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体验新版 GitCode,发现更多精彩内容 >>
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ff6329bd
编写于
10月 29, 2018
作者:
D
dengkaipeng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix some inappropriate expressions in api doc for grid_sampler. test=develop
上级
593e1b18
变更
8
显示空白变更内容
内联
并排
Showing
8 changed file
with
436 addition
and
409 deletion
+436
-409
paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc
paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc
+88
-84
paddle/fluid/operators/grid_sampler_op.cc
paddle/fluid/operators/grid_sampler_op.cc
+97
-91
paddle/fluid/operators/grid_sampler_op.h
paddle/fluid/operators/grid_sampler_op.h
+171
-164
paddle/fluid/platform/cudnn_helper.h
paddle/fluid/platform/cudnn_helper.h
+5
-5
paddle/fluid/platform/dynload/cudnn.h
paddle/fluid/platform/dynload/cudnn.h
+45
-45
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+18
-11
python/paddle/fluid/tests/unittests/test_grid_sampler_op.py
python/paddle/fluid/tests/unittests/test_grid_sampler_op.py
+10
-6
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+2
-3
未找到文件。
paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc
浏览文件 @
ff6329bd
...
...
@@ -60,10 +60,10 @@ class CUDNNGridSampleOpKernel : public framework::OpKernel<T> {
DataLayout
::
kNCHW
,
framework
::
vectorize2int
(
output
->
dims
()));
CUDNN_ENFORCE
(
platform
::
dynload
::
cudnnSpatialTfSamplerForward
(
handle
,
cudnn_st_desc
,
CudnnDataType
<
T
>::
kOne
(),
cudnn_input_desc
,
input_data
,
grid_data
,
CudnnDataType
<
T
>::
kZero
(),
cudnn_output_desc
,
output_data
));
handle
,
cudnn_st_desc
,
CudnnDataType
<
T
>::
kOne
(),
cudnn_input_desc
,
input_data
,
grid_data
,
CudnnDataType
<
T
>::
kZero
(),
cudnn_output_desc
,
output_data
));
}
};
template
<
typename
T
>
...
...
@@ -94,25 +94,29 @@ class CUDNNGridSampleGradOpKernel : public framework::OpKernel<T> {
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
grid_data
=
grid
->
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
output_grad_dims
,
ctx
.
GetPlace
());
T
*
grid_grad_data
=
grid_grad
->
mutable_data
<
T
>
({
n
,
h
,
w
,
2
},
ctx
.
GetPlace
());
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
output_grad_dims
,
ctx
.
GetPlace
());
T
*
grid_grad_data
=
grid_grad
->
mutable_data
<
T
>
({
n
,
h
,
w
,
2
},
ctx
.
GetPlace
());
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
input_grad_desc
;
ScopedTensorDescriptor
output_grad_desc
;
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
DataLayout
::
kNCHW
,
framework
::
vectorize2int
(
input
->
dims
()));
cudnnTensorDescriptor_t
cudnn_input_grad_desc
=
input_grad_desc
.
descriptor
<
T
>
(
cudnnTensorDescriptor_t
cudnn_input_grad_desc
=
input_grad_desc
.
descriptor
<
T
>
(
DataLayout
::
kNCHW
,
framework
::
vectorize2int
(
input_grad
->
dims
()));
cudnnTensorDescriptor_t
cudnn_output_grad_desc
=
output_grad_desc
.
descriptor
<
T
>
(
cudnnTensorDescriptor_t
cudnn_output_grad_desc
=
output_grad_desc
.
