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体验新版 GitCode,发现更多精彩内容 >>
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7bc8481c
编写于
1月 29, 2019
作者:
J
jerrywgz
提交者:
GitHub
1月 29, 2019
浏览文件
操作
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差异文件
Merge pull request #15418 from jerrywgz/refine_nms
Refine nms
上级
ab471584
3118a5e8
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
466 addition
and
104 deletion
+466
-104
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/detection/multiclass_nms_op.cc
paddle/fluid/operators/detection/multiclass_nms_op.cc
+183
-78
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+120
-1
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+11
-0
python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
...on/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
+151
-25
未找到文件。
paddle/fluid/API.spec
浏览文件 @
7bc8481c
...
...
@@ -325,6 +325,7 @@ paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
paddle.fluid.layers.multiclass_nms ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None))
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
...
...
paddle/fluid/operators/detection/multiclass_nms_op.cc
浏览文件 @
7bc8481c
...
...
@@ -9,9 +9,9 @@ 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.
limitations under the License. */
#include <glog/logging.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/poly_util.h"
...
...
@@ -35,30 +35,45 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
auto
box_dims
=
ctx
->
GetInputDim
(
"BBoxes"
);
auto
score_dims
=
ctx
->
GetInputDim
(
"Scores"
);
auto
score_size
=
score_dims
.
size
();
if
(
ctx
->
IsRuntime
())
{
PADDLE_ENFORCE
(
score_size
==
2
||
score_size
==
3
,
"The rank of Input(Scores) must be 2 or 3"
);
PADDLE_ENFORCE_EQ
(
box_dims
.
size
(),
3
,
"The rank of Input(BBoxes) must be 3."
);
PADDLE_ENFORCE_EQ
(
score_dims
.
size
(),
3
,
"The rank of Input(Scores) must be 3."
);
PADDLE_ENFORCE
(
box_dims
[
2
]
==
4
||
box_dims
[
2
]
==
8
||
box_dims
[
2
]
==
16
||
box_dims
[
2
]
==
24
||
box_dims
[
2
]
==
32
,
"The 2nd dimension of Input(BBoxes) must be 4 or 8, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax] or "
"4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
"8 points: [xi, yi] i= 1,2,...,8 or "
"12 points: [xi, yi] i= 1,2,...,12 or "
"16 points: [xi, yi] i= 1,2,...,16"
);
PADDLE_ENFORCE_EQ
(
box_dims
[
1
],
score_dims
[
2
],
"The 1st dimensiong of Input(BBoxes) must be equal to "
"3rd dimension of Input(Scores), which represents the "
"predicted bboxes."
);
"The rank of Input(BBoxes) must be 3"
);
if
(
score_size
==
3
)
{
PADDLE_ENFORCE
(
box_dims
[
2
]
==
4
||
box_dims
[
2
]
==
8
||
box_dims
[
2
]
==
16
||
box_dims
[
2
]
==
24
||
box_dims
[
2
]
==
32
,
"The last dimension of Input(BBoxes) must be 4 or 8, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax] or "
"4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
"8 points: [xi, yi] i= 1,2,...,8 or "
"12 points: [xi, yi] i= 1,2,...,12 or "
"16 points: [xi, yi] i= 1,2,...,16"
);
PADDLE_ENFORCE_EQ
(
box_dims
[
1
],
score_dims
[
2
],
"The 2nd dimension of Input(BBoxes) must be equal to "
"last dimension of Input(Scores), which represents the "
"predicted bboxes."
);
}
else
{
PADDLE_ENFORCE
(
box_dims
[
2
]
==
4
,
"The last dimension of Input(BBoxes) must be 4"
);
PADDLE_ENFORCE_EQ
(
box_dims
[
1
],
score_dims
[
1
],
"The 2nd dimension of Input(BBoxes)"
"must be equal to the 2nd dimension"
" of Input(Scores)"
);
}
}
// Here the box_dims[0] is not the real dimension of output.
// It will be rewritten in the computing kernel.
ctx
->
SetOutputDim
(
"Out"
,
{
box_dims
[
1
],
box_dims
[
2
]
+
2
});
if
(
score_size
==
3
)
{
ctx
->
SetOutputDim
(
"Out"
,
{
box_dims
[
1
],
box_dims
[
2
]
+
2
});
}
else
{
ctx
->
SetOutputDim
(
"Out"
,
{
-
1
,
box_dims
[
2
]
+
2
});
}
}
protected:
...
...
@@ -123,8 +138,9 @@ static inline T JaccardOverlap(const T* box1, const T* box2,
const
T
inter_ymin
=
std
::
max
(
box1
[
1
],
box2
[
1
]);
const
T
inter_xmax
=
std
::
min
(
box1
[
2
],
box2
[
2
]);
const
T
inter_ymax
=
std
::
min
(
box1
[
3
],
box2
[
3
]);
const
T
inter_w
=
inter_xmax
-
inter_xmin
;
const
T
inter_h
=
inter_ymax
-
inter_ymin
;
T
norm
=
normalized
?
static_cast
<
T
>
(
0.
)
:
static_cast
<
T
>
(
1.
