
stride_height
-
padding_height
;
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
}
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
if
(
adaptive
)
{
wstart
=
AdaptStartIndex
(
pw
,
input_width
,
output_width
);
wend
=
AdaptEndIndex
(
pw
,
input_width
,
output_width
);
}
else
{
wstart
=
pw
*
stride_width
-
padding_width
;
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
}
int
output_idx
=
(
pd
*
output_height
+
ph
)
*
output_width
+
pw
;
T1
ele
=
static_cast
<
T1
>
(
-
FLT_MAX
);
...
...
@@ -718,7 +809,7 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUDeviceContext, T1, T2> {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input_grad
->
dims
()[
0
];
const
int
input_depth
=
input_grad
->
dims
()[
2
];
...
...
paddle/fluid/operators/math/pooling.cu
浏览文件 @
dc76e4b0
此差异已折叠。
点击以展开。
paddle/fluid/operators/math/pooling.h
浏览文件 @
dc76e4b0
...
...
@@ -68,6 +68,18 @@ class AvgPoolGrad {
}
};
/* used for adaptive pool to calculate start and end index of each divided grid
*/
HOSTDEVICE
inline
int
AdaptStartIndex
(
int
ph
,
int
input_size
,
int
output_size
)
{
return
static_cast
<
int
>
(
floor
(
static_cast
<
double
>
(
ph
*
input_size
)
/
output_size
));
}
HOSTDEVICE
inline
int
AdaptEndIndex
(
int
ph
,
int
input_size
,
int
output_size
)
{
return
static_cast
<
int
>
(
ceil
(
static_cast
<
double
>
((
ph
+
1
)
*
input_size
)
/
output_size
));
}
/*
* \brief Getting pooling results, and calculating gradient.
*
...
...
@@ -102,7 +114,7 @@ class Pool2dFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
output
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
);
};
template
<
typename
DeviceContext
,
typename
PoolProcess
,
typename
T
>
...
...
@@ -114,7 +126,7 @@ class Pool2dGradFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
template
<
typename
DeviceContext
,
class
T
>
...
...
@@ -136,7 +148,7 @@ class Pool3dFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
output
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
output
);
};
template
<
typename
DeviceContext
,
typename
PoolProcess
,
typename
T
>
...
...
@@ -148,7 +160,7 @@ class Pool3dGradFunctor {
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
,
bool
exclusive
,
framework
::
Tensor
*
input_grad
);
bool
exclusive
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
template
<
typename
DeviceContext
,
class
T
>
...
...
@@ -176,8 +188,8 @@ class MaxPool2dWithIndexFunctor {
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
};
template
<
typename
DeviceContext
,
typename
T1
,
typename
T2
>
...
...
@@ -187,7 +199,7 @@ class MaxPool2dWithIndexGradFunctor {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
...
...
@@ -197,8 +209,8 @@ class MaxPool3dWithIndexFunctor {
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
};
template
<
typename
DeviceContext
,
typename
T1
,
typename
T2
>
...
...
@@ -208,7 +220,7 @@ class MaxPool3dWithIndexGradFunctor {
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
const
std
::
vector
<
int
>&
ksize
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
paddings
,
bool
adaptive
,
framework
::
Tensor
*
input_grad
);
};
...
...
paddle/fluid/operators/pool_op.cc
浏览文件 @
dc76e4b0
...
...
@@ -52,6 +52,7 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
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"
);
bool
adaptive
=
ctx
->
Attrs
().
Get
<
bool
>
(
"adaptive"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
||
in_x_dims
.
size
()
==
5
,
"Pooling intput should be 4-D or 5-D tensor."
);
...
...
@@ -72,9 +73,13 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
"Paddings size and pooling size should be the same."
);
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
],
ceil_mode
));
if
(
adaptive
)
{
output_shape
.
insert
(
output_shape
.
end
(),
ksize
.
begin
(),
ksize
.
end
());
}
else
{
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
],
ceil_mode
));
}
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
ctx
->
ShareLoD
(
"X"
,
"Out"
);
...
...
