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356d6954
编写于
11月 14, 2017
作者:
C
chengduoZH
浏览文件
操作
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电子邮件补丁
差异文件
follow comments
上级
7d73b8fc
变更
13
隐藏空白更改
内联
并排
Showing
13 changed file
with
487 addition
and
541 deletion
+487
-541
paddle/operators/conv_op.cc
paddle/operators/conv_op.cc
+25
-15
paddle/operators/conv_op.h
paddle/operators/conv_op.h
+97
-160
paddle/operators/conv_transpose_op.cc
paddle/operators/conv_transpose_op.cc
+12
-11
paddle/operators/conv_transpose_op.h
paddle/operators/conv_transpose_op.h
+24
-28
paddle/operators/math/context_project.h
paddle/operators/math/context_project.h
+8
-11
paddle/operators/math/im2col.cc
paddle/operators/math/im2col.cc
+80
-88
paddle/operators/math/im2col.cu
paddle/operators/math/im2col.cu
+80
-80
paddle/operators/math/im2col.h
paddle/operators/math/im2col.h
+17
-8
paddle/operators/math/im2col_test.cc
paddle/operators/math/im2col_test.cc
+12
-14
paddle/operators/math/vol2col.cc
paddle/operators/math/vol2col.cc
+54
-58
paddle/operators/math/vol2col.cu
paddle/operators/math/vol2col.cu
+48
-48
paddle/operators/math/vol2col.h
paddle/operators/math/vol2col.h
+19
-10
paddle/operators/math/vol2col_test.cc
paddle/operators/math/vol2col_test.cc
+11
-10
未找到文件。
paddle/operators/conv_op.cc
浏览文件 @
356d6954
...
...
@@ -13,6 +13,7 @@
limitations under the License. */
#include "paddle/operators/conv_op.h"
#include <vector>
namespace
paddle
{
namespace
operators
{
...
...
@@ -53,7 +54,7 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
"The number of output channels should be divided by groups."
);
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
0
]});
for
(
size_t
i
=
0
;
i
<
padding
s
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
stride
s
.
size
();
++
i
)
{
PADDLE_ENFORCE
(
in_dims
[
i
+
2
]
+
2
*
paddings
[
i
]
-
(
dilations
[
i
]
*
(
filter_dims
[
i
+
2
]
-
1
)
+
1
)
>
0
,
...
...
@@ -61,8 +62,7 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
"dilations, the output size is less than 0, please check "
"again."
);
output_shape
.
push_back
(
OutputSize
(
in_dims
[
i
+
2
],
filter_dims
[
i
+
2
],
dilations
[
i
],
paddings
[
i
],
paddings
[
i
],
strides
[
i
]));
dilations
[
i
],
paddings
[
i
],
strides
[
i
]));
}
ctx
->
SetOutputDim
(
"Output"
,
framework
::
make_ddim
(
output_shape
));
}
...
...
@@ -86,9 +86,15 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides of convolution operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride) of "
"convolution operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings of convolution operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector<int> default:{0, 0}), the "
"paddings(h_pad, w_pad) of "
"convolution operator."
)
.
SetDefault
({
0
,
0
});
AddAttr
<
int
>
(
"groups"
,
...
...
@@ -99,9 +105,10 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
"is only connected to the second half of the input channels."
)
.
SetDefault
(
1
);
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"(vector default:{1, 1}), the dilations of "
"(vector<int> default:{1, 1}), the "
"dilations(h_dilation, w_dilation) of "
"convolution operator."
)
.
SetDefault
(
std
::
vector
<
int
>
{
1
,
1
});
.
SetDefault
({
1
,
1
});
AddComment
(
R"DOC(
Convolution Operator.
...
...
@@ -147,13 +154,15 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector, default:{0, 0, 0}), the strides of convolution operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
"convolution operator."
)
.
SetDefault
({
1
,
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector, default:{0, 0, 0}), the paddings of convolution operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector<int>, default:{0, 0, 0}), the "
"paddings(d_pad, h_pad, w_pad) of convolution "
"operator."
)
.
SetDefault
({
0
,
0
,
0
});
AddAttr
<
int
>
(
"groups"
,
...
...
@@ -164,10 +173,11 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
"is only connected to the second half of the input channels."
)
.
SetDefault
(
1
);
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"(vector default:{1, 1, 1}), the dilations of "
"(vector<int> default:{1, 1, 1}), the "
"dilations(d_dilation, h_dilation, w_dilation) of "
"convolution operator. Currently, conv3d doesn't "
"support dilation."
)
.
SetDefault
(
std
::
vector
<
int
>
{
1
,
1
,
1
});
.
SetDefault
({
1
,
1
,
1
});
AddComment
(
R"DOC(
Convolution3D Operator.
...
...
paddle/operators/conv_op.h
浏览文件 @
356d6954
...
...
@@ -28,24 +28,22 @@ using Tensor = framework::Tensor;
// Base convolution operator definations for other conv
// like operators to reuse the implementation.
inline
int
OutputSize
(
int
input_size
,
int
filter_size
,
int
dilation
,
int
padding_up
,
int
padding_down
,
int
stride
)
{
int
output_size
=
(
input_size
+
padding_up
+
padding_down
-
(
dilation
*
(
filter_size
-
1
)
+
1
))
/
stride
+
1
;
int
padding
,
int
stride
)
{
const
int
dkernel
=
dilation
*
(
filter_size
-
1
)
+
1
;
const
int
output_size
=
(
input_size
+
2
*
padding
-
dkernel
)
/
stride
+
1
;
return
output_size
;
}
inline
bool
Not
Expand
(
std
::
vector
<
int64_t
>&
filter_dim
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
dilations
)
{
inline
bool
Is
Expand
(
std
::
vector
<
int64_t
>&
filter_dim
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
dilations
)
{
bool
filter_1
=
true
,
strides_1
=
true
,
padding_0
=
true
,
dilation_1
=
true
;
for
(
size_t
j
=
0
;
j
<
strides
.
size
();
++
j
)
{
filter_1
&=
(
static_cast
<
int
>
(
filter_dim
[
j
])
==
1
);
strides_1
&=
(
strides
[
j
]
==
1
);
padding_0
&=
(
paddings
[
j
]
==
0
);
dilation_1
&=
(
dilations
[
j
]
==
1
);
filter_1
=
filter_1
&&
(
static_cast
<
int
>
(
filter_dim
[
j
])
==
1
);
strides_1
=
strides_1
&&
(
strides
[
j
]
==
1
);
padding_0
=
padding_0
&&
(
paddings
[
j
]
==
0
);
dilation_1
=
dilation_1
&&
(
dilations
[
j
]
==
1
);
}
return
filter_1
&&
strides_1
&&
padding_0
&&
dilation_1
;
return
!
(
filter_1
&&
strides_1
&&
padding_0
&&
dilation_1
)
;
}
// Define Op classes in .h file so that other conv
...
...
@@ -65,14 +63,12 @@ class Conv3DOpMaker : public framework::OpProtoAndCheckerMaker {
class
ConvOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
class
ConvOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
...
...
@@ -88,9 +84,9 @@ class GemmConvKernel : public framework::OpKernel<T> {
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
...
@@ -122,13 +118,13 @@ class GemmConvKernel : public framework::OpKernel<T> {
framework
::
DDim
col_matrix_shape
=
framework
::
flatten_to_2d
(
col_shape
,
filter_shape_vec
.
size
()
+
1
);
bool
not_expand
=
Not
Expand
(
filter_shape_vec
,
strides
,
paddings
,
dilations
);
bool
is_expand
=
Is
Expand
(
filter_shape_vec
,
strides
,
paddings
,
dilations
);
Tensor
col
;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor
col_matrix
;
if
(
!
not
_expand
)
{
if
(
is
_expand
)
{
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
...
...
