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7554f428
编写于
4月 05, 2022
作者:
R
RichardWooSJTU
提交者:
GitHub
4月 05, 2022
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电子邮件补丁
差异文件
Add nms op and batched_nms api (#40962)
* add nms op and batched_nms api
上级
510347f9
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
740 addition
and
0 deletion
+740
-0
paddle/fluid/operators/detection/CMakeLists.txt
paddle/fluid/operators/detection/CMakeLists.txt
+1
-0
paddle/fluid/operators/detection/nms_op.cc
paddle/fluid/operators/detection/nms_op.cc
+147
-0
paddle/fluid/operators/detection/nms_op.cu
paddle/fluid/operators/detection/nms_op.cu
+108
-0
paddle/fluid/operators/detection/nms_op.h
paddle/fluid/operators/detection/nms_op.h
+51
-0
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+1
-0
python/paddle/fluid/tests/unittests/test_nms_op.py
python/paddle/fluid/tests/unittests/test_nms_op.py
+92
-0
python/paddle/fluid/tests/unittests/test_ops_nms.py
python/paddle/fluid/tests/unittests/test_ops_nms.py
+190
-0
python/paddle/vision/ops.py
python/paddle/vision/ops.py
+149
-0
tools/static_mode_white_list.py
tools/static_mode_white_list.py
+1
-0
未找到文件。
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @
7554f428
...
...
@@ -66,6 +66,7 @@ detection_library(yolo_box_op SRCS yolo_box_op.cc)
detection_library
(
box_decoder_and_assign_op SRCS box_decoder_and_assign_op.cc box_decoder_and_assign_op.cu
)
detection_library
(
sigmoid_focal_loss_op SRCS sigmoid_focal_loss_op.cc sigmoid_focal_loss_op.cu
)
detection_library
(
retinanet_detection_output_op SRCS retinanet_detection_output_op.cc
)
detection_library
(
nms_op SRCS nms_op.cc nms_op.cu
)
if
(
WITH_GPU OR WITH_ROCM
)
set
(
TMPDEPS memory
)
...
...
paddle/fluid/operators/detection/nms_op.cc
0 → 100644
浏览文件 @
7554f428
/* Copyright (c) 2022 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. */
#include "paddle/fluid/operators/detection/nms_op.h"
#include <vector>
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
NMSOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"Boxes"
,
"(Tensor) "
"Boxes is a Tensor with shape [N, 4] "
"N is the number of boxes "
"in last dimension in format [x1, x2, y1, y2] "
"the relation should be ``0 <= x1 < x2 && 0 <= y1 < y2``."
);
AddOutput
(
"KeepBoxesIdxs"
,
"(Tensor) "
"KeepBoxesIdxs is a Tensor with shape [N] "
);
AddAttr
<
float
>
(
"iou_threshold"
,
"iou_threshold is a threshold value used to compress similar boxes "
"boxes with IoU > iou_threshold will be considered as overlapping "
"and just one of them can be kept."
)
.
SetDefault
(
1.0
f
)
.
AddCustomChecker
([](
const
float
&
iou_threshold
)
{
PADDLE_ENFORCE_LE
(
iou_threshold
,
1.0
f
,
platform
::
errors
::
InvalidArgument
(
"iou_threshold should less equal than 1.0 "
"but got %f"
,
iou_threshold
));
PADDLE_ENFORCE_GE
(
iou_threshold
,
0.0
f
,
platform
::
errors
::
InvalidArgument
(
"iou_threshold should greater equal than 0.0 "
"but got %f"
,
iou_threshold
));
});
AddComment
(
R"DOC(
NMS Operator.
This Operator is used to perform Non-Maximum Compress for input boxes.
Indices of boxes kept by NMS will be sorted by scores and output.
