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51113cfe
编写于
10月 27, 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
write together
上级
ed120ee7
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
292 addition
and
408 deletion
+292
-408
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+9
-2
paddle/operators/conv2dtranspose_op.cc
paddle/operators/conv2dtranspose_op.cc
+0
-107
paddle/operators/conv2dtranspose_op.cu
paddle/operators/conv2dtranspose_op.cu
+0
-24
paddle/operators/conv2dtranspose_op.h
paddle/operators/conv2dtranspose_op.h
+0
-254
paddle/operators/conv_transpose_op.cc
paddle/operators/conv_transpose_op.cc
+64
-16
paddle/operators/conv_transpose_op.cu
paddle/operators/conv_transpose_op.cu
+8
-1
paddle/operators/conv_transpose_op.h
paddle/operators/conv_transpose_op.h
+210
-3
python/paddle/v2/framework/tests/test_conv2dtranspose_op.py
python/paddle/v2/framework/tests/test_conv2dtranspose_op.py
+1
-1
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
51113cfe
...
...
@@ -69,6 +69,13 @@ function(op_library TARGET)
file
(
APPEND
${
pybind_file
}
"USE_OP(max_pool2d_with_index);
\n
"
)
endif
()
# conv_transpose_op contains several operators
if
(
"
${
TARGET
}
"
STREQUAL
"conv_transpose_op"
)
set
(
pybind_flag 1
)
# It's enough to just adding one operator to pybind
file
(
APPEND
${
pybind_file
}
"USE_OP(conv2dtranspose);
\n
"
)
endif
()
# save_restore_op contains several operators
if
(
"
${
TARGET
}
"
STREQUAL
"save_restore_op"
)
set
(
pybind_flag 1
)
...
...
@@ -124,7 +131,7 @@ set(DEPS_OPS
pool_op
pool_with_index_op
lstm_op
conv
3d
transpose_op
)
conv
_
transpose_op
)
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
...
...
@@ -136,7 +143,7 @@ op_library(sum_op DEPS net_op)
op_library
(
pool_op DEPS pooling
)
op_library
(
pool_with_index_op DEPS pooling
)
op_library
(
lstm_op DEPS sequence2batch lstm_compute
)
op_library
(
conv
3d
transpose_op DEPS vol2col
)
op_library
(
conv
_
transpose_op DEPS vol2col
)
list
(
REMOVE_ITEM GENERAL_OPS
${
DEPS_OPS
}
)
foreach
(
src
${
GENERAL_OPS
}
)
...
...
paddle/operators/conv2dtranspose_op.cc
已删除
100644 → 0
浏览文件 @
ed120ee7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv2dtranspose_op.h"
namespace
paddle
{
namespace
operators
{
void
Conv2DTransposeOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Input"
),
"Input(Input) of Conv2DTransposeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Filter"
),
"Input(Filter) of Conv2DTransposeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Output"
),
"Output(Output) of Conv2DTransposeOp should not be null."
);
auto
in_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
paddings
[
i
],
0
,
"No Padding allowed in conv transpose op."
);
}
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
4
,
"Conv2DTransposeOp input should be 4-D tensor."
);
PADDLE_ENFORCE_EQ
(
filter_dims
.
size
(),
4
,
"Conv2DTransposeOp filter should be 4-D tensor."
);
PADDLE_ENFORCE_EQ
(
in_dims
[
1
],
filter_dims
[
0
],
"input and kernel input dimension should be equal."
);
auto
output_height
=
(
in_dims
[
2
]
-
1
)
*
strides
[
0
]
+
filter_dims
[
2
];
auto
output_width
=
(
in_dims
[
3
]
-
1
)
*
strides
[
1
]
+
filter_dims
[
3
];
ctx
->
SetOutputDim
(
"Output"
,
{
in_dims
[
0
],
filter_dims
[
1
],
output_height
,
output_width
});
}
Conv2DTransposeOpMaker
::
Conv2DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Input"
,
"(Tensor) The input tensor of convolution transpose operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of input channels, H and W is the height and width of image."
);
AddInput
(
"Filter"
,
"(Tensor) The filter tensor of convolution transpose operator."
