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9a14ca91
编写于
10月 18, 2018
作者:
J
jerrywgz
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
test=develop
上级
4c9884e7
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
118 addition
and
136 deletion
+118
-136
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/roi_align_op.cc
paddle/fluid/operators/roi_align_op.cc
+1
-2
paddle/fluid/operators/roi_align_op.cu
paddle/fluid/operators/roi_align_op.cu
+47
-54
paddle/fluid/operators/roi_align_op.h
paddle/fluid/operators/roi_align_op.h
+67
-78
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+2
-1
未找到文件。
paddle/fluid/API.spec
浏览文件 @
9a14ca91
...
...
@@ -114,7 +114,7 @@ paddle.fluid.layers.pad ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], var
paddle.fluid.layers.pad_constant_like ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None))
paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None))
paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0))
paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio'
], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1
))
paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio'
, 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None
))
paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,))
paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR'))
paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
...
...
paddle/fluid/operators/roi_align_op.cc
浏览文件 @
9a14ca91
...
...
@@ -94,7 +94,7 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor), "
"
t
he input of ROIAlignOp. "
"
T
he input of ROIAlignOp. "
"The format of input tensor is NCHW. Where N is batch size, "
"C is the number of input channels, "
"H is the height of the feature, and "
...
...
@@ -104,7 +104,6 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], …]. "
"Where batch_id is the id of the data, "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates."
);
AddOutput
(
"Out"
,
...
...
paddle/fluid/operators/roi_align_op.cu
浏览文件 @
9a14ca91
...
...
@@ -34,17 +34,13 @@ static inline int NumBlocks(const int N) {
i += blockDim.x * gridDim.x)
template
<
class
T
>
__device__
T
bilinear_i
nterpolate
(
const
T
*
input_data
,
const
int
height
,
const
int
width
,
T
y
,
T
x
)
{
__device__
T
BilinearI
nterpolate
(
const
T
*
input_data
,
const
int
height
,
const
int
width
,
T
y
,
T
x
)
{
if
(
y
<
-
1.0
||
y
>
height
||
x
<
-
1.0
||
x
>
width
)
{
return
0
;
}
if
(
y
<=
0
)
{
y
=
0
;
}
if
(
x
<=
0
)
{
x
=
0
;
}
y
=
y
<=
0
?
0
:
y
;
x
=
x
<=
0
?
0
:
x
;
int
y_low
=
static_cast
<
int
>
(
y
);
int
x_low
=
static_cast
<
int
>
(
x
);
int
y_high
;
...
...
@@ -75,20 +71,16 @@ __device__ T bilinear_interpolate(const T* input_data, const int height,
}
template
<
class
T
>
__device__
void
bilinear_interpolate_g
radient
(
const
int
height
,
const
int
width
,
T
y
,
T
x
,
T
*
w1
,
T
*
w2
,
T
*
w3
,
T
*
w4
,
int
*
x_low
,
int
*
x_high
,
int
*
y_low
,
int
*
y_high
)
{
__device__
void
BilinearInterpolateG
radient
(
const
int
height
,
const
int
width
,
T
y
,
T
x
,
T
*
w1
,
T
*
w2
,
T
*
w3
,
T
*
w4
,
int
*
x_low
,
int
*
x_high
,
int
*
y_low
,
int
*
y_high
)
{
if
(
y
<
-
1.0
||
y
>
height
||
x
<
-
1.0
||
x
>
width
)
{
return
;
}
if
(
y
<=
0
)
{
y
=
0
;
}
if
(
x
<=
0
)
{
x
=
0
;
}
y
=
y
<=
0
?
0
:
y
;
x
=
x
<=
0
?
0
:
x
;
*
y_low
=
static_cast
<
int
>
(
y
);
*
x_low
=
static_cast
<
int
>
(
x
);
if
(
*
y_low
>=
height
-
1
)
{
...
...
@@ -153,7 +145,7 @@ __global__ void GPUROIAlignForward(
const
T
x
=
roi_xmin
+
pw
*
bin_size_w
+
static_cast
<
T
>
(
ix
+
.5
f
)
*
bin_size_w
/
static_cast
<
T
>
(
roi_bin_grid_w
);
T
val
=
bilinear_i
nterpolate
(
offset_input_data
,
height
,
width
,
y
,
x
);
T
val
=
BilinearI
nterpolate
(
offset_input_data
,
height
,
width
,
y
,
x
);
output_val
+=
val
;
}
}
...
...
