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体验新版 GitCode,发现更多精彩内容 >>
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8ac11787
编写于
11月 06, 2017
作者:
C
chengduoZH
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电子邮件补丁
差异文件
fix doc
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17248153
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1
隐藏空白更改
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Showing
1 changed file
with
40 addition
and
36 deletion
+40
-36
paddle/operators/conv_op.cc
paddle/operators/conv_op.cc
+40
-36
未找到文件。
paddle/operators/conv_op.cc
浏览文件 @
8ac11787
...
...
@@ -64,42 +64,41 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Input"
,
"(Tensor), the input tensor of convolution operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of image."
);
"(Tensor) The input tensor of convolution operator. "
"The format of input tensor is NCHW, where N is batch size, C is the "
"number of channels, H is the height of the feature, "
"and W is the width of the feature."
);
AddInput
(
"Filter"
,
"(Tensor)
, the filter tensor of convolution operator.
"
"(Tensor)
The filter tensor of convolution operator.
"
"The format of the filter tensor 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 the groups attribute is greater than 1, C equal the number of "
"H
is the height of the filter, and W is the width of the
filter. "
"If the groups attribute is greater than 1, C equal
s
the number of "
"input image channels divided by the groups."
);
AddOutput
(
"Output"
,
"(Tensor), the output tensor of convolution operator."
"The format of output tensor is also NCHW. Where N is batch size, "
"C is the "
"number of channels, H and W is the height and width of image."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector default:{1, 1}), strides of convolution operator."
)
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides of convolution operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector default:{0, 0}), paddings of convolution operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings of convolution operator."
)
.
SetDefault
({
0
,
0
});
AddAttr
<
int
>
(
"groups"
,
"(int
, default:1),
group size of convolution operator. "
"
Refer to grouped convolution in Alex Krizhevsky's
paper: "
"when group=2, the first half of the filters
are
only connected to the "
"first half of the input channels,
and the second half only connected
"
"
to the second half
."
)
"(int
default:1), the
group size of convolution operator. "
"
According to grouped convolution in Alex Krizhevsky's Deep CNN
paper: "
"when group=2, the first half of the filters
is
only connected to the "
"first half of the input channels,
while the second half of the filters
"
"
is only connected to the second half of the input channels
."
)
.
SetDefault
(
1
);
AddComment
(
R"DOC(
Convolution Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
size, C is the number of channels, H
and W is the height and
width of
feature. Parameters(ksize, strides, paddings) are two elements.
size, C is the number of channels, H
is the height of the feature, and W is
the width of the
feature. Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
...
...
@@ -120,19 +119,21 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Input"
,
"(Tensor)
, t
he input tensor of convolution operator. "
"(Tensor)
T
he input tensor of convolution operator. "
"The format of input tensor is NCDHW. Where N is batch size, C is the "
"number of channels, D, H and W is the depth, height and width of "
"image."
);
"number of channels, D is the depth of the feature, H is the height of "
"the feature, "
"and W is the width of the feature."
);
AddInput
(
"Filter"
,
"(Tensor)
, the filter tensor of convolution operator.
"
"(Tensor)
The filter tensor of convolution operator.
"
"The format of the filter tensor is MCDHW, where M is the number of "
"output image channels, C is the number of input image channels, "
"D, H and W is depth, height and width of filter. "
"If the groups attribute is greater than 1, C equal the number of "
"D is the depth of the filter, H is the height of the filter, and W "
"is the width of the filter."
"If the groups attribute is greater than 1, C equals the number of "
"input image channels divided by the groups."
);
AddOutput
(
"Output"
,
"(Tensor)
, t
he output tensor of convolution operator."
"(Tensor)
T
he output tensor of convolution operator."
"The format of output tensor is also NCDHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
...
...
@@ -144,20 +145,23 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
.
SetDefault
({
0
,
0
,
0
});
AddAttr
<
int
>
(
"groups"
,
"(int
, default:1)
the group size of convolution operator. "
"
Refer to grouped convolution in Alex Krizhevsky's
paper: "
"when group=2, the first half of the filters
are
only connected to the "
"first half of the input channels,
and the second half only connected
"
"
to the second half
."
)
"(int
default:1),
the group size of convolution operator. "
"
According to grouped convolution in Alex Krizhevsky's Deep CNN
paper: "
"when group=2, the first half of the filters
is
only connected to the "
"first half of the input channels,
while the second half of the filters
"
"
is only connected to the second half of the input channels
."
)
.
SetDefault
(
1
);
AddComment
(
R"DOC(
Convolution3D Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCDHW format. Where N is batch
size, C is the number of channels,
D, H and W is the depth, height and
width of feature. Parameters(ksize, strides, paddings) are three elements.
These three elements represent depth, height and width, respectively.
size, C is the number of channels,
D is the depth of the feature, H is the height of
the feature, and W is the width of the feature. Parameters(ksize, strides, paddings)
are three elements.
These three elements represent depth, height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
...
...
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