descriptor
<
T
>
(
DataLayout
::
kNCHW
,
framework
::
vectorize2int
(
output_grad
->
dims
()));
CUDNN_ENFORCE
(
platform
::
dynload
::
cudnnSpatialTfSamplerBackward
(
handle
,
cudnn_st_dest
,
CudnnDataType
<
T
>::
kOne
()
,
cudnn_input_desc
,
input_data
,
CudnnDataType
<
T
>::
kZero
()
,
cudnn_input_grad_desc
,
input_grad_data
,
CudnnDataType
<
T
>::
kOne
()
,
cudnn_output_grad_desc
,
output_grad_data
,
grid_data
,
CudnnDataType
<
T
>::
kZero
(),
grid_grad_data
));
handle
,
cudnn_st_dest
,
CudnnDataType
<
T
>::
kOne
(),
cudnn_input_desc
,
input_data
,
CudnnDataType
<
T
>::
kZero
(),
cudnn_input_grad_desc
,
input_grad_data
,
CudnnDataType
<
T
>::
kOne
(),
cudnn_output_grad_desc
,
output_grad_data
,
grid_data
,
CudnnDataType
<
T
>::
kZero
()
,
grid_grad_data
));
}
};
...
...
paddle/fluid/operators/grid_sampler_op.cc
浏览文件 @
ff6329bd
...
...
@@ -36,12 +36,19 @@ class GridSampleOp : public framework::OperatorWithKernel {
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
grid_dims
=
ctx
->
GetInputDim
(
"Grid"
);
PADDLE_ENFORCE
(
x_dims
.
size
()
==
4
,
"Input(X) of GridSampleOp should be 4-D Tensor."
);
PADDLE_ENFORCE
(
grid_dims
.
size
()
==
4
,
"Input(Grid) of GridSampleOp should be 4-D Tensor."
);
PADDLE_ENFORCE
(
x_dims
.
size
()
==
4
,
"Input(X) of GridSampleOp should be 4-D Tensor."
);
PADDLE_ENFORCE
(
grid_dims
.
size
()
==
4
,
"Input(Grid) of GridSampleOp should be 4-D Tensor."
);
PADDLE_ENFORCE
(
grid_dims
[
3
]
==
2
,
"Input(Grid) dims[3] should be 2."
);
PADDLE_ENFORCE_EQ
(
grid_dims
[
0
],
x_dims
[
0
],
"Input(X) and Input(Grid) dims[0] should be equal."
);
PADDLE_ENFORCE_EQ
(
grid_dims
[
1
],
x_dims
[
2
],
"Input(X) dims[2] and Input(Grid) dims[1] should be equal."
);
PADDLE_ENFORCE_EQ
(
grid_dims
[
2
],
x_dims
[
3
],
"Input(X) dims[3] and Input(Grid) dims[2] should be equal."
);
PADDLE_ENFORCE_EQ
(
grid_dims
[
0
],
x_dims
[
0
],
"Input(X) and Input(Grid) dims[0] should be equal."
);
PADDLE_ENFORCE_EQ
(
grid_dims
[
1
],
x_dims
[
2
],
"Input(X) dims[2] and Input(Grid) dims[1] should be equal."
);
PADDLE_ENFORCE_EQ
(
grid_dims
[
2
],
x_dims
[
3
],
"Input(X) dims[3] and Input(Grid) dims[2] should be equal."
);
ctx
->
SetOutputDim
(
"Output"
,
x_dims
);
ctx
->
ShareLoD
(
"X"
,
"Output"
);
...
...
@@ -57,16 +64,15 @@ class GridSampleOp : public framework::OperatorWithKernel {
}
#endif
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()
),
ctx
.
GetPlace
(),
framework
::
DataLayout
::
kAnyLayout
,
library_
);
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
(
),
framework
::
DataLayout
::
kAnyLayout
,
library_
);
}
};
class
GridSampleOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
AddInput
(
"X"
,
"(Tensor) The input data of GridSampleOp, "
"This is a 4-D tensor with shape of [N, C, H, W]"
);
AddInput
(
...
...
@@ -74,20 +80,20 @@ class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor) The input grid of GridSampleOp generated by AffineGridOp, "
"This is a 4-D tensor with shape of [N, H, W, 2] is the concatenation "
"of x and y coordinates with shape [N, H, W] in last dimention"
);
AddOutput
(
"Output"
,
"(Tensor) Output tensor with shape [N, C, H, W]"
);
AddOutput
(
"Output"
,
"(Tensor) Output tensor with shape [N, C, H, W]"
);
AddAttr
<
bool
>
(
"use_cudnn"
,
"(bool, default true) Only used in cudnn kernel, need install cudnn"
)
.
