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
afeixing77
Paddle
提交
6a62b9d8
P
Paddle
项目概览
afeixing77
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
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
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
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
6a62b9d8
编写于
3月 07, 2019
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
add temporal_shift_op. test=develop
上级
d2065823
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
509 addition
and
0 deletion
+509
-0
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/temporal_shift_op.cc
paddle/fluid/operators/temporal_shift_op.cc
+115
-0
paddle/fluid/operators/temporal_shift_op.cu
paddle/fluid/operators/temporal_shift_op.cu
+151
-0
paddle/fluid/operators/temporal_shift_op.h
paddle/fluid/operators/temporal_shift_op.h
+117
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+40
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+8
-0
python/paddle/fluid/tests/unittests/test_temporal_shift_op.py
...on/paddle/fluid/tests/unittests/test_temporal_shift_op.py
+77
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
6a62b9d8
...
...
@@ -216,6 +216,7 @@ paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=Non
paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7ffc849e71f31dfe29030ff94e662de6'))
paddle.fluid.layers.lstm (ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)), ('document', 'd5e6c494ac35100e2ed4d4bd9a1ed932'))
paddle.fluid.layers.shuffle_channel (ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2fa6782d43d02ae64482d21235a82949'))
paddle.fluid.layers.temporal_shift(ArgSpec(args=['x', 'seg_num', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2fa6782d43d02ae64482d21235a82949'))
paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None)), ('document', '8404e472ac12b4a30a505d3d3a3e5fdb'))
paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1546136806fef5c08f6918544bd9151d'))
paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '2f6ff96864054a31aa4bb659c6722c99'))
...
...
paddle/fluid/operators/temporal_shift_op.cc
0 → 100644
浏览文件 @
6a62b9d8
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/temporal_shift_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
TemporalShiftOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of TemporalShiftOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of TemporalShiftOp should not be null."
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
dim_x
.
size
(),
4
,
"Input(X) rank should be 4 in shape of [N*T, C, H, W]."
);
int
seg_num
=
ctx
->
Attrs
().
Get
<
int
>
(
"seg_num"
);
PADDLE_ENFORCE_GT
(
seg_num
,
0
,
"Attr(seg_num) should be greater then 0."
);
if
(
ctx
->
IsRuntime
())
{
PADDLE_ENFORCE_EQ
(
dim_x
[
0
]
%
seg_num
,
0
,
"Input(X) dims[0] should be divided exactly by Attr(seg_num)."
);
}
ctx
->
SetOutputDim
(
"Out"
,
dim_x
);
ctx
->
ShareLoD
(
"X"
,
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
(),
ctx
.
GetPlace
());
}
};
class
TemporalShiftOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"The input tensor of temporal shift operator. "
"This is a 4-D tensor with shape of [N*T, C, H, W]. "
"While N is the batch size, T is the temporal segment "
"number, C is the channel number, H is the height of "
"features and W is the width of features."
);
AddOutput
(
"Out"
,
"The output tensor of temporal shift operator. "
"This is a 4-D tensor in the same shape with Input(X)."
);
AddAttr
<
int
>
(
"seg_num"
,
"The temporal segment number, this should be a positive "
"interger."
);
AddComment
(
R"DOC(
This operator calculates the temporal shift features for Input(X).
For details of spectral normalization, please refer to paper:
`Temporal Shift Module <arxiv.org/abs/1802.0595://arxiv.org/abs/1811.08383>`_ .
)DOC"
);
}
};
class
TemporalShiftOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
dim_x
);
}
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
(),
ctx
.
