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90a33ddd
编写于
1月 02, 2018
作者:
S
sweetsky0901
提交者:
GitHub
1月 02, 2018
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差异文件
Merge pull request #6488 from sweetsky0901/detection_output
add Detection output op for SSD
上级
6a5cf28a
59c14f0b
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
665 addition
and
1 deletion
+665
-1
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+31
-1
paddle/operators/detection_output_op.cc
paddle/operators/detection_output_op.cc
+89
-0
paddle/operators/detection_output_op.cu.cc
paddle/operators/detection_output_op.cu.cc
+21
-0
paddle/operators/detection_output_op.h
paddle/operators/detection_output_op.h
+167
-0
paddle/operators/math/detection_util.h
paddle/operators/math/detection_util.h
+300
-0
python/paddle/v2/fluid/tests/test_detection_output_op.py
python/paddle/v2/fluid/tests/test_detection_output_op.py
+57
-0
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
90a33ddd
...
...
@@ -53,7 +53,6 @@ function(op_library TARGET)
if
(
${
op_library_DEPS_len
}
GREATER 0
)
set
(
DEPS_OPS
${
TARGET
}
${
DEPS_OPS
}
PARENT_SCOPE
)
endif
()
if
(
WITH_GPU
)
nv_library
(
${
TARGET
}
SRCS
${
cc_srcs
}
${
cu_cc_srcs
}
${
cu_srcs
}
DEPS
${
op_library_DEPS
}
${
op_common_deps
}
)
...
...
@@ -187,6 +186,36 @@ endfunction()
add_subdirectory
(
math
)
add_subdirectory
(
nccl
)
set
(
DEPS_OPS
cond_op
cross_entropy_op
recurrent_op
softmax_with_cross_entropy_op
softmax_op
sequence_softmax_op
sum_op
pool_op
maxout_op
unpool_op
pool_with_index_op
conv_op
conv_transpose_op
nccl_op
sequence_conv_op
sequence_pool_op
lod_rank_table_op
lod_tensor_to_array_op
array_to_lod_tensor_op
max_sequence_len_op
lstm_op
gru_op
adagrad_op
sgd_op
save_op
load_op
send_op
recv_op
detection_output_op
)
if
(
WITH_GPU
)
op_library
(
nccl_op DEPS nccl_common
)
else
()
...
...
@@ -210,6 +239,7 @@ op_library(cond_op DEPS framework_proto tensor net_op)
op_library
(
cross_entropy_op DEPS cross_entropy
)
op_library
(
softmax_with_cross_entropy_op DEPS cross_entropy softmax
)
op_library
(
softmax_op DEPS softmax
)
op_library
(
detection_output_op DEPS softmax
)
op_library
(
sequence_softmax_op DEPS softmax
)
op_library
(
sum_op DEPS selected_rows_functor
)
op_library
(
sgd_op DEPS selected_rows_functor
)
...
...
paddle/operators/detection_output_op.cc
0 → 100644
浏览文件 @
90a33ddd
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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/operators/detection_output_op.h"
namespace
paddle
{
namespace
operators
{
class
DetectionOutputOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
DetectionOutputOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Loc"
,
"(Tensor) The input tensor of detection_output operator."
"The input predict locations"
"The format of input tensor is kNCHW. Where K is priorbox point "
"numbers,"
"N is How many boxes are there on each point, "
"C is 4, H and W both are 1."
);
AddInput
(
"Conf"
,
"(Tensor) The input tensor of detection_output operator."
"The input priorbox confidence."
"The format of input tensor is kNCHW. Where K is priorbox point "
"numbers,"
"N is How many boxes are there on each point, "
"C is the number of classes, H and W both are 1."
);
AddInput
(
"PriorBox"
,
"(Tensor) The input tensor of detection_output operator."
"The format of input tensor is the position and variance "
"of the boxes"
);
AddOutput
(
"Out"
,
"(Tensor) The output tensor of detection_output operator."
);
AddAttr
<
int
>
(
"background_label_id"
,
"(int), The background class index."
);
AddAttr
<
int
>
(
"num_classes"
,
"(int), The number of the classification."
);
AddAttr
<
float
>
(
"nms_threshold"
,
"(float), The Non-maximum suppression threshold."
);
AddAttr
<
float
>
(
"confidence_threshold"
,
"(float), The classification confidence threshold."
);
AddAttr
<
int
>
(
"top_k"
,
"(int), The bbox number kept of the layer’s output."
