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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
30cc8b7a
编写于
1月 30, 2019
作者:
X
Xin Pan
提交者:
GitHub
1月 30, 2019
浏览文件
操作
浏览文件
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差异文件
Merge pull request #15554 from heavengate/yolo_loss_darknet
Yolo loss darknet
上级
1a252f4b
23d34d1f
变更
8
展开全部
隐藏空白更改
内联
并排
Showing
8 changed file
with
694 addition
and
691 deletion
+694
-691
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/detection/CMakeLists.txt
paddle/fluid/operators/detection/CMakeLists.txt
+1
-0
paddle/fluid/operators/detection/yolov3_loss_op.cc
paddle/fluid/operators/detection/yolov3_loss_op.cc
+74
-33
paddle/fluid/operators/detection/yolov3_loss_op.h
paddle/fluid/operators/detection/yolov3_loss_op.h
+447
-0
paddle/fluid/operators/yolov3_loss_op.h
paddle/fluid/operators/yolov3_loss_op.h
+0
-483
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+26
-31
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+2
-2
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
+143
-141
未找到文件。
paddle/fluid/API.spec
浏览文件 @
30cc8b7a
...
...
@@ -324,7 +324,7 @@ paddle.fluid.layers.generate_mask_labels ArgSpec(args=['im_info', 'gt_classes',
paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', '
class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None
))
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.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.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/operators/detection/CMakeLists.txt
浏览文件 @
30cc8b7a
...
...
@@ -31,6 +31,7 @@ detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc
polygon_box_transform_op.cu
)
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
(
yolov3_loss_op SRCS yolov3_loss_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/yolov3_loss_op.cc
→
paddle/fluid/operators/
detection/
yolov3_loss_op.cc
浏览文件 @
30cc8b7a
...
...
@@ -9,7 +9,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/yolov3_loss_op.h"
#include "paddle/fluid/operators/
detection/
yolov3_loss_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
...
...
@@ -29,23 +29,33 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Input(GTLabel) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Loss"
),
"Output(Loss) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ObjectnessMask"
),
"Output(ObjectnessMask) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"GTMatchMask"
),
"Output(GTMatchMask) of Yolov3LossOp should not be null."
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
auto
dim_gtbox
=
ctx
->
GetInputDim
(
"GTBox"
);
auto
dim_gtlabel
=
ctx
->
GetInputDim
(
"GTLabel"
);
auto
anchors
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"anchors"
);
int
anchor_num
=
anchors
.
size
()
/
2
;
auto
anchor_mask
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"anchor_mask"
);
int
mask_num
=
anchor_mask
.
size
();
auto
class_num
=
ctx
->
Attrs
().
Get
<
int
>
(
"class_num"
);
PADDLE_ENFORCE_EQ
(
dim_x
.
size
(),
4
,
"Input(X) should be a 4-D tensor."
);
PADDLE_ENFORCE_EQ
(
dim_x
[
2
],
dim_x
[
3
],
"Input(X) dim[3] and dim[4] should be euqal."
);
PADDLE_ENFORCE_EQ
(
dim_x
[
1
],
anchors
.
size
()
/
2
*
(
5
+
class_num
),
"Input(X) dim[1] should be equal to (anchor_number * (5 "
"+ class_num))."
);
PADDLE_ENFORCE_EQ
(
dim_x
[
1
],
mask_num
*
(
5
+
class_num
),
"Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
"+ class_num))."
);
PADDLE_ENFORCE_EQ
(
dim_gtbox
.
size
(),
3
,
"Input(GTBox) should be a 3-D tensor"
);
PADDLE_ENFORCE_EQ
(
dim_gtbox
[
2
],
4
,
"Input(GTBox) dim[2] should be 5"
);
PADDLE_ENFORCE_EQ
(
dim_gtlabel
.
size
(),
2
,
"Input(GT
Box
) should be a 2-D tensor"
);
"Input(GT
Label
) should be a 2-D tensor"
);
PADDLE_ENFORCE_EQ
(
dim_gtlabel
[
0
],
dim_gtbox
[
0
],
"Input(GTBox) and Input(GTLabel) dim[0] should be same"
);
PADDLE_ENFORCE_EQ
(
dim_gtlabel
[
1
],
dim_gtbox
[
1
],
...
...
@@ -54,11 +64,22 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Attr(anchors) length should be greater then 0."
);
PADDLE_ENFORCE_EQ
(
anchors
.
size
()
%
2
,
0
,
"Attr(anchors) length should be even integer."
);
for
(
size_t
i
=
0
;
i
<
anchor_mask
.
size
();
i
++
)
{
PADDLE_ENFORCE_LT
(
anchor_mask
[
i
],
anchor_num
,
"Attr(anchor_mask) should not crossover Attr(anchors)."
);
}
PADDLE_ENFORCE_GT
(
class_num
,
0
,
"Attr(class_num) should be an integer greater then 0."
);
std
::
vector
<
int64_t
>
dim_out
({
1
});
std
::
vector
<
int64_t
>
dim_out
({
dim_x
[
0
]
});
ctx
->
SetOutputDim
(
"Loss"
,
framework
::
make_ddim
(
dim_out
));
std
::
vector
<
int64_t
>
dim_obj_mask
({
dim_x
[
0
],
mask_num
,
dim_x
[
2
],
dim_x
[
3
]});
ctx
->
SetOutputDim
(
"ObjectnessMask"
,
framework
::
make_ddim
(
dim_obj_mask
));
std
::
vector
<
int64_t
>
dim_gt_match_mask
({
dim_gtbox
[
0
],
dim_gtbox
[
1
]});
ctx
->
SetOutputDim
(
"GTMatchMask"
,
framework
::
make_ddim
(
dim_gt_match_mask
));
}
protected:
...
...
@@ -73,11 +94,11 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void
Make
()
override
{
AddInput
(
"X"
,
"The input tensor of YOLO
v3 loss operator, "
"The input tensor of YOLOv3 loss operator, "
"This is a 4-D tensor with shape of [N, C, H, W]."
"H and W should be same, and the second dimention(C) stores"
"box locations, confidence score and classification one-hot"
"key of each anchor box"
);
"key
s
of each anchor box"
);
AddInput
(
"GTBox"
,
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
...
...
@@ -89,32 +110,39 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"GTLabel"
,
"The input tensor of ground truth label, "
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element shou
dl
be an integer to indicate the "
"and each element shou
ld
be an integer to indicate the "
"box class id."
);
AddOutput
(
"Loss"
,
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [1]"
);
"This is a 1-D tensor with shape of [N]"
);
AddOutput
(
"ObjectnessMask"
,
"This is an intermediate tensor with shape of [N, M, H, W], "
"M is the number of anchor masks. This parameter caches the "
"mask for calculate objectness loss in gradient kernel."
)
.
AsIntermediate
();
AddOutput
(
"GTMatchMask"
,
"This is an intermediate tensor with shape of [N, B], "
"B is the max box number of GT boxes. This parameter caches "
"matched mask index of each GT boxes for gradient calculate."
)
.
AsIntermediate
();
AddAttr
<
int
>
(
"class_num"
,
"The number of classes to predict."
);
AddAttr
<
std
::
vector
<
int
>>
(
"anchors"
,
"The anchor width and height, "
"it will be parsed pair by pair."
);
"it will be parsed pair by pair."
)
.