GetPlace
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
temporal_shift
,
ops
::
TemporalShiftOp
,
ops
::
TemporalShiftOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
temporal_shift_grad
,
ops
::
TemporalShiftOpGrad
);
REGISTER_OP_CPU_KERNEL
(
temporal_shift
,
ops
::
TemporalShiftKernel
<
float
>
,
ops
::
TemporalShiftKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
temporal_shift_grad
,
ops
::
TemporalShiftGradKernel
<
float
>
,
ops
::
TemporalShiftGradKernel
<
double
>
);
paddle/fluid/operators/temporal_shift_op.cu
0 → 100644
浏览文件 @
6a62b9d8
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/temporal_shift_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
template
<
typename
T
>
__global__
void
KeTemporalShiftFw
(
const
T
*
input
,
T
*
output
,
const
int
ntchw
,
const
int
tchw
,
const
int
chw
,
const
int
hw
,
const
int
w
,
const
int
t
,
const
int
c
)
{
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
int
src_it
=
0
;
for
(;
tid
<
ntchw
;
tid
+=
stride
)
{
int
in
=
tid
/
tchw
;
int
it
=
(
tid
%
tchw
)
/
chw
;
int
ic
=
(
tid
%
chw
)
/
hw
;
int
ih
=
(
tid
%
hw
)
/
w
;
int
iw
=
tid
%
w
;
if
(
ic
<
c
/
4
)
{
src_it
=
it
-
1
;
}
else
if
(
ic
<
c
/
2
)
{
src_it
=
it
+
1
;
}
else
{
src_it
=
it
;
}
if
(
src_it
<
0
||
src_it
>=
t
)
{
output
[
tid
]
=
0
;
}
else
{
int
src_idx
=
GetEntryIndex
(
in
,
src_it
,
ic
,
ih
,
iw
,
tchw
,
chw
,
hw
,
w
);
output
[
tid
]
=
input
[
src_idx
];
}
}
}
template
<
typename
T
>
__global__
void
KeTemporalShiftBw
(
const
T
*
output_grad
,
T
*
input_grad
,
const
int
ntchw
,
const
int
tchw
,
const
int
chw
,
const
int
hw
,
const
int
w
,
const
int
t
,
const
int
c
)
{
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
int
src_it
=
0
;
for
(;
tid
<
ntchw
;
tid
+=
stride
)
{
int
in
=
tid
/
tchw
;
int
it
=
(
tid
%
tchw
)
/
chw
;
int
ic
=
(
tid
%
chw
)
/
hw
;
int
ih
=
(
tid
%
hw
)
/
w
;
int
iw
=
tid
%
w
;
if
(
ic
<
c
/
4
)
{
src_it
=
it
-
1
;
}
else
if
(
ic
<
c
/
2
)
{
src_it
=
it
+
1
;
}
else
{
src_it
=
it
;
}
if
(
src_it
>=
0
&&
src_it
<
t
)
{
int
src_idx
=
GetEntryIndex
(
in
,
src_it
,
ic
,
ih
,
iw
,
tchw
,
chw
,
hw
,
w
);
input_grad
[
src_idx
]
=
output_grad
[
tid
];
}
}
}
template
<
typename
T
>
class
TemporalShiftOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"This kernel only runs on GPU device."
);
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
int
t
=
ctx
.
Attr
<
int
>
(
"seg_num"
);
const
int
nt
=
input
->
dims
()[
0
];
const
int
c
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
hw
=
h
*
w
;
const
int
chw
=
c
*
hw
;
const
int
tchw
=
t
*
chw
;
const
int
ntchw
=
nt
*
chw
;
const
T
*
input_data
=
input
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
({
nt
,
c
,
h
,
w
},
ctx
.
GetPlace
());
int
pixelNum
=
nt
*
chw
;
int
grid_dim
=
(
pixelNum
+
512
-
1
)
/
512
;
grid_dim
=
grid_dim
>
8
?
8
:
grid_dim
;
KeTemporalShiftFw
<
T
><<<
grid_dim
,
512
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
input_data
,
output_data
,
ntchw
,
tchw
,
chw
,
hw
,
w
,
t
,
c
);
}
};
template
<
typename
T
>
class
TemporalShiftGradOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
int
t
=
ctx
.
Attr
<
int
>
(
"seg_num"
);
const
int
nt
=
output_grad
->
dims
()[
0
];
const
int
c
=
output_grad
->
dims
()[
1
];
const
int
h
=
output_grad
->
dims
()[
2
];
const
int
w
=
output_grad
->
dims
()[
3
];
const
int
hw
=
h
*
w
;
const
int
chw
=
c
*
hw
;
const
int
tchw
=
t
*
chw
;
const
int
ntchw
=
nt
*
chw
;
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
({
nt
,
c
,
h
,
w
},
ctx
.