@@ -149,51 +145,37 @@ class GemmConvKernel : public framework::OpKernel<T> {
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
out_step
=
static_cast
<
int
>
(
output
->
dims
()[
1
])
/
groups
;
if
(
!
not_expand
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
Tensor
out_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
if
(
filter_shape_vec
.
size
()
==
2
)
{
// im2col
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
im2col
(
context
.
device_context
(),
in_slice
,
col
,
dilations
[
0
],
dilations
[
1
],
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
}
else
if
(
filter_shape_vec
.
size
()
==
3
)
{
// vol2col
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
vol2col
(
context
.
device_context
(),
in_slice
,
col
,
dilations
[
0
],
dilations
[
1
],
dilations
[
2
],
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
}
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
Tensor
out_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
// gemm
Tensor
out_slice
=
out_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter_slice
,
false
,
col_matrix
,
false
,
T
(
1.0
),
&
out_slice
,
T
(
0.0
));
}
}
}
else
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
Tensor
out_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
if
(
!
is_expand
)
{
col
.
ShareDataWith
(
in_slice
);
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
// gemm
Tensor
out_slice
=
out_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter_slice
,
false
,
col_matrix
,
false
,
T
(
1.0
),
&
out_slice
,
T
(
0.0
));
}
else
if
(
filter_shape_vec
.
size
()
==
2
)
{
// im2col
im2col
(
context
.
device_context
(),
in_slice
,
dilations
,
strides
,
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
col
);
}
else
if
(
filter_shape_vec
.
size
()
==
3
)
{
// vol2col
vol2col
(
context
.
device_context
(),
in_slice
,
dilations
,
strides
,
paddings
,
&
col
);
}
// gemm
Tensor
out_slice
=
out_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter_slice
,
false
,
col_matrix
,
false
,
T
(
1.0
),
&
out_slice
,
T
(
0.0
));
}
}
}
...
...
@@ -217,9 +199,9 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
if
(
!
input_grad
&&
!
filter_grad
)
return
;
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
...
@@ -270,13 +252,13 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
out_step
=
static_cast
<
int
>
(
output_grad
->
dims
()[
1
])
/
groups
;
bool
not_expand
=
Not
Expand
(
filter_shape_vec
,
strides
,
paddings
,
dilations
);
bool
is_expand
=
Is
Expand
(
filter_shape_vec
,
strides
,
paddings
,
dilations
);
Tensor
col
;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor
col_matrix
;
if
(
!
not
_expand
)
{
if
(
is
_expand
)
{
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
...
...
@@ -288,61 +270,38 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
set_zero
(
context
.
device_context
(),
input_grad
,
static_cast
<
T
>
(
0
));
if
(
!
not_expand
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
out_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
Tensor
in_grad_batch
=
input_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// gemm
Tensor
out_grad_slice
=
out_grad_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter_slice
,
true
,
out_grad_slice
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
Tensor
in_grad_slice
=
in_grad_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
if
(
filter_shape_vec
.
size
()
==
2
)
{
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
col2im
(
context
.
device_context
(),
in_grad_slice
,
col
,
dilations
[
0
],
dilations
[
1
],
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
}
else
if
(
filter_shape_vec
.
size
()
==
3
)
{
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
col2vol
(
context
.
device_context
(),
in_grad_slice
,
col
,
dilations
[
0
],
dilations
[
1
],
dilations
[
2
],
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
}
}
}
}
else
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
out_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
Tensor
in_grad_batch
=
input_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// gemm
Tensor
out_grad_slice
=
out_grad_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
in_grad_slice
=
in_grad_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
out_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
Tensor
in_grad_batch
=
input_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// gemm
Tensor
out_grad_slice
=
out_grad_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
in_grad_slice
=
in_grad_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
if
(
!
is_expand
)
{
col_matrix
.
ShareDataWith
(
in_grad_slice
);
col_matrix
.
Resize
(
col_matrix_shape
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter_slice
,
true
,
out_grad_slice
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
}
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter_slice
,
true
,
out_grad_slice
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
if
(
is_expand
&&
filter_shape_vec
.
size
()
==
2
)
{
col2im
(
context
.
device_context
(),
col
,
dilations
,
strides
,
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
in_grad_slice
);
}
else
if
(
is_expand
&&
filter_shape_vec
.
size
()
==
3
)
{
col2vol
(
context
.
device_context
(),
col
,
dilations
,
strides
,
paddings
,
&
in_grad_slice
);
}
}
}
...
...
@@ -353,60 +312,38 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
Tensor
filter_grad_
=
*
filter_grad
;
filter_grad_
.
Resize
(
filter_matrix_shape
);
set_zero
(
context
.
device_context
(),
filter_grad
,
static_cast
<
T
>
(
0
));
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
out_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// im2col
Tensor
out_grad_slice
=
out_grad_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
if
(
!
not_expand
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
out_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// im2col
Tensor
out_grad_slice
=
out_grad_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
if
(
filter_shape_vec
.
size
()
==
2
)
{
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
im2col
(
context
.
device_context
(),
in_slice
,
col
,
dilations
[
0
],
dilations
[
1
],
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
}
else
if
(
filter_shape_vec
.
size
()
==
3
)
{
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
vol2col
(
context
.
device_context
(),
in_slice
,
col
,
dilations
[
0
],
dilations
[
1
],
dilations
[
2
],
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
}
// gemm
Tensor
filter_grad_slice
=
filter_grad_
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
out_grad_slice
,
false
,
col_matrix
,
true
,
T
(
1.0
),
&
filter_grad_slice
,
T
(
1.0
));
}
}
}
else
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
out_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_shape
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// im2col
Tensor
out_grad_slice
=
out_grad_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
if
(
!
is_expand
)
{
col
.
ShareDataWith
(
in_slice
);
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
// gemm
Tensor
filter_grad_slice
=
filter_grad_
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
out_grad_slice
,
false
,
col_matrix
,
true
,
T
(
1.0
),
&
filter_grad_slice
,
T
(
1.0
));
}
else
if
(
filter_shape_vec
.
size
()
==
2
)
{
im2col
(
context
.
device_context
(),
in_slice
,
dilations
,
strides
,
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
col
);
}
else
if
(
filter_shape_vec
.
size
()
==
3
)
{
vol2col
(
context
.
device_context
(),
in_slice
,
dilations
,
strides
,
paddings
,
&
col
);
}
// gemm
Tensor
filter_grad_slice
=
filter_grad_
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
out_grad_slice
,
false
,
col_matrix
,
true
,
T
(
1.0
),
&
filter_grad_slice
,
T
(
1.0
));
}
}
}
...
...
paddle/operators/conv_transpose_op.cc
浏览文件 @
356d6954
...
...
@@ -51,7 +51,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"as the number of filters."
);
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
1
]});
for
(
size_t
i
=
0
;
i
<
padding
s
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
stride
s
.
size
();
++
i
)
{
output_shape
.
push_back
((
in_dims
[
i
+
2
]
-
1
)
*
strides
[
i
]
+
filter_dims
[
i
+
2
]);
}
...
...
@@ -77,13 +77,14 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector defalut:{1, 1}), strides of
convolution transpose operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int> defalut:{1, 1}), strides of "
"
convolution transpose operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector defalut:{0, 0}), paddings of convolution transpose operator."
)
"(vector<int> defalut:{0, 0}), paddings(h_pad, w_pad) of convolution "
"transpose operator."
)
.
SetDefault
({
0
,
0
});
AddComment
(
R"DOC(
Convolution2D Transpose Operator.
...
...
@@ -132,13 +133,13 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
"Where N is batch 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."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector defalut:{1, 1, 1}), strides of
convolution transpose operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int> defalut:{1, 1, 1}), strides of "
"
convolution transpose operator."
)
.
SetDefault
({
1
,
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector defalut:{0, 0, 0}), paddings
of convolution transpose operator."
)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector<int> defalut:{0, 0, 0}), paddings(d_pad, "
"h_pad, w_pad)
of convolution transpose operator."
)
.
SetDefault
({
0
,
0
,
0
});
AddComment
(
R"DOC(
Convolution3D Transpose Operator.
...
...
paddle/operators/conv_transpose_op.h
浏览文件 @
356d6954
...
