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0eac3992
编写于
12月 13, 2016
作者:
Y
yuan
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priorbox layer for ssd
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4 changed file
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196 addition
and
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+196
-0
paddle/gserver/layers/PriorBox.cpp
paddle/gserver/layers/PriorBox.cpp
+137
-0
proto/ModelConfig.proto
proto/ModelConfig.proto
+10
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+13
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+36
-0
未找到文件。
paddle/gserver/layers/PriorBox.cpp
0 → 100644
浏览文件 @
0eac3992
/* Copyright (c) 2016 Baidu, Inc. 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 "Layer.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/BaseMatrix.h"
namespace
paddle
{
class
PriorBoxLayer
:
public
Layer
{
public:
explicit
PriorBoxLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
);
void
forward
(
PassType
passType
);
void
backward
(
const
UpdateCallback
&
callback
)
{}
int
numPriors_
;
std
::
vector
<
int
>
minSize_
;
std
::
vector
<
int
>
maxSize_
;
std
::
vector
<
float
>
aspectRatio_
;
std
::
vector
<
float
>
variance_
;
MatrixPtr
buffer_
;
};
bool
PriorBoxLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
std
::
copy
(
config_
.
inputs
(
0
).
priorbox_conf
().
min_size
().
begin
(),
config_
.
inputs
(
0
).
priorbox_conf
().
min_size
().
end
(),
std
::
back_inserter
(
minSize_
));
std
::
copy
(
config_
.
inputs
(
0
).
priorbox_conf
().
max_size
().
begin
(),
config_
.
inputs
(
0
).
priorbox_conf
().
max_size
().
end
(),
std
::
back_inserter
(
maxSize_
));
std
::
copy
(
config_
.
inputs
(
0
).
priorbox_conf
().
aspect_ratio
().
begin
(),
config_
.
inputs
(
0
).
priorbox_conf
().
aspect_ratio
().
end
(),
std
::
back_inserter
(
aspectRatio_
));
std
::
copy
(
config_
.
inputs
(
0
).
priorbox_conf
().
variance
().
begin
(),
config_
.
inputs
(
0
).
priorbox_conf
().
variance
().
end
(),
std
::
back_inserter
(
variance_
));
// flip
int
input_ratio_length
=
aspectRatio_
.
size
();
for
(
int
index
=
0
;
index
<
input_ratio_length
;
index
++
)
aspectRatio_
.
push_back
(
1
/
aspectRatio_
[
index
]);
aspectRatio_
.
push_back
(
1.
);
numPriors_
=
aspectRatio_
.
size
();
if
(
maxSize_
.
size
()
>
0
)
numPriors_
++
;
buffer_
=
Matrix
::
create
(
1
,
1
,
false
,
false
);
return
true
;
}
void
PriorBoxLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
auto
input
=
getInput
(
0
);
int
layer_width
=
input
.
getFrameWidth
();
int
layer_height
=
input
.
getFrameHeight
();
MatrixPtr
inV1
=
getInputValue
(
1
);
int
image_width
=
inV1
->
getElement
(
0
,
0
);
int
image_height
=
inV1
->
getElement
(
0
,
1
);
float
step_w
=
static_cast
<
float
>
(
image_width
)
/
layer_width
;
float
step_h
=
static_cast
<
float
>
(
image_height
)
/
layer_height
;
int
dim
=
layer_height
*
layer_width
*
numPriors_
*
4
;
reserveOutput
(
1
,
dim
*
2
);
// use a cpu buffer to compute
Matrix
::
resizeOrCreate
(
buffer_
,
1
,
dim
*
2
,
false
,
false
);
auto
*
tmp_ptr
=
buffer_
->
getData
();
int
idx
=
0
;
for
(
int
h
=
0
;
h
<
layer_height
;
++
h
)
{
for
(
int
w
=
0
;
w
<
layer_width
;
++
w
)
{
float
center_x
=
(
w
+
0.5
)
*
step_w
;
float
center_y
=
(
h
+
0.5
)
*
step_h
;
int
min_size
=
0
;
for
(
size_t
s
=
0
;
s
<
minSize_
.
size
();
s
++
)
{
// first prior.
min_size
=
minSize_
[
s
];
int
box_width
=
min_size
;
int
box_height
=
min_size
;
// xmin, ymin, xmax, ymax.
tmp_ptr
[
idx
++
]
=
(
center_x
-
box_width
/
2.
