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88bd7e1a
编写于
1月 25, 2019
作者:
R
ruri
提交者:
GitHub
1月 25, 2019
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差异文件
Merge pull request #15027 from shippingwang/shufflechannel
Add Shuffle Channel Operator
上级
e043ea96
14f2a106
变更
7
显示空白变更内容
内联
并排
Showing
7 changed file
with
468 addition
and
0 deletion
+468
-0
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/shuffle_channel_op.cc
paddle/fluid/operators/shuffle_channel_op.cc
+113
-0
paddle/fluid/operators/shuffle_channel_op.cu
paddle/fluid/operators/shuffle_channel_op.cu
+125
-0
paddle/fluid/operators/shuffle_channel_op.h
paddle/fluid/operators/shuffle_channel_op.h
+95
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+74
-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_shuffle_channel_op.py
...n/paddle/fluid/tests/unittests/test_shuffle_channel_op.py
+52
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
88bd7e1a
...
...
@@ -213,6 +213,7 @@ paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act
paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
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))
paddle.fluid.layers.shuffle_channel ArgSpec(args=['x', 'group', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.py_func ArgSpec(args=['func', 'x', 'out', 'backward_func', 'skip_vars_in_backward_input'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
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))
...
...
paddle/fluid/operators/shuffle_channel_op.cc
0 → 100644
浏览文件 @
88bd7e1a
/*Copyright (c) 2018 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. */
#include "paddle/fluid/operators/shuffle_channel_op.h"
namespace
paddle
{
namespace
operators
{
class
ShuffleChannelOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of ShuffleChannelOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of ShuffleChannelOp should not be null."
);
auto
input_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE
(
input_dims
.
size
()
==
4
,
"The layout of input is NCHW."
);
ctx
->
SetOutputDim
(
"Out"
,
input_dims
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
(),
ctx
.
device_context
());
}
};
class
ShuffleChannelOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), "
"the input feature data of ShuffleChannelOp, the layout is NCHW."
);
AddOutput
(
"Out"
,
"(Tensor, default Tensor<float>), the output of "
"ShuffleChannelOp. The layout is NCHW."
);
AddAttr
<
int
>
(
"group"
,
"the number of groups."
)
.
SetDefault
(
1
)
.
AddCustomChecker
([](
const
int
&
group
)
{
PADDLE_ENFORCE_GE
(
group
,
1
,
"group should be larger than 0."
);
});
AddComment
(
R"DOC(
Shuffle Channel operator
This opearator shuffles the channels of input x.
It divide the input channels in each group into several subgroups,
and obtain a new order by selecting element from every subgroup one by one.
Shuffle channel operation makes it possible to build more powerful structures
with multiple group convolutional layers.
please get more information from the following paper:
https://arxiv.org/pdf/1707.01083.pdf
)DOC"
);
}
};
class
ShuffleChannelGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@Grad) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Output(X@Grad) should not be null"
);
auto
input_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE
(
input_dims
.
size
()
==
4
,
"The layout of input is NCHW."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
input_dims
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
(),
ctx
.
device_context
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
shuffle_channel
,
ops
::
ShuffleChannelOp
,
ops
::
ShuffleChannelOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
shuffle_channel_grad
,
ops
::
ShuffleChannelGradOp
);
REGISTER_OP_CPU_KERNEL
(
shuffle_channel
,
ops
::
ShuffleChannelOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ShuffleChannelOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
shuffle_channel_grad
,
ops
::
ShuffleChannelGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
ShuffleChannelGradOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/shuffle_channel_op.cu
0 → 100644
浏览文件 @
88bd7e1a
/* Copyright (c) 2018 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. */
#include "paddle/fluid/operators/shuffle_channel_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/gpu_info.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
static
constexpr
int
kNumCUDAThreads
=
512
;
static
constexpr
int
kNumMaximumNumBlocks
=
4096
;
static
inline
int
NumBlocks
(
const
int
N
)
{
return
std
::
min
((
N
+
kNumCUDAThreads
-
1
)
/
kNumCUDAThreads
,
kNumMaximumNumBlocks
);
}
template
<
typename
T
>
__global__
void
ShuffleChannel
(
const
int
nthreads
,
const
int
feature_map_size
,
T
*
output
,
const
T
*
input
,
int
group_row
,
int
group_column
,
int
len
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
offset
=
blockDim
.
x
*
gridDim
.
x
;
for
(
size_t
ii
=
index
;
ii
<
nthreads
;
ii
+=
offset
)
{
const
int
n
=
index
/
group_row
/
group_column
/
len
;
const
int
i
=
(
index
/
group_column
/
len
)
%
group_row
;
const
int
j
=
index
/
len
%
group_column
;
const
int
k
=
index
-
(
n
*
feature_map_size
+
(
i
*
group_column
+
j
)
*
len
);
T
*
p_o
=
output
+
n
*
feature_map_size
+
(
j
*
group_row
+
i
)
*
len
;
p_o
[
k
]
=
input
[
index
];
}
}
template
<
typename
DeviceContext
,
typename
T
>
class
ShuffleChannelOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
int
group
=
ctx
.