);
T
inter_w
=
inter_xmax
-
inter_xmin
+
norm
;
T
inter_h
=
inter_ymax
-
inter_ymin
+
norm
;
const
T
inter_area
=
inter_w
*
inter_h
;
const
T
bbox1_area
=
BBoxArea
<
T
>
(
box1
,
normalized
);
const
T
bbox2_area
=
BBoxArea
<
T
>
(
box2
,
normalized
);
...
...
@@ -139,7 +155,7 @@ T PolyIoU(const T* box1, const T* box2, const size_t box_size,
T
bbox2_area
=
PolyArea
<
T
>
(
box2
,
box_size
,
normalized
);
T
inter_area
=
PolyOverlapArea
<
T
>
(
box1
,
box2
,
box_size
,
normalized
);
if
(
bbox1_area
==
0
||
bbox2_area
==
0
||
inter_area
==
0
)
{
// If coordinate values are i
s i
nvalid
// If coordinate values are invalid
// if area size <= 0, return 0.
return
T
(
0.
);
}
else
{
...
...
@@ -147,12 +163,35 @@ T PolyIoU(const T* box1, const T* box2, const size_t box_size,
}
}
template
<
class
T
>
void
SliceOneClass
(
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Tensor
&
items
,
const
int
class_id
,
framework
::
Tensor
*
one_class_item
)
{
T
*
item_data
=
one_class_item
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
items_data
=
items
.
data
<
T
>
();
const
int64_t
num_item
=
items
.
dims
()[
0
];
const
int
class_num
=
items
.
dims
()[
1
];
if
(
items
.
dims
().
size
()
==
3
)
{
int
item_size
=
items
.
dims
()[
2
];
for
(
int
i
=
0
;
i
<
num_item
;
++
i
)
{
std
::
memcpy
(
item_data
+
i
*
item_size
,
items_data
+
i
*
class_num
*
item_size
+
class_id
*
item_size
,
sizeof
(
T
)
*
item_size
);
}
}
else
{
for
(
int
i
=
0
;
i
<
num_item
;
++
i
)
{
item_data
[
i
]
=
items_data
[
i
*
class_num
+
class_id
];
}
}
}
template
<
typename
T
>
class
MultiClassNMSKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
NMSFast
(
const
Tensor
&
bbox
,
const
Tensor
&
scores
,
const
T
score_threshold
,
const
T
nms_threshold
,
const
T
eta
,
const
int64_t
top_k
,
std
::
vector
<
int
>*
selected_indices
)
const
{
const
int64_t
top_k
,
std
::
vector
<
int
>*
selected_indices
,
const
bool
normalized
)
const
{
// The total boxes for each instance.
int64_t
num_boxes
=
bbox
.
dims
()[
0
];
// 4: [xmin ymin xmax ymax]
...
...
@@ -178,15 +217,16 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
T
overlap
=
T
(
0.
);
// 4: [xmin ymin xmax ymax]
if
(
box_size
==
4
)
{
overlap
=
JaccardOverlap
<
T
>
(
bbox_data
+
idx
*
box_size
,
bbox_data
+
kept_idx
*
box_size
,
true
);
overlap
=
JaccardOverlap
<
T
>
(
bbox_data
+
idx
*
box_size
,
bbox_data
+
kept_idx
*
box_size
,
normalized
);
}
// 8: [x1 y1 x2 y2 x3 y3 x4 y4] or 16, 24, 32
if
(
box_size
==
8
||
box_size
==
16
||
box_size
==
24
||
box_size
==
32
)
{
overlap
=
PolyIoU
<
T
>
(
bbox_data
+
idx
*
box_size
,
bbox_data
+
kept_idx
*
box_size
,
box_size
,
true
);
overlap
=
PolyIoU
<
T
>
(
bbox_data
+
idx
*
box_size
,
bbox_data
+
kept_idx
*
box_size
,
box_size
,
normalized
);
}
keep
=
overlap
<=
adaptive_threshold
;
}
else
{
...
...
@@ -205,37 +245,58 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
void
MultiClassNMS
(
const
framework
::
ExecutionContext
&
ctx
,
const
Tensor
&
scores
,
const
Tensor
&
bboxes
,
const
int
scores_size
,
std
::
map
<
int
,
std
::
vector
<
int
>>*
indices
,
int
*
num_nmsed_out
)
const
{
int64_t
background_label
=
ctx
.
Attr
<
int
>
(
"background_label"
);
int64_t
nms_top_k
=
ctx
.
Attr
<
int
>
(
"nms_top_k"
);
int64_t
keep_top_k
=
ctx
.
Attr
<
int
>
(
"keep_top_k"
);
bool
normalized
=
ctx
.
Attr
<
bool
>
(
"normalized"
);
T
nms_threshold
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"nms_threshold"
));
T
nms_eta
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"nms_eta"
));
T
score_threshold
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"score_threshold"
));
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>();
int64_t
class_num
=
scores
.
dims
()[
0
];
int64_t
predict_dim
=
scores
.
dims
()[
1
];
int
num_det
=
0
;
int64_t
class_num
=
scores_size
==
3
?
scores
.
dims
()[
0
]
:
scores
.
dims
()[
1
];
Tensor
bbox_slice
,
score_slice
;
for
(
int64_t
c
=
0
;
c
<
class_num
;
++
c
)
{
if
(
c
==
background_label
)
continue
;
Tensor
score
=
scores
.