"The format of the filter tensor is CMHW, where C is the number of "
"output image channels, M is the number of input image channels, "
"H and W is height and width of filter. "
"We enforce groups number == 1 and padding == 0 in "
"convolution transpose Scenario."
);
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution transpose operator."
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides of convolution transpose operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings of convolution transpose operator."
)
.
SetDefault
({
0
,
0
});
AddComment
(
R"DOC(
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
)DOC"
);
}
void
Conv2DTransposeOpGrad
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
auto
in_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Input"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Input"
),
in_dims
);
}
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Filter"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Filter"
),
filter_dims
);
}
}
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
conv2dtranspose
,
ops
::
Conv2DTransposeOp
,
ops
::
Conv2DTransposeOpMaker
,
conv2dtranspose_grad
,
ops
::
Conv2DTransposeOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/conv2dtranspose_op.cu
已删除
100644 → 0
浏览文件 @
ed120ee7
/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv2dtranspose_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/conv2dtranspose_op.h
已删除
100644 → 0
浏览文件 @
ed120ee7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
// Define Op classes in .h file so that other conv transpose
// operator implementations can reuse the code.
class
Conv2DTransposeOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Conv2DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
);
};
class
Conv2DTransposeOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
class
Conv2DTransposeOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
template
<
typename
Place
,
typename
T
>
class
GemmConv2DTransposeKernel
:
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, so it should not be constant pointer
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
const
int
batch_size
=
input
->
dims
()[
0
];
const
int
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output
->
dims
()[
1
];
// output channels
const
int
o_h
=
output
->
dims
()[
2
];
const
int
o_w
=
output
->
dims
()[
3
];
paddle
::
operators
::
math
::
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
// use col_shape in the im2col and col2im calculation
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
// use col_matrix_shape in the gemm calculation
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
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.
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose: gemm + col2im (similar to conv-backward on input)
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
output
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
// batch with size (M, h * w)
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)
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// col_matrix = filter * input_batch
// of shape (c * k_h * k_w, h * w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
true
,
input_batch
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
col2im
(
context
.
device_context
(),
output_batch
,
col
,
strides
[
0
],
strides
[
1
],
0
,
0
,
0
,
0
);
}
}
};
template
<
typename
Place
,
typename
T
>
class
GemmConv2DTransposeGradKernel
:
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"
));
// For filter, we do not use const pointer b/c we will do reshape,
// but we should avoid modifying its value.
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
input_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
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"
);
const
int
batch_size
=
input
->
dims
()[
0
];
const
int
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output_grad
->
dims
()[
1
];
// output channels
const
int
o_h
=
output_grad
->
dims
()[
2
];
const
int
o_w
=
output_grad
->
dims
()[
3
];
// Only im2col functor required for bp to get to the right shape
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col and col2im calculation
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
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
if
(
input_grad
)
{
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
w
};
col_matrix
.
Resize
(
col_matrix_shape
);
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_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
=
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)
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
// gemm: dx = filter * dy
// (m, c * k_h * k_w) * (c * k_h * k_w, h * w) -> (m, c, h)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
false
,
col_matrix
,
false
,
T
(
1.0
),
&
input_grad_batch
,
T
(
0.0
));
}
}
// filter gradient required
if
(
filter_grad
)
{
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
=
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
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
// gemm: d_filter = x * y_grad^T
// (m, c * h * w) * (k_h * k_w, c * h * w) -> (m, c, h)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
in_batch
,
false
,
col_matrix_f
,
true
,
T
(
1.0
),
&
filter_grad_
,
T
(
1.0
));
}
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/operators/conv
3d
transpose_op.cc
→
paddle/operators/conv
_
transpose_op.cc
浏览文件 @
51113cfe
...
...
@@ -12,18 +12,18 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv
3d
transpose_op.h"
#include "paddle/operators/conv
_
transpose_op.h"
namespace
paddle
{
namespace
operators
{
void
Conv
3D
TransposeOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
void
ConvTransposeOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Input"
),
"Input(Input) of Conv
3D
TransposeOp should not be null."
);
"Input(Input) of ConvTransposeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Filter"
),
"Input(Filter) of Conv
3D
TransposeOp should not be null."
