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体验新版 GitCode,发现更多精彩内容 >>
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17248153
编写于
10月 30, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix code format and doc
上级
9ee8a0d0
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
40 addition
and
22 deletion
+40
-22
paddle/operators/conv_op.cc
paddle/operators/conv_op.cc
+31
-10
paddle/operators/conv_op.h
paddle/operators/conv_op.h
+6
-12
python/paddle/v2/framework/tests/test_conv2d_op.py
python/paddle/v2/framework/tests/test_conv2d_op.py
+3
-0
未找到文件。
paddle/operators/conv_op.cc
浏览文件 @
17248153
...
...
@@ -33,6 +33,8 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
int
input_channels
=
in_dims
[
1
];
int
output_channels
=
filter_dims
[
0
];
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
"Conv intput should be 4-D or 5-D tensor."
);
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
filter_dims
.
size
(),
"Conv input dimension and filter dimension should be the same."
);
...
...
@@ -62,26 +64,30 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Input"
,
"
T
he input tensor of convolution operator. "
"
(Tensor), t
he 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."
);
AddInput
(
"Filter"
,
"
T
he filter tensor of convolution operator."
"
(Tensor), t
he 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 "
"input image channels divided by the groups."
);
AddOutput
(
"Output"
,
"The output tensor of convolution operator."
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides of convolution operator."
)
"(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."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings of convolution operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector default:{0, 0}), paddings of convolution operator."
)
.
SetDefault
({
0
,
0
});
AddAttr
<
int
>
(
"groups"
,
"group size of convolution operator. "
"
(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 "
...
...
@@ -91,6 +97,21 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
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.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
Input shape: (N, C_in, H_in, W_in)
Filter shape: (C_out, C_in, H_f, W_f)
Output:
Output shape: (N, C_out, H_out, W_out)
where
H_out = (H_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
W_out = (W_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
)DOC"
);
}
...
...
@@ -115,15 +136,15 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
"The format of output tensor is also NCDHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector, default
{0,0,
0}), the strides of convolution operator."
)
"(vector, default
:{0, 0,
0}), the strides of convolution operator."
)
.
SetDefault
({
1
,
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector, default
{0,0,
0}), the paddings of convolution operator."
)
"(vector, default
:{0, 0,
0}), the paddings of convolution operator."
)
.
SetDefault
({
0
,
0
,
0
});
AddAttr
<
int
>
(
"groups"
,
"(int, default
1) the group size of convolution operator. "
"(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 "
...
...
paddle/operators/conv_op.h
浏览文件 @
17248153
...
...
@@ -85,9 +85,7 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
int
output_height
=
output
->
dims
()[
2
];
int
output_width
=
output
->
dims
()[
3
];
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_height
,
filter_width
,
output_height
,
output_width
};
...
...
@@ -162,12 +160,8 @@ class GemmConvGrad2DKernel : public framework::OpKernel<T> {
int
output_height
=
output_grad
->
dims
()[
2
];
int
output_width
=
output_grad
->
dims
()[
3
];
paddle
::
operators
::
math
::
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col and col2im calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_height
,
filter_width
,
output_height
,
output_width
};
...
...
@@ -283,7 +277,7 @@ class GemmConv3DKernel : public framework::OpKernel<T> {
int
output_height
=
output
->
dims
()[
3
];
int
output_width
=
output
->
dims
()[
4
];
paddle
::
operators
::
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
// use col_shape in the vol2col calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_depth
,
...
...
@@ -369,8 +363,8 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
int
output_height
=
output_grad
->
dims
()[
3
];
int
output_width
=
output_grad
->
dims
()[
4
];
paddle
::
operators
::
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
paddle
::
operators
::
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
// use col_shape in the vol2col and col2vol calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_depth
,
...
...
python/paddle/v2/framework/tests/test_conv2d_op.py
浏览文件 @
17248153
...
...
@@ -103,6 +103,9 @@ class TestWithGroup(TestConv2dOp):
self
.
op_type
=
"conv2d"
#----------------Conv2dCudnn----------------
class
TestCudnn
(
TestConv2dOp
):
def
init_group
(
self
):
self
.
groups
=
1
...
...
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