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
wzpei9
Paddle
提交
669c0df6
P
Paddle
项目概览
wzpei9
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
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
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
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
669c0df6
编写于
5月 22, 2018
作者:
Y
Yibing Liu
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add groups for conv transpose
上级
8b1b7564
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
103 addition
and
42 deletion
+103
-42
paddle/fluid/operators/conv_transpose_op.cc
paddle/fluid/operators/conv_transpose_op.cc
+8
-4
paddle/fluid/operators/conv_transpose_op.h
paddle/fluid/operators/conv_transpose_op.h
+58
-28
python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
.../paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
+37
-10
未找到文件。
paddle/fluid/operators/conv_transpose_op.cc
浏览文件 @
669c0df6
...
@@ -32,6 +32,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
...
@@ -32,6 +32,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"dilations"
);
std
::
vector
<
int
>
dilations
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"dilations"
);
int
groups
=
ctx
->
Attrs
().
Get
<
int
>
(
"groups"
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
"ConvTransposeOp intput should be 4-D or 5-D tensor."
);
"ConvTransposeOp intput should be 4-D or 5-D tensor."
);
...
@@ -48,10 +49,10 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
...
@@ -48,10 +49,10 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"ConvTransposeOp paddings dimension and dilations "
"ConvTransposeOp paddings dimension and dilations "
"dimension should be the same."
);
"dimension should be the same."
);
PADDLE_ENFORCE_EQ
(
in_dims
[
1
],
filter_dims
[
0
],
PADDLE_ENFORCE_EQ
(
in_dims
[
1
],
filter_dims
[
0
],
"In ConvTransposeOp, The
input channel should be the same
"
"In ConvTransposeOp, The
number of input channels should
"
"
as the number of filter
s."
);
"
be equal to the number of filter' channel
s."
);
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
1
]});
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
1
]
*
groups
});
for
(
size_t
i
=
0
;
i
<
strides
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
strides
.
size
();
++
i
)
{
auto
filter_extent
=
dilations
[
i
]
*
(
filter_dims
[
i
+
2
]
-
1
)
+
1
;
auto
filter_extent
=
dilations
[
i
]
*
(
filter_dims
[
i
+
2
]
-
1
)
+
1
;
output_shape
.
push_back
((
in_dims
[
i
+
2
]
-
1
)
*
strides
[
i
]
-
2
*
paddings
[
i
]
+
output_shape
.
push_back
((
in_dims
[
i
+
2
]
-
1
)
*
strides
[
i
]
-
2
*
paddings
[
i
]
+
...
@@ -102,7 +103,10 @@ void Conv2DTransposeOpMaker::Make() {
...
@@ -102,7 +103,10 @@ void Conv2DTransposeOpMaker::Make() {
AddOutput
(
"Output"
,
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution transpose operator. "
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW."
);
"The format of output tensor is also NCHW."
);
AddAttr
<
int
>
(
"groups"
,
"(int default:1), the groups number of the convolution "
"transpose operator. "
)
.
SetDefault
(
1
);
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"(vector<int> default:{1, 1}), the "
"(vector<int> default:{1, 1}), the "
"dilations(h_dilation, w_dilation) of convolution "
"dilations(h_dilation, w_dilation) of convolution "
...
...
paddle/fluid/operators/conv_transpose_op.h
浏览文件 @
669c0df6
...
@@ -70,7 +70,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
...
@@ -70,7 +70,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
// groups will alway be disabled in conv2dtranspose.
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
@@ -81,10 +81,10 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
...
@@ -81,10 +81,10 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// use col_shape in the im2col and col2im (or vol2col and col2vol)
// use col_shape in the im2col and col2im (or vol2col and col2vol)
// calculation
// calculation
// col_shape_vec: {c
, k_h, k_w, h, w} or {c
, k_d, k_h, k_w, d, h, w}
// col_shape_vec: {c
/g, k_h, k_w, h, w} or {c/g
, k_d, k_h, k_w, d, h, w}
size_t
data_dim
=
filter_shape_vec
.
size
()
-
2
;
size_t
data_dim
=
filter_shape_vec
.
size
()
-
2
;
std
::
vector
<
int64_t
>
col_shape_vec
(
1
+
2
*
data_dim
);
std
::
vector
<
int64_t
>
col_shape_vec
(
1
+
2
*
data_dim
);
col_shape_vec
[
0
]
=
output
->
dims
()[
1
];
col_shape_vec
[
0
]
=
output
->
dims
()[
1
]
/
groups
;
for
(
size_t
j
=
0
;
j
<
data_dim
;
++
j
)
{
for
(
size_t
j
=
0
;
j
<
data_dim
;
++
j
)
{
col_shape_vec
[
j
+
1
]
=
filter_shape_vec
[
j
+
2
];
col_shape_vec
[
j
+
1
]
=
filter_shape_vec
[
j
+
2
];
col_shape_vec
[
j
+
1
+
data_dim
]
=
input_shape_vec
[
j
+
2
];
col_shape_vec
[
j
+
1
+
data_dim
]
=
input_shape_vec
[
j
+
2
];
...