)DOC"
);
}
};
class
NMSOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Boxes"
),
"Input"
,
"Boxes"
,
"NMS"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"KeepBoxesIdxs"
),
"Output"
,
"KeepBoxesIdxs"
,
"NMS"
);
auto
boxes_dim
=
ctx
->
GetInputDim
(
"Boxes"
);
PADDLE_ENFORCE_EQ
(
boxes_dim
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"The Input Boxes must be 2-dimention "
"whose shape must be [N, 4] "
"N is the number of boxes "
"in last dimension in format [x1, x2, y1, y2]. "
));
auto
num_boxes
=
boxes_dim
[
0
];
ctx
->
SetOutputDim
(
"KeepBoxesIdxs"
,
{
num_boxes
});
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"Boxes"
),
ctx
.
GetPlace
());
}
};
template
<
typename
T
>
static
void
NMS
(
const
T
*
boxes_data
,
int64_t
*
output_data
,
float
threshold
,
int64_t
num_boxes
)
{
auto
num_masks
=
CeilDivide
(
num_boxes
,
64
);
std
::
vector
<
uint64_t
>
masks
(
num_masks
,
0
);
for
(
int64_t
i
=
0
;
i
<
num_boxes
;
++
i
)
{
if
(
masks
[
i
/
64
]
&
1ULL
<<
(
i
%
64
))
continue
;
T
box_1
[
4
];
for
(
int
k
=
0
;
k
<
4
;
++
k
)
{
box_1
[
k
]
=
boxes_data
[
i
*
4
+
k
];
}
for
(
int64_t
j
=
i
+
1
;
j
<
num_boxes
;
++
j
)
{
if
(
masks
[
j
/
64
]
&
1ULL
<<
(
j
%
64
))
continue
;
T
box_2
[
4
];
for
(
int
k
=
0
;
k
<
4
;
++
k
)
{
box_2
[
k
]
=
boxes_data
[
j
*
4
+
k
];
}
bool
is_overlap
=
CalculateIoU
<
T
>
(
box_1
,
box_2
,
threshold
);
if
(
is_overlap
)
{
masks
[
j
/
64
]
|=
1ULL
<<
(
j
%
64
);
}
}
}
int64_t
output_data_idx
=
0
;
for
(
int64_t
i
=
0
;
i
<
num_boxes
;
++
i
)
{
if
(
masks
[
i
/
64
]
&
1ULL
<<
(
i
%
64
))
continue
;
output_data
[
output_data_idx
++
]
=
i
;
}
for
(;
output_data_idx
<
num_boxes
;
++
output_data_idx
)
{
output_data
[
output_data_idx
]
=
0
;
}
}
template
<
typename
T
>
class
NMSKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
boxes
=
context
.
Input
<
Tensor
>
(
"Boxes"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"KeepBoxesIdxs"
);
int64_t
*
output_data
=
output
->
mutable_data
<
int64_t
>
(
context
.
GetPlace
());
auto
threshold
=
context
.
template
Attr
<
float
>(
"iou_threshold"
);
NMS
<
T
>
(
boxes
->
data
<
T
>
(),
output_data
,
threshold
,
boxes
->
dims
()[
0
]);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
nms
,
ops
::
NMSOp
,
ops
::
NMSOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
imperative
::
OpBase
>
);
REGISTER_OP_CPU_KERNEL
(
nms
,
ops
::
NMSKernel
<
float
>
,
ops
::
NMSKernel
<
double
>
);
paddle/fluid/operators/detection/nms_op.cu
0 → 100644
浏览文件 @
7554f428
/* Copyright (c) 2022 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. */
#include <vector>
#include "paddle/fluid/operators/detection/nms_op.h"
#include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
static
const
int64_t
threadsPerBlock
=
sizeof
(
int64_t
)
*
8
;
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
template
<
typename
T
>
static
__global__
void
NMS
(
const
T
*
boxes_data
,
float
threshold
,
int64_t
num_boxes
,
uint64_t
*
masks
)
{
auto
raw_start
=
blockIdx
.
y
;
auto
col_start
=
blockIdx
.
x
;
if
(
raw_start
>
col_start
)
return
;
const
int
raw_last_storage
=
min
(
num_boxes
-
raw_start
*
threadsPerBlock
,
threadsPerBlock
);
const
int
col_last_storage
=
min
(
num_boxes
-
col_start
*
threadsPerBlock
,
threadsPerBlock
);
if
(
threadIdx
.