...
@@ -43,16 +43,12 @@ class Conv3DTransposeOpMaker : public framework::OpProtoAndCheckerMaker {
class
ConvTransposeOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
class
ConvTransposeOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
...
...
@@ -66,13 +62,11 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// Actually, no paddings and groups allowed in conv transpose.
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
int
dilaiton_d
=
1
;
int
dilation_h
=
1
;
int
dilation_w
=
1
;
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
// input_shape_vec: {h, w} or {d, h, w}
...
...
@@ -124,6 +118,10 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
math
::
SetConstant
<
Place
,
T
>
set_zero
;
set_zero
(
context
.
device_context
(),
output
,
static_cast
<
T
>
(
0
));
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
std
::
vector
<
int
>
dilations
({
1
,
1
,
1
});
// convolution transpose: gemm + col2im or col2vol (similar to conv-backward
// on input)
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
...
...
@@ -142,17 +140,16 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
if
(
filter_shape_vec
.
size
()
==
2
)
{
// col2im: col_matrix -> dy
// from (c * k_h * k_w, h * w) to (c, o_h, o_w)
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
col2im
(
context
.
device_context
(),
output_batch
,
col
,
dilation_h
,
dilation_w
,
strides
[
0
],
strides
[
1
],
0
,
0
,
0
,
0
);
col2im
(
context
.
device_context
(),
col
,
std
::
vector
<
int
>
{
dilations
[
0
],
dilations
[
1
]},
strides
,
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
output_batch
);
}
else
if
(
filter_shape_vec
.
size
()
==
3
)
{
// col2vol: col_matrix -> dy
// from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
col2vol
(
context
.
device_context
(),
output_batch
,
col
,
dilaiton_d
,
dilation_h
,
dilation_w
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
0
,
0
,
0
);
col2vol
(
context
.
device_context
(),
col
,
dilations
,
strides
,
std
::
vector
<
int
>
{
0
,
0
,
0
},
&
output_batch
);
}
}
}
...
...
@@ -179,10 +176,6 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// Actually, no paddings and groups allowed in conv transpose.
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
dilaiton_d
=
1
;
int
dilation_h
=
1
;
int
dilation_w
=
1
;
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
// input_shape_vec: {h, w} or {d, h, w}
...
...
@@ -237,6 +230,10 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
Tensor
filter_grad_
;
math
::
SetConstant
<
Place
,
T
>
set_zero
;
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
std
::
vector
<
int
>
dilations
({
1
,
1
,
1
});
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
set_zero
(
context
.
device_context
(),
input_grad
,
static_cast
<
T
>
(
0
));
...
...
@@ -256,17 +253,16 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
if
(
filter_shape_vec
.
size
()
==
2
)
{
// im2col: dy -> col matrix
// from (c, o_h, o_w) to (c * k_h * k_w, h * w)
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
dilation_h
,
dilation_w
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
im2col
(
context
.
device_context
(),
output_grad_batch
,
std
::
vector
<
int
>
{
dilations
[
0
],
dilations
[
1
]},
strides
,
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
col
);
}
else
if
(
filter_shape_vec
.
size
()
==
3
)
{
// vol2col: dy -> col_matrix
// from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
vol2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
dilaiton_d
,
dilation_h
,
dilation_w
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
vol2col
(
context
.
device_context
(),
output_grad_batch
,
dilations
,
strides
,
paddings
,
&
col
);
}
if
(
input_grad
)
{
...
...
paddle/operators/math/context_project.h
浏览文件 @
356d6954
...
...
@@ -95,8 +95,9 @@ class ContextProjectFunctor {
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kOCF
,
Place
,
float
>
im2col_ocf
;
int
dilation_h
=
1
;
int
dilation_w
=
1
;
std
::
vector
<
int
>
dilation
({
1
,
1
});
std
::
vector
<
int
>
padding
({
up_pad
,
0
,
down_pad
,
0
});
std
::
vector
<
int
>
stride
({
context_stride
,
1
});
int
input_row_begin
,
input_row_end
;
int
sequence_height
,
sequence_width
;
...
...
@@ -126,10 +127,7 @@ class ContextProjectFunctor {
{
1
,
input_row_end
-
input_row_begin
,
sequence_width
});
// input_channels, input_height, input_width
in_t
.
Resize
(
framework
::
make_ddim
(
input_shape
));
im2col_ocf
(
context
,
in_t
,
out_t
,
dilation_h
,
dilation_w
,
/*stride_height*/
context_stride
,
/*stride_width*/
1
,
up_pad
,
down_pad
,
0
,
0
);
im2col_ocf
(
context
,
in_t
,
dilation
,
stride
,
padding
,
&
out_t
);
out_t
.
Resize
({
sequence_height
,
context_length
*
sequence_width
});
}
}
...
...
@@ -207,8 +205,9 @@ class ContextProjectGradFunctor {
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kOCF
,
Place
,
float
>
col2im_ocf
;
int
dilation_h
=
1
;
int
dilation_w
=
1
;
std
::
vector
<
int
>
dilation
({
1
,
1
});
std
::
vector
<
int
>
padding
({
up_pad
,
0
,
down_pad
,
0
});
std
::
vector
<
int
>
stride
({
context_stride
,
1
});
int
input_row_begin
,
input_row_end
;
int
sequence_height
,
sequence_width
;
...
...
@@ -240,9 +239,7 @@ class ContextProjectGradFunctor {
sequence_width
});
// input_channels, input_height, input_width
in_t
.
Resize
(
framework
::
make_ddim
(
input_shape
));
col2im_ocf
(
context
,
in_t
,
out_t
,
dilation_h
,
dilation_w
,
/*stride_height*/
context_stride
,
/*stride_width*/
1
,
up_pad
,
down_pad
,
0
,
0
);
col2im_ocf
(
context
,
out_t
,
dilation
,
stride
,
padding
,
&
in_t
);
out_t
.
Resize
({
sequence_height
,
context_length
*
sequence_width
});
}
}
...
...
paddle/operators/math/im2col.cc
浏览文件 @
356d6954
...
...
@@ -28,40 +28,39 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
im
,
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
const
framework
::
Tensor
&
im
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
col
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
PADDLE_ENFORCE
(
col
->
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
1
];
int
filter_width
=
col
.
dims
()[
2
];
int
col_height
=
col
.
dims
()[
3
];
int
col_width
=
col
.
dims
()[
4
];
int
filter_height
=
col
->
dims
()[
1
];
int
filter_width
=
col
->
dims
()[
2
];
int
col_height
=
col
->
dims
()[
3
];
int
col_width
=
col
->
dims
()[
4
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
_up
+
padding_down
-
((
dilation
_h
*
(
filter_height
-
1
)
+
1
)))
/
stride
_height
+
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
((
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
)))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
_left
+
padding_right
-
((
dilation
_w
*
(
filter_width
-
1
)
+
1
)))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
((
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
)))
/
stride
[
1
]
+
1
,
col_width
,
"
col_width and padding(padding_left, padding_right
) are "
"
Output_height and padding(padding_up, padding_down
) are "
"inconsistent."
);
int
channels_col
=
im_channels
*
filter_height
*
filter_width
;
const
T
*
im_data
=
im
.
data
<
T
>
();
T
*
col_data
=
col
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
for
(
int
c
=
0
;
c
<
channels_col
;
++
c
)
{
int
w_offset
=
c
%
filter_width
;
...
...
@@ -69,10 +68,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int
c_im
=
c
/
filter_width
/
filter_height
;
for
(
int
h
=
0
;
h
<
col_height
;
++
h
)
{
for
(
int
w
=
0
;
w
<
col_width
;
++
w
)
{
int
im_row_idx
=
h
*
stride_height
-
padding_up
+
h_offset
*
dilation_h
;
int
im_col_idx
=
w
*
stride_width
-
padding_left
+
w_offset
*
dilation_w
;
int
im_row_idx
=
h
*
stride
[
0
]
-
padding
[
0
]
+
h_offset
*
dilation
[
0
];
int
im_col_idx
=
w
*
stride
[
1
]
-
padding
[
1
]
+
w_offset
*
dilation
[
1
];
int
col_idx
=
(
c
*
col_height
+
h
)
*
col_width
+
w
;
int
im_idx
=
(
im_row_idx
+
c_im
*
im_height
)
*
im_width
+
im_col_idx
;
...
