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03799bdb
编写于
8月 10, 2017
作者:
H
hedaoyuan
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电子邮件补丁
差异文件
Refine the unit test of convolution function.
上级
7c8e5c3b
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
333 addition
and
0 deletion
+333
-0
paddle/function/CMakeLists.txt
paddle/function/CMakeLists.txt
+2
-0
paddle/function/ConvOpTest.h
paddle/function/ConvOpTest.h
+244
-0
paddle/function/DepthwiseConvOpTest.cpp
paddle/function/DepthwiseConvOpTest.cpp
+37
-0
paddle/function/GemmConvOpTest.cpp
paddle/function/GemmConvOpTest.cpp
+50
-0
未找到文件。
paddle/function/CMakeLists.txt
浏览文件 @
03799bdb
...
...
@@ -38,10 +38,12 @@ if(WITH_GPU)
add_simple_unittest
(
RowConvOpTest
)
add_simple_unittest
(
BlockExpandOpTest
)
add_simple_unittest
(
CropOpTest
)
add_simple_unittest
(
DepthwiseConvOpTest
)
endif
()
add_simple_unittest
(
ConvOpTest
)
add_simple_unittest
(
Im2ColTest
)
add_simple_unittest
(
GemmConvOpTest
)
endif
()
add_style_check_target
(
paddle_function
${
h_files
}
)
...
...
paddle/function/ConvOpTest.h
0 → 100644
浏览文件 @
03799bdb
/* Copyright (c) 2016 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 "FunctionTest.h"
namespace
paddle
{
template
<
DeviceType
DType1
,
DeviceType
DType2
>
void
forward
(
Compare2Function
<
DType1
,
DType2
>&
test
,
const
TensorShape
&
input
,
const
TensorShape
&
filter
,
const
TensorShape
&
output
)
{
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
input
));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
filter
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
output
));
test
.
run
();
}
template
<
DeviceType
DType1
,
DeviceType
DType2
>
void
backward_input
(
Compare2Function
<
DType1
,
DType2
>&
test
,
const
TensorShape
&
input
,
const
TensorShape
&
filter
,
const
TensorShape
&
output
)
{
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
output
));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
filter
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
input
),
ADD_TO
);
test
.
run
();
}
template
<
DeviceType
DType1
,
DeviceType
DType2
>
void
backward_filter
(
Compare2Function
<
DType1
,
DType2
>&
test
,
const
TensorShape
&
input
,
const
TensorShape
&
filter
,
const
TensorShape
&
output
)
{
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
output
));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
input
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
filter
),
ADD_TO
);
test
.
run
();
}
template
<
DeviceType
DType1
,
DeviceType
DType2
>
using
Function
=
void
(
*
)(
Compare2Function
<
DType1
,
DType2
>&
test
,
const
TensorShape
&
input
,
const
TensorShape
&
filter
,
const
TensorShape
&
output
);
/**
* \brief A basic convolution function test interface.
*
* \param conv1 type name of convolution function 1.
* \param conv2 type name of convolution function 2.
* \param function test function, can be one of the forward, backward_input
* backward_filter function.
* Example:
* 1. Compare GemmConv's CPU and GPU implementation:
* Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
* "GemmConv-CPU", "GemmConv-GPU", forward);
*/
template
<
DeviceType
DType1
,
DeviceType
DType2
>
void
Convolution
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
Function
<
DType1
,
DType2
>
function
)
{
for
(
size_t
batchSize
:
{
1
,
5
})
{
for
(
size_t
inputSize
:
{
7
,
14
,
31
})
{
for
(
size_t
filterSize
:
{
1
,
3
,
5
})
{
for
(
size_t
inputChannels
:
{
3
,
16
})
{
for
(
size_t
outputChannels
:
{
3
,
16
})
{
if
(
outputChannels
<
inputChannels
)
continue
;
for
(
size_t
stride
:
{
1
,
2
})
{
for
(
size_t
padding
:
{
0
,
1
})
{
if
(
padding
>=
filterSize
)
break
;
size_t
outputSize
=
(
inputSize
-
filterSize
+
2
*
padding
+
stride
)
/
stride
;
VLOG
(
3
)
<<
" batchSize="
<<
batchSize
<<
" inputChannels="
<<
inputChannels
<<
" inputHeight="
<<
inputSize
<<
" inputWidth="
<<
inputSize
<<
" outputChannels="
<<
outputChannels
<<
" filterHeight="
<<
filterSize
<<
" filterWidth="
<<
filterSize
<<
" outputHeight="
<<
outputSize
<<
" outputWidth="
<<
outputSize
<<
" stride="
<<
stride
<<
" padding="
<<
padding
;
std
::
vector
<
size_t
>
paddings
=
{
padding
,
padding
};
std
::
vector
<
size_t
>
strides
=
{
stride
,
stride
};
Compare2Function
<
DType1
,
DType2
>
test
(
conv1
,
conv2
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
1
)
.