Attr
<
int
>
(
"group"
);
auto
input_dims
=
input
->
dims
();
auto
num
=
input_dims
[
0
];
auto
channel
=
input_dims
[
1
];
auto
height
=
input_dims
[
2
];
auto
weight
=
input_dims
[
3
];
auto
feature_map_size
=
channel
*
height
*
weight
;
auto
sp_sz
=
height
*
weight
;
int
group_row
=
group
;
int
group_column
=
channel
/
group_row
;
// count is the product of NCHW same as numel()
int
count
=
num
*
group_column
*
group_row
*
sp_sz
;
int
blocks
=
NumBlocks
(
output
->
numel
());
int
threads
=
kNumCUDAThreads
;
const
T
*
input_data
=
input
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
ShuffleChannel
<
T
><<<
blocks
,
threads
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
count
,
feature_map_size
,
output_data
,
input_data
,
group_row
,
group_column
,
sp_sz
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
ShuffleChannelGradOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
int
group
=
ctx
.
Attr
<
int
>
(
"group"
);
auto
input_dims
=
input
->
dims
();
auto
num
=
input_dims
[
0
];
auto
channel
=
input_dims
[
1
];
auto
height
=
input_dims
[
2
];
auto
weight
=
input_dims
[
3
];
auto
feature_map_size
=
channel
*
height
*
weight
;
auto
sp_sz
=
height
*
weight
;
int
group_row
=
group
;
int
group_column
=
channel
/
group_row
;
auto
*
output_grad
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
input_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
int
blocks
=
NumBlocks
(
output_grad
->
numel
());
int
threads
=
kNumCUDAThreads
;
int
count
=
num
*
group_column
*
group_row
*
sp_sz
;
ShuffleChannel
<
T
><<<
blocks
,
threads
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
count
,
feature_map_size
,
input_grad_data
,
output_grad_data
,
group_row
,
group_column
,
sp_sz
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
shuffle_channel
,
ops
::
ShuffleChannelOpCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ShuffleChannelOpCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
shuffle_channel_grad
,
ops
::
ShuffleChannelGradOpCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
ShuffleChannelGradOpCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/shuffle_channel_op.h
0 → 100644
浏览文件 @
88bd7e1a
/* Copyright (c) 2018 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. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
class
ShuffleChannelOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
int
group
=
ctx
.
Attr
<
int
>
(
"group"
);
auto
input_dims
=
input
->
dims
();
auto
num
=
input_dims
[
0
];
auto
channel
=
input_dims
[
1
];
auto
height
=
input_dims
[
2
];
auto
weight
=
input_dims
[
3
];
auto
feature_map_size
=
channel
*
height
*
weight
;
auto
sp_sz
=
height
*
weight
;
int
group_row
=
group
;
int
group_column
=
channel
/
group_row
;
const
T
*
input_data
=
input
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
for
(
int
n
=
0
;
n
<
num
;
++
n
)
{
for
(
int
i
=
0
;
i
<
group_row
;
++
i
)
{
for
(
int
j
=
0
;
j
<
group_column
;
++
j
)
{
const
T
*
p_i
=
input_data
+
n
*
feature_map_size
+
(
i
*
group_column
+
j
)
*
sp_sz
;
T
*
p_o
=
output_data
+
n
*
feature_map_size
+
(
j
*
group_row
+
i
)
*
sp_sz
;
memcpy
(
p_o
,
p_i
,
sizeof
(
int
)
*
sp_sz
);
}
}
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
ShuffleChannelGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
int
group
=
ctx
.
