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49c50c9f
编写于
2月 11, 2018
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
Add multiBox API
上级
cb4eacb1
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
251 addition
and
2 deletion
+251
-2
python/paddle/v2/fluid/layers/detection.py
python/paddle/v2/fluid/layers/detection.py
+157
-2
python/paddle/v2/fluid/nets.py
python/paddle/v2/fluid/nets.py
+33
-0
python/paddle/v2/fluid/tests/test_detection.py
python/paddle/v2/fluid/tests/test_detection.py
+61
-0
未找到文件。
python/paddle/v2/fluid/layers/detection.py
浏览文件 @
49c50c9f
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve
d
.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
...
...
@@ -16,8 +16,19 @@ All layers just related to the detection neural network.
"""
from
..layer_helper
import
LayerHelper
from
..param_attr
import
ParamAttr
from
..framework
import
Variable
from
layer_function_generator
import
autodoc
from
tensor
import
concat
from
ops
import
reshape
from
..nets
import
img_conv_with_bn
from
nn
import
transpose
import
math
__all__
=
[
'detection_output'
,
]
__all__
=
[
'detection_output'
,
'multi_box_head'
,
]
def
detection_output
(
scores
,
...
...
@@ -114,3 +125,147 @@ def detection_output(scores,
'nms_eta'
:
1.0
})
return
nmsed_outs
def
multi_box_head
(
inputs
,
num_classes
,
min_sizes
=
None
,
max_sizes
=
None
,
min_ratio
=
None
,
max_ratio
=
None
,
aspect_ratios
=
None
,
flip
=
False
,
share_location
=
True
,
kernel_size
=
1
,
pad
=
1
,
stride
=
1
,
use_batchnorm
=
False
,
base_size
=
None
,
name
=
None
):
"""
**Multi Box Head**
input many Variable, and return mbox_loc, mbox_conf
Args:
inputs(list): The list of input Variables, the format
of all Variables is NCHW.
num_classes(int): The number of calss.
min_sizes(list, optional, default=None): The length of
min_size is used to compute the the number of prior box.
If the min_size is None, it will be computed according
to min_ratio and max_ratio.
max_sizes(list, optional, default=None): The length of max_size
is used to compute the the number of prior box.
min_ratio(int): If the min_sizes is None, min_ratio and min_ratio
will be used to compute the min_sizes and max_sizes.
max_ratio(int): If the min_sizes is None, min_ratio and min_ratio
will be used to compute the min_sizes and max_sizes.
aspect_ratios(list): The number of the aspect ratios is used to
compute the number of prior box.
base_size(int): the base_size is used to get min_size
and max_size according to min_ratio and max_ratio.
flip(bool, optional, default=False): Whether to flip
aspect ratios.
name(str, optional, None): Name of the prior box layer.
Returns:
mbox_loc(Variable): the output prior boxes of PriorBoxOp. The layout is
[num_priors, 4]. num_priors is the total box count of each
position of inputs.
mbox_conf(Variable): the expanded variances of PriorBoxOp. The layout
is [num_priors, 4]. num_priors is the total box count of each
position of inputs
Examples:
.. code-block:: python
"""
assert
isinstance
(
inputs
,
list
),
'inputs should be a list.'
if
min_sizes
is
not
None
:
assert
len
(
inputs
)
==
len
(
min_sizes
)
if
max_sizes
is
not
None
:
assert
len
(
inputs
)
==
len
(
max_sizes
)
if
min_sizes
is
None
:
# if min_sizes is None, min_sizes and max_sizes
# will be set according to max_ratio and min_ratio
assert
max_ratio
is
not
None
and
min_ratio
is
not
None
min_sizes
=
[]
max_sizes
=
[]
num_layer
=
len
(
inputs
)
step
=
int
(
math
.
floor
(((
max_ratio
-
min_ratio
))
/
(
num_layer
-
2
)))
for
ratio
in
xrange
(
min_ratio
,
max_ratio
+
1
,
step
):
min_sizes
.
append
(
base_size
*
ratio
/
100.
)
max_sizes
.
append
(
base_size
*
(
ratio
+
step
)
/
100.
)
min_sizes
=
[
base_size
*
.
10
]
+
min_sizes
max_sizes
=
[
base_size
*
.
20
]
+
max_sizes
if
aspect_ratios
is
not
None
:
assert
len
(
inputs
)
==
len
(
aspect_ratios
)
mbox_locs
=
[]
mbox_confs
=
[]
for
i
,
input
in
enumerate
(
inputs
):
min_size
=
min_sizes
[
i
]
if
type
(
min_size
)
is
not
list
:
min_size
=
[
min_size
]
max_size
=
[]
if
max_sizes
is
not
None
:
max_size
=
max_sizes
[
i
]
if
type
(
max_size
)
is
not
list
:
max_size
=
[
max_size
]
if
max_size
:
assert
len
(
max_size
)
==
len
(
min_size
),
"max_size and min_size should have same length."
aspect_ratio
=
[]
if
aspect_ratios
is
not
None
:
aspect_ratio
=
aspect_ratios
[
i
]
if
type
(
aspect_ratio
)
is
not
list
:
aspect_ratio
=
[
aspect_ratio
]
num_priors_per_location
=
0
if
max_sizes
is
not
None
:
num_priors_per_location
=
len
(
min_size
)
+
len
(
aspect_ratio
)
*
len
(
min_size
)
+
len
(
max_size
)
else
:
num_priors_per_location
=
len
(
min_size
)
+
len
(
aspect_ratio
)
*
len
(
min_size
)
if
flip
:
num_priors_per_location
+=
len
(
aspect_ratio
)
*
len
(
min_size
)
# mbox_loc
num_loc_output
=
num_priors_per_location
*
4
if
share_location
:
num_loc_output
*=
num_classes
mbox_loc
=
img_conv_with_bn
(
input
=
input
,
conv_num_filter
=
num_loc_output
,
conv_padding
=
pad
,
conv_stride
=
stride
,
conv_filter_size
=
kernel_size
,
conv_with_batchnorm
=
use_batchnorm
)
mbox_loc
=
transpose
(
mbox_loc
,
perm
=
[
0
,
2
,
3
,
1
])
mbox_locs
.
append
(
mbox_loc
)
# get the number of prior box
num_conf_output
=
num_priors_per_location
*
num_classes
conf_loc
=
img_conv_with_bn
(
input
=
input
,
conv_num_filter
=
num_conf_output
,
conv_padding
=
pad
,
conv_stride
=
stride
,
conv_filter_size
=
kernel_size
,
conv_with_batchnorm
=
use_batchnorm
)
conf_loc
=
transpose
(
conf_loc
,
perm
=
[
0
,
2
,
3
,
1
])
mbox_confs
.
append
(
conf_loc
)
return
mbox_locs
,
mbox_confs
python/paddle/v2/fluid/nets.py
浏览文件 @
49c50c9f
...
...
@@ -18,6 +18,7 @@ __all__ = [
"sequence_conv_pool"
,
"glu"
,
"scaled_dot_product_attention"
,
"img_conv_with_bn"
,
]
...
...
@@ -107,6 +108,38 @@ def img_conv_group(input,
return
pool_out
def
img_conv_with_bn
(
input
,
conv_num_filter
,
conv_padding
=
1
,
conv_filter_size
=
3
,
conv_stride
=
1
,
conv_act
=
None
,
param_attr
=
None
,
conv_with_batchnorm
=
False
,
conv_batchnorm_drop_rate
=
0.0
,
use_cudnn
=
True
):
"""
Image Convolution Group, Used for vgg net.
"""
conv2d
=
layers
.
conv2d
(
input
=
input
,
num_filters
=
conv_num_filter
,
filter_size
=
conv_filter_size
,
padding
=
conv_padding
,
stride
=
conv_stride
,
param_attr
=
param_attr
,
act
=
conv_act
,
use_cudnn
=
use_cudnn
)
if
conv_with_batchnorm
:
conv2d
=
layers
.
batch_norm
(
input
=
conv2d
)
drop_rate
=
conv_batchnorm_drop_rate
if
abs
(
drop_rate
)
>
1e-5
:
conv2d
=
layers
.
dropout
(
x
=
conv2d
,
dropout_prob
=
drop_rate
)
return
conv2d
def
sequence_conv_pool
(
input
,
num_filters
,
filter_size
,
...
...
python/paddle/v2/fluid/tests/test_detection.py
浏览文件 @
49c50c9f
...
...
@@ -13,7 +13,13 @@
# limitations under the License.
from
__future__
import
print_function
import
paddle.v2.fluid
as
fluid
import
paddle.v2.fluid.core
as
core
import
paddle.v2.fluid.layers
as
layers
import
paddle.v2.fluid.layers.detection
as
detection
from
paddle.v2.fluid.framework
import
Program
,
program_guard
import
unittest
import
numpy
as
np
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.framework
import
Program
,
program_guard
...
...
@@ -49,5 +55,60 @@ class TestBook(unittest.TestCase):
print
(
str
(
program
))
class
TestMultiBoxHead
(
unittest
.
TestCase
):
def
test_prior_box
(
self
):
data_shape
=
[
3
,
224
,
224
]
mbox_locs
,
mbox_confs
=
self
.
multi_box_output
(
data_shape
)
# print mbox_locs.shape
# print mbox_confs.shape
# assert len(box.shape) == 2
# assert box.shape == var.shape
# assert box.shape[1] == 4
def
multi_box_output
(
self
,
data_shape
):
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
data_shape
,
dtype
=
'float32'
)
conv1
=
fluid
.
layers
.
conv2d
(
input
=
images
,
num_filters
=
3
,
filter_size
=
3
,
stride
=
2
,
use_cudnn
=
False
)
conv2
=
fluid
.
layers
.
conv2d
(
input
=
conv1
,
num_filters
=
3
,
filter_size
=
3
,
stride
=
2
,
use_cudnn
=
False
)
conv3
=
fluid
.
layers
.
conv2d
(
input
=
conv2
,
num_filters
=
3
,
filter_size
=
3
,
stride
=
2
,
use_cudnn
=
False
)
conv4
=
fluid
.
layers
.
conv2d
(
input
=
conv3
,
num_filters
=
3
,
filter_size
=
3
,
stride
=
2
,
use_cudnn
=
False
)
conv5
=
fluid
.
layers
.
conv2d
(
input
=
conv4
,
num_filters
=
3
,
filter_size
=
3
,
stride
=
2
,
use_cudnn
=
False
)
mbox_locs
,
mbox_confs
=
detection
.
multi_box_head
(
inputs
=
[
conv1
,
conv2
,
conv3
,
conv4
,
conv5
,
conv5
],
num_classes
=
21
,
min_ratio
=
20
,
max_ratio
=
90
,
aspect_ratios
=
[[
2.
],
[
2.
,
3.
],
[
2.
,
3.
],
[
2.
,
3.
],
[
2.
],
[
2.
]],
base_size
=
300
,
flip
=
True
)
return
mbox_locs
,
mbox_confs
if
__name__
==
'__main__'
:
unittest
.
main
()
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