SetDefault
(
std
::
vector
<
int
>
{});
AddAttr
<
std
::
vector
<
int
>>
(
"anchor_mask"
,
"The mask index of anchors used in "
"current YOLOv3 loss calculation."
)
.
SetDefault
(
std
::
vector
<
int
>
{});
AddAttr
<
int
>
(
"downsample_ratio"
,
"The downsample ratio from network input to YOLOv3 loss "
"input, so 32, 16, 8 should be set for the first, second, "
"and thrid YOLOv3 loss operators."
)
.
SetDefault
(
32
);
AddAttr
<
float
>
(
"ignore_thresh"
,
"The ignore threshold to ignore confidence loss."
);
AddAttr
<
float
>
(
"loss_weight_xy"
,
"The weight of x, y location loss."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_wh"
,
"The weight of w, h location loss."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_conf_target"
,
"The weight of confidence score loss in locations with target object."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_conf_notarget"
,
"The weight of confidence score loss in locations without "
"target object."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_class"
,
"The weight of classification loss."
)
.
SetDefault
(
1.0
);
"The ignore threshold to ignore confidence loss."
)
.
SetDefault
(
0.7
);
AddComment
(
R"DOC(
This operator generate yolov3 loss by given predict result and ground
truth boxes.
...
...
@@ -147,17 +175,28 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
confidence score loss, and classification loss. The MSE loss is used for
box location, and binary cross entropy loss is used for confidence score
loss and classification loss.
confidence score loss, and classification loss. The L2 loss is used for
box coordinates (w, h), and sigmoid cross entropy loss is used for box
coordinates (x, y), confidence score loss and classification loss.
Each groud truth box find a best matching anchor box in all anchors,
prediction of this anchor box will incur all three parts of losses, and
prediction of anchor boxes with no GT box matched will only incur objectness
loss.
In order to trade off box coordinate losses between big boxes and small
boxes, box coordinate losses will be mutiplied by scale weight, which is
calculated as follow.
$$
weight_{box} = 2.0 - t_w * t_h
$$
Final loss will be represented as follow.
$$
loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+ \loss_weight_{conf_target} * loss_{conf_target}
+ \loss_weight_{conf_notarget} * loss_{conf_notarget}
+ \loss_weight_{class} * loss_{class}
loss = (loss_{xy} + loss_{wh}) * weight_{box}
+ loss_{conf} + loss_{class}
$$
)DOC"
);
}
...
...
@@ -196,6 +235,8 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op
->
SetInput
(
"GTBox"
,
Input
(
"GTBox"
));
op
->
SetInput
(
"GTLabel"
,
Input
(
"GTLabel"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Loss"
),
OutputGrad
(
"Loss"
));
op
->
SetInput
(
"ObjectnessMask"
,
Output
(
"ObjectnessMask"
));
op
->
SetInput
(
"GTMatchMask"
,
Output
(
"GTMatchMask"
));
op
->
SetAttrMap
(
Attrs
());
...
...
paddle/fluid/operators/detection/yolov3_loss_op.h
0 → 100644
浏览文件 @
30cc8b7a
/* Copyright (c) 2018 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 <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
size_t
D
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
>
static
inline
bool
LessEqualZero
(
T
x
)
{
return
x
<
1e-6
;
}
template
<
typename
T
>
static
T
SigmoidCrossEntropy
(
T
x
,
T
label
)
{
return
(
x
>
0
?
x
:
0.0
)
-
x
*
label
+
std
::
log
(
1.0
+
std
::
exp
(
-
std
::
abs
(
x
)));
}
template
<
typename
T
>
static
T
L2Loss
(
T
x
,
T
y
)
{
return
0.5
*
(
y
-
x
)
*
(
y
-
x
);
}
template
<
typename
T
>
static
T
SigmoidCrossEntropyGrad
(
T
x
,
T
label
)
{
return
1.0
/
(
1.0
+
std
::
exp
(
-
x
))
-
label
;
}
template
<
typename
T
>
static
T
L2LossGrad
(
T
x
,
T
y
)
{
return
x
-
y
;
}
static
int
GetMaskIndex
(
std
::
vector
<
int
>
mask
,
int
val
)
{
for
(
size_t
i
=
0
;
i
<
mask
.
size
();
i
++
)
{
if
(
mask
[
i
]
==
val
)
{
return
i
;
}
}
return
-
1
;
}
template
<
typename
T
>
struct
Box
{
T
x
,
y
,
w
,
h
;
};
template
<
typename
T
>
static
inline
T
sigmoid
(
T
x
)
{
return
1.0
/
(
1.0
+
std
::
exp
(
-
x
));
}
template
<
typename
T
>
static
inline
Box
<
T
>
GetYoloBox
(
const
T
*
x
,
std
::
vector
<
int
>
anchors
,
int
i
,
int
j
,
int
an_idx
,
int
grid_size
,
int
input_size
,
int
index
,
int
stride
)
{
Box
<
T
>
b
;
b
.
x
=
(
i
+
sigmoid
<
T
>
(
x
[
index
]))
/
grid_size
;
b
.
y
=
(
j
+
sigmoid
<
T
>
(
x
[
index
+
stride
]))
/
grid_size
;
b
.
w
=
std
::
exp
(
x
[
index
+
2
*
stride
])
*
anchors
[
2
*
an_idx
]
/
input_size
;
b
.
h
=
std
::
exp
(
x
[
index
+
3
*
stride
])
*
anchors
[
2
*
an_idx
+
1
]
/
input_size
;
return
b
;
}
template
<
typename
T
>
static
inline
Box
<
T
>
GetGtBox
(
const
T
*
gt
,
int
batch
,
int
max_boxes
,
int
idx
)
{
Box
<
T
>
b
;
b
.
x
=
gt
[(
batch
*
max_boxes
+
idx
)
*
4
];
b
.
y
=
gt
[(
batch
*
max_boxes
+
idx
)
*
4
+
1
];
b
.
w
=
gt
[(
batch
*
max_boxes
+
idx
)
*
4
+
2
];
b
.
h
=
gt
[(
batch
*
max_boxes
+
idx
)
*
4
+
3
];
return
b
;
}
template
<
typename
T
>
static
inline
T
BoxOverlap
(
T
c1
,
T
w1
,
T
c2
,
T
w2
)
{
T
l1
=
c1
-
w1
/
2.0
;
T
l2
=
c2
-
w2
/
2.0
;
T
left
=
l1
>
l2
?
l1
:
l2
;
T
r1
=
c1
+
w1
/
2.0
;
T
r2
=
c2
+
w2
/
2.0
;
T
right
=
r1
<
r2
?
r1
:
r2
;
return
right
-
left
;
}
template
<
typename
T
>
static
inline
T
CalcBoxIoU
(
Box
<
T
>
b1
,
Box
<
T
>
b2
)
{
T
w
=
BoxOverlap
(
b1
.
x
,
b1
.
w
,
b2
.
x
,
b2
.
w
);
T
h
=
BoxOverlap
(
b1
.
y
,
b1
.
h
,
b2
.
y
,
b2
.
h
);
T
inter_area
=
(
w
<
0
||
h
<
0
)
?