SetDefault
(
true
);
AddComment
(
R"DOC(
It sample input X by grid gennerate by AffineGridOp. The grid of shape
[N, H, W, 2] is the concatenation of (x, y) coordinates with shape
[N, H, W] each, with x indexing the 4th-D(W) of input feature map and y to
indexng the 3rd-D(H), finally results is the bilinear interpolation value
of 4 nearest corner points.
This operation samples input X by using bilinear interpolation based on
flow field grid, which is usually gennerated by affine_grid. The grid of
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexng the 3rd
dimention (in height dimension), finally results is the bilinear
interpolation value of 4 nearest corner points.
Step 1:
Get (x, y) grid coordinates and scale to [0, H-1/W-1].
...
...
@@ -154,8 +160,8 @@ class GridSampleOpGrad : public framework::OperatorWithKernel {
}
#endif
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()
),
ctx
.
GetPlace
(),
framework
::
DataLayout
::
kAnyLayout
,
library_
);
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
(
),
framework
::
DataLayout
::
kAnyLayout
,
library_
);
}
};
...
...
paddle/fluid/operators/grid_sampler_op.h
浏览文件 @
ff6329bd
...
...
@@ -19,7 +19,6 @@ limitations under the License. */
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -31,7 +30,6 @@ using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using
Array3
=
Eigen
::
DSizes
<
int64_t
,
3
>
;
using
Array4
=
Eigen
::
DSizes
<
int64_t
,
4
>
;
template
<
typename
T
>
static
inline
bool
isInBound
(
T
x
,
T
y
,
T
x_max
,
T
y_max
)
{
if
(
x
<
0
||
x
>
x_max
||
y
<
0
||
y
>
y_max
)
{
...
...
@@ -40,16 +38,17 @@ static inline bool isInBound(T x, T y, T x_max, T y_max) {
return
true
;
}
template
<
typename
DeviceContext
,
typename
T
>
static
void
CalcGridLocations
(
const
DeviceContext
&
ctx
,
const
Tensor
&
grid
,
Tensor
*
x_w
,
Tensor
*
x_e
,
Tensor
*
y_n
,
Tensor
*
y_s
,
Tensor
*
d_w
,
Tensor
*
d_e
,
Tensor
*
d_n
,
Tensor
*
d_s
)
{
template
<
typename
T
>
static
void
CalcGridLocations
(
const
platform
::
CPUDeviceContext
&
ctx
,
const
Tensor
&
grid
,
Tensor
*
x_w
,
Tensor
*
x_e
,
Tensor
*
y_n
,
Tensor
*
y_s
,
Tensor
*
d_w
,
Tensor
*
d_e
,
Tensor
*
d_n
,
Tensor
*
d_s
)
{
auto
&
place
=
*
ctx
.
eigen_device
();
const
int
n
=
grid
.
dims
()[
0
];
const
int
h
=
grid
.
dims
()[
1
];
const
int
w
=
grid
.
dims
()[
2
];
const
T
x_max
=
static_cast
<
T
>
(
w
-
1
);
const
T
y_max
=
static_cast
<
T
>
(
h
-
1
);
const
T
x_max
=
static_cast
<
T
>
(
w
-
1
);
const
T
y_max
=
static_cast
<
T
>
(
h
-
1
);
// split grid with shape (n, h, w, 2) into (x, y) by the 3rd Dim
Tensor
grid_x
,
grid_y
;
...
...
@@ -117,7 +116,9 @@ static void GetGridPointValue(const Tensor& input, Tensor* output,
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
if
(
isInBound
(
x_t
(
i
,
k
,
l
),
y_t
(
i
,
k
,
l
),
(
T
)(
w
-
1
),
(
T
)(
h
-
1
)))
{
for
(
int
j
=
0
;
j
<
c
;
j
++
)
{
output_t
(
i
,
j
,
k
,
l
)
=
input_t
(
i
,
j
,
(
int
)
round
(
y_t
(
i
,
k
,
l
)),
(
int
)
round
(
x_t
(
i
,
k
,
l
)));
output_t
(
i
,
j
,
k
,
l
)
=
input_t
(
i
,
j
,
static_cast
<
int
>
(
round
(
y_t
(
i
,
k
,
l
))),
static_cast
<
int
>
(
round
(
x_t
(
i
,
k
,
l
))));
}
}
}
...