);
"Input(Filter) of ConvTransposeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Output"
),
"Output(Output) of Conv
3D
TransposeOp should not be null."
);
"Output(Output) of ConvTransposeOp should not be null."
);
auto
in_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
...
...
@@ -35,12 +35,20 @@ void Conv3DTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"No Padding allowed in conv transpose op."
);
}
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
5
,
"Conv3DTransposeOp input should be 5-D tensor."
);
PADDLE_ENFORCE_EQ
(
filter_dims
.
size
(),
5
,
"Conv3DTransposeOp filter should be 5-D tensor."
);
PADDLE_ENFORCE_EQ
(
in_dims
[
1
],
filter_dims
[
0
],
"input and kernel input dimension should be equal."
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
"ConvTransposeOp intput should be 4-D or 5-D tensor."
);
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
filter_dims
.
size
(),
"ConvTransposeOp input dimension and filter dimension "
"should be the same."
);
PADDLE_ENFORCE
(
in_dims
.
size
()
-
strides
.
size
()
==
2U
,
"ConvTransposeOp input dimension and strides dimension should "
"be consistent."
);
PADDLE_ENFORCE_EQ
(
paddings
.
size
(),
strides
.
size
(),
"ConvTransposeOp paddings dimension and Conv strides "
"dimension should be the same."
);
PADDLE_ENFORCE_EQ
(
in_dims
[
1
],
filter_dims
[
0
],
"ConvTransposeOp input and kernel input dimension should be equal."
);
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
1
]});
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
...
...
@@ -50,6 +58,37 @@ void Conv3DTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
ctx
->
SetOutputDim
(
"Output"
,
framework
::
make_ddim
(
output_shape
));
}
Conv2DTransposeOpMaker
::
Conv2DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Input"
,
"(Tensor) The input tensor of convolution transpose operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of input channels, H and W is the height and width of image."
);
AddInput
(
"Filter"
,
"(Tensor) The filter tensor of convolution transpose operator."
"The format of the filter tensor is CMHW, where C is the number of "
"output image channels, M is the number of input image channels, "
"H and W is height and width of filter. "
"We enforce groups number == 1 and padding == 0 in "
"convolution transpose Scenario."
);
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution transpose operator."
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides of convolution transpose operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings of convolution transpose operator."
)
.
SetDefault
({
0
,
0
});
AddComment
(
R"DOC(
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
)DOC"
);
}
Conv3DTransposeOpMaker
::
Conv3DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
...
...
@@ -85,8 +124,7 @@ parameters is checked in the infer-shape.
)DOC"
);
}
void
Conv3DTransposeOpGrad
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
void
ConvTransposeOpGrad
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
auto
in_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Input"
)))
{
...
...
@@ -101,9 +139,19 @@ void Conv3DTransposeOpGrad::InferShape(
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
conv3dtranspose
,
ops
::
Conv3DTransposeOp
,
ops
::
Conv3DTransposeOpMaker
,
conv3dtranspose_grad
,
ops
::
Conv3DTransposeOpGrad
);
REGISTER_OP
(
conv2dtranspose
,
ops
::
ConvTransposeOp
,
ops
::
Conv2DTransposeOpMaker
,
conv2dtranspose_grad
,
ops
::
ConvTransposeOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP
(
conv3dtranspose
,
ops
::
ConvTransposeOp
,
ops
::
Conv3DTransposeOpMaker
,
conv3dtranspose_grad
,
ops
::
ConvTransposeOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv3dtranspose
,
...
...
paddle/operators/conv
3d
transpose_op.cu
→
paddle/operators/conv
_
transpose_op.cu
浏览文件 @
51113cfe
...
...
@@ -12,10 +12,17 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv
3d
transpose_op.h"
#include "paddle/operators/conv
_
transpose_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv3dtranspose
,
ops
::
GemmConv3DTransposeKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
...
...
paddle/operators/conv
3d
transpose_op.h
→
paddle/operators/conv
_
transpose_op.h
浏览文件 @
51113cfe
...
...
@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/vol2col.h"
...
...