@@ -186,6 +191,14 @@ void Pool2dOpMaker::Make() {
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True."
)
.
SetDefault
(
true
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"use_cudnn"
,
"(bool, default false) Only used in cudnn kernel, need install cudnn"
)
...
...
@@ -264,6 +277,14 @@ Example:
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
hstart = floor(i * H_{in} / H_{out})
hend = ceil((i + 1) * H_{in} / H_{out})
wstart = floor(j * W_{in} / W_{out})
wend = ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
)DOC"
);
}
...
...
@@ -325,6 +346,13 @@ void Pool3dOpMaker::Make() {
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True."
)
.
SetDefault
(
true
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"use_cudnn"
,
...
...
@@ -376,6 +404,37 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
For exclusive = true:
$$
dstart = i * strides[0] - paddings[0]
dend = dstart + ksize[0]
hstart = j * strides[1] - paddings[1]
hend = hstart + ksize[1]
wstart = k * strides[2] - paddings[2]
wend = wstart + ksize[2]
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
For exclusive = false:
$$
dstart = max(0, i * strides[0] - paddings[0])
dend = min(D, dstart + ksize[0])
hstart = max(0, j * strides[1] - paddings[1])
hend = min(H, hstart + ksize[1])
wstart = max(0, k * strides[2] - paddings[2])
wend = min(W, wstart + ksize[2])
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
dstart = floor(i * D_{in} / D_{out})
dend = ceil((i + 1) * D_{in} / D_{out})
hstart = floor(j * H_{in} / H_{out})
hend = ceil((j + 1) * H_{in} / H_{out})
wstart = floor(k * W_{in} / W_{out})
wend = ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
)DOC"
);
}
...
...
paddle/fluid/operators/pool_op.h
浏览文件 @
dc76e4b0
...
...
@@ -70,6 +70,7 @@ class PoolKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
exclusive
=
context
.
Attr
<
bool
>
(
"exclusive"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
paddings
[
i
]
=
0
;
...
...
@@ -85,7 +86,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
true
,
out
);
true
,
false
,
out
);
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool2dFunctor
<
...
...
@@ -93,7 +94,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
out
);
exclusive
,
adaptive
,
out
);
}
}
break
;
case
3
:
{
...
...
@@ -103,14 +104,14 @@ class PoolKernel : public framework::OpKernel<T> {
pool3d_forward
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
true
,
out
);
true
,
false
,
out
);
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool3dFunctor
<
DeviceContext
,
paddle
::
operators
::
math
::
AvgPool
<
T
>
,
T
>
pool3d_forward
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
out
);
exclusive
,
adaptive
,
out
);
}
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
...
...
@@ -133,6 +134,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
exclusive
=
context
.
Attr
<
bool
>
(
"exclusive"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
...
...
@@ -159,7 +161,8 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool2d_backward
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
pool2d_backward
(
dev_ctx
,
*
in_x
,
*
out
,
*
out_grad
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
in_x_grad
);
paddings
,
pool_process
,
exclusive
,
adaptive
,
in_x_grad
);
}
}
break
;
case
3
:
{
...
...
@@ -174,7 +177,8 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool3d_backward
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
pool3d_backward
(
dev_ctx
,
*
in_x
,
*
out
,
*
out_grad
,
ksize
,
strides
,
paddings
,
pool_process
,
exclusive
,
in_x_grad
);
paddings
,
pool_process
,
exclusive
,
adaptive
,
in_x_grad
);
}
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
...
...
paddle/fluid/operators/pool_with_index_op.cc
浏览文件 @
dc76e4b0
...
...
@@ -40,6 +40,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
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
adaptive
=
ctx
->
Attrs
().
Get
<
bool
>
(
"adaptive"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
||
in_x_dims
.
size
()
==
5
,
"Pooling intput should be 4-D or 5-D tensor."
);
...
...
@@ -60,9 +61,13 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
"Paddings size and pooling size should be the same."
);
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
(
MaxPoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
if
(
adaptive
)
{
output_shape
.
insert
(
output_shape
.
end
(),
ksize
.
begin
(),
ksize
.
end
());
}
else
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
output_shape
.
push_back
(
MaxPoolOutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
ctx
->
SetOutputDim
(
"Mask"
,
framework
::
make_ddim
(
output_shape
));
...