...
@@ -126,9 +127,10 @@ static void GetGridPointValue(const Tensor& input, Tensor* output,
}
template
<
typename
T
>
static
void
GatherOutputGradToInputGrad
(
const
Tensor
&
output_grad
,
Tensor
*
input_grad
,
const
Tensor
&
x
,
const
Tensor
&
y
,
const
Tensor
&
d1
,
const
Tensor
&
d2
)
{
static
void
GatherOutputGradToInputGrad
(
const
Tensor
&
output_grad
,
Tensor
*
input_grad
,
const
Tensor
&
x
,
const
Tensor
&
y
,
const
Tensor
&
d1
,
const
Tensor
&
d2
)
{
const
int
n
=
output_grad
.
dims
()[
0
];
const
int
c
=
output_grad
.
dims
()[
1
];
const
int
h
=
output_grad
.
dims
()[
2
];
...
...
@@ -143,10 +145,11 @@ static void GatherOutputGradToInputGrad(const Tensor& output_grad, Tensor* input
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
k
=
0
;
k
<
h
;
k
++
)
{
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
if
(
isInBound
(
x_t
(
i
,
k
,
l
),
y_t
(
i
,
k
,
l
),
(
T
)(
w
-
1
),
(
T
)(
h
-
1
)))
{
if
(
isInBound
(
x_t
(
i
,
k
,
l
),
y_t
(
i
,
k
,
l
),
(
T
)(
w
-
1
),
(
T
)(
h
-
1
)))
{
for
(
int
j
=
0
;
j
<
c
;
j
++
)
{
input_grad_t
(
i
,
j
,
(
int
)
y_t
(
i
,
k
,
l
),
(
int
)
x_t
(
i
,
k
,
l
))
+=
output_grad_t
(
i
,
j
,
k
,
l
)
*
d1_t
(
i
,
k
,
l
)
*
d2_t
(
i
,
k
,
l
);
input_grad_t
(
i
,
j
,
static_cast
<
int
>
(
round
(
y_t
(
i
,
k
,
l
))),
static_cast
<
int
>
(
round
(
x_t
(
i
,
k
,
l
))))
+=
output_grad_t
(
i
,
j
,
k
,
l
)
*
d1_t
(
i
,
k
,
l
)
*
d2_t
(
i
,
k
,
l
);
}
}
}
...
...
@@ -154,8 +157,6 @@ static void GatherOutputGradToInputGrad(const Tensor& output_grad, Tensor* input
}
}
template
<
typename
DeviceContext
,
typename
T
>
class
GridSampleOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -172,10 +173,9 @@ class GridSampleOpKernel : public framework::OpKernel<T> {
// calc locations and distances of 4 corner points
Tensor
x_w
,
x_e
,
y_n
,
y_s
;
Tensor
d_w
,
d_e
,
d_n
,
d_s
;
CalcGridLocations
<
DeviceContext
,
T
>
(
ctx
.
template
device_context
<
DeviceContext
>(),
*
grid
,
&
x_w
,
&
x_e
,
&
y_n
,
&
y_s
,
&
d_w
,
&
d_e
,
&
d_n
,
&
d_s
);
CalcGridLocations
<
T
>
(
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>(),
*
grid
,
&
x_w
,
&
x_e
,
&
y_n
,
&
y_s
,
&
d_w
,
&
d_e
,
&
d_n
,
&
d_s
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Output"
);
output
->
mutable_data
<
T
>
({
n
,
c
,
h
,
w
},
ctx
.
GetPlace
());
...
...