);
AddAttr
<
int
>
(
"nms_top_k"
,
"(int), The bbox number kept of the NMS’s output."
);
AddComment
(
R"DOC(
detection output for SSD(single shot multibox detector)
Apply the NMS to the output of network and compute the predict
bounding box location. The output’s shape of this layer could
be zero if there is no valid bounding box.
)DOC"
);
}
};
class
DetectionOutputOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Loc"
),
"Input(X) of DetectionOutputOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Conf"
),
"Input(X) of DetectionOutputOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"PriorBox"
),
"Input(X) of DetectionOutputOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of DetectionOutputOp should not be null."
);
std
::
vector
<
int64_t
>
output_shape
({
1
,
7
});
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_WITHOUT_GRADIENT
(
detection_output
,
ops
::
DetectionOutputOp
,
ops
::
DetectionOutputOpMaker
);
REGISTER_OP_CPU_KERNEL
(
detection_output
,
ops
::
DetectionOutputKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
DetectionOutputKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/operators/detection_output_op.cu.cc
0 → 100644
浏览文件 @
90a33ddd
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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/operators/detection_output_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
detection_output
,
ops
::
DetectionOutputKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
DetectionOutputKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/operators/detection_output_op.h
0 → 100644
浏览文件 @
90a33ddd
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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/framework/op_registry.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/detection_util.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/softmax.h"
#include "paddle/operators/strided_memcpy.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
inline
void
transpose_fun
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
src
,
framework
::
Tensor
*
dst
)
{
int
input_nums
=
src
.
dims
()[
0
];
int
offset
=
0
;
for
(
int
j
=
0
;
j
<
input_nums
;
++
j
)
{
framework
::
Tensor
in_p_tensor
=
src
.
Slice
(
j
,
j
+
1
);
std
::
vector
<
int64_t
>
shape_vec
(
{
in_p_tensor
.
dims
()[
0
],
in_p_tensor
.
dims
()[
1
],
in_p_tensor
.
dims
()[
3
],
in_p_tensor
.
dims
()[
4
],
in_p_tensor
.
dims
()[
2
]});
framework
::
DDim
shape
(
framework
::
make_ddim
(
shape_vec
));
framework
::
Tensor
in_p_tensor_transpose
;
in_p_tensor_transpose
.
mutable_data
<
T
>
(
shape
,
context
.
GetPlace
());
std
::
vector
<
int
>
shape_axis
({
0
,
1
,
3
,
4
,
2
});
math
::
Transpose
<
DeviceContext
,
T
,
5
>
trans5
;
trans5
(
context
.
template
device_context
<
DeviceContext
>(),
in_p_tensor
,
&
in_p_tensor_transpose
,
shape_axis
);
auto
dst_stride
=
framework
::
stride
(
dst
->
dims
());
auto
src_stride
=
framework
::
stride
(
in_p_tensor_transpose
.
dims
());
StridedMemcpy
<
T
>
(
context
.
device_context
(),
in_p_tensor_transpose
.
data
<
T
>
(),
src_stride
,
in_p_tensor_transpose
.
dims
(),
dst_stride
,
dst
->
data
<
T
>
()
+
offset
);
offset
+=
in_p_tensor_transpose
.
dims
()[
4
]
*
src_stride
[
4
];
}
}
template
<
typename
DeviceContext
,
typename
T
>
class
DetectionOutputKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
in_loc
=
context
.
Input
<
framework
::
Tensor
>
(
"Loc"
);
const
framework
::
Tensor
*
in_conf
=
context
.
Input
<
framework
::
Tensor
>
(
"Conf"
);
const
framework
::
Tensor
*
in_priorbox
=
context
.
Input
<
framework
::
Tensor
>
(
"PriorBox"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
int
num_classes
=
context
.
template
Attr
<
int
>(
"num_classes"
);
int
top_k
=
context
.
template
Attr
<
int
>(
"top_k"
);
int
nms_top_k
=
context
.
template
Attr
<
int
>(
"nms_top_k"
);
int
background_label_id
=
context
.
template
Attr
<
int
>(
"background_label_id"
);
float
nms_threshold
=
context
.
template
Attr
<
float
>(
"nms_threshold"
);
float
confidence_threshold
=
context
.
