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a3ce6aa8
编写于
7月 10, 2017
作者:
X
xzl
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add depthwise conv test
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198164ad
变更
2
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Showing
2 changed file
with
209 addition
and
0 deletion
+209
-0
paddle/function/CMakeLists.txt
paddle/function/CMakeLists.txt
+1
-0
paddle/function/DepthwiseConvOpTest.cpp
paddle/function/DepthwiseConvOpTest.cpp
+208
-0
未找到文件。
paddle/function/CMakeLists.txt
浏览文件 @
a3ce6aa8
...
...
@@ -37,6 +37,7 @@ if(WITH_GPU)
add_simple_unittest
(
MulOpTest
)
add_simple_unittest
(
CosSimOpTest
)
add_simple_unittest
(
RowConvOpTest
)
add_simple_unittest
(
DepthwiseConvOpTest
)
endif
()
add_simple_unittest
(
ConvOpTest
)
...
...
paddle/function/DepthwiseConvOpTest.cpp
0 → 100644
浏览文件 @
a3ce6aa8
/* 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
DepthwiseConvolutionTest
{
public:
DepthwiseConvolutionTest
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
TestType
type
,
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
:
{
64
,
128
})
{
size_t
outputChannels
=
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
};
size_t
groups
=
inputChannels
;
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
{
inputChannels
,
1
,
1
,
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
));
test
.
run
();
}
}
}
}
}
}
}
}
};
// Mainly used to test cases where the height and width (input, filter)
// are not equal.
template
<
DeviceType
DType1
,
DeviceType
DType2
>
class
DepthwiseConvolutionTest2
{
public:
DepthwiseConvolutionTest2
(
const
std
::
string
&
conv1
,
const
std
::
string
&
conv2
,
TestType
type
,
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
:
{
32
})
{
size_t
outputChannels
=
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
};
size_t
groups
=
inputChannels
;
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
{
inputChannels
,
1
,
1
,
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
));
test
.
run
();
}
}
}
}
}
}
}
}
};
#ifndef PADDLE_ONLY_CPU
TEST
(
Forward
,
GEMM2
)
{
DepthwiseConvolutionTest
<
DEVICE_TYPE_GPU
,
DEVICE_TYPE_GPU
>
test
(
"DepthwiseConv-GPU"
,
"DepthwiseConv-GPU"
,
kForwardTest
);
DepthwiseConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"DepthwiseConv-GPU"
,
"DepthwiseConv-GPU"
,
kForwardTest
);
}
TEST
(
BackwardInput
,
GEMM
)
{
DepthwiseConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"DepthwiseConvGradInput-GPU"
,
"DepthwiseConvGradInput-GPU"
,
kBackwardInputTest
);
DepthwiseConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"DepthwiseConvGradInput-GPU"
,
"DepthwiseConvGradInput-GPU"
,
kBackwardInputTest
);
}
TEST
(
BackwardFilter
,
GEMM
)
{
DepthwiseConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"DepthwiseConvGradFilter-GPU"
,
"DepthwiseConvGradFilter-GPU"
,
kBackwardFilterTest
);
DepthwiseConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"DepthwiseConvGradFilter-GPU"
,
"DepthwiseConvGradFilter-GPU"
,
kBackwardFilterTest
);
}
#endif
}
// namespace paddle
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