Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
Paddle
提交
d2ee3c98
P
Paddle
项目概览
PaddlePaddle
/
Paddle
11 个月 前同步成功
通知
2292
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
d2ee3c98
编写于
11月 29, 2017
作者:
S
sweetsky0901
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
format code
上级
2d42fa74
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
110 addition
and
100 deletion
+110
-100
paddle/operators/math/unpooling.cc
paddle/operators/math/unpooling.cc
+9
-7
paddle/operators/math/unpooling.cu
paddle/operators/math/unpooling.cu
+28
-22
paddle/operators/math/unpooling.h
paddle/operators/math/unpooling.h
+10
-7
paddle/operators/unpool_op.cc
paddle/operators/unpool_op.cc
+56
-58
paddle/operators/unpool_op.cu.cc
paddle/operators/unpool_op.cu.cc
+4
-4
python/paddle/v2/fluid/tests/test_unpool_op.py
python/paddle/v2/fluid/tests/test_unpool_op.py
+3
-2
未找到文件。
paddle/operators/math/unpooling.cc
浏览文件 @
d2ee3c98
...
...
@@ -19,9 +19,9 @@ namespace math {
template
<
typename
T
>
class
Unpool2dMaxFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -50,10 +50,12 @@ class Unpool2dMaxFunctor<platform::CPUPlace, T> {
template
<
class
T
>
class
Unpool2dMaxGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
framework
::
Tensor
*
input_grad
)
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
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
];
...
...
paddle/operators/math/unpooling.cu
浏览文件 @
d2ee3c98
...
...
@@ -19,10 +19,12 @@ namespace paddle {
namespace
operators
{
namespace
math
{
template
<
typename
T
>
__global__
void
KernelUnpool2dMax
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
T
*
output_data
,
const
int
output_height
,
const
int
output_width
)
{
__global__
void
KernelUnpool2dMax
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
T
*
output_data
,
const
int
output_height
,
const
int
output_width
)
{
int
in_n_stride
=
input_height
*
input_width
*
channels
;
int
in_c_stride
=
input_height
*
input_width
;
int
out_n_stride
=
output_height
*
output_width
*
channels
;
...
...
@@ -40,11 +42,12 @@ __global__ void KernelUnpool2dMax(
}
}
template
<
typename
T
>
__global__
void
KernelUnpool2dMaxGrad
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
const
T
*
output_data
,
const
T
*
output_grad
,
const
int
output_height
,
const
int
output_width
,
T
*
input_grad
)
{
__global__
void
KernelUnpool2dMaxGrad
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
const
T
*
output_data
,
const
T
*
output_grad
,
const
int
output_height
,
const
int
output_width
,
T
*
input_grad
)
{
int
in_n_stride
=
input_height
*
input_width
*
channels
;
int
in_c_stride
=
input_height
*
input_width
;
int
out_n_stride
=
output_height
*
output_width
*
channels
;
...
...
@@ -67,9 +70,9 @@ __global__ void KernelUnpool2dMaxGrad(
template
<
typename
T
>
class
Unpool2dMaxFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -81,11 +84,12 @@ class Unpool2dMaxFunctor<platform::GPUPlace, T> {
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
threads
=
1024
;
int
grid
=
(
input
.
numel
()
+
threads
-
1
)
/
threads
;
KernelUnpool2dMax
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
input
.
numel
(),
input_data
,
indices_data
,
input_height
,
input_width
,
output_channels
,
output_data
,
output_height
,
output_width
);
KernelUnpool2dMax
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
input
.
numel
(),
input_data
,
indices_data
,
input_height
,
input_width
,
output_channels
,
output_data
,
output_height
,
output_width
);
}
};
/*
...
...
@@ -113,11 +117,13 @@ class Unpool2dMaxGradFunctor<platform::GPUPlace, T> {
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
threads
=
1024
;
int
grid
=
(
input
.
numel
()
+
threads
-
1
)
/
threads
;
KernelUnpool2dMaxGrad
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
input
.
numel
(),
input_data
,
indices_data
,
input_height
,
input_width
,
output_channels
,
output_data
,
output_grad_data
,
output_height
,
output_width
,
input_grad_data
);
KernelUnpool2dMaxGrad
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
input
.
numel
(),
input_data
,
indices_data
,
input_height
,
input_width
,
output_channels
,
output_data
,
output_grad_data
,
output_height
,
output_width
,
input_grad_data
);
}
};
template
class
Unpool2dMaxGradFunctor
<
platform
::
GPUPlace
,
float
>;
...
