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fd4b1136
编写于
7月 12, 2017
作者:
X
xzl
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
move DepthwiseConvOpTest.cpp to ConvOpTest.cpp
上级
e92f0021
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
194 addition
and
209 deletion
+194
-209
paddle/function/CMakeLists.txt
paddle/function/CMakeLists.txt
+0
-1
paddle/function/ConvOpTest.cpp
paddle/function/ConvOpTest.cpp
+194
-0
paddle/function/DepthwiseConvOpTest.cpp
paddle/function/DepthwiseConvOpTest.cpp
+0
-208
未找到文件。
paddle/function/CMakeLists.txt
浏览文件 @
fd4b1136
...
@@ -37,7 +37,6 @@ if(WITH_GPU)
...
@@ -37,7 +37,6 @@ if(WITH_GPU)
add_simple_unittest
(
MulOpTest
)
add_simple_unittest
(
MulOpTest
)
add_simple_unittest
(
CosSimOpTest
)
add_simple_unittest
(
CosSimOpTest
)
add_simple_unittest
(
RowConvOpTest
)
add_simple_unittest
(
RowConvOpTest
)
add_simple_unittest
(
DepthwiseConvOpTest
)
endif
()
endif
()
add_simple_unittest
(
ConvOpTest
)
add_simple_unittest
(
ConvOpTest
)
...
...
paddle/function/ConvOpTest.cpp
浏览文件 @
fd4b1136
...
@@ -177,6 +177,156 @@ public:
...
@@ -177,6 +177,156 @@ public:
}
}
};
};
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
();
}
}
}
}
}
}
}
}
};
// ======Start Convolution TEST======
TEST
(
Forward
,
GEMM
)
{
TEST
(
Forward
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
test
(
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
test
(
"NaiveConv-CPU"
,
"GemmConv-CPU"
,
kForwardTest
);
"NaiveConv-CPU"
,
"GemmConv-CPU"
,
kForwardTest
);
...
@@ -206,5 +356,49 @@ TEST(BackwardFilter, GEMM) {
...
@@ -206,5 +356,49 @@ TEST(BackwardFilter, GEMM) {
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
kBackwardFilterTest
);
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
kBackwardFilterTest
);
}
}
#endif
#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
,
GEMM
)
{
DepthwiseConvolutionTest
<
DEVICE_TYPE_GPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConv-GPU"
,
"DepthwiseConv-GPU"
,
kForwardTest
);
DepthwiseConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConv-GPU"
,
"DepthwiseConv-GPU"
,
kForwardTest
);
}
TEST
(
DepthwiseConvForward
,
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
(
DepthwiseConvBackwardInput
,
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
(
DepthwiseConvBackwardFilter
,
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
// ======End DepthwiseConvolution TEST======
}
// namespace paddle
}
// namespace paddle
paddle/function/DepthwiseConvOpTest.cpp
已删除
100644 → 0
浏览文件 @
e92f0021
/* 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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