@@ -27,13 +28,19 @@ using DDim = framework::DDim;
// Define Op classes in .h file so that other conv transpose
// operator implementations can reuse the code.
class
Conv2DTransposeOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Conv2DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
);
};
class
Conv3DTransposeOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Conv3DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
);
};
class
Conv
3D
TransposeOp
:
public
framework
::
OperatorWithKernel
{
class
ConvTransposeOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -41,7 +48,7 @@ class Conv3DTransposeOp : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
class
Conv
3D
TransposeOpGrad
:
public
framework
::
OperatorWithKernel
{
class
ConvTransposeOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -50,7 +57,7 @@ class Conv3DTransposeOpGrad : public framework::OperatorWithKernel {
};
template
<
typename
Place
,
typename
T
>
class
GemmConv
3
DTransposeKernel
:
public
framework
::
OpKernel
<
T
>
{
class
GemmConv
2
DTransposeKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
...
...
@@ -61,6 +68,206 @@ class GemmConv3DTransposeKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
const
int
batch_size
=
input
->
dims
()[
0
];
const
int
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output
->
dims
()[
1
];
// output channels
const
int
o_h
=
output
->
dims
()[
2
];
const
int
o_w
=
output
->
dims
()[
3
];
paddle
::
operators
::
math
::
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
// use col_shape in the im2col and col2im calculation
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
// use col_matrix_shape in the gemm calculation
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
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.
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose: gemm + col2im (similar to conv-backward on input)
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
output
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
// batch with size (M, h * w)
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)
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// col_matrix = filter * input_batch
// of shape (c * k_h * k_w, h * w)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
true
,
input_batch
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
col2im
(
context
.
device_context
(),
output_batch
,
col
,
strides
[
0
],
strides
[
1
],
0
,
0
,
0
,
0
);
}
}
};
template
<
typename
Place
,
typename
T
>
class
GemmConv2DTransposeGradKernel
:
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"
));
// For filter, we do not use const pointer b/c we will do reshape,
// but we should avoid modifying its value.
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
input_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
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"
);
const
int
batch_size
=
input
->
dims
()[
0
];
const
int
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output_grad
->
dims
()[
1
];
// output channels
const
int
o_h
=
output_grad
->
dims
()[
2
];
const
int
o_w
=
output_grad
->
dims
()[
3
];
// Only im2col functor required for bp to get to the right shape
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col and col2im calculation
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
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
if
(
input_grad
)
{
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
w
};
col_matrix
.
Resize
(
col_matrix_shape
);
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_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
=
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)
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
// gemm: dx = filter * dy
// (m, c * k_h * k_w) * (c * k_h * k_w, h * w) -> (m, c, h)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
false
,
col_matrix
,
false
,
T
(
1.0
),
&
input_grad_batch
,
T
(
0.0
));
}
}
// filter gradient required
if
(
filter_grad
)
{
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
=
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
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
0
],
paddings
[
1
],
paddings
[
1
]);
// gemm: d_filter = x * y_grad^T
// (m, c * h * w) * (k_h * k_w, c * h * w) -> (m, c, h)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
in_batch
,
false
,
col_matrix_f
,
true
,
T
(
1.0
),
&
filter_grad_
,
T
(
1.0
));
}
}
}
};
template
<
typename
Place
,
typename
T
>
class
GemmConv3DTransposeKernel
:
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, so it should not be constant pointer
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// TODO(chengduo): Paddings can be added in future.
// groups will alway be disabled in conv3dtranspose.
...
...
python/paddle/v2/framework/tests/test_conv2dtranspose_op.py
浏览文件 @
51113cfe
...
...
@@ -44,7 +44,7 @@ class TestConv2dTransposeOp(OpTest):
input_
=
np
.
random
.
random
(
self
.
input_size
).
astype
(
"float32"
)
filter_
=
np
.
random
.
random
(
self
.
filter_size
).
astype
(
"float32"
)
output
=
conv2dtranspose_forward_naive
(
input_
,
filter_
,
conv2dtranspose_param
).
astype
(
"float32"
)
input_
,
filter_
,
conv2dtranspose_param
).
astype
(
'float32'
)
# print 'deconv output py', output, output.shape
self
.
inputs
=
{
'Input'
:
input_
,
'Filter'
:
filter_
}
...
...
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