Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
17248153
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
12 个月 前同步成功
通知
692
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
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
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录