@@ -213,8 +205,8 @@ __global__ void GPUROIAlignBackward(const int nthreads, const T* input_rois,
static_cast
<
T
>
(
roi_bin_grid_w
);
T
w1
=
0
,
w2
=
0
,
w3
=
0
,
w4
=
0
;
int
x_low
=
-
1
,
x_high
=
-
1
,
y_low
=
-
1
,
y_high
=
-
1
;
bilinear_interpolate_g
radient
(
height
,
width
,
y
,
x
,
&
w1
,
&
w2
,
&
w3
,
&
w4
,
&
x_low
,
&
x_high
,
&
y_low
,
&
y_high
);
BilinearInterpolateG
radient
(
height
,
width
,
y
,
x
,
&
w1
,
&
w2
,
&
w3
,
&
w4
,
&
x_low
,
&
x_high
,
&
y_low
,
&
y_high
);
T
diff1
=
out_grad_this_bin
*
w1
/
count
;
T
diff2
=
out_grad_this_bin
*
w2
/
count
;
T
diff3
=
out_grad_this_bin
*
w3
/
count
;
...
...
@@ -279,8 +271,8 @@ class GPUROIAlignOpKernel : public framework::OpKernel<T> {
}
}
Tensor
roi_batch_id_list_gpu
;
framework
::
TensorCopy
(
roi_batch_id_list
,
ctx
.
GetPlace
(),
ctx
.
device_context
(),
&
roi_batch_id_list_gpu
);
framework
::
TensorCopy
Sync
(
roi_batch_id_list
,
ctx
.
GetPlace
(),
&
roi_batch_id_list_gpu
);
GPUROIAlignForward
<
T
><<<
blocks
,
threads
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
output_size
,
in
->
data
<
T
>
(),
rois
->
data
<
T
>
(),
spatial_scale
,
channels
,
...
...
@@ -310,39 +302,40 @@ class GPUROIAlignGradOpKernel : public framework::OpKernel<T> {
int
height
=
in
->
dims
()[
2
];
int
width
=
in
->
dims
()[
3
];
if
(
in_grad
)
{
Tensor
roi_batch_id_list
;
roi_batch_id_list
.
Resize
({
rois_num
});
int
*
roi_batch_id_data
=
roi_batch_id_list
.
mutable_data
<
int
>
(
platform
::
CPUPlace
());
auto
rois_lod
=
rois
->
lod
().
back
();
int
rois_batch_size
=
rois_lod
.
size
()
-
1
;
for
(
int
n
=
0
;
n
<
rois_batch_size
;
++
n
)
{
for
(
size_t
i
=
rois_lod
[
n
];
i
<
rois_lod
[
n
+
1
];
++
i
)
{
roi_batch_id_data
[
i
]
=
n
;
}
}
Tensor
roi_batch_id_list_gpu
;
framework
::
TensorCopy
(
roi_batch_id_list
,
ctx
.
GetPlace
(),
ctx
.
device_context
(),
&
roi_batch_id_list_gpu
);
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
math
::
SetConstant
<
Place
,
T
>
set_zero
;
set_zero
(
ctx
.
cuda_device_context
(),
in_grad
,
static_cast
<
T
>
(
0
));
int
output_grad_size
=
out_grad
->
numel
();
int
blocks
=
NumBlocks
(
output_grad_size
);
int
threads
=
kNumCUDAThreads
;
if
(
output_grad_size
>
0
)
{
GPUROIAlignBackward
<
T
><<<
blocks
,
threads
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
output_grad_size
,
rois
->
data
<
T
>
(),
out_grad
->
data
<
T
>
(),
rois_num
,
spatial_scale
,
channels
,
height
,
width
,
pooled_height
,
pooled_width
,
sampling_ratio
,
roi_batch_id_list_gpu
.
data
<
int
>
(),
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()));
if
(
!
in_grad
)
{
return
;
}
Tensor
roi_batch_id_list
;
roi_batch_id_list
.
Resize
({
rois_num
});
int
*
roi_batch_id_data
=
roi_batch_id_list
.
mutable_data
<
int
>
(
platform
::
CPUPlace
());
auto
rois_lod
=
rois
->
lod
().
back
();
int
rois_batch_size
=
rois_lod
.
size
()
-
1
;
for
(
int
n
=
0
;
n
<
rois_batch_size
;
++
n
)
{
for
(
size_t
i
=
rois_lod
[
n
];
i
<
rois_lod
[
n
+
1
];
++
i
)
{
roi_batch_id_data
[
i
]
=
n
;
}
}
Tensor
roi_batch_id_list_gpu
;
framework
::
TensorCopySync
(
roi_batch_id_list
,
ctx
.
