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f3415ec5
编写于
2月 01, 2018
作者:
D
dangqingqing
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Follow comments.
上级
53788640
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
72 addition
and
56 deletion
+72
-56
paddle/operators/bipartite_match_op.cc
paddle/operators/bipartite_match_op.cc
+12
-6
paddle/operators/multiclass_nms_op.cc
paddle/operators/multiclass_nms_op.cc
+57
-47
python/paddle/v2/fluid/tests/test_bipartite_match_op.py
python/paddle/v2/fluid/tests/test_bipartite_match_op.py
+2
-2
python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
+1
-1
未找到文件。
paddle/operators/bipartite_match_op.cc
浏览文件 @
f3415ec5
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 201
8
PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
you may not use this file except in compliance with the License.
...
@@ -28,12 +28,18 @@ class BipartiteMatchOp : public framework::OperatorWithKernel {
...
@@ -28,12 +28,18 @@ class BipartiteMatchOp : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"DistMat"
),
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"DistMat"
),
"Input(DistMat) of BipartiteMatch should not be null."
);
"Input(DistMat) of BipartiteMatch should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ColToRowMatchIndices"
),
"Output(ColToRowMatchIndices) of BipartiteMatch should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ColToRowMatchDist"
),
"Output(ColToRowMatchDist) of BipartiteMatch should not be null."
);
auto
dims
=
ctx
->
GetInputDim
(
"DistMat"
);
auto
dims
=
ctx
->
GetInputDim
(
"DistMat"
);
PADDLE_ENFORCE_EQ
(
dims
.
size
(),
2
,
"The rank of Input(DistMat) must be 2."
);
PADDLE_ENFORCE_EQ
(
dims
.
size
(),
2
,
"The rank of Input(DistMat) must be 2."
);
ctx
->
SetOutputDim
(
"ColToRowMatchIndices"
,
dims
);
ctx
->
SetOutputDim
(
"ColToRowMatchIndices"
,
dims
);
ctx
->
SetOutputDim
(
"ColToRowMatchDis"
,
dims
);
ctx
->
SetOutputDim
(
"ColToRowMatchDis
t
"
,
dims
);
}
}
};
};
...
@@ -91,7 +97,7 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
...
@@ -91,7 +97,7 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
dist_mat
=
context
.
Input
<
LoDTensor
>
(
"DistMat"
);
auto
*
dist_mat
=
context
.
Input
<
LoDTensor
>
(
"DistMat"
);
auto
*
match_indices
=
context
.
Output
<
Tensor
>
(
"ColToRowMatchIndices"
);
auto
*
match_indices
=
context
.
Output
<
Tensor
>
(
"ColToRowMatchIndices"
);
auto
*
match_dist
=
context
.
Output
<
Tensor
>
(
"ColToRowMatchDis"
);
auto
*
match_dist
=
context
.
Output
<
Tensor
>
(
"ColToRowMatchDis
t
"
);
auto
&
dev_ctx
=
context
.
device_context
<
platform
::
CPUDeviceContext
>
();
auto
&
dev_ctx
=
context
.
device_context
<
platform
::
CPUDeviceContext
>
();
...
@@ -148,13 +154,13 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -148,13 +154,13 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker {
"Otherwise, it means B[j] is matched to row "
"Otherwise, it means B[j] is matched to row "
"ColToRowMatchIndices[i][j] in i-th instance. The row number of "
"ColToRowMatchIndices[i][j] in i-th instance. The row number of "
"i-th instance is saved in ColToRowMatchIndices[i][j]."
);
"i-th instance is saved in ColToRowMatchIndices[i][j]."
