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a824da91
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a824da91
编写于
2月 12, 2018
作者:
W
Wang Hao
提交者:
GitHub
2月 12, 2018
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Merge pull request #6588 from wanghaox/detection_map
detection map evaluator for SSD
上级
e9d30991
91a21883
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paddle/fluid/operators/detection_map_op.cc
paddle/fluid/operators/detection_map_op.cc
+184
-0
paddle/fluid/operators/detection_map_op.h
paddle/fluid/operators/detection_map_op.h
+451
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python/paddle/v2/fluid/tests/test_detection_map_op.py
python/paddle/v2/fluid/tests/test_detection_map_op.py
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paddle/fluid/operators/detection_map_op.cc
0 → 100644
浏览文件 @
a824da91
/* 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. */
#include "paddle/fluid/operators/detection_map_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
class
DetectionMAPOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"DetectRes"
),
"Input(DetectRes) of DetectionMAPOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) of DetectionMAPOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"AccumPosCount"
),
"Output(AccumPosCount) of DetectionMAPOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"AccumTruePos"
),
"Output(AccumTruePos) of DetectionMAPOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"AccumFalsePos"
),
"Output(AccumFalsePos) of DetectionMAPOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"MAP"
),
"Output(MAP) of DetectionMAPOp should not be null."
);
auto
det_dims
=
ctx
->
GetInputDim
(
"DetectRes"
);
PADDLE_ENFORCE_EQ
(
det_dims
.
size
(),
2UL
,
"The rank of Input(DetectRes) must be 2, "
"the shape is [N, 6]."
);
PADDLE_ENFORCE_EQ
(
det_dims
[
1
],
6UL
,
"The shape is of Input(DetectRes) [N, 6]."
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
2UL
,
"The rank of Input(Label) must be 2, "
"the shape is [N, 6]."
);
PADDLE_ENFORCE_EQ
(
label_dims
[
1
],
6UL
,
"The shape is of Input(Label) [N, 6]."
);
if
(
ctx
->
HasInput
(
"PosCount"
))
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"TruePos"
),
"Input(TruePos) of DetectionMAPOp should not be null when "
"Input(TruePos) is not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"FalsePos"
),
"Input(FalsePos) of DetectionMAPOp should not be null when "
"Input(FalsePos) is not null."
);
}
ctx
->
SetOutputDim
(
"MAP"
,
framework
::
make_ddim
({
1
}));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"DetectRes"
)
->
type
()),
ctx
.
device_context
());
}
};
class
DetectionMAPOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
DetectionMAPOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"DetectRes"
,
"(LoDTensor) A 2-D LoDTensor with shape [M, 6] represents the "
"detections. Each row has 6 values: "
"[label, confidence, xmin, ymin, xmax, ymax], M is the total "
"number of detect results in this mini-batch. For each instance, "
"the offsets in first dimension are called LoD, the number of "
"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
"no detected data."
);
AddInput
(
"Label"
,
"(LoDTensor) A 2-D LoDTensor with shape[N, 6] represents the"
"Labeled ground-truth data. Each row has 6 values: "
"[label, is_difficult, xmin, ymin, xmax, ymax], N is the total "
"number of ground-truth data in this mini-batch. For each "
"instance, the offsets in first dimension are called LoD, "
"the number of offset is N + 1, if LoD[i + 1] - LoD[i] == 0, "
"means there is no ground-truth data."
);
AddInput
(
"PosCount"
,
"(Tensor) A tensor with shape [Ncls, 1], store the "
"input positive example count of each class, Ncls is the count of "
"input classification. "
"This input is used to pass the AccumPosCount generated by the "
"previous mini-batch when the multi mini-batches cumulative "
"calculation carried out. "
"When the input(PosCount) is empty, the cumulative "
"calculation is not carried out, and only the results of the "
"current mini-batch are calculated."
)
.
AsDispensable
();
AddInput
(
"TruePos"
,
"(LoDTensor) A 2-D LoDTensor with shape [Ntp, 2], store the "
"input true positive example of each class."
"This input is used to pass the AccumTruePos generated by the "
"previous mini-batch when the multi mini-batches cumulative "
"calculation carried out. "
)
.
AsDispensable
();
AddInput
(
"FalsePos"
,
"(LoDTensor) A 2-D LoDTensor with shape [Nfp, 2], store the "
"input false positive example of each class."
"This input is used to pass the AccumFalsePos generated by the "
"previous mini-batch when the multi mini-batches cumulative "
"calculation carried out. "
)
.
AsDispensable
();
AddOutput
(
"AccumPosCount"
,
"(Tensor) A tensor with shape [Ncls, 1], store the "
"positive example count of each class. It combines the input "
"input(PosCount) and the positive example count computed from "
"input(Detection) and input(Label)."
);
AddOutput
(
"AccumTruePos"
,
"(LoDTensor) A LoDTensor with shape [Ntp', 2], store the "
"true positive example of each class. It combines the "
"input(TruePos) and the true positive examples computed from "
"input(Detection) and input(Label)."