template
Attr
<
float
>(
"confidence_threshold"
);
size_t
batch_size
=
in_conf
->
dims
()[
1
];
int
conf_sum_size
=
in_conf
->
numel
();
// for softmax
std
::
vector
<
int64_t
>
conf_shape_softmax_vec
(
{
conf_sum_size
/
num_classes
,
num_classes
});
framework
::
DDim
conf_shape_softmax
(
framework
::
make_ddim
(
conf_shape_softmax_vec
));
// for knchw => nhwc
std
::
vector
<
int64_t
>
loc_shape_vec
({
1
,
in_loc
->
dims
()[
1
],
in_loc
->
dims
()[
3
],
in_loc
->
dims
()[
4
],
in_loc
->
dims
()[
2
]
*
in_loc
->
dims
()[
0
]});
std
::
vector
<
int64_t
>
conf_shape_vec
(
{
1
,
in_conf
->
dims
()[
1
],
in_conf
->
dims
()[
3
],
in_conf
->
dims
()[
4
],
in_conf
->
dims
()[
2
]
*
in_conf
->
dims
()[
0
]});
framework
::
DDim
loc_shape
(
framework
::
make_ddim
(
loc_shape_vec
));
framework
::
DDim
conf_shape
(
framework
::
make_ddim
(
conf_shape_vec
));
framework
::
Tensor
loc_tensor
;
framework
::
Tensor
conf_tensor
;
loc_tensor
.
mutable_data
<
T
>
(
loc_shape
,
context
.
GetPlace
());
conf_tensor
.
mutable_data
<
T
>
(
conf_shape
,
context
.
GetPlace
());
// for cpu
framework
::
Tensor
loc_cpu
;
framework
::
Tensor
conf_cpu
;
framework
::
Tensor
priorbox_cpu
;
const
T
*
priorbox_data
=
in_priorbox
->
data
<
T
>
();
transpose_fun
<
DeviceContext
,
T
>
(
context
,
*
in_loc
,
&
loc_tensor
);
transpose_fun
<
DeviceContext
,
T
>
(
context
,
*
in_conf
,
&
conf_tensor
);
conf_tensor
.
Resize
(
conf_shape_softmax
);
math
::
SoftmaxFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
&
conf_tensor
,
&
conf_tensor
);
T
*
loc_data
=
loc_tensor
.
data
<
T
>
();
T
*
conf_data
=
conf_tensor
.
data
<
T
>
();
if
(
platform
::
is_gpu_place
(
context
.
GetPlace
()))
{
loc_cpu
.
mutable_data
<
T
>
(
loc_tensor
.
dims
(),
platform
::
CPUPlace
());
framework
::
CopyFrom
(
loc_tensor
,
platform
::
CPUPlace
(),
context
.
device_context
(),
&
loc_cpu
);
loc_data
=
loc_cpu
.
data
<
T
>
();
conf_cpu
.
mutable_data
<
T
>
(
conf_tensor
.
dims
(),
platform
::
CPUPlace
());
framework
::
CopyFrom
(
conf_tensor
,
platform
::
CPUPlace
(),
context
.
device_context
(),
&
conf_cpu
);
conf_data
=
conf_cpu
.
data
<
T
>
();
priorbox_cpu
.
mutable_data
<
T
>
(
in_priorbox
->
dims
(),
platform
::
CPUPlace
());
framework
::
CopyFrom
(
*
in_priorbox
,
platform
::
CPUPlace
(),
context
.
device_context
(),
&
priorbox_cpu
);
priorbox_data
=
priorbox_cpu
.
data
<
T
>
();
}
// get decode bboxes
size_t
num_priors
=
in_priorbox
->
numel
()
/
8
;
std
::
vector
<
std
::
vector
<
operators
::
math
::
BBox
<
T
>>>
all_decoded_bboxes
;
for
(
size_t
n
=
0
;
n
<
batch_size
;
++
n
)
{
std
::
vector
<
operators
::
math
::
BBox
<
T
>>
decoded_bboxes
;
for
(
size_t
i
=
0
;
i
<
num_priors
;
++
i
)
{
size_t
prior_offset
=
i
*
8
;
size_t
loc_pred_offset
=
n
*
num_priors
*
4
+
i
*
4
;
std
::
vector
<
math
::
BBox
<
T
>>
prior_bbox_vec
;
math
::
GetBBoxFromPriorData
<
T
>
(
priorbox_data
+
prior_offset
,
1
,
prior_bbox_vec
);
std
::
vector
<
std
::
vector
<
T
>>
prior_bbox_var
;
math
::
GetBBoxVarFromPriorData
<
T
>
(
priorbox_data
+
prior_offset
,
1
,
prior_bbox_var
);
std
::
vector
<
T
>
loc_pred_data
;
for
(
size_t
j
=
0
;
j
<
4
;
++
j
)
loc_pred_data
.