x
<
raw_last_storage
)
{
uint64_t
mask
=
0
;
auto
current_box_idx
=
raw_start
*
threadsPerBlock
+
threadIdx
.
x
;
const
T
*
current_box
=
boxes_data
+
current_box_idx
*
4
;
for
(
int
i
=
0
;
i
<
col_last_storage
;
++
i
)
{
const
T
*
target_box
=
boxes_data
+
(
col_start
*
threadsPerBlock
+
i
)
*
4
;
if
(
CalculateIoU
<
T
>
(
current_box
,
target_box
,
threshold
))
{
mask
|=
1ULL
<<
i
;
}
}
const
int
blocks_per_line
=
CeilDivide
(
num_boxes
,
threadsPerBlock
);
masks
[
current_box_idx
*
blocks_per_line
+
col_start
]
=
mask
;
}
}
template
<
typename
T
>
class
NMSCudaKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
boxes
=
context
.
Input
<
Tensor
>
(
"Boxes"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"KeepBoxesIdxs"
);
auto
*
output_data
=
output
->
mutable_data
<
int64_t
>
(
context
.
GetPlace
());
auto
threshold
=
context
.
template
Attr
<
float
>(
"iou_threshold"
);
const
int64_t
num_boxes
=
boxes
->
dims
()[
0
];
const
auto
blocks_per_line
=
CeilDivide
(
num_boxes
,
threadsPerBlock
);
dim3
block
(
threadsPerBlock
);
dim3
grid
(
blocks_per_line
,
blocks_per_line
);
auto
mask_data
=
memory
::
Alloc
(
context
.
cuda_device_context
(),
num_boxes
*
blocks_per_line
*
sizeof
(
uint64_t
));
uint64_t
*
mask_dev
=
reinterpret_cast
<
uint64_t
*>
(
mask_data
->
ptr
());
NMS
<
T
><<<
grid
,
block
,
0
,
context
.
cuda_device_context
().
stream
()
>>>
(
boxes
->
data
<
T
>
(),
threshold
,
num_boxes
,
mask_dev
);
std
::
vector
<
uint64_t
>
mask_host
(
num_boxes
*
blocks_per_line
);
memory
::
Copy
(
platform
::
CPUPlace
(),
mask_host
.
data
(),
context
.
GetPlace
(),
mask_dev
,
num_boxes
*
blocks_per_line
*
sizeof
(
uint64_t
),
context
.
cuda_device_context
().
stream
());
std
::
vector
<
int64_t
>
remv
(
blocks_per_line
);
std
::
vector
<
int64_t
>
keep_boxes_idxs
(
num_boxes
);
int64_t
*
output_host
=
keep_boxes_idxs
.
data
();
int64_t
last_box_num
=
0
;
for
(
int64_t
i
=
0
;
i
<
num_boxes
;
++
i
)
{
auto
remv_element_id
=
i
/
threadsPerBlock
;
auto
remv_bit_id
=
i
%
threadsPerBlock
;
if
(
!
(
remv
[
remv_element_id
]
&
1ULL
<<
remv_bit_id
))
{
output_host
[
last_box_num
++
]
=
i
;
uint64_t
*
current_mask
=
mask_host
.
data
()
+
i
*
blocks_per_line
;
for
(
auto
j
=
remv_element_id
;
j
<
blocks_per_line
;
++
j
)
{
remv
[
j
]
|=
current_mask
[
j
];
}
}
}
memory
::
Copy
(
context
.
GetPlace
(),
output_data
,
platform
::
CPUPlace
(),
output_host
,
sizeof
(
int64_t
)
*
num_boxes
,
context
.
cuda_device_context
().