);
AddOutput
(
"AccumFalsePos"
,
"(LoDTensor) A LoDTensor with shape [Nfp', 2], store the "
"false positive example of each class. It combines the "
"input(FalsePos) and the false positive examples computed from "
"input(Detection) and input(Label)."
);
AddOutput
(
"MAP"
,
"(Tensor) A tensor with shape [1], store the mAP evaluate "
"result of the detection."
);
AddAttr
<
float
>
(
"overlap_threshold"
,
"(float) "
"The lower bound jaccard overlap threshold of detection output and "
"ground-truth data."
)
.
SetDefault
(
.3
f
);
AddAttr
<
bool
>
(
"evaluate_difficult"
,
"(bool, default true) "
"Switch to control whether the difficult data is evaluated."
)
.
SetDefault
(
true
);
AddAttr
<
std
::
string
>
(
"ap_type"
,
"(string, default 'integral') "
"The AP algorithm type, 'integral' or '11point'."
)
.
SetDefault
(
"integral"
)
.
InEnum
({
"integral"
,
"11point"
})
.
AddCustomChecker
([](
const
std
::
string
&
ap_type
)
{
PADDLE_ENFORCE_NE
(
GetAPType
(
ap_type
),
APType
::
kNone
,
"The ap_type should be 'integral' or '11point."
);
});
AddComment
(
R"DOC(
Detection mAP evaluate operator.
The general steps are as follows. First, calculate the true positive and
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_WITHOUT_GRADIENT
(
detection_map
,
ops
::
DetectionMAPOp
,
ops
::
DetectionMAPOpMaker
);
REGISTER_OP_CPU_KERNEL
(
detection_map
,
ops
::
DetectionMAPOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
DetectionMAPOpKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
paddle/fluid/operators/detection_map_op.h
0 → 100644
浏览文件 @
a824da91
/* 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
enum
APType
{
kNone
=
0
,
kIntegral
,
k11point
};
APType
GetAPType
(
std
::
string
str
)
{
if
(
str
==
"integral"
)
{
return
APType
::
kIntegral
;
}
else
if
(
str
==
"11point"
)
{
return
APType
::
k11point
;
}
else
{
return
APType
::
kNone
;
}
}
template
<
typename
T
>
inline
bool
SortScorePairDescend
(
const
std
::
pair
<
float
,
T
>&
pair1
,
const
std
::
pair
<
float
,
T
>&
pair2
)
{
return
pair1
.
first
>
pair2
.
first
;
}
template
<
typename
T
>
inline
void
GetAccumulation
(
std
::
vector
<
std
::
pair
<
T
,
int
>>
in_pairs
,
std
::
vector
<
int
>*
accu_vec
)
{
std
::
stable_sort
(
in_pairs
.
begin
(),
in_pairs
.
end
(),
SortScorePairDescend
<
int
>
);
accu_vec
->
clear
();
size_t
sum
=
0
;
for
(
size_t
i
=
0
;
i
<
in_pairs
.
size
();
++
i
)
{
auto
count
=
in_pairs
[
i
].
second
;
sum
+=
count
;
accu_vec
->
push_back
(
sum
);
}
}
template
<
typename
Place
,
typename
T
>
class
DetectionMAPOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in_detect
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"DetectRes"
);
auto
*
in_label
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Label"
);
auto
*
out_map
=
ctx
.
Output
<
framework
::
Tensor
>
(
"MAP"
);
auto
*
in_pos_count
=
ctx
.
Input
<
framework
::
Tensor
>
(
"PosCount"
);
auto
*
in_true_pos
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"TruePos"
);
auto
*
in_false_pos
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"FalsePos"
);
auto
*
out_pos_count
=
ctx
.
Output
<
framework
::
Tensor
>
(
"AccumPosCount"
);
auto
*
out_true_pos
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"AccumTruePos"
);
auto
*
out_false_pos
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"AccumFalsePos"
);
float
overlap_threshold
=
ctx
.
Attr
<
float
>
(
"overlap_threshold"
);
float
evaluate_difficult
=
ctx
.
Attr
<
bool
>
(
"evaluate_difficult"
);
auto
ap_type
=
GetAPType
(
ctx
.
Attr
<
std
::
string
>
(
"ap_type"
));
auto
label_lod
=
in_label
->
lod
();
auto
detect_lod
=
in_detect
->
lod
();
PADDLE_ENFORCE_EQ
(
label_lod
.
size
(),
1UL
,
"Only support one level sequence now."
);
PADDLE_ENFORCE_EQ
(
label_lod
[
0
].
size
(),
detect_lod
[
0
].
size
(),
"The batch_size of input(Label) and input(Detection) "
"must be the same."