0.0
:
w
*
h
;
T
union_area
=
b1
.
w
*
b1
.
h
+
b2
.
w
*
b2
.
h
-
inter_area
;
return
inter_area
/
union_area
;
}
static
inline
int
GetEntryIndex
(
int
batch
,
int
an_idx
,
int
hw_idx
,
int
an_num
,
int
an_stride
,
int
stride
,
int
entry
)
{
return
(
batch
*
an_num
+
an_idx
)
*
an_stride
+
entry
*
stride
+
hw_idx
;
}
template
<
typename
T
>
static
void
CalcBoxLocationLoss
(
T
*
loss
,
const
T
*
input
,
Box
<
T
>
gt
,
std
::
vector
<
int
>
anchors
,
int
an_idx
,
int
box_idx
,
int
gi
,
int
gj
,
int
grid_size
,
int
input_size
,
int
stride
)
{
T
tx
=
gt
.
x
*
grid_size
-
gi
;
T
ty
=
gt
.
y
*
grid_size
-
gj
;
T
tw
=
std
::
log
(
gt
.
w
*
input_size
/
anchors
[
2
*
an_idx
]);
T
th
=
std
::
log
(
gt
.
h
*
input_size
/
anchors
[
2
*
an_idx
+
1
]);
T
scale
=
(
2.0
-
gt
.
w
*
gt
.
h
);
loss
[
0
]
+=
SigmoidCrossEntropy
<
T
>
(
input
[
box_idx
],
tx
)
*
scale
;
loss
[
0
]
+=
SigmoidCrossEntropy
<
T
>
(
input
[
box_idx
+
stride
],
ty
)
*
scale
;
loss
[
0
]
+=
L2Loss
<
T
>
(
input
[
box_idx
+
2
*
stride
],
tw
)
*
scale
;
loss
[
0
]
+=
L2Loss
<
T
>
(
input
[
box_idx
+
3
*
stride
],
th
)
*
scale
;
}
template
<
typename
T
>
static
void
CalcBoxLocationLossGrad
(
T
*
input_grad
,
const
T
loss
,
const
T
*
input
,
Box
<
T
>
gt
,
std
::
vector
<
int
>
anchors
,
int
an_idx
,
int
box_idx
,
int
gi
,
int
gj
,
int
grid_size
,
int
input_size
,
int
stride
)
{
T
tx
=
gt
.
x
*
grid_size
-
gi
;
T
ty
=
gt
.
y
*
grid_size
-
gj
;
T
tw
=
std
::
log
(
gt
.
w
*
input_size
/
anchors
[
2
*
an_idx
]);
T
th
=
std
::
log
(
gt
.
h
*
input_size
/
anchors
[
2
*
an_idx
+
1
]);
T
scale
=
(
2.0
-
gt
.
w
*
gt
.
h
);
input_grad
[
box_idx
]
=
SigmoidCrossEntropyGrad
<
T
>
(
input
[
box_idx
],
tx
)
*
scale
*
loss
;
input_grad
[
box_idx
+
stride
]
=
SigmoidCrossEntropyGrad
<
T
>
(
input
[
box_idx
+
stride
],
ty
)
*
scale
*
loss
;
input_grad
[
box_idx
+
2
*
stride
]
=
L2LossGrad
<
T
>
(
input
[
box_idx
+
2
*
stride
],
tw
)
*
scale
*
loss
;
input_grad
[
box_idx
+
3
*
stride
]
=
L2LossGrad
<
T
>
(
input
[
box_idx
+
3
*
stride
],
th
)
*
scale
*
loss
;
}
template
<
typename
T
>
static
inline
void
CalcLabelLoss
(
T
*
loss
,
const
T
*
input
,
const
int
index
,
const
int
label
,
const
int
class_num
,
const
int
stride
)
{
for
(
int
i
=
0
;
i
<
class_num
;
i
++
)
{
T
pred
=
input
[
index
+
i
*
stride
];
loss
[
0
]
+=
SigmoidCrossEntropy
<
T
>
(
pred
,
(
i
==
label
)
?
1.0
:
0.0
);
}
}
template
<
typename
T
>
static
inline
void
CalcLabelLossGrad
(
T
*
input_grad
,
const
T
loss
,
const
T
*
input
,
const
int
index
,
const
int
label
,
const
int
class_num
,
const
int
stride
)
{
for
(
int
i
=
0
;
i
<
class_num
;
i
++
)
{
T
pred
=
input
[
index
+
i
*
stride
];
input_grad
[
index
+
i
*
stride
]
=
SigmoidCrossEntropyGrad
<
T
>
(
pred
,
(
i
==
label
)
?
1.0
:
0.0
)
*
loss
;
}
}
template
<
typename
T
>
static
inline
void
CalcObjnessLoss
(
T
*
loss
,
const
T
*
input
,
const
T
*
objness
,
const
int
n
,
const
int
an_num
,
const
int
h
,
const
int
w
,
const
int
stride
,
const
int
an_stride
)
{
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
an_num
;
j
++
)
{
for
(
int
k
=
0
;
k
<
h
;
k
++
)
{
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
T
obj
=
objness
[
k
*
w
+
l
];
if
(
obj
>
1e-5
)
{
// positive sample: obj = 1
loss
[
i
]
+=
SigmoidCrossEntropy
<
T
>
(
input
[
k
*
w
+
l
],
1.0
);
}
else
if
(
obj
>
-
0.5
)
{
// negetive sample: obj = 0
loss
[
i
]
+=
SigmoidCrossEntropy
<
T
>
(
input
[
k
*
w
+
l
],
0.0
);
}
}
}
objness
+=
stride
;
input
+=
an_stride
;
}
}
}
template
<
typename
T
>
static
inline
void
CalcObjnessLossGrad
(
T
*
input_grad
,
const
T
*
loss
,
const
T
*
input
,
const
T
*
objness
,
const
int
n
,
const
int
an_num
,
const
int
h
,
const
int
w
,
const
int
stride
,
const
int
an_stride
)
{
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
an_num
;
j
++
)
{
for
(
int
k
=
0
;
k
<
h
;
k
++
)
{
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
T
obj
=
objness
[
k
*
w
+
l
];
if
(
obj
>
1e-5
)
{
input_grad
[
k
*
w
+
l
]
=
SigmoidCrossEntropyGrad
<
T
>
(
input
[
k
*
w
+
l
],
1.0
)
*
loss
[
i
];
}
else
if
(
obj
>
-
0.5
)
{
input_grad
[
k
*
w
+
l
]
=
SigmoidCrossEntropyGrad
<
T
>
(
input
[
k
*
w
+
l
],
0.0
)
*
loss
[
i
];
}
}
}
objness
+=
stride
;
input
+=
an_stride
;
input_grad
+=
an_stride
;
}
}
}
template
<
typename
T
>
static
void
inline
GtValid
(
bool
*
valid
,
const
T
*
gtbox
,
const
int
n
,
const
int
b
)
{
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
b
;
j
++
)
{
if
(
LessEqualZero
(
gtbox
[
j
*
4
+
2
])
||
LessEqualZero
(
gtbox
[
j
*
4
+
3
]))
{
valid
[
j
]
=
false
;
}
else
{
valid
[
j
]
=
true
;
}
}
valid
+=
b
;
gtbox
+=
b
*
4
;
}
}
template
<
typename
T
>
class
Yolov3LossKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
gt_box
=
ctx
.
Input
<
Tensor
>
(
"GTBox"
);
auto
*
gt_label
=
ctx
.
Input
<
Tensor
>
(
"GTLabel"
);
auto
*
loss
=
ctx
.
Output
<
Tensor
>
(
"Loss"
);
auto
*
objness_mask
=
ctx
.
Output
<
Tensor
>
(
"ObjectnessMask"
);
auto
*
gt_match_mask
=
ctx
.