...
@@ -133,6 +138,14 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, default:false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings will be ignored."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling "
"instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int>, default {1, 1}), strides(height, "
"width) of pooling operator."
)
...
...
@@ -169,6 +182,12 @@ Example:
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
$$
For adaptive = true:
$$
H_{out} = ksize[0] W_{out} = ksize[1]
$$
)DOC"
);
}
...
...
@@ -209,6 +228,14 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, default false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings will be ignored."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"adaptive"
,
"(bool, default False) When true, will perform adaptive pooling "
"instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int>, default {1,1,1}), strides(depth, "
"height, width) of pooling operator."
)
...
...
@@ -246,6 +273,11 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
For adaptive = true:
$$
D_{out} = ksize[0] H_{out} = ksize[1] W_{out} = ksize[2]
$$
)DOC"
);
}
...
...
paddle/fluid/operators/pool_with_index_op.h
浏览文件 @
dc76e4b0
...
...
@@ -36,6 +36,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
...
...
@@ -50,13 +51,15 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
paddle
::
operators
::
math
::
MaxPool2dWithIndexFunctor
<
DeviceContext
,
T1
,
T2
>
pool2d_forward
;
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
out
,
mask
);
pool2d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
adaptive
,
out
,
mask
);
}
break
;
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexFunctor
<
DeviceContext
,
T1
,
T2
>
pool3d_forward
;
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
out
,
mask
);
pool3d_forward
(
dev_ctx
,
*
in_x
,
ksize
,
strides
,
paddings
,
adaptive
,
out
,
mask
);
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
}
...
...
@@ -75,6 +78,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
bool
adaptive
=
context
.
Attr
<
bool
>
(
"adaptive"
);
if
(
context
.
Attr
<
bool
>
(
"global_pooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
paddings
[
i
]
=
0
;
...
...
@@ -93,14 +97,14 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
T1
,
T2
>
pool2d_backward
;
pool2d_backward
(
device_ctx
,
*
out_grad
,
*
mask
,
ksize
,
strides
,
paddings
,
in_x_grad
);
paddings
,
adaptive
,
in_x_grad
);
}
break
;
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexGradFunctor
<
DeviceContext
,
T1
,
T2
>
pool3d_backward
;
pool3d_backward
(
device_ctx
,
*
out_grad
,
*
mask
,
ksize
,
strides
,
paddings
,
in_x_grad
);
paddings
,
adaptive
,
in_x_grad
);
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
}
...
...
paddle/fluid/operators/spp_op.h
浏览文件 @
dc76e4b0
...
...
@@ -56,13 +56,13 @@ class SppKernel : public framework::OpKernel<T> {
math
::
Pool2dFunctor
<
DeviceContext
,
math
::
MaxPool
<
T
>
,
T
>
pool_forward
;
math
::
MaxPool
<
T
>
max_process
;
pool_forward
(
context
.
template
device_context
<
DeviceContext
>(),
*
in_x
,
kernel_size
,
strides
,
paddings
,
max_process
,
true
,
kernel_size
,
strides
,
paddings
,
max_process
,
true
,
false
,
&
out_level
);
}
else
if
(
pooling_type
==
"avg"
)
{
math
::
Pool2dFunctor
<
DeviceContext
,
math
::
AvgPool
<
T
>
,
T
>
pool_forward
;
math
::
AvgPool
<
T
>
avg_process
;
pool_forward
(
context
.
template
device_context
<
DeviceContext
>(),
*
in_x
,
kernel_size
,
strides
,
paddings
,
avg_process
,
true
,
kernel_size
,
strides
,
paddings
,
avg_process
,
true
,
false
,
&
out_level
);
}
// flatten pooling output shape
...
...