);
std
::
vector
<
std
::
map
<
int
,
std
::
vector
<
Box
>>>
gt_boxes
;
std
::
vector
<
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
Box
>>>>
detect_boxes
;
GetBoxes
(
*
in_label
,
*
in_detect
,
gt_boxes
,
detect_boxes
);
std
::
map
<
int
,
int
>
label_pos_count
;
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>
true_pos
;
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>
false_pos
;
if
(
in_pos_count
!=
nullptr
)
{
GetInputPos
(
*
in_pos_count
,
*
in_true_pos
,
*
in_false_pos
,
label_pos_count
,
true_pos
,
false_pos
);
}
CalcTrueAndFalsePositive
(
gt_boxes
,
detect_boxes
,
evaluate_difficult
,
overlap_threshold
,
label_pos_count
,
true_pos
,
false_pos
);
T
map
=
CalcMAP
(
ap_type
,
label_pos_count
,
true_pos
,
false_pos
);
GetOutputPos
(
ctx
,
label_pos_count
,
true_pos
,
false_pos
,
*
out_pos_count
,
*
out_true_pos
,
*
out_false_pos
);
T
*
map_data
=
out_map
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
map_data
[
0
]
=
map
;
}
protected:
struct
Box
{
Box
(
T
xmin
,
T
ymin
,
T
xmax
,
T
ymax
)
:
xmin
(
xmin
),
ymin
(
ymin
),
xmax
(
xmax
),
ymax
(
ymax
),
is_difficult
(
false
)
{}
T
xmin
,
ymin
,
xmax
,
ymax
;
bool
is_difficult
;
};
inline
T
JaccardOverlap
(
const
Box
&
box1
,
const
Box
&
box2
)
const
{
if
(
box2
.
xmin
>
box1
.
xmax
||
box2
.
xmax
<
box1
.
xmin
||
box2
.
ymin
>
box1
.
ymax
||
box2
.
ymax
<
box1
.
ymin
)
{
return
0.0
;
}
else
{
T
inter_xmin
=
std
::
max
(
box1
.
xmin
,
box2
.
xmin
);
T
inter_ymin
=
std
::
max
(
box1
.
ymin
,
box2
.
ymin
);
T
inter_xmax
=
std
::
min
(
box1
.
xmax
,
box2
.
xmax
);
T
inter_ymax
=
std
::
min
(
box1
.
ymax
,
box2
.
ymax
);
T
inter_width
=
inter_xmax
-
inter_xmin
;
T
inter_height
=
inter_ymax
-
inter_ymin
;
T
inter_area
=
inter_width
*
inter_height
;
T
bbox_area1
=
(
box1
.
xmax
-
box1
.
xmin
)
*
(
box1
.
ymax
-
box1
.
ymin
);
T
bbox_area2
=
(
box2
.
xmax
-
box2
.
xmin
)
*
(
box2
.
ymax
-
box2
.
ymin
);
return
inter_area
/
(
bbox_area1
+
bbox_area2
-
inter_area
);
}
}
void
GetBoxes
(
const
framework
::
LoDTensor
&
input_label
,
const
framework
::
LoDTensor
&
input_detect
,
std
::
vector
<
std
::
map
<
int
,
std
::
vector
<
Box
>>>&
gt_boxes
,
std
::
vector
<
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
Box
>>>>&
detect_boxes
)
const
{
auto
labels
=
framework
::
EigenTensor
<
T
,
2
>::
From
(
input_label
);
auto
detect
=
framework
::
EigenTensor
<
T
,
2
>::
From
(
input_detect
);
auto
label_lod
=
input_label
.
lod
();
auto
detect_lod
=
input_detect
.
lod
();
int
batch_size
=
label_lod
[
0
].
size
()
-
1
;
auto
label_index
=
label_lod
[
0
];
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
std
::
map
<
int
,
std
::
vector
<
Box
>>
boxes
;
for
(
int
i
=
label_index
[
n
];
i
<
label_index
[
n
+
1
];
++
i
)
{
Box
box
(
labels
(
i
,
2
),
labels
(
i
,
3
),
labels
(
i
,
4
),
labels
(
i
,
5
));
int
label
=
labels
(
i
,
0
);
auto
is_difficult
=
labels
(
i
,
1
);
if
(
std
::
abs
(
is_difficult
-
0.0
)
<
1e-6
)
box
.
is_difficult
=
false
;
else
box
.
is_difficult
=
true
;
boxes
[
label
].
push_back
(
box
);
}
gt_boxes
.
push_back
(
boxes
);
}
auto
detect_index
=
detect_lod
[
0
];
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
Box
>>>
boxes
;
for
(
int
i
=
detect_index
[
n
];
i
<
detect_index
[
n
+
1
];
++
i
)
{
Box
box
(
detect
(
i
,
2
),
detect
(
i
,
3
),
detect
(
i
,
4
),
detect
(
i
,
5
));
int
label
=
detect
(
i
,
0
);
auto
score
=
detect
(
i
,
1
);
boxes
[
label
].
push_back
(
std
::
make_pair
(
score
,
box
));
}
detect_boxes
.
push_back
(
boxes
);
}
}
void
GetOutputPos
(
const
framework
::
ExecutionContext
&
ctx
,
const
std
::
map
<
int
,
int
>&
label_pos_count
,
const
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
true_pos
,
const
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
false_pos
,
framework
::
Tensor
&
output_pos_count
,
framework
::
LoDTensor
&
output_true_pos
,
framework
::
LoDTensor
&
output_false_pos
)
const
{
int
max_class_id
=
0
;
int
true_pos_count
=
0
;
int
false_pos_count
=
0
;
for
(
auto
it
=
label_pos_count
.