push_back
(
*
(
loc_data
+
loc_pred_offset
+
j
));
math
::
BBox
<
T
>
bbox
=
math
::
DecodeBBoxWithVar
<
T
>
(
prior_bbox_vec
[
0
],
prior_bbox_var
[
0
],
loc_pred_data
);
decoded_bboxes
.
push_back
(
bbox
);
}
all_decoded_bboxes
.
push_back
(
decoded_bboxes
);
}
std
::
vector
<
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>>
all_indices
;
int
num_kept
=
math
::
GetDetectionIndices
<
T
>
(
conf_data
,
num_priors
,
num_classes
,
background_label_id
,
batch_size
,
confidence_threshold
,
nms_top_k
,
nms_threshold
,
top_k
,
all_decoded_bboxes
,
&
all_indices
);
if
(
num_kept
<=
0
)
{
std
::
vector
<
int64_t
>
out_shape_vec
({
0
,
0
});
framework
::
DDim
out_shape
(
framework
::
make_ddim
(
out_shape_vec
));
out
->
Resize
(
out_shape
);
return
;
}
std
::
vector
<
int64_t
>
out_shape_vec
({
num_kept
,
7
});
framework
::
DDim
out_shape
(
framework
::
make_ddim
(
out_shape_vec
));
out
->
mutable_data
<
T
>
(
out_shape
,
context
.
GetPlace
());
framework
::
Tensor
out_cpu
;
T
*
out_data
=
out
->
data
<
T
>
();
if
(
platform
::
is_gpu_place
(
context
.
GetPlace
()))
{
out_cpu
.
mutable_data
<
T
>
(
out
->
dims
(),
platform
::
CPUPlace
());
out_data
=
out_cpu
.
data
<
T
>
();
}
math
::
GetDetectionOutput
<
T
>
(
conf_data
,
num_kept
,
num_priors
,
num_classes
,
batch_size
,
all_indices
,
all_decoded_bboxes
,
out_data
);
if
(
platform
::
is_gpu_place
(
context
.
GetPlace
()))
{
framework
::
CopyFrom
(
out_cpu
,
platform
::
CUDAPlace
(),
context
.
device_context
(),
out
);
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/operators/math/detection_util.h
0 → 100644
浏览文件 @
90a33ddd
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <map>
#include "paddle/framework/selected_rows.h"
#include "paddle/platform/device_context.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
>
struct
BBox
{
BBox
(
T
x_min
,
T
y_min
,
T
x_max
,
T
y_max
)
:
x_min
(
x_min
),
y_min
(
y_min
),
x_max
(
x_max
),
y_max
(
y_max
),
is_difficult
(
false
)
{}
BBox
()
{}
T
get_width
()
const
{
return
x_max
-
x_min
;
}
T
get_height
()
const
{
return
y_max
-
y_min
;
}
T
get_center_x
()
const
{
return
(
x_min
+
x_max
)
/
2
;
}
T
get_center_y
()
const
{
return
(
y_min
+
y_max
)
/
2
;
}
T
get_area
()
const
{
return
get_width
()
*
get_height
();
}
// coordinate of bounding box
T
x_min
;
T
y_min
;
T
x_max
;
T
y_max
;
// whether difficult object (e.g. object with heavy occlusion is difficult)
bool
is_difficult
;
};
// KNCHW ==> NHWC
// template <typename T>
template
<
typename
T
>
void
GetBBoxFromPriorData
(
const
T
*
prior_data
,
const
size_t
num_bboxes
,
std
::
vector
<
BBox
<
T
>>&
bbox_vec
);
template
<
typename
T
>
void
GetBBoxVarFromPriorData
(
const
T
*
prior_data
,
const
size_t
num
,
std
::
vector
<
std
::
vector
<
T
>>&
var_vec
);
template
<
typename
T
>
BBox
<
T
>
DecodeBBoxWithVar
(
BBox
<
T
>&
prior_bbox
,
const
std
::
vector
<
T
>&
prior_bbox_var
,
const
std
::
vector
<
T
>&
loc_pred_data
);
template
<