stream
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
nms
,
ops
::
NMSCudaKernel
<
float
>
,
ops
::
NMSCudaKernel
<
double
>
);
paddle/fluid/operators/detection/nms_op.h
0 → 100644
浏览文件 @
7554f428
/* Copyright (c) 2022 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. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace
paddle
{
namespace
operators
{
HOSTDEVICE
static
inline
int64_t
CeilDivide
(
int64_t
n
,
int64_t
m
)
{
return
(
n
+
m
-
1
)
/
m
;
}
template
<
typename
T
>
HOSTDEVICE
inline
bool
CalculateIoU
(
const
T
*
const
box_1
,
const
T
*
const
box_2
,
const
float
threshold
)
{
auto
box_1_x0
=
box_1
[
0
],
box_1_y0
=
box_1
[
1
];
auto
box_1_x1
=
box_1
[
2
],
box_1_y1
=
box_1
[
3
];
auto
box_2_x0
=
box_2
[
0
],
box_2_y0
=
box_2
[
1
];
auto
box_2_x1
=
box_2
[
2
],
box_2_y1
=
box_2
[
3
];
auto
inter_box_x0
=
box_1_x0
>
box_2_x0
?
box_1_x0
:
box_2_x0
;
auto
inter_box_y0
=
box_1_y0
>
box_2_y0
?
box_1_y0
:
box_2_y0
;
auto
inter_box_x1
=
box_1_x1
<
box_2_x1
?
box_1_x1
:
box_2_x1
;
auto
inter_box_y1
=
box_1_y1
<
box_2_y1
?
box_1_y1
:
box_2_y1
;
auto
inter_width
=
inter_box_x1
-
inter_box_x0
>
0
?
inter_box_x1
-
inter_box_x0
:
0
;
auto
inter_height
=
inter_box_y1
-
inter_box_y0
>
0
?
inter_box_y1
-
inter_box_y0
:
0
;
auto
inter_area
=
inter_width
*
inter_height
;
auto
union_area
=
(
box_1_x1
-
box_1_x0
)
*
(
box_1_y1
-
box_1_y0
)
+
(
box_2_x1
-
box_2_x0
)
*
(
box_2_y1
-
box_2_y0
)
-
inter_area
;
return
inter_area
/
union_area
>
threshold
;
}
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
7554f428
...
...
@@ -234,6 +234,7 @@ endif()
if
(
WIN32
)
LIST
(
REMOVE_ITEM TEST_OPS test_complex_matmul
)
LIST
(
REMOVE_ITEM TEST_OPS test_ops_nms
)
endif
()
LIST
(
REMOVE_ITEM TEST_OPS test_fleet_checkpoint
)
...
...
python/paddle/fluid/tests/unittests/test_nms_op.py
0 → 100644
浏览文件 @
7554f428
# Copyright (c) 2022 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
from
op_test
import
OpTest
def
iou
(
box_a
,
box_b
):
"""Apply intersection-over-union overlap between box_a and box_b
"""
xmin_a
=
min
(
box_a
[
0
],
box_a
[
2
])
ymin_a
=
min
(
box_a
[
1
],
box_a
[
3
])
xmax_a
=
max
(
box_a
[
0
],
box_a
[
2
])
ymax_a
=
max
(
box_a
[
1
],
box_a
[
3
])
xmin_b
=
min
(
box_b
[
0
],
box_b
[
2
])
ymin_b
=
min
(
box_b
[
1
],
box_b
[
3
])
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
)
if
area_a
<=
0
and
area_b
<=
0
:
return
0.0
xa
=
max
(
xmin_a
,
xmin_b
)
ya
=
max
(
ymin_a
,
ymin_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
)
iou_ratio
=
inter_area
/
(
area_a
+
area_b
-
inter_area
)
return
iou_ratio
def
nms
(
boxes
,
nms_threshold
):
selected_indices
=
np
.
zeros
(
boxes
.
shape
[
0
],
dtype
=
np
.
int64
)
keep
=
np
.
ones
(
boxes
.
shape
[
0
],
dtype
=
int
)
io_ratio
=
np
.
ones
((
boxes
.
shape
[
0
],
boxes
.
shape
[
0
]),
dtype
=
np
.
float64
)
cnt
=
0
for
i
in
range
(
boxes
.
shape
[
0
]):
if
keep
[
i
]
==
0
:
continue
selected_indices
[
cnt
]
=
i
cnt
+=
1
for
j
in
range
(
i
+
1
,
boxes
.
