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6d2cfe92
编写于
1月 23, 2018
作者:
W
whs
提交者:
GitHub
1月 23, 2018
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差异文件
Merge pull request #4866 from gongweibao/blockexpand
Add im2sequence op.
上级
23f5c182
09544bc1
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
484 addition
and
0 deletion
+484
-0
paddle/operators/im2sequence_op.cc
paddle/operators/im2sequence_op.cc
+157
-0
paddle/operators/im2sequence_op.cu
paddle/operators/im2sequence_op.cu
+25
-0
paddle/operators/im2sequence_op.h
paddle/operators/im2sequence_op.h
+135
-0
python/paddle/v2/fluid/tests/test_im2sequence_op.py
python/paddle/v2/fluid/tests/test_im2sequence_op.py
+167
-0
未找到文件。
paddle/operators/im2sequence_op.cc
0 → 100644
浏览文件 @
6d2cfe92
/* 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/im2sequence_op.h"
namespace
paddle
{
namespace
operators
{
class
Im2SequenceOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of Im2SequenceOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of Im2SequenceOp op should not be null."
);
auto
in_dim
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
in_dim
.
size
(),
4
,
"Input(X) format must be 4D tensor, eg., NCHW."
);
auto
kernels
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"kernels"
);
auto
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
auto
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
int
batch_size
=
in_dim
[
0
];
int
img_channels
=
in_dim
[
1
];
int
img_height
=
in_dim
[
2
];
int
img_width
=
in_dim
[
3
];
int
output_height
=
OutputSize
(
img_height
,
kernels
[
0
],
paddings
[
0
],
paddings
[
2
],
strides
[
0
]);
int
output_width
=
OutputSize
(
img_width
,
kernels
[
1
],
paddings
[
1
],
paddings
[
3
],
strides
[
1
]);
ctx
->
SetOutputDim
(
"Out"
,
{
batch_size
*
output_height
*
output_width
,
img_channels
*
kernels
[
0
]
*
kernels
[
1
]});
}
};
class
Im2SequenceOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Im2SequenceOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(Tensor) The input tensor has NCHW format."
"N: batch size"
"C: channels"
"H: height"
"W: width"
);
AddOutput
(
"Out"
,
"(LodTensor) The output data of im2sequence op,"
);
AddAttr
<
std
::
vector
<
int
>>
(
"kernels"
,
"(vector<int>), the "
"kernels(kernel_height, kernel_width)"
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride)"
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector<int> default:{0, 0, 0, 0}), the "
"paddings(up_pad, left_pad, down_pad, right_pad)"
)
.
SetDefault
({
0
,
0
,
0
,
0
});
AddComment
(
R"DOC(
This op uses kernels to scan images and converts these images to sequences.
After expanding, The number of time steps are output_height * output_width
and the dimension of each time step is kernel_height * kernel_width * channels,
in which:
output_height =
1 + (padding_height + padding_down + img_height - kernel_height + stride_height - 1) /
stride_height;
output_width =
1 + (padding_left + padding+right + img_width - kernel_width + stride_width - 1) /
stride_width;
This op can be used after convolution neural network, and before recurrent neural network.
Given:
x = [[[[ 6. 2. 1.]
[ 8. 3. 5.]
[ 0. 2. 6.]]
[[ 2. 4. 4.]
[ 6. 3. 0.]
[ 6. 4. 7.]]]
[[[ 6. 7. 1.]
[ 5. 7. 9.]
[ 2. 4. 8.]]
[[ 1. 2. 1.]
[ 1. 3. 5.]
[ 9. 0. 8.]]]]
x.dims = {2, 2, 3, 3}
And:
kernels = [2, 2]
strides = [1, 1]
paddings = [0, 0, 0, 0]
Then:
output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 2. 1. 3. 5. 4. 4. 3. 0.]
[ 8. 3. 0. 2. 6. 3. 6. 4.]
[ 3. 5. 2. 6. 3. 0. 4. 7.]
[ 6. 7. 5. 7. 1. 2. 1. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.lod = [[0, 4, 8]]
)DOC"
);
}
};
class
Im2SequenceGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) shouldn't be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
im2sequence
,
ops
::
Im2SequenceOp
,
ops
::
Im2SequenceOpMaker
,
im2sequence_grad
,
ops
::
Im2SequenceGradOp
);
REGISTER_OP_CPU_KERNEL
(
im2sequence
,
ops
::
Im2SequenceKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
im2sequence_grad
,
ops
::
Im2SequenceGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
paddle/operators/im2sequence_op.cu
0 → 100644
浏览文件 @
6d2cfe92
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/im2sequence_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
im2sequence
,
ops
::
Im2SequenceKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
im2sequence_grad
,
ops
::
Im2SequenceGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
paddle/operators/im2sequence_op.h
0 → 100644
浏览文件 @
6d2cfe92
/* 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/data_layout.h"
#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
LoDTensor
=
framework
::
LoDTensor
;
inline
int
OutputSize
(
int
input_size
,
int
filter_size
,
int
padding_0
,
int
padding_1
,
int
stride
)
{
const
int
output_size
=
(
input_size
+
padding_0
+
padding_1
-
filter_size
)
/
stride
+
1
;
return
output_size
;
}
template
<
typename
DeviceContext
,
typename
T
>
class
Im2SequenceKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
Tensor
*
in
=
ctx
.