begin
();
it
!=
label_pos_count
.
end
();
++
it
)
{
int
label
=
it
->
first
;
if
(
label
>
max_class_id
)
max_class_id
=
label
;
int
label_num_pos
=
it
->
second
;
if
(
label_num_pos
==
0
||
true_pos
.
find
(
label
)
==
true_pos
.
end
())
continue
;
auto
label_true_pos
=
true_pos
.
find
(
label
)
->
second
;
auto
label_false_pos
=
false_pos
.
find
(
label
)
->
second
;
true_pos_count
+=
label_true_pos
.
size
();
false_pos_count
+=
label_false_pos
.
size
();
}
int
*
pos_count_data
=
output_pos_count
.
mutable_data
<
int
>
(
framework
::
make_ddim
({
max_class_id
+
1
,
1
}),
ctx
.
GetPlace
());
T
*
true_pos_data
=
output_true_pos
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
true_pos_count
,
2
}),
ctx
.
GetPlace
());
T
*
false_pos_data
=
output_false_pos
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
false_pos_count
,
2
}),
ctx
.
GetPlace
());
true_pos_count
=
0
;
false_pos_count
=
0
;
std
::
vector
<
size_t
>
true_pos_starts
=
{
0
};
std
::
vector
<
size_t
>
false_pos_starts
=
{
0
};
for
(
int
i
=
0
;
i
<=
max_class_id
;
++
i
)
{
auto
it_count
=
label_pos_count
.
find
(
i
);
pos_count_data
[
i
]
=
0
;
if
(
it_count
!=
label_pos_count
.
end
())
{
pos_count_data
[
i
]
=
it_count
->
second
;
}
auto
it_true_pos
=
true_pos
.
find
(
i
);
if
(
it_true_pos
!=
true_pos
.
end
())
{
const
std
::
vector
<
std
::
pair
<
T
,
int
>>&
true_pos_vec
=
it_true_pos
->
second
;
for
(
const
std
::
pair
<
T
,
int
>&
tp
:
true_pos_vec
)
{
true_pos_data
[
true_pos_count
*
2
]
=
tp
.
first
;
true_pos_data
[
true_pos_count
*
2
+
1
]
=
static_cast
<
T
>
(
tp
.
second
);
true_pos_count
++
;
}
}
true_pos_starts
.
push_back
(
true_pos_count
);
auto
it_false_pos
=
false_pos
.
find
(
i
);
if
(
it_false_pos
!=
false_pos
.
end
())
{
const
std
::
vector
<
std
::
pair
<
T
,
int
>>&
false_pos_vec
=
it_false_pos
->
second
;
for
(
const
std
::
pair
<
T
,
int
>&
fp
:
false_pos_vec
)
{
false_pos_data
[
false_pos_count
*
2
]
=
fp
.
first
;
false_pos_data
[
false_pos_count
*
2
+
1
]
=
static_cast
<
T
>
(
fp
.
second
);
false_pos_count
++
;
}
}
false_pos_starts
.
push_back
(
false_pos_count
);
}
framework
::
LoD
true_pos_lod
;
true_pos_lod
.
emplace_back
(
true_pos_starts
);
framework
::
LoD
false_pos_lod
;
false_pos_lod
.
emplace_back
(
false_pos_starts
);
output_true_pos
.
set_lod
(
true_pos_lod
);
output_false_pos
.
set_lod
(
false_pos_lod
);
return
;
}
void
GetInputPos
(
const
framework
::
Tensor
&
input_pos_count
,
const
framework
::
LoDTensor
&
input_true_pos
,
const
framework
::
LoDTensor
&
input_false_pos
,
std
::
map
<
int
,
int
>&
label_pos_count
,
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
true_pos
,
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
false_pos
)
const
{
constexpr
T
kEPS
=
static_cast
<
T
>
(
1e-6
);
int
class_number
=
input_pos_count
.
dims
()[
0
];
const
int
*
pos_count_data
=
input_pos_count
.
data
<
int
>
();
for
(
int
i
=
0
;
i
<
class_number
;
++
i
)
{
label_pos_count
[
i
]
=
pos_count_data
[
i
];
}
auto
SetData
=
[](
const
framework
::
LoDTensor
&
pos_tensor
,
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
pos
)
{
const
T
*
pos_data
=
pos_tensor
.
data
<
T
>
();
auto
pos_data_lod
=
pos_tensor
.
lod
();
for
(
int
i
=
0
;
i
<
pos_data_lod
.
size
();
++
i
)
{
for
(
int
j
=
pos_data_lod
[
0
][
i
];
j
<
pos_data_lod
[
0
][
i
+
1
];
++
j
)
{
T
score
=
pos_data
[
j
*
2
];
int
flag
=
1
;
if
(
pos_data
[
j
*
2
+
1
]
<
kEPS
)
flag
=
0
;
pos
[
i
].