shape
[
0
]):
io_ratio
[
i
][
j
]
=
iou
(
boxes
[
i
],
boxes
[
j
])
if
keep
[
j
]:
overlap
=
iou
(
boxes
[
i
],
boxes
[
j
])
keep
[
j
]
=
1
if
overlap
<=
nms_threshold
else
0
else
:
continue
return
selected_indices
class
TestNMSOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'nms'
self
.
dtype
=
np
.
float64
self
.
init_dtype_type
()
boxes
=
np
.
random
.
rand
(
32
,
4
).
astype
(
self
.
dtype
)
boxes
[:,
2
]
=
boxes
[:,
0
]
+
boxes
[:,
2
]
boxes
[:,
3
]
=
boxes
[:,
1
]
+
boxes
[:,
3
]
self
.
inputs
=
{
'Boxes'
:
boxes
}
self
.
attrs
=
{
'iou_threshold'
:
0.5
}
out_py
=
nms
(
boxes
,
self
.
attrs
[
'iou_threshold'
])
self
.
outputs
=
{
'KeepBoxesIdxs'
:
out_py
}
def
init_dtype_type
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_ops_nms.py
0 → 100644
浏览文件 @
7554f428
# Copyright (c) 2022 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
paddle
from
test_nms_op
import
nms
def
_find
(
condition
):
"""
Find the indices of elements saticfied the condition.
Args:
condition(Tensor[N] or np.ndarray([N,])): Element should be bool type.
Returns:
Tensor: Indices of True element.
"""
res
=
[]
for
i
in
range
(
condition
.
shape
[
0
]):
if
condition
[
i
]:
res
.
append
(
i
)
return
np
.
array
(
res
)
def
multiclass_nms
(
boxes
,
scores
,
category_idxs
,
iou_threshold
,
top_k
):
mask
=
np
.
zeros_like
(
scores
)
for
category_id
in
np
.
unique
(
category_idxs
):
cur_category_boxes_idxs
=
_find
(
category_idxs
==
category_id
)
cur_category_boxes
=
boxes
[
cur_category_boxes_idxs
]
cur_category_scores
=
scores
[
cur_category_boxes_idxs
]
cur_category_sorted_indices
=
np
.
argsort
(
-
cur_category_scores
)
cur_category_sorted_boxes
=
cur_category_boxes
[
cur_category_sorted_indices
]
cur_category_keep_boxes_sub_idxs
=
cur_category_sorted_indices
[
nms
(
cur_category_sorted_boxes
,
iou_threshold
)]
mask
[
cur_category_boxes_idxs
[
cur_category_keep_boxes_sub_idxs
]]
=
True
keep_boxes_idxs
=
_find
(
mask
==
True
)
topK_sub_indices
=
np
.
argsort
(
-
scores
[
keep_boxes_idxs
])[:
top_k
]
return
keep_boxes_idxs
[
topK_sub_indices
]
def
gen_args
(
num_boxes
,
dtype
):
boxes
=
np
.
random
.
rand
(
num_boxes
,
4
).
astype
(
dtype
)
boxes
[:,
2
]
=
boxes
[:,
0
]
+
boxes
[:,
2
]
boxes
[:,
3
]
=
boxes
[:,
1
]
+
boxes
[:,
3
]
scores
=
np
.
random
.
rand
(
num_boxes
).
astype
(
dtype
)
categories
=
[
0
,
1
,
2
,
3
]
category_idxs
=
np
.
random
.
choice
(
categories
,
num_boxes
)
return
boxes
,
scores
,
category_idxs
,
categories
class
TestOpsNMS
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
num_boxes
=
64
self
.
threshold
=
0.5
self
.
topk
=
20
self
.
dtypes
=
[
'float32'
]
self
.
devices
=
[
'cpu'
]
if
paddle
.
is_compiled_with_cuda
():
self
.
devices
.
append
(
'gpu'
)
def
test_nms
(
self
):
for
device
in
self
.
devices
:
for
dtype
in
self
.
dtypes
:
boxes
,
scores
,
category_idxs
,
categories
=
gen_args
(
self
.
num_boxes
,
dtype
)
paddle
.
set_device
(
device
)
out
=
paddle
.
vision
.
ops
.
nms
(
paddle
.
