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dff1bf33
编写于
2月 12, 2018
作者:
C
chengduoZH
浏览文件
操作
浏览文件
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差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into feature/add_prior_box_py
上级
adc058aa
cb4eacb1
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
68 addition
and
65 deletion
+68
-65
paddle/fluid/framework/mixed_vector.h
paddle/fluid/framework/mixed_vector.h
+16
-4
paddle/fluid/framework/mixed_vector_test.cu
paddle/fluid/framework/mixed_vector_test.cu
+6
-0
paddle/fluid/inference/tests/book/CMakeLists.txt
paddle/fluid/inference/tests/book/CMakeLists.txt
+1
-1
python/paddle/v2/fluid/layers/detection.py
python/paddle/v2/fluid/layers/detection.py
+39
-32
python/paddle/v2/fluid/tests/book/notest_rnn_encoder_decoer.py
...n/paddle/v2/fluid/tests/book/notest_rnn_encoder_decoer.py
+0
-0
python/paddle/v2/fluid/tests/test_detection.py
python/paddle/v2/fluid/tests/test_detection.py
+6
-28
未找到文件。
paddle/fluid/framework/mixed_vector.h
浏览文件 @
dff1bf33
...
...
@@ -106,9 +106,11 @@ class Vector {
// std::vector iterator methods. Based on CPU data access method
size_t
size
()
const
{
return
size_
;
}
T
*
begin
()
{
return
&
this
->
operator
[](
0
);
}
T
*
begin
()
{
return
capacity
()
==
0
?
&
EmptyDummy
()
:
&
this
->
operator
[](
0
);
}
T
*
end
()
{
return
&
this
->
operator
[](
size
());
}
T
*
end
()
{
return
capacity
()
==
0
?
&
EmptyDummy
()
:
&
this
->
operator
[](
size
());
}
T
&
front
()
{
return
*
begin
();
}
...
...
@@ -118,8 +120,13 @@ class Vector {
return
*
it
;
}
const
T
*
begin
()
const
{
return
&
this
->
operator
[](
0
);
}
const
T
*
end
()
const
{
return
&
this
->
operator
[](
size
());
}
const
T
*
begin
()
const
{
return
capacity
()
==
0
?
&
EmptyDummy
()
:
&
this
->
operator
[](
0
);
}
const
T
*
end
()
const
{
return
capacity
()
==
0
?
&
EmptyDummy
()
:
&
this
->
operator
[](
size
());
}
const
T
*
cbegin
()
const
{
return
begin
();
}
...
...
@@ -358,6 +365,11 @@ class Vector {
}
}
static
T
&
EmptyDummy
()
{
static
T
dummy
=
T
();
return
dummy
;
}
mutable
int
flag_
;
mutable
Tensor
cpu_vec_
;
mutable
Tensor
cuda_vec_
;
...
...
paddle/fluid/framework/mixed_vector_test.cu
浏览文件 @
dff1bf33
...
...
@@ -98,3 +98,9 @@ TEST(mixed_vector, InitWithCount) {
ASSERT_EQ
(
vec
[
i
],
10
);
}
}
TEST
(
mixed_vector
,
ForEach
)
{
vec
<
int
>
tmp
;
for
(
auto
&
v
:
tmp
)
{
}
}
paddle/fluid/inference/tests/book/CMakeLists.txt
浏览文件 @
dff1bf33
...
...
@@ -29,6 +29,6 @@ inference_test(image_classification ARGS vgg resnet)
inference_test
(
label_semantic_roles
)
inference_test
(
recognize_digits ARGS mlp
)
inference_test
(
recommender_system
)
inference_test
(
rnn_encoder_decoder
)
#
inference_test(rnn_encoder_decoder)
inference_test
(
understand_sentiment
)
inference_test
(
word2vec
)
python/paddle/v2/fluid/layers/detection.py
浏览文件 @
dff1bf33
...
...
@@ -19,7 +19,6 @@ from ..layer_helper import LayerHelper
from
..framework
import
Variable
from
tensor
import
concat
from
ops
import
reshape
from
operator
import
mul
import
math
__all__
=
[
...
...
@@ -143,43 +142,50 @@ def prior_box(inputs,
"""
**Prior_boxes**
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
The details of this algorithm, please refer the section 2.2 of SSD paper
(SSD: Single Shot MultiBox Detector)<https://arxiv.org/abs/1512.02325>`_ .
Generate prior boxes for SSD(Single Shot MultiBox Detector)
algorithm. The details of this algorithm, please refer the
section 2.2 of SSD paper (SSD: Single Shot MultiBox Detector)
<https://arxiv.org/abs/1512.02325>`_ .
Args:
inputs(list): The list of input Variables, the format of all Variables is NCHW.
image(Variable): The input image data of PriorBoxOp, the layout is NCHW.
inputs(list): The list of input Variables, the format
of all Variables is NCHW.
image(Variable): The input image data of PriorBoxOp,
the layout is NCHW.
min_ratio(int): the min ratio of generated prior boxes.
max_ratio(int): the max ratio of generated prior boxes.
aspect_ratios(list): the aspect ratios of generated prior
boxes.
The length of input and aspect_ratios must be equal.
base_size(int): the base_size is used to get min_size
and max_size
according to min_ratio and max_ratio.
step_w(list, optional, default=None): Prior boxes step
across width.