GetPlace
(),
&
roi_batch_id_list_gpu
);
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
math
::
SetConstant
<
Place
,
T
>
set_zero
;
set_zero
(
ctx
.
cuda_device_context
(),
in_grad
,
static_cast
<
T
>
(
0
));
int
output_grad_size
=
out_grad
->
numel
();
int
blocks
=
NumBlocks
(
output_grad_size
);
int
threads
=
kNumCUDAThreads
;
if
(
output_grad_size
>
0
)
{
GPUROIAlignBackward
<
T
><<<
blocks
,
threads
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
output_grad_size
,
rois
->
data
<
T
>
(),
out_grad
->
data
<
T
>
(),
rois_num
,
spatial_scale
,
channels
,
height
,
width
,
pooled_height
,
pooled_width
,
sampling_ratio
,
roi_batch_id_list_gpu
.
data
<
int
>
(),
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()));
}
}
};
...
...
paddle/fluid/operators/roi_align_op.h
浏览文件 @
9a14ca91
...
...
@@ -24,7 +24,7 @@ using LoDTensor = framework::LoDTensor;
static
constexpr
int
kROISize
=
4
;
template
<
class
T
>
void
pre_calc_for_bilinear_i
nterpolate
(
void
PreCalcForBilinearI
nterpolate
(
const
platform
::
DeviceContext
&
ctx
,
const
int
height
,
const
int
width
,
const
int
pooled_height
,
const
int
pooled_width
,
const
int
iy_upper
,
const
int
ix_upper
,
T
roi_ymin
,
T
roi_xmin
,
T
bin_size_h
,
T
bin_size_w
,
...
...
@@ -53,12 +53,8 @@ void pre_calc_for_bilinear_interpolate(
pre_calc_index
+=
1
;
continue
;
}
if
(
y
<=
0
)
{
y
=
0
;
}
if
(
x
<=
0
)
{
x
=
0
;
}
y
=
y
<=
0
?
0
:
y
;
x
=
x
<=
0
?
0
:
x
;
int
y_low
=
static_cast
<
int
>
(
y
);
int
x_low
=
static_cast
<
int
>
(
x
);
...
...
@@ -104,12 +100,8 @@ void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
x_low
=
x_high
=
y_low
=
y_high
=
-
1
;
return
;
}
if
(
y
<=
0
)
{
y
=
0
;
}
if
(
x
<=
0
)
{
x
=
0
;
}
y
=
y
<=
0
?
0
:
y
;
x
=
x
<=
0
?
0
:
x
;
y_low
=
static_cast
<
int
>
(
y
);
x_low
=
static_cast
<
int
>
(
x
);
if
(
y_low
>=
height
-
1
)
{
...
...
@@ -139,7 +131,6 @@ void bilinear_interpolate_gradient(const int height, const int width, T y, T x,
*
(
batch_grad_data
+
y_high
*
width
+
x_low
)
+=
diff3
;
*
(
batch_grad_data
+
y_high
*
width
+
x_high
)
+=
diff4
;
}
return
;
}
template
<
typename
DeviceContext
,
typename
T
>
...
...
@@ -214,7 +205,7 @@ class CPUROIAlignOpKernel : public framework::OpKernel<T> {
pre_pos
.
Resize
({
pre_size
,
kROISize
});
pre_w
.
Resize
({
pre_size
,
kROISize
});
pre_calc_for_bilinear_i
nterpolate
(
PreCalcForBilinearI
nterpolate
(
dev_ctx
,
height
,
width
,
pooled_height
,
pooled_width
,
roi_bin_grid_h
,
roi_bin_grid_w
,
roi_ymin
,
roi_xmin
,
bin_size_h
,
bin_size_w
,
roi_bin_grid_h
,
roi_bin_grid_w
,
&
pre_pos
,
&
pre_w
);
...
...
@@ -245,7 +236,6 @@ class CPUROIAlignOpKernel : public framework::OpKernel<T> {
}
rois_data
+=
roi_stride
[
0
];
}
return
;
}
};
...
...
@@ -264,79 +254,78 @@ class CPUROIAlignGradOpKernel : public framework::OpKernel<T> {
auto
spatial_scale
=
ctx
.
Attr
<
float
>
(
"spatial_scale"
);
auto
sampling_ratio
=
ctx
.