Slice
(
c
,
c
+
1
);
NMSFast
(
bboxes
,
score
,
score_threshold
,
nms_threshold
,
nms_eta
,
nms_top_k
,
&
((
*
indices
)[
c
]));
if
(
scores_size
==
3
)
{
score_slice
=
scores
.
Slice
(
c
,
c
+
1
);
bbox_slice
=
bboxes
;
}
else
{
score_slice
.
Resize
({
scores
.
dims
()[
0
],
1
});
bbox_slice
.
Resize
({
scores
.
dims
()[
0
],
4
});
SliceOneClass
<
T
>
(
dev_ctx
,
scores
,
c
,
&
score_slice
);
SliceOneClass
<
T
>
(
dev_ctx
,
bboxes
,
c
,
&
bbox_slice
);
}
NMSFast
(
bbox_slice
,
score_slice
,
score_threshold
,
nms_threshold
,
nms_eta
,
nms_top_k
,
&
((
*
indices
)[
c
]),
normalized
);
if
(
scores_size
==
2
)
{
std
::
stable_sort
((
*
indices
)[
c
].
begin
(),
(
*
indices
)[
c
].
end
());
}
num_det
+=
(
*
indices
)[
c
].
size
();
}
*
num_nmsed_out
=
num_det
;
const
T
*
scores_data
=
scores
.
data
<
T
>
();
if
(
keep_top_k
>
-
1
&&
num_det
>
keep_top_k
)
{
const
T
*
sdata
;
std
::
vector
<
std
::
pair
<
float
,
std
::
pair
<
int
,
int
>>>
score_index_pairs
;
for
(
const
auto
&
it
:
*
indices
)
{
int
label
=
it
.
first
;
const
T
*
sdata
=
scores_data
+
label
*
predict_dim
;
if
(
scores_size
==
3
)
{
sdata
=
scores_data
+
label
*
scores
.
dims
()[
1
];
}
else
{
score_slice
.
Resize
({
scores
.
dims
()[
0
],
1
});
SliceOneClass
<
T
>
(
dev_ctx
,
scores
,
label
,
&
score_slice
);
sdata
=
score_slice
.
data
<
T
>
();
}
const
std
::
vector
<
int
>&
label_indices
=
it
.
second
;
for
(
size_t
j
=
0
;
j
<
label_indices
.
size
();
++
j
)
{
int
idx
=
label_indices
[
j
];
PADDLE_ENFORCE_LT
(
idx
,
predict_dim
);
score_index_pairs
.
push_back
(
std
::
make_pair
(
sdata
[
idx
],
std
::
make_pair
(
label
,
idx
)));
}
...
...
@@ -252,31 +313,55 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
int
idx
=
score_index_pairs
[
j
].
second
.
second
;
new_indices
[
label
].
push_back
(
idx
);
}
if
(
scores_size
==
2
)
{
for
(
const
auto
&
it
:
new_indices
)
{
int
label
=
it
.
first
;
std
::
stable_sort
(
new_indices
[
label
].
begin
(),
new_indices
[
label
].
end
());
}
}
new_indices
.
swap
(
*
indices
);
*
num_nmsed_out
=
keep_top_k
;
}
}
void
MultiClassOutput
(
const
Tensor
&
scores
,
const
Tensor
&
bboxes
,
void
MultiClassOutput
(
const
platform
::
DeviceContext
&
ctx
,
const
Tensor
&
scores
,
const
Tensor
&
bboxes
,
const
std
::
map
<
int
,
std
::
vector
<
int
>>&
selected_indices
,
Tensor
*
outs
)
const
{
const
int
scores_size
,
Tensor
*
outs
)
const
{
int64_t
class_num
=
scores
.
dims
()[
1
];
int64_t
predict_dim
=
scores
.
dims
()[
1
];
int64_t
box_size
=
bboxes
.
dims
()[
1
];
int64_t
out_dim
=
bboxes
.
dims
()[
1
]
+
2
;
if
(
scores_size
==
2
)
{
box_size
=
bboxes
.
dims
()[
2
];
}
int64_t
out_dim
=
box_size
+
2
;
auto
*
scores_data
=
scores
.
data
<
T
>
();
auto
*
bboxes_data
=
bboxes
.
data
<
T
>
();
auto
*
odata
=
outs
->
data
<
T
>
();
const
T
*
sdata
;
Tensor
bbox
;
bbox
.