push_back
(
std
::
make_pair
(
score
,
flag
));
}
}
};
SetData
(
input_true_pos
,
true_pos
);
SetData
(
input_false_pos
,
false_pos
);
return
;
}
void
CalcTrueAndFalsePositive
(
const
std
::
vector
<
std
::
map
<
int
,
std
::
vector
<
Box
>>>&
gt_boxes
,
const
std
::
vector
<
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
Box
>>>>&
detect_boxes
,
bool
evaluate_difficult
,
float
overlap_threshold
,
std
::
map
<
int
,
int
>&
label_pos_count
,
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
true_pos
,
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
false_pos
)
const
{
int
batch_size
=
gt_boxes
.
size
();
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
auto
image_gt_boxes
=
gt_boxes
[
n
];
for
(
auto
it
=
image_gt_boxes
.
begin
();
it
!=
image_gt_boxes
.
end
();
++
it
)
{
size_t
count
=
0
;
auto
labeled_bboxes
=
it
->
second
;
if
(
evaluate_difficult
)
{
count
=
labeled_bboxes
.
size
();
}
else
{
for
(
size_t
i
=
0
;
i
<
labeled_bboxes
.
size
();
++
i
)
if
(
!
(
labeled_bboxes
[
i
].
is_difficult
))
++
count
;
}
if
(
count
==
0
)
{
continue
;
}
int
label
=
it
->
first
;
if
(
label_pos_count
.
find
(
label
)
==
label_pos_count
.
end
())
{
label_pos_count
[
label
]
=
count
;
}
else
{
label_pos_count
[
label
]
+=
count
;
}
}
}
for
(
size_t
n
=
0
;
n
<
detect_boxes
.
size
();
++
n
)
{
auto
image_gt_boxes
=
gt_boxes
[
n
];
auto
detections
=
detect_boxes
[
n
];
if
(
image_gt_boxes
.
size
()
==
0
)
{
for
(
auto
it
=
detections
.
begin
();
it
!=
detections
.
end
();
++
it
)
{
auto
pred_boxes
=
it
->
second
;
int
label
=
it
->
first
;
for
(
size_t
i
=
0
;
i
<
pred_boxes
.
size
();
++
i
)
{
auto
score
=
pred_boxes
[
i
].
first
;
true_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
0
));
false_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
1
));
}
}
continue
;
}
for
(
auto
it
=
detections
.
begin
();
it
!=
detections
.
end
();
++
it
)
{
int
label
=
it
->
first
;
auto
pred_boxes
=
it
->
second
;
if
(
image_gt_boxes
.
find
(
label
)
==
image_gt_boxes
.
end
())
{
for
(
size_t
i
=
0
;
i
<
pred_boxes
.
size
();
++
i
)
{
auto
score
=
pred_boxes
[
i
].
first
;
true_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
0
));
false_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
1
));
}
continue
;
}
auto
matched_bboxes
=
image_gt_boxes
.
find
(
label
)
->
second
;
std
::
vector
<
bool
>
visited
(
matched_bboxes
.
size
(),
false
);
// Sort detections in descend order based on scores
std
::
sort
(
pred_boxes
.
begin
(),
pred_boxes
.
end
(),
SortScorePairDescend
<
Box
>
);
for
(
size_t
i
=
0
;
i
<
pred_boxes
.
size
();
++
i
)
{
T
max_overlap
=
-
1.0
;
size_t
max_idx
=
0
;
auto
score
=
pred_boxes
[
i
].
first
;
for
(
size_t
j
=
0
;
j
<
matched_bboxes
.
size
();
++
j
)
{
T
overlap
=
JaccardOverlap
(
pred_boxes
[
i
].
second
,
matched_bboxes
[
j
]);
if
(
overlap
>
max_overlap
)
{
max_overlap
=
overlap
;
max_idx
=
j
;
}
}
if
(
max_overlap
>
overlap_threshold
)
{
bool
match_evaluate_difficult
=
evaluate_difficult
||
(
!
evaluate_difficult
&&
!
matched_bboxes
[
max_idx
].
is_difficult
);
if
(
match_evaluate_difficult
)
{
if
(
!
visited
[
max_idx
])
{
true_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
1
));
false_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
0
));
visited
[
max_idx
]
=
true
;
}
else
{
true_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
0
));
false_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
1
));
}
}
}
else
{
true_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
0
));
false_pos
[
label
].
push_back
(
std
::
make_pair
(
score
,
1
));
}
}
}
}
}
T
CalcMAP
(
APType
ap_type
,
const
std
::
map
<
int
,
int
>&
label_pos_count
,
const
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
true_pos
,
const
std
::
map
<
int
,
std
::
vector
<
std
::
pair
<
T
,
int
>>>&
false_pos
)
const
{
T
mAP
=
0.0
;
int
count
=
0
;
for
(
auto
it
=
label_pos_count
.
begin
();
it
!=
label_pos_count
.
end
();
++
it
)
{
int
label
=
it
->
first
;
int
label_num_pos
=
it
->
second
;
if
(
label_num_pos
==
0
||
true_pos
.
find
(
label
)
==
true_pos
.
end
())
continue
;
auto
label_true_pos
=
true_pos
.
