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体验新版 GitCode,发现更多精彩内容 >>
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7c1a0b77
编写于
3月 16, 2018
作者:
Q
qingqing01
提交者:
GitHub
3月 16, 2018
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电子邮件补丁
差异文件
Delete the detection_output_op, which had been split into several operators. (#9121)
上级
26a92083
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
0 addition
and
650 deletion
+0
-650
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+0
-1
paddle/fluid/operators/detection_output_op.cc
paddle/fluid/operators/detection_output_op.cc
+0
-89
paddle/fluid/operators/detection_output_op.cu.cc
paddle/fluid/operators/detection_output_op.cu.cc
+0
-21
paddle/fluid/operators/detection_output_op.h
paddle/fluid/operators/detection_output_op.h
+0
-167
paddle/fluid/operators/math/detection_util.h
paddle/fluid/operators/math/detection_util.h
+0
-300
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+0
-1
python/paddle/fluid/tests/unittests/test_detection_output_op.py
.../paddle/fluid/tests/unittests/test_detection_output_op.py
+0
-71
未找到文件。
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
7c1a0b77
...
...
@@ -165,7 +165,6 @@ 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/fluid/operators/detection_output_op.cc
已删除
100644 → 0
浏览文件 @
26a92083
/* Copyright (c) 2016 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.
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/fluid/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/fluid/operators/detection_output_op.cu.cc
已删除
100644 → 0
浏览文件 @
26a92083
/* Copyright (c) 2016 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.
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/fluid/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/fluid/operators/detection_output_op.h
已删除
100644 → 0
浏览文件 @
26a92083
/* Copyright (c) 2016 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.
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/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/detection_util.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/softmax.h"
#include "paddle/fluid/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
::
TensorCopy
(
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
::
TensorCopy
(
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
::
TensorCopy
(
*
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
::
TensorCopy
(
out_cpu
,
platform
::
CUDAPlace
(),
context
.
device_context
(),
out
);
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/detection_util.h
已删除
100644 → 0
浏览文件 @
26a92083
/* Copyright (c) 2016 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 <map>
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/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/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
7c1a0b77
...
...
@@ -11,7 +11,6 @@ list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com
list
(
REMOVE_ITEM TEST_OPS test_nce
)
# IXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/7778
list
(
REMOVE_ITEM TEST_OPS test_recurrent_op
)
# FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/6152
list
(
REMOVE_ITEM TEST_OPS test_cond_op
)
# FIXME(qijun): https://github.com/PaddlePaddle/Paddle/issues/5101#issuecomment-339814957
list
(
REMOVE_ITEM TEST_OPS test_detection_output_op
)
# FIXME: detection_output_op will be rewritten. This unittest should be
list
(
REMOVE_ITEM TEST_OPS op_test
)
# op_test is a helper python file, not a test
list
(
REMOVE_ITEM TEST_OPS decorators
)
# decorators is a helper python file, not a test
...
...
python/paddle/fluid/tests/unittests/test_detection_output_op.py
已删除
100644 → 0
浏览文件 @
26a92083
# Copyright (c) 2018 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
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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