Resize
({
scores
.
dims
()[
0
],
box_size
});
int
count
=
0
;
for
(
const
auto
&
it
:
selected_indices
)
{
int
label
=
it
.
first
;
const
T
*
sdata
=
scores_data
+
label
*
predict_dim
;
const
std
::
vector
<
int
>&
indices
=
it
.
second
;
if
(
scores_size
==
2
)
{
SliceOneClass
<
T
>
(
ctx
,
bboxes
,
label
,
&
bbox
);
}
else
{
sdata
=
scores_data
+
label
*
predict_dim
;
}
for
(
size_t
j
=
0
;
j
<
indices
.
size
();
++
j
)
{
int
idx
=
indices
[
j
];
const
T
*
bdata
=
bboxes_data
+
idx
*
box_size
;
odata
[
count
*
out_dim
]
=
label
;
// label
odata
[
count
*
out_dim
+
1
]
=
sdata
[
idx
];
// score
odata
[
count
*
out_dim
]
=
label
;
// label
const
T
*
bdata
;
if
(
scores_size
==
3
)
{
bdata
=
bboxes_data
+
idx
*
box_size
;
odata
[
count
*
out_dim
+
1
]
=
sdata
[
idx
];
// score
}
else
{
bdata
=
bbox
.
data
<
T
>
()
+
idx
*
box_size
;
odata
[
count
*
out_dim
+
1
]
=
*
(
scores_data
+
idx
*
class_num
+
label
);
}
// xmin, ymin, xmax, ymax or multi-points coordinates
std
::
memcpy
(
odata
+
count
*
out_dim
+
2
,
bdata
,
box_size
*
sizeof
(
T
));
count
++
;
...
...
@@ -285,52 +370,64 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
}
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
boxes
=
ctx
.
Input
<
Tensor
>
(
"BBoxes"
);
auto
*
scores
=
ctx
.
Input
<
Tensor
>
(
"Scores"
);
auto
*
boxes
=
ctx
.
Input
<
LoD
Tensor
>
(
"BBoxes"
);
auto
*
scores
=
ctx
.
Input
<
LoD
Tensor
>
(
"Scores"
);
auto
*
outs
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
auto
score_dims
=
scores
->
dims
();
int64_t
batch_size
=
score_dims
[
0
];
int64_t
class_num
=
score_dims
[
1
];
int64_t
predict_dim
=
score_dims
[
2
];
int64_t
box_dim
=
boxes
->
dims
()[
2
];
int64_t
out_dim
=
boxes
->
dims
()[
2
]
+
2
;
auto
score_size
=
score_dims
.
size
();
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>();
std
::
vector
<
std
::
map
<
int
,
std
::
vector
<
int
>>>
all_indices
;
std
::
vector
<
size_t
>
batch_starts
=
{
0
};
for
(
int64_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
Tensor
ins_score
=
scores
->
Slice
(
i
,
i
+
1
);
ins_score
.
Resize
({
class_num
,
predict_dim
});
Tensor
ins_boxes
=
boxes
->
Slice
(
i
,
i
+
1
);
ins_boxes
.
Resize
({
predict_dim
,
box_dim
});
int64_t
batch_size
=
score_dims
[
0
];
int64_t
box_dim
=
boxes
->
dims
()[
2
];
int64_t
out_dim
=
box_dim
+
2
;
int
num_nmsed_out
=
0
;
Tensor
boxes_slice
,
scores_slice
;
int
n
=
score_size
==
3
?
batch_size
:
boxes
->
lod
().
back
().
size
()
-
1
;
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
if
(
score_size
==
3
)
{
scores_slice
=
scores
->
Slice
(
i
,
i
+
1
);
scores_slice
.
Resize
({
score_dims
[
1
],
score_dims
[
2
]});
boxes_slice
=
boxes
->
Slice
(
i
,
i
+
1
);
boxes_slice
.
Resize
({
score_dims
[
2
],
box_dim
});
}
else
{
auto
boxes_lod
=
boxes
->
lod
().
back
();
scores_slice
=
scores
->
Slice
(
boxes_lod
[
i
],
boxes_lod
[
i
+
1
]);
boxes_slice
=
boxes
->
Slice
(
boxes_lod
[
i
],
boxes_lod
[
i
+
1
]);
}
std
::
map
<
int
,
std
::
vector
<
int
>>
indices
;
int
num_nmsed_out
=
0
;
MultiClassNMS
(
ctx
,
ins_score
,
ins_boxes
,
&
indices
,
&
num_nmsed_out
);
MultiClassNMS
(
ctx
,
scores_slice
,
boxes_slice
,
score_size
,
&
indices
,
&
num_nmsed_out
);
all_indices
.
push_back
(
indices
);
batch_starts
.
push_back
(
batch_starts
.
back
()
+
num_nmsed_out
);
}
int
num_kept
=
batch_starts
.
back
();
if
(
num_kept
==
0
)
{
T
*
od
=
outs
->
mutable_data
<
T
>
({
1
},
ctx
.
GetPlace
());
T
*
od
=
outs
->
mutable_data
<
T
>
({
1
,
1
},
ctx
.
GetPlace
());
od
[
0
]
=
-
1
;
batch_starts
=
{
0
,
1
};
}
else
{
outs
->
mutable_data
<
T
>
({
num_kept
,
out_dim
},
ctx
.
GetPlace
());
for
(
int64_t
i
=
0
;
i
<
batch_size
;
++
i
)
{
Tensor
ins_score
=
scores
->
Slice
(
i
,
i
+
1
);
ins_score
.
Resize
({
class_num
,
predict_dim
});
Tensor
ins_boxes
=
boxes
->
Slice
(
i
,
i
+
1
);
ins_boxes
.
Resize
({
predict_dim
,
box_dim
});
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
if
(
score_size
==
3
)
{
scores_slice
=
scores
->
Slice
(
i
,
i
+
1
);
boxes_slice
=
boxes
->
Slice
(
i
,
i
+
1
);
scores_slice
.
