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体验新版 GitCode,发现更多精彩内容 >>
提交
a0c63f11
编写于
1月 27, 2019
作者:
T
tink2123
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add align_flag
test=develop
上级
b64cdaf6
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
36 addition
and
45 deletion
+36
-45
paddle/fluid/operators/interpolate_op.cc
paddle/fluid/operators/interpolate_op.cc
+1
-1
paddle/fluid/operators/interpolate_op.cu
paddle/fluid/operators/interpolate_op.cu
+16
-20
paddle/fluid/operators/interpolate_op.h
paddle/fluid/operators/interpolate_op.h
+16
-21
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+3
-3
未找到文件。
paddle/fluid/operators/interpolate_op.cc
浏览文件 @
a0c63f11
...
@@ -110,7 +110,7 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -110,7 +110,7 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
to perform linear interpolation first in one direction, and then
to perform linear interpolation first in one direction, and then
again in the other direction.
again in the other direction.
Align_corners and align_mode are optinal parameters,
T
he calculation method
Align_corners and align_mode are optinal parameters,
t
he calculation method
of interpolation can be selected by them.
of interpolation can be selected by them.
Example:
Example:
...
...
paddle/fluid/operators/interpolate_op.cu
浏览文件 @
a0c63f11
...
@@ -94,6 +94,7 @@ __global__ void KeBilinearInterpFw(
...
@@ -94,6 +94,7 @@ __global__ void KeBilinearInterpFw(
int
nthreads
=
output_h
*
output_w
;
int
nthreads
=
output_h
*
output_w
;
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
bool
align_flag
=
(
align_mode
==
0
&&
!
align_corners
);
for
(;
tid
<
nthreads
;
tid
+=
stride
)
{
for
(;
tid
<
nthreads
;
tid
+=
stride
)
{
int
out_id_h
=
tid
/
output_w
;
int
out_id_h
=
tid
/
output_w
;
int
out_id_w
=
tid
%
output_w
;
int
out_id_w
=
tid
%
output_w
;
...
@@ -102,25 +103,23 @@ __global__ void KeBilinearInterpFw(
...
@@ -102,25 +103,23 @@ __global__ void KeBilinearInterpFw(
int
channel_id
=
out_id_w
/
out_img_size
;
int
channel_id
=
out_id_w
/
out_img_size
;
int
out_img_idy
=
(
out_id_w
%
out_img_size
)
/
out_img_w
;
int
out_img_idy
=
(
out_id_w
%
out_img_size
)
/
out_img_w
;
int
in_img_idy
=
(
align_mode
==
0
&&
!
align_corners
)
int
in_img_idy
=
align_flag
?
static_cast
<
int
>
(
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
)
?
static_cast
<
int
>
(
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
)
:
static_cast
<
int
>
(
ratio_h
*
out_img_idy
);
:
static_cast
<
int
>
(
ratio_h
*
out_img_idy
);
in_img_idy
=
(
in_img_idy
>
0
)
?
in_img_idy
:
0
;
in_img_idy
=
(
in_img_idy
>
0
)
?
in_img_idy
:
0
;
int
h_id
=
(
in_img_idy
<
in_img_h
-
1
)
?
1
:
0
;
int
h_id
=
(
in_img_idy
<
in_img_h
-
1
)
?
1
:
0
;
T
h1lambda
=
(
align_mode
==
0
&&
!
align_corners
)
T
h1lambda
=
align_flag
?
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
-
in_img_idy
?
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
-
in_img_idy
:
ratio_h
*
out_img_idy
-
in_img_idy
;
:
ratio_h
*
out_img_idy
-
in_img_idy
;
T
h2lambda
=
1.
f
-
h1lambda
;
T
h2lambda
=
1.
f
-
h1lambda
;
int
out_img_idx
=
tid
%
out_img_w
;
int
out_img_idx
=
tid
%
out_img_w
;
int
in_img_idx
=
(
align_mode
==
0
&&
!
align_corners
)
int
in_img_idx
=
align_flag
?
static_cast
<
int
>
(
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
)
?
static_cast
<
int
>
(
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
)
:
static_cast
<
int
>
(
ratio_w
*
out_img_idx
);
:
static_cast
<
int
>
(
ratio_w
*
out_img_idx
);
in_img_idx
=
(
in_img_idx
>
0
)
?
in_img_idx
:
0
;
in_img_idx
=
(
in_img_idx
>
0
)
?
in_img_idx
:
0
;
int
w_id
=
(
in_img_idx
<
in_img_w
-
1
)
?
