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1d74d16c
编写于
8月 10, 2017
作者:
H
hedaoyuan
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Remove the useless code.
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03799bdb
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2 changed file
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307 deletion
+0
-307
paddle/function/CMakeLists.txt
paddle/function/CMakeLists.txt
+0
-1
paddle/function/ConvOpTest.cpp
paddle/function/ConvOpTest.cpp
+0
-306
未找到文件。
paddle/function/CMakeLists.txt
浏览文件 @
1d74d16c
...
...
@@ -41,7 +41,6 @@ if(WITH_GPU)
add_simple_unittest
(
DepthwiseConvOpTest
)
endif
()
add_simple_unittest
(
ConvOpTest
)
add_simple_unittest
(
Im2ColTest
)
add_simple_unittest
(
GemmConvOpTest
)
endif
()
...
...
paddle/function/ConvOpTest.cpp
已删除
100644 → 0
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 <memory>
#include "Function.h"
#include "FunctionTest.h"
namespace
paddle
{
enum
TestType
{
kForwardTest
=
0
,
kBackwardInputTest
=
1
,
kBackwardFilterTest
=
2
,
};
template
<
DeviceType
DType1
,
DeviceType
DType2
>
class
ConvolutionTest
{
public:
ConvolutionTest
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
TestType
type
,
bool
useGroups
=
true
,
std
::
string
algo
=
"auto"
)
{
for
(
size_t
batchSize
:
{
1
,
32
})
{
for
(
size_t
inputSize
:
{
7
,
14
,
54
})
{
for
(
size_t
filterSize
:
{
1
,
3
,
5
})
{
for
(
size_t
inputChannels
:
{
3
,
64
})
{
for
(
size_t
outputChannels
:
{
3
,
64
})
{
if
(
inputChannels
>
outputChannels
)
break
;
size_t
groups
;
if
(
!
useGroups
)
{
groups
=
1
;
}
else
{
if
(
outputChannels
%
inputChannels
!=
0
)
continue
;
groups
=
inputChannels
;
}
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"
,
groups
)
.
set
(
"algo"
,
algo
));
TensorShape
input
{
batchSize
,
inputChannels
,
inputSize
,
inputSize
};
TensorShape
filter
;
if
(
groups
>
1
)
filter
=
TensorShape
({
groups
,
outputChannels
/
groups
,
inputChannels
/
groups
,
filterSize
,
filterSize
});
else
filter
=
TensorShape
({
outputChannels
,
inputChannels
,
filterSize
,
filterSize
});
TensorShape
output
{
batchSize
,
outputChannels
,
outputSize
,
outputSize
};
if
(
type
==
kForwardTest
)
{
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
input
));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
filter
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
output
));
test
.
run
();
}
else
if
(
type
==
kBackwardInputTest
)
{
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
();
}
else
if
(
type
==
kBackwardFilterTest
)
{
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
();
}
}
}
}
}
}
}
}
}
};
// Mainly used to test cases where the height and width (input, filter)
// are not equal.
template
<
DeviceType
DType1
,
DeviceType
DType2
>
class
ConvolutionTest2
{
public:
ConvolutionTest2
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
TestType
type
,
bool
useGroups
=
true
,
std
::
string
algo
=
"auto"
)
{
for
(
size_t
batchSize
:
{
16
})
{
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
groups
;
if
(
!
useGroups
)
{
groups
=
1
;
}
else
{
if
(
outputChannels
%
inputChannels
!=
0
)
continue
;
groups
=
inputChannels
;
}
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"
,
groups
)
.
set
(
"algo"
,
algo
));
TensorShape
input
{
batchSize
,
inputChannels
,
inputHeight
,
inputWidth
};
TensorShape
filter
;
if
(
groups
>
1
)
filter
=
TensorShape
({
groups
,
outputChannels
/
groups
,
inputChannels
/
groups
,
filterHeight
,
filterWidth
});
else
filter
=
TensorShape
({
outputChannels
,
inputChannels
,
filterHeight
,
filterWidth
});
TensorShape
output
{
batchSize
,
outputChannels
,
outputHeight
,
outputWidth
};
if
(
type
==
kForwardTest
)
{
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
input
));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
filter
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
output
));
test
.
run
();
}
else
if
(
type
==
kBackwardInputTest
)
{
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
();
}
else
if
(
type
==
kBackwardFilterTest
)
{
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
();
}
}
}
}
}
}
}
}
}
};
// ======Start Convolution TEST======
TEST
(
Forward
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
test
(
"NaiveConv-CPU"
,
"GemmConv-CPU"
,
kForwardTest
,
false
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
test2
(
"NaiveConv-CPU"
,
"GemmConv-CPU"
,
kForwardTest
,
false
);
}
#ifndef PADDLE_ONLY_CPU
TEST
(
Forward
,
GEMM2
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConv-CPU"
,
"GemmConv-GPU"
,
kForwardTest
,
false
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConv-CPU"
,
"GemmConv-GPU"
,
kForwardTest
,
false
);
}
TEST
(
BackwardInput
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
kBackwardInputTest
,
false
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
kBackwardInputTest
,
false
);
}
TEST
(
BackwardFilter
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
kBackwardFilterTest
,
false
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
kBackwardFilterTest
,
false
);
}
#endif
// ======End Convolution TEST======
// ======Start DepthwiseConvolution TEST======
// TODO(zhaolong) The depthwise convolution cpu test will be added when the cpu
// version of depthwiseConv is implemented.
#ifndef PADDLE_ONLY_CPU
TEST
(
DepthwiseConvForward
,
GEMM2
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConv-CPU"
,
"DepthwiseConv-GPU"
,
kForwardTest
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConv-CPU"
,
"DepthwiseConv-GPU"
,
kForwardTest
);
}
TEST
(
DepthwiseConvBackwardInput
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConvGradInput-CPU"
,
"DepthwiseConvGradInput-GPU"
,
kBackwardInputTest
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConvGradInput-CPU"
,
"DepthwiseConvGradInput-GPU"
,
kBackwardInputTest
);
}
TEST
(
DepthwiseConvBackwardFilter
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConvGradFilter-CPU"
,
"DepthwiseConvGradFilter-GPU"
,
kBackwardFilterTest
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConvGradFilter-CPU"
,
"DepthwiseConvGradFilter-GPU"
,
kBackwardFilterTest
);
}
#endif
// ======End DepthwiseConvolution TEST======
}
// namespace paddle
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