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0f652f30
编写于
2月 27, 2019
作者:
J
jerrywgz
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电子邮件补丁
差异文件
add distribute fpn proposals op, test=develop
上级
685a20ef
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
450 addition
and
0 deletion
+450
-0
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/detection/CMakeLists.txt
paddle/fluid/operators/detection/CMakeLists.txt
+1
-0
paddle/fluid/operators/detection/distribute_fpn_proposals_op.cc
.../fluid/operators/detection/distribute_fpn_proposals_op.cc
+93
-0
paddle/fluid/operators/detection/distribute_fpn_proposals_op.h
...e/fluid/operators/detection/distribute_fpn_proposals_op.h
+147
-0
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+75
-0
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+16
-0
python/paddle/fluid/tests/unittests/test_distribute_fpn_proposals_op.py
...fluid/tests/unittests/test_distribute_fpn_proposals_op.py
+117
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
0f652f30
...
...
@@ -327,6 +327,7 @@ paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], vararg
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_clip ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(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.distribute_fpn_proposals ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(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/CMakeLists.txt
浏览文件 @
0f652f30
...
...
@@ -33,6 +33,7 @@ detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
detection_library
(
generate_proposal_labels_op SRCS generate_proposal_labels_op.cc
)
detection_library
(
box_clip_op SRCS box_clip_op.cc box_clip_op.cu
)
detection_library
(
yolov3_loss_op SRCS yolov3_loss_op.cc
)
detection_library
(
distribute_fpn_proposals_op SRCS distribute_fpn_proposals_op.cc
)
if
(
WITH_GPU
)
detection_library
(
generate_proposals_op SRCS generate_proposals_op.cc generate_proposals_op.cu DEPS memory cub
)
...
...
paddle/fluid/operators/detection/distribute_fpn_proposals_op.cc
0 → 100644
浏览文件 @
0f652f30
/* Copyright (c) 2019 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/distribute_fpn_proposals_op.h"
namespace
paddle
{
namespace
operators
{
class
DistributeFpnProposalsOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"FpnRois"
),
"Input(FpnRois) shouldn't be null"
);
PADDLE_ENFORCE_GE
(
ctx
->
Outputs
(
"MultiFpnRois"
).
size
(),
1UL
,
"Outputs(MultiFpnRois) of DistributeOp should not be empty"
);
size_t
min_level
=
static_cast
<
size_t
>
(
ctx
->
Attrs
().
Get
<
int
>
(
"min_level"
));
size_t
max_level
=
static_cast
<
size_t
>
(
ctx
->
Attrs
().
Get
<
int
>
(
"max_level"
));
PADDLE_ENFORCE_GE
(
max_level
,
min_level
,
"max_level must not lower than min_level"
);
// Set the output shape
size_t
num_out_rois
=
max_level
-
min_level
+
1
;
std
::
vector
<
framework
::
DDim
>
outs_dims
;
outs_dims
.
reserve
(
num_out_rois
);
for
(
size_t
i
=
0
;
i
<
num_out_rois
;
++
i
)
{
framework
::
DDim
out_dim
=
{
-
1
,
4
};
outs_dims
.
push_back
(
out_dim
);
}
ctx
->
SetOutputsDim
(
"MultiFpnRois"
,
outs_dims
);
ctx
->
SetOutputDim
(
"RestoreIndex"
,
{
1
,
-
1
});
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
data_type
=
framework
::
GetDataTypeOfVar
(
ctx
.
InputVar
(
"FpnRois"
));
return
framework
::
OpKernelType
(
data_type
,
platform
::
CPUPlace
());
}
};
class
DistributeFpnProposalsOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"FpnRois"
,
"(LoDTensor) The rois at all levels in shape (-1, 4)"
);
AddOutput
(
"MultiFpnRois"
,
"(LoDTensor) Output with distribute operator"
)
.