Output
<
Tensor
>
(
"GTMatchMask"
);
auto
anchors
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"anchors"
);
auto
anchor_mask
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"anchor_mask"
);
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
float
ignore_thresh
=
ctx
.
Attr
<
float
>
(
"ignore_thresh"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
an_num
=
anchors
.
size
()
/
2
;
const
int
mask_num
=
anchor_mask
.
size
();
const
int
b
=
gt_box
->
dims
()[
1
];
int
input_size
=
downsample_ratio
*
h
;
const
int
stride
=
h
*
w
;
const
int
an_stride
=
(
class_num
+
5
)
*
stride
;
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
gt_box_data
=
gt_box
->
data
<
T
>
();
const
int
*
gt_label_data
=
gt_label
->
data
<
int
>
();
T
*
loss_data
=
loss
->
mutable_data
<
T
>
({
n
},
ctx
.
GetPlace
());
memset
(
loss_data
,
0
,
loss
->
numel
()
*
sizeof
(
T
));
T
*
obj_mask_data
=
objness_mask
->
mutable_data
<
T
>
({
n
,
mask_num
,
h
,
w
},
ctx
.
GetPlace
());
memset
(
obj_mask_data
,
0
,
objness_mask
->
numel
()
*
sizeof
(
T
));
int
*
gt_match_mask_data
=
gt_match_mask
->
mutable_data
<
int
>
({
n
,
b
},
ctx
.
GetPlace
());
// calc valid gt box mask, avoid calc duplicately in following code
Tensor
gt_valid_mask
;
bool
*
gt_valid_mask_data
=
gt_valid_mask
.
mutable_data
<
bool
>
({
n
,
b
},
ctx
.
GetPlace
());
GtValid
<
T
>
(
gt_valid_mask_data
,
gt_box_data
,
n
,
b
);
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
mask_num
;
j
++
)
{
for
(
int
k
=
0
;
k
<
h
;
k
++
)
{
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
// each predict box find a best match gt box, if overlap is bigger
// then ignore_thresh, ignore the objectness loss.
int
box_idx
=
GetEntryIndex
(
i
,
j
,
k
*
w
+
l
,
mask_num
,
an_stride
,
stride
,
0
);
Box
<
T
>
pred
=
GetYoloBox
(
input_data
,
anchors
,
l
,
k
,
anchor_mask
[
j
],
h
,
input_size
,
box_idx
,
stride
);
T
best_iou
=
0
;
for
(
int
t
=
0
;
t
<
b
;
t
++
)
{
if
(
!
gt_valid_mask_data
[
i
*
b
+
t
])
{
continue
;
}
Box
<
T
>
gt
=
GetGtBox
(
gt_box_data
,
i
,
b
,
t
);
T
iou
=
CalcBoxIoU
(
pred
,
gt
);
if
(
iou
>
best_iou
)
{
best_iou
=
iou
;
}
}
// If best IoU is bigger then ignore_thresh,
// ignore the objectness loss.
if
(
best_iou
>
ignore_thresh
)
{
int
obj_idx
=
(
i
*
mask_num
+
j
)
*
stride
+
k
*
w
+
l
;
obj_mask_data
[
obj_idx
]
=
static_cast
<
T
>
(
-
1
);
}
// all losses should be calculated if best IoU
// is bigger then truth thresh, but currently,
// truth thresh is an unreachable value as 1.0.
}
}
}
for
(
int
t
=
0
;
t
<
b
;
t
++
)
{
if
(
!
gt_valid_mask_data
[
i
*
b
+
t
])
{
gt_match_mask_data
[
i
*
b
+
t
]
=
-
1
;
continue
;
}
Box
<
T
>
gt
=
GetGtBox
(
gt_box_data
,
i
,
b
,
t
);
int
gi
=
static_cast
<
int
>
(
gt
.
x
*
w
);
int
gj
=
static_cast
<
int
>
(
gt
.
y
*
h
);
Box
<
T
>
gt_shift
=
gt
;
gt_shift
.
x
=
0.0
;
gt_shift
.
y
=
0.0
;
T
best_iou
=
0.0
;
int
best_n
=
0
;
// each gt box find a best match anchor box as positive sample,
// for positive sample, all losses should be calculated, and for
// other samples, only objectness loss is required.
for
(
int
an_idx
=
0
;
an_idx
<
an_num
;
an_idx
++
)
{
Box
<
T
>
an_box
;
an_box
.
x
=
0.0
;
an_box
.
y
=
0.0
;
an_box
.
w
=
anchors
[
2
*
an_idx
]
/
static_cast
<
T
>
(
input_size
);
an_box
.
h
=
anchors
[
2
*
an_idx
+
1
]
/
static_cast
<
T
>
(
input_size
);
float
iou
=
CalcBoxIoU
<
T
>
(
an_box
,
gt_shift
);
if
(
iou
>
best_iou
)
{
best_iou
=
iou
;
best_n
=
an_idx
;
}
}
int
mask_idx
=
GetMaskIndex
(
anchor_mask
,
best_n
);
gt_match_mask_data
[
i
*
b
+
t
]
=
mask_idx
;
if
(
mask_idx
>=
0
)
{
int
box_idx
=
GetEntryIndex
(
i
,
mask_idx
,
gj
*
w
+
gi
,
mask_num
,
an_stride
,
stride
,
0
);
CalcBoxLocationLoss
<
T
>
(
loss_data
+
i
,
input_data
,
gt
,
anchors
,
best_n
,
box_idx
,
gi
,
gj
,
h
,
input_size
,
stride
);
int
obj_idx
=
(
i
*
mask_num
+
mask_idx
)
*
stride
+
gj
*
w
+
gi
;
obj_mask_data
[
obj_idx
]
=
1.0
;
int
label
=
gt_label_data
[
i
*
b
+
t
];
int
label_idx
=
GetEntryIndex
(
i
,
mask_idx
,
gj
*
w
+
gi
,
mask_num
,
an_stride
,
stride
,
5
);
CalcLabelLoss
<
T
>
(
loss_data
+
i
,
input_data
,
label_idx
,
label
,
class_num
,
stride
);
}
}
}
CalcObjnessLoss
<
T
>
(
loss_data
,
input_data
+
4
*
stride
,
obj_mask_data
,
n
,
mask_num
,
h
,
w
,
stride
,
an_stride
);
}
};
template
<
typename
T
>
class
Yolov3LossGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
gt_box
=
ctx
.
Input
<
Tensor
>
(
"GTBox"
);
auto
*
gt_label
=
ctx
.
Input
<
Tensor
>
(
"GTLabel"
);
auto
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
loss_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Loss"
));
auto
*
objness_mask
=
ctx
.
Input
<
Tensor
>
(
"ObjectnessMask"
);
auto
*
gt_match_mask
=
ctx
.