@@ -156,7 +156,7 @@ class SppGradKernel : public framework::OpKernel<T> {
math
::
AvgPoolGrad
<
T
>
avg_process
;
pool_backward
(
context
.
template
device_context
<
DeviceContext
>(),
*
in_x
,
*&
out_level
,
*&
outgrad_level
,
kernel_size
,
strides
,
paddings
,
avg_process
,
true
,
in_x_grad
);
paddings
,
avg_process
,
true
,
false
,
in_x_grad
);
}
}
}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
dc76e4b0
...
...
@@ -52,6 +52,8 @@ __all__ = [
'softmax'
,
'pool2d'
,
'pool3d'
,
'adaptive_pool2d'
,
'adaptive_pool3d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
...
...
@@ -2500,6 +2502,204 @@ def pool3d(input,
return
pool_out
@
templatedoc
(
op_type
=
"pool2d"
)
def
adaptive_pool2d
(
input
,
pool_size
,
pool_type
=
"max"
,
require_index
=
False
,
name
=
None
):
"""
${comment}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is
the number of channels, H is the height of the
feature, and W is the width of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The pooling result.
Raises:
ValueError: 'pool_type' is not 'max' nor 'avg'.
ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'.
ValueError: 'pool_size' should be a list or tuple with length as 2.
Examples:
.. code-block:: python
# suppose input data in shape of [N, C, H, W], `pool_size` is [m, n],
# output shape is [N, C, m, n], adaptive pool divide H and W dimentions
# of input data into m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive average pool performs calculations as follow:
#
# for i in range(m):
# for j in range(n):
# hstart = floor(i * H / m)
# hend = ceil((i + 1) * H / m)
# wstart = floor(i * W / n)
# wend = ceil((i + 1) * W / n)
# output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
#
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
pool_out = fluid.layers.adaptive_pool2d(
input=data,
pool_size=[3, 3],
pool_type='avg')
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'."
,
str
(
pool_type
))
if
pool_type
==
"avg"
and
require_index
:
raise
ValueError
(
"invalid setting 'require_index' true when 'pool_type' is 'avg'."
)
def
_is_list_or_tuple_
(
data
):
return
(
isinstance
(
data
,
list
)
or
isinstance
(
data
,
tuple
))
if
not
_is_list_or_tuple_
(
pool_size
)
or
len
(
pool_size
)
!=
2
:
raise
ValueError
(
"'pool_size' should be a list or tuple with length as 2."
)
if
pool_type
==
"max"
:
l_type
=
'max_pool2d_with_index'
else
:
l_type
=
"pool2d"
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
=
{
"Out"
:
pool_out
}
if
pool_type
==
"max"
:
mask
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
[
"Mask"
]
=
mask
helper
.
append_op
(
type
=
l_type
,
inputs
=
{
"X"
:
input
},
outputs
=
outputs
,
attrs
=
{
"pooling_type"
:
pool_type
,
"ksize"
:
pool_size
,
"adaptive"
:
True
,
})
return
(
pool_out
,
mask
)
if
require_index
else
pool_out
@
templatedoc
(
op_type
=
"pool3d"
)
def
adaptive_pool3d
(
input
,
pool_size
,
pool_type
=
"max"
,
require_index
=
False
,
name
=
None
):
"""
${comment}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is
the number of channels, H is the height of the
feature, and W is the width of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (Depth, Height, Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable: The pooling result.
Raises:
ValueError: 'pool_type' is not 'max' nor 'avg'.
ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'.
ValueError: 'pool_size' should be a list or tuple with length as 2.
Examples:
.. code-block:: python
# suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n],
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions
# of input data into l * m * n grids averagely and performs poolings in each
# grid to get output.
# adaptive average pool performs calculations as follow:
#
# for i in range(l):
# for j in range(m):
# for k in range(n):
# dstart = floor(i * D / l)
# dend = ceil((i + 1) * D / l)
# hstart = floor(j * H / m)
# hend = ceil((j + 1) * H / m)
# wstart = floor(k * W / n)
# wend = ceil((k + 1) * W / n)
# output[:, :, i, j, k] =
# avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
#
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
pool_out, mask = fluid.layers.adaptive_pool3d(
input=data,
pool_size=[3, 3],
pool_type='avg')
"""
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'."
