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784e2184
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784e2184
编写于
7年前
作者:
H
hedaoyuan
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix the error of group convolution.
上级
7aac38c7
7 合并请求
!11636
[IMPORTANT] MKLDNN layout: Support for sum operator
,
!8482
Release/0.11.0
,
!8190
Release/0.11.0
,
!8189
Release/0.11.0
,
!6633
给线性回归的get-started代码加上了预测的示例~~
,
!4615
Feature/tensor array add python binding
,
!2282
Add convolution Function
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
67 addition
and
28 deletion
+67
-28
paddle/function/ConvOp.h
paddle/function/ConvOp.h
+40
-8
paddle/function/ConvOpTest.cpp
paddle/function/ConvOpTest.cpp
+1
-1
paddle/function/GemmConvOp.cpp
paddle/function/GemmConvOp.cpp
+18
-15
paddle/gserver/layers/ExpandConvLayer.cpp
paddle/gserver/layers/ExpandConvLayer.cpp
+8
-4
未找到文件。
paddle/function/ConvOp.h
浏览文件 @
784e2184
...
...
@@ -46,8 +46,13 @@ namespace paddle {
* are all NCHW format. Where N is batch size, C is the number of channels,
* H and W is the height and width of image or image gradient.
*
* 2. The format of the filter data is MCHW, where M is the number of
* output image channels, C is the number of input image channels,
* 2. The format of the filter data is MCHW, where M is the number of output
* image channels, C is the number of input image channels,
* H and W is height and width of filter.
*
* If groups is greater than 1, the filter's data format should be GMCHW,
* where G is the groups, and G * M is the number of output image channels,
* G * C is the number of input image channels,
* H and W is height and width of filter.
*/
class
ConvFunctionBase
:
public
FunctionBase
{
...
...
@@ -73,20 +78,47 @@ public:
const
TensorShape
&
output
)
{
// inputs and outputs arguments should be 4-dimensional.
CHECK_EQ
(
input
.
ndims
(),
(
size_t
)
4
);
CHECK_EQ
(
filter
.
ndims
(),
(
size_t
)
4
);
CHECK_EQ
(
output
.
ndims
(),
(
size_t
)
4
);
// The batchSize of the input needs to be equal to
// the batchSize of the output.
CHECK_EQ
(
input
[
0
],
output
[
0
]);
// The input and output channel dimensions are the second and first
// dimensions of the filter shape.
CHECK_EQ
(
input
[
1
]
/
groups_
,
filter
[
1
]);
CHECK_EQ
(
output
[
1
],
filter
[
0
]);
if
(
filter
.
ndims
()
==
(
size_t
)
4
)
{
// If the filter's dimension is 4, groups convolution is not supported.
CHECK_EQ
(
groups_
,
(
size_t
)
1
);
// The input and output channel dimensions are the second and first
// dimensions of the filter shape.
CHECK_EQ
(
input
[
1
],
filter
[
1
]);
CHECK_EQ
(
output
[
1
],
filter
[
0
]);
}
else
{
// filter argument should be 5-dimensional.
CHECK_EQ
(
filter
.
ndims
(),
(
size_t
)
5
);
// The first dimension of the filter is the size of the group
CHECK_EQ
(
filter
[
0
],
groups_
);
// The input and output channel dimensions are the third and second
// dimensions of the filter shape.
CHECK_EQ
(
input
[
1
],
filter
[
2
]
*
groups_
);
CHECK_EQ
(
output
[
1
],
filter
[
1
]
*
groups_
);
}
}
protected:
size_t
getFilterHeight
(
const
TensorShape
&
filter
)
const
{
if
(
filter
.
ndims
()
==
5
)
{
return
filter
[
3
];
}
else
{
return
filter
[
2
];
}
}
size_t
getFilterWidth
(
const
TensorShape
&
filter
)
const
{
if
(
filter
.
ndims
()
==
5
)
{
return
filter
[
4
];
}
else
{
return
filter
[
3
];
}
}
std
::
vector
<
size_t
>
strides_
;
std
::
vector
<
size_t
>
paddings_
;
...
...
This diff is collapsed.
Click to expand it.
paddle/function/ConvOpTest.cpp
浏览文件 @
784e2184
...
...
@@ -80,7 +80,7 @@ public:
}
else
if
(
type
==
BACKWARD_INPUT_TEST
)
{
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
output
));
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
filter
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
input
));
test
.
addOutputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
input
)
,
ADD_TO
);
test
.
run
();
}
else
if
(
type
==
BACKWARD_FILTER_TEST
)
{
test
.
addInputs
(
BufferArg
(
VALUE_TYPE_FLOAT
,
output
));
...
...
This diff is collapsed.
Click to expand it.
paddle/function/GemmConvOp.cpp
浏览文件 @
784e2184
...
...