Input
<
Tensor
>
(
"GTMatchMask"
);
auto
anchors
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"anchors"
);
auto
anchor_mask
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"anchor_mask"
);
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
const
int
n
=
input_grad
->
dims
()[
0
];
const
int
c
=
input_grad
->
dims
()[
1
];
const
int
h
=
input_grad
->
dims
()[
2
];
const
int
w
=
input_grad
->
dims
()[
3
];
const
int
mask_num
=
anchor_mask
.
size
();
const
int
b
=
gt_match_mask
->
dims
()[
1
];
int
input_size
=
downsample_ratio
*
h
;
const
int
stride
=
h
*
w
;
const
int
an_stride
=
(
class_num
+
5
)
*
stride
;
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
gt_box_data
=
gt_box
->
data
<
T
>
();
const
int
*
gt_label_data
=
gt_label
->
data
<
int
>
();
const
T
*
loss_grad_data
=
loss_grad
->
data
<
T
>
();
const
T
*
obj_mask_data
=
objness_mask
->
data
<
T
>
();
const
int
*
gt_match_mask_data
=
gt_match_mask
->
data
<
int
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
({
n
,
c
,
h
,
w
},
ctx
.
GetPlace
());
memset
(
input_grad_data
,
0
,
input_grad
->
numel
()
*
sizeof
(
T
));
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
t
=
0
;
t
<
b
;
t
++
)
{
int
mask_idx
=
gt_match_mask_data
[
i
*
b
+
t
];
if
(
mask_idx
>=
0
)
{
Box
<
T
>
gt
=
GetGtBox
(
gt_box_data
,
i
,
b
,
t
);
int
gi
=
static_cast
<
int
>
(
gt
.
x
*
w
);
int
gj
=
static_cast
<
int
>
(
gt
.
y
*
h
);
int
box_idx
=
GetEntryIndex
(
i
,
mask_idx
,
gj
*
w
+
gi
,
mask_num
,
an_stride
,
stride
,
0
);
CalcBoxLocationLossGrad
<
T
>
(
input_grad_data
,
loss_grad_data
[
i
],
input_data
,
gt
,
anchors
,
anchor_mask
[
mask_idx
],
box_idx
,
gi
,
gj
,
h
,
input_size
,
stride
);
int
label
=
gt_label_data
[
i
*
b
+
t
];
int
label_idx
=
GetEntryIndex
(
i
,
mask_idx
,
gj
*
w
+
gi
,
mask_num
,
an_stride
,
stride
,
5
);
CalcLabelLossGrad
<
T
>
(
input_grad_data
,
loss_grad_data
[
i
],
input_data
,
label_idx
,
label
,
class_num
,
stride
);
}
}
}
CalcObjnessLossGrad
<
T
>
(
input_grad_data
+
4
*
stride
,
loss_grad_data
,
input_data
+
4
*
stride
,
obj_mask_data
,
n
,
mask_num
,
h
,
w
,
stride
,
an_stride
);
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/yolov3_loss_op.h
已删除
100644 → 0
浏览文件 @
1a252f4b
此差异已折叠。
点击以展开。
python/paddle/fluid/layers/detection.py
浏览文件 @
30cc8b7a
...
...
@@ -508,13 +508,10 @@ def yolov3_loss(x,
gtbox
,
gtlabel
,
anchors
,
anchor_mask
,
class_num
,
ignore_thresh
,
loss_weight_xy
=
None
,
loss_weight_wh
=
None
,
loss_weight_conf_target
=
None
,
loss_weight_conf_notarget
=
None
,
loss_weight_class
=
None
,
downsample_ratio
,
name
=
None
):
"""
${comment}
...
...
@@ -526,16 +523,13 @@ def yolov3_loss(x,
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be in
s
shape
gtlabel (Variable): class id of ground truth boxes, shoud be in shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
anchor_mask (list|tuple): ${anchor_mask_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
loss_weight_xy (float|None): ${loss_weight_xy_comment}
loss_weight_wh (float|None): ${loss_weight_wh_comment}
loss_weight_conf_target (float|None): ${loss_weight_conf_target_comment}
loss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget_comment}
loss_weight_class (float|None): ${loss_weight_class_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss
Returns:
...
...
@@ -555,9 +549,10 @@ def yolov3_loss(x,
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
anchors=anchors, ignore_thresh=0.5)
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchors = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80, anchors=anchors,
ignore_thresh=0.5, downsample_ratio=32)
"""
helper
=
LayerHelper
(
'yolov3_loss'
,
**
locals
())
...
...
@@ -569,6 +564,8 @@ def yolov3_loss(x,
raise
TypeError
(
"Input gtlabel of yolov3_loss must be Variable"
)
if
not
isinstance
(
anchors
,
list
)
and
not
isinstance
(
anchors
,
tuple
):
raise
TypeError
(
"Attr anchors of yolov3_loss must be list or tuple"
)
if
not
isinstance
(
anchor_mask
,
list
)
and
not
isinstance
(
anchor_mask
,
tuple
):
raise
TypeError
(
"Attr anchor_mask of yolov3_loss must be list or tuple"
)
if
not
isinstance
(
class_num
,
int
):
raise
TypeError
(
"Attr class_num of yolov3_loss must be an integer"
)
if
not
isinstance
(
ignore_thresh
,
float
):
...
...
@@ -581,31 +578,29 @@ def yolov3_loss(x,
loss
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
objectness_mask
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
gt_match_mask
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
attrs
=
{
"anchors"
:
anchors
,
"anchor_mask"
:
anchor_mask
,
"class_num"
:
class_num
,
"ignore_thresh"
:
ignore_thresh
,
"downsample_ratio"
:
downsample_ratio
,
}
if
loss_weight_xy
is
not
None
and
isinstance
(
loss_weight_xy
,
float
):
self
.
attrs
[
'loss_weight_xy'
]
=
loss_weight_xy
if
loss_weight_wh
is
not
None
and
isinstance
(
loss_weight_wh
,
float
):
self
.
attrs
[
'loss_weight_wh'
]
=
loss_weight_wh
if
loss_weight_conf_target
is
not
None
and
isinstance
(
loss_weight_conf_target
,
float
):
self
.
attrs
[
'loss_weight_conf_target'
]
=
loss_weight_conf_target
if
loss_weight_conf_notarget
is
not
None
and
isinstance
(
loss_weight_conf_notarget
,
float
):
self
.
attrs
[
'loss_weight_conf_notarget'
]
=
loss_weight_conf_notarget
if
loss_weight_class
is
not
None
and
isinstance
(
loss_weight_class
,
float
):
self
.
attrs
[
'loss_weight_class'
]
=
loss_weight_class
helper
.
append_op
(
type
=
'yolov3_loss'
,
inputs
=
{
"X"
:
x
,
"GTBox"
:
gtbox
,
"GTLabel"
:
gtlabel
},
outputs
=
{
'Loss'
:
loss
},
inputs
=
{
"X"
:
x
,
"GTBox"
:
gtbox
,
"GTLabel"
:
gtlabel
,
},
outputs
=
{
'Loss'
:
loss
,
'ObjectnessMask'
:
objectness_mask
,
'GTMatchMask'
:
gt_match_mask
},
attrs
=
attrs
)
return
loss
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
30cc8b7a
...
...
@@ -476,8 +476,8 @@ class TestYoloDetection(unittest.TestCase):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
30
,
7
,
7
],
dtype
=
'float32'
)
gtbox
=
layers
.
data
(
name
=
'gtbox'
,
shape
=
[
10
,
4
],
dtype
=
'float32'
)
gtlabel
=
layers
.
data
(
name
=
'gtlabel'
,
shape
=
[
10
],
dtype
=
'int32'
)
loss
=
layers
.
yolov3_loss
(
x
,
gtbox
,
gtlabel
,
[
10
,
13
,
30
,
13
],
10
,
0.5
)
loss
=
layers
.
yolov3_loss
(
x
,
gtbox
,
gtlabel
,
[
10
,
13
,
30
,
13
],
[
0
,
1
],
10
,
0.7
,
32
)
self
.
assertIsNotNone
(
loss
)
...