...
paddle/operators/math/unpooling.h
浏览文件 @
d2ee3c98
...
...
@@ -21,17 +21,20 @@ namespace math {
template
<
typename
Place
,
typename
T
>
class
Unpool2dMaxFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
);
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
);
};
template
<
typename
Place
,
class
T
>
class
Unpool2dMaxGradFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
framework
::
Tensor
*
input_grad
);
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
framework
::
Tensor
*
input_grad
);
};
}
// namespace math
}
// namespace operators
...
...
paddle/operators/unpool_op.cc
浏览文件 @
d2ee3c98
...
...
@@ -32,24 +32,22 @@ class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature."
);
AddOutput
(
"Out"
,
"(Tensor) The output tensor of unpool operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of feature."
);
"(Tensor) The output tensor of unpool operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of feature."
);
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"(vector), the unpooling window size(height, width) "
"of unpooling operator."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector, default:{1, 1}), "
"strides (height, width) of unpooling operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector, default:{1, 1}), "
"strides (height, width) of unpooling operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector defalut:{0,0}), "
"paddings (height, width) of unpooling operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector defalut:{0,0}), "
"paddings (height, width) of unpooling operator."
)
.
SetDefault
({
0
,
0
});
AddAttr
<
std
::
string
>
(
"unpooling_type"
,
...
...
@@ -75,71 +73,71 @@ int OutputSize(int input_size, int ksize, int padding, int stride) {
}
class
UnpoolOp
:
public
framework
::
OperatorWithKernel
{
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of UnpoolOp"
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of UnpoolOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Indices"
),
"Input(Indices) of UnpoolOp"
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Indices"
),
"Input(Indices) of UnpoolOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of UnpoolOp should not be null."
);
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
in_y_dims
=
ctx
->
GetInputDim
(
"Indices"
);
std
::
string
unpooling_type
=
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
in_y_dims
=
ctx
->
GetInputDim
(
"Indices"
);
std
::
string
unpooling_type
=
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"unpooling_type"
);
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
=
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"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
,
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
,
"Unpooling intput must be of 4-dimensional."
);
PADDLE_ENFORCE_EQ
(
in_x_dims
,
in_y_dims
);
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
(
OutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
PADDLE_ENFORCE_EQ
(
in_x_dims
,
in_y_dims
);
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
(
OutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
};
class
UnpoolOpGrad
:
public
framework
::
OperatorWithKernel
{
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input(X@GRAD) should not be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
unpool
,
ops
::
UnpoolOp
,
ops
::
Unpool2dOpMaker
,
unpool_grad
,
ops
::
UnpoolOpGrad
);
REGISTER_OP_CPU_KERNEL
(
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
unpool_grad
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
unpool_grad
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/operators/unpool_op.cu.cc
浏览文件 @
d2ee3c98
...
...
@@ -16,8 +16,8 @@ limitations under the License. */
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
unpool_grad
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
unpool_grad
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
python/paddle/v2/fluid/tests/test_unpool_op.py
浏览文件 @
d2ee3c98
...
...
@@ -55,13 +55,13 @@ class TestUnpoolOp(OpTest):
self
.
inputs
=
{
'X'
:
input
.
astype
(
'float32'
),
'Indices'
:
indices
.
astype
(
'int32'
)
}
}
self
.
attrs
=
{
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'unpooling_type'
:
self
.
unpooling_type
,
}
}
self
.
outputs
=
{
'Out'
:
output
.
astype
(
'float32'
)}
def
test_check_output
(
self
):
...
...
@@ -78,5 +78,6 @@ class TestUnpoolOp(OpTest):
self
.
strides
=
[
2
,
2
]
self
.
paddings
=
[
0
,
0
]
if
__name__
==
'__main__'
:
unittest
.
main
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录