typename
T1
,
typename
T2
>
bool
SortScorePairDescend
(
const
std
::
pair
<
T1
,
T2
>&
pair1
,
const
std
::
pair
<
T1
,
T2
>&
pair2
);
template
<
typename
T
>
bool
SortScorePairDescend
(
const
std
::
pair
<
T
,
BBox
<
T
>>&
pair1
,
const
std
::
pair
<
T
,
BBox
<
T
>>&
pair2
);
template
<
typename
T
>
T
jaccard_overlap
(
const
BBox
<
T
>&
bbox1
,
const
BBox
<
T
>&
bbox2
);
template
<
typename
T
>
void
ApplyNmsFast
(
const
std
::
vector
<
BBox
<
T
>>&
bboxes
,
const
T
*
conf_score_data
,
size_t
class_idx
,
size_t
top_k
,
T
conf_threshold
,
T
nms_threshold
,
size_t
num_priors
,
size_t
num_classes
,
std
::
vector
<
size_t
>*
indices
);
template
<
typename
T
>
int
GetDetectionIndices
(
const
T
*
conf_data
,
const
size_t
num_priors
,
const
size_t
num_classes
,
const
size_t
background_label_id
,
const
size_t
batch_size
,
const
T
conf_threshold
,
const
size_t
nms_top_k
,
const
T
nms_threshold
,
const
size_t
top_k
,
const
std
::
vector
<
std
::
vector
<
BBox
<
T
>>>&
all_decoded_bboxes
,
std
::
vector
<
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>>*
all_detection_indices
);
template
<
typename
T
>
BBox
<
T
>
ClipBBox
(
const
BBox
<
T
>&
bbox
);
template
<
typename
T
>
void
GetDetectionOutput
(
const
T
*
conf_data
,
const
size_t
num_kept
,
const
size_t
num_priors
,
const
size_t
num_classes
,
const
size_t
batch_size
,
const
std
::
vector
<
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>>&
all_indices
,
const
std
::
vector
<
std
::
vector
<
BBox
<
T
>>>&
all_decoded_bboxes
,
T
*
out_data
);
template
<
typename
T
>
void
GetBBoxFromPriorData
(
const
T
*
prior_data
,
const
size_t
num_bboxes
,
std
::
vector
<
BBox
<
T
>>&
bbox_vec
)
{
size_t
out_offset
=
bbox_vec
.
size
();
bbox_vec
.
resize
(
bbox_vec
.
size
()
+
num_bboxes
);
for
(
size_t
i
=
0
;
i
<
num_bboxes
;
++
i
)
{
BBox
<
T
>
bbox
;
bbox
.
x_min
=
*
(
prior_data
+
i
*
8
);
bbox
.
y_min
=
*
(
prior_data
+
i
*
8
+
1
);
bbox
.
x_max
=
*
(
prior_data
+
i
*
8
+
2
);
bbox
.
y_max
=
*
(
prior_data
+
i
*
8
+
3
);
bbox_vec
[
out_offset
+
i
]
=
bbox
;
}
}
template
<
typename
T
>
void
GetBBoxVarFromPriorData
(
const
T
*
prior_data
,
const
size_t
num
,
std
::
vector
<
std
::
vector
<
T
>>&
var_vec
)
{
size_t
out_offset
=
var_vec
.
size
();
var_vec
.
resize
(
var_vec
.
size
()
+
num
);
for
(
size_t
i
=
0
;
i
<
num
;
++
i
)
{
std
::
vector
<
T
>
var
;
var
.
push_back
(
*
(
prior_data
+
i
*
8
+
4
));
var
.
push_back
(
*
(
prior_data
+
i
*
8
+
5
));
var
.
push_back
(
*
(
prior_data
+
i
*
8
+
6
));
var
.
push_back
(
*
(
prior_data
+
i
*
8
+
7
));
var_vec
[
out_offset
+
i
]
=
var
;
}
}
template
<
typename
T
>
BBox
<
T
>
DecodeBBoxWithVar
(
BBox
<
T
>&
prior_bbox
,
const
std
::
vector
<
T
>&
prior_bbox_var
,
const
std
::
vector
<
T
>&
loc_pred_data
)
{
T
prior_bbox_width
=
prior_bbox
.
get_width
();
T
prior_bbox_height
=
prior_bbox
.
get_height
();
T
prior_bbox_center_x
=
prior_bbox
.
get_center_x
();
T
prior_bbox_center_y
=
prior_bbox
.