...
@@ -95,38 +92,39 @@ template <class T>
class
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
im
,
const
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
im
)
{
PADDLE_ENFORCE
(
im
->
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
im_channels
=
im
->
dims
()[
0
];
int
im_height
=
im
->
dims
()[
1
];
int
im_width
=
im
->
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
1
];
int
filter_width
=
col
.
dims
()[
2
];
int
col_height
=
col
.
dims
()[
3
];
int
col_width
=
col
.
dims
()[
4
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
_up
+
padding_down
-
((
dilation
_h
*
(
filter_height
-
1
)
+
1
)))
/
stride
_height
+
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
((
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
)))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
_left
+
padding_right
-
((
dilation
_w
*
(
filter_width
-
1
)
+
1
)))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
((
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
)))
/
stride
[
1
]
+
1
,
col_width
,
"
col_width and padding(padding_left, padding_right
) are "
"
Output_height and padding(padding_up, padding_down
) are "
"inconsistent."
);
int
channels_col
=
im_channels
*
filter_height
*
filter_width
;
T
*
im_data
=
im
.
data
<
T
>
();
T
*
im_data
=
im
->
data
<
T
>
();
const
T
*
col_data
=
col
.
data
<
T
>
();
for
(
int
c
=
0
;
c
<
channels_col
;
++
c
)
{
...
...
@@ -135,10 +133,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
int
c_im
=
c
/
filter_width
/
filter_height
;
for
(
int
h
=
0
;
h
<
col_height
;
++
h
)
{
for
(
int
w
=
0
;
w
<
col_width
;
++
w
)
{
int
im_row_idx
=
h
*
stride_height
-
padding_up
+
h_offset
*
dilation_h
;
int
im_col_idx
=
w
*
stride_width
-
padding_left
+
w_offset
*
dilation_w
;
int
im_row_idx
=
h
*
stride
[
0
]
-
padding
[
0
]
+
h_offset
*
dilation
[
0
];
int
im_col_idx
=
w
*
stride
[
1
]
-
padding
[
1
]
+
w_offset
*
dilation
[
1
];
if
((
im_row_idx
)
>=
0
&&
(
im_row_idx
)
<
im_height
&&
(
im_col_idx
)
>=
0
&&
(
im_col_idx
)
<
im_width
)
{
...
...
@@ -171,35 +167,32 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
im
,
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
const
framework
::
Tensor
&
im
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
col
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
PADDLE_ENFORCE
(
col
->
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
3
];
int
filter_width
=
col
.
dims
()[
4
];
int
col_height
=
col
.
dims
()[
0
];
int
col_width
=
col
.
dims
()[
1
];
int
filter_height
=
col
->
dims
()[
3
];
int
filter_width
=
col
->
dims
()[
4
];
int
col_height
=
col
->
dims
()[
0
];
int
col_width
=
col
->
dims
()[
1
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding_up
+
padding_down
-
filter_height
)
/
stride_height
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding_left
+
padding_right
-
filter_width
)
/
stride_width
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
(
(
im_height
+
padding
[
0
]
+
padding
[
2
]
-
filter_height
)
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
(
(
im_width
+
padding
[
1
]
+
padding
[
3
]
-
filter_width
)
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
"inconsistent."
);
const
T
*
im_data
=
im
.
data
<
T
>
();
T
*
col_data
=
col
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
for
(
int
col_row_idx
=
0
;
col_row_idx
<
col_height
;
++
col_row_idx
)
{
for
(
int
col_col_idx
=
0
;
col_col_idx
<
col_width
;
++
col_col_idx
)
{
...
...
@@ -209,9 +202,9 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
for
(
int
filter_col_idx
=
0
;
filter_col_idx
<
filter_width
;
++
filter_col_idx
)
{
int
im_row_offset
=
col_row_idx
*
stride
_height
+
filter_row_idx
-
padding_up
;
col_row_idx
*
stride
[
0
]
+
filter_row_idx
-
padding
[
0
]
;
int
im_col_offset
=
col_col_idx
*
stride
_width
+
filter_col_idx
-
padding_left
;
col_col_idx
*
stride
[
1
]
+
filter_col_idx
-
padding
[
1
]
;
int
col_offset
=
((((
col_row_idx
)
*
col_width
+
col_col_idx
)
*
im_channels
+
channel
)
*
...
...
@@ -244,34 +237,33 @@ template <class T>
class
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
im
,
const
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
im
)
{
PADDLE_ENFORCE
(
im
->
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
im_channels
=
im
->
dims
()[
0
];
int
im_height
=
im
->
dims
()[
1
];
int
im_width
=
im
->
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
3
];
int
filter_width
=
col
.
dims
()[
4
];
int
col_height
=
col
.
dims
()[
0
];
int
col_width
=
col
.
dims
()[
1
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding_up
+
padding_down
-
filter_height
)
/
stride_height
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding_left
+
padding_right
-
filter_width
)
/
stride_width
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
(
(
im_height
+
padding
[
0
]
+
padding
[
2
]
-
filter_height
)
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
(
(
im_width
+
padding
[
1
]
+
padding
[
3
]
-
filter_width
)
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
"inconsistent."
);
T
*
im_data
=
im
.
data
<
T
>
();
T
*
im_data
=
im
->
data
<
T
>
();
const
T
*
col_data
=
col
.
data
<
T
>
();
for
(
int
col_row_idx
=
0
;
col_row_idx
<
col_height
;
++
col_row_idx
)
{
...
...
@@ -282,9 +274,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
for
(
int
filter_col_idx
=
0
;
filter_col_idx
<
filter_width
;
++
filter_col_idx
)
{
int
im_row_offset
=
col_row_idx
*
stride
_height
+
filter_row_idx
-
padding_up
;
col_row_idx
*
stride
[
0
]
+
filter_row_idx
-
padding
[
0
]
;
int
im_col_offset
=
col_col_idx
*
stride
_width
+
filter_col_idx
-
padding_left
;
col_col_idx
*
stride
[
1
]
+
filter_col_idx
-
padding
[
1
]
;
int
col_offset
=
(((
col_row_idx
*
col_width
+
col_col_idx
)
*
im_channels
+
channel
)
*
...
...
paddle/operators/math/im2col.cu
浏览文件 @
356d6954
...
...
@@ -61,31 +61,30 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
im
,
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
const
framework
::
Tensor
&
im
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
col
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
PADDLE_ENFORCE
(
col
->
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
1
];
int
filter_width
=
col
.
dims
()[
2
];
int
col_height
=
col
.
dims
()[
3
];
int
col_width
=
col
.
dims
()[
4
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
_up
+
padding_down
-
(
dilation
_h
*
(
filter_height
-
1
)
+
1
))
/
stride
_height
+
int
filter_height
=
col
->
dims
()[
1
];
int
filter_width
=
col
->
dims
()[
2
];
int
col_height
=
col
->
dims
()[
3
];
int
col_width
=
col
->
dims
()[
4
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
(
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
_left
+
padding_right
-
(
dilation
_w
*
(
filter_width
-
1
)
+
1
))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
(
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
))
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
...
...
@@ -100,9 +99,9 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
im2col
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
im
.
data
<
T
>
(),
num_outputs
,
im_height
,
im_width
,
dilation
_h
,
dilation_w
,
filter_height
,
filter_width
,
stride_height
,
stride_width
,
padding_up
,
padding
_left
,
col_height
,
col_width
,
col
.
data
<
T
>
());
im
.
data
<
T
>
(),
num_outputs
,
im_height
,
im_width
,
dilation
[
0
]
,
dilation
[
1
],
filter_height
,
filter_width
,
stride
[
0
],
stride
[
1
]
,
padding
[
0
],
padding
[
1
],
col_height
,
col_width
,
col
->
data
<
T
>
());
}
};
...