...
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @
30cc8b7a
...
...
@@ -16,174 +16,179 @@ from __future__ import division
import
unittest
import
numpy
as
np
from
scipy.special
import
logit
from
scipy.special
import
expit
from
op_test
import
OpTest
from
paddle.fluid
import
core
def
sigmoid
(
x
):
return
1.0
/
(
1.0
+
np
.
exp
(
-
1.0
*
x
)
)
def
l2loss
(
x
,
y
):
return
0.5
*
(
y
-
x
)
*
(
y
-
x
)
def
mse
(
x
,
y
,
num
):
return
((
y
-
x
)
**
2
).
sum
()
/
num
def
sce
(
x
,
label
):
sigmoid_x
=
expit
(
x
)
term1
=
label
*
np
.
log
(
sigmoid_x
)
term2
=
(
1.0
-
label
)
*
np
.
log
(
1.0
-
sigmoid_x
)
return
-
term1
-
term2
def
bce
(
x
,
y
,
mask
):
x
=
x
.
reshape
((
-
1
))
y
=
y
.
reshape
((
-
1
))
mask
=
mask
.
reshape
((
-
1
))
def
sigmoid
(
x
):
return
1.0
/
(
1.0
+
np
.
exp
(
-
1.0
*
x
))
error_sum
=
0.0
count
=
0
for
i
in
range
(
x
.
shape
[
0
]):
if
mask
[
i
]
>
0
:
error_sum
+=
y
[
i
]
*
np
.
log
(
x
[
i
])
+
(
1
-
y
[
i
])
*
np
.
log
(
1
-
x
[
i
])
count
+=
1
return
error_sum
/
(
-
1.0
*
count
)
def
batch_xywh_box_iou
(
box1
,
box2
):
b1_left
=
box1
[:,
:,
0
]
-
box1
[:,
:,
2
]
/
2
b1_right
=
box1
[:,
:,
0
]
+
box1
[:,
:,
2
]
/
2
b1_top
=
box1
[:,
:,
1
]
-
box1
[:,
:,
3
]
/
2
b1_bottom
=
box1
[:,
:,
1
]
+
box1
[:,
:,
3
]
/
2
def
box_iou
(
box1
,
box2
):
b1_x1
=
box1
[
0
]
-
box1
[
2
]
/
2
b1_x2
=
box1
[
0
]
+
box1
[
2
]
/
2
b1_y1
=
box1
[
1
]
-
box1
[
3
]
/
2
b1_y2
=
box1
[
1
]
+
box1
[
3
]
/
2
b2_x1
=
box2
[
0
]
-
box2
[
2
]
/
2
b2_x2
=
box2
[
0
]
+
box2
[
2
]
/
2
b2_y1
=
box2
[
1
]
-
box2
[
3
]
/
2
b2_y2
=
box2
[
1
]
+
box2
[
3
]
/
2
b2_left
=
box2
[:,
:,
0
]
-
box2
[:,
:,
2
]
/
2
b2_right
=
box2
[:,
:,
0
]
+
box2
[:,
:,
2
]
/
2
b2_top
=
box2
[:,
:,
1
]
-
box2
[:,
:,
3
]
/
2
b2_bottom
=
box2
[:,
:,
1
]
+
box2
[:,
:,
3
]
/
2
b1_area
=
(
b1_x2
-
b1_x1
)
*
(
b1_y2
-
b1_y1
)
b2_area
=
(
b2_x2
-
b2_x1
)
*
(
b2_y2
-
b2_y1
)
left
=
np
.
maximum
(
b1_left
[:,
:,
np
.
newaxis
],
b2_left
[:,
np
.
newaxis
,
:])
right
=
np
.
minimum
(
b1_right
[:,
:,
np
.
newaxis
],
b2_right
[:,
np
.
newaxis
,
:])
top
=
np
.
maximum
(
b1_top
[:,
:,
np
.
newaxis
],
b2_top
[:,
np
.
newaxis
,
:])
bottom
=
np
.
minimum
(
b1_bottom
[:,
:,
np
.
newaxis
],
b2_bottom
[:,
np
.
newaxis
,
:])
inter_rect_x1
=
max
(
b1_x1
,
b2_x1
)
inter_rect_y1
=
max
(
b1_y1
,
b2_y1
)
inter_rect_x2
=
min
(
b1_x2
,
b2_x2
)
inter_rect_y2
=
min
(
b1_y2
,
b2_y2
)
inter_area
=
max
(
inter_rect_x2
-
inter_rect_x1
,
0
)
*
max
(
inter_rect_y2
-
inter_rect_y1
,
0
)
inter_w
=
np
.
clip
(
right
-
left
,
0.
,
1.
)
inter_h
=
np
.
clip
(
bottom
-
top
,
0.
,
1.
)
inter_area
=
inter_w
*
inter_h
return
inter_area
/
(
b1_area
+
b2_area
+
inter_area
)
b1_area
=
(
b1_right
-
b1_left
)
*
(
b1_bottom
-
b1_top
)
b2_area
=
(
b2_right
-
b2_left
)
*
(
b2_bottom
-
b2_top
)
union
=
b1_area
[:,
:,
np
.
newaxis
]
+
b2_area
[:,
np
.
newaxis
,
:]
-
inter_area
return
inter_area
/
union
def
build_target
(
gtboxs
,
gtlabel
,
attrs
,
grid_size
):
n
,
b
,
_
=
gtboxs
.
shape
ignore_thresh
=
attrs
[
"ignore_thresh"
]
anchors
=
attrs
[
"anchors"
]
class_num
=
attrs
[
"class_num"
]
an_num
=
len
(
anchors
)
//
2
obj_mask
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
noobj_mask
=
np
.
ones
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tx
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
ty
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tw
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
th
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tconf
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tcls
=
np
.
zeros
(
(
n
,
an_num
,
grid_size
,
grid_size
,
class_num
)).
astype
(
'float32'
)
def
YOLOv3Loss
(
x
,
gtbox
,
gtlabel
,
attrs
):
n
,
c
,
h
,
w
=
x
.
shape
b
=
gtbox
.
shape
[
1
]
anchors
=
attrs
[
'anchors'
]
an_num
=
len
(
anchors
)
//
2
anchor_mask
=
attrs
[
'anchor_mask'
]
mask_num
=
len
(
anchor_mask
)
class_num
=
attrs
[
"class_num"
]
ignore_thresh
=
attrs
[
'ignore_thresh'
]
downsample
=
attrs
[
'downsample'
]
input_size
=
downsample
*
h
x
=
x
.
reshape
((
n
,
mask_num
,
5
+
class_num
,
h
,
w
)).
transpose
((
0
,
1
,
3
,
4
,
2
))
loss
=
np
.
zeros
((
n
)).
astype
(
'float32'
)
pred_box
=
x
[:,
:,
:,
:,
:
4
].
copy
()
grid_x
=
np
.
tile
(
np
.
arange
(
w
).
reshape
((
1
,
w
)),
(
h
,
1
))
grid_y
=
np
.
tile
(
np
.
arange
(
h
).
