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82aa5693
编写于
11月 04, 2017
作者:
C
chengduoZH
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-180
paddle/operators/conv_transpose_op.h
paddle/operators/conv_transpose_op.h
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paddle/operators/conv_transpose_op.h
浏览文件 @
82aa5693
...
@@ -63,29 +63,25 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
...
@@ -63,29 +63,25 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
// The filter will be reshaped, so it should not be constant pointer
// The filter will be reshaped, so it should not be constant pointer
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// TODO(Zhuoyuan): Paddings can be added in future.
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
// groups will alway be disabled in conv2dtranspose.
const
int
batch_size
=
input
->
dims
()[
0
]
;
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
])
;
const
int
m
=
input
->
dims
()[
1
];
const
int
64_t
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
64_t
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
64_t
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
64_t
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
64_t
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output
->
dims
()[
1
];
// output channels
const
int
64_t
c
=
output
->
dims
()[
1
];
// output channels
const
int
o_h
=
output
->
dims
()[
2
];
const
int
64_t
o_h
=
output
->
dims
()[
2
];
const
int
o_w
=
output
->
dims
()[
3
];
const
int
64_t
o_w
=
output
->
dims
()[
3
];
paddle
::
operators
::
math
::
Col2ImFunctor
<
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
// use col_shape in the im2col and col2im calculation
// use col_shape in the im2col and col2im calculation
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
...
@@ -105,19 +101,18 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
...
@@ -105,19 +101,18 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
// filter size: (m, c * k_h * k_w)
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose: gemm + col2im (similar to conv-backward on input)
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
output
)
;
math
::
SetConstant
<
Place
,
T
>
set_zero
;
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
set_zero
(
context
.
device_context
(),
output
,
static_cast
<
T
>
(
0
));
// convolution transpose: gemm + col2im (similar to conv-backward on input)
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
// batch with size (
M
, h * w)
// batch with size (
m
, h * w)
Tensor
input_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
Tensor
input_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// filter size: (M, c * k_h * k_w)
// output size: (c, o_h, o_w)
// output size: (c, o_h, o_w)
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
...
@@ -125,7 +120,11 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
...
@@ -125,7 +120,11 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
// col_matrix = filter * input_batch
// col_matrix = filter * input_batch
// of shape (c * k_h * k_w, h * w)
// of shape (c * k_h * k_w, h * w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
true
,
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
true
,
input_batch
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
input_batch
,
false
,
static_cast
<
T
>
(
1.0
),
&
col_matrix
,
static_cast
<
T
>
(
0.0
));
// col2im: col_matrix -> dy
// from (c * k_h * k_w, h * w) to (c, o_h, o_w)
col2im
(
context
.
device_context
(),
output_batch
,
col
,
strides
[
0
],
col2im
(
context
.
device_context
(),
output_batch
,
col
,
strides
[
0
],
strides
[
1
],
0
,
0
,
0
,
0
);
strides
[
1
],
0
,
0
,
0
,
0
);
}
}
...
@@ -143,7 +142,6 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -143,7 +142,6 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
// For filter, we do not use const pointer b/c we will do reshape,
// For filter, we do not use const pointer b/c we will do reshape,
// but we should avoid modifying its value.
// but we should avoid modifying its value.
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
input_grad
=
Tensor
*
input_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
Tensor
*
filter_grad
=
...
@@ -153,35 +151,24 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -153,35 +151,24 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
// Actually, no paddings and groups allowed in conv transpose.
// Actually, no paddings and groups allowed in conv transpose.
