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c1083765
编写于
8月 15, 2018
作者:
J
jerrywgz
提交者:
qingqing01
8月 15, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add three modes for prelu_op (#12630)
* Add three modes for prelu_op.
上级
d0684930
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
237 addition
and
101 deletion
+237
-101
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/prelu_op.cc
paddle/fluid/operators/prelu_op.cc
+52
-13
paddle/fluid/operators/prelu_op.cu
paddle/fluid/operators/prelu_op.cu
+0
-22
paddle/fluid/operators/prelu_op.h
paddle/fluid/operators/prelu_op.h
+73
-52
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+54
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+15
-0
python/paddle/fluid/tests/unittests/test_prelu_op.py
python/paddle/fluid/tests/unittests/test_prelu_op.py
+42
-14
未找到文件。
paddle/fluid/API.spec
浏览文件 @
c1083765
...
...
@@ -159,6 +159,7 @@ paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaul
paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
...
...
paddle/fluid/operators/prelu_op.cc
浏览文件 @
c1083765
/* Copyright (c) 2016 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.
...
...
@@ -26,14 +23,40 @@ class PReluOp : public framework::OperatorWithKernel {
:
OperatorWithKernel
(
type
,
inputs
,
outputs
,
attrs
)
{}
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
std
::
string
mode
=
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"mode"
);
auto
x_dim
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Alpha"
),
"Input(Alpha) should not be null"
);
PADDLE_ENFORCE
(
product
(
ctx
->
GetInputDim
(
"Alpha"
))
==
1
,
"Size of weight Alpha must be one."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) should not be null"
);
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
if
(
mode
==
"all"
)
{
PADDLE_ENFORCE
(
product
(
ctx
->
GetInputDim
(
"Alpha"
))
==
1
,
"For mode 'all', size of weight Alpha must be one."
);
}
else
if
(
mode
==
"channel"
)
{
PADDLE_ENFORCE
(
product
(
ctx
->
GetInputDim
(
"Alpha"
))
==
x_dim
[
1
],
"For channel-wise mode, size of weight Alpha must be "
"equal to the number of channels, should be %d"
,
x_dim
[
1
]);
}
else
if
(
mode
==
"element"
)
{
PADDLE_ENFORCE
(
product
(
ctx
->
GetInputDim
(
"Alpha"
))
==
product
(
x_dim
),
"For element-wise mode, size of weight Alpha must be "
"equal to the number of input, should be %d"
,
product
(
x_dim
));
}
else
{
PADDLE_THROW
(
"Unkown mode %s"
,
mode
);
}
ctx
->
SetOutputDim
(
"Out"
,
x_dim
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
platform
::
CPUPlace
());
}
};
class
PReluOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
...
@@ -44,9 +67,7 @@ class PReluOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput
(
"Out"
,
"The output tensor of prelu operator."
);
AddComment
(
R"DOC(
PRelu Operator.
The equation is:
$$
f(x) =
\begin{cases}
...
...
@@ -54,11 +75,15 @@ f(x) =
x, \qquad \text{if} \ x >= 0
\end{cases}
$$
The input `X` can carry the LoD (Level of Details) information,
or not. And the output shares the LoD information with input `X`.
There are modes:
all: all elements share same weight
channel: elements in a channel share same weight
element: each element has a weight
)DOC"
);
AddAttr
<
std
::
string
>
(
"mode"
,
"The mode for inputs to share weights."
)
.
SetDefault
(
"all"
);
}
};
...
...
@@ -71,9 +96,23 @@ class PReluGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Alpha"
),
ctx
->
GetInputDim
(
"Alpha"
));
auto
x_grad_name
=
framework
::
GradVarName
(
"X"
);
auto
alpha_grad_name
=
framework
::
GradVarName
(
"Alpha"
);
if
(
ctx
->
HasOutput
(
x_grad_name
))
{
ctx
->
SetOutputDim
(
x_grad_name
,
ctx
->
GetInputDim
(
"X"
));
}
if
(
ctx
->
HasOutput
(
alpha_grad_name
))
{
ctx
->
SetOutputDim
(
alpha_grad_name
,
ctx
->
GetInputDim
(
"Alpha"
));
}
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
platform
::
CPUPlace
());
}
};
...