@@ -198,22 +198,25 @@ class GridSampleOpKernel : public framework::OpKernel<T> {
auto
d_e_t
=
EigenTensor
<
T
,
3
>::
From
(
d_e
);
auto
d_n_t
=
EigenTensor
<
T
,
3
>::
From
(
d_n
);
auto
d_s_t
=
EigenTensor
<
T
,
3
>::
From
(
d_s
);
auto
d_w_scaled_t
=
d_w_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
d_e_scaled_t
=
d_e_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
d_n_scaled_t
=
d_n_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
d_s_scaled_t
=
d_s_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
d_w_scaled_t
=
d_w_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
d_e_scaled_t
=
d_e_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
d_n_scaled_t
=
d_n_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
d_s_scaled_t
=
d_s_t
.
reshape
(
Array4
(
n
,
1
,
h
,
w
)).
broadcast
(
Array4
(
1
,
c
,
1
,
1
));
auto
v_wn_t
=
EigenTensor
<
T
,
4
>::
From
(
v_wn
);
auto
v_en_t
=
EigenTensor
<
T
,
4
>::
From
(
v_en
);
auto
v_ws_t
=
EigenTensor
<
T
,
4
>::
From
(
v_ws
);
auto
v_es_t
=
EigenTensor
<
T
,
4
>::
From
(
v_es
);
auto
output_t
=
EigenTensor
<
T
,
4
>::
From
(
*
output
);
//
bilinear interpolaetion by 4 corner points
output_t
.
device
(
place
)
=
v_wn_t
*
d_e_scaled_t
*
d_s_scaled_t
+
v_en_t
*
d_w_scaled_t
*
d_s_scaled_t
+
v_ws_t
*
d_e_scaled_t
*
d_n_scaled_t
+
v_es_t
*
d_w_scaled_t
*
d_n_scaled_t
;
//
bilinear interpolaetion by 4 corner points
output_t
.
device
(
place
)
=
v_wn_t
*
d_e_scaled_t
*
d_s_scaled_t
+
v_en_t
*
d_w_scaled_t
*
d_s_scaled_t
+
v_ws_t
*
d_e_scaled_t
*
d_n_scaled_t
+
v_es_t
*
d_w_scaled_t
*
d_n_scaled_t
;
}
};
template
<
typename
DeviceContext
,
typename
T
>
...
...
@@ -242,16 +245,19 @@ class GridSampleGradOpKernel : public framework::OpKernel<T> {
Tensor
x_w
,
x_e
,
y_n
,
y_s
;
Tensor
d_w
,
d_e
,
d_n
,
d_s
;
CalcGridLocations
<
DeviceContext
,
T
>
(
ctx
.
template
device_context
<
DeviceContext
>(),
*
grid
,
&
x_w
,
&
x_e
,
&
y_n
,
&
y_s
,
&
d_w
,
&
d_e
,
&
d_n
,
&
d_s
);
CalcGridLocations
<
T
>
(
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>(),
*
grid
,
&
x_w
,
&
x_e
,
&
y_n
,
&
y_s
,
&
d_w
,
&
d_e
,
&
d_n
,
&
d_s
);
// gather output grad value to input grad by corner point coords and weight
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_w
,
y_n
,
d_e
,
d_s
);
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_w
,
y_s
,
d_e
,
d_n
);
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_e
,
y_n
,
d_w
,
d_s
);
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_e
,
y_s
,
d_w
,
d_n
);
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_w
,
y_n
,
d_e
,
d_s
);
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_w
,
y_s
,
d_e
,
d_n
);
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_e
,
y_n
,
d_w
,
d_s
);
GatherOutputGradToInputGrad
<
T
>
(
*
output_grad
,
input_grad
,
x_e
,
y_s
,
d_w
,
d_n
);
// calc 4 corner points value
Tensor
v_wn
,
v_en
,
v_ws
,
v_es
;
...
...
