Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
TonyTonyFun
Paddle
提交
f115eb0d
P
Paddle
项目概览
TonyTonyFun
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
f115eb0d
编写于
11月 15, 2018
作者:
D
dengkaipeng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
enhance api. test=develop
上级
95d5060d
变更
7
显示空白变更内容
内联
并排
Showing
7 changed file
with
199 addition
and
159 deletion
+199
-159
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/yolov3_loss_op.cc
paddle/fluid/operators/yolov3_loss_op.cc
+33
-17
paddle/fluid/operators/yolov3_loss_op.h
paddle/fluid/operators/yolov3_loss_op.h
+69
-60
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+39
-28
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+13
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+0
-9
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
+44
-44
未找到文件。
paddle/fluid/API.spec
浏览文件 @
f115eb0d
...
...
@@ -288,7 +288,7 @@ paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'i
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'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
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', '
anchors', 'class_num', 'ignore_thresh', 'lambda_xy', 'lambda_wh', 'lambda_conf_obj', 'lambda_conf_noobj', 'lambda
_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 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.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
...
...
paddle/fluid/operators/yolov3_loss_op.cc
浏览文件 @
f115eb0d
...
...
@@ -25,11 +25,14 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
"Input(X) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"GTBox"
),
"Input(GTBox) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"GTLabel"
),
"Input(GTLabel) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Loss"
),
"Output(Loss) of Yolov3LossOp should not be null."
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
auto
dim_gt
=
ctx
->
GetInputDim
(
"GTBox"
);
auto
dim_gtbox
=
ctx
->
GetInputDim
(
"GTBox"
);
auto
dim_gtlabel
=
ctx
->
GetInputDim
(
"GTLabel"
);
auto
anchors
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"anchors"
);
auto
class_num
=
ctx
->
Attrs
().
Get
<
int
>
(
"class_num"
);
PADDLE_ENFORCE_EQ
(
dim_x
.
size
(),
4
,
"Input(X) should be a 4-D tensor."
);
...
...
@@ -38,8 +41,15 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
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_gt
.
size
(),
3
,
"Input(GTBox) should be a 3-D tensor"
);
PADDLE_ENFORCE_EQ
(
dim_gt
[
2
],
5
,
"Input(GTBox) dim[2] should be 5"
);
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(GTBox) 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
],
"Input(GTBox) and Input(GTLabel) dim[1] should be same"
);
PADDLE_ENFORCE_GT
(
anchors
.
size
(),
0
,
"Attr(anchors) length should be greater then 0."
);
PADDLE_ENFORCE_EQ
(
anchors
.
size
()
%
2
,
0
,
...
...
@@ -73,11 +83,15 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
"max_box_num is the max number of boxes in each image, "
"In the third dimention, stores label, x, y, w, h, "
"label is an integer to specify box class, x, y is the "
"center cordinate of boxes and w, h is the width and height"
"and x, y, w, h should be divided by input image height to "
"scale to [0, 1]."
);
"In the third dimention, stores x, y, w, h coordinates, "
"x, y is the center cordinate of boxes and w, h is the "
"width and height and x, y, w, h should be divided by "
"input image height to scale to [0, 1]."
);
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 shoudl 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]"
);
...
...
@@ -88,19 +102,19 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"it will be parsed pair by pair."
);
AddAttr
<
float
>
(
"ignore_thresh"
,
"The ignore threshold to ignore confidence loss."
);
AddAttr
<
float
>
(
"l
ambda
_xy"
,
"The weight of x, y location loss."
)
AddAttr
<
float
>
(
"l
oss_weight
_xy"
,
"The weight of x, y location loss."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"l
ambda
_wh"
,
"The weight of w, h location loss."
)
AddAttr
<
float
>
(
"l
oss_weight
_wh"
,
"The weight of w, h location loss."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"l
ambda_conf_obj
"
,
"l
oss_weight_conf_target
"
,
"The weight of confidence score loss in locations with target object."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"l
ambda_conf_noobj
"
,
AddAttr
<
float
>
(
"l
oss_weight_conf_notarget
"
,
"The weight of confidence score loss in locations without "
"target object."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"l
ambda
_class"
,
"The weight of classification loss."
)
AddAttr
<
float
>
(
"l
oss_weight
_class"
,
"The weight of classification loss."
)
.
SetDefault
(
1.0
);
AddComment
(
R"DOC(
This operator generate yolov3 loss by given predict result and ground
...
...
@@ -141,10 +155,10 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
Final loss will be represented as follow.
$$
loss = \l
ambda_{xy} * loss_{xy} + \lambda
_{wh} * loss_{wh}
+ \l
ambda_{conf_obj} * loss_{conf_obj
}
+ \l
ambda_{conf_noobj} * loss_{conf_noobj
}
+ \l
ambda
_{class} * loss_{class}
loss = \l
oss_weