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体验新版 GitCode,发现更多精彩内容 >>
提交
f302c6a3
编写于
11月 06, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
write conv2d and conv3d together
上级
ba7db29d
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
145 addition
and
294 deletion
+145
-294
paddle/operators/conv_cudnn_op.cc
paddle/operators/conv_cudnn_op.cc
+3
-3
paddle/operators/conv_op.cc
paddle/operators/conv_op.cc
+6
-6
paddle/operators/conv_op.cu
paddle/operators/conv_op.cu
+6
-6
paddle/operators/conv_op.h
paddle/operators/conv_op.h
+124
-271
python/paddle/v2/framework/tests/test_conv2d_op.py
python/paddle/v2/framework/tests/test_conv2d_op.py
+3
-5
python/paddle/v2/framework/tests/test_conv3d_op.py
python/paddle/v2/framework/tests/test_conv3d_op.py
+3
-3
未找到文件。
paddle/operators/conv_cudnn_op.cc
浏览文件 @
f302c6a3
...
...
@@ -41,8 +41,8 @@ namespace ops = paddle::operators;
REGISTER_OP
(
conv_cudnn
,
ops
::
ConvOp
,
ops
::
CudnnConvOpMaker
,
conv_cudnn_grad
,
ops
::
ConvOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv_cudnn
,
ops
::
GemmConv2D
Kernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv_cudnn
,
ops
::
GemmConv
Kernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv_cudnn_grad
,
ops
::
GemmConvGrad
2D
Kernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/conv_op.cc
浏览文件 @
f302c6a3
...
...
@@ -198,12 +198,12 @@ namespace ops = paddle::operators;
REGISTER_OP
(
conv3d
,
ops
::
ConvOp
,
ops
::
Conv3DOpMaker
,
conv3d_grad
,
ops
::
ConvOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv2d
,
ops
::
GemmConvKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv2d
,
ops
::
GemmConv2DKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv2d_grad
,
ops
::
GemmConvGrad2DKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
conv2d_grad
,
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv3d
,
ops
::
GemmConvKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv3d
,
ops
::
GemmConv3DKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv3d_grad
,
ops
::
GemmConvGrad3DKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
conv3d_grad
,
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/conv_op.cu
浏览文件 @
f302c6a3
...
...
@@ -16,12 +16,12 @@
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
conv2d
,
ops
::
GemmConvKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv2d
,
ops
::
GemmConv2DKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv2d_grad
,
ops
::
GemmConvGrad2DKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
conv2d_grad
,
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv3d
,
ops
::
GemmConvKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv3d
,
ops
::
GemmConv3DKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv3d_grad
,
ops
::
GemmConvGrad3DKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
conv3d_grad
,
ops
::
GemmConvGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/conv_op.h
浏览文件 @
f302c6a3
...
...
@@ -62,7 +62,7 @@ class ConvOpGrad : public framework::OperatorWithKernel {
};
template
<
typename
Place
,
typename
T
>
class
GemmConv
2D
Kernel
:
public
framework
::
OpKernel
<
T
>
{
class
GemmConvKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
...
...
@@ -77,49 +77,78 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
int
batch_size
=
input
->
dims
()[
0
];
int
input_channels
=
input
->
dims
()[
1
];
int
filter_height
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
2
];
int
filter_width
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
1
];
int
output_channels
=
output
->
dims
()[
1
];
int
output_height
=
output
->
dims
()[
2
];
int
output_width
=
output
->
dims
()[
3
];
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
// filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
std
::
vector
<
int64_t
>
filter_shape_vec
(
framework
::
vectorize
(
filter
.
dims
()));
filter_shape_vec
.
erase
(
filter_shape_vec
.
begin
(),
filter_shape_vec
.
begin
()
+
2
);
// output_shape_vec: {o_h, o_w} or {o_d, o_h, o_w}
std
::
vector
<
int64_t
>
output_shape_vec
(
framework
::
vectorize
(
output
->
dims
()));
output_shape_vec
.
erase
(
output_shape_vec
.
begin
(),
output_shape_vec
.
begin
()
+
2
);
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_height
,
filter_width
,
output_height
,
output_width
};
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w, o_d,
// o_h, o_w}
std
::
vector
<
int64_t
>
col_shape_vec
;
col_shape_vec
.
push_back
(
input
->
dims
()[
1
]
/
groups
);
col_shape_vec
.
insert
(
col_shape_vec
.
end
(),
filter_shape_vec
.
begin
(),
filter_shape_vec
.
end
());
col_shape_vec
.
insert
(
col_shape_vec
.
end
(),
output_shape_vec
.
begin
(),
output_shape_vec
.