get_center_y
();
T
decoded_bbox_center_x
=
prior_bbox_var
[
0
]
*
loc_pred_data
[
0
]
*
prior_bbox_width
+
prior_bbox_center_x
;
T
decoded_bbox_center_y
=
prior_bbox_var
[
1
]
*
loc_pred_data
[
1
]
*
prior_bbox_height
+
prior_bbox_center_y
;
T
decoded_bbox_width
=
std
::
exp
(
prior_bbox_var
[
2
]
*
loc_pred_data
[
2
])
*
prior_bbox_width
;
T
decoded_bbox_height
=
std
::
exp
(
prior_bbox_var
[
3
]
*
loc_pred_data
[
3
])
*
prior_bbox_height
;
BBox
<
T
>
decoded_bbox
;
decoded_bbox
.
x_min
=
decoded_bbox_center_x
-
decoded_bbox_width
/
2
;
decoded_bbox
.
y_min
=
decoded_bbox_center_y
-
decoded_bbox_height
/
2
;
decoded_bbox
.
x_max
=
decoded_bbox_center_x
+
decoded_bbox_width
/
2
;
decoded_bbox
.
y_max
=
decoded_bbox_center_y
+
decoded_bbox_height
/
2
;
return
decoded_bbox
;
}
template
<
typename
T1
,
typename
T2
>
bool
SortScorePairDescend
(
const
std
::
pair
<
T1
,
T2
>&
pair1
,
const
std
::
pair
<
T1
,
T2
>&
pair2
)
{
return
pair1
.
first
>
pair2
.
first
;
}
template
<
typename
T
>
T
jaccard_overlap
(
const
BBox
<
T
>&
bbox1
,
const
BBox
<
T
>&
bbox2
)
{
if
(
bbox2
.
x_min
>
bbox1
.
x_max
||
bbox2
.
x_max
<
bbox1
.
x_min
||
bbox2
.
y_min
>
bbox1
.
y_max
||
bbox2
.
y_max
<
bbox1
.
y_min
)
{
return
0.0
;
}
else
{
T
inter_x_min
=
std
::
max
(
bbox1
.
x_min
,
bbox2
.
x_min
);
T
inter_y_min
=
std
::
max
(
bbox1
.
y_min
,
bbox2
.
y_min
);
T
interX_max
=
std
::
min
(
bbox1
.
x_max
,
bbox2
.
x_max
);
T
interY_max
=
std
::
min
(
bbox1
.
y_max
,
bbox2
.
y_max
);
T
inter_width
=
interX_max
-
inter_x_min
;
T
inter_height
=
interY_max
-
inter_y_min
;
T
inter_area
=
inter_width
*
inter_height
;
T
bbox_area1
=
bbox1
.
get_area
();
T
bbox_area2
=
bbox2
.
get_area
();
return
inter_area
/
(
bbox_area1
+
bbox_area2
-
inter_area
);
}
}
template
<
typename
T
>
void
ApplyNmsFast
(
const
std
::
vector
<
BBox
<
T
>>&
bboxes
,
const
T
*
conf_score_data
,
size_t
class_idx
,
size_t
top_k
,
T
conf_threshold
,
T
nms_threshold
,
size_t
num_priors
,
size_t
num_classes
,
std
::
vector
<
size_t
>*
indices
)
{
std
::
vector
<
std
::
pair
<
T
,
size_t
>>
scores
;
for
(
size_t
i
=
0
;
i
<
num_priors
;
++
i
)
{
size_t
conf_offset
=
i
*
num_classes
+
class_idx
;
if
(
conf_score_data
[
conf_offset
]
>
conf_threshold
)
scores
.
push_back
(
std
::
make_pair
(
conf_score_data
[
conf_offset
],
i
));
}
std
::
stable_sort
(
scores
.
begin
(),
scores
.
end
(),
SortScorePairDescend
<
T
,
size_t
>
);
if
(
top_k
>
0
&&
top_k
<
scores
.
size
())
scores
.
resize
(
top_k
);
while
(
scores
.
size
()
>
0
)
{
const
size_t
idx
=
scores
.
front
().
second
;
bool
keep
=
true
;
for
(
size_t
i
=
0
;
i
<
indices
->
size
();
++
i
)
{
if
(
keep
)
{
const
size_t
saved_idx
=
(
*
indices
)[
i
];
T
overlap
=
jaccard_overlap
<
T
>
(
bboxes
[
idx
],
bboxes
[
saved_idx
]);
keep
=
overlap
<=
nms_threshold
;
}
else
{
break
;
}
}
if
(
keep
)
indices
->
push_back
(
idx
);
scores
.