GetPlace
());
int
pixelNum
=
nt
*
chw
;
int
grid_dim
=
(
pixelNum
+
512
-
1
)
/
512
;
grid_dim
=
grid_dim
>
8
?
8
:
grid_dim
;
KeTemporalShiftBw
<
T
><<<
grid_dim
,
512
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
output_grad_data
,
input_grad_data
,
ntchw
,
tchw
,
chw
,
hw
,
w
,
t
,
c
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
temporal_shift
,
ops
::
TemporalShiftOpCUDAKernel
<
float
>
,
ops
::
TemporalShiftOpCUDAKernel
<
double
>
);
REGISTER_OP_CUDA_KERNEL
(
temporal_shift_grad
,
ops
::
TemporalShiftGradOpCUDAKernel
<
float
>
,
ops
::
TemporalShiftGradOpCUDAKernel
<
double
>
);
paddle/fluid/operators/temporal_shift_op.h
0 → 100644
浏览文件 @
6a62b9d8
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
static
HOSTDEVICE
inline
int
GetEntryIndex
(
int
in
,
int
it
,
int
ic
,
int
ih
,
int
iw
,
const
int
tchw
,
const
int
chw
,
const
int
hw
,
const
int
w
)
{
return
in
*
tchw
+
it
*
chw
+
ic
*
hw
+
ih
*
w
+
iw
;
}
template
<
typename
T
>
class
TemporalShiftKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
int
t
=
ctx
.
Attr
<
int
>
(
"seg_num"
);
const
int
nt
=
input
->
dims
()[
0
];
const
int
c
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
hw
=
h
*
w
;
const
int
chw
=
c
*
hw
;
const
int
tchw
=
t
*
chw
;
const
T
*
input_data
=
input
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
({
nt
,
c
,
h
,
w
},
ctx
.
GetPlace
());
int
src_it
=
0
;
for
(
int
i
=
0
;
i
<
output
->
numel
();
i
++
)
{
int
in
=
i
/
tchw
;
int
it
=
(
i
%
tchw
)
/
chw
;
int
ic
=
(
i
%
chw
)
/
hw
;
int
ih
=
(
i
%
hw
)
/
w
;
int
iw
=
i
%
w
;
if
(
ic
<
c
/
4
)
{
src_it
=
it
-
1
;
}
else
if
(
ic
<
c
/
2
)
{
src_it
=
it
+
1
;
}
else
{
src_it
=
it
;
}
if
(
src_it
<
0
||
src_it
>=
t
)
{
output_data
[
i
]
=
0
;
}
else
{
int
src_idx
=
GetEntryIndex
(
in
,
src_it
,
ic
,
ih
,
iw
,
tchw
,
chw
,
hw
,
w
);
output_data
[
i
]
=
input_data
[
src_idx
];
}
}
}
};
template
<
typename
T
>
class
TemporalShiftGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
int
t
=
ctx
.
Attr
<
int
>
(
"seg_num"
);
const
int
nt
=
output_grad
->
dims
()[
0
];
const
int
c
=
output_grad
->
dims
()[
1
];
const
int
h
=
output_grad
->
dims
()[
2
];
const
int
w
=
output_grad
->
dims
()[
3
];
const
int
hw
=
h
*
w
;
const
int
chw
=
c
*
hw
;
const
int
tchw
=
t
*
chw
;
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
({
nt
,
c
,
h
,
w
},
ctx
.
GetPlace
());
int
src_it
=
0
;
for
(
int
i
=
0
;
i
<
output_grad
->
numel
();
i
++
)
{
int
in
=
i
/
tchw
;
int
it
=
(
i
%
tchw
)
/
chw
;
int
ic
=
(
i
%
chw
)
/
hw
;
int
ih
=
(
i
%
hw
)
/
w
;
int
iw
=
i
%
w
;
if
(
ic
<
c
/
4
)
{
src_it
=
it
-
1
;
}
else
if
(
ic
<
c
/
2
)
{
src_it
=
it
+
1
;
}
else
{
src_it
=
it
;
}
if
(
src_it
>=
0
&&
src_it
<
t
)
{
int
src_idx
=
GetEntryIndex
(
in
,
src_it
,
ic
,
ih
,
iw
,
tchw
,
chw
,
hw
,
w
);
input_grad_data
[
src_idx
]
=
output_grad_data
[
i
];
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
6a62b9d8
...