,
str
(
pool_type
))
if
pool_type
==
"avg"
and
require_index
:
raise
ValueError
(
"invalid setting 'require_index' true when 'pool_type' is 'avg'."
)
def
_is_list_or_tuple_
(
data
):
return
(
isinstance
(
data
,
list
)
or
isinstance
(
data
,
tuple
))
if
not
_is_list_or_tuple_
(
pool_size
)
or
len
(
pool_size
)
!=
3
:
raise
ValueError
(
"'pool_size' should be a list or tuple with length as 3."
)
if
pool_type
==
"max"
:
l_type
=
'max_pool3d_with_index'
else
:
l_type
=
"pool3d"
helper
=
LayerHelper
(
l_type
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
=
{
"Out"
:
pool_out
}
if
pool_type
==
"max"
:
mask
=
helper
.
create_variable_for_type_inference
(
dtype
)
outputs
[
"Mask"
]
=
mask
helper
.
append_op
(
type
=
l_type
,
inputs
=
{
"X"
:
input
},
outputs
=
outputs
,
attrs
=
{
"pooling_type"
:
pool_type
,
"ksize"
:
pool_size
,
"adaptive"
:
True
,
})
return
(
pool_out
,
mask
)
if
require_index
else
pool_out
def
batch_norm
(
input
,
act
=
None
,
is_test
=
False
,
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
dc76e4b0
...
...
@@ -233,6 +233,29 @@ class TestBook(unittest.TestCase):
pool_stride
=
[
1
,
2
],
pool_padding
=
(
2
,
1
)))
def
test_adaptive_pool2d
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
3
,
224
,
224
],
dtype
=
'float32'
)
self
.
assertIsNotNone
(
layers
.
adaptive_pool2d
(
x
,
[
3
,
3
],
pool_type
=
'avg'
))
pool
,
mask
=
layers
.
adaptive_pool2d
(
x
,
[
3
,
3
],
require_index
=
True
)
self
.
assertIsNotNone
(
pool
)
self
.
assertIsNotNone
(
mask
)
def
test_adaptive_pool3d
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
3
,
244
,
224
,
224
],
dtype
=
'float32'
)
self
.
assertIsNotNone
(
layers
.
adaptive_pool3d
(
x
,
[
3
,
3
,
3
],
pool_type
=
'avg'
))
pool
,
mask
=
layers
.
adaptive_pool3d
(
x
,
[
3
,
3
,
3
],
require_index
=
True
)
self
.
assertIsNotNone
(
pool
)
self
.
assertIsNotNone
(
mask
)
def
test_lstm_unit
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @
dc76e4b0
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
from
__future__
import
print_function
from
__future__
import
division
import
unittest
import
numpy
as
np
...
...
@@ -21,29 +22,47 @@ import paddle.fluid.core as core
from
op_test
import
OpTest
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
):
exclusive
=
True
,
adaptive
=
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
)
//
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
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
):
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
))
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
))
...
...
@@ -56,27 +75,37 @@ def avg_pool2D_forward_naive(x,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
):
exclusive
=
True
,
adaptive
=
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
)
//
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
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
):
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
))
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
\
else
(
ksize
[
0
]
*
ksize
[
1
])
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
...
...
@@ -93,12 +122,13 @@ class TestPool2D_Op(OpTest):
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
).
astype
(
self
.
dtype
)
self
.
ceil_mode
,
self
.
exclusive
,
self
.
adaptive
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
input
)}
self
.
attrs
=
{
...
...
@@ -112,7 +142,8 @@ class TestPool2D_Op(OpTest):
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
,
# TODO(dzhwinter) : should be fix latter
'exclusive'
:
self
.
exclusive
'exclusive'
:
self
.
exclusive
,
'adaptive'
:
self
.
adaptive
}
self
.
outputs
=
{
'Out'
:
output
}
...
...
@@ -159,6 +190,9 @@ class TestPool2D_Op(OpTest):
def
init_exclusive
(
self
):
self
.
exclusive
=
True
def
init_adaptive
(
self
):
self
.
adaptive
=
False
class
TestCase1
(
TestPool2D_Op
):
def
init_test_case
(
self
):
...