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
const
int
batch_size
=
input
->
dims
()[
0
]
;
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
])
;
const
int
m
=
input
->
dims
()[
1
];
const
int
64_t
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
64_t
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
64_t
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
64_t
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
64_t
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output_grad
->
dims
()[
1
];
// output channels
const
int
64_t
c
=
output_grad
->
dims
()[
1
];
// output channels
const
int
o_h
=
output_grad
->
dims
()[
2
];
const
int
64_t
o_h
=
output_grad
->
dims
()[
2
];
const
int
o_w
=
output_grad
->
dims
()[
3
];
const
int
64_t
o_w
=
output_grad
->
dims
()[
3
];
// Only im2col functor required for bp to get to the right shape
// Only im2col functor required for bp to get to the right shape
paddle
::
operators
::
math
::
Im2ColFunctor
<
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col and col2im calculation
// use col_shape in the im2col and col2im calculation
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
// use col_matrix_shape in the gemm calculation
DDim
col_matrix_shape_f
=
{
c
*
h
*
w
,
k_h
*
k_w
};
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.
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
...
@@ -191,67 +178,60 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -191,67 +178,60 @@ class GemmConv2DTransposeGradKernel : public framework::OpKernel<T> {
// convolution transpose grad on input:
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
// input need to compute gradient
if
(
input_grad
)
{
if
(
input_grad
||
filter_grad
)
{
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
;
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
ShareDataWith
(
col
);
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
w
};
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
w
};
col_matrix
.
Resize
(
col_matrix_shape
);
col_matrix
.
Resize
(
col_matrix_shape
);
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
filter_grad_
;
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_grad
);
math
::
SetConstant
<
Place
,
T
>
set_zero
;
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
// batch with size (c, o_h * o_w)
Tensor
output_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// filter of size (m, c * k_h * k_w)
// batch with size (m, h, w)
Tensor
input_grad_batch
=
input_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// im2col: dy from (c, o_h, o_w) -> (c * k_h * k_w, h * w)
if
(
input_grad
)
{
i
m2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
i
nput_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]
);
set_zero
(
context
.
device_context
(),
input_grad
,
static_cast
<
T
>
(
0
)
);
}
// gemm: dx = filter * dy
if
(
filter_grad
)
{
// filter size (m, c, k_h, k_w)
// (m, c * k_h * k_w) * (c * k_h * k_w, h * w) -> (m, c, h)
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
false
,
set_zero
(
context
.
device_context
(),
filter_grad
,
static_cast
<
T
>
(
0
));
col_matrix
,
false
,
T
(
1.0
),
&
input_grad_batch
,
filter_grad_
=
*
filter_grad
;
T
(
0.0
)
);
filter_grad_
.
Resize
(
filter_matrix_shape
);
}
}
}
// filter gradient required
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
if
(
filter_grad
)
{
// batch with size (c, o_h * o_w)
Tensor
col_matrix_f
;
col_matrix_f
.
ShareDataWith
(
col
);
DDim
col_matrix_shape_f
=
{
c
*
h
*
w
,
k_h
*
k_w
};
col_matrix_f
.
Resize
(
col_matrix_shape_f
);
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
filter_grad_
=
*
filter_grad
;
filter_grad_
.
Resize
(
filter_matrix_shape
);
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
filter_grad_
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
// batch with size (c, o_h, o_w)
Tensor
output_grad_batch
=
Tensor
output_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// input batch
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// im2col: (c * h * w, k_h * k_w)
// im2col: dy -> col matrix
// from (c, o_h, o_w) to (c * k_h * k_w, h * w)
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
// gemm: d_filter = x * y_grad^T
if
(
input_grad
)
{
// (m, c * h * w) * (k_h * k_w, c * h * w) -> (m, c, h)
// batch with size (m, h, w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
in_batch
,
false
,
Tensor
input_grad_batch
=
col_matrix_f
,
true
,
T
(
1.0
),
&
filter_grad_
,
input_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
T
(
1.0
));
// gemm: dx = filter * dy
// (m, c * k_h * k_w) * (c * k_h * k_w, h * w) -> (m, h * w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
false
,
col_matrix
,
false
,
static_cast
<
T
>
(
1.0
),
&
input_grad_batch
,
static_cast
<
T
>
(
0.0
));
}
if
(
filter_grad
)
{
// input batch
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// gemm: d_filter = x * dy^T
// (m, c * h * w) * (k_h * k_w, c * h * w) -> (m, k_h * k_w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
in_batch
,
false
,
col_matrix
,
true
,
static_cast
<
T
>
(
1.0
),
&
filter_grad_
,
static_cast
<
T
>
(
1.0
));
}
}
}
}
}
}
}
...