);
AddOutput
(
"ColToRowMatchDis"
,
AddOutput
(
"ColToRowMatchDis
t
"
,
"(Tensor) A 2-D Tensor with shape [N, M] in float type. "
"(Tensor) A 2-D Tensor with shape [N, M] in float type. "
"N is batch size. If ColToRowMatchIndices[i][j] is -1, "
"N is batch size. If ColToRowMatchIndices[i][j] is -1, "
"ColToRowMatchDis[i][j] is also -1.0. Otherwise, assumed "
"ColToRowMatchDis
t
[i][j] is also -1.0. Otherwise, assumed "
"ColToRowMatchIndices[i][j] = d, and the row offsets of each "
"ColToRowMatchIndices[i][j] = d, and the row offsets of each "
"instance are called LoD. Then "
"instance are called LoD. Then "
"ColToRowMatchDis[i][j] = DistMat[d+LoD[i]][j]"
);
"ColToRowMatchDis
t
[i][j] = DistMat[d+LoD[i]][j]"
);
AddComment
(
R"DOC(
AddComment
(
R"DOC(
This operator is a greedy bipartite matching algorithm, which is used to
This operator is a greedy bipartite matching algorithm, which is used to
obtain the matching with the maximum distance based on the input
obtain the matching with the maximum distance based on the input
...
...
paddle/operators/multiclass_nms_op.cc
浏览文件 @
f3415ec5
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 201
8
PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
you may not use this file except in compliance with the License.
...
@@ -24,25 +24,33 @@ using LoDTensor = framework::LoDTensor;
...
@@ -24,25 +24,33 @@ using LoDTensor = framework::LoDTensor;
constexpr
int64_t
kOutputDim
=
6
;
constexpr
int64_t
kOutputDim
=
6
;
constexpr
int64_t
kBBoxSize
=
4
;
constexpr
int64_t
kBBoxSize
=
4
;
class
Multi
c
lassNMSOp
:
public
framework
::
OperatorWithKernel
{
class
Multi
C
lassNMSOp
:
public
framework
::
OperatorWithKernel
{
public:
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"B
b
oxes"
),
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"B
B
oxes"
),
"Input(B
boxes) of Multic
lassNMS should not be null."
);
"Input(B
Boxes) of MultiC
lassNMS should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Scores"
),
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Scores"
),
"Input(Scores) of MulticlassNMS should not be null."
);
"Input(Scores) of MultiClassNMS should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of MultiClassNMS should not be null."
);
auto
box_dims
=
ctx
->
GetInputDim
(
"B
b
oxes"
);
auto
box_dims
=
ctx
->
GetInputDim
(
"B
B
oxes"
);
auto
score_dims
=
ctx
->
GetInputDim
(
"Scores"
);
auto
score_dims
=
ctx
->
GetInputDim
(
"Scores"
);
PADDLE_ENFORCE_EQ
(
box_dims
.
size
(),
2
,
PADDLE_ENFORCE_EQ
(
box_dims
.
size
(),
2
,
"The rank of Input(B
boxes) must be 3
."
);
"The rank of Input(B
Boxes) must be 2
."
);
PADDLE_ENFORCE_EQ
(
score_dims
.
size
(),
3
,
PADDLE_ENFORCE_EQ
(
score_dims
.
size
(),
3
,
"The rank of Input(Scores) must be 3."
);
"The rank of Input(Scores) must be 3."
);
PADDLE_ENFORCE_EQ
(
box_dims
[
1
],
4
);
PADDLE_ENFORCE_EQ
(
box_dims
[
1
],
4
,
PADDLE_ENFORCE_EQ
(
box_dims
[
0
],
score_dims
[
2
]);
"The 2nd dimension of Input(BBoxes) must be 4, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax]"
);
PADDLE_ENFORCE_EQ
(
box_dims
[
0
],
score_dims
[
2
],
"The 1st dimensiong of Input(BBoxes) must be equal to "
"3rd dimension of Input(Scores), which represents the "
"predicted bboxes."
);
// Here the box_dims[0] is not the real dimension of output.
// Here the box_dims[0] is not the real dimension of output.
// It will be rewritten in the computing kernel.
// It will be rewritten in the computing kernel.
...
@@ -86,15 +94,16 @@ static inline void GetMaxScoreIndex(
...