to_tensor
(
boxes
),
self
.
threshold
,
paddle
.
to_tensor
(
scores
))
out
=
paddle
.
vision
.
ops
.
nms
(
paddle
.
to_tensor
(
boxes
),
self
.
threshold
)
out_py
=
nms
(
boxes
,
self
.
threshold
)
self
.
assertTrue
(
np
.
array_equal
(
out
.
numpy
(),
out_py
),
"paddle out: {}
\n
py out: {}
\n
"
.
format
(
out
,
out_py
))
def
test_multiclass_nms_dynamic
(
self
):
for
device
in
self
.
devices
:
for
dtype
in
self
.
dtypes
:
boxes
,
scores
,
category_idxs
,
categories
=
gen_args
(
self
.
num_boxes
,
dtype
)
paddle
.
set_device
(
device
)
out
=
paddle
.
vision
.
ops
.
nms
(
paddle
.
to_tensor
(
boxes
),
self
.
threshold
,
paddle
.
to_tensor
(
scores
),
paddle
.
to_tensor
(
category_idxs
),
categories
,
self
.
topk
)
out_py
=
multiclass_nms
(
boxes
,
scores
,
category_idxs
,
self
.
threshold
,
self
.
topk
)
self
.
assertTrue
(
np
.
array_equal
(
out
.
numpy
(),
out_py
),
"paddle out: {}
\n
py out: {}
\n
"
.
format
(
out
,
out_py
))
def
test_multiclass_nms_static
(
self
):
for
device
in
self
.
devices
:
for
dtype
in
self
.
dtypes
:
paddle
.
enable_static
()
boxes
,
scores
,
category_idxs
,
categories
=
gen_args
(
self
.
num_boxes
,
dtype
)
boxes_static
=
paddle
.
static
.
data
(
shape
=
boxes
.
shape
,
dtype
=
boxes
.
dtype
,
name
=
"boxes"
)
scores_static
=
paddle
.
static
.
data
(
shape
=
scores
.
shape
,
dtype
=
scores
.
dtype
,
name
=
"scores"
)
category_idxs_static
=
paddle
.
static
.
data
(
shape
=
category_idxs
.
shape
,
dtype
=
category_idxs
.
dtype
,
name
=
"category_idxs"
)
out
=
paddle
.
vision
.
ops
.
nms
(
boxes_static
,
self
.
threshold
,
scores_static
,
category_idxs_static
,
categories
,
self
.
topk
)
place
=
paddle
.
CPUPlace
()
if
device
==
'gpu'
:
place
=
paddle
.
CUDAPlace
(
0
)
exe
=
paddle
.
static
.
Executor
(
place
)
out
=
exe
.
run
(
paddle
.
static
.
default_main_program
(),
feed
=
{
'boxes'
:
boxes
,
'scores'
:
scores
,
'category_idxs'
:
category_idxs
},
fetch_list
=
[
out
])
paddle
.
disable_static
()
out_py
=
multiclass_nms
(
boxes
,
scores
,
category_idxs
,
self
.
threshold
,
self
.
topk
)
out
=
np
.
array
(
out
)
out
=
np
.
squeeze
(
out
)
self
.
assertTrue
(
np
.
array_equal
(
out
,
out_py
),
"paddle out: {}
\n
py out: {}
\n
"
.
format
(
out
,
out_py
))
def
test_multiclass_nms_dynamic_to_static
(
self
):
for
device
in
self
.
devices
:
for
dtype
in
self
.
dtypes
:
paddle
.
set_device
(
device
)
def
fun
(
x
):
scores
=
np
.
arange
(
0
,
64
).
astype
(
'float32'
)
categories
=
np
.
array
([
0
,
1
,
2
,
3
])
category_idxs
=
categories
.
repeat
(
16
)
out
=
paddle
.
vision
.
ops
.
nms
(
x
,
0.1
,
paddle
.
to_tensor
(
scores
),
paddle
.
to_tensor
(
category_idxs
),
categories
,
10
)
return
out
path
=
"./net"
boxes
=
np
.
random
.
rand
(
64
,
4
).
astype
(
'float32'
)
boxes
[:,
2
]
=
boxes
[:,
0
]
+
boxes
[:,
2
]
boxes
[:,
3
]
=
boxes
[:,
1
]
+
boxes
[:,
3
]
origin
=
fun
(
paddle
.
to_tensor
(
boxes
))
paddle
.
jit
.
save
(
fun
,
path
,
input_spec
=
[
paddle
.
static
.