Attr
<
int
>
(
"sampling_ratio"
);
auto
in_dims
=
in
->
dims
();
if
(
in_grad
)
{
int
channels
=
in_dims
[
1
];
int
height
=
in_dims
[
2
];
int
width
=
in_dims
[
3
];
int
rois_num
=
rois
->
dims
()[
0
];
Tensor
roi_batch_id_list
;
roi_batch_id_list
.
Resize
({
rois_num
});
int
*
roi_batch_id_data
=
roi_batch_id_list
.
mutable_data
<
int
>
(
ctx
.
GetPlace
());
if
(
!
in_grad
)
{
return
;
}
int
channels
=
in_dims
[
1
];
int
height
=
in_dims
[
2
];
int
width
=
in_dims
[
3
];
int
rois_num
=
rois
->
dims
()[
0
];
Tensor
roi_batch_id_list
;
roi_batch_id_list
.
Resize
({
rois_num
});
int
*
roi_batch_id_data
=
roi_batch_id_list
.
mutable_data
<
int
>
(
ctx
.
GetPlace
());
auto
rois_lod
=
rois
->
lod
().
back
();
int
rois_batch_size
=
rois_lod
.
size
()
-
1
;
for
(
int
n
=
0
;
n
<
rois_batch_size
;
++
n
)
{
for
(
size_t
i
=
rois_lod
[
n
];
i
<
rois_lod
[
n
+
1
];
++
i
)
{
roi_batch_id_data
[
i
]
=
n
;
}
auto
rois_lod
=
rois
->
lod
().
back
();
int
rois_batch_size
=
rois_lod
.
size
()
-
1
;
for
(
int
n
=
0
;
n
<
rois_batch_size
;
++
n
)
{
for
(
size_t
i
=
rois_lod
[
n
];
i
<
rois_lod
[
n
+
1
];
++
i
)
{
roi_batch_id_data
[
i
]
=
n
;
}
}
const
T
*
rois_data
=
rois
->
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
->
data
<
T
>
();
T
*
in_grad_data
=
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
rois_data
=
rois
->
data
<
T
>
();
const
T
*
out_grad_data
=
out_grad
->
data
<
T
>
();
T
*
in_grad_data
=
in_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
in_stride
=
framework
::
stride
(
in
->
dims
());
auto
roi_stride
=
framework
::
stride
(
rois
->
dims
());
auto
out_stride
=
framework
::
stride
(
out_grad
->
dims
());
auto
in_stride
=
framework
::
stride
(
in
->
dims
());
auto
roi_stride
=
framework
::
stride
(
rois
->
dims
());
auto
out_stride
=
framework
::
stride
(
out_grad
->
dims
());
for
(
int
n
=
0
;
n
<
rois_num
;
++
n
)
{
int
roi_batch_idx
=
roi_batch_id_data
[
n
];
T
roi_xmin
=
rois_data
[
0
]
*
spatial_scale
;
T
roi_ymin
=
rois_data
[
1
]
*
spatial_scale
;
T
roi_xmax
=
rois_data
[
2
]
*
spatial_scale
;
T
roi_ymax
=
rois_data
[
3
]
*
spatial_scale
;
T
roi_width
=
std
::
max
(
roi_xmax
-
roi_xmin
,
static_cast
<
T
>
(
1.
));
T
roi_height
=
std
::
max
(
roi_ymax
-
roi_ymin
,
static_cast
<
T
>
(
1.
));
T
bin_size_h
=
static_cast
<
T
>
(
roi_height
)
/
static_cast
<
T
>
(
pooled_height
);
T
bin_size_w
=
static_cast
<
T
>
(
roi_width
)
/
static_cast
<
T
>
(
pooled_width
);
for
(
int
c
=
0
;
c
<
channels
;
++
c
)
{
T
*
batch_grad_data
=
in_grad_data
+
roi_batch_idx
*
in_stride
[
0
]
+
c
*
in_stride
[
1
];
const
T
*
batch_out_grad_data
=
out_grad_data
+
n
*
out_stride
[
0
]
+
c
*
out_stride
[
1
];
for
(
int
ph
=
0
;
ph
<
pooled_height
;
++
ph
)
{
for
(
int
pw
=
0
;
pw
<
pooled_width
;
++
pw
)
{
int
pool_index
=
ph
*
pooled_width
+
pw
;
T
out_grad_this_bin
=
batch_out_grad_data
[
pool_index
];
int
roi_bin_grid_h
=
(
sampling_ratio
>
0
)
?
sampling_ratio
:
ceil
(
roi_height
/
pooled_height
);
int
roi_bin_grid_w
=
(
sampling_ratio
>
0
)
?