@@ -134,15 +134,15 @@ public:
beta
=
0.0
;
}
size_t
batchSize
=
input
s
[
0
].
shape
()
[
0
];
size_t
inputChannels
=
input
s
[
0
].
shape
()
[
1
];
size_t
inputHeight
=
input
s
[
0
].
shape
()
[
2
];
size_t
inputWidth
=
input
s
[
0
].
shape
()
[
3
];
size_t
filterHeight
=
inputs
[
1
].
shape
()[
2
]
;
size_t
filterWidth
=
inputs
[
1
].
shape
()[
3
]
;
size_t
outputChannels
=
output
s
[
0
].
shape
()
[
1
];
size_t
outputHeight
=
output
s
[
0
].
shape
()
[
2
];
size_t
outputWidth
=
output
s
[
0
].
shape
()
[
3
];
size_t
batchSize
=
input
[
0
];
size_t
inputChannels
=
input
[
1
];
size_t
inputHeight
=
input
[
2
];
size_t
inputWidth
=
input
[
3
];
size_t
filterHeight
=
getFilterHeight
(
filter
)
;
size_t
filterWidth
=
getFilterWidth
(
filter
)
;
size_t
outputChannels
=
output
[
1
];
size_t
outputHeight
=
output
[
2
];
size_t
outputWidth
=
output
[
3
];
real
*
inputData
=
inputs
[
0
].
data
<
real
>
();
real
*
filterData
=
inputs
[
1
].
data
<
real
>
();
...
...
@@ -158,7 +158,8 @@ public:
size_t
inputOffset
=
(
inputChannels
/
groups_
)
*
inputHeight
*
inputWidth
;
size_t
outputOffset
=
(
outputChannels
/
groups_
)
*
outputHeight
*
outputWidth
;
size_t
filterOffset
=
inputs
[
1
].
shape
().
getElements
()
/
groups_
;
size_t
filterOffset
=
filter
.
getElements
()
/
groups_
;
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
for
(
size_t
g
=
0
;
g
<
groups_
;
g
++
)
{
im2col
(
inputData
+
g
*
inputOffset
,
...
...
@@ -211,7 +212,9 @@ public:
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
numInputs_
,
inputs
.
size
());
CHECK_EQ
(
numOutputs_
,
outputs
.
size
());
// CHECK_EQ(outputs[0].getArgType(), ADD_TO);
// Since the implementation of Col2ImFunctor is ADD_TO,
// this function only supports ADD_TO mode.
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ADD_TO
);
const
TensorShape
&
output
=
inputs
[
0
].
shape
();
const
TensorShape
&
filter
=
inputs
[
1
].
shape
();
const
TensorShape
&
input
=
outputs
[
0
].
shape
();
...
...
@@ -221,8 +224,8 @@ public:
size_t
inputChannels
=
input
[
1
];
size_t
inputHeight
=
input
[
2
];
size_t
inputWidth
=
input
[
3
];
size_t
filterHeight
=
filter
[
2
]
;
size_t
filterWidth
=
filter
[
3
]
;
size_t
filterHeight
=
getFilterHeight
(
filter
)
;
size_t
filterWidth
=
getFilterWidth
(
filter
)
;
size_t
outputChannels
=
output
[
1
];
size_t
outputHeight
=
output
[
2
];
size_t
outputWidth
=
output
[
3
];
...
...
@@ -311,8 +314,8 @@ public:
size_t
inputChannels
=
input
[
1
];
size_t
inputHeight
=
input
[
2
];
size_t
inputWidth
=
input
[
3
];
size_t
filterHeight
=
filter
[
2
]
;
size_t
filterWidth
=
filter
[
3
]
;
size_t
filterHeight
=
getFilterHeight
(
filter
)
;
size_t
filterWidth
=
getFilterWidth
(
filter
)
;
size_t
outputChannels
=
output
[
1
];
size_t
outputHeight
=
output
[
2
];
size_t
outputWidth
=
output
[
3
];
...
...
This diff is collapsed.
Click to expand it.
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @
784e2184
...
...
@@ -80,8 +80,11 @@ void ExpandConvLayer::forward(PassType passType) {
(
size_t
)
imgSizeH_
[
i
],
(
size_t
)
imgSizeW_
[
i
]});
filterShape_
[
i
]
=
TensorShape
({
!
isDeconv_
?
(
size_t
)
numFilters_
:
(
size_t
)
channels_
[
i
],
!
isDeconv_
?
(
size_t
)
channels_
[
i
]
:
(
size_t
)
numFilters_
,
TensorShape
({(
size_t
)
groups_
[
i
],
!
isDeconv_
?
(
size_t
)
numFilters_
/
groups_
[
i
]
:
(
size_t
)
channels_
[
i
]
/
groups_
[
i
],
!
isDeconv_
?
(
size_t
)
channels_
[
i
]
/
groups_
[
i
]
:
(
size_t
)
numFilters_
/
groups_
[
i
],
(
size_t
)
filterSizeY_
[
i
],
(
size_t
)
filterSize_
[
i
]});
outputShape_
[
i
]
=
TensorShape
({(
size_t
)
batchSize
,
...
...
@@ -96,8 +99,9 @@ void ExpandConvLayer::forward(PassType passType) {
BufferArgs
outputs
;
inputs
.
addArg
(
*
getInputValue
(
i
),
inputShape_
[
i
]);
inputs
.
addArg
(
*
weights_
[
i
]
->
getW
(),
filterShape_
[
i
]);
outputs
.
addArg
(
*
getOutputValue
(),
outputShape_
[
i
],
i
==
0
?
ASSIGN_TO
:
ADD_TO
);
outputs
.
addArg
(
*
getOutputValue
(),
outputShape_
[
i
],
!
isDeconv_
&&
i
==
0
?
ASSIGN_TO
:
ADD_TO
);
forward_
[
i
]
->
calc
(
inputs
,
outputs
);
}
...
...
This diff is collapsed.
Click to expand it.
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