...
@@ -182,6 +182,7 @@ __all__ = [
'get_tensor_from_selected_rows'
,
'lstm'
,
'shuffle_channel'
,
'temporal_shift'
,
'py_func'
,
'psroi_pool'
,
'teacher_student_sigmoid_loss'
,
...
...
@@ -10264,6 +10265,45 @@ def shuffle_channel(x, group, name=None):
return
out
@
templatedoc
()
def
temporal_shift
(
x
,
seg_num
,
name
=
None
):
"""
**Temporal Shift Operator**
${comment}
Args:
x(Variable): ${x_comment}
seg_num(int): ${seg_num_comment}
Returns:
out(Variable): The temporal shifting result is a tensor variable with the
same shape and same type as the input.
Raises:
TypeError: seg_num must be int type.
Examples:
.. code-block:: python
input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32')
out = fluid.layers.temporal_shift(x=input, seg_num=2)
"""
helper
=
LayerHelper
(
"temporal_shift"
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
if
not
isinstance
(
seg_num
,
int
):
raise
TypeError
(
"seg_num must be int type."
)
helper
.
append_op
(
type
=
"temporal_shift"
,
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
out
},
attrs
=
{
"seg_num"
:
seg_num
})
return
out
class
PyFuncRegistry
(
object
):
_register_funcs
=
[]
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
6a62b9d8
...
...
@@ -1048,6 +1048,14 @@ class TestBook(unittest.TestCase):
print
(
str
(
program
))
def
test_temporal_shift
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
"X"
,
shape
=
[
16
,
4
,
4
],
dtype
=
"float32"
)
out
=
layers
.
temporal_shift
(
x
,
seg_num
=
4
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_shuffle_channel
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
python/paddle/fluid/tests/unittests/test_temporal_shift_op.py
0 → 100644
浏览文件 @
6a62b9d8
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
division
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
from
paddle.fluid
import
core
def
temporal_shift
(
x
,
seg_num
):
shape
=
x
.
shape
reshape_x
=
x
.
reshape
((
-
1
,
seg_num
,
shape
[
1
],
shape
[
2
],
shape
[
3
]))
pad_x
=
np
.
pad
(
reshape_x
,
((
0
,
0
),
(
1
,
1
),
(
0
,
0
),
(
0
,
0
),
(
0
,
0
)),
'constant'
)
slice1
=
pad_x
[:,
:
seg_num
,
:
shape
[
1
]
//
4
,
:,
:]
slice2
=
pad_x
[:,
2
:
seg_num
+
2
,
shape
[
1
]
//
4
:
shape
[
1
]
//
2
,
:,
:]
slice3
=
pad_x
[:,
1
:
seg_num
+
1
,
shape
[
1
]
//
2
:,
:,
:]
concat_x
=
np
.
concatenate
([
slice1
,
slice2
,
slice3
],
axis
=
2
)
return
concat_x
.
reshape
(
shape
)
class
TestTemporalShift
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
op_type
=
'temporal_shift'
x
=
np
.
random
.
random
(
self
.
x_shape
).
astype
(
'float32'
)
self
.
attrs
=
{
"seg_num"
:
self
.
seg_num
,
}
self
.
inputs
=
{
"X"
:
x
,
}
output
=
temporal_shift
(
x
,
self
.
seg_num
)
self
.
outputs
=
{
"Out"
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_ignore_uv
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.01
)
def
initTestCase
(
self
):
self
.
x_shape
=
(
6
,
4
,
4
,
4
)
self
.
seg_num
=
3
class
TestTemporalShift2
(
TestTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
4
,
9
,
7
,
7
)
self
.
seg_num
=
2
class
TestTemporalShift2
(
TestTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
3
,
10
,
5
,
5
)
self
.
seg_num
=
1
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