1
:
0
;
int
w_id
=
(
in_img_idx
<
in_img_w
-
1
)
?
1
:
0
;
T
w1lambda
=
(
align_mode
==
0
&&
!
align_corners
)
T
w1lambda
=
align_flag
?
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
-
in_img_idx
?
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
-
in_img_idx
:
ratio_w
*
out_img_idx
-
in_img_idx
;
:
ratio_w
*
out_img_idx
-
in_img_idx
;
T
w2lambda
=
1.
f
-
w1lambda
;
T
w2lambda
=
1.
f
-
w1lambda
;
const
T
*
in_pos
=
&
in
[
out_id_h
*
input_w
+
channel_id
*
in_img_size
+
const
T
*
in_pos
=
&
in
[
out_id_h
*
input_w
+
channel_id
*
in_img_size
+
...
@@ -144,6 +143,7 @@ __global__ void KeBilinearInterpBw(
...
@@ -144,6 +143,7 @@ __global__ void KeBilinearInterpBw(
int
nthreads
=
output_h
*
output_w
;
int
nthreads
=
output_h
*
output_w
;
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
bool
align_flag
=
(
align_mode
==
0
&&
!
align_corners
);
for
(;
tid
<
nthreads
;
tid
+=
stride
)
{
for
(;
tid
<
nthreads
;
tid
+=
stride
)
{
int
out_id_h
=
tid
/
output_w
;
int
out_id_h
=
tid
/
output_w
;
int
out_id_w
=
tid
%
output_w
;
int
out_id_w
=
tid
%
output_w
;
...
@@ -152,26 +152,22 @@ __global__ void KeBilinearInterpBw(
...
@@ -152,26 +152,22 @@ __global__ void KeBilinearInterpBw(
int
channel_id
=
out_id_w
/
out_img_size
;
int
channel_id
=
out_id_w
/
out_img_size
;
int
out_img_idy
=
(
out_id_w
%
out_img_size
)
/
out_img_w
;
int
out_img_idy
=
(
out_id_w
%
out_img_size
)
/
out_img_w
;
int
in_img_idy
=
(
align_mode
==
0
&&
!
align_corners
)
int
in_img_idy
=
align_flag
?
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
?
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
:
ratio_h
*
out_img_idy
;
:
ratio_h
*
out_img_idy
;
in_img_idy
=
(
in_img_idy
>
0
)
?
in_img_idy
:
0
;
in_img_idy
=
(
in_img_idy
>
0
)
?
in_img_idy
:
0
;
int
h_id
=
(
in_img_idy
<
in_img_h
-
1
)
?
1
:
0
;
int
h_id
=
(
in_img_idy
<
in_img_h
-
1
)
?
1
:
0
;
T
h1lambda
=
(
align_mode
==
0
&&
!
align_corners
)
T
h1lambda
=
align_flag
?
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
-
in_img_idy
?
ratio_h
*
(
out_img_idy
+
0.5
)
-
0.5
-
in_img_idy
:
ratio_h
*
out_img_idy
-
in_img_idy
;
:
ratio_h
*
out_img_idy
-
in_img_idy
;
T
h2lambda
=
1.
f
-
h1lambda
;
T
h2lambda
=
1.
f
-
h1lambda
;
int
out_img_idx
=
tid
%
out_img_w
;
int
out_img_idx
=
tid
%
out_img_w
;
int
in_img_idx
=
(
align_mode
==
0
&&
!
align_corners
)
int
in_img_idx
=
align_flag
?
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
?
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
:
ratio_w
*
out_img_idx
;
:
ratio_w
*
out_img_idx
;
in_img_idx
=
(
in_img_idx
>
0
)
?
in_img_idx
:
0
;
in_img_idx
=
(
in_img_idx
>
0
)
?
in_img_idx
:
0
;
int
w_id
=
(
in_img_idx
<
in_img_w
-
1
)
?
1
:
0
;
int
w_id
=
(
in_img_idx
<
in_img_w
-
1
)
?
1
:
0
;
T
w1lambda
=
(
align_mode
==
0
&&
!
align_corners
)
T
w1lambda
=
align_flag
?
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
-
in_img_idx
?
ratio_w
*
(
out_img_idx
+
0.5
)
-
0.5
-
in_img_idx
:
ratio_w
*
out_img_idx
-
in_img_idx
;
:
ratio_w
*
out_img_idx
-
in_img_idx
;
T
w2lambda
=
1.
f
-
w1lambda
;
T
w2lambda
=
1.
f
-
w1lambda
;
T
*
in_pos
=
&
in
[
out_id_h
*
input_w
+
channel_id
*
in_img_size
+
T
*
in_pos
=
&
in
[
out_id_h
*
input_w
+
channel_id
*
in_img_size
+
...