@@ -92,7 +92,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
...
@@ -92,7 +92,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
DDim
col_shape
(
framework
::
make_ddim
(
col_shape_vec
));
DDim
col_shape
(
framework
::
make_ddim
(
col_shape_vec
));
// use col_matrix_shape in the gemm calculation
// use col_matrix_shape in the gemm calculation
// size: (c
* k_h * k_w, h * w) or (c
* k_d * k_h * k_w, d * h * w)
// size: (c
/g * k_h * k_w, h * w) or (c/g
* k_d * k_h * k_w, d * h * w)
DDim
col_matrix_shape
=
framework
::
flatten_to_2d
(
col_shape
,
data_dim
+
1
);
DDim
col_matrix_shape
=
framework
::
flatten_to_2d
(
col_shape
,
data_dim
+
1
);
Tensor
col
;
Tensor
col
;
...
@@ -111,7 +111,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
...
@@ -111,7 +111,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// input matrix size: (m, h * w) or (m, d * h * w)
// input matrix size: (m, h * w) or (m, d * h * w)
DDim
input_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
1
]};
DDim
input_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
1
]};
// filter size: (m, c
* k_h * k_w) or (m, c
* k_d * k_h * k_w)
// filter size: (m, c
/g * k_h * k_w) or (m, c/g
* k_d * k_h * k_w)
DDim
filter_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
0
]};
DDim
filter_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
0
]};
filter
.
Resize
(
filter_matrix_shape
);
filter
.
Resize
(
filter_matrix_shape
);
...
@@ -121,6 +121,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
...
@@ -121,6 +121,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
set_zero
(
dev_ctx
,
output
,
static_cast
<
T
>
(
0
));
set_zero
(
dev_ctx
,
output
,
static_cast
<
T
>
(
0
));
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
out_step
=
static_cast
<
int
>
(
output
->
dims
()[
1
])
/
groups
;
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
DeviceContext
,
T
>
col2im
;
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
DeviceContext
,
T
>
col2im
;
math
::
Col2VolFunctor
<
DeviceContext
,
T
>
col2vol
;
math
::
Col2VolFunctor
<
DeviceContext
,
T
>
col2vol
;
...
@@ -133,22 +135,29 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
...
@@ -133,22 +135,29 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// output size: (c, o_h, o_w) or (c, o_d, o_h, o_w)
// output size: (c, o_h, o_w) or (c, o_d, o_h, o_w)
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// col_matrix = filter * input_batch
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// of shape (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
Tensor
in_slice
=
input_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
blas
.
MatMul
(
filter
,
true
,
input_batch
,
false
,
static_cast
<
T
>
(
1.0
),
Tensor
filter_slice
=
filter
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
&
col_matrix
,
static_cast
<
T
>
(
0.0
));
Tensor
out_slice
=
output_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
if
(
data_dim
==
2U
)
{
// col_matrix = filter_slice * input_slice
// col2im: col_matrix -> dy
// of shape (c/g * k_h * k_w, h * w)
// from (c * k_h * k_w, h * w) to (c, o_h, o_w)
// or (c/g * k_d * k_h * k_w, d * h * w)
col2im
(
dev_ctx
,
col
,
dilations
,
strides
,
blas
.
MatMul
(
filter_slice
,
true
,
in_slice
,
false
,
static_cast
<
T
>
(
1.0
),
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
&
col_matrix
,
static_cast
<
T
>
(
0.0
));
paddings
[
1
]},
&
output_batch
);
if
(
data_dim
==
2U
)
{
}
else
if
(
data_dim
==
3U
)
{
// col2im: col_matrix -> dy
// col2vol: col_matrix -> dy
// from (c/g * k_h * k_w, h * w) to (c/g, o_h, o_w)
// from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
col2im
(
dev_ctx
,
col
,
dilations
,
strides
,
col2vol
(
dev_ctx
,
col
,
dilations
,
strides
,
paddings
,
&
output_batch
);
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
out_slice
);
}
else
if
(
data_dim
==
3U
)
{
// col2vol: col_matrix -> dy
// from (c/g * k_d * k_h * k_w, d * h * w) to (c/g, o_d, o_h, o_w)
col2vol
(
dev_ctx
,
col
,
dilations
,
strides
,
paddings
,
&
out_slice
);
}
}
}
}
}
}
}
...