AsDuplicable
();
AddOutput
(
"RestoreIndex"
,
"(Tensor) An array of positive number which is "
"used to restore the order of FpnRois"
);
AddAttr
<
int
>
(
"min_level"
,
"The lowest level of FPN layer where the"
" proposals come from"
);
AddAttr
<
int
>
(
"max_level"
,
"The highest level of FPN layer where the"
" proposals come from"
);
AddAttr
<
int
>
(
"refer_level"
,
"The referring level of FPN layer with"
" specified scale"
);
AddAttr
<
int
>
(
"refer_scale"
,
"The referring scale of FPN layer with"
" specified level"
);
AddComment
(
R"DOC(
This operator distribute all proposals into different fpn level,
with respect to scale of the proposals, the referring scale and
the referring level. Besides, to restore the order of proposals,
we return an array which indicate the original index of rois in
current proposals.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
distribute_fpn_proposals
,
ops
::
DistributeFpnProposalsOp
,
ops
::
DistributeFpnProposalsOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
distribute_fpn_proposals
,
ops
::
DistributeFpnProposalsOpKernel
<
float
>
,
ops
::
DistributeFpnProposalsOpKernel
<
double
>
);
paddle/fluid/operators/detection/distribute_fpn_proposals_op.h
0 → 100644
浏览文件 @
0f652f30
/* Copyright (c) 2019 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 <algorithm>
#include <cmath>
#include <cstring>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
const
int
kBoxDim
=
4
;
template
<
typename
T
>
static
inline
T
BBoxArea
(
const
T
*
box
,
bool
normalized
)
{
if
(
box
[
2
]
<
box
[
0
]
||
box
[
3
]
<
box
[
1
])
{
// If coordinate values are is invalid
// (e.g. xmax < xmin or ymax < ymin), return 0.
return
static_cast
<
T
>
(
0.
);
}
else
{
const
T
w
=
box
[
2
]
-
box
[
0
];
const
T
h
=
box
[
3
]
-
box
[
1
];
if
(
normalized
)
{
return
w
*
h
;
}
else
{
// If coordinate values are not within range [0, 1].
return
(
w
+
1
)
*
(
h
+
1
);
}
}
}
template
<
typename
T
>
class
DistributeFpnProposalsOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
fpn_rois
=
context
.
Input
<
paddle
::
framework
::
LoDTensor
>
(
"FpnRois"
);
auto
multi_fpn_rois
=
context
.
MultiOutput
<
paddle
::
framework
::
LoDTensor
>
(
"MultiFpnRois"
);
auto
*
restore_index
=
context
.
Output
<
paddle
::
framework
::
Tensor
>
(
"RestoreIndex"
);
const
int
min_level
=
context
.
Attr
<
int
>
(
"min_level"
);
const
int
max_level
=
context
.
Attr
<
int
>
(
"max_level"
);
const
int
refer_level
=
context
.
Attr
<
int
>
(
"refer_level"
);
const
int
refer_scale
=
context
.
Attr
<
int
>
(
"refer_scale"
);
const
int
num_level
=
max_level
-
min_level
+
1
;
// check that the fpn_rois is not empty
PADDLE_ENFORCE_EQ
(
fpn_rois
->
lod
().
size
(),
1UL
,
"DistributeFpnProposalsOp need 1 level of LoD"
);
auto
fpn_rois_lod
=
fpn_rois
->
lod
().
back
();
int
fpn_rois_num
=
fpn_rois_lod
[
fpn_rois_lod
.
size
()
-
1
];
std
::
vector
<
int
>
target_level
;
// std::vector<int> target_level(fpn_rois_num, -1);
// record the number of rois in each level
std
::
vector
<
int
>
num_rois_level
(
num_level
,
0
);
std
::
vector
<
int
>
num_rois_level_integral
(
num_level
+
1
,
0
);
for
(
int
i
=
0
;
i
<
fpn_rois_lod
.
size
()
-
1
;
++
i
)
{
Tensor
fpn_rois_slice
=
fpn_rois
->
Slice
(
fpn_rois_lod
[
i
],
fpn_rois_lod
[
i
+
1
]);
const
T
*
rois_data
=
fpn_rois_slice
.
data
<
T
>
();
for
(
int
j
=
0
;
j
<
fpn_rois_slice
.
dims
()[
0
];
++
j
)
{
// get the target level of current rois
T
roi_scale
=
std
::
sqrt
(
BBoxArea
(
rois_data
,
false
));
int
tgt_lvl
=
std
::
floor
(
std
::
log2
(
roi_scale
/
refer_scale
)
+
refer_level
);
tgt_lvl
=
std
::
min
(
max_level
,
std
::
max
(
tgt_lvl
,
min_level
));
target_level
.