Input
<
Tensor
>
(
"X"
);
LoDTensor
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// TODO(wanghaoshuang): Add layout checker after 'set_layout'
// being available for python API
// PADDLE_ENFORCE_EQ(in->layout(), framework::DataLayout::kNCHW,
// "Input(X) layout must be NCHW");
auto
in_dim
=
in
->
dims
();
int
batch_size
=
in_dim
[
0
];
int
img_channels
=
in_dim
[
1
];
int
img_height
=
in_dim
[
2
];
int
img_width
=
in_dim
[
3
];
auto
kernels
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"kernels"
);
auto
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
auto
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
output_height
=
OutputSize
(
img_height
,
kernels
[
0
],
paddings
[
0
],
paddings
[
2
],
strides
[
0
]);
int
output_width
=
OutputSize
(
img_width
,
kernels
[
1
],
paddings
[
1
],
paddings
[
3
],
strides
[
1
]);
const
std
::
vector
<
int
>
dilations
({
1
,
1
});
auto
out_dims
=
out
->
dims
();
out
->
Resize
({
batch_size
,
out
->
numel
()
/
batch_size
});
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
const
Tensor
src
=
in
->
Slice
(
i
,
i
+
1
).
Resize
({
img_channels
,
img_height
,
img_width
});
Tensor
dst
=
out
->
Slice
(
i
,
i
+
1
).
Resize
(
{
output_height
,
output_width
,
img_channels
,
kernels
[
0
],
kernels
[
1
]});
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kOCF
,
DeviceContext
,
T
>
f
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
f
(
dev_ctx
,
src
,
dilations
,
strides
,
paddings
,
&
dst
);
}
out
->
Resize
(
out_dims
);
// set lod information
// TODO(wanghaoshuang): Move this to InferShape
framework
::
LoD
lod
(
1
);
lod
[
0
].
reserve
(
batch_size
+
1
);
for
(
int
i
=
0
,
offset
=
0
;
i
<
batch_size
+
1
;
++
i
)
{
lod
[
0
][
i
]
=
offset
;
offset
+=
output_height
*
output_width
;
}
out
->
set_lod
(
lod
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
Im2SequenceGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
Tensor
>
(
"X"
);
Tensor
*
d_out
=
const_cast
<
Tensor
*>
(
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
)));
auto
*
d_x
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
d_x
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
x_v
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_x
);
auto
&
place
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
x_v
.
device
(
place
)
=
x_v
.
constant
(
0.0
);
auto
in_dim
=
in
->
dims
();
int
batch_size
=
in_dim
[
0
];
int
img_channels
=
in_dim
[
1
];
int
img_height
=
in_dim
[
2
];
int
img_width
=
in_dim
[
3
];
auto
kernels
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"kernels"
);
auto
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
auto
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
output_height
=
OutputSize
(
img_height
,
kernels
[
0
],
paddings
[
0
],
paddings
[
2
],
strides
[
0
]);
int
output_width
=
OutputSize
(
img_width
,
kernels
[
1
],
paddings
[
1
],
paddings
[
3
],
strides
[
1
]);
const
std
::
vector
<
int
>
dilations
({
1
,
1
});
auto
d_out_dims
=
d_out
->
dims
();
d_out
->
Resize
({
batch_size
,
d_out
->
numel
()
/
batch_size
});
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
Tensor
dst
=
d_x
->
Slice
(
i
,
i
+
1
).
Resize
({
img_channels
,
img_height
,
img_width
});
const
Tensor
src
=
d_out
->
Slice
(
i
,
i
+
1
).
Resize
(
{
output_height
,
output_width
,
img_channels
,
kernels
[
0
],
kernels
[
1
]});
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kOCF
,
DeviceContext
,
T
>
f
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
f
(
dev_ctx
,
src
,
dilations
,
strides
,
paddings
,
&
dst
);
}
d_out
->
Resize
(
d_out_dims
);
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/tests/test_im2sequence_op.py
0 → 100644
浏览文件 @
6d2cfe92
# Copyright (c) 2018 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
get_output_shape
(
attrs
,
in_shape
):
img_height
=
in_shape
[
2
]
img_width
=
in_shape
[
3
]
paddings
=
attrs
[
'paddings'
]
kernels
=
attrs
[
'kernels'
]
strides
=
attrs
[
'strides'
]
output_height
=
\
1
+
\
(
img_height
+
paddings
[
0
]
+
paddings
[
2
]
-
kernels
[
0
]
+
strides
[
0
]
-
1
)
/
\
strides
[
0
]
output_width
=
\
1
+
\
(
img_width
+
paddings
[
1
]
+
paddings
[
3
]
-
kernels
[
1
]
+
strides
[
1
]
-
1
)
/
\
strides
[
1
]
return
output_height
,
output_width
def
im2col
(
attrs
,
im
,
col
):
"""
im: {CHW}
col:
{outputHeight, outputWidth, inputChannels, filterHeight, filterWidth}
"""
input_channels
,
input_height
,
input_width
=
im
.