Resize
({
score_dims
[
1
],
score_dims
[
2
]});
boxes_slice
.
Resize
({
score_dims
[
2
],
box_dim
});
}
else
{
auto
boxes_lod
=
boxes
->
lod
().
back
();
scores_slice
=
scores
->
Slice
(
boxes_lod
[
i
],
boxes_lod
[
i
+
1
]);
boxes_slice
=
boxes
->
Slice
(
boxes_lod
[
i
],
boxes_lod
[
i
+
1
]);
}
int64_t
s
=
batch_starts
[
i
];
int64_t
e
=
batch_starts
[
i
+
1
];
if
(
e
>
s
)
{
Tensor
out
=
outs
->
Slice
(
s
,
e
);
MultiClassOutput
(
ins_score
,
ins_boxes
,
all_indices
[
i
],
&
out
);
MultiClassOutput
(
dev_ctx
,
scores_slice
,
boxes_slice
,
all_indices
[
i
],
score_dims
.
size
(),
&
out
);
}
}
}
...
...
@@ -346,17 +443,24 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void
Make
()
override
{
AddInput
(
"BBoxes"
,
"(Tensor) A 3-D Tensor with shape "
"Two types of bboxes are supported:"
"1. (Tensor) A 3-D Tensor with shape "
"[N, M, 4 or 8 16 24 32] represents the "
"predicted locations of M bounding bboxes, N is the batch size. "
"Each bounding box has four coordinate values and the layout is "
"[xmin, ymin, xmax, ymax], when box size equals to 4."
);
"[xmin, ymin, xmax, ymax], when box size equals to 4."
"2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]"
"M is the number of bounding boxes, C is the class number"
);
AddInput
(
"Scores"
,
"(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"Two types of scores are supported:"
"1. (Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"predicted confidence predictions. N is the batch size, C is the "
"class number, M is number of bounding boxes. For each category "
"there are total M scores which corresponding M bounding boxes. "
" Please note, M is equal to the 1st dimension of BBoxes. "
);
" Please note, M is equal to the 2nd dimension of BBoxes. "
"2. (LoDTensor) A 2-D LoDTensor with shape [M, C]. "
"M is the number of bbox, C is the class number. In this case, "
"Input BBoxes should be the second case with shape [M, C, 4]."
);
AddAttr
<
int
>
(
"background_label"
,
"(int, defalut: 0) "
...
...
@@ -384,6 +488,10 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
"(int64_t) "
"Number of total bboxes to be kept per image after NMS "
"step. -1 means keeping all bboxes after NMS step."
);
AddAttr
<
bool
>
(
"normalized"
,
"(bool, default true) "
"Whether detections are normalized."
)
.
SetDefault
(
true
);
AddOutput
(
"Out"
,
"(LoDTensor) A 2-D LoDTensor with shape [No, 6] represents the "
"detections. Each row has 6 values: "
...
...
@@ -399,24 +507,21 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
This operator is to do multi-class non maximum suppression (NMS) on a batched
of boxes and scores.
In the NMS step, this operator greedily selects a subset of detection bounding
boxes that have high scores larger than score_threshold, if providing this
threshold, then selects the largest nms_top_k confidences scores if nms_top_k
is larger than -1. Then this operator pruns away boxes that have high IOU
(intersection over union) overlap with already selected boxes by adaptive
threshold NMS based on parameters of nms_threshold and nms_eta.
Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
per image if keep_top_k is larger than -1.
This operator support multi-class and batched inputs. It applying NMS
independently for each class. The outputs is a 2-D LoDTenosr, for each
image, the offsets in first dimension of LoDTensor are called LoD, the number
of offset is N + 1, where N is the batch size. If LoD[i + 1] - LoD[i] == 0,
means there is no detected bbox for this image. If there is no detected boxes
for all images, all the elements in LoD are
0, and the Out only contains one
value which is -1.
for all images, all the elements in LoD are
set to {1}, and the Out only
contains one
value which is -1.
)DOC"
);
}
};
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
7bc8481c
...
...
@@ -49,6 +49,7 @@ __all__ = [
'box_coder'
,
'polygon_box_transform'
,
'yolov3_loss'
,
'multiclass_nms'
,
]
...
...
@@ -262,8 +263,10 @@ def detection_output(loc,
number is N + 1, N is the batch size. The i-th image has
`LoD[i + 1] - LoD[i]` detected results, if it is 0, the i-th image
has no detected results. If all images have not detected results,
all the elements in LoD are 0
, and output tensor only contains one
LoD will be set to {1}
, and output tensor only contains one
value, which is -1.
(After version 1.3, when no boxes detected, the lod is changed
from {0} to {1}.)
Examples:
.. code-block:: python
...
...
@@ -1960,3 +1963,119 @@ def generate_proposals(scores,
rpn_roi_probs
.
stop_gradient
=
True
return
rpn_rois
,
rpn_roi_probs
def
multiclass_nms
(
bboxes
,
scores
,
score_threshold
,
nms_top_k
,
keep_top_k
,
nms_threshold
=
0.3
,
normalized
=
True
,
nms_eta
=
1.