...
paddle/fluid/operators/prelu_op.cu
已删除
100644 → 0
浏览文件 @
d0684930
/* Copyright (c) 2016 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/prelu_op.h"
REGISTER_OP_CUDA_KERNEL
(
prelu
,
paddle
::
operators
::
PReluKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
prelu_grad
,
paddle
::
operators
::
PReluGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
paddle/fluid/operators/prelu_op.h
浏览文件 @
c1083765
/* Copyright (c) 2016 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.
...
...
@@ -13,32 +10,16 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/transform.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
platform
::
Transform
;
template
<
typename
T
>
class
PReluFunctor
{
public:
explicit
PReluFunctor
(
const
T
*
alpha
)
:
alpha_
(
alpha
)
{}
HOSTDEVICE
T
operator
()(
const
T
&
x
)
const
{
if
(
x
>
0
)
return
x
;
else
return
x
*
(
*
alpha_
);
}
private:
const
T
*
alpha_
;
};
template
<
typename
DeviceContext
,
typename
T
>
class
PReluKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -50,53 +31,93 @@ class PReluKernel : public framework::OpKernel<T> {
const
T
*
x_ptr
=
x
->
data
<
T
>
();
T
*
o_ptr
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
alpha_ptr
=
alpha
->
data
<
T
>
();
const
T
*
alpha_ptr
=
alpha
->
data
<
T
>
();
std
::
string
mode
=
context
.
Attr
<
std
::
string
>
(
"mode"
);
int
numel
=
x
->
numel
();
Transform
<
DeviceContext
>
trans
;
trans
(
context
.
template
device_context
<
DeviceContext
>(),
x_ptr
,
x_ptr
+
numel
,
o_ptr
,
PReluFunctor
<
T
>
(
alpha_ptr
));
}
};
template
<
typename
T
>
class
PReluGradFunctor
{
public:
explicit
PReluGradFunctor
(
const
T
*
alpha
)
:
alpha_
(
alpha
)
{}
HOSTDEVICE
T
operator
()(
const
T
&
out
,
const
T
&
dout
)
const
{
if
(
out
>
0
)
return
dout
;
else
return
dout
*
(
*
alpha_
);
auto
dim
=
x
->
dims
();
int
index
=
0
;
int
i
=
0
;
int
temp
=
0
;
if
(
mode
==
"channel"
)
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
temp
=
numel
/
(
dim
[
0
]
*
dim
[
1
]);
index
=
(
i
/
temp
)
%
dim
[
1
];
o_ptr
[
i
]
=
x_ptr
[
i
]
>
0
?
x_ptr
[
i
]
:
alpha_ptr
[
index
]
*
x_ptr
[
i
];
}
}
else
if
(
mode
==
"element"
)
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
o_ptr
[
i
]
=
x_ptr
[
i
]
>
0
?
x_ptr
[
i
]
:
alpha_ptr
[
i
]
*
x_ptr
[
i
];
}
}
else
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
o_ptr
[
i
]
=
x_ptr
[
i
]
>
0
?
x_ptr
[
i
]
:
alpha_ptr
[
0
]
*
x_ptr
[
i
];
}
}
}
private:
const
T
*
alpha_
;
};
template
<
typename
DeviceContext
,
typename
T
>
class
PReluGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
dx
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dout
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dalpha
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Alpha"
));
auto
*
out
=
context
.
Input
<
Tensor
>
(
"Out"
);
auto
*
alpha
=
context
.
Input
<
Tensor
>
(
"Alpha"
);
auto
*
alpha_ptr
=
alpha
->
data
<
T
>
();
T
*
dx_ptr
=
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
alpha_ptr
=
alpha
->
data
<
T
>
();
const
T
*
x_ptr
=
x
->
data
<
T
>
();
const
T
*
dout_ptr
=
dout
->
data
<
T
>
();
const
T
*
out_ptr
=
out
->
data
<
T
>
();
int
numel
=
dx
->
numel
();
Transform
<
DeviceContext
>
trans
;
trans
(
context
.
template
device_context
<
DeviceContext
>(),
out_ptr
,
out_ptr
+
numel
,
dout_ptr
,
dx_ptr
,
PReluGradFunctor
<
T
>
(
alpha_ptr
));
// TODO(Zhuoyuan): add dalpha upgrade when GPU kernels ready
std
::
string
mode
=
context
.