...
@@ -163,31 +162,32 @@ template <class T>
class
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
im
,
const
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
im
)
{
PADDLE_ENFORCE
(
im
->
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
im_channels
=
im
->
dims
()[
0
];
int
im_height
=
im
->
dims
()[
1
];
int
im_width
=
im
->
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
1
];
int
filter_width
=
col
.
dims
()[
2
];
int
col_height
=
col
.
dims
()[
3
];
int
col_width
=
col
.
dims
()[
4
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
_up
+
padding_down
-
(
dilation
_h
*
(
filter_height
-
1
)
+
1
))
/
stride
_height
+
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
(
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
_left
+
padding_right
-
(
dilation
_w
*
(
filter_width
-
1
)
+
1
))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
(
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
))
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
...
...
@@ -206,9 +206,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
col2im
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
num_kernels
,
col
.
data
<
T
>
(),
im_height
,
im_width
,
dilation
_h
,
dilation_w
,
filter_height
,
filter_width
,
stride_height
,
stride_width
,
padding_up
,
padding
_left
,
col_height
,
col_width
,
im
.
data
<
T
>
());
num_kernels
,
col
.
data
<
T
>
(),
im_height
,
im_width
,
dilation
[
0
]
,
dilation
[
1
],
filter_height
,
filter_width
,
stride
[
0
],
stride
[
1
]
,
padding
[
0
],
padding
[
2
],
col_height
,
col_width
,
im
->
data
<
T
>
());
}
};
...
...
@@ -222,11 +222,11 @@ template class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
platform
::
GPUPlace
,
double
>;
template
<
class
T
>
__global__
void
im2colOCF
(
const
T
*
im_data
,
T
*
col_data
,
int
im_channels
,
int
im_
height
,
int
im_width
,
int
filter_height
,
int
filter_width
,
int
stride_height
,
int
stride_width
,
__global__
void
im2colOCF
(
const
T
*
im_data
,
int
im_channels
,
int
im_height
,
int
im_
width
,
int
filter_height
,
int
filter_width
,
int
stride_height
,
int
stride_width
,
int
padding_height
,
int
padding_width
,
int
col_height
,
int
col_width
)
{
int
col_width
,
T
*
col_data
)
{
int
swid
=
blockIdx
.
x
;
int
shid
=
blockIdx
.
y
;
for
(
int
channelid
=
threadIdx
.
z
;
channelid
<
im_channels
;
...
...
@@ -263,30 +263,29 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
im
,
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
const
framework
::
Tensor
&
im
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
col
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
PADDLE_ENFORCE
(
col
->
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
3
];
int
filter_width
=
col
.
dims
()[
4
];
int
col_height
=
col
.
dims
()[
0
];
int
col_width
=
col
.
dims
()[
1
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
_up
+
padding_down
-
(
dilation
_h
*
(
filter_height
-
1
)
+
1
))
/
stride
_height
+
int
filter_height
=
col
->
dims
()[
3
];
int
filter_width
=
col
->
dims
()[
4
];
int
col_height
=
col
->
dims
()[
0
];
int
col_width
=
col
->
dims
()[
1
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
(
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
_left
+
padding_right
-
(
dilation
_w
*
(
filter_width
-
1
)
+
1
))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
(
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
))
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
...
...
@@ -314,18 +313,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
im2colOCF
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
im
.
data
<
T
>
(),
col
.
data
<
T
>
(),
im_channels
,
im_height
,
im_width
,
filter_
height
,
filter_width
,
stride_height
,
stride_width
,
padding_up
,
padding_left
,
col_height
,
col_width
);
im
.
data
<
T
>
(),
im_channels
,
im_height
,
im_width
,
filter_height
,
filter_
width
,
stride
[
0
],
stride
[
1
],
padding
[
0
],
padding
[
1
],
col_height
,
col_width
,
col
->
data
<
T
>
()
);
}
};
template
<
class
T
>
__global__
void
col2imOCF
(
T
*
im_data
,
const
T
*
col_data
,
int
im_channels
,
int
im_
height
,
int
im_width
,
int
filter_height
,
int
filter_width
,
int
stride_height
,
int
stride_width
,
__global__
void
col2imOCF
(
const
T
*
col_data
,
int
im_channels
,
int
im_height
,
int
im_
width
,
int
filter_height
,
int
filter_width
,
int
stride_height
,
int
stride_width
,
int
padding_height
,
int
padding_width
,
int
col_height
,
int
col_width
)
{
int
col_width
,
T
*
im_data
)
{
int
swid
=
blockIdx
.
x
;
int
shid
=
blockIdx
.
y
;
for
(
int
channelid
=
threadIdx
.
z
;
channelid
<
im_channels
;
...
...
@@ -361,30 +360,31 @@ template <class T>
class
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
im
,
const
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
)
{
PADDLE_ENFORCE
(
im
.
dims
().
size
()
==
3
);
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
im
)
{
PADDLE_ENFORCE
(
im
->
dims
().
size
()
==
3
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
5
);
int
im_channels
=
im
.
dims
()[
0
];
int
im_height
=
im
.
dims
()[
1
];
int
im_width
=
im
.
dims
()[
2
];
int
im_channels
=
im
->
dims
()[
0
];
int
im_height
=
im
->
dims
()[
1
];
int
im_width
=
im
->
dims
()[
2
];
int
filter_height
=
col
.
dims
()[
3
];
int
filter_width
=
col
.
dims
()[
4
];
int
col_height
=
col
.
dims
()[
0
];
int
col_width
=
col
.
dims
()[
1
];
PADDLE_ENFORCE_EQ
((
im_height
+
padding
_up
+
padding_down
-
(
dilation
_h
*
(
filter_height
-
1
)
+
1
))
/
stride
_height
+
PADDLE_ENFORCE_EQ
((
im_height
+
padding
[
0
]
+
padding
[
2
]
-
(
dilation
[
0
]
*
(
filter_height
-
1
)
+
1
))
/
stride
[
0
]
+
1
,
col_height
,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent."
);
PADDLE_ENFORCE_EQ
((
im_width
+
padding
_left
+
padding_right
-
(
dilation
_w
*
(
filter_width
-
1
)
+
1
))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
im_width
+
padding
[
1
]
+
padding
[
3
]
-
(
dilation
[
1
]
*
(
filter_width
-
1
)
+
1
))
/
stride
[
1
]
+
1
,
col_width
,
"col_width and padding(padding_left, padding_right) are "
...
...
@@ -412,9 +412,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
col2imOCF
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
im
.
data
<
T
>
(),
col
.
data
<
T
>
(),
im_channels
,
im_height
,
im_width
,
filter_
height
,
filter_width
,
stride_height
,
stride_width
,
padding_up
,
padding_left
,
col_height
,
col_width
);
col
.
data
<
T
>
(),
im_channels
,
im_height
,
im_width
,
filter_height
,
filter_
width
,
stride
[
0
],
stride
[
1
],
padding
[
0
],
padding
[
1
],
col_height
,
col_width
,
im
->
data
<
T
>
()
);
}
};
...
...
paddle/operators/math/im2col.h
浏览文件 @
356d6954
...
...
@@ -35,6 +35,15 @@ enum class ColFormat { kCFO = 0, kOCF = 1 };
* \param colData Column data.
* \param colShape The shape of colData.
*
* \param dilations dilation data.
* \param 2-dimension [dilation_height, dilation_width].
*
* \param strides stride data.
* \param 2-dimension [stride_height, stride_width].
*
* \param paddings padding data.
* \param 4-dimension [up_pad, left_pad, down_pad, right_pad].
*
* If the template argument Format is kCFO, the shape of colData is:
* [input_channels, filter_height, filter_width, output_height, output_width]
* So, it is easy to reshape into a convolution matrix for convolution
...
...