push_back
(
tgt_lvl
);
num_rois_level
[
tgt_lvl
-
min_level
]
++
;
rois_data
+=
kBoxDim
;
}
}
// define the output rois
// pointer which point to each level fpn rois
T
*
multi_fpn_rois_data
[
num_level
];
// lod0 which will record the offset information of each level rois
std
::
vector
<
std
::
vector
<
size_t
>>
multi_fpn_rois_lod0
;
for
(
int
i
=
0
;
i
<
num_level
;
++
i
)
{
// allocate memory for each level rois
multi_fpn_rois
[
i
]
->
mutable_data
<
T
>
({
num_rois_level
[
i
],
kBoxDim
},
context
.
GetPlace
());
multi_fpn_rois_data
[
i
]
=
multi_fpn_rois
[
i
]
->
data
<
T
>
();
std
::
vector
<
size_t
>
lod0
(
1
,
0
);
multi_fpn_rois_lod0
.
push_back
(
lod0
);
// statistic start point for each level rois
num_rois_level_integral
[
i
+
1
]
=
num_rois_level_integral
[
i
]
+
num_rois_level
[
i
];
}
restore_index
->
mutable_data
<
int
>
({
1
,
fpn_rois_num
},
context
.
GetPlace
());
int
*
restore_index_data
=
restore_index
->
data
<
int
>
();
std
::
vector
<
int
>
restore_index_inter
(
fpn_rois_num
,
-
1
);
// distribute the rois into different fpn level by target level
for
(
int
i
=
0
;
i
<
fpn_rois_lod
.
size
()
-
1
;
++
i
)
{
Tensor
fpn_rois_slice
=
fpn_rois
->
Slice
(
fpn_rois_lod
[
i
],
fpn_rois_lod
[
i
+
1
]);
const
T
*
rois_data
=
fpn_rois_slice
.
data
<
T
>
();
size_t
cur_offset
=
fpn_rois_lod
[
i
];
// std::vector<size_t > lod_offset[num_level];
for
(
int
j
=
0
;
j
<
num_level
;
j
++
)
{
multi_fpn_rois_lod0
[
j
].
push_back
(
multi_fpn_rois_lod0
[
j
][
i
]);
}
for
(
int
j
=
0
;
j
<
fpn_rois_slice
.
dims
()[
0
];
++
j
)
{
int
lvl
=
target_level
[
cur_offset
+
j
];
memcpy
(
multi_fpn_rois_data
[
lvl
-
min_level
],
rois_data
,
kBoxDim
*
sizeof
(
T
));
multi_fpn_rois_data
[
lvl
-
min_level
]
+=
kBoxDim
;
int
index_in_shuffle
=
num_rois_level_integral
[
lvl
-
min_level
]
+
multi_fpn_rois_lod0
[
lvl
-
min_level
][
i
+
1
];
restore_index_inter
[
index_in_shuffle
]
=
cur_offset
+
j
;
multi_fpn_rois_lod0
[
lvl
-
min_level
][
i
+
1
]
++
;
rois_data
+=
kBoxDim
;
}
}
for
(
int
i
=
0
;
i
<
fpn_rois_num
;
++
i
)
{
restore_index_data
[
restore_index_inter
[
i
]]
=
i
;
}
// merge lod information into LoDTensor
for
(
int
i
=
0
;
i
<
num_level
;
++
i
)
{
framework
::
LoD
lod
;
lod
.
emplace_back
(
multi_fpn_rois_lod0
[
i
]);
multi_fpn_rois
[
i
]
->
set_lod
(
lod
);
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/detection.py
浏览文件 @
0f652f30
...
...
@@ -51,6 +51,7 @@ __all__ = [
'yolov3_loss'
,
'box_clip'
,
'multiclass_nms'
,
'distribute_fpn_proposals'
,
]
...
...