Attr
<
std
::
string
>
(
"mode"
);
int
numel
=
x
->
numel
();
auto
dim
=
x
->
dims
();
int
index
=
0
;
int
i
=
0
;
int
temp
=
0
;
if
(
dx
)
{
T
*
dx_ptr
=
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
());
if
(
mode
==
"channel"
)
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
temp
=
numel
/
(
dim
[
0
]
*
dim
[
1
]);
index
=
(
i
/
temp
)
%
dim
[
1
];
dx_ptr
[
i
]
=
out_ptr
[
i
]
>
0
?
dout_ptr
[
i
]
:
alpha_ptr
[
index
]
*
dout_ptr
[
i
];
}
}
else
if
(
mode
==
"element"
)
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
dx_ptr
[
i
]
=
out_ptr
[
i
]
>
0
?
dout_ptr
[
i
]
:
alpha_ptr
[
i
]
*
dout_ptr
[
i
];
}
}
else
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
dx_ptr
[
i
]
=
out_ptr
[
i
]
>
0
?
dout_ptr
[
i
]
:
alpha_ptr
[
0
]
*
dout_ptr
[
i
];
}
}
}
index
=
0
;
if
(
dalpha
)
{
T
*
dalpha_ptr
=
dalpha
->
mutable_data
<
T
>
(
context
.
GetPlace
());
if
(
mode
==
"channel"
)
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
temp
=
numel
/
(
dim
[
0
]
*
dim
[
1
]);
index
=
(
i
/
temp
)
%
dim
[
1
];
dalpha_ptr
[
index
]
+=
out_ptr
[
i
]
>
0
?
0
:
x_ptr
[
i
]
*
dout_ptr
[
i
];
}
}
else
if
(
mode
==
"element"
)
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
dalpha_ptr
[
i
]
+=
out_ptr
[
i
]
>
0
?
0
:
x_ptr
[
i
]
*
dout_ptr
[
i
];
}
}
else
{
for
(
i
=
0
;
i
<
numel
;
i
++
)
{
dalpha_ptr
[
0
]
+=
out_ptr
[
i
]
>
0
?
0
:
x_ptr
[
i
]
*
dout_ptr
[
i
];
}
}
}
// TODO(Guanzhong): add GPU kernels
}
};
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
c1083765
...
...
@@ -112,6 +112,7 @@ __all__ = [
'log'
,
'crop'
,
'rank_loss'
,
'prelu'
,
'flatten'
,
]
...
...
@@ -5364,6 +5365,59 @@ def rank_loss(label, left, right, name=None):
return
out
def
prelu
(
x
,
mode
,
param_attr
=
None
,
name
=
None
):
"""
Equation:
y = \max(0, x) + alpha \min(0, x)
Args:
x (Variable): The input tensor.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha).
mode (string): The mode for weight sharing
all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
Examples:
.. code-block:: python
x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
mode = 'channel'
output = fluid.layers.prelu(x,mode)
"""
helper
=
LayerHelper
(
'prelu'
,
**
locals
())
if
mode
not
in
[
'all'
,
'channel'
,
'element'
]:
raise
ValueError
(
'mode should be one of all, channel, element.'
)
alpha_shape
=
[
1
]
if
mode
==
'channel'
:
alpha_shape
=
[
1
,
x
.
shape
[
1
],
1
,
1
]
elif
mode
==
'element'
:
alpha_shape
=
x
.
shape
dtype
=
helper
.
input_dtype
(
input_param_name
=
'x'
)
alpha
=
helper
.
create_parameter
(
attr
=
param_attr
,
shape
=
alpha_shape
,
dtype
=
'float32'
,
is_bias
=
False
,
default_initializer
=
Constant
(
1.0
))
out
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"prelu"
,
inputs
=
{
"X"
:
x
,
'Alpha'
:
alpha
},
attrs
=
{
"mode"
:
mode
},
outputs
=
{
"Out"
:
out
})
return
out
def
flatten
(
x
,
axis
=
1
,
name
=
None
):
"""
**Flatten layer**
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
c1083765
...