@@ -86,15 +94,16 @@ static inline void GetMaxScoreIndex(
template
<
class
T
>
template
<
class
T
>
T
BBoxArea
(
const
T
*
box
,
const
bool
normalized
)
{
T
BBoxArea
(
const
T
*
box
,
const
bool
normalized
)
{
if
(
box
[
2
]
<
box
[
0
]
||
box
[
3
]
<
box
[
1
])
{
if
(
box
[
2
]
<
box
[
0
]
||
box
[
3
]
<
box
[
1
])
{
// If bbox is invalid (e.g. xmax < xmin or ymax < ymin), return 0.
// If coordinate values are is invalid
return
T
(
0.
);
// (e.g. xmax < xmin or ymax < ymin), return 0.
return
static_cast
<
T
>
(
0.
);
}
else
{
}
else
{
const
T
w
=
box
[
2
]
-
box
[
0
];
const
T
w
=
box
[
2
]
-
box
[
0
];
const
T
h
=
box
[
3
]
-
box
[
1
];
const
T
h
=
box
[
3
]
-
box
[
1
];
if
(
normalized
)
{
if
(
normalized
)
{
return
w
*
h
;
return
w
*
h
;
}
else
{
}
else
{
// If
bbox is
not within range [0, 1].
// If
coordinate values are
not within range [0, 1].
return
(
w
+
1
)
*
(
h
+
1
);
return
(
w
+
1
)
*
(
h
+
1
);
}
}
}
}
...
@@ -121,7 +130,7 @@ static inline T JaccardOverlap(const T* box1, const T* box2,
...
@@ -121,7 +130,7 @@ static inline T JaccardOverlap(const T* box1, const T* box2,
}
}
template
<
typename
T
>
template
<
typename
T
>
class
Multi
c
lassNMSKernel
:
public
framework
::
OpKernel
<
T
>
{
class
Multi
C
lassNMSKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
void
NMSFast
(
const
Tensor
&
bbox
,
const
Tensor
&
scores
,
void
NMSFast
(
const
Tensor
&
bbox
,
const
Tensor
&
scores
,
const
T
score_threshold
,
const
T
nms_threshold
,
const
T
eta
,
const
T
score_threshold
,
const
T
nms_threshold
,
const
T
eta
,
...
@@ -163,10 +172,10 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
...
@@ -163,10 +172,10 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
}
}
}
}
void
Multi
c
lassNMS
(
const
framework
::
ExecutionContext
&
ctx
,
void
Multi
C
lassNMS
(
const
framework
::
ExecutionContext
&
ctx
,
const
Tensor
&
scores
,
const
Tensor
&
bboxes
,
const
Tensor
&
scores
,
const
Tensor
&
bboxes
,
std
::
map
<
int
,
std
::
vector
<
int
>>
*
indices
,
std
::
map
<
int
,
std
::
vector
<
int
>>
&
indices
,
int
*
num_nmsed_out
)
const
{
int
&
num_nmsed_out
)
const
{
int64_t
background_label
=
ctx
.
Attr
<
int
>
(
"background_label"
);
int64_t
background_label
=
ctx
.
Attr
<
int
>
(
"background_label"
);
int64_t
nms_top_k
=
ctx
.
Attr
<
int
>
(
"nms_top_k"
);
int64_t
nms_top_k
=
ctx
.
Attr
<
int
>
(
"nms_top_k"
);
int64_t
keep_top_k
=
ctx
.
Attr
<
int
>
(
"keep_top_k"
);
int64_t
keep_top_k
=
ctx
.
Attr
<
int
>
(
"keep_top_k"
);
...
@@ -181,15 +190,15 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
...
@@ -181,15 +190,15 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
if
(
c
==
background_label
)
continue
;
if
(
c
==
background_label
)
continue
;
Tensor
score
=
scores
.
Slice
(
c
,
c
+
1
);
Tensor
score
=
scores
.