@@ -73,19 +82,19 @@ template <ColFormat Format, typename Place, typename T>
class
Im2ColFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
im
,
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
);
const
framework
::
Tensor
&
im
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
col
);
};
template
<
ColFormat
Format
,
typename
Place
,
typename
T
>
class
Col2ImFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
im
,
const
framework
::
Tensor
&
col
,
int
dilation_h
,
int
dilation_w
,
int
stride_height
,
int
stride_width
,
int
padding_up
,
int
padding_down
,
int
padding_left
,
int
padding_right
);
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilation
,
const
std
::
vector
<
int
>&
stride
,
const
std
::
vector
<
int
>&
padding
,
framework
::
Tensor
*
im
);
};
}
// namespace math
...
...
paddle/operators/math/im2col_test.cc
浏览文件 @
356d6954
...
...
@@ -45,12 +45,14 @@ void testIm2col() {
int
input_height
=
2
;
int
input_width
=
3
;
int
filter_size
=
2
;
int
stride
=
1
;
int
padding
=
0
;
int
dilation_h
=
1
;
int
dilation_w
=
1
;
int
output_height
=
(
input_height
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
int
output_width
=
(
input_width
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
std
::
vector
<
int
>
stride
({
1
,
1
});
// stride_y, stride_x
std
::
vector
<
int
>
padding
(
{
0
,
0
,
0
,
0
});
// up_pad, left_pad, down_pad, right_pad
std
::
vector
<
int
>
dilation
({
1
,
1
});
// dilation_y, dilation_x
int
output_height
=
(
input_height
-
filter_size
+
padding
[
0
]
+
padding
[
1
])
/
stride
[
0
]
+
1
;
int
output_width
=
(
input_width
-
filter_size
+
padding
[
2
]
+
padding
[
3
])
/
stride
[
1
]
+
1
;
float
*
input_ptr
=
input_tmp
.
mutable_data
<
float
>
(
{
1
,
input_height
,
input_width
},
paddle
::
platform
::
CPUPlace
());
float
arr
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
...
...
@@ -87,10 +89,8 @@ void testIm2col() {
paddle
::
operators
::
math
::
ColFormat
::
kOCF
,
Place
,
float
>
im2col_ocf
;
im2col
(
*
context
,
input
,
output_cfo
,
dilation_h
,
dilation_w
,
stride
,
stride
,
padding
,
padding
,
padding
,
padding
);
im2col_ocf
(
*
context
,
input
,
output_ocf
,
dilation_h
,
dilation_w
,
stride
,
stride
,
padding
,
padding
,
padding
,
padding
);
im2col
(
*
context
,
input
,
dilation
,
stride
,
padding
,
&
output_cfo
);
im2col_ocf
(
*
context
,
input
,
dilation
,
stride
,
padding
,
&
output_ocf
);
float
out_cfo_data
[]
=
{
0
,
1
,
1
,
2
,
3
,
4
,
4
,
5
};
float
out_ocf_data
[]
=
{
0
,
1
,
3
,
4
,
1
,
2
,
4
,
5
};
...
...
@@ -133,8 +133,7 @@ void testIm2col() {
input
.
CopyFrom
(
input_tmp
,
*
place
,
*
context
);
}
col2im
(
*
context
,
input
,
output_cfo
,
dilation_h
,
dilation_w
,
stride
,
stride
,
padding
,
padding
,
padding
,
padding
);
col2im
(
*
context
,
output_cfo
,
dilation
,
stride
,
padding
,
&
input
);
float
*
in_ptr
;
if
(
paddle
::
platform
::
is_cpu_place
(
*
place
))
{
...
...
@@ -155,8 +154,7 @@ void testIm2col() {
input
.
CopyFrom
(
input_tmp
,
*
place
,
*
context
);
}
col2im_ocf
(
*
context
,
input
,
output_ocf
,
dilation_h
,
dilation_w
,
stride
,
stride
,
padding
,
padding
,
padding
,
padding
);
col2im_ocf
(
*
context
,
output_ocf
,
dilation
,
stride
,
padding
,
&
input
);
if
(
paddle
::
platform
::
is_cpu_place
(
*
place
))
{
in_ptr
=
input
.
data
<
float
>
();
...
...
paddle/operators/math/vol2col.cc
浏览文件 @
356d6954
...
...
@@ -28,51 +28,51 @@ template <class T>
class
Vol2ColFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
vol
,
framework
::
Tensor
&
col
,
int
dilation_d
,
int
dilation_h
,
int
dilation_w
,
int
stride_depth
,
int
stride_height
,
int
stride_width
,
int
padding_depth
,
int
padding_height
,
int
padding_width
)
const
{
const
framework
::
Tensor
&
vol
,
const
std
::
vector
<
int
>&
dilations
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
col
)
const
{
PADDLE_ENFORCE
(
vol
.
dims
().
size
()
==
4
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
7
);
PADDLE_ENFORCE
(
col
->
dims
().
size
()
==
7
);
int
input_channels
=
vol
.
dims
()[
0
];
int
input_depth
=
vol
.
dims
()[
1
];
int
input_height
=
vol
.
dims
()[
2
];
int
input_width
=
vol
.
dims
()[
3
];
int
filter_depth
=
col
.
dims
()[
1
];
int
filter_height
=
col
.
dims
()[
2
];
int
filter_width
=
col
.
dims
()[
3
];
int
output_depth
=
col
.
dims
()[
4
];
int
output_height
=
col
.
dims
()[
5
];
int
output_width
=
col
.
dims
()[
6
];
int
filter_depth
=
col
->
dims
()[
1
];
int
filter_height
=
col
->
dims
()[
2
];
int
filter_width
=
col
->
dims
()[
3
];
int
output_depth
=
col
->
dims
()[
4
];
int
output_height
=
col
->
dims
()[
5
];
int
output_width
=
col
->
dims
()[
6
];
int
channels_col
=
input_channels
*
filter_depth
*
filter_height
*
filter_width
;
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
_depth
-
((
dilation
_d
*
(
filter_depth
-
1
)
+
1
)))
/
stride
_depth
+
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
s
[
0
]
-
((
dilation
s
[
0
]
*
(
filter_depth
-
1
)
+
1
)))
/
stride
s
[
0
]
+
1
,
output_depth
,
"input_depth and output_depth are "
"
M
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
_height
-
((
dilation
_h
*
(
filter_height
-
1
)
+
1
)))
/
stride
_height
+
"
m
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
s
[
1
]
-
((
dilation
s
[
1
]
*
(
filter_height
-
1
)
+
1
)))
/
stride
s
[
1
]
+
1
,
output_height
,
"input_height and output_height are "
"
M
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
_width
-
((
dilation
_w
*
(
filter_width
-
1
)
+
1
)))
/
stride
_width
+
"
m
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
s
[
2
]
-
((
dilation
s
[
2
]
*
(
filter_width
-
1
)
+
1
)))
/
stride
s
[
2
]
+
1
,
output_width
,
"input_width and output_width are "
"
M
ismatching."
);
"
m
ismatching."
);
const
T
*
vol_data
=
vol
.
data
<
T
>
();
T
*
col_data
=
col
.
data
<
T
>
();
T
*
col_data
=
col
->
data
<
T
>
();
for
(
int
c
=
0
;
c
<
channels_col
;
++
c
)
{
int
w_offset
=
c
%
filter_width
;
...
...
@@ -80,13 +80,11 @@ class Vol2ColFunctor<platform::CPUPlace, T> {
int
d_offset
=
(
c
/
filter_width
/
filter_height
)
%
filter_depth
;
int
c_in
=
c
/
filter_width
/
filter_height
/
filter_depth
;
for
(
int
d
=
0
;
d
<
output_depth
;
++
d
)
{
int
d_pad
=
d
*
stride
_depth
-
padding_depth
+
d_offset
*
dilation_d
;
int
d_pad
=
d
*
stride
s
[
0
]
-
paddings
[
0
]
+
d_offset
*
dilations
[
0
]
;
for
(
int
h
=
0
;
h
<
output_height
;
++
h
)
{
int
h_pad
=
h
*
stride_height
-
padding_height
+
h_offset
*
dilation_h
;
int
h_pad
=
h
*
strides
[
1
]
-
paddings
[
1
]
+
h_offset
*
dilations
[
1
];
for
(
int
w
=
0
;
w
<
output_width
;
++
w
)
{
int
w_pad
=
w
*
stride_width
-
padding_width
+
w_offset
*
dilation_w
;
int
w_pad
=
w
*
strides
[
2
]
-
paddings
[
2
]
+
w_offset
*
dilations
[
2
];
int
col_idx
=
((
c
*
output_depth
+
d
)
*
output_height
+
h
)
*
output_width
+
w
;
...