...
@@ -21,6 +21,7 @@ import paddle.fluid.nets as nets
from
paddle.fluid.framework
import
Program
,
program_guard
,
default_main_program
from
paddle.fluid.param_attr
import
ParamAttr
import
decorators
from
paddle.fluid.initializer
import
Constant
class
TestBook
(
unittest
.
TestCase
):
...
...
@@ -485,6 +486,20 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
def
test_prelu
(
self
):
program
=
Program
()
with
program_guard
(
program
):
input
=
layers
.
data
(
name
=
"input"
,
shape
=
[
5
,
200
,
100
,
100
],
dtype
=
"float32"
)
mode
=
'channel'
out
=
layers
.
prelu
(
input
,
mode
,
param_attr
=
ParamAttr
(
initializer
=
Constant
(
1.0
)),
name
=
'prelu'
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_prelu_op.py
浏览文件 @
c1083765
...
...
@@ -20,30 +20,58 @@ from op_test import OpTest
class
PReluTest
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"prelu"
x_np
=
np
.
random
.
normal
(
size
=
(
10
,
10
)).
astype
(
"float32"
)
for
pos
,
val
in
np
.
ndenumerate
(
x_np
):
# Since zero point in prelu is not differentiable, avoid randomize
# zero.
while
abs
(
val
)
<
1e-3
:
x_np
[
pos
]
=
np
.
random
.
normal
()
val
=
x_np
[
pos
]
x_np_sign
=
np
.
sign
(
x_np
)
x_np
=
x_np_sign
*
np
.
maximum
(
x_np
,
.
005
)
alpha_np
=
np
.
array
([.
1
],
dtype
=
"float32"
)
self
.
inputs
=
{
'X'
:
x_np
,
'Alpha'
:
alpha_np
}
self
.
initTestCase
()
x_np
=
np
.
random
.
normal
(
size
=
(
3
,
5
,
5
,
10
)).
astype
(
"float32"
)
# Since zero point in prelu is not differentiable, avoid randomize
# zero.
x_np
[
np
.
abs
(
x_np
)
<
0.005
]
=
0.02
if
self
.
attrs
==
{
'mode'
:
"all"
}:
alpha_np
=
np
.
random
.
rand
(
1
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x_np
,
'Alpha'
:
alpha_np
}
elif
self
.
attrs
==
{
'mode'
:
"channel"
}:
alpha_np
=
np
.
random
.
rand
(
1
,
x_np
.
shape
[
1
],
1
,
1
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x_np
,
'Alpha'
:
alpha_np
}
else
:
alpha_np
=
np
.
random
.
rand
(
*
x_np
.
shape
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x_np
,
'Alpha'
:
alpha_np
}
out_np
=
np
.
maximum
(
self
.
inputs
[
'X'
],
0.
)
out_np
=
out_np
+
np
.
minimum
(
self
.
inputs
[
'X'
],
0.
)
*
self
.
inputs
[
'Alpha'
]
assert
out_np
is
not
self
.
inputs
[
'X'
]
self
.
outputs
=
{
'Out'
:
out_np
}
def
initTestCase
(
self
):
self
.
attrs
=
{
'mode'
:
"channel"
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
self
.
check_grad
([
'X'
,
'Alpha'
],
'Out'
)
def
test_check_grad_ignore_x
(
self
):
self
.
check_grad
([
'Alpha'
],
'Out'
,
no_grad_set
=
set
(
'X'
))
def
test_check_grad_ignore_alpha
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
no_grad_set
=
set
(
'Alpha'
))
class
TestCase1
(
PReluTest
):
def
initTestCase
(
self
):
self
.
attrs
=
{
'mode'
:
"all"
}
class
TestCase2
(
PReluTest
):
def
initTestCase
(
self
):
self
.
attrs
=
{
'mode'
:
"channel"
}
class
TestCase3
(
PReluTest
):
def
initTestCase
(
self
):
self
.
attrs
=
{
'mode'
:
"element"
}
if
__name__
==
"__main__"
:
...
...
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