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f3669ca3
编写于
9月 18, 2017
作者:
H
hedaoyuan
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Support input_grad = null or filter_grad = null.
上级
64b0b756
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
68 addition
and
33 deletion
+68
-33
paddle/operators/conv2d_op.cc
paddle/operators/conv2d_op.cc
+9
-2
paddle/operators/gemm_conv2d_op.h
paddle/operators/gemm_conv2d_op.h
+53
-31
python/paddle/v2/framework/tests/test_conv2d_op.py
python/paddle/v2/framework/tests/test_conv2d_op.py
+6
-0
未找到文件。
paddle/operators/conv2d_op.cc
浏览文件 @
f3669ca3
...
...
@@ -28,6 +28,13 @@ class Conv2DOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Input"
),
"Input(Input) of Conv2DOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Filter"
),
"Input(Filter) of Conv2DOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Output"
),
"Output(Output) of Conv2DOp should not be null."
);
auto
in
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
auto
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Output"
);
...
...
@@ -108,8 +115,8 @@ class Conv2DOpGrad : public framework::OperatorWithKernel {
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Input"
));
auto
d_filter
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Filter"
));
d_in
->
Resize
(
in
->
dims
());
d_filter
->
Resize
(
filter
->
dims
());
if
(
d_in
)
d_in
->
Resize
(
in
->
dims
());
if
(
d_filter
)
d_filter
->
Resize
(
filter
->
dims
());
}
};
...
...
paddle/operators/gemm_conv2d_op.h
浏览文件 @
f3669ca3
...
...
@@ -111,14 +111,16 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad_
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
filter_grad_
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// The filter and filter_grad will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
filter_grad
=
*
filter_grad_
;
Tensor
filter_grad
;
if
(
filter_grad_
)
{
filter_grad_
->
mutable_data
<
T
>
(
context
.
GetPlace
());
filter_grad
=
*
filter_grad_
;
}
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
...
...
@@ -162,12 +164,20 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
framework
::
DDim
filter_matrix_shape
=
{
filter
.
dims
()[
0
],
filter
.
numel
()
/
filter
.
dims
()[
0
]};
filter
.
Resize
(
filter_matrix_shape
);
filter_grad
.
Resize
(
filter_matrix_shape
);
if
(
filter_grad_
)
{
filter_grad
.
Resize
(
filter_matrix_shape
);
auto
t1
=
framework
::
EigenVector
<
T
>::
Flatten
(
filter_grad
);
t1
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t1
.
constant
(
static_cast
<
T
>
(
0
));
t1
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t1
.
constant
(
static_cast
<
T
>
(
0
));
}
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t2
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_grad
);
t2
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t2
.
constant
(
static_cast
<
T
>
(
0
));
t2
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t2
.
constant
(
static_cast
<
T
>
(
0
));
}
auto
*
device_context
=
const_cast
<
platform
::
DeviceContext
*>
(
context
.
device_context_
);
...
...
@@ -176,16 +186,24 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
// convolution backward weight operator: im2col + gemm
int
in_step
=
input_channels
/
groups
;
int
out_step
=
output_channels
/
groups
;
Tensor
in_grad_batch
;
Tensor
in_batch
;
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
out_grad_batch
=
output_grad
->
Slice
<
T
>
(
i
,
i
+
1
).
Resize
(
output_matrix_shape
);
Tensor
in_grad_batch
=
input_grad
->
Slice
<
T
>
(
i
,
i
+
1
).
Resize
(
input_shape
);
Tensor
in_batch
=
input
->
Slice
<
T
>
(
i
,
i
+
1
).
Resize
(
input_shape
);
if
(
input_grad
)
{
in_grad_batch
=
input_grad
->
Slice
<
T
>
(
i
,
i
+
1
).
Resize
(
input_shape
);
}
if
(
filter_grad_
)
{
in_batch
=
input
->
Slice
<
T
>
(
i
,
i
+
1
).
Resize
(
input_shape
);
}
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
// gemm
Tensor
out_grad_slice
=
out_grad_batch
.
Slice
<
T
>
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
Tensor
filter_slice
=
filter
.
Slice
<
T
>
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
if
(
input_grad
)
{
// gemm
Tensor
filter_slice
=
filter
.
Slice
<
T
>
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
filter_slice
,
true
,
out_grad_slice
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
),
device_context
);
...
...
@@ -194,17 +212,21 @@ class GemmConvGrad2DKernel : public framework::OpKernel {
in_grad_batch
.
Slice
<
T
>
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
col2im
(
in_grad_slice
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
1
],
device_context
);
}
if
(
filter_grad_
)
{
// im2col
Tensor
in_slice
=
in_batch
.
Slice
<
T
>
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
im2col
(
in_slice
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
1
],
device_context
);
im2col
(
in_slice
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
1
],
device_context
);
// gemm
Tensor
filter_grad_slice
=
filter_grad
.
Slice
<
T
>
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
math
::
matmul
<
Place
,
T
>
(
out_grad_slice
,
false
,
col_matrix
,
true
,
T
(
1.0
),
&
filter_grad_slice
,
T
(
1.0
),
device_context
);
math
::
matmul
<
Place
,
T
>
(
out_grad_slice
,
false
,
col_matrix
,
true
,
T
(
1.0
),
&
filter_grad_slice
,
T
(
1.0
),
device_context
);
}
}
}
}
...
...
python/paddle/v2/framework/tests/test_conv2d_op.py
浏览文件 @
f3669ca3
...
...
@@ -75,6 +75,12 @@ class TestConv2dOp(OpTest):
def
test_check_grad
(
self
):
self
.
check_grad
(
set
([
'Input'
,
'Filter'
]),
'Output'
)
def
test_check_grad_no_filter
(
self
):
self
.
check_grad
([
'Input'
],
'Output'
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad_no_input
(
self
):
self
.
check_grad
([
'Filter'
],
'Output'
,
no_grad_set
=
set
([
'Input'
]))
def
init_groups
(
self
):
self
.
groups
=
1
...
...
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