...
@@ -116,18 +114,18 @@ template <class T>
class
Col2VolFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
vol
,
const
framework
::
Tensor
&
col
,
int
dilation_d
,
int
dilation_h
,
int
dilation_w
,
int
stride_depth
,
int
stride_height
,
int
stride_width
,
int
padding_depth
,
int
padding_height
,
int
padding_width
)
const
{
PADDLE_ENFORCE
(
vol
.
dims
().
size
()
==
4
);
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilations
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
vol
)
const
{
PADDLE_ENFORCE
(
vol
->
dims
().
size
()
==
4
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
7
);
int
input_channels
=
vol
.
dims
()[
0
];
int
input_depth
=
vol
.
dims
()[
1
];
int
input_height
=
vol
.
dims
()[
2
];
int
input_width
=
vol
.
dims
()[
3
];
int
input_channels
=
vol
->
dims
()[
0
];
int
input_depth
=
vol
->
dims
()[
1
];
int
input_height
=
vol
->
dims
()[
2
];
int
input_width
=
vol
->
dims
()[
3
];
int
filter_depth
=
col
.
dims
()[
1
];
int
filter_height
=
col
.
dims
()[
2
];
int
filter_width
=
col
.
dims
()[
3
];
...
...
@@ -137,28 +135,28 @@ class Col2VolFunctor<platform::CPUPlace, T> {
int
channels_col
=
input_channels
*
filter_depth
*
filter_height
*
filter_width
;
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
_depth
-
((
dilation
_d
*
(
filter_depth
-
1
)
+
1
)))
/
stride
_depth
+
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
s
[
0
]
-
((
dilation
s
[
0
]
*
(
filter_depth
-
1
)
+
1
)))
/
stride
s
[
0
]
+
1
,
output_depth
,
"input_depth and output_depth are "
"
M
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
_height
-
((
dilation
_h
*
(
filter_height
-
1
)
+
1
)))
/
stride
_height
+
"
m
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
s
[
1
]
-
((
dilation
s
[
1
]
*
(
filter_height
-
1
)
+
1
)))
/
stride
s
[
1
]
+
1
,
output_height
,
"input_height and output_height are "
"
M
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
_width
-
((
dilation
_w
*
(
filter_width
-
1
)
+
1
)))
/
stride
_width
+
"
m
ismatching."
);
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
s
[
2
]
-
((
dilation
s
[
2
]
*
(
filter_width
-
1
)
+
1
)))
/
stride
s
[
2
]
+
1
,
output_width
,
"input_width and output_width are "
"
M
ismatching."
);
T
*
vol_data
=
vol
.
data
<
T
>
();
"
m
ismatching."
);
T
*
vol_data
=
vol
->
data
<
T
>
();
const
T
*
col_data
=
col
.
data
<
T
>
();
for
(
int
c
=
0
;
c
<
channels_col
;
++
c
)
{
...
...
@@ -167,13 +165,11 @@ class Col2VolFunctor<platform::CPUPlace, T> {
int
d_offset
=
(
c
/
filter_width
/
filter_height
)
%
filter_depth
;
int
cIm
=
c
/
filter_width
/
filter_height
/
filter_depth
;
for
(
int
d
=
0
;
d
<
output_depth
;
++
d
)
{
int
d_pad
=
d
*
stride
_depth
-
padding_depth
+
d_offset
*
dilation_d
;
int
d_pad
=
d
*
stride
s
[
0
]
-
paddings
[
0
]
+
d_offset
*
dilations
[
0
]
;
for
(
int
h
=
0
;
h
<
output_height
;
++
h
)
{
int
h_pad
=
h
*
stride_height
-
padding_height
+
h_offset
*
dilation_h
;
int
h_pad
=
h
*
strides
[
1
]
-
paddings
[
1
]
+
h_offset
*
dilations
[
1
];
for
(
int
w
=
0
;
w
<
output_width
;
++
w
)
{
int
w_pad
=
w
*
stride_width
-
padding_width
+
w_offset
*
dilation_w
;
int
w_pad
=
w
*
strides
[
2
]
-
paddings
[
2
]
+
w_offset
*
dilations
[
2
];
if
(
h_pad
>=
0
&&
h_pad
<
input_height
&&
w_pad
>=
0
&&
w_pad
<
input_width
&&
d_pad
>=
0
&&
d_pad
<
input_depth
)
{
...
...
paddle/operators/math/vol2col.cu
浏览文件 @
356d6954
...
...
@@ -71,42 +71,42 @@ template <class T>
class
Vol2ColFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
vol
,
framework
::
Tensor
&
col
,
int
dilation_d
,
int
dilation_h
,
int
dilation_w
,
int
stride_depth
,
int
stride_height
,
int
stride_width
,
int
padding_depth
,
int
padding_height
,
int
padding_width
)
const
{
const
framework
::
Tensor
&
vol
,
const
std
::
vector
<
int
>&
dilations
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
col
)
const
{
PADDLE_ENFORCE
(
vol
.
dims
().
size
()
==
4
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
7
);
PADDLE_ENFORCE
(
col
->
dims
().
size
()
==
7
);
int
input_channels
=
vol
.
dims
()[
0
];
int
input_depth
=
vol
.
dims
()[
1
];
int
input_height
=
vol
.
dims
()[
2
];
int
input_width
=
vol
.
dims
()[
3
];
int
filter_depth
=
col
.
dims
()[
1
];
int
filter_height
=
col
.
dims
()[
2
];
int
filter_width
=
col
.
dims
()[
3
];
int
output_depth
=
col
.
dims
()[
4
];
int
output_height
=
col
.
dims
()[
5
];
int
output_width
=
col
.
dims
()[
6
];
int
filter_depth
=
col
->
dims
()[
1
];
int
filter_height
=
col
->
dims
()[
2
];
int
filter_width
=
col
->
dims
()[
3
];
int
output_depth
=
col
->
dims
()[
4
];
int
output_height
=
col
->
dims
()[
5
];
int
output_width
=
col
->
dims
()[
6
];
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
_depth
-
((
dilation
_d
*
(
filter_depth
-
1
)
+
1
)))
/
stride
_depth
+
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
s
[
0
]
-
((
dilation
s
[
0
]
*
(
filter_depth
-
1
)
+
1
)))
/
stride
s
[
0
]
+
1
,
output_depth
,
"input_depth and output_depth are "
"Mismatching."
);
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
_height
-
((
dilation
_h
*
(
filter_height
-
1
)
+
1
)))
/
stride
_height
+
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
s
[
1
]
-
((
dilation
s
[
1
]
*
(
filter_height
-
1
)
+
1
)))
/
stride
s
[
1
]
+
1
,
output_height
,
"input_height and output_height are "
"Mismatching."
);
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
_width
-
((
dilation
_w
*
(
filter_width
-
1
)
+
1
)))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
s
[
2
]
-
((
dilation
s
[
2
]
*
(
filter_width
-
1
)
+
1
)))
/
stride
s
[
2
]
+
1
,
output_width
,
"input_width and output_width are "
...
...