...
paddle/fluid/operators/interpolate_op.h
浏览文件 @
a0c63f11
...
@@ -56,15 +56,14 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output,
...
@@ -56,15 +56,14 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output,
const
bool
align_mode
)
{
const
bool
align_mode
)
{
auto
input_t
=
EigenTensor
<
T
,
4
>::
From
(
input
);
auto
input_t
=
EigenTensor
<
T
,
4
>::
From
(
input
);
auto
output_t
=
EigenTensor
<
T
,
4
>::
From
(
*
output
);
auto
output_t
=
EigenTensor
<
T
,
4
>::
From
(
*
output
);
bool
align_flag
=
(
align_mode
==
0
&&
!
align_corners
);
for
(
int
k
=
0
;
k
<
out_h
;
k
++
)
{
// loop for images
for
(
int
k
=
0
;
k
<
out_h
;
k
++
)
{
// loop for images
int
y_n
=
(
align_mode
==
0
&&
!
align_corners
)
int
y_n
=
align_flag
?
static_cast
<
int
>
(
ratio_h
*
(
k
+
0.5
)
-
0.5
)
?
static_cast
<
int
>
(
ratio_h
*
(
k
+
0.5
)
-
0.5
)
:
static_cast
<
int
>
(
ratio_h
*
k
);
:
static_cast
<
int
>
(
ratio_h
*
k
);
y_n
=
(
y_n
>
0
)
?
y_n
:
0
;
y_n
=
(
y_n
>
0
)
?
y_n
:
0
;
int
y_s
=
(
y_n
+
1
)
<
(
in_h
-
1
)
?
(
y_n
+
1
)
:
(
in_h
-
1
);
int
y_s
=
(
y_n
+
1
)
<
(
in_h
-
1
)
?
(
y_n
+
1
)
:
(
in_h
-
1
);
float
d_n
=
(
align_mode
==
0
&&
!
align_corners
)
float
d_n
=
?
ratio_h
*
(
k
+
0.5
)
-
0.5
-
y_n
align_flag
?
ratio_h
*
(
k
+
0.5
)
-
0.5
-
y_n
:
ratio_h
*
k
-
y_n
;
:
ratio_h
*
k
-
y_n
;
float
d_s
=
1.
f
-
d_n
;
float
d_s
=
1.
f
-
d_n
;
for
(
int
l
=
0
;
l
<
out_w
;
l
++
)
{
for
(
int
l
=
0
;
l
<
out_w
;
l
++
)
{
...
@@ -73,9 +72,8 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output,
...
@@ -73,9 +72,8 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output,
:
static_cast
<
int
>
(
ratio_w
*
l
);
:
static_cast
<
int
>
(
ratio_w
*
l
);
x_w
=
(
x_w
>
0
)
?
x_w
:
0
;
x_w
=
(
x_w
>
0
)
?
x_w
:
0
;
int
x_e
=
(
x_w
+
1
)
<
(
in_w
-
1
)
?
(
x_w
+
1
)
:
(
in_w
-
1
);
int
x_e
=
(
x_w
+
1
)
<
(
in_w
-
1
)
?
(
x_w
+
1
)
:
(
in_w
-
1
);
float
d_w
=
(
align_mode
==
0
&&
!
align_corners
)
float
d_w
=
?
ratio_w
*
(
l
+
0.5
)
-
0.5
-
x_w
align_flag
?
ratio_w
*
(
l
+
0.5
)
-
0.5
-
x_w
:
ratio_w
*
l
-
x_w
;
:
ratio_w
*
l
-
x_w
;
float
d_e
=
1.
f
-
d_w
;
float
d_e
=
1.
f
-
d_w
;
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
// loop for batches
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
// loop for batches
...
@@ -126,26 +124,23 @@ static void BilinearInterpolationGrad(const Tensor& output_grad,
...