end
());
framework
::
DDim
col_shape
(
framework
::
make_ddim
(
col_shape_vec
));
// use col_matrix_shape in the gemm calculation
framework
::
DDim
col_matrix_shape
=
{
input_channels
/
groups
*
filter_height
*
filter_width
,
output_height
*
output_width
};
// size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w, o_d *
// o_h * o_w)
framework
::
DDim
col_matrix_shape
=
framework
::
flatten_to_2d
(
col_shape
,
filter_shape_vec
.
size
()
+
1
);
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// 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
=
col
;
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
framework
::
DDim
input_shape
=
{
input
->
dims
()[
1
],
input
->
dims
()[
2
],
input
->
dims
()[
3
]};
framework
::
DDim
input_shape
=
framework
::
slice_ddim
(
input
->
dims
(),
1
,
static_cast
<
int
>
(
input
->
dims
().
size
()));
framework
::
DDim
filter_matrix_shape
=
{
filter
.
dims
()[
0
],
filter
.
numel
()
/
filter
.
dims
()[
0
]};
filter
.
Resize
(
filter_matrix_shape
);
framework
::
DDim
output_matrix_shape
=
{
output_channels
,
output_height
*
output_width
};
// convolution operator: im2col + gemm
int
in_step
=
input_channels
/
groups
;
int
out_step
=
output_channels
/
groups
;
framework
::
DDim
output_matrix_shape
=
{
output
->
dims
()[
1
],
output
->
numel
()
/
(
output
->
dims
()[
0
]
*
output
->
dims
()[
1
])};
// convolution operator: im2col(or vol2col) + gemm
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
out_step
=
static_cast
<
int
>
(
output
->
dims
()[
1
])
/
groups
;
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
++
)
{
// im2col
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
im2col
(
context
.
device_context
(),
in_slice
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
if
(
filter_shape_vec
.
size
()
==
2
)
{
// im2col
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
im2col
(
context
.
device_context
(),
in_slice
,
col
,
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
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
}
// gemm
Tensor
out_slice
=
out_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
...
...
@@ -132,7 +161,7 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
};
template
<
typename
Place
,
typename
T
>
class
GemmConvGrad
2D
Kernel
:
public
framework
::
OpKernel
<
T
>
{
class
GemmConvGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
...
...
@@ -142,267 +171,74 @@ class GemmConvGrad2DKernel : public framework::OpKernel<T> {
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
// 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"
);
if
(
!
input_grad
&&
!
filter_grad
)
return
;
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"
);
int
batch_size
=
input
->
dims
()[
0
];
int
input_channels
=
input
->
dims
()[
1
];
int
filter_height
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
2
];
int
filter_width
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
1
];
int
output_channels
=
output_grad
->
dims
()[
1
];
int
output_height
=
output_grad
->
dims
()[
2
];
int
output_width
=
output_grad
->
dims
()[
3
];
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col and col2im calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_height
,
filter_width
,
output_height
,
output_width
};
// use col_matrix_shape in the gemm calculation
framework
::
DDim
col_matrix_shape
=
{
input_channels
/
groups
*
filter_height
*
filter_width
,
output_height
*
output_width
};
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// 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
=
col
;
col_matrix
.
Resize
(
col_matrix_shape
);
framework
::
DDim
input_shape
=
{
input
->
dims
()[
1
],
input
->
dims
()[
2
],
input
->
dims
()[
3
]};
framework
::
DDim
output_matrix_shape
=
{
output_grad
->
dims
()[
1
],
output_grad
->
dims
()[
2
]
*
output_grad
->
dims
()[
3
]};
framework
::
DDim
filter_matrix_shape
=
{
filter
.
dims
()[
0
],
filter
.
numel
()
/
filter
.
dims
()[
0
]};
filter
.
Resize
(
filter_matrix_shape
);
// convolution backward input operator: gemm + col2im
// convolution backward weight operator: im2col + gemm
int
in_step
=
input_channels
/
groups
;
int
out_step
=
output_channels
/
groups
;
math
::
SetConstant
<
Place
,
T
>
set_zero
;
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
set_zero
(
context
.
device_context
(),
input_grad
,
static_cast
<
T
>
(
0
));
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
));
// col2im
Tensor
in_grad_slice
=
in_grad_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
col2im
(
context
.
device_context
(),
in_grad_slice
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
}
}
}
if
(
filter_grad
)
{
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
filter_grad_
=
*
filter_grad
;
filter_grad_
.