@@ -281,15 +287,17 @@ class GridSampleGradOpKernel : public framework::OpKernel<T> {
auto
grid_grad_x_t
=
EigenTensor
<
T
,
3
>::
From
(
grid_grad_x
).
setConstant
(
0.0
);
auto
grid_grad_y_t
=
EigenTensor
<
T
,
3
>::
From
(
grid_grad_y
).
setConstant
(
0.0
);
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
c
;
j
++
)
{
for
(
int
k
=
0
;
k
<
h
;
k
++
)
{
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
grid_grad_x_t
(
i
,
k
,
l
)
+=
((
v_en_t
(
i
,
j
,
k
,
l
)
-
v_wn_t
(
i
,
j
,
k
,
l
))
*
d_s_t
(
i
,
k
,
l
)
+
(
v_es_t
(
i
,
j
,
k
,
l
)
-
v_ws_t
(
i
,
j
,
k
,
l
))
*
d_n_t
(
i
,
k
,
l
))
*
output_grad_t
(
i
,
j
,
k
,
l
);
grid_grad_y_t
(
i
,
k
,
l
)
+=
((
v_ws_t
(
i
,
j
,
k
,
l
)
-
v_wn_t
(
i
,
j
,
k
,
l
))
*
d_e_t
(
i
,
k
,
l
)
+
(
v_es_t
(
i
,
j
,
k
,
l
)
-
v_en_t
(
i
,
j
,
k
,
l
))
*
d_w_t
(
i
,
k
,
l
))
*
output_grad_t
(
i
,
j
,
k
,
l
);
for
(
int
j
=
0
;
j
<
c
;
j
++
)
{
for
(
int
k
=
0
;
k
<
h
;
k
++
)
{
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
grid_grad_x_t
(
i
,
k
,
l
)
+=
((
v_en_t
(
i
,
j
,
k
,
l
)
-
v_wn_t
(
i
,
j
,
k
,
l
))
*
d_s_t
(
i
,
k
,
l
)
+
(
v_es_t
(
i
,
j
,
k
,
l
)
-
v_ws_t
(
i
,
j
,
k
,
l
))
*
d_n_t
(
i
,
k
,
l
))
*
output_grad_t
(
i
,
j
,
k
,
l
);
grid_grad_y_t
(
i
,
k
,
l
)
+=
((
v_ws_t
(
i
,
j
,
k
,
l
)
-
v_wn_t
(
i
,
j
,
k
,
l
))
*
d_e_t
(
i
,
k
,
l
)
+
(
v_es_t
(
i
,
j
,
k
,
l
)
-
v_en_t
(
i
,
j
,
k
,
l
))
*
d_w_t
(
i
,
k
,
l
))
*
output_grad_t
(
i
,
j
,
k
,
l
);
}
}
}
...
...
@@ -308,7 +316,6 @@ class GridSampleGradOpKernel : public framework::OpKernel<T> {
grid_grad_data
[
2
*
i
+
1
]
=
grid_grad_y_data
[
i
];
}
}
};
}
// namespace operators
...
...
paddle/fluid/platform/cudnn_helper.h
浏览文件 @
ff6329bd
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @
ff6329bd
...
...
@@ -92,7 +92,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
__macro(cudnnDeriveBNTensorDescriptor); \
__macro(cudnnCreateSpatialTransformerDescriptor); \
__macro(cudnnSetSpatialTransformerNdDescriptor); \
__macro(cudnnDestroySpatialTransformerDescriptor);\
__macro(cudnnDestroySpatialTransformerDescriptor);
\
__macro(cudnnSpatialTfGridGeneratorForward); \
__macro(cudnnSpatialTfGridGeneratorBackward); \
__macro(cudnnSpatialTfSamplerForward); \
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
ff6329bd
...
...
@@ -7586,11 +7586,13 @@ def hash(input, hash_size, num_hash=1, name=None):
@
templatedoc
()
def
grid_sampler
(
x
,
grid
,
name
=
None
):
"""
It sample input X by grid gennerate by AffineGridOp. The grid of shape
[N, H, W, 2] is the concatenation of (x, y) coordinates with shape
[N, H, W] each, with x indexing the 4th-D(W) of input feature map and y to
indexng the 3rd-D(H), finally results is the bilinear interpolation value
of 4 nearest corner points.