,
background_label
=
0
,
name
=
None
):
"""
**Multiclass NMS**
This operator is to do multi-class non maximum suppression (NMS) on
boxes and scores.
In the NMS step, this operator greedily selects a subset of detection bounding
boxes that have high scores larger than score_threshold, if providing this
threshold, then selects the largest nms_top_k confidences scores if nms_top_k
is larger than -1. Then this operator pruns away boxes that have high IOU
(intersection over union) overlap with already selected boxes by adaptive
threshold NMS based on parameters of nms_threshold and nms_eta.
Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
per image if keep_top_k is larger than -1.
Args:
bboxes (Variable): Two types of bboxes are supported:
1. (Tensor) A 3-D Tensor with shape
[N, M, 4 or 8 16 24 32] represents the
predicted locations of M bounding bboxes,
N is the batch size. Each bounding box has four
coordinate values and the layout is
[xmin, ymin, xmax, ymax], when box size equals to 4.
2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
M is the number of bounding boxes, C is the
class number
scores (Variable): Two types of scores are supported:
1. (Tensor) A 3-D Tensor with shape [N, C, M]
represents the predicted confidence predictions.
N is the batch size, C is the class number, M is
number of bounding boxes. For each category there
are total M scores which corresponding M bounding
boxes. Please note, M is equal to the 2nd dimension
of BBoxes.
2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
M is the number of bbox, C is the class number.
In this case, input BBoxes should be the second
case with shape [M, C, 4].
background_label (int): The index of background label, the background
label will be ignored. If set to -1, then all
categories will be considered. Default: 0
score_threshold (float): Threshold to filter out bounding boxes with
low confidence score. If not provided,
consider all boxes.
nms_top_k (int): Maximum number of detections to be kept according to
the confidences aftern the filtering detections based
on score_threshold.
nms_threshold (float): The threshold to be used in NMS. Default: 0.3
nms_eta (float): The threshold to be used in NMS. Default: 1.0
keep_top_k (int): Number of total bboxes to be kept per image after NMS
step. -1 means keeping all bboxes after NMS step.
normalized (bool): Whether detections are normalized. Default: True
name(str): Name of the multiclass nms op. Default: None.
Returns:
Out: A 2-D LoDTensor with shape [No, 6] represents the detections.
Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
or A 2-D LoDTensor with shape [No, 10] represents the detections.
Each row has 10 values:
[label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the
total number of detections. If there is no detected boxes for all
images, lod will be set to {1} and Out only contains one value
which is -1.
(After version 1.3, when no boxes detected, the lod is changed
from {0} to {1})
Examples:
.. code-block:: python
boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
dtype='float32', lod_level=1)
scores = fluid.layers.data(name='scores', shape=[81],
dtype='float32', lod_level=1)
out = fluid.layers.multiclass_nms(bboxes=boxes,
scores=scores,
background_label=0,
score_threshold=0.5,
nms_top_k=400,
nms_threshold=0.3,
keep_top_k=200,
normalized=False)
"""
helper
=
LayerHelper
(
'multiclass_nms'
,
**
locals
())
output
=
helper
.
create_variable_for_type_inference
(
dtype
=
bboxes
.
dtype
)
helper
.
append_op
(
type
=
"multiclass_nms"
,
inputs
=
{
'BBoxes'
:
bboxes
,
'Scores'
:
scores
},
attrs
=
{
'background_label'
:
background_label
,
'score_threshold'
:
score_threshold
,
'nms_top_k'
:
nms_top_k
,
'nms_threshold'
:
nms_threshold
,
'nms_eta'
:
nms_eta
,
'keep_top_k'
:
keep_top_k
,
'nms_eta'
:
nms_eta
,
'normalized'
:
normalized
},
outputs
=
{
'Out'
:
output
})
output
.
stop_gradient
=
True
return
output
python/paddle/fluid/tests/test_detection.py
浏览文件 @
7bc8481c
...
...
@@ -469,5 +469,16 @@ class TestYoloDetection(unittest.TestCase):
self
.
assertIsNotNone
(
loss
)
class
TestMulticlassNMS
(
unittest
.
TestCase
):
def
test_multiclass_nms
(
self
):
program
=
Program
()
with
program_guard
(
program
):
bboxes
=
layers
.
data
(
name
=
'bboxes'
,
shape
=
[
-
1
,
10
,
4
],
dtype
=
'float32'
)
scores
=
layers
.
data
(
name
=
'scores'
,
shape
=
[
-
1
,
10
],
dtype
=
'float32'
)
output
=
layers
.
multiclass_nms
(
bboxes
,
scores
,
0.3
,
400
,
200
,
0.7
)
self
.
assertIsNotNone
(
output
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
浏览文件 @
7bc8481c
...
...
@@ -19,7 +19,7 @@ import copy
from
op_test
import
OpTest
def
iou
(
box_a
,
box_b
):
def
iou
(
box_a
,
box_b
,
norm
):
"""Apply intersection-over-union overlap between box_a and box_b
"""
xmin_a
=
min
(
box_a
[
0
],
box_a
[
2
])
...
...