reshape
((
h
,
1
)),
(
1
,
w
))
pred_box
[:,
:,
:,
:,
0
]
=
(
grid_x
+
sigmoid
(
pred_box
[:,
:,
:,
:,
0
]))
/
w
pred_box
[:,
:,
:,
:,
1
]
=
(
grid_y
+
sigmoid
(
pred_box
[:,
:,
:,
:,
1
]))
/
h
x
[:,
:,
:,
:,
5
:]
=
np
.
where
(
x
[:,
:,
:,
:,
5
:]
<
-
0.5
,
x
[:,
:,
:,
:,
5
:],
np
.
ones_like
(
x
[:,
:,
:,
:,
5
:])
*
1.0
/
class_num
)
mask_anchors
=
[]
for
m
in
anchor_mask
:
mask_anchors
.
append
((
anchors
[
2
*
m
],
anchors
[
2
*
m
+
1
]))
anchors_s
=
np
.
array
(
[(
an_w
/
input_size
,
an_h
/
input_size
)
for
an_w
,
an_h
in
mask_anchors
])
anchor_w
=
anchors_s
[:,
0
:
1
].
reshape
((
1
,
mask_num
,
1
,
1
))
anchor_h
=
anchors_s
[:,
1
:
2
].
reshape
((
1
,
mask_num
,
1
,
1
))
pred_box
[:,
:,
:,
:,
2
]
=
np
.
exp
(
pred_box
[:,
:,
:,
:,
2
])
*
anchor_w
pred_box
[:,
:,
:,
:,
3
]
=
np
.
exp
(
pred_box
[:,
:,
:,
:,
3
])
*
anchor_h
pred_box
=
pred_box
.
reshape
((
n
,
-
1
,
4
))
pred_obj
=
x
[:,
:,
:,
:,
4
].
reshape
((
n
,
-
1
))
objness
=
np
.
zeros
(
pred_box
.
shape
[:
2
]).
astype
(
'float32'
)
ious
=
batch_xywh_box_iou
(
pred_box
,
gtbox
)
ious_max
=
np
.
max
(
ious
,
axis
=-
1
)
objness
=
np
.
where
(
ious_max
>
ignore_thresh
,
-
np
.
ones_like
(
objness
),
objness
)
gtbox_shift
=
gtbox
.
copy
()
gtbox_shift
[:,
:,
0
]
=
0
gtbox_shift
[:,
:,
1
]
=
0
anchors
=
[(
anchors
[
2
*
i
],
anchors
[
2
*
i
+
1
])
for
i
in
range
(
0
,
an_num
)]
anchors_s
=
np
.
array
(
[(
an_w
/
input_size
,
an_h
/
input_size
)
for
an_w
,
an_h
in
anchors
])
anchor_boxes
=
np
.
concatenate
(
[
np
.
zeros_like
(
anchors_s
),
anchors_s
],
axis
=-
1
)
anchor_boxes
=
np
.
tile
(
anchor_boxes
[
np
.
newaxis
,
:,
:],
(
n
,
1
,
1
))
ious
=
batch_xywh_box_iou
(
gtbox_shift
,
anchor_boxes
)
iou_matches
=
np
.
argmax
(
ious
,
axis
=-
1
)
gt_matches
=
iou_matches
.
copy
()
for
i
in
range
(
n
):
for
j
in
range
(
b
):
if
gtboxs
[
i
,
j
,
:].
sum
()
==
0
:
if
gtbox
[
i
,
j
,
2
:].
sum
()
==
0
:
gt_matches
[
i
,
j
]
=
-
1
continue
if
iou_matches
[
i
,
j
]
not
in
anchor_mask
:
gt_matches
[
i
,
j
]
=
-
1
continue
an_idx
=
anchor_mask
.
index
(
iou_matches
[
i
,
j
])
gt_matches
[
i
,
j
]
=
an_idx
gi
=
int
(
gtbox
[
i
,
j
,
0
]
*
w
)
gj
=
int
(
gtbox
[
i
,
j
,
1
]
*
h
)
gt_label
=
gtlabel
[
i
,
j
]
gx
=
gtboxs
[
i
,
j
,
0
]
*
grid_size
gy
=
gtboxs
[
i
,
j
,
1
]
*
grid_size
gw
=
gtboxs
[
i
,
j
,
2
]
*
grid_size
gh
=
gtboxs
[
i
,
j
,
3
]
*
grid_size
gi
=
int
(
gx
)
gj
=
int
(
gy
)
gtbox
=
[
0
,
0
,
gw
,
gh
]
max_iou
=
0
for
k
in
range
(
an_num
):
anchor_box
=
[
0
,
0
,
anchors
[
2
*
k
],
anchors
[
2
*
k
+
1
]]
iou
=
box_iou
(
gtbox
,
anchor_box
)
if
iou
>
max_iou
:
max_iou
=
iou
best_an_index
=
k
if
iou
>
ignore_thresh
:
noobj_mask
[
i
,
best_an_index
,
gj
,
gi
]
=
0
obj_mask
[
i
,
best_an_index
,
gj
,
gi
]
=
1
noobj_mask
[
i
,
best_an_index
,
gj
,
gi
]
=
0
tx
[
i
,
best_an_index
,
gj
,
gi
]
=
gx
-
gi
ty
[
i
,
best_an_index
,
gj
,
gi
]
=
gy
-
gj
tw
[
i
,
best_an_index
,
gj
,
gi
]
=
np
.
log
(
gw
/
anchors
[
2
*
best_an_index
])
th
[
i
,
best_an_index
,
gj
,
gi
]
=
np
.
log
(
gh
/
anchors
[
2
*
best_an_index
+
1
])
tconf
[
i
,
best_an_index
,
gj
,
gi
]
=
1
tcls
[
i
,
best_an_index
,
gj
,
gi
,
gt_label
]
=
1
return
(
tx
,
ty
,
tw
,
th
,
tconf
,
tcls
,
obj_mask
,
noobj_mask
)
def
YoloV3Loss
(
x
,
gtbox
,
gtlabel
,
attrs
):
n
,
c
,
h
,
w
=
x
.
shape
an_num
=
len
(
attrs
[
'anchors'
])
//
2
class_num
=
attrs
[
"class_num"
]
x
=
x
.
reshape
((
n
,
an_num
,
5
+
class_num
,
h
,
w
)).
transpose
((
0
,
1
,
3
,
4
,
2
))
pred_x
=
sigmoid
(
x
[:,
:,
:,
:,
0
])
pred_y
=
sigmoid
(
x
[:,
:,
:,
:,
1
])
pred_w
=
x
[:,
:,
:,
:,
2
]
pred_h
=
x
[:,
:,
:,
:,
3
]
pred_conf
=
sigmoid
(
x
[:,
:,
:,
:,
4
])
pred_cls
=
sigmoid
(
x
[:,
:,
:,
:,
5
:])
tx
,
ty
,
tw
,
th
,
tconf
,
tcls
,
obj_mask
,
noobj_mask
=
build_target
(
gtbox
,
gtlabel
,
attrs
,
x
.
shape
[
2
])
obj_mask_expand
=
np
.
tile
(
np
.
expand_dims
(
obj_mask
,
4
),
(
1
,
1
,
1
,
1
,
int
(
attrs
[
'class_num'
])))
loss_x
=
mse
(
pred_x
*
obj_mask
,
tx
*
obj_mask
,
obj_mask
.
sum
())
loss_y
=
mse
(
pred_y
*
obj_mask
,
ty
*
obj_mask
,
obj_mask
.
sum
())
loss_w
=
mse
(
pred_w
*
obj_mask
,
tw
*
obj_mask
,
obj_mask
.