InputSpec
(
shape
=
[
None
,
4
],
dtype
=
'float32'
,
name
=
'x'
)
],
)
load_func
=
paddle
.
jit
.
load
(
path
)
res
=
load_func
(
paddle
.
to_tensor
(
boxes
))
self
.
assertTrue
(
np
.
array_equal
(
origin
,
res
),
"origin out: {}
\n
inference model out: {}
\n
"
.
format
(
origin
,
res
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/vision/ops.py
浏览文件 @
7554f428
...
...
@@ -36,6 +36,7 @@ __all__ = [ #noqa
'PSRoIPool'
,
'roi_align'
,
'RoIAlign'
,
'nms'
,
]
...
...
@@ -1357,3 +1358,151 @@ class ConvNormActivation(Sequential):
if
activation_layer
is
not
None
:
layers
.
append
(
activation_layer
())
super
().
__init__
(
*
layers
)
def
nms
(
boxes
,
iou_threshold
=
0.3
,
scores
=
None
,
category_idxs
=
None
,
categories
=
None
,
top_k
=
None
):
r
"""
This operator implements non-maximum suppression. Non-maximum suppression (NMS)
is used to select one bounding box out of many overlapping bounding boxes in object detection.
Boxes with IoU > iou_threshold will be considered as overlapping boxes,
just one with highest score can be kept. Here IoU is Intersection Over Union,
which can be computed by:
.. math::
IoU = \frac{intersection\_area(box1, box2)}{union\_area(box1, box2)}
If scores are provided, input boxes will be sorted by their scores firstly.
If category_idxs and categories are provided, NMS will be performed with a batched style,
which means NMS will be applied to each category respectively and results of each category
will be concated and sorted by scores.
If K is provided, only the first k elements will be returned. Otherwise, all box indices sorted by scores will be returned.
Args:
boxes(Tensor): The input boxes data to be computed, it's a 2D-Tensor with
the shape of [num_boxes, 4] and boxes should be sorted by their
confidence scores. The data type is float32 or float64.
Given as [[x1, y1, x2, y2], …], (x1, y1) is the top left coordinates,
and (x2, y2) is the bottom right coordinates.
Their relation should be ``0 <= x1 < x2 && 0 <= y1 < y2``.
iou_threshold(float32): IoU threshold for determine overlapping boxes. Default value: 0.3.
scores(Tensor, optional): Scores corresponding to boxes, it's a 1D-Tensor with
shape of [num_boxes]. The data type is float32 or float64.
category_idxs(Tensor, optional): Category indices corresponding to boxes.
it's a 1D-Tensor with shape of [num_boxes]. The data type is int64.
categories(List, optional): A list of unique id of all categories. The data type is int64.
top_k(int64, optional): The top K boxes who has higher score and kept by NMS preds to
consider. top_k should be smaller equal than num_boxes.
Returns:
Tensor: 1D-Tensor with the shape of [num_boxes]. Indices of boxes kept by NMS.
Examples:
.. code-block:: python
import paddle
import numpy as np
boxes = np.random.rand(4, 4).astype('float32')
boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
# [[0.06287421 0.5809351 0.3443958 0.8713329 ]
# [0.0749094 0.9713205 0.99241287 1.2799143 ]
# [0.46246734 0.6753201 1.346266 1.3821303 ]
# [0.8984796 0.5619834 1.1254641 1.0201943 ]]
out = paddle.vision.ops.nms(paddle.to_tensor(boxes), 0.1)
# [0, 1, 3, 0]
scores = np.random.rand(4).astype('float32')
# [0.98015213 0.3156527 0.8199343 0.874901 ]
categories = [0, 1, 2, 3]
category_idxs = np.random.choice(categories, 4)
# [2 0 0 3]
out = paddle.vision.ops.nms(paddle.to_tensor(boxes),
0.1,
paddle.to_tensor(scores),
paddle.to_tensor(category_idxs),
categories,
4)
# [0, 3, 2]
"""
def
_nms
(
boxes
,
iou_threshold
):
if
_non_static_mode
():
return
_C_ops
.