@@ -32,8 +32,10 @@ def iou(box_a, box_b):
xmax_b
=
max
(
box_b
[
0
],
box_b
[
2
])
ymax_b
=
max
(
box_b
[
1
],
box_b
[
3
])
area_a
=
(
ymax_a
-
ymin_a
)
*
(
xmax_a
-
xmin_a
)
area_b
=
(
ymax_b
-
ymin_b
)
*
(
xmax_b
-
xmin_b
)
area_a
=
(
ymax_a
-
ymin_a
+
(
norm
==
False
))
*
(
xmax_a
-
xmin_a
+
(
norm
==
False
))
area_b
=
(
ymax_b
-
ymin_b
+
(
norm
==
False
))
*
(
xmax_b
-
xmin_b
+
(
norm
==
False
))
if
area_a
<=
0
and
area_b
<=
0
:
return
0.0
...
...
@@ -42,17 +44,21 @@ def iou(box_a, box_b):
xb
=
min
(
xmax_a
,
xmax_b
)
yb
=
min
(
ymax_a
,
ymax_b
)
inter_area
=
max
(
xb
-
xa
,
0.0
)
*
max
(
yb
-
ya
,
0.0
)
box_a_area
=
(
box_a
[
2
]
-
box_a
[
0
])
*
(
box_a
[
3
]
-
box_a
[
1
])
box_b_area
=
(
box_b
[
2
]
-
box_b
[
0
])
*
(
box_b
[
3
]
-
box_b
[
1
])
inter_area
=
max
(
xb
-
xa
+
(
norm
==
False
),
0.0
)
*
max
(
yb
-
ya
+
(
norm
==
False
),
0.0
)
iou_ratio
=
inter_area
/
(
area_a
+
area_b
-
inter_area
)
return
iou_ratio
def
nms
(
boxes
,
scores
,
score_threshold
,
nms_threshold
,
top_k
=
200
,
eta
=
1.0
):
def
nms
(
boxes
,
scores
,
score_threshold
,
nms_threshold
,
top_k
=
200
,
normalized
=
True
,
eta
=
1.0
):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
...
...
@@ -87,7 +93,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
for
k
in
range
(
len
(
selected_indices
)):
if
keep
:
kept_idx
=
selected_indices
[
k
]
overlap
=
iou
(
boxes
[
idx
],
boxes
[
kept_idx
])
overlap
=
iou
(
boxes
[
idx
],
boxes
[
kept_idx
]
,
normalized
)
keep
=
True
if
overlap
<=
adaptive_threshold
else
False
else
:
break
...
...
@@ -99,16 +105,24 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
def
multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
):
class_num
=
scores
.
shape
[
0
]
priorbox_num
=
scores
.
shape
[
1
]
nms_top_k
,
keep_top_k
,
normalized
,
shared
):
if
shared
:
class_num
=
scores
.
shape
[
0
]
priorbox_num
=
scores
.
shape
[
1
]
else
:
box_num
=
scores
.
shape
[
0
]
class_num
=
scores
.
shape
[
1
]
selected_indices
=
{}
num_det
=
0
for
c
in
range
(
class_num
):
if
c
==
background
:
continue
indices
=
nms
(
boxes
,
scores
[
c
],
score_threshold
,
nms_threshold
,
nms_top_k
)
if
shared
:
indices
=
nms
(
boxes
,
scores
[
c
],
score_threshold
,
nms_threshold
,
nms_top_k
,
normalized
)
else
:
indices
=
nms
(
boxes
[:,
c
,
:],
scores
[:,
c
],
score_threshold
,
nms_threshold
,
nms_top_k
,
normalized
)
selected_indices
[
c
]
=
indices
num_det
+=
len
(
indices
)
...
...
@@ -116,7 +130,10 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
score_index
=
[]
for
c
,
indices
in
selected_indices
.
items
():
for
idx
in
indices
:
score_index
.
append
((
scores
[
c
][
idx
],
c
,
idx
))
if
shared
:
score_index
.
append
((
scores
[
c
][
idx
],
c
,
idx
))
else
:
score_index
.
append
((
scores
[
idx
][
c
],
c
,
idx
))
sorted_score_index
=
sorted
(
score_index
,
key
=
lambda
tup
:
tup
[
0
],
reverse
=
True
)
...
...
@@ -127,24 +144,75 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
selected_indices
[
c
]
=
[]
for
s
,
c
,
idx
in
sorted_score_index
:
selected_indices
[
c
].
append
(
idx
)
if
not
shared
:
for
labels
in
selected_indices
:
selected_indices
[
labels
].
sort
()
num_det
=
keep_top_k
return
selected_indices
,
num_det
def
batched_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
):
def
lod_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
box_lod
,
normalized
):
det_outs
=
[]
lod
=
[]
head
=
0
for
n
in
range
(
len
(
box_lod
[
0
])):
box
=
boxes
[
head
:
head
+
box_lod
[
0
][
n
]]
score
=
scores
[
head
:
head
+
box_lod
[
0
][
n
]]
head
=
head
+
box_lod
[
0
][
n
]
nmsed_outs
,
nmsed_num
=
multiclass_nms
(
box
,
score
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
,
shared
=
False
)
if
nmsed_num
==
0
:
#lod.append(1)
continue
lod
.
append
(
nmsed_num
)
for
c
,
indices
in
nmsed_outs
.