set
(
"algo"
,
"auto"
));
TensorShape
input
{
batchSize
,
inputChannels
,
inputSize
,
inputSize
};
TensorShape
filter
{
outputChannels
,
inputChannels
,
filterSize
,
filterSize
};
TensorShape
output
{
batchSize
,
outputChannels
,
outputSize
,
outputSize
};
function
(
test
,
input
,
filter
,
output
);
}
}
}
}
}
}
}
}
/**
* \brief A convolution function test interface for
* image height is not equal image width.
*/
template
<
DeviceType
DType1
,
DeviceType
DType2
>
void
Convolution2
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
Function
<
DType1
,
DType2
>
function
)
{
for
(
size_t
batchSize
:
{
4
})
{
for
(
size_t
inputHeight
:
{
7
,
31
})
{
for
(
size_t
inputWidth
:
{
10
,
54
})
{
for
(
size_t
filterHeight
:
{
1
,
5
})
{
for
(
size_t
filterWidth
:
{
3
,
7
})
{
for
(
size_t
inputChannels
:
{
7
})
{
for
(
size_t
outputChannels
:
{
7
})
{
size_t
stride
=
1
;
size_t
padding
=
0
;
size_t
outputHeight
=
(
inputHeight
-
filterHeight
+
2
*
padding
+
stride
)
/
stride
;
size_t
outputWidth
=
(
inputWidth
-
filterWidth
+
2
*
padding
+
stride
)
/
stride
;
VLOG
(
3
)
<<
" batchSize="
<<
batchSize
<<
" inputChannels="
<<
inputChannels
<<
" inputHeight="
<<
inputHeight
<<
" inputWidth="
<<
inputWidth
<<
" outputChannels="
<<
outputChannels
<<
" filterHeight="
<<
filterHeight
<<
" filterWidth="
<<
filterWidth
<<
" outputHeight="
<<
outputHeight
<<
" outputWidth="
<<
outputWidth
<<
" stride="
<<
stride
<<
" padding="
<<
padding
;
std
::
vector
<
size_t
>
paddings
=
{
padding
,
padding
};
std
::
vector
<
size_t
>
strides
=
{
stride
,
stride
};
Compare2Function
<
DType1
,
DType2
>
test
(
conv1
,
conv2
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
(
size_t
)
1
)
.
set
(
"algo"
,
"auto"
));
TensorShape
input
{
batchSize
,
inputChannels
,
inputHeight
,
inputWidth
};
TensorShape
filter
{
outputChannels
,
inputChannels
,
filterHeight
,
filterWidth
};
TensorShape
output
{
batchSize
,
outputChannels
,
outputHeight
,
outputWidth
};
function
(
test
,
input
,
filter
,
output
);
}
}
}
}
}
}
}
}
/**
* \brief A convolution function test interface for depthwise convolution.