@@ -126,26 +124,23 @@ static void BilinearInterpolationGrad(const Tensor& output_grad,
const
int
align_mode
)
{
const
int
align_mode
)
{
auto
input_grad_t
=
EigenTensor
<
T
,
4
>::
From
(
*
input_grad
);
auto
input_grad_t
=
EigenTensor
<
T
,
4
>::
From
(
*
input_grad
);
auto
output_grad_t
=
EigenTensor
<
T
,
4
>::
From
(
output_grad
);
auto
output_grad_t
=
EigenTensor
<
T
,
4
>::
From
(
output_grad
);
bool
align_flag
=
(
align_mode
==
0
&&
!
align_corners
);
for
(
int
k
=
0
;
k
<
out_h
;
k
++
)
{
// loop for images
for
(
int
k
=
0
;
k
<
out_h
;
k
++
)
{
// loop for images
int
y_n
=
(
align_mode
==
0
&&
!
align_corners
)
int
y_n
=
align_flag
?
static_cast
<
int
>
(
ratio_h
*
(
k
+
0.5
)
-
0.5
)
?
static_cast
<
int
>
(
ratio_h
*
(
k
+
0.5
)
-
0.5
)
:
static_cast
<
int
>
(
ratio_h
*
k
);
:
static_cast
<
int
>
(
ratio_h
*
k
);
y_n
=
(
y_n
>
0
)
?
y_n
:
0
;
y_n
=
(
y_n
>
0
)
?
y_n
:
0
;
int
y_s
=
(
y_n
+
1
)
<
(
in_h
-
1
)
?
(
y_n
+
1
)
:
(
in_h
-
1
);
int
y_s
=
(
y_n
+
1
)
<
(
in_h
-
1
)
?
(
y_n
+
1
)
:
(
in_h
-
1
);
float
d_n
=
(
align_mode
==
0
&&
!
align_corners
)
float
d_n
=
?
ratio_h
*
(
k
+
0.5
)
-
0.5
-
y_n
align_flag
?
ratio_h
*
(
k
+
0.5
)
-
0.5
-
y_n
:
ratio_h
*
k
-
y_n
;
:
ratio_h
*
k
-
y_n
;
float
d_s
=
1.
f
-
d_n
;
float
d_s
=
1.
f
-
d_n
;
for
(
int
l
=
0
;
l
<
out_w
;
l
++
)
{
for
(
int
l
=
0
;
l
<
out_w
;
l
++
)
{
int
x_w
=
(
align_mode
==
0
&&
!
align_corners
)
int
x_w
=
align_flag
?
static_cast
<
int
>
(
ratio_w
*
(
l
+
0.5
)
-
0.5
)
?
static_cast
<
int
>
(
ratio_w
*
(
l
+
0.5
)
-
0.5
)
:
static_cast
<
int
>
(
ratio_w
*
l
);
:
static_cast
<
int
>
(
ratio_w
*
l
);
x_w
=
(
x_w
>
0
)
?
x_w
:
0
;
x_w
=
(
x_w
>
0
)
?
x_w
:
0
;
int
x_e
=
(
x_w
+
1
)
<
(
in_w
-
1
)
?
(
x_w
+
1
)
:
(
in_w
-
1
);
int
x_e
=
(
x_w
+
1
)
<
(
in_w
-
1
)
?
(
x_w
+
1
)
:
(
in_w
-
1
);
float
d_w
=
(
align_mode
==
0
&&
!
align_corners
)
float
d_w
=
?
ratio_w
*
(
l
+
0.5
)
-
0.5
-
x_w
align_flag
?
ratio_w
*
(
l
+
0.5
)
-
0.5
-
x_w
:
ratio_w
*
l
-
x_w
;
:
ratio_w
*
l
-
x_w
;
float
d_e
=
1.
f
-
d_w
;
float
d_e
=
1.
f
-
d_w
;
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
// loop for batches
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
// loop for batches
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
a0c63f11
...
@@ -6552,7 +6552,7 @@ def image_resize(input,
...
@@ -6552,7 +6552,7 @@ def image_resize(input,
to perform linear interpolation first in one direction, and then
to perform linear interpolation first in one direction, and then
again in the other direction.
again in the other direction.
Align_corners and align_mode are optinal parameters,
T
he calculation method
Align_corners and align_mode are optinal parameters,
t
he calculation method
of interpolation can be selected by them.
of interpolation can be selected by them.
Example:
Example:
...
@@ -6758,11 +6758,11 @@ def resize_bilinear(input,
...
@@ -6758,11 +6758,11 @@ def resize_bilinear(input,
For details of bilinear interpolation, please refer to Wikipedia:
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
https://en.wikipedia.org/wiki/Bilinear_interpolation
Align_corners and align_mode are optinal parameters,
T
he calculation
Align_corners and align_mode are optinal parameters,
t
he calculation
method of interpolation can be selected by them.
method of interpolation can be selected by them.
Align_corners and align_mode are optinal parameters,
T
he calculation method
Align_corners and align_mode are optinal parameters,
t
he calculation method
of interpolation can be selected by them.
of interpolation can be selected by them.
Example:
Example:
...
...
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