Slice
(
c
,
c
+
1
);
NMSFast
(
bboxes
,
score
,
score_threshold
,
nms_threshold
,
nms_eta
,
nms_top_k
,
NMSFast
(
bboxes
,
score
,
score_threshold
,
nms_threshold
,
nms_eta
,
nms_top_k
,
&
(
(
*
indices
)
[
c
]));
&
(
indices
[
c
]));
num_det
+=
(
*
indices
)
[
c
].
size
();
num_det
+=
indices
[
c
].
size
();
}
}
*
num_nmsed_out
=
num_det
;
num_nmsed_out
=
num_det
;
const
T
*
scores_data
=
scores
.
data
<
T
>
();
const
T
*
scores_data
=
scores
.
data
<
T
>
();
if
(
keep_top_k
>
-
1
&&
num_det
>
keep_top_k
)
{
if
(
keep_top_k
>
-
1
&&
num_det
>
keep_top_k
)
{
std
::
vector
<
std
::
pair
<
float
,
std
::
pair
<
int
,
int
>>>
score_index_pairs
;
std
::
vector
<
std
::
pair
<
float
,
std
::
pair
<
int
,
int
>>>
score_index_pairs
;
for
(
const
auto
&
it
:
*
indices
)
{
for
(
const
auto
&
it
:
indices
)
{
int
label
=
it
.
first
;
int
label
=
it
.
first
;
const
T
*
sdata
=
scores_data
+
label
*
predict_dim
;
const
T
*
sdata
=
scores_data
+
label
*
predict_dim
;
const
std
::
vector
<
int
>&
label_indices
=
it
.
second
;
const
std
::
vector
<
int
>&
label_indices
=
it
.
second
;
...
@@ -212,12 +221,12 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
...
@@ -212,12 +221,12 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
int
idx
=
score_index_pairs
[
j
].
second
.
second
;
int
idx
=
score_index_pairs
[
j
].
second
.
second
;
new_indices
[
label
].
push_back
(
idx
);
new_indices
[
label
].
push_back
(
idx
);
}
}
new_indices
.
swap
(
*
indices
);
new_indices
.
swap
(
indices
);
*
num_nmsed_out
=
keep_top_k
;
num_nmsed_out
=
keep_top_k
;
}
}
}
}
void
Multi
c
lassOutput
(
const
Tensor
&
scores
,
const
Tensor
&
bboxes
,
void
Multi
C
lassOutput
(
const
Tensor
&
scores
,
const
Tensor
&
bboxes
,
std
::
map
<
int
,
std
::
vector
<
int
>>&
selected_indices
,
std
::
map
<
int
,
std
::
vector
<
int
>>&
selected_indices
,
Tensor
*
outs
)
const
{
Tensor
*
outs
)
const
{
int
predict_dim
=
scores
.
dims
()[
1
];
int
predict_dim
=
scores
.
dims
()[
1
];
...
@@ -229,23 +238,21 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
...
@@ -229,23 +238,21 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
for
(
const
auto
&
it
:
selected_indices
)
{
for
(
const
auto
&
it
:
selected_indices
)
{
int
label
=
it
.
first
;
int
label
=
it
.
first
;
const
T
*
sdata
=
scores_data
+
label
*
predict_dim
;
const
T
*
sdata
=
scores_data
+
label
*
predict_dim
;
std
::
vector
<
int
>
indices
=
it
.
second
;
const
std
::
vector
<
int
>&
indices
=
it
.
second
;
for
(
int
j
=
0
;
j
<
indices
.
size
();
++
j
)
{
for
(
int
j
=
0
;
j
<
indices
.
size
();
++
j
)
{
int
idx
=
indices
[
j
];
int
idx
=
indices
[
j
];
const
T
*
bdata
=
bboxes_data
+
idx
*
kBBoxSize
;
const
T
*
bdata
=
bboxes_data
+
idx
*
kBBoxSize
;
odata
[
count
*
kOutputDim
]
=
label
;
// label
odata
[
count
*
kOutputDim
]
=
label
;
// label
odata
[
count
*
kOutputDim
+
1
]
=
sdata
[
idx
];
// score
odata
[
count
*
kOutputDim
+
1
]
=
sdata
[
idx
];
// score
odata
[
count
*
kOutputDim
+
2
]
=
bdata
[
0
];
// xmin
// xmin, ymin, xmax, ymax
odata
[
count
*
kOutputDim
+
3
]
=
bdata
[
1
];
// ymin
std
::
memcpy
(
odata
+
count
*
kOutputDim
+
2
,
bdata
,
4
*
sizeof
(
T
));
odata
[
count
*
kOutputDim
+
4
]
=
bdata
[
2
];
// xmax
odata
[
count
*
kOutputDim
+
5
]
=
bdata
[
3
];
// ymax
count
++
;
count
++
;
}
}
}
}
}
}
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
boxes
=
ctx
.