)
/
image_width
;
tmp_ptr
[
idx
++
]
=
(
center_y
-
box_height
/
2.
)
/
image_height
;
tmp_ptr
[
idx
++
]
=
(
center_x
+
box_width
/
2.
)
/
image_width
;
tmp_ptr
[
idx
++
]
=
(
center_y
+
box_height
/
2.
)
/
image_height
;
if
(
maxSize_
.
size
()
>
0
)
{
CHECK_EQ
(
minSize_
.
size
(),
maxSize_
.
size
());
// second prior.
for
(
size_t
s
=
0
;
s
<
maxSize_
.
size
();
s
++
)
{
int
max_size
=
maxSize_
[
s
];
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
);
tmp_ptr
[
idx
++
]
=
(
center_x
-
box_width
/
2.
)
/
image_width
;
tmp_ptr
[
idx
++
]
=
(
center_y
-
box_height
/
2.
)
/
image_height
;
tmp_ptr
[
idx
++
]
=
(
center_x
+
box_width
/
2.
)
/
image_width
;
tmp_ptr
[
idx
++
]
=
(
center_y
+
box_height
/
2.
)
/
image_height
;
}
}
}
// rest of priors.
for
(
size_t
r
=
0
;
r
<
aspectRatio_
.
size
();
r
++
)
{
float
ar
=
aspectRatio_
[
r
];
if
(
fabs
(
ar
-
1.
)
<
1e-6
)
continue
;
float
box_width
=
min_size
*
sqrt
(
ar
);
float
box_height
=
min_size
/
sqrt
(
ar
);
tmp_ptr
[
idx
++
]
=
(
center_x
-
box_width
/
2.
)
/
image_width
;
tmp_ptr
[
idx
++
]
=
(
center_y
-
box_height
/
2.
)
/
image_height
;
tmp_ptr
[
idx
++
]
=
(
center_x
+
box_width
/
2.
)
/
image_width
;
tmp_ptr
[
idx
++
]
=
(
center_y
+
box_height
/
2.
)
/
image_height
;
}
}
}
// clip the prior's coordidate such that it is within [0, 1]
for
(
int
d
=
0
;
d
<
dim
;
++
d
)
tmp_ptr
[
d
]
=
std
::
min
(
std
::
max
(
tmp_ptr
[
d
],
(
float
)
0.
),
(
float
)
1.
);
// set the variance.
for
(
int
h
=
0
;
h
<
layer_height
;
h
++
)
for
(
int
w
=
0
;
w
<
layer_width
;
w
++
)
for
(
int
i
=
0
;
i
<
numPriors_
;
i
++
)
for
(
int
j
=
0
;
j
<
4
;
j
++
)
tmp_ptr
[
idx
++
]
=
variance_
[
j
];
MatrixPtr
outV
=
getOutputValue
();
outV
->
copyFrom
(
buffer_
->
data_
,
dim
*
2
);
}
REGISTER_LAYER
(
priorbox
,
PriorBoxLayer
);
}
// namespace paddle
proto/ModelConfig.proto
浏览文件 @
0eac3992
...
...
@@ -248,6 +248,15 @@ message ImageConfig {
required
uint32
img_size_y
=
9
;
}
message
PriorBoxConfig
{
repeated
uint32
min_size
=
1
;
repeated
uint32
max_size
=
2
;
repeated
float
aspect_ratio
=
3
;
repeated
float
variance
=
4
;
optional
bool
flip
=
5
[
default
=
true
];
optional
bool
clip
=
6
[
default
=
true
];
}
message
LayerInputConfig
{
required
string
input_layer_name
=
1
;
optional
string
input_parameter_name
=
2
;
...
...