Resize
(
filter_matrix_shape
);
set_zero
(
context
.
device_context
(),
filter_grad
,
static_cast
<
T
>
(
0
));
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
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
);
im2col
(
context
.
device_context
(),
in_slice
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
// 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
));
}
}
}
}
};
// filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
std
::
vector
<
int64_t
>
filter_shape_vec
(
framework
::
vectorize
(
filter
.
dims
()));
filter_shape_vec
.
erase
(
filter_shape_vec
.
begin
(),
filter_shape_vec
.
begin
()
+
2
);
template
<
typename
Place
,
typename
T
>
class
GemmConv3DKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
// The filter 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
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// output_shape_vec: {o_h, o_w} or {o_d, o_h, o_w}
std
::
vector
<
int64_t
>
output_shape_vec
(
framework
::
vectorize
(
output_grad
->
dims
()));
output_shape_vec
.
erase
(
output_shape_vec
.
begin
(),
output_shape_vec
.
begin
()
+
2
);
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"
);
// use col_shape in the im2col calculation
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w, o_d,
// o_h, o_w}
std
::
vector
<
int64_t
>
col_shape_vec
;
col_shape_vec
.
push_back
(
input
->
dims
()[
1
]
/
groups
);
col_shape_vec
.
insert
(
col_shape_vec
.
end
(),
filter_shape_vec
.
begin
(),
filter_shape_vec
.
end
());
col_shape_vec
.
insert
(
col_shape_vec
.
end
(),
output_shape_vec
.
begin
(),
output_shape_vec
.
end
());
framework
::
DDim
col_shape
(
framework
::
make_ddim
(
col_shape_vec
));
int
batch_size
=
input
->
dims
()[
0
];
int
input_channels
=
input
->
dims
()[
1
];
int
filter_depth
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
3
];
int
filter_height
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
2
];
int
filter_width
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
1
];
int
output_channels
=
output
->
dims
()[
1
];
int
output_depth
=
output
->
dims
()[
2
];
int
output_height
=
output
->
dims
()[
3
];
int
output_width
=
output
->
dims
()[
4
];
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
// use col_shape in the vol2col calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_depth
,
filter_height
,
filter_width
,
output_depth
,
output_height
,
output_width
};
// use col_matrix_shape in the gemm calculation
framework
::
DDim
col_matrix_shape
=
{
input_channels
/
groups
*
filter_depth
*
filter_height
*
filter_width
,
output_depth
*
output_height
*
output_width
};
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// 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
=
col
;
col_matrix
.
Resize
(
col_matrix_shape
);
// size: (i_c/g * k_h * k_w, o_h * o_w)
// or
// (i_c/g * k_d * k_h * k_w, o_d * o_h * o_w)
framework
::
DDim
col_matrix_shape
=
framework
::
flatten_to_2d
(
col_shape
,
filter_shape_vec
.
size
()
+
1
);
framework
::
DDim
input_shape
=
framework
::
slice_ddim
(
input
->
dims
(),
1
,
static_cast
<
int
>
(
input
->
dims
().
size
()));
framework
::
DDim
input_shape
=
{
input
->
dims
()[
1
],
input
->
dims
()[
2
],
input
->
dims
()[
3
],
input
->
dims
()[
4
]};
// channel, depth, height, width
framework
::
DDim
filter_matrix_shape
=
{
filter
.
dims
()[
0
],
filter
.
numel
()
/
filter
.
dims
()[
0
]};
// filter_out_channel,
// filter_in_channel*filter_depth*filter_height*filter_width
framework
::
DDim
filter_matrix_shape
=
{
filter
.
dims
()[
0
],
filter
.
numel
()
/
filter
.
dims
()[
0
]};
filter
.
Resize
(
filter_matrix_shape
);
framework
::
DDim
output_matrix_shape
=
{
output_channels
,
output_depth
*
output_height
*
output_width
};
// convolution operator: vol2col + gemm
int
in_step
=
input_channels
/
groups
;
int
out_step
=
output_channels
/
groups
;
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
++
)
{
// vol2col
Tensor
in_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
vol2col
(
context
.
device_context
(),
in_slice
,
col
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
// 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
));
}
}
}
};
template
<
typename
Place
,
typename
T
>
class
GemmConvGrad3DKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
const
Tensor
*
output_grad
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Output"
));
Tensor
*
input_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
// 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"
);
output_grad
->
dims
()[
1
],
output_grad
->
numel
()
/
(
output_grad
->
dims
()[
0
]
*
output_grad
->
dims
()[
1
])};
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"
);
// convolution backward input operator: gemm + col2im(or col2vol)
// convolution backward weight operator: im2col(or vol2col) + gemm
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
out_step
=
static_cast
<
int
>
(
output_grad
->
dims
()[
1
])
/
groups
;
int
batch_size
=
input
->
dims
()[
0
];
int
input_channels
=
input
->
dims
()[
1
];
int
filter_depth
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
3
];
int
filter_height
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
2
];
int
filter_width
=
filter
.