*/
template
<
DeviceType
DType1
,
DeviceType
DType2
>
void
DepthwiseConvolution
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
Function
<
DType1
,
DType2
>
function
)
{
for
(
size_t
batchSize
:
{
1
,
32
})
{
for
(
size_t
inputSize
:
{
7
,
14
,
54
})
{
for
(
size_t
filterSize
:
{
3
,
4
})
{
for
(
size_t
inputChannels
:
{
32
})
{
for
(
size_t
outputChannels
:
{
32
,
64
})
{
for
(
size_t
stride
:
{
1
,
2
})
{
for
(
size_t
padding
:
{
0
,
1
})
{
size_t
outputSize
=
(
inputSize
-
filterSize
+
2
*
padding
+
stride
)
/
stride
;
VLOG
(
3
)
<<
" batchSize="
<<
batchSize
<<
" inputChannels="
<<
inputChannels
<<
" inputHeight="
<<
inputSize
<<
" inputWidth="
<<
inputSize
<<
" outputChannels="
<<
outputChannels
<<
" filterHeight="
<<
filterSize
<<
" filterWidth="
<<
filterSize
<<
" outputHeight="
<<
outputSize
<<
" outputWidth="
<<
outputSize
<<
" stride="
<<
stride
<<
" padding="
<<
padding
;
std
::
vector
<
size_t
>
paddings
=
{
padding
,
padding
};
std
::
vector
<
size_t
>
strides
=
{
stride
,
stride
};
size_t
groups
=
inputChannels
;
Compare2Function
<
DType1
,
DType2
>
test
(
conv1
,
conv2
,
FuncConfig
()
.
set
(
"paddings"
,
paddings
)
.
set
(
"strides"
,
strides
)
.
set
(
"groups"
,
groups
)
.
set
(
"algo"
,
"auto"
));
TensorShape
input
{
batchSize
,
inputChannels
,
inputSize
,
inputSize
};
TensorShape
filter
{
groups
,
outputChannels
/
groups
,
inputChannels
/
groups
,
filterSize
,
filterSize
};
TensorShape
output
{
batchSize
,
outputChannels
,
outputSize
,
outputSize
};
function
(
test
,
input
,
filter
,
output
);
}
}
}
}
}
}
}
}
}
// namespace paddle
paddle/function/DepthwiseConvOpTest.cpp
0 → 100644
浏览文件 @
03799bdb
/* Copyright (c) 2016 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 <gtest/gtest.h>
#include "ConvOpTest.h"
namespace
paddle
{
#ifndef PADDLE_ONLY_CPU
TEST
(
DepthwiseConv
,
Forward
)
{
DepthwiseConvolution
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConv-CPU"
,
"DepthwiseConv-GPU"
,
forward
);
}
TEST
(
DepthwiseConv
,
BackwardInput
)
{
DepthwiseConvolution
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConvGradInput-CPU"
,
"DepthwiseConvGradInput-GPU"
,
backward_input
);
}
TEST
(
DepthwiseConv
,
BackwardFilter
)
{
DepthwiseConvolution
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConvGradFilter-CPU"
,
"DepthwiseConvGradFilter-GPU"
,
backward_filter
);
}
#endif
}
// namespace paddle
paddle/function/GemmConvOpTest.cpp
0 → 100644
浏览文件 @
03799bdb
/* Copyright (c) 2016 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 <gtest/gtest.h>
#include "ConvOpTest.h"
namespace
paddle
{
TEST
(
GemmConv
,
NaiveConv
)
{
Convolution
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
(
"NaiveConv-CPU"
,
"GemmConv-CPU"
,
forward
);
Convolution2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
(
"NaiveConv-CPU"
,
"GemmConv-CPU"
,
forward
);
}
#ifndef PADDLE_ONLY_CPU
TEST
(
GemmConv
,
Forward
)
{
Convolution
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConv-CPU"
,
"GemmConv-GPU"
,
forward
);
Convolution2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConv-CPU"
,
"GemmConv-GPU"
,
forward
);
}
TEST
(
GemmConv
,
BackwardInput
)
{
Convolution
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
backward_input
);
Convolution2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
backward_input
);
}
TEST
(
GemmConv
,
BackwardFilter
)
{
Convolution
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
backward_filter
);
Convolution2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
(
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
backward_filter
);
}
#endif
}
// namespace paddle
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