Input
<
Tensor
>
(
"B
b
oxes"
);
auto
*
boxes
=
ctx
.
Input
<
Tensor
>
(
"B
B
oxes"
);
auto
*
scores
=
ctx
.
Input
<
Tensor
>
(
"Scores"
);
auto
*
scores
=
ctx
.
Input
<
Tensor
>
(
"Scores"
);
auto
*
outs
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
auto
*
outs
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
...
@@ -262,7 +269,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
...
@@ -262,7 +269,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
ins_score
.
Resize
({
class_num
,
predict_dim
});
ins_score
.
Resize
({
class_num
,
predict_dim
});
std
::
map
<
int
,
std
::
vector
<
int
>>
indices
;
std
::
map
<
int
,
std
::
vector
<
int
>>
indices
;
int
num_nmsed_out
=
0
;
int
num_nmsed_out
=
0
;
Multi
classNMS
(
ctx
,
ins_score
,
*
boxes
,
&
indices
,
&
num_nmsed_out
);
Multi
ClassNMS
(
ctx
,
ins_score
,
*
boxes
,
indices
,
num_nmsed_out
);
all_indices
.
push_back
(
indices
);
all_indices
.
push_back
(
indices
);
batch_starts
.
push_back
(
batch_starts
.
back
()
+
num_nmsed_out
);
batch_starts
.
push_back
(
batch_starts
.
back
()
+
num_nmsed_out
);
}
}
...
@@ -280,7 +287,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
...
@@ -280,7 +287,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
int64_t
e
=
batch_starts
[
i
+
1
];
int64_t
e
=
batch_starts
[
i
+
1
];
if
(
e
>
s
)
{
if
(
e
>
s
)
{
Tensor
out
=
outs
->
Slice
(
s
,
e
);
Tensor
out
=
outs
->
Slice
(
s
,
e
);
Multi
c
lassOutput
(
ins_score
,
*
boxes
,
all_indices
[
i
],
&
out
);
Multi
C
lassOutput
(
ins_score
,
*
boxes
,
all_indices
[
i
],
&
out
);
}
}
}
}
}
}
...
@@ -292,28 +299,31 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
...
@@ -292,28 +299,31 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
}
}
};
};
class
Multi
c
lassNMSOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
class
Multi
C
lassNMSOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
public:
Multi
c
lassNMSOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
Multi
C
lassNMSOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Bboxes"
,
AddInput
(
"BBoxes"
,
"(Tensor) A 2-D Tensor with shape [M, 4] represents the location "
"(Tensor) A 2-D Tensor with shape [M, 4] represents the "
"predictions with M bboxes. 4 is the number of "
"predicted locations of M bounding bboxes. Each bounding box "
"each location coordinates."
);
"has four coordinate values and the layout is "
"[xmin, ymin, xmax, ymax]."
);
AddInput
(
"Scores"
,
AddInput
(
"Scores"
,
"(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"confidence predictions. N is the batch size, C is the class "
"predicted confidence predictions. N is the batch size, C is the "
"number, M is number of predictions for each class, which is "
"class number, M is number of bounding boxes. For each category "
"the same with Bboxes."
);
"there are total M scores which corresponding M bounding boxes. "
" Please note, M is equal to the 1st dimension of BBoxes. "
);
AddAttr
<
int
>
(
AddAttr
<
int
>
(
"background_label"
,
"background_label"
,
"(int64_t, defalut: 0) "
"(int64_t, defalut: 0) "
"The index of background label, the background label will be ignored."
)
"The index of background label, the background label will be ignored. "
"If set to -1, then all categories will be considered."
)
.
SetDefault
(
0
);
.