shape
output_height
,
output_width
,
_
,
filter_height
,
filter_width
=
col
.
shape
stride_height
,
stride_width
=
attrs
[
'strides'
]
padding_height
,
padding_width
=
attrs
[
'paddings'
][
0
:
2
]
for
col_row_idx
in
range
(
0
,
output_height
):
for
col_col_idx
in
range
(
0
,
output_width
):
for
channel
in
range
(
0
,
input_channels
):
for
filter_row_idx
in
range
(
0
,
filter_height
):
for
filter_col_idx
in
range
(
0
,
filter_width
):
im_row_offset
=
col_row_idx
*
stride_height
\
+
filter_row_idx
-
padding_height
im_col_offset
=
col_col_idx
*
stride_width
\
+
filter_col_idx
-
padding_width
if
(
im_row_offset
<
0
or
im_row_offset
>=
input_height
or
im_col_offset
<
0
or
im_col_offset
>=
input_width
):
col
[
col_row_idx
][
col_col_idx
][
channel
][
\
filter_row_idx
][
filter_col_idx
]
=
0.0
else
:
im_offset
=
(
channel
*
input_height
+
im_row_offset
\
)
*
input_width
+
im_col_offset
col
[
col_row_idx
][
col_col_idx
][
channel
][
\
filter_row_idx
][
filter_col_idx
]
=
im
[
channel
][
\
im_row_offset
][
im_col_offset
]
def
Im2Sequence
(
inputs
,
attrs
):
output_height
,
output_width
=
get_output_shape
(
attrs
,
inputs
.
shape
)
img_channels
=
inputs
.
shape
[
1
]
batch_size
=
inputs
.
shape
[
0
]
out
=
np
.
zeros
([
batch_size
,
output_height
,
output_width
,
img_channels
,
attrs
[
'kernels'
][
0
],
attrs
[
'kernels'
][
1
]
]).
astype
(
"float32"
)
for
i
in
range
(
len
(
inputs
)):
im2col
(
attrs
,
inputs
[
i
],
out
[
i
])
out
=
out
.
reshape
([
batch_size
*
output_height
*
output_width
,
img_channels
*
attrs
[
'kernels'
][
0
]
*
attrs
[
'kernels'
][
1
]
])
return
out
class
TestBlockExpandOp
(
OpTest
):
def
config
(
self
):
self
.
batch_size
=
1
self
.
img_channels
=
3
self
.
img_height
=
4
self
.
img_width
=
4
self
.
attrs
=
{
'kernels'
:
[
2
,
2
],
'strides'
:
[
1
,
1
],
'paddings'
:
[
1
,
1
,
1
,
1
]
}
def
setUp
(
self
):
self
.
config
()
self
.
op_type
=
"im2sequence"
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
self
.
batch_size
,
self
.
img_channels
,
self
.
img_height
,
self
.
img_width
]).
astype
(
"float32"
)
out
=
Im2Sequence
(
x
,
self
.
attrs
)
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestBlockExpandOpCase2
(
TestBlockExpandOp
):
def
config
(
self
):
self
.
batch_size
=
2
self
.
img_channels
=
3
self
.
img_height
=
4
self
.
img_width
=
5
self
.
attrs
=
{
'kernels'
:
[
2
,
1
],
'strides'
:
[
2
,
1
],
'paddings'
:
[
2
,
1
,
2
,
1
]
}
class
TestBlockExpandOpCase3
(
TestBlockExpandOp
):
def
config
(
self
):
self
.
batch_size
=
3
self
.
img_channels
=
1
self
.
img_height
=
4
self
.
img_width
=
5
self
.
attrs
=
{
'kernels'
:
[
2
,
1
],
'strides'
:
[
2
,
1
],
'paddings'
:
[
2
,
0
,
2
,
0
]
}
class
TestBlockExpandOpCase4
(
TestBlockExpandOp
):
def
config
(
self
):
self
.
batch_size
=
2
self
.
img_channels
=
2
self
.
img_height
=
3
self
.
img_width
=
3
self
.
attrs
=
{
'kernels'
:
[
2
,
2
],
'strides'
:
[
1
,
1
],
'paddings'
:
[
0
,
0
,
0
,
0
]
}
if
__name__
==
'__main__'
:
unittest
.
main
()
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