@@ -121,10 +121,10 @@ class Vol2ColFunctor<platform::GPUPlace, T> {
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
num_outputs
,
vol
.
data
<
T
>
(),
input_depth
,
input_height
,
input_width
,
dilation
_d
,
dilation_h
,
dilation_w
,
filter_depth
,
filter_height
,
filter_width
,
stride
_depth
,
stride_height
,
stride_width
,
padding_depth
,
padding
_height
,
padding_width
,
output_depth
,
output_height
,
output_width
,
col
.
data
<
T
>
());
dilation
s
[
0
],
dilations
[
1
],
dilations
[
2
]
,
filter_depth
,
filter_height
,
filter_width
,
stride
s
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
]
,
padding
s
[
1
],
paddings
[
2
],
output_depth
,
output_height
,
output_width
,
col
->
data
<
T
>
());
}
};
...
...
@@ -200,18 +200,18 @@ template <class T>
class
Col2VolFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
vol
,
const
framework
::
Tensor
&
col
,
int
dilation_d
,
int
dilation_h
,
int
dilation_w
,
int
stride_depth
,
int
stride_height
,
int
stride_width
,
int
padding_depth
,
int
padding_height
,
int
padding_width
)
const
{
PADDLE_ENFORCE
(
vol
.
dims
().
size
()
==
4
);
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilations
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
vol
)
const
{
PADDLE_ENFORCE
(
vol
->
dims
().
size
()
==
4
);
PADDLE_ENFORCE
(
col
.
dims
().
size
()
==
7
);
int
input_channels
=
vol
.
dims
()[
0
];
int
input_depth
=
vol
.
dims
()[
1
];
int
input_height
=
vol
.
dims
()[
2
];
int
input_width
=
vol
.
dims
()[
3
];
int
input_channels
=
vol
->
dims
()[
0
];
int
input_depth
=
vol
->
dims
()[
1
];
int
input_height
=
vol
->
dims
()[
2
];
int
input_width
=
vol
->
dims
()[
3
];
int
filter_depth
=
col
.
dims
()[
1
];
int
filter_height
=
col
.
dims
()[
2
];
int
filter_width
=
col
.
dims
()[
3
];
...
...
@@ -219,23 +219,23 @@ class Col2VolFunctor<platform::GPUPlace, T> {
int
output_height
=
col
.
dims
()[
5
];
int
output_width
=
col
.
dims
()[
6
];
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
_depth
-
((
dilation
_d
*
(
filter_depth
-
1
)
+
1
)))
/
stride
_depth
+
PADDLE_ENFORCE_EQ
((
input_depth
+
2
*
padding
s
[
0
]
-
((
dilation
s
[
0
]
*
(
filter_depth
-
1
)
+
1
)))
/
stride
s
[
0
]
+
1
,
output_depth
,
"input_depth and output_depth are "
"Mismatching."
);
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
_height
-
((
dilation
_h
*
(
filter_height
-
1
)
+
1
)))
/
stride
_height
+
PADDLE_ENFORCE_EQ
((
input_height
+
2
*
padding
s
[
1
]
-
((
dilation
s
[
1
]
*
(
filter_height
-
1
)
+
1
)))
/
stride
s
[
1
]
+
1
,
output_height
,
"input_height and output_height are "
"Mismatching."
);
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
_width
-
((
dilation
_w
*
(
filter_width
-
1
)
+
1
)))
/
stride
_width
+
PADDLE_ENFORCE_EQ
((
input_width
+
2
*
padding
s
[
2
]
-
((
dilation
s
[
2
]
*
(
filter_width
-
1
)
+
1
)))
/
stride
s
[
2
]
+
1
,
output_width
,
"input_width and output_width are "
...
...
@@ -250,10 +250,10 @@ class Col2VolFunctor<platform::GPUPlace, T> {
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
num_kernels
,
col
.
data
<
T
>
(),
input_depth
,
input_height
,
input_width
,
dilation
_d
,
dilation_h
,
dilation_w
,
filter_depth
,
filter_height
,
filter_width
,
stride
_depth
,
stride_height
,
stride_width
,
padding_depth
,
padding
_height
,
padding_width
,
output_depth
,
output_height
,
output_width
,
vol
.
data
<
T
>
());
dilation
s
[
0
],
dilations
[
1
],
dilations
[
2
]
,
filter_depth
,
filter_height
,
filter_width
,
stride
s
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
]
,
padding
s
[
1
],
paddings
[
2
],
output_depth
,
output_height
,
output_width
,
vol
->
data
<
T
>
());
}
};
...
...
paddle/operators/math/vol2col.h
浏览文件 @
356d6954
...
...
@@ -31,6 +31,15 @@ namespace math {
* \param colData Column data.
* \param colShape The shape of colData.
*
* \param dilations dilation data.
* \param 3-dimension [dilation_depth, dilation_height, dilation_width].
*
* \param strides stride data.
* \param 3-dimension [stride_depth, stride_height, stride_width].
*
* \param paddings padding data.
* \param 3-dimension [d_pad, h_pad, w_pad].
*
* The shape of colData is:
* [input_channels, filter_depth, filter_height, filter_width, output_depth,
* output_height, output_width]
...
...
@@ -57,22 +66,22 @@ template <typename Place, typename T>
class
Vol2ColFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
vol
,
framework
::
Tensor
&
col
,
int
dilation_d
,
int
dilation_h
,
int
dilation_w
,
int
stride_depth
,
int
stride_height
,
int
stride_width
,
int
padding_depth
,
int
padding_height
,
int
padding_width
)
const
;
const
framework
::
Tensor
&
vol
,
const
std
::
vector
<
int
>&
dilations
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
col
)
const
;
};
template
<
typename
Place
,
typename
T
>
class
Col2VolFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
vol
,
const
framework
::
Tensor
&
col
,
int
dilation_d
,
int
dilation_h
,
int
dilation_w
,
int
stride_depth
,
int
stride_height
,
int
stride_width
,
int
padding_depth
,
int
padding_height
,
int
padding_width
)
const
;
const
framework
::
Tensor
&
col
,
const
std
::
vector
<
int
>&
dilations
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
vol
)
const
;
};
}
// namespace math
...
...
paddle/operators/math/vol2col_test.cc
浏览文件 @
356d6954
...
...
@@ -62,12 +62,15 @@ void testVol2col() {
int
input_height
=
2
;
int
input_width
=
3
;
int
filter_size
=
2
;
int
stride
=
1
;
int
padding
=
0
;
int
dilation
=
1
;
int
output_depth
=
(
input_depth
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
int
output_height
=
(
input_height
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
int
output_width
=
(
input_width
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
std
::
vector
<
int
>
strides
({
1
,
1
,
1
});
std
::
vector
<
int
>
paddings
({
0
,
0
,
0
});
std
::
vector
<
int
>
dilations
({
1
,
1
,
1
});
int
output_depth
=
(
input_depth
-
filter_size
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
;
int
output_height
=
(
input_height
-
filter_size
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
;
int
output_width
=
(
input_width
-
filter_size
+
2
*
paddings
[
2
])
/
strides
[
2
]
+
1
;
// Vol2Col test
float
*
input_ptr
=
...
...
@@ -86,8 +89,7 @@ void testVol2col() {
*
place
);
paddle
::
operators
::
math
::
Vol2ColFunctor
<
Place
,
float
>
vol2col
;
vol2col
(
*
context
,
input
,
output
,
dilation
,
dilation
,
dilation
,
stride
,
stride
,
stride
,
padding
,
padding
,
padding
);
vol2col
(
*
context
,
input
,
dilations
,
strides
,
paddings
,
&
output
);
float
vol_2_col
[]
=
{
0
,
1
,
1
,
2
,
3
,
4
,
4
,
5
,
6
,
7
,
7
,
8
,
9
,
10
,
10
,
11
};
float
*
out_cfo_ptr
;
...
...
@@ -112,8 +114,7 @@ void testVol2col() {
}
paddle
::
operators
::
math
::
Col2VolFunctor
<
Place
,
float
>
col2vol
;
col2vol
(
*
context
,
input
,
output
,
dilation
,
dilation
,
dilation
,
stride
,
stride
,
stride
,
padding
,
padding
,
padding
);
col2vol
(
*
context
,
output
,
dilations
,
strides
,
paddings
,
&
input
);
float
*
in_ptr
;
if
(
paddle
::
platform
::
is_cpu_place
(
*
place
))
{
...
...
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