@@ -2220,3 +2221,77 @@ def multiclass_nms(bboxes,
output
.
stop_gradient
=
True
return
output
def
distribute_fpn_proposals
(
fpn_rois
,
min_level
,
max_level
,
refer_level
,
refer_scale
,
name
=
None
):
"""
Distribute all proposals into different fpn level, with respect to scale
of the proposals, the referring scale and the referring level. Besides, to
restore the order of proposals, we return an array which indicate the
original index of rois in current proposals. To compute fpn level for each
roi, the formula is given as follows:
.. code-block:: text
roi_scale = sqrt(BBoxArea(fpn_roi));
level = floor(log2(roi_scale / refer_scale) + refer_level)
where BBoxArea is the function to compute the area of each roi:
.. code-block:: text
w = fpn_roi[2] - fpn_roi[0]
h = fpn_roi[3] - fpn_roi[1]
area = (w + 1) * (h + 1)
Args:
fpn_rois(variable): The input fpn_rois, the last dimension is 4.
min_level(int): The lowest level of FPN layer where the proposals come
from.
max_level(int): The highest level of FPN layer where the proposals
come from.
refer_level(int): The referring level of FPN layer with specified scale.
refer_scale(int): The referring scale of FPN layer with specified level.
Returns:
List(variable): The list of segmented tensor variables.
Variable: An array of positive number which is used to restore the
order of fpn_rois.
Examples:
.. code-block:: python
fpn_rois = fluid.layers.data(
name='data', shape=[4], dtype='float32', lod_level=1)
multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
fpn_rois=fpn_rois,
min_level=2,
max_level=5,
refer_level=4,
refer_scale=224)
"""
helper
=
LayerHelper
(
'distribute_fpn_proposals'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
num_lvl
=
max_level
-
min_level
+
1
multi_rois
=
[
helper
.
create_variable_for_type_inference
(
dtype
)
for
i
in
range
(
num_lvl
)
]
restore_ind
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
helper
.
append_op
(
type
=
'distribute_fpn_proposals'
,
inputs
=
{
'FpnRois'
:
fpn_rois
},
outputs
=
{
'MultiFpnRois'
:
multi_rois
,
'RestoreIndex'
:
restore_ind
},
attrs
=
{
'min_level'
:
min_level
,
'max_level'
:
max_level
,
'refer_level'
:
refer_level
,
'refer_scale'
:
refer_scale
})
return
multi_rois
,
restore_ind
python/paddle/fluid/tests/test_detection.py
浏览文件 @
0f652f30
...
...
@@ -504,5 +504,21 @@ class TestMulticlassNMS(unittest.TestCase):
self
.
assertIsNotNone
(
output
)
class
TestDistributeFpnProposals
(
unittest
.
TestCase
):
def
test_distribute_fpn_proposals
(
self
):
program
=
Program
()
with
program_guard
(
program
):
fpn_rois
=
fluid
.
layers
.
data
(
name
=
'data'
,
shape
=
[
4
],
dtype
=
'float32'
,
lod_level
=
1
)
multi_rois
,
restore_ind
=
layers
.
distribute_fpn_proposals
(
fpn_rois
=
fpn_rois
,
min_level
=
2
,
max_level
=
5
,
refer_level
=
4
,
refer_scale
=
224
)
self
.
assertIsNotNone
(
multi_rois
)
self
.
assertIsNotNone
(
restore_ind
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_distribute_fpn_proposals_op.py
0 → 100644
浏览文件 @
0f652f30
# Copyright (c) 2019 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
math
import
sys
from
op_test
import
OpTest
class
TestDistributeFPNProposalsOp
(
OpTest
):
def
set_data
(
self
):
self
.
init_test_case
()
self
.
make_rois
()
self
.
rois_fpn
,
self
.
rois_idx_restore
=
self
.
calc_rois_distribute
()
self
.
inputs
=
{
'FpnRois'
:
(
self
.
rois
[:,
1
:
5
],
self
.
rois_lod
)}
self
.
attrs
=
{
'max_level'
:
self
.
roi_max_level
,
'min_level'
:
self
.
roi_min_level
,
'refer_scale'
:
self
.
canonical_scale
,
'refer_level'
:
self
.
canonical_level
}
output
=
[(
'out%d'
%
i
,
self
.
rois_fpn
[
i
])
for
i
in
range
(
len
(
self
.
rois_fpn
))]
self
.