@@ -267,30 +247,28 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
...
@@ -267,30 +247,28 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// TODO(chengduo): Paddings can be added in future.
// TODO(chengduo): Paddings can be added in future.
// groups will alway be disabled in conv3dtranspose.
// groups will alway be disabled in conv3dtranspose.
const
int
batch_size
=
input
->
dims
()[
0
]
;
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
])
;
const
int
m
=
input
->
dims
()[
1
];
const
int
64_t
m
=
input
->
dims
()[
1
];
const
int
d
=
input
->
dims
()[
2
];
const
int
64_t
d
=
input
->
dims
()[
2
];
const
int
h
=
input
->
dims
()[
3
];
const
int
64_t
h
=
input
->
dims
()[
3
];
const
int
w
=
input
->
dims
()[
4
];
const
int
64_t
w
=
input
->
dims
()[
4
];
const
int
k_d
=
filter
.
dims
()[
2
];
const
int
64_t
k_d
=
filter
.
dims
()[
2
];
const
int
k_h
=
filter
.
dims
()[
3
];
const
int
64_t
k_h
=
filter
.
dims
()[
3
];
const
int
k_w
=
filter
.
dims
()[
4
];
const
int
64_t
k_w
=
filter
.
dims
()[
4
];
const
int
c
=
output
->
dims
()[
1
];
// output channels
const
int
64_t
c
=
output
->
dims
()[
1
];
// output channels
const
int
o_d
=
output
->
dims
()[
2
];
const
int
64_t
o_d
=
output
->
dims
()[
2
];
const
int
o_h
=
output
->
dims
()[
3
];
const
int
64_t
o_h
=
output
->
dims
()[
3
];
const
int
o_w
=
output
->
dims
()[
4
];
const
int
64_t
o_w
=
output
->
dims
()[
4
];
paddle
::
operators
::
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
paddle
::
operators
::
math
::
Col2VolFunctor
<
Place
,
T
>
col2vol
;
// use col_shape in the vol2col and col2vol calculation
// use col_shape in the vol2col and col2vol calculation
DDim
col_shape
=
{
c
,
k_d
,
k_h
,
k_w
,
d
,
h
,
w
};
DDim
col_shape
=
{
c
,
k_d
,
k_h
,
k_w
,
d
,
h
,
w
};
// use col_matrix_shape in the gemm calculation
// use col_matrix_shape in the gemm calculation
DDim
col_matrix_shape
=
{
c
*
k_d
*
k_h
*
k_w
,
d
*
h
*
w
};
DDim
col_matrix_shape
=
{
c
*
k_d
*
k_h
*
k_w
,
d
*
h
*
w
};
...
@@ -306,19 +284,18 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
...
@@ -306,19 +284,18 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
DDim
output_shape
=
{
c
,
o_d
,
o_h
,
o_w
};
DDim
output_shape
=
{
c
,
o_d
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
d
*
h
*
w
};
DDim
input_matrix_shape
=
{
m
,
d
*
h
*
w
};
// filter size: (m, c * k_d * k_h * k_w)
DDim
filter_matrix_shape
=
{
m
,
c
*
k_d
*
k_h
*
k_w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_d
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose: gemm + col2vol (similar to conv-backward on input)
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
output
)
;
math
::
SetConstant
<
Place
,
T
>
set_zero
;
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
set_zero
(
context
.
device_context
(),
output
,
static_cast
<
T
>
(
0
));
// convolution transpose: gemm + col2vol (similar to conv-backward on input)
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
// batch with size (
M
, d * h * w)
// batch with size (
m
, d * h * w)
Tensor
input_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
Tensor
input_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// filter size: (M, c * k_d * k_h * k_w)
// output size: (c, o_d, o_h, o_w)
// output size: (c, o_d, o_h, o_w)
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
...
@@ -326,7 +303,10 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
...