...
@@ -315,5 +349,10 @@ class TestCUDNNAvgInclude(TestCase2):
self
.
exclusive
=
False
class
TestAvgPoolAdaptive
(
TestCase1
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_pool3d_op.py
浏览文件 @
dc76e4b0
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
from
__future__
import
print_function
from
__future__
import
division
import
unittest
import
numpy
as
np
...
...
@@ -21,35 +22,59 @@ import paddle.fluid.core as core
from
op_test
import
OpTest
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_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
):
exclusive
=
True
,
adaptive
=
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
)
//
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
if
adaptive
:
D_out
,
H_out
,
W_out
=
ksize
else
:
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
range
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
if
adaptive
:
d_start
=
adaptive_start_index
(
k
,
D
,
ksize
[
0
])
d_end
=
adaptive_end_index
(
k
,
D
,
ksize
[
0
])
else
:
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
for
i
in
range
(
H_out
):
h_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
if
adaptive
:
h_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
1
])
h_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
1
])
else
:
h_start
=
np
.
max
((
i
*
strides
[
1
]
-
paddings
[
1
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
H
))
for
j
in
range
(
W_out
):
w_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
w_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
2
])
w_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
2
])
else
:
w_start
=
np
.
max
((
j
*
strides
[
2
]
-
paddings
[
2
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
2
]
+
ksize
[
2
]
-
paddings
[
2
],
W
))
x_masked
=
x
[:,
:,
d_start
:
d_end
,
h_start
:
h_end
,
w_start
:
w_end
]
out
[:,
:,
k
,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
,
4
))
...
...
@@ -62,33 +87,49 @@ def avg_pool3D_forward_naive(x,
paddings
,
global_pool
=
0
,
ceil_mode
=
False
,
exclusive
=
True
):
exclusive
=
True
,
adaptive
=
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
)
//
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
if
adaptive
:
D_out
,
H_out
,
W_out
=
ksize
else
:
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
range
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
if
adaptive
:
d_start
=
adaptive_start_index
(
k
,
D
,
ksize
[
0
])
d_end
=
adaptive_end_index
(
k
,
D
,
ksize
[
0
])
else
:
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
for
i
in
range
(
H_out
):
h_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
if
adaptive
:
h_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
1
])
h_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
1
])
else
:
h_start
=
np
.
max
((
i
*
strides
[
1
]
-
paddings
[
1
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
H
))
for
j
in
range
(
W_out
):
w_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
w_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
2
])
w_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
2
])
else
:
w_start
=
np
.
max
((
j
*
strides
[
2
]
-
paddings
[
2
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
2
]
+
ksize
[
2
]
-
paddings
[
2
],
W
))
x_masked
=
x
[:,
:,
d_start
:
d_end
,
h_start
:
h_end
,
w_start
:
w_end
]
field_size
=
(
d_end
-
d_start
)
*
(
h_end
-
h_start
)
*
(
w_end
-
w_start
)
\
if
exclusive
else
ksize
[
0
]
*
ksize
[
1
]
*
ksize
[
2
]
if
(
exclusive
or
adaptive
)
else
ksize
[
0
]
*
ksize
[
1
]
*
ksize
[
2
]
out
[:,
:,
k
,
i
,
j
]
=
np
.
sum
(
x_masked
,
axis
=
(
2
,
3
,
4
))
/
field_size
return
out
...
...
@@ -105,13 +146,14 @@ class TestPool3d_Op(OpTest):
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
.
pool3D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
,
self
.
ceil_mode
,
self
.
exclusive
).
astype
(
self
.
dtype
)
self
.
ceil_mode
,
self
.
exclusive
,
self
.
adaptive
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
input
)}
self
.
attrs
=
{
...
...
@@ -124,7 +166,8 @@ class TestPool3d_Op(OpTest):
'ceil_mode'
:
self
.
ceil_mode
,
'data_format'
:
'AnyLayout'
,
# TODO(dzhwinter) : should be fix latter
'exclusive'
:
self
.
exclusive
'exclusive'
:
self
.
exclusive
,
'adaptive'
:
self
.
adaptive
}
self
.
outputs
=
{
'Out'
:
output
}
...