outputs
=
{
'MultiFpnRois'
:
output
,
'RestoreIndex'
:
self
.
rois_idx_restore
}
def
init_test_case
(
self
):
self
.
roi_max_level
=
5
self
.
roi_min_level
=
2
self
.
canonical_scale
=
224
self
.
canonical_level
=
4
self
.
images_shape
=
[
512
,
512
]
def
boxes_area
(
self
,
boxes
):
w
=
(
boxes
[:,
2
]
-
boxes
[:,
0
]
+
1
)
h
=
(
boxes
[:,
3
]
-
boxes
[:,
1
]
+
1
)
areas
=
w
*
h
assert
np
.
all
(
areas
>=
0
),
'Negative areas founds'
return
areas
def
map_rois_to_fpn_levels
(
self
,
rois
,
lvl_min
,
lvl_max
):
s
=
np
.
sqrt
(
self
.
boxes_area
(
rois
))
s0
=
self
.
canonical_scale
lvl0
=
self
.
canonical_level
target_lvls
=
np
.
floor
(
lvl0
+
np
.
log2
(
s
/
s0
+
1e-6
))
target_lvls
=
np
.
clip
(
target_lvls
,
lvl_min
,
lvl_max
)
return
target_lvls
def
get_sub_lod
(
self
,
sub_lvl
):
sub_lod
=
[]
max_batch_id
=
sub_lvl
[
-
1
]
for
i
in
range
(
max_batch_id
.
astype
(
np
.
int32
)
+
1
):
sub_lod
.
append
(
np
.
where
(
sub_lvl
==
i
)[
0
].
size
)
return
sub_lod
def
add_multilevel_roi
(
self
,
rois
,
target_lvls
,
lvl_min
,
lvl_max
):
rois_idx_order
=
np
.
empty
((
0
,
))
rois_fpn
=
[]
for
lvl
in
range
(
lvl_min
,
lvl_max
+
1
):
idx_lvl
=
np
.
where
(
target_lvls
==
lvl
)[
0
]
if
len
(
idx_lvl
)
==
0
:
rois_fpn
.
append
((
np
.
empty
(
shape
=
(
0
,
4
)),
[[
0
,
0
]]))
continue
sub_lod
=
self
.
get_sub_lod
(
rois
[
idx_lvl
,
0
])
rois_fpn
.
append
((
rois
[
idx_lvl
,
1
:],
[
sub_lod
]))
rois_idx_order
=
np
.
concatenate
((
rois_idx_order
,
idx_lvl
))
rois_idx_restore
=
np
.
argsort
(
rois_idx_order
).
astype
(
np
.
int32
,
copy
=
False
)
return
rois_fpn
,
rois_idx_restore
def
calc_rois_distribute
(
self
):
lvl_min
=
self
.
roi_min_level
lvl_max
=
self
.
roi_max_level
target_lvls
=
self
.
map_rois_to_fpn_levels
(
self
.
rois
[:,
1
:
5
],
lvl_min
,
lvl_max
)
rois_fpn
,
rois_idx_restore
=
self
.
add_multilevel_roi
(
self
.
rois
,
target_lvls
,
lvl_min
,
lvl_max
)
return
rois_fpn
,
rois_idx_restore
def
make_rois
(
self
):
self
.
rois_lod
=
[[
100
,
200
]]
rois
=
[]
lod
=
self
.
rois_lod
[
0
]
bno
=
0
for
roi_num
in
lod
:
for
i
in
range
(
roi_num
):
xywh
=
np
.
random
.
rand
(
4
)
xy1
=
xywh
[
0
:
2
]
*
20
wh
=
xywh
[
2
:
4
]
*
(
self
.
images_shape
-
xy1
)
xy2
=
xy1
+
wh
roi
=
[
bno
,
xy1
[
0
],
xy1
[
1
],
xy2
[
0
],
xy2
[
1
]]
rois
.
append
(
roi
)
bno
+=
1
self
.
rois
=
np
.
array
(
rois
).
astype
(
"float32"
)
def
setUp
(
self
):
self
.
op_type
=
"distribute_fpn_proposals"
self
.
set_data
()
def
test_check_output
(
self
):
self
.
check_output
()
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