find
(
label
)
->
second
;
auto
label_false_pos
=
false_pos
.
find
(
label
)
->
second
;
// Compute average precision.
std
::
vector
<
int
>
tp_sum
;
GetAccumulation
<
T
>
(
label_true_pos
,
&
tp_sum
);
std
::
vector
<
int
>
fp_sum
;
GetAccumulation
<
T
>
(
label_false_pos
,
&
fp_sum
);
std
::
vector
<
T
>
precision
,
recall
;
size_t
num
=
tp_sum
.
size
();
// Compute Precision.
for
(
size_t
i
=
0
;
i
<
num
;
++
i
)
{
precision
.
push_back
(
static_cast
<
T
>
(
tp_sum
[
i
])
/
static_cast
<
T
>
(
tp_sum
[
i
]
+
fp_sum
[
i
]));
recall
.
push_back
(
static_cast
<
T
>
(
tp_sum
[
i
])
/
label_num_pos
);
}
// VOC2007 style
if
(
ap_type
==
APType
::
k11point
)
{
std
::
vector
<
T
>
max_precisions
(
11
,
0.0
);
int
start_idx
=
num
-
1
;
for
(
int
j
=
10
;
j
>=
0
;
--
j
)
for
(
int
i
=
start_idx
;
i
>=
0
;
--
i
)
{
if
(
recall
[
i
]
<
j
/
10.
)
{
start_idx
=
i
;
if
(
j
>
0
)
max_precisions
[
j
-
1
]
=
max_precisions
[
j
];
break
;
}
else
{
if
(
max_precisions
[
j
]
<
precision
[
i
])
max_precisions
[
j
]
=
precision
[
i
];
}
}
for
(
int
j
=
10
;
j
>=
0
;
--
j
)
mAP
+=
max_precisions
[
j
]
/
11
;
++
count
;
}
else
if
(
ap_type
==
APType
::
kIntegral
)
{
// Nature integral
float
average_precisions
=
0.
;
float
prev_recall
=
0.
;
for
(
size_t
i
=
0
;
i
<
num
;
++
i
)
{
if
(
fabs
(
recall
[
i
]
-
prev_recall
)
>
1e-6
)
average_precisions
+=
precision
[
i
]
*
fabs
(
recall
[
i
]
-
prev_recall
);
prev_recall
=
recall
[
i
];
}
mAP
+=
average_precisions
;
++
count
;
}
else
{
LOG
(
FATAL
)
<<
"Unkown ap version: "
<<
ap_type
;
}
}
if
(
count
!=
0
)
mAP
/=
count
;
return
mAP
*
100
;
}
};
// namespace operators
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/tests/test_detection_map_op.py
0 → 100644
浏览文件 @
a824da91
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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
import
sys
import
collections
import
math
from
op_test
import
OpTest
class
TestDetectionMAPOp
(
OpTest
):
def
set_data
(
self
):
self
.
init_test_case
()
self
.
mAP
=
[
self
.
calc_map
(
self
.
tf_pos
,
self
.
tf_pos_lod
)]
self
.
label
=
np
.
array
(
self
.
label
).
astype
(
'float32'
)
self
.
detect
=
np
.
array
(
self
.
detect
).
astype
(
'float32'
)
self
.
mAP
=
np
.
array
(
self
.
mAP
).
astype
(
'float32'
)
if
(
len
(
self
.
class_pos_count
)
>
0
):
self
.
class_pos_count
=
np
.
array
(
self
.
class_pos_count
).
astype
(
'int32'
)
self
.
true_pos
=
np
.
array
(
self
.
true_pos
).
astype
(
'float32'
)
self
.
false_pos
=
np
.
array
(
self
.
false_pos
).
astype
(
'float32'
)
self
.
inputs
=
{
'Label'
:
(
self
.
label
,
self
.
label_lod
),
'DetectRes'
:
(
self
.
detect
,
self
.
detect_lod
),
'PosCount'
:
self
.
class_pos_count
,
'TruePos'
:
(
self
.
true_pos
,
self
.
true_pos_lod
),
'FalsePos'
:
(
self
.
false_pos
,
self
.
false_pos_lod
)
}
else
:
self
.
inputs
=
{
'Label'
:
(
self
.
label
,
self
.
label_lod
),
'DetectRes'
:
(
self
.
detect
,
self
.
detect_lod
),
}
self
.
attrs
=
{
'overlap_threshold'
:
self
.
overlap_threshold
,
'evaluate_difficult'
:
self
.
evaluate_difficult
,
'ap_type'
:
self
.
ap_type
}
self
.
out_class_pos_count
=
np
.
array
(
self
.
out_class_pos_count
).
astype
(
'int'
)
self
.
out_true_pos
=
np
.
array
(
self
.
out_true_pos
).
astype
(
'float32'
)
self
.
out_false_pos
=
np
.
array
(
self
.
out_false_pos
).
astype
(
'float32'
)
self
.
outputs
=
{
'MAP'
:
self
.
mAP
,
'AccumPosCount'
:
self
.
out_class_pos_count
,
'AccumTruePos'
:
(
self
.
out_true_pos
,
self
.