dims
()[
filter
.
dims
().
size
()
-
1
];
int
output_channels
=
output_grad
->
dims
()[
1
];
int
output_depth
=
output_grad
->
dims
()[
2
];
int
output_height
=
output_grad
->
dims
()[
3
];
int
output_width
=
output_grad
->
dims
()[
4
];
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
// use col_shape in the vol2col and col2vol calculation
framework
::
DDim
col_shape
=
{
input_channels
/
groups
,
filter_depth
,
filter_height
,
filter_width
,
output_depth
,
output_height
,
output_width
};
// use col_matrix_shape in the gemm calculation
framework
::
DDim
col_matrix_shape
=
{
input_channels
/
groups
*
filter_depth
*
filter_height
*
filter_width
,
output_depth
*
output_height
*
output_width
};
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// 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
=
col
;
Tensor
col_matrix
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
framework
::
DDim
input_shape
=
{
input
->
dims
()[
1
],
input
->
dims
()[
2
],
input
->
dims
()[
3
],
input
->
dims
()[
4
]};
// channel, depth, height, width
framework
::
DDim
output_matrix_shape
=
{
output_grad
->
dims
()[
1
],
output_grad
->
dims
()[
2
]
*
output_grad
->
dims
()[
3
]
*
output_grad
->
dims
()[
4
]};
framework
::
DDim
filter_matrix_shape
=
{
filter
.
dims
()[
0
],
filter
.
numel
()
/
filter
.
dims
()[
0
]};
// filter_out_channel,
// filter_in_channel*filter_depth*filter_height*filter_width
filter
.
Resize
(
filter_matrix_shape
);
// convolution backward input operator: gemm + col2vol
// convolution backward weight operator: vol2col + gemm
int
in_step
=
input_channels
/
groups
;
int
out_step
=
output_channels
/
groups
;
math
::
SetConstant
<
Place
,
T
>
set_zero
;
if
(
input_grad
)
{
...
...
@@ -421,13 +257,22 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter_slice
,
true
,
out_grad_slice
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
// col2vol
// col2im
Tensor
in_grad_slice
=
in_grad_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
col2vol
(
context
.
device_context
(),
in_grad_slice
,
col
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
if
(
filter_shape_vec
.
size
()
==
2
)
{
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
col2im
(
context
.
device_context
(),
in_grad_slice
,
col
,
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
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
}
}
}
}
...
...
@@ -443,13 +288,22 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
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
++
)
{
//
vol
2col
//
im
2col
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
);
vol2col
(
context
.
device_context
(),
in_slice
,
col
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
if
(
filter_shape_vec
.
size
()
==
2
)
{
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
im2col
(
context
.
device_context
(),
in_slice
,
col
,
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
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
}
// gemm
Tensor
filter_grad_slice
=
...
...
@@ -462,6 +316,5 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_conv2d_op.py
浏览文件 @
f302c6a3
...
...
@@ -61,25 +61,23 @@ class TestConv2dOp(OpTest):
def
test_check_grad
(
self
):
self
.
check_grad
(
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.0
5
)
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.0
2
)
def
test_check_grad_no_filter
(
self
):
self
.
check_grad
(
[
'Input'
],
'Output'
,
max_relative_error
=
0.0
5
,
max_relative_error
=
0.0
2
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad_no_input
(
self
):
self
.
check_grad
(
[
'Filter'
],
'Output'
,
max_relative_error
=
0.0
5
,
max_relative_error
=
0.0
2
,
no_grad_set
=
set
([
'Input'
]))
def
init_test_case
(
self
):
# self.groups = 1
# self.op_type = "conv2d"
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
...
...
python/paddle/v2/framework/tests/test_conv3d_op.py
浏览文件 @
f302c6a3
...
...
@@ -64,20 +64,20 @@ class TestConv3dOp(OpTest):
def
test_check_grad
(
self
):
self
.
check_grad
(
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.0
5
)
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.0
3
)
def
test_check_grad_no_filter
(
self
):
self
.
check_grad
(
[
'Input'
],
'Output'
,
max_relative_error
=
0.0
5
,
max_relative_error
=
0.0
3
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad_no_input
(
self
):
self
.
check_grad
(
[
'Filter'
],
'Output'
,
max_relative_error
=
0.0
5
,
max_relative_error
=
0.0
3
,
no_grad_set
=
set
([
'Input'
]))
def
init_test_case
(
self
):
...
...
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