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248149f4
编写于
7月 21, 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
add depthwiseconv test and fix the little bug of the convOpTest
上级
5b07d4e0
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
202 addition
and
147 deletion
+202
-147
paddle/function/ConvOpTest.cpp
paddle/function/ConvOpTest.cpp
+202
-147
未找到文件。
paddle/function/ConvOpTest.cpp
浏览文件 @
248149f4
...
...
@@ -38,76 +38,76 @@ public:
for
(
size_t
filterSize
:
{
1
,
3
,
5
})
{
for
(
size_t
inputChannels
:
{
3
,
64
})
{
for
(
size_t
outputChannels
:
{
3
,
64
})
{
for
(
size_t
groups
:
{
1
,
3
,
64
})
{
if
(
inputChannels
>
outputChannels
)
break
;
if
(
groups
!=
1
&&
(
inputChannels
!=
groups
||
outputChannels
%
groups
!=
0
))
continue
;
if
(
!
useGroups
)
groups
=
1
;
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="
<<
output
Size
<<
" 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"
,
stride
s
)
.
set
(
"groups"
,
group
s
)
.
set
(
"algo"
,
algo
));
TensorShape
input
{
batchSize
,
inputChannels
,
inputSize
,
inputSize
};
TensorShape
filter
;
if
(
groups
>
1
)
filter
=
TensorShape
({
groups
,
outputChannels
/
groups
,
in
putChannels
/
groups
,
filterSize
,
filterSize
});
else
filter
=
TensorShape
({
outputChannels
,
in
putChannels
,
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
(
);
}
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="
<<
filter
Size
<<
" 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"
,
padding
s
)
.
set
(
"strides"
,
stride
s
)
.
set
(
"groups"
,
groups
)
.
set
(
"algo"
,
algo
));
TensorShape
input
{
batchSize
,
inputChannels
,
inputSize
,
inputSize
};
TensorShape
filter
;
if
(
groups
>
1
)
filter
=
TensorShape
({
groups
,
out
putChannels
/
groups
,
inputChannels
/
groups
,
filterSize
,
filterSize
});
else
filter
=
TensorShape
({
out
putChannels
,
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
();
}
}
}
...
...
@@ -136,77 +136,78 @@ public:
for
(
size_t
filterWidth
:
{
3
,
7
})
{
for
(
size_t
inputChannels
:
{
7
})
{
for
(
size_t
outputChannels
:
{
7
})
{
for
(
size_t
groups
:
{
1
,
7
})
{
if
(
groups
!=
1
&&
(
inputChannels
!=
groups
||
outputChannels
%
groups
!=
0
))
continue
;
if
(
!
useGroups
)
groups
=
1
;
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
));
test
.
run
();
}
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
();
}
}
}
...
...
@@ -218,6 +219,8 @@ public:
}
};
// ======Start Convolution TEST======
TEST
(
Forward
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_CPU
>
test
(
"NaiveConv-CPU"
,
"GemmConv-CPU"
,
kForwardTest
,
false
);
...
...
@@ -228,24 +231,76 @@ TEST(Forward, GEMM) {
#ifndef PADDLE_ONLY_CPU
TEST
(
Forward
,
GEMM2
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConv-CPU"
,
"GemmConv-GPU"
,
kForwardTest
);
"GemmConv-CPU"
,
"GemmConv-GPU"
,
kForwardTest
,
false
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConv-CPU"
,
"GemmConv-GPU"
,
kForwardTest
);
"GemmConv-CPU"
,
"GemmConv-GPU"
,
kForwardTest
,
false
);
}
TEST
(
BackwardInput
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
kBackwardInputTest
);
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
kBackwardInputTest
,
false
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
kBackwardInputTest
);
"GemmConvGradInput-CPU"
,
"GemmConvGradInput-GPU"
,
kBackwardInputTest
,
false
);
}
TEST
(
BackwardFilter
,
GEMM
)
{
ConvolutionTest
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test
(
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
kBackwardFilterTest
);
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
kBackwardFilterTest
,
false
);
ConvolutionTest2
<
DEVICE_TYPE_CPU
,
DEVICE_TYPE_GPU
>
test2
(
"GemmConvGradFilter-CPU"
,
"GemmConvGradFilter-GPU"
,
kBackwardFilterTest
);
"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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