Attr
<
int
>
(
"group"
);
auto
input_dims
=
input
->
dims
();
auto
num
=
input_dims
[
0
];
auto
channel
=
input_dims
[
1
];
auto
height
=
input_dims
[
2
];
auto
weight
=
input_dims
[
3
];
auto
feature_map_size
=
channel
*
height
*
weight
;
auto
sp_sz
=
height
*
weight
;
int
group_row
=
group
;
int
group_column
=
channel
/
group_row
;
auto
*
output_grad
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
input_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
for
(
int
n
=
0
;
n
<
num
;
++
n
)
{
for
(
int
i
=
0
;
i
<
group_row
;
++
i
)
{
for
(
int
j
=
0
;
j
<
group_column
;
++
j
)
{
const
T
*
p_i
=
output_grad_data
+
n
*
feature_map_size
+
(
i
*
group_column
+
j
)
*
sp_sz
;
T
*
p_o
=
input_grad_data
+
n
*
feature_map_size
+
(
j
*
group_row
+
i
)
*
sp_sz
;
memcpy
(
p_o
,
p_i
,
sizeof
(
int
)
*
sp_sz
);
}
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
88bd7e1a
...
...
@@ -179,6 +179,7 @@ __all__ = [
'merge_selected_rows'
,
'get_tensor_from_selected_rows'
,
'lstm'
,
'shuffle_channel'
,
'py_func'
,
'psroi_pool'
,
'teacher_student_sigmoid_loss'
,
...
...
@@ -9646,6 +9647,79 @@ def get_tensor_from_selected_rows(x, name=None):
return
out
def
shuffle_channel
(
x
,
group
,
name
=
None
):
"""
**Shuffle Channel Operator**
This operator shuffles the channels of input x.
It divide the input channels in each group into :attr:`group` subgroups,
and obtain a new order by selecting element from every subgroup one by one.
Please refer to the paper
https://arxiv.org/pdf/1707.01083.pdf
.. code-block:: text
Given a 4-D tensor input with the shape (N, C, H, W):
input.shape = (1, 4, 2, 2)
input.data =[[[[0.1, 0.2],
[0.2, 0.3]],
[[0.3, 0.4],
[0.4, 0.5]],
[[0.5, 0.6],
[0.6, 0.7]],
[[0.7, 0.8],
[0.8, 0.9]]]]
Given group: 2
then we get a 4-D tensor out whth the same shape of input:
out.shape = (1, 4, 2, 2)
out.data = [[[[0.1, 0.2],
[0.2, 0.3]],
[[0.5, 0.6],
[0.6, 0.7]],
[[0.3, 0.4],
[0.4, 0.5]],
[[0.7, 0.8],
[0.8, 0.9]]]]
Args:
x(Variable): The input tensor variable. It should be a 4-D tensor with shape [N, C, H, W]
group(int): Indicating the conuts of subgroups, It should divide the number of channels.
Returns:
out(Variable): the channels shuffling result is a tensor variable with the
same shape and same type as the input.
Raises:
ValueError: If group is not an int type variable.
Examples:
.. code-block:: python
input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32')
out = fluid.layers.shuffle_channel(x=input, group=2)
"""
helper
=
LayerHelper
(
"shuffle_channel"
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
if
not
isinstance
(
group
,
int
):
raise
TypeError
(
"group must be int type"
)
helper
.
append_op
(
type
=
"shuffle_channel"
,
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
out
},
attrs
=
{
"group"
:
group
})
return
out
class
PyFuncRegistry
(
object
):
_register_funcs
=
[]
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
88bd7e1a
...
...
@@ -1023,6 +1023,14 @@ class TestBook(unittest.TestCase):
print
(
str
(
program
))
def
test_shuffle_channel
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
"X"
,
shape
=
[
16
,
4
,
4
],
dtype
=
"float32"
)
out
=
layers
.
shuffle_channel
(
x
,
group
=
4
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_shuffle_channel_op.py
0 → 100644
浏览文件 @
88bd7e1a
# Copyright (c) 2018 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
print_function
import
unittest
import
numpy
as
np
import
sys
import
math
from
op_test
import
OpTest
import
paddle.fluid.core
as
core
class
TestShuffleChannelOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"shuffle_channel"
self
.
batch_size
=
10
self
.
input_channels
=
16
self
.
layer_h
=
4
self
.
layer_w
=
4
self
.
group
=
4
self
.
x
=
np
.
random
.
random
(
(
self
.
batch_size
,
self
.
input_channels
,
self
.
layer_h
,
self
.
layer_w
)).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
self
.
x
}
self
.
attrs
=
{
'group'
:
self
.
group
}
n
,
c
,
h
,
w
=
self
.
x
.
shape
input_reshaped
=
np
.
reshape
(
self
.
x
,
(
-
1
,
self
.
group
,
c
//
self
.
group
,
h
,
w
))
input_transposed
=
np
.
transpose
(
input_reshaped
,
(
0
,
2
,
1
,
3
,
4
))
self
.
outputs
=
{
'Out'
:
np
.
reshape
(
input_transposed
,
(
-
1
,
c
,
h
,
w
))}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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