...
@@ -171,6 +214,9 @@ class TestPool3d_Op(OpTest):
def
init_exclusive
(
self
):
self
.
exclusive
=
True
def
init_adaptive
(
self
):
self
.
adaptive
=
False
class
TestCase1
(
TestPool3d_Op
):
def
init_test_case
(
self
):
...
...
@@ -353,5 +399,10 @@ class TestCUDNNAvgInclude(TestCUDNNCase3):
self
.
exclusive
=
False
class
TestAvgPoolAdaptive
(
TestCase1
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_pool_max_op.py
浏览文件 @
dc76e4b0
...
...
@@ -13,33 +13,62 @@
# limitations under the License.
from
__future__
import
print_function
from
__future__
import
division
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
):
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_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
,
adaptive
=
False
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
:
ksize
=
[
D
,
H
,
W
]
paddings
=
[
0
,
0
,
0
]
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
if
adaptive
:
D_out
,
H_out
,
W_out
=
ksize
else
:
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
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
mask
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
range
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
if
adaptive
:
d_start
=
adaptive_start_index
(
k
,
D
,
ksize
[
0
])
d_end
=
adaptive_end_index
(
k
,
D
,
ksize
[
0
])
else
:
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
for
i
in
range
(
H_out
):
h_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
if
adaptive
:
h_start
=
adaptive_start_index
(
i
,
H
,
ksize
[
1
])
h_end
=
adaptive_end_index
(
i
,
H
,
ksize
[
1
])
else
:
h_start
=
np
.
max
((
i
*
strides
[
1
]
-
paddings
[
1
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
H
))
for
j
in
range
(
W_out
):
w_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
if
adaptive
:
w_start
=
adaptive_start_index
(
j
,
W
,
ksize
[
2
])
w_end
=
adaptive_end_index
(
j
,
W
,
ksize
[
2
])
else
:
w_start
=
np
.
max
((
j
*
strides
[
2
]
-
paddings
[
2
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
2
]
+
ksize
[
2
]
-
paddings
[
2
],
W
))
x_masked
=
x
[:,
:,
d_start
:
d_end
,
h_start
:
h_end
,
w_start
:
w_end
]
out
[:,
:,
k
,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
,
4
))
...
...
@@ -58,23 +87,37 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=False):
return
out
,
mask
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
):
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
,
global_pool
=
False
,
adaptive
=
False
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
:
ksize
=
[
H
,
W
]
paddings
=
[
0
,
0
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
//
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
//
strides
[
1
]
+
1
if
adaptive
:
H_out
,
W_out
=
ksize
else
:
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
))
mask
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
for
j
in
range
(
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
))
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
))
...
...
@@ -95,10 +138,12 @@ class TestMaxPoolWithIndex_Op(OpTest):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
init_global
()
self
.
init_adaptive
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
,
mask
=
self
.
pool_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
)
self
.
paddings
,
self
.
global_pool
,
self
.
adaptive
)
output
=
output
.
astype
(
"float32"
)
mask
=
mask
.
astype
(
"int32"
)
...
...
@@ -107,6 +152,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'global_pooling'
:
self
.
global_pool
,
'adaptive'
:
self
.
adaptive
,
}
self
.
inputs
=
{
'X'
:
input
}
...
...
@@ -129,6 +175,9 @@ class TestMaxPoolWithIndex_Op(OpTest):
def
init_global
(
self
):
self
.
global_pool
=
False
def
init_adaptive
(
self
):
self
.
adaptive
=
False
class
TestCase1
(
TestMaxPoolWithIndex_Op
):
def
init_global
(
self
):
...
...
@@ -190,5 +239,15 @@ class TestCase7(TestCase6):
self
.
global_pool
=
False
class
TestCastAdaptive2d
(
TestCase6
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
class
TestCastAdaptive3d
(
TestMaxPoolWithIndex_Op
):
def
init_adaptive
(
self
):
self
.
adaptive
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
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