sampling_ratio
:
ceil
(
roi_width
/
pooled_width
);
T
count
=
roi_bin_grid_h
*
roi_bin_grid_w
;
for
(
int
iy
=
0
;
iy
<
roi_bin_grid_h
;
iy
++
)
{
const
T
y
=
roi_ymin
+
ph
*
bin_size_h
+
static_cast
<
T
>
(
iy
+
.5
f
)
*
bin_size_h
/
static_cast
<
T
>
(
roi_bin_grid_h
);
for
(
int
ix
=
0
;
ix
<
roi_bin_grid_w
;
ix
++
)
{
const
T
x
=
roi_xmin
+
pw
*
bin_size_w
+
static_cast
<
T
>
(
ix
+
.5
f
)
*
bin_size_w
/
static_cast
<
T
>
(
roi_bin_grid_w
);
bilinear_interpolate_gradient
(
height
,
width
,
y
,
x
,
out_grad_this_bin
,
count
,
batch_grad_data
);
}
for
(
int
n
=
0
;
n
<
rois_num
;
++
n
)
{
int
roi_batch_idx
=
roi_batch_id_data
[
n
];
T
roi_xmin
=
rois_data
[
0
]
*
spatial_scale
;
T
roi_ymin
=
rois_data
[
1
]
*
spatial_scale
;
T
roi_xmax
=
rois_data
[
2
]
*
spatial_scale
;
T
roi_ymax
=
rois_data
[
3
]
*
spatial_scale
;
T
roi_width
=
std
::
max
(
roi_xmax
-
roi_xmin
,
static_cast
<
T
>
(
1.
));
T
roi_height
=
std
::
max
(
roi_ymax
-
roi_ymin
,
static_cast
<
T
>
(
1.
));
T
bin_size_h
=
static_cast
<
T
>
(
roi_height
)
/
static_cast
<
T
>
(
pooled_height
);
T
bin_size_w
=
static_cast
<
T
>
(
roi_width
)
/
static_cast
<
T
>
(
pooled_width
);
for
(
int
c
=
0
;
c
<
channels
;
++
c
)
{
T
*
batch_grad_data
=
in_grad_data
+
roi_batch_idx
*
in_stride
[
0
]
+
c
*
in_stride
[
1
];
const
T
*
batch_out_grad_data
=
out_grad_data
+
n
*
out_stride
[
0
]
+
c
*
out_stride
[
1
];
for
(
int
ph
=
0
;
ph
<
pooled_height
;
++
ph
)
{
for
(
int
pw
=
0
;
pw
<
pooled_width
;
++
pw
)
{
int
pool_index
=
ph
*
pooled_width
+
pw
;
T
out_grad_this_bin
=
batch_out_grad_data
[
pool_index
];
int
roi_bin_grid_h
=
(
sampling_ratio
>
0
)
?
sampling_ratio
:
ceil
(
roi_height
/
pooled_height
);
int
roi_bin_grid_w
=
(
sampling_ratio
>
0
)
?
sampling_ratio
:
ceil
(
roi_width
/
pooled_width
);
T
count
=
roi_bin_grid_h
*
roi_bin_grid_w
;
for
(
int
iy
=
0
;
iy
<
roi_bin_grid_h
;
iy
++
)
{
const
T
y
=
roi_ymin
+
ph
*
bin_size_h
+
static_cast
<
T
>
(
iy
+
.5
f
)
*
bin_size_h
/
static_cast
<
T
>
(
roi_bin_grid_h
);
for
(
int
ix
=
0
;
ix
<
roi_bin_grid_w
;
ix
++
)
{
const
T
x
=
roi_xmin
+
pw
*
bin_size_w
+
static_cast
<
T
>
(
ix
+
.5
f
)
*
bin_size_w
/
static_cast
<
T
>
(
roi_bin_grid_w
);
bilinear_interpolate_gradient
(
height
,
width
,
y
,
x
,
out_grad_this_bin
,
count
,
batch_grad_data
);
}
}
}
}
rois_data
+=
roi_stride
[
0
];
}
rois_data
+=
roi_stride
[
0
];
}
return
;
}
};
}
// namespace operators
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
9a14ca91
...
...
@@ -5184,7 +5184,8 @@ def roi_align(input,
pooled_height
=
1
,
pooled_width
=
1
,
spatial_scale
=
1.0
,
sampling_ratio
=-
1
):
sampling_ratio
=-
1
,
name
=
None
):
"""
${comment}
...
...
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