@@ -326,7 +303,10 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
// col_matrix = filter * input_batch
// col_matrix = filter * input_batch
// of shape (c * k_d * k_h * k_w, d * h * w)
// of shape (c * k_d * k_h * k_w, d * h * w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
true
,
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
true
,
input_batch
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
input_batch
,
false
,
static_cast
<
T
>
(
1.0
),
&
col_matrix
,
static_cast
<
T
>
(
0.0
));
// col2vol: col_matrix -> dy
// from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
col2vol
(
context
.
device_context
(),
output_batch
,
col
,
strides
[
0
],
col2vol
(
context
.
device_context
(),
output_batch
,
col
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
0
,
0
,
0
);
strides
[
1
],
strides
[
2
],
0
,
0
,
0
);
}
}
...
@@ -344,7 +324,6 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -344,7 +324,6 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
// For filter, we do not use const pointer b/c we will do reshape,
// For filter, we do not use const pointer b/c we will do reshape,
// but we should avoid modifying its value.
// but we should avoid modifying its value.
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
input_grad
=
Tensor
*
input_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
Tensor
*
filter_grad
=
...
@@ -354,20 +333,20 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -354,20 +333,20 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
// Actually, no paddings and groups allowed in conv transpose.
// Actually, no paddings and groups allowed in conv transpose.
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
const
int
batch_size
=
input
->
dims
()[
0
]
;
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
])
;
const
int
m
=
input
->
dims
()[
1
];
const
int
64_t
m
=
input
->
dims
()[
1
];
const
int
d
=
input
->
dims
()[
2
];
const
int
64_t
d
=
input
->
dims
()[
2
];
const
int
h
=
input
->
dims
()[
3
];
const
int
64_t
h
=
input
->
dims
()[
3
];
const
int
w
=
input
->
dims
()[
4
];
const
int
64_t
w
=
input
->
dims
()[
4
];
const
int
k_d
=
filter
.
dims
()[
2
];
const
int
64_t
k_d
=
filter
.
dims
()[
2
];
const
int
k_h
=
filter
.
dims
()[
3
];
const
int
64_t
k_h
=
filter
.
dims
()[
3
];
const
int
k_w
=
filter
.
dims
()[
4
];
const
int
64_t
k_w
=
filter
.
dims
()[
4
];
const
int
c
=
output_grad
->
dims
()[
1
];
// output channels
const
int
64_t
c
=
output_grad
->
dims
()[
1
];
// output channels
const
int
o_d
=
output_grad
->
dims
()[
2
];
const
int
64_t
o_d
=
output_grad
->
dims
()[
2
];
const
int
o_h
=
output_grad
->
dims
()[
3
];
const
int
64_t
o_h
=
output_grad
->
dims
()[
3
];
const
int
o_w
=
output_grad
->
dims
()[
4
];
const
int
64_t
o_w
=
output_grad
->
dims
()[
4
];
// Only vol2col functor required for bp to get to the right shape
// Only vol2col functor required for bp to get to the right shape
paddle
::
operators
::
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
paddle
::
operators
::
math
::
Vol2ColFunctor
<
Place
,
T
>
vol2col
;
...
@@ -378,12 +357,6 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -378,12 +357,6 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
// use col_matrix_shape in the gemm calculation
// use col_matrix_shape in the gemm calculation
DDim
col_matrix_shape_f
=
{
c
*
d
*
h
*
w
,
k_d
*
k_h
*
k_w
};
DDim
col_matrix_shape_f
=
{
c
*
d
*
h
*
w
,
k_d
*
k_h
*
k_w
};
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.
DDim
output_shape
=
{
c
,
o_d
,
o_h
,
o_w
};
DDim
output_shape
=
{
c
,
o_d
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
d
*
h
*
w
};
DDim
input_matrix_shape
=
{
m
,
d
*
h
*
w
};
...
@@ -393,70 +366,62 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
...