@@ -174,6 +183,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -174,6 +183,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
@@ -205,9 +215,11 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -205,9 +215,11 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// input matrix size: (m, h * w) or (m, d * h * w)
// input matrix size: (m, h * w) or (m, d * h * w)
DDim
input_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
1
]};
DDim
input_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
1
]};
// filter size: (m, c
* k_h * k_w) or (m, c
* k_d * k_h * k_w)
// filter size: (m, c
/g * k_h * k_w) or (m, c/g
* k_d * k_h * k_w)
DDim
filter_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
0
]};
DDim
filter_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
0
]
/
groups
};
filter
.
Resize
(
filter_matrix_shape
);
filter
.
Resize
(
filter_matrix_shape
);
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
col_step
=
static_cast
<
int
>
(
col_matrix_shape
[
0
])
/
groups
;
// convolution transpose grad on input:
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// im2col + gemm (similar to conv-forward)
...
@@ -233,7 +245,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -233,7 +245,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
if
(
input_grad
)
{
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
}
}
if
(
filter_grad
)
{
// filter size (m, c, k_h, k_w)
if
(
filter_grad
)
{
// filter size (m, c
/g
, k_h, k_w)
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
set_zero
(
dev_ctx
,
filter_grad
,
static_cast
<
T
>
(
0
));
set_zero
(
dev_ctx
,
filter_grad
,
static_cast
<
T
>
(
0
));
filter_grad_
=
*
filter_grad
;
filter_grad_
=
*
filter_grad
;
...
@@ -268,8 +280,17 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -268,8 +280,17 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// or
// (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
// (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
// d, h, w)
// d, h, w)
blas
.
MatMul
(
filter
,
false
,
col_matrix
,
false
,
static_cast
<
T
>
(
1.0
),
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
&
input_grad_batch
,
static_cast
<
T
>
(
0.0
));
Tensor
input_grad_slice
=
input_grad_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
col_matrix_slice
=
col_matrix
.
Slice
(
g
*
col_step
,
(
g
+
1
)
*
col_step
);
blas
.
MatMul
(
filter_slice
,
false
,
col_matrix_slice
,
false
,
static_cast
<
T
>
(
1.0
),
&
input_grad_slice
,
static_cast
<
T
>
(
0.0
));
}
}
}
if
(
filter_grad
)
{
if
(
filter_grad
)
{
// input batch
// input batch
...
@@ -279,8 +300,17 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -279,8 +300,17 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// or
// (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
// (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
// k_h * k_w)
// k_h * k_w)
blas
.
MatMul
(
in_batch
,
false
,
col_matrix
,
true
,
static_cast
<
T
>
(
1.0
),
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
&
filter_grad_
,
static_cast
<
T
>
(
1.0
));
Tensor
in_batch_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
filter_grad_slice
=
filter_grad_
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
col_matrix_slice
=
col_matrix
.
Slice
(
g
*
col_step
,
(
g
+
1
)
*
col_step
);
blas
.
MatMul
(
in_batch_slice
,
false
,
col_matrix_slice
,
true
,
static_cast
<
T
>
(
1.0
),
&
filter_grad_slice
,
static_cast
<
T
>
(
1.0
));
}
}
}
}
}
}
}
...
...
python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
浏览文件 @
669c0df6
...
@@ -21,8 +21,11 @@ from op_test import OpTest
...
@@ -21,8 +21,11 @@ from op_test import OpTest
def
conv2dtranspose_forward_naive
(
input_
,
filter_
,
attrs
):
def
conv2dtranspose_forward_naive
(
input_
,
filter_
,
attrs
):
in_n
,
in_c
,
in_h
,
in_w
=
input_
.
shape
in_n
,
in_c
,
in_h
,
in_w
=
input_
.
shape
f_c
,
out_c
,
f_h
,
f_w
=
filter_
.
shape
f_c
,
f_out_c
,
f_h
,
f_w
=
filter_
.
shape
groups
=
attrs
[
'groups'
]
assert
in_c
==
f_c
assert
in_c
==
f_c
out_c
=
f_out_c
*
groups
sub_in_c
=
in_c
/
groups
stride
,
pad
,
dilations
=
attrs
[
'strides'
],
attrs
[
'paddings'
],
attrs
[
stride
,
pad
,
dilations
=
attrs
[
'strides'
],
attrs
[
'paddings'
],
attrs
[
'dilations'
]
'dilations'
]
...
@@ -36,15 +39,21 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
...