This operation samples input X by using bilinear interpolation based on
flow field grid, which is usually gennerated by affine_grid. The grid of
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexng the 3rd
dimention (in height dimension), finally results is the bilinear
interpolation value of 4 nearest corner points.
Step 1:
Get (x, y) grid coordinates and scale to [0, H-1/W-1].
...
...
@@ -7636,7 +7638,16 @@ def grid_sampler(x, grid, name=None):
name (str, default None): The name of this layer.
Returns:
out(Variable): Output data indices by grid from x of shape [N, C, H, W].
out(Variable): Output of shape [N, C, H, W] data samples input X
using bilnear interpolation based on input grid.
Exmples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32')
theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32')
grid = fluid.layers.affine_grid(input=theta, size=[3, 10, 32, 32]})
out = fluid.layers.grid_sampler(x=x, grid=grid)
"""
helper
=
LayerHelper
(
"grid_sampler"
,
**
locals
())
...
...
@@ -7649,10 +7660,6 @@ def grid_sampler(x, grid, name=None):
out
=
helper
.
create_tmp_variable
(
x
.
dtype
)
ipts
=
{
'X'
:
x
,
'Grid'
:
grid
}
helper
.
apppend_op
(
type
=
'grid_sampler'
,
inputs
=
ipts
,
outputs
=
{
'Output'
,
out
})
helper
.
apppend_op
(
type
=
'grid_sampler'
,
inputs
=
ipts
,
outputs
=
{
'Output'
,
out
})
return
out
python/paddle/fluid/tests/unittests/test_grid_sampler_op.py
浏览文件 @
ff6329bd
...
...
@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
...
...
@@ -37,6 +36,7 @@ def AffineGrid(theta, size):
return
ret
.
reshape
([
n
,
h
,
w
,
2
]).
astype
(
"float32"
)
def
getGridPointValue
(
data
,
x
,
y
):
data_shape
=
data
.
shape
N
=
data_shape
[
0
]
...
...
@@ -47,13 +47,15 @@ def getGridPointValue(data, x, y):
for
i
in
range
(
N
):
for
j
in
range
(
H
):
for
k
in
range
(
W
):
if
y
[
i
,
j
,
k
]
<
0
or
y
[
i
,
j
,
k
]
>
H
-
1
or
x
[
i
,
j
,
k
]
<
0
or
x
[
i
,
j
,
k
]
>
W
-
1
:
if
y
[
i
,
j
,
k
]
<
0
or
y
[
i
,
j
,
k
]
>
H
-
1
or
x
[
i
,
j
,
k
]
<
0
or
x
[
i
,
j
,
k
]
>
W
-
1
:
out
[
i
,
:,
j
,
k
]
=
0
else
:
out
[
i
,
:,
j
,
k
]
=
data
[
i
,
:,
y
[
i
,
j
,
k
],
x
[
i
,
j
,
k
]]
return
out
def
GridSampler
(
data
,
grid
):
dims
=
data
.
shape
N
=
dims
[
0
]
...
...
@@ -87,6 +89,7 @@ def GridSampler(data, grid):
out
=
(
wa
*
va
+
wb
*
vb
+
wc
*
vc
+
wd
*
vd
).
astype
(
'float32'
)
return
out
class
TestGridSamplerOp
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
...
...
@@ -115,5 +118,6 @@ class TestGridSamplerOp(OpTest):
self
.
grid_shape
=
(
2
,
7
,
3
,
2
)
self
.
theta_shape
=
(
2
,
2
,
3
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
ff6329bd
...
...
@@ -868,13 +868,12 @@ class TestBook(unittest.TestCase):
def
test_affine_grid_gen
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
2
,
5
,
7
,
3
],
dtype
=
'float32'
)
grid
=
layers
.
data
(
name
=
'grid'
,
shape
=
[
2
,
5
,
7
,
2
],
dtype
=
'float32'
)
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
2
,
5
,
7
,
3
],
dtype
=
'float32'
)
grid
=
layers
.
data
(
name
=
'grid'
,
shape
=
[
2
,
5
,
7
,
2
],
dtype
=
'float32'
)
out
=
layers
.
grid_sampler
(
x
,
grid
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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