@@ -263,6 +272,7 @@ message LayerInputConfig {
optional
BilinearInterpConfig
bilinear_interp_conf
=
10
;
optional
MaxOutConfig
maxout_conf
=
11
;
optional
SppConfig
spp_conf
=
12
;
optional
PriorBoxConfig
priorbox_conf
=
13
;
}
message
LayerConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
0eac3992
...
...
@@ -1577,6 +1577,19 @@ class PrintLayer(LayerBase):
def
__init__
(
self
,
name
,
inputs
):
super
(
PrintLayer
,
self
).
__init__
(
name
,
'print'
,
0
,
inputs
)
@
config_layer
(
'priorbox'
)
class
PriorBoxLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
size
,
min_size
,
max_size
,
aspect_ratio
,
variance
):
super
(
PriorBoxLayer
,
self
).
__init__
(
name
,
'priorbox'
,
0
,
inputs
)
config_assert
(
len
(
inputs
)
==
2
,
'PriorBoxLayer must have 2 input'
)
self
.
config
.
inputs
[
0
].
priorbox_conf
.
min_size
.
extend
(
min_size
)
self
.
config
.
inputs
[
0
].
priorbox_conf
.
max_size
.
extend
(
max_size
)
self
.
config
.
inputs
[
0
].
priorbox_conf
.
aspect_ratio
.
extend
(
aspect_ratio
)
self
.
config
.
inputs
[
0
].
priorbox_conf
.
variance
.
extend
(
variance
)
self
.
config
.
size
=
size
input_layer0
=
self
.
get_input_layer
(
0
)
input_layer1
=
self
.
get_input_layer
(
1
)
@
config_layer
(
'data'
)
class
DataLayer
(
LayerBase
):
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
0eac3992
...
...
@@ -106,6 +106,7 @@ __all__ = [
'maxout_layer'
,
'out_prod_layer'
,
'print_layer'
,
'priorbox_layer'
,
'spp_layer'
,
]
...
...
@@ -171,6 +172,7 @@ class LayerType(object):
SPP_LAYER
=
"spp"
PRINT_LAYER
=
"print"
PRIORBOX_LAYER
=
"priorbox"
CTC_LAYER
=
"ctc"
WARP_CTC_LAYER
=
"warp_ctc"
...
...
@@ -933,6 +935,40 @@ def print_layer(input, name=None):
inputs
=
[
l
.
name
for
l
in
input
],
)
# this layer don't return anything, can not be input of other layer.
@
wrap_name_default
(
"priorbox"
)
def
priorbox_layer
(
input
,
img_shape
,
aspect_ratio
,
variance
,
min_size
,
max_size
=
[],
name
=
None
):
"""
Compute the priorbox and set the variance. This layer is necessary for ssd.
:param name: The Layer Name.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput
:param img_shape: The width and height of the network input image.
:type img_shape: LayerOutput
:param aspect_ratio: The aspect ratio.
:type aspect_ratio: list
:param variance: The bounding box variance.
:type min_size: The min size of the priorbox width/height.
:param min_size: list
:type max_size: The max size of the priorbox width/height. Could be NULL.
:param max_size: list
:return: LayerOutput
"""
# plus one for ratio 1.
num_filters
=
(
len
(
aspect_ratio
)
*
2
+
1
+
len
(
max_size
))
*
4
size
=
(
input
.
size
/
input
.
num_filters
)
*
num_filters
*
2
Layer
(
name
=
name
,
type
=
LayerType
.
PRIORBOX_LAYER
,
inputs
=
[
input
.
name
,
img_shape
.
name
],
size
=
size
,
min_size
=
min_size
,
max_size
=
max_size
,
aspect_ratio
=
aspect_ratio
,
variance
=
variance
)
return
LayerOutput
(
name
,
LayerType
.
PRIORBOX_LAYER
,
parents
=
[
input
,
img_shape
],
num_filters
=
num_filters
,
size
=
size
)
@
wrap_name_default
(
"seq_pooling"
)
@
wrap_bias_attr_default
(
has_bias
=
False
)
...
...
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