erase
(
scores
.
begin
());
}
}
template
<
typename
T
>
int
GetDetectionIndices
(
const
T
*
conf_data
,
const
size_t
num_priors
,
const
size_t
num_classes
,
const
size_t
background_label_id
,
const
size_t
batch_size
,
const
T
conf_threshold
,
const
size_t
nms_top_k
,
const
T
nms_threshold
,
const
size_t
top_k
,
const
std
::
vector
<
std
::
vector
<
BBox
<
T
>>>&
all_decoded_bboxes
,
std
::
vector
<
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>>*
all_detection_indices
)
{
int
total_keep_num
=
0
;
for
(
size_t
n
=
0
;
n
<
batch_size
;
++
n
)
{
const
std
::
vector
<
BBox
<
T
>>&
decoded_bboxes
=
all_decoded_bboxes
[
n
];
size_t
num_detected
=
0
;
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>
indices
;
size_t
conf_offset
=
n
*
num_priors
*
num_classes
;
for
(
size_t
c
=
0
;
c
<
num_classes
;
++
c
)
{
if
(
c
==
background_label_id
)
continue
;
ApplyNmsFast
<
T
>
(
decoded_bboxes
,
conf_data
+
conf_offset
,
c
,
nms_top_k
,
conf_threshold
,
nms_threshold
,
num_priors
,
num_classes
,
&
(
indices
[
c
]));
num_detected
+=
indices
[
c
].
size
();
}
if
(
top_k
>
0
&&
num_detected
>
top_k
)
{
// std::vector<pair<T,T>> score_index_pairs;
std
::
vector
<
std
::
pair
<
T
,
std
::
pair
<
size_t
,
size_t
>>>
score_index_pairs
;
for
(
size_t
c
=
0
;
c
<
num_classes
;
++
c
)
{
const
std
::
vector
<
size_t
>&
label_indices
=
indices
[
c
];
for
(
size_t
i
=
0
;
i
<
label_indices
.
size
();
++
i
)
{
size_t
idx
=
label_indices
[
i
];
score_index_pairs
.
push_back
(
std
::
make_pair
((
conf_data
+
conf_offset
)[
idx
*
num_classes
+
c
],
std
::
make_pair
(
c
,
idx
)));
}
}
std
::
sort
(
score_index_pairs
.
begin
(),
score_index_pairs
.
end
(),
SortScorePairDescend
<
T
,
std
::
pair
<
size_t
,
size_t
>>
);
score_index_pairs
.
resize
(
top_k
);
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>
new_indices
;
for
(
size_t
i
=
0
;
i
<
score_index_pairs
.
size
();
++
i
)
{
size_t
label
=
score_index_pairs
[
i
].
second
.
first
;
size_t
idx
=
score_index_pairs
[
i
].
second
.
second
;
new_indices
[
label
].
push_back
(
idx
);
}
all_detection_indices
->
push_back
(
new_indices
);
total_keep_num
+=
top_k
;
}
else
{
all_detection_indices
->
push_back
(
indices
);
total_keep_num
+=
num_detected
;
}
}
return
total_keep_num
;
}
template
<
typename
T
>
BBox
<
T
>
ClipBBox
(
const
BBox
<
T
>&
bbox
)
{
T
one
=
static_cast
<
T
>
(
1.0
);
T
zero
=
static_cast
<
T
>
(
0.0
);
BBox
<
T
>
clipped_bbox
;
clipped_bbox
.
x_min
=
std
::
max
(
std
::
min
(
bbox
.
x_min
,
one
),
zero
);
clipped_bbox
.
y_min
=
std
::
max
(
std
::
min
(
bbox
.
y_min
,
one
),
zero
);
clipped_bbox
.
x_max
=
std
::
max
(
std
::
min
(
bbox
.
x_max
,
one
),
zero
);
clipped_bbox
.
y_max
=
std
::
max
(
std
::
min
(
bbox
.
y_max
,
one
),
zero
);
return
clipped_bbox
;
}
template
<
typename
T
>
void
GetDetectionOutput
(
const
T
*
conf_data
,
const
size_t
num_kept
,
const
size_t
num_priors
,
const
size_t
num_classes
,
const
size_t
batch_size
,
const
std
::
vector
<
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>>&
all_indices
,
const
std
::
vector
<
std
::
vector
<
BBox
<
T
>>>&
all_decoded_bboxes
,
T
*
out_data
)
{
size_t
count
=
0
;
for
(
size_t
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
std
::
map
<
size_t
,
std
::
vector
<
size_t
>>::
const_iterator
it
=
all_indices
[
n
].