items
():
for
idx
in
indices
:
xmin
,
ymin
,
xmax
,
ymax
=
box
[
idx
,
c
,
:]
det_outs
.
append
([
c
,
score
[
idx
][
c
],
xmin
,
ymin
,
xmax
,
ymax
])
if
len
(
lod
)
==
0
:
lod
.
append
(
1
)
return
det_outs
,
lod
def
batched_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
=
True
):
batch_size
=
scores
.
shape
[
0
]
det_outs
=
[]
lod
=
[]
for
n
in
range
(
batch_size
):
nmsed_outs
,
nmsed_num
=
multiclass_nms
(
boxes
[
n
],
scores
[
n
],
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
)
lod
.
append
(
nmsed_num
)
if
nmsed_num
==
0
:
continue
nmsed_outs
,
nmsed_num
=
multiclass_nms
(
boxes
[
n
],
scores
[
n
],
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
,
shared
=
True
)
if
nmsed_num
==
0
:
continue
lod
.
append
(
nmsed_num
)
tmp_det_out
=
[]
for
c
,
indices
in
nmsed_outs
.
items
():
for
idx
in
indices
:
...
...
@@ -154,7 +222,8 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
sorted_det_out
=
sorted
(
tmp_det_out
,
key
=
lambda
tup
:
tup
[
0
],
reverse
=
False
)
det_outs
.
extend
(
sorted_det_out
)
if
len
(
lod
)
==
0
:
lod
+=
[
1
]
return
det_outs
,
lod
...
...
@@ -168,7 +237,6 @@ class TestMulticlassNMSOp(OpTest):
M
=
1200
C
=
21
BOX_SIZE
=
4
background
=
0
nms_threshold
=
0.3
nms_top_k
=
400
...
...
@@ -206,6 +274,7 @@ class TestMulticlassNMSOp(OpTest):
'keep_top_k'
:
keep_top_k
,
'score_threshold'
:
score_threshold
,
'nms_eta'
:
1.0
,
'normalized'
:
True
,
}
def
test_check_output
(
self
):
...
...
@@ -219,13 +288,70 @@ class TestMulticlassNMSOpNoOutput(TestMulticlassNMSOp):
self
.
score_threshold
=
2.0
class
TestMulticlassNMSLoDInput
(
OpTest
):
def
set_argument
(
self
):
self
.
score_threshold
=
0.01
def
setUp
(
self
):
self
.
set_argument
()
M
=
1200
C
=
21
BOX_SIZE
=
4
box_lod
=
[[
1200
]]
background
=
0
nms_threshold
=
0.3
nms_top_k
=
400
keep_top_k
=
200
score_threshold
=
self
.
score_threshold
normalized
=
False
scores
=
np
.
random
.
random
((
M
,
C
)).
astype
(
'float32'
)
def
softmax
(
x
):
shiftx
=
x
-
np
.
max
(
x
).
clip
(
-
64.
)
exps
=
np
.
exp
(
shiftx
)
return
exps
/
np
.
sum
(
exps
)
scores
=
np
.
apply_along_axis
(
softmax
,
1
,
scores
)
boxes
=
np
.
random
.
random
((
M
,
C
,
BOX_SIZE
)).
astype
(
'float32'
)
boxes
[:,
:,
0
]
=
boxes
[:,
:,
0
]
*
10
boxes
[:,
:,
1
]
=
boxes
[:,
:,
1
]
*
10
boxes
[:,
:,
2
]
=
boxes
[:,
:,
2
]
*
10
+
10
boxes
[:,
:,
3
]
=
boxes
[:,
:,
3
]
*
10
+
10
nmsed_outs
,
lod
=
lod_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
box_lod
,
normalized
)
nmsed_outs
=
[
-
1
]
if
not
nmsed_outs
else
nmsed_outs
nmsed_outs
=
np
.
array
(
nmsed_outs
).
astype
(
'float32'
)
self
.
op_type
=
'multiclass_nms'
self
.
inputs
=
{
'BBoxes'
:
(
boxes
,
box_lod
),
'Scores'
:
(
scores
,
box_lod
),
}
self
.
outputs
=
{
'Out'
:
(
nmsed_outs
,
[
lod
])}
self
.
attrs
=
{
'background_label'
:
0
,
'nms_threshold'
:
nms_threshold
,
'nms_top_k'
:
nms_top_k
,
'keep_top_k'
:
keep_top_k
,
'score_threshold'
:
score_threshold
,
'nms_eta'
:
1.0
,
'normalized'
:
normalized
,
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestIOU
(
unittest
.
TestCase
):
def
test_iou
(
self
):
box1
=
np
.
array
([
4.0
,
3.0
,
7.0
,
5.0
]).
astype
(
'float32'
)
box2
=
np
.
array
([
3.0
,
4.0
,
6.0
,
8.0
]).
astype
(
'float32'
)
expt_output
=
np
.
array
([
2.0
/
16.0
]).
astype
(
'float32'
)
calc_output
=
np
.
array
([
iou
(
box1
,
box2
)]).
astype
(
'float32'
)
calc_output
=
np
.
array
([
iou
(
box1
,
box2
,
True
)]).
astype
(
'float32'
)
self
.
assertTrue
(
np
.
allclose
(
calc_output
,
expt_output
))
...
...
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