@@ -36,15 +39,21 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
for
n
in
range
(
in_n
):
for
n
in
range
(
in_n
):
for
i
in
range
(
in_h
):
for
i
in
range
(
in_h
):
for
j
in
range
(
in_w
):
for
j
in
range
(
in_w
):
input_masked
=
input_
[
n
,
:,
i
,
j
]
# (c)
for
g
in
range
(
groups
):
input_masked
=
np
.
reshape
(
input_masked
,
(
in_c
,
1
,
1
))
input_masked
=
input_
[
n
,
g
*
sub_in_c
:(
g
+
1
)
*
sub_in_c
,
i
,
input_masked
=
np
.
tile
(
input_masked
,
(
1
,
f_h
,
f_w
))
j
]
# (c)
input_masked
=
np
.
reshape
(
input_masked
,
(
sub_in_c
,
1
,
1
))
for
k
in
range
(
out_c
):
input_masked
=
np
.
tile
(
input_masked
,
(
1
,
f_h
,
f_w
))
tmp_out
=
np
.
sum
(
input_masked
*
filter_
[:,
k
,
:,
:],
axis
=
0
)
i1
,
i2
=
i
*
stride
[
0
],
i
*
stride
[
0
]
+
d_bolck_h
for
k
in
range
(
f_out_c
):
j1
,
j2
=
j
*
stride
[
0
],
j
*
stride
[
0
]
+
d_bolck_h
tmp_out
=
np
.
sum
(
out
[
n
,
k
,
i1
:
i2
:
dilations
[
0
],
j1
:
j2
:
dilations
[
1
]]
+=
tmp_out
input_masked
*
filter_
[
g
*
sub_in_c
:(
g
+
1
)
*
sub_in_c
,
k
,
:,
:],
axis
=
0
)
i1
,
i2
=
i
*
stride
[
0
],
i
*
stride
[
0
]
+
d_bolck_h
j1
,
j2
=
j
*
stride
[
0
],
j
*
stride
[
0
]
+
d_bolck_h
out
[
n
,
g
*
f_out_c
+
k
,
i1
:
i2
:
dilations
[
0
],
j1
:
j2
:
dilations
[
1
]]
+=
tmp_out
out
=
out
[:,
:,
pad
[
0
]:
out_h
-
pad
[
0
],
pad
[
1
]:
out_w
-
pad
[
1
]]
out
=
out
[:,
:,
pad
[
0
]:
out_h
-
pad
[
0
],
pad
[
1
]:
out_w
-
pad
[
1
]]
return
out
return
out
...
@@ -64,6 +73,7 @@ class TestConv2dTransposeOp(OpTest):
...
@@ -64,6 +73,7 @@ class TestConv2dTransposeOp(OpTest):
self
.
attrs
=
{
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
'paddings'
:
self
.
pad
,
'groups'
:
self
.
groups
,
'dilations'
:
self
.
dilations
,
'dilations'
:
self
.
dilations
,
'use_cudnn'
:
self
.
use_cudnn
,
'use_cudnn'
:
self
.
use_cudnn
,
'data_format'
:
'AnyLayout'
# TODO(dzhwinter) : should be fix latter
'data_format'
:
'AnyLayout'
# TODO(dzhwinter) : should be fix latter
...
@@ -127,6 +137,7 @@ class TestConv2dTransposeOp(OpTest):
...
@@ -127,6 +137,7 @@ class TestConv2dTransposeOp(OpTest):
self
.
pad
=
[
0
,
0
]
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
...
@@ -140,16 +151,29 @@ class TestWithPad(TestConv2dTransposeOp):
...
@@ -140,16 +151,29 @@ class TestWithPad(TestConv2dTransposeOp):
self
.
pad
=
[
1
,
1
]
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestWithGroups
(
TestConv2dTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
2
self
.
input_size
=
[
2
,
4
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
3
,
3
,
3
]
class
TestWithStride
(
TestConv2dTransposeOp
):
class
TestWithStride
(
TestConv2dTransposeOp
):
def
init_test_case
(
self
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
self
.
stride
=
[
2
,
2
]
self
.
dilations
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
...
@@ -159,6 +183,7 @@ class TestWithDilation(TestConv2dTransposeOp):
...
@@ -159,6 +183,7 @@ class TestWithDilation(TestConv2dTransposeOp):
def
init_test_case
(
self
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
groups
=
1
self
.
dilations
=
[
2
,
2
]
self
.
dilations
=
[
2
,
2
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
f_c
=
self
.
input_size
[
1
]
...
@@ -176,6 +201,7 @@ class TestCUDNNWithPad(TestWithPad):
...
@@ -176,6 +201,7 @@ class TestCUDNNWithPad(TestWithPad):
def
init_test_case
(
self
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
groups
=
1
self
.
dilations
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
f_c
=
self
.
input_size
[
1
]
...
@@ -190,6 +216,7 @@ class TestCUDNNWithStride(TestWithStride):
...
@@ -190,6 +216,7 @@ class TestCUDNNWithStride(TestWithStride):
def
init_test_case
(
self
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
self
.
stride
=
[
2
,
2
]
self
.
groups
=
1
self
.
dilations
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
f_c
=
self
.
input_size
[
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