out_true_pos_lod
),
'AccumFalsePos'
:
(
self
.
out_false_pos
,
self
.
out_false_pos_lod
)
}
def
init_test_case
(
self
):
self
.
overlap_threshold
=
0.3
self
.
evaluate_difficult
=
True
self
.
ap_type
=
"integral"
self
.
label_lod
=
[[
0
,
2
,
4
]]
# label difficult xmin ymin xmax ymax
self
.
label
=
[[
1
,
0
,
0.1
,
0.1
,
0.3
,
0.3
],
[
1
,
1
,
0.6
,
0.6
,
0.8
,
0.8
],
[
2
,
0
,
0.3
,
0.3
,
0.6
,
0.5
],
[
1
,
0
,
0.7
,
0.1
,
0.9
,
0.3
]]
# label score xmin ymin xmax ymax difficult
self
.
detect_lod
=
[[
0
,
3
,
7
]]
self
.
detect
=
[
[
1
,
0.3
,
0.1
,
0.0
,
0.4
,
0.3
],
[
1
,
0.7
,
0.0
,
0.1
,
0.2
,
0.3
],
[
1
,
0.9
,
0.7
,
0.6
,
0.8
,
0.8
],
[
2
,
0.8
,
0.2
,
0.1
,
0.4
,
0.4
],
[
2
,
0.1
,
0.4
,
0.3
,
0.7
,
0.5
],
[
1
,
0.2
,
0.8
,
0.1
,
1.0
,
0.3
],
[
3
,
0.2
,
0.8
,
0.1
,
1.0
,
0.3
]
]
# label score true_pos false_pos
self
.
tf_pos_lod
=
[[
0
,
3
,
7
]]
self
.
tf_pos
=
[[
1
,
0.9
,
1
,
0
],
[
1
,
0.7
,
1
,
0
],
[
1
,
0.3
,
0
,
1
],
[
1
,
0.2
,
1
,
0
],
[
2
,
0.8
,
0
,
1
],
[
2
,
0.1
,
1
,
0
],
[
3
,
0.2
,
0
,
1
]]
self
.
class_pos_count
=
[]
self
.
true_pos_lod
=
[[]]
self
.
true_pos
=
[[]]
self
.
false_pos_lod
=
[[]]
self
.
false_pos
=
[[]]
def
calc_map
(
self
,
tf_pos
,
tf_pos_lod
):
mAP
=
0.0
count
=
0
def
get_input_pos
(
class_pos_count
,
true_pos
,
true_pos_lod
,
false_pos
,
false_pos_lod
):
class_pos_count_dict
=
collections
.
Counter
()
true_pos_dict
=
collections
.
defaultdict
(
list
)
false_pos_dict
=
collections
.
defaultdict
(
list
)
for
i
,
count
in
enumerate
(
class_pos_count
):
class_pos_count_dict
[
i
]
=
count
for
i
in
range
(
len
(
true_pos_lod
[
0
])
-
1
):
start
=
true_pos_lod
[
0
][
i
]
end
=
true_pos_lod
[
0
][
i
+
1
]
for
j
in
range
(
start
,
end
):
true_pos_dict
[
i
].
append
(
true_pos
[
j
])
for
i
in
range
(
len
(
false_pos_lod
[
0
])
-
1
):
start
=
false_pos_lod
[
0
][
i
]
end
=
false_pos_lod
[
0
][
i
+
1
]
for
j
in
range
(
start
,
end
):
false_pos_dict
[
i
].
append
(
false_pos
[
j
])
return
class_pos_count_dict
,
true_pos_dict
,
false_pos_dict
def
get_output_pos
(
label_count
,
true_pos
,
false_pos
):
max_label
=
0
for
(
label
,
label_pos_num
)
in
label_count
.
items
():
if
max_label
<
label
:
max_label
=
label
label_number
=
max_label
+
1
out_class_pos_count
=
[]
out_true_pos_lod
=
[
0
]
out_true_pos
=
[]
out_false_pos_lod
=
[
0
]
out_false_pos
=
[]
for
i
in
range
(
label_number
):
out_class_pos_count
.
append
([
label_count
[
i
]])
true_pos_list
=
true_pos
[
i
]
out_true_pos
+=
true_pos_list
out_true_pos_lod
.
append
(
len
(
out_true_pos
))
false_pos_list
=
false_pos
[
i
]
out_false_pos
+=
false_pos_list
out_false_pos_lod
.
append
(
len
(
out_false_pos
))
return
out_class_pos_count
,
out_true_pos
,
[
out_true_pos_lod
],
out_false_pos
,
[
out_false_pos_lod
]
def
get_accumulation
(
pos_list
):
sorted_list
=
sorted
(
pos_list
,
key
=
lambda
pos
:
pos
[
0
],
reverse
=
True
)
sum
=
0
accu_list
=
[]
for
(
score
,
count
)
in
sorted_list
:
sum
+=
count
accu_list
.
append
(
sum
)
return
accu_list
label_count
,
true_pos
,
false_pos
=
get_input_pos
(
self
.
class_pos_count
,
self
.