nms
(
boxes
,
'iou_threshold'
,
iou_threshold
)
helper
=
LayerHelper
(
'nms'
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
'int64'
)
helper
.
append_op
(
type
=
'nms'
,
inputs
=
{
'Boxes'
:
boxes
},
outputs
=
{
'KeepBoxesIdxs'
:
out
},
attrs
=
{
'iou_threshold'
:
iou_threshold
})
return
out
if
scores
is
None
:
return
_nms
(
boxes
,
iou_threshold
)
import
paddle
if
category_idxs
is
None
:
sorted_global_indices
=
paddle
.
argsort
(
scores
,
descending
=
True
)
return
_nms
(
boxes
[
sorted_global_indices
],
iou_threshold
)
if
top_k
is
not
None
:
assert
top_k
<=
scores
.
shape
[
0
],
"top_k should be smaller equal than the number of boxes"
assert
categories
is
not
None
,
"if category_idxs is given, categories which is a list of unique id of all categories is necessary"
mask
=
paddle
.
zeros_like
(
scores
,
dtype
=
paddle
.
int32
)
for
category_id
in
categories
:
cur_category_boxes_idxs
=
paddle
.
where
(
category_idxs
==
category_id
)[
0
]
shape
=
cur_category_boxes_idxs
.
shape
[
0
]
cur_category_boxes_idxs
=
paddle
.
reshape
(
cur_category_boxes_idxs
,
[
shape
])
if
shape
==
0
:
continue
elif
shape
==
1
:
mask
[
cur_category_boxes_idxs
]
=
1
continue
cur_category_boxes
=
boxes
[
cur_category_boxes_idxs
]
cur_category_scores
=
scores
[
cur_category_boxes_idxs
]
cur_category_sorted_indices
=
paddle
.
argsort
(
cur_category_scores
,
descending
=
True
)
cur_category_sorted_boxes
=
cur_category_boxes
[
cur_category_sorted_indices
]
cur_category_keep_boxes_sub_idxs
=
cur_category_sorted_indices
[
_nms
(
cur_category_sorted_boxes
,
iou_threshold
)]
updates
=
paddle
.
ones_like
(
cur_category_boxes_idxs
[
cur_category_keep_boxes_sub_idxs
],
dtype
=
paddle
.
int32
)
mask
=
paddle
.
scatter
(
mask
,
cur_category_boxes_idxs
[
cur_category_keep_boxes_sub_idxs
],
updates
,
overwrite
=
True
)
keep_boxes_idxs
=
paddle
.
where
(
mask
)[
0
]
shape
=
keep_boxes_idxs
.
shape
[
0
]
keep_boxes_idxs
=
paddle
.
reshape
(
keep_boxes_idxs
,
[
shape
])
sorted_sub_indices
=
paddle
.
argsort
(
scores
[
keep_boxes_idxs
],
descending
=
True
)
if
top_k
is
None
:
return
keep_boxes_idxs
[
sorted_sub_indices
]
if
_non_static_mode
():
top_k
=
shape
if
shape
<
top_k
else
top_k
_
,
topk_sub_indices
=
paddle
.
topk
(
scores
[
keep_boxes_idxs
],
top_k
)
return
keep_boxes_idxs
[
topk_sub_indices
]
return
keep_boxes_idxs
[
sorted_sub_indices
][:
top_k
]
tools/static_mode_white_list.py
浏览文件 @
7554f428
...
...
@@ -349,6 +349,7 @@ STATIC_MODE_TESTING_LIST = [
'test_nearest_interp_v2_op'
,
'test_network_with_dtype'
,
'test_nll_loss'
,
'test_nms_op'
,
'test_nn_functional_embedding_static'
,
'test_nn_functional_hot_op'
,
'test_nonzero_api'
,
...
...
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