begin
();
it
!=
all_indices
[
n
].
end
();
++
it
)
{
size_t
label
=
it
->
first
;
const
std
::
vector
<
size_t
>&
indices
=
it
->
second
;
const
std
::
vector
<
BBox
<
T
>>&
decoded_bboxes
=
all_decoded_bboxes
[
n
];
for
(
size_t
i
=
0
;
i
<
indices
.
size
();
++
i
)
{
size_t
idx
=
indices
[
i
];
size_t
conf_offset
=
n
*
num_priors
*
num_classes
+
idx
*
num_classes
;
out_data
[
count
*
7
]
=
n
;
out_data
[
count
*
7
+
1
]
=
label
;
out_data
[
count
*
7
+
2
]
=
(
conf_data
+
conf_offset
)[
label
];
BBox
<
T
>
clipped_bbox
=
ClipBBox
<
T
>
(
decoded_bboxes
[
idx
]);
out_data
[
count
*
7
+
3
]
=
clipped_bbox
.
x_min
;
out_data
[
count
*
7
+
4
]
=
clipped_bbox
.
y_min
;
out_data
[
count
*
7
+
5
]
=
clipped_bbox
.
x_max
;
out_data
[
count
*
7
+
6
]
=
clipped_bbox
.
y_max
;
++
count
;
}
}
}
}
}
// namespace math
}
// namespace operators
}
// namespace paddle
python/paddle/v2/fluid/tests/test_detection_output_op.py
0 → 100644
浏览文件 @
90a33ddd
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestUnpoolOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"detection_output"
self
.
init_test_case
()
#loc.shape ((1, 4, 4, 1, 1))
#conf.shape ((1, 4, 2, 1, 1))
loc
=
np
.
array
([[[[[
0.1
]],
[[
0.1
]],
[[
0.1
]],
[[
0.1
]]],
[[[
0.1
]],
[[
0.1
]],
[[
0.1
]],
[[
0.1
]]],
[[[
0.1
]],
[[
0.1
]],
[[
0.1
]],
[[
0.1
]]],
[[[
0.1
]],
[[
0.1
]],
[[
0.1
]],
[[
0.1
]]]]])
conf
=
np
.
array
([[[[[
0.1
]],
[[
0.9
]]],
[[[
0.2
]],
[[
0.8
]]],
[[[
0.3
]],
[[
0.7
]]],
[[[
0.4
]],
[[
0.6
]]]]])
priorbox
=
np
.
array
([
0.1
,
0.1
,
0.5
,
0.5
,
0.1
,
0.1
,
0.2
,
0.2
,
0.2
,
0.2
,
0.6
,
0.6
,
0.1
,
0.1
,
0.2
,
0.2
,
0.3
,
0.3
,
0.7
,
0.7
,
0.1
,
0.1
,
0.2
,
0.2
,
0.4
,
0.4
,
0.8
,
0.8
,
0.1
,
0.1
,
0.2
,
0.2
])
output
=
np
.
array
([
0
,
1
,
0.68997443
,
0.099959746
,
0.099959746
,
0.50804031
,
0.50804031
])
self
.
inputs
=
{
'Loc'
:
loc
.
astype
(
'float32'
),
'Conf'
:
conf
.
astype
(
'float32'
),
'PriorBox'
:
priorbox
.
astype
(
'float32'
)
}
self
.
attrs
=
{
'num_classes'
:
self
.
num_classes
,
'top_k'
:
self
.
top_k
,
'nms_top_k'
:
self
.
nms_top_k
,
'background_label_id'
:
self
.
background_label_id
,
'nms_threshold'
:
self
.
nms_threshold
,
'confidence_threshold'
:
self
.
confidence_threshold
,
}
self
.
outputs
=
{
'Out'
:
output
.
astype
(
'float32'
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
init_test_case
(
self
):
self
.
num_classes
=
2
self
.
top_k
=
10
self
.
nms_top_k
=
20
self
.
background_label_id
=
0
self
.
nms_threshold
=
0.01
self
.
confidence_threshold
=
0.01
if
__name__
==
'__main__'
:
unittest
.
main
()
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