true_pos
,
self
.
true_pos_lod
,
self
.
false_pos
,
self
.
false_pos_lod
)
for
(
label
,
difficult
,
xmin
,
ymin
,
xmax
,
ymax
)
in
self
.
label
:
if
self
.
evaluate_difficult
:
label_count
[
label
]
+=
1
elif
not
difficult
:
label_count
[
label
]
+=
1
true_pos
=
collections
.
defaultdict
(
list
)
false_pos
=
collections
.
defaultdict
(
list
)
for
(
label
,
score
,
tp
,
fp
)
in
tf_pos
:
true_pos
[
label
].
append
([
score
,
tp
])
false_pos
[
label
].
append
([
score
,
fp
])
for
(
label
,
label_pos_num
)
in
label_count
.
items
():
if
label_pos_num
==
0
or
label
not
in
true_pos
:
continue
label_true_pos
=
true_pos
[
label
]
label_false_pos
=
false_pos
[
label
]
accu_tp_sum
=
get_accumulation
(
label_true_pos
)
accu_fp_sum
=
get_accumulation
(
label_false_pos
)
precision
=
[]
recall
=
[]
for
i
in
range
(
len
(
accu_tp_sum
)):
precision
.
append
(
float
(
accu_tp_sum
[
i
])
/
float
(
accu_tp_sum
[
i
]
+
accu_fp_sum
[
i
]))
recall
.
append
(
float
(
accu_tp_sum
[
i
])
/
label_pos_num
)
if
self
.
ap_type
==
"11point"
:
max_precisions
=
[
0.0
]
*
11
start_idx
=
len
(
accu_tp_sum
)
-
1
for
j
in
range
(
10
,
-
1
,
-
1
):
for
i
in
range
(
start_idx
,
-
1
,
-
1
):
if
recall
[
i
]
<
float
(
j
)
/
10.0
:
start_idx
=
i
if
j
>
0
:
max_precisions
[
j
-
1
]
=
max_precisions
[
j
]
break
else
:
if
max_precisions
[
j
]
<
precision
[
i
]:
max_precisions
[
j
]
=
precision
[
i
]
for
j
in
range
(
10
,
-
1
,
-
1
):
mAP
+=
max_precisions
[
j
]
/
11
count
+=
1
elif
self
.
ap_type
==
"integral"
:
average_precisions
=
0.0
prev_recall
=
0.0
for
i
in
range
(
len
(
accu_tp_sum
)):
if
math
.
fabs
(
recall
[
i
]
-
prev_recall
)
>
1e-6
:
average_precisions
+=
precision
[
i
]
*
\
math
.
fabs
(
recall
[
i
]
-
prev_recall
)
prev_recall
=
recall
[
i
]
mAP
+=
average_precisions
count
+=
1
self
.
out_class_pos_count
,
self
.
out_true_pos
,
self
.
out_true_pos_lod
,
self
.
out_false_pos
,
self
.
out_false_pos_lod
=
get_output_pos
(
label_count
,
true_pos
,
false_pos
)
if
count
!=
0
:
mAP
/=
count
return
mAP
*
100.0
def
setUp
(
self
):
self
.
op_type
=
"detection_map"
self
.
set_data
()
def
test_check_output
(
self
):
self
.
check_output
()
class
TestDetectionMAPOpSkipDiff
(
TestDetectionMAPOp
):
def
init_test_case
(
self
):
super
(
TestDetectionMAPOpSkipDiff
,
self
).
init_test_case
()
self
.
evaluate_difficult
=
False
self
.
tf_pos_lod
=
[[
0
,
2
,
6
]]
# label score true_pos false_pos
self
.
tf_pos
=
[[
1
,
0.7
,
1
,
0
],
[
1
,
0.3
,
0
,
1
],
[
1
,
0.2
,
1
,
0
],
[
2
,
0.8
,
0
,
1
],
[
2
,
0.1
,
1
,
0
],
[
3
,
0.2
,
0
,
1
]]
class
TestDetectionMAPOp11Point
(
TestDetectionMAPOp
):
def
init_test_case
(
self
):
super
(
TestDetectionMAPOp11Point
,
self
).
init_test_case
()
self
.
ap_type
=
"11point"
class
TestDetectionMAPOpMultiBatch
(
TestDetectionMAPOp
):
def
init_test_case
(
self
):
super
(
TestDetectionMAPOpMultiBatch
,
self
).
init_test_case
()
self
.
class_pos_count
=
[
0
,
2
,
1
]
self
.
true_pos_lod
=
[[
0
,
0
,
3
,
5
]]
self
.
true_pos
=
[[
0.7
,
1.
],
[
0.3
,
0.
],
[
0.2
,
1.
],
[
0.8
,
0.
],
[
0.1
,
1.
]]
self
.
false_pos_lod
=
[[
0
,
0
,
3
,
5
]]
self
.
false_pos
=
[[
0.7
,
0.
],
[
0.3
,
1.
],
[
0.2
,
0.
],
[
0.8
,
1.
],
[
0.1
,
0.
]]
if
__name__
==
'__main__'
:
unittest
.
main
()
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