sum
())
loss_h
=
mse
(
pred_h
*
obj_mask
,
th
*
obj_mask
,
obj_mask
.
sum
())
loss_conf_target
=
bce
(
pred_conf
*
obj_mask
,
tconf
*
obj_mask
,
obj_mask
)
loss_conf_notarget
=
bce
(
pred_conf
*
noobj_mask
,
tconf
*
noobj_mask
,
noobj_mask
)
loss_class
=
bce
(
pred_cls
*
obj_mask_expand
,
tcls
*
obj_mask_expand
,
obj_mask_expand
)
return
attrs
[
'loss_weight_xy'
]
*
(
loss_x
+
loss_y
)
\
+
attrs
[
'loss_weight_wh'
]
*
(
loss_w
+
loss_h
)
\
+
attrs
[
'loss_weight_conf_target'
]
*
loss_conf_target
\
+
attrs
[
'loss_weight_conf_notarget'
]
*
loss_conf_notarget
\
+
attrs
[
'loss_weight_class'
]
*
loss_class
tx
=
gtbox
[
i
,
j
,
0
]
*
w
-
gi
ty
=
gtbox
[
i
,
j
,
1
]
*
w
-
gj
tw
=
np
.
log
(
gtbox
[
i
,
j
,
2
]
*
input_size
/
mask_anchors
[
an_idx
][
0
])
th
=
np
.
log
(
gtbox
[
i
,
j
,
3
]
*
input_size
/
mask_anchors
[
an_idx
][
1
])
scale
=
(
2.0
-
gtbox
[
i
,
j
,
2
]
*
gtbox
[
i
,
j
,
3
])
loss
[
i
]
+=
sce
(
x
[
i
,
an_idx
,
gj
,
gi
,
0
],
tx
)
*
scale
loss
[
i
]
+=
sce
(
x
[
i
,
an_idx
,
gj
,
gi
,
1
],
ty
)
*
scale
loss
[
i
]
+=
l2loss
(
x
[
i
,
an_idx
,
gj
,
gi
,
2
],
tw
)
*
scale
loss
[
i
]
+=
l2loss
(
x
[
i
,
an_idx
,
gj
,
gi
,
3
],
th
)
*
scale
objness
[
i
,
an_idx
*
h
*
w
+
gj
*
w
+
gi
]
=
1.0
for
label_idx
in
range
(
class_num
):
loss
[
i
]
+=
sce
(
x
[
i
,
an_idx
,
gj
,
gi
,
5
+
label_idx
],
float
(
label_idx
==
gtlabel
[
i
,
j
]))
for
j
in
range
(
mask_num
*
h
*
w
):
if
objness
[
i
,
j
]
>
0
:
loss
[
i
]
+=
sce
(
pred_obj
[
i
,
j
],
1.0
)
elif
objness
[
i
,
j
]
==
0
:
loss
[
i
]
+=
sce
(
pred_obj
[
i
,
j
],
0.0
)
return
(
loss
,
objness
.
reshape
((
n
,
mask_num
,
h
,
w
)).
astype
(
'float32'
),
\
gt_matches
.
astype
(
'int32'
))
class
TestYolov3LossOp
(
OpTest
):
def
setUp
(
self
):
self
.
loss_weight_xy
=
1.0
self
.
loss_weight_wh
=
1.0
self
.
loss_weight_conf_target
=
1.0
self
.
loss_weight_conf_notarget
=
1.0
self
.
loss_weight_class
=
1.0
self
.
initTestCase
()
self
.
op_type
=
'yolov3_loss'
x
=
np
.
random
.
random
(
size
=
self
.
x_shape
).
astype
(
'float32'
)
x
=
logit
(
np
.
random
.
uniform
(
0
,
1
,
self
.
x_shape
).
astype
(
'float32'
)
)
gtbox
=
np
.
random
.
random
(
size
=
self
.
gtbox_shape
).
astype
(
'float32'
)
gtlabel
=
np
.
random
.
randint
(
0
,
self
.
class_num
,
self
.
gtbox_shape
[:
2
]).
astype
(
'int32'
)
gtlabel
=
np
.
random
.
randint
(
0
,
self
.
class_num
,
self
.
gtbox_shape
[:
2
])
gtmask
=
np
.
random
.
randint
(
0
,
2
,
self
.
gtbox_shape
[:
2
])
gtbox
=
gtbox
*
gtmask
[:,
:,
np
.
newaxis
]
gtlabel
=
gtlabel
*
gtmask
self
.
attrs
=
{
"anchors"
:
self
.
anchors
,
"anchor_mask"
:
self
.
anchor_mask
,
"class_num"
:
self
.
class_num
,
"ignore_thresh"
:
self
.
ignore_thresh
,
"loss_weight_xy"
:
self
.
loss_weight_xy
,
"loss_weight_wh"
:
self
.
loss_weight_wh
,
"loss_weight_conf_target"
:
self
.
loss_weight_conf_target
,
"loss_weight_conf_notarget"
:
self
.
loss_weight_conf_notarget
,
"loss_weight_class"
:
self
.
loss_weight_class
,
"downsample"
:
self
.
downsample
,
}
self
.
inputs
=
{
'X'
:
x
,
'GTBox'
:
gtbox
,
'GTLabel'
:
gtlabel
}
self
.
inputs
=
{
'X'
:
x
,
'GTBox'
:
gtbox
.
astype
(
'float32'
),
'GTLabel'
:
gtlabel
.
astype
(
'int32'
),
}
loss
,
objness
,
gt_matches
=
YOLOv3Loss
(
x
,
gtbox
,
gtlabel
,
self
.
attrs
)
self
.
outputs
=
{
'Loss'
:
np
.
array
(
[
YoloV3Loss
(
x
,
gtbox
,
gtlabel
,
self
.
attrs
)]).
astype
(
'float32'
)
'Loss'
:
loss
,
'ObjectnessMask'
:
objness
,
"GTMatchMask"
:
gt_matches
}
def
test_check_output
(
self
):
...
...
@@ -196,19 +201,16 @@ class TestYolov3LossOp(OpTest):
place
,
[
'X'
],
'Loss'
,
no_grad_set
=
set
([
"GTBox"
,
"GTLabel"
]),
max_relative_error
=
0.
06
)
max_relative_error
=
0.
3
)
def
initTestCase
(
self
):
self
.
anchors
=
[
10
,
13
,
12
,
12
]
self
.
class_num
=
10
self
.
anchors
=
[
10
,
13
,
16
,
30
,
33
,
23
]
self
.
anchor_mask
=
[
1
,
2
]
self
.
class_num
=
5
self
.
ignore_thresh
=
0.5
self
.
x_shape
=
(
5
,
len
(
self
.
anchors
)
//
2
*
(
5
+
self
.
class_num
),
7
,
7
)
self
.
gtbox_shape
=
(
5
,
10
,
4
)
self
.
loss_weight_xy
=
2.5
self
.
loss_weight_wh
=
0.8
self
.
loss_weight_conf_target
=
1.5
self
.
loss_weight_conf_notarget
=
0.5
self
.
loss_weight_class
=
1.2
self
.
downsample
=
32
self
.
x_shape
=
(
3
,
len
(
self
.
anchor_mask
)
*
(
5
+
self
.
class_num
),
5
,
5
)
self
.
gtbox_shape
=
(
3
,
5
,
4
)
if
__name__
==
"__main__"
:
...
...
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