SetDefault
(
0
);
AddAttr
<
float
>
(
"score_threshold"
,
AddAttr
<
float
>
(
"score_threshold"
,
"(float) "
"(float) "
"
Only consider detections whose confidences are larger than
"
"
Threshold to filter out bounding boxes with low
"
"
a threshold
. If not provided, consider all boxes."
);
"
confidence score
. If not provided, consider all boxes."
);
AddAttr
<
int
>
(
"nms_top_k"
,
AddAttr
<
int
>
(
"nms_top_k"
,
"(int64_t) "
"(int64_t) "
"Maximum number of detections to be kept according to the "
"Maximum number of detections to be kept according to the "
...
@@ -368,8 +378,8 @@ value which is -1.
...
@@ -368,8 +378,8 @@ value which is -1.
}
// namespace paddle
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
multiclass_nms
,
ops
::
Multi
c
lassNMSOp
,
REGISTER_OPERATOR
(
multiclass_nms
,
ops
::
Multi
C
lassNMSOp
,
ops
::
Multi
c
lassNMSOpMaker
,
ops
::
Multi
C
lassNMSOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
multiclass_nms
,
ops
::
Multi
c
lassNMSKernel
<
float
>
,
REGISTER_OP_CPU_KERNEL
(
multiclass_nms
,
ops
::
Multi
C
lassNMSKernel
<
float
>
,
ops
::
Multi
c
lassNMSKernel
<
double
>
);
ops
::
Multi
C
lassNMSKernel
<
double
>
);
python/paddle/v2/fluid/tests/test_bipartite_match_op.py
浏览文件 @
f3415ec5
...
@@ -72,7 +72,7 @@ class TestBipartiteMatchOpWithLoD(OpTest):
...
@@ -72,7 +72,7 @@ class TestBipartiteMatchOpWithLoD(OpTest):
self
.
inputs
=
{
'DistMat'
:
(
dist
,
lod
)}
self
.
inputs
=
{
'DistMat'
:
(
dist
,
lod
)}
self
.
outputs
=
{
self
.
outputs
=
{
'ColToRowMatchIndices'
:
(
match_indices
),
'ColToRowMatchIndices'
:
(
match_indices
),
'ColToRowMatchDis'
:
(
match_dist
),
'ColToRowMatchDis
t
'
:
(
match_dist
),
}
}
def
test_check_output
(
self
):
def
test_check_output
(
self
):
...
@@ -89,7 +89,7 @@ class TestBipartiteMatchOpWithoutLoD(OpTest):
...
@@ -89,7 +89,7 @@ class TestBipartiteMatchOpWithoutLoD(OpTest):
self
.
inputs
=
{
'DistMat'
:
dist
}
self
.
inputs
=
{
'DistMat'
:
dist
}
self
.
outputs
=
{
self
.
outputs
=
{
'ColToRowMatchIndices'
:
match_indices
,
'ColToRowMatchIndices'
:
match_indices
,
'ColToRowMatchDis'
:
match_dist
,
'ColToRowMatchDis
t
'
:
match_dist
,
}
}
def
test_check_output
(
self
):
def
test_check_output
(
self
):
...
...
python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
浏览文件 @
f3415ec5
...
@@ -190,7 +190,7 @@ class TestMulticlassNMSOp(OpTest):
...
@@ -190,7 +190,7 @@ class TestMulticlassNMSOp(OpTest):
nmsed_outs
=
np
.
array
(
nmsed_outs
).
astype
(
'float32'
)
nmsed_outs
=
np
.
array
(
nmsed_outs
).
astype
(
'float32'
)
self
.
op_type
=
'multiclass_nms'
self
.
op_type
=
'multiclass_nms'
self
.
inputs
=
{
'B
b
oxes'
:
boxes
,
'Scores'
:
scores
}
self
.
inputs
=
{
'B
B
oxes'
:
boxes
,
'Scores'
:
scores
}
self
.
outputs
=
{
'Out'
:
(
nmsed_outs
,
[
lod
])}
self
.
outputs
=
{
'Out'
:
(
nmsed_outs
,
[
lod
])}
self
.
attrs
=
{
self
.
attrs
=
{
'background_label'
:
0
,
'background_label'
:
0
,
...
...
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