@@ -393,70 +366,62 @@ class GemmConv3DTransposeGradKernel : public framework::OpKernel<T> {
// convolution transpose grad on input:
// convolution transpose grad on input:
// vol2col + gemm (similar to conv-forward)
// vol2col + gemm (similar to conv-forward)
// input need to compute gradient
// input need to compute gradient
if
(
input_grad
)
{
if
(
input_grad
||
filter_grad
)
{
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
;
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
ShareDataWith
(
col
);
DDim
col_matrix_shape
=
{
c
*
k_d
*
k_h
*
k_w
,
d
*
h
*
w
};
DDim
col_matrix_shape
=
{
c
*
k_d
*
k_h
*
k_w
,
d
*
h
*
w
};
col_matrix
.
Resize
(
col_matrix_shape
);
col_matrix
.
Resize
(
col_matrix_shape
);
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
filter_grad_
;
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_grad
);
math
::
SetConstant
<
Place
,
T
>
set_zero
;
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
if
(
input_grad
)
{
// batch with size (c, o_d * o_h * o_w)
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
output_grad_batch
=
set_zero
(
context
.
device_context
(),
input_grad
,
static_cast
<
T
>
(
0
));
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
}
// filter of size (m, c * k_d * k_h * k_w)
if
(
filter_grad
)
{
// filter size (m, c * k_d * k_h * k_w)
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// batch with size (m, d, h, w)
set_zero
(
context
.
device_context
(),
filter_grad
,
static_cast
<
T
>
(
0
));
Tensor
input_grad_batch
=
filter_grad_
=
*
filter_grad
;
input_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
filter_grad_
.
Resize
(
filter_matrix_shape
);
// vol2col: dy from (c, o_d, o_h, o_w) -> (c * k_d * k_h * k_w, d * h *
// w)
vol2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
// gemm: dx = filter * dy
// (m, c *k_d * k_h * k_w) * (c * k_d * k_h * k_w, d* h * w) -> (m, c,
// d, h, w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
false
,
col_matrix
,
false
,
T
(
1.0
),
&
input_grad_batch
,
T
(
0.0
));
}
}
}
// filter gradient required
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
if
(
filter_grad
)
{
// batch with size (c, o_d * o_h * o_w)
Tensor
col_matrix_f
;
col_matrix_f
.
ShareDataWith
(
col
);
DDim
col_matrix_shape_f
=
{
c
*
d
*
h
*
w
,
k_d
*
k_h
*
k_w
};
col_matrix_f
.
Resize
(
col_matrix_shape_f
);
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
filter_grad_
=
*
filter_grad
;
filter_grad_
.
Resize
(
filter_matrix_shape
);
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
filter_grad_
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
// batch with size (c, o_d, o_h, o_w)
Tensor
output_grad_batch
=
Tensor
output_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// input batch
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// vol2col: (c * d * h * w, k_d * k_h * k_w)
// vol2col: dy -> col_matrix
// from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
vol2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
vol2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
strides
[
1
],
strides
[
2
],
paddings
[
0
],
paddings
[
1
],
paddings
[
2
]);
// gemm: d_filter = x * y_grad^T
if
(
input_grad
)
{
// (m, c * d * h * w) * (k_d * k_h * k_w, c * d * h * w) -> (m, c, d, h,
// batch with size (m, d, h, w)
// w)
Tensor
input_grad_batch
=
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
in_batch
,
false
,
input_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
col_matrix_f
,
true
,
T
(
1.0
),
&
filter_grad_
,
// gemm: dx = filter * dy
T
(
1.0
));
// (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
// d, h, w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
false
,
col_matrix
,
false
,
static_cast
<
T
>
(
1.0
),
&
input_grad_batch
,
static_cast
<
T
>
(
0.0
));
}
if
(
filter_grad
)
{
// input batch
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// gemm: d_filter = x * dy^T
// (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
// k_h * k_w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
in_batch
,
false
,
col_matrix
,
true
,
static_cast
<
T
>
(
1.0
),
&
filter_grad_
,
static_cast
<
T
>
(
1.0
));
}
}
}
}
}
}
}
...
...
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