If step_w[i] == 0.0, the prior boxes step across width of the inputs[i]
will be automatically calculated.
step_h(list, optional, default=None): Prior boxes step
across height,
If step_h[i] == 0.0, the prior boxes step across height of the inputs[i]
will be automatically calculated.
aspect_ratios(list): the aspect ratios of generated prior
boxes.
The length of input and aspect_ratios must be equal.
base_size(int): the base_size is used to get min_size
a
nd max_size a
ccording to min_ratio and max_ratio.
step_w(list, optional, default=None): Prior boxes step
across width. If step_w[i] == 0.0, the prior boxes step
across width of the inputs[i]
will be automatically calculated.
step_h(list, optional, default=None): Prior boxes step
across height, If step_h[i] == 0.0, the prior boxes
step across height of the inputs[i]
will be automatically calculated.
offset(float, optional, default=0.5): Prior boxes center offset.
variance(list, optional, default=[0.1, 0.1, 0.1, 0.1]): the variances
to be encoded in prior boxes.
flip(bool, optional, default=False): Whether to flip aspect ratios.
clip(bool, optional, default=False): Whether to clip out-of-boundary boxes.
min_sizes(list, optional, default=None): If `len(inputs) <=2`, min_sizes must
be set up, and the length of min_sizes should equal to the length of inputs.
max_sizes(list, optional, default=None): If `len(inputs) <=2`, max_sizes must
be set up, and the length of min_sizes should equal to the length of inputs.
flip(bool, optional, default=False): Whether to flip
aspect ratios.
clip(bool, optional, default=False): Whether to clip
out-of-boundary boxes.
min_sizes(list, optional, default=None): If `len(inputs) <=2`,
min_sizes must be set up, and the length of min_sizes
should equal to the length of inputs.
max_sizes(list, optional, default=None): If `len(inputs) <=2`,
max_sizes must be set up, and the length of min_sizes
should equal to the length of inputs.
name(str, optional, None): Name of the prior box layer.
Returns:
boxes(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.
Variances(Variable): the expanded variances of PriorBoxOp.
The layout
is [num_priors, 4]. num_priors is the total box count of each
position of inputs
boxes(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.
Variances(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
...
...
@@ -235,10 +241,11 @@ def prior_box(inputs,
def
_reshape_with_axis_
(
input
,
axis
=
1
):
if
not
(
axis
>
0
and
axis
<
len
(
input
.
shape
)):
raise
ValueError
(
"The axis should be smaller than the arity of input and bigger than 0."
)
new_shape
=
[
-
1
,
reduce
(
mul
,
input
.
shape
[
axis
:
len
(
input
.
shape
)],
1
)]
raise
ValueError
(
"The axis should be smaller than "
"the arity of input and bigger than 0."
)
new_shape
=
[
-
1
,
reduce
(
lambda
x
,
y
:
x
*
y
,
input
.
shape
[
axis
:
len
(
input
.
shape
)])
]
out
=
reshape
(
x
=
input
,
shape
=
new_shape
)
return
out
...
...
python/paddle/v2/fluid/tests/book/
test_rnn_encoder_decod
er.py
→
python/paddle/v2/fluid/tests/book/
notest_rnn_encoder_deco
er.py
浏览文件 @
dff1bf33
文件已移动
python/paddle/v2/fluid/tests/test_detection.py
浏览文件 @
dff1bf33
...
...
@@ -54,8 +54,12 @@ class TestBook(unittest.TestCase):
class
TestPriorBox
(
unittest
.
TestCase
):
def
test_prior_box
(
self
):
self
.
check_prior_box
(
use_cuda
=
False
)
self
.
check_prior_box
(
use_cuda
=
True
)
data_shape
=
[
3
,
224
,
224
]
box
,
var
=
self
.
prior_box_output
(
data_shape
)
assert
len
(
box
.
shape
)
==
2
assert
box
.
shape
==
var
.
shape
assert
box
.
shape
[
1
]
==
4
def
prior_box_output
(
self
,
data_shape
):
images
=
fluid
.
layers
.
data
(
...
...
@@ -104,32 +108,6 @@ class TestPriorBox(unittest.TestCase):
clip
=
True
)
return
box
,
var
def
check_prior_box
(
self
,
use_cuda
):
if
use_cuda
:
# prior_box only support CPU.
return
data_shape
=
[
3
,
224
,
224
]
box
,
var
=
self
.
prior_box_output
(
data_shape
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
batch
=
[
4
]
# batch is not used in the prior_box.
assert
box
.
shape
[
1
]
==
4
assert
var
.
shape
[
1
]
==
4
assert
box
.
shape
==
var
.
shape
assert
len
(
box
.
shape
)
==
2
x
=
np
.
random
.
random
(
batch
+
data_shape
).
astype
(
"float32"
)
tensor_x
=
core
.
LoDTensor
()
tensor_x
.
set
(
x
,
place
)
boxes
,
vars
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
'pixel'
:
tensor_x
},
fetch_list
=
[
box
,
var
])
assert
vars
.
shape
==
var
.
shape
assert
boxes
.
shape
==
box
.
shape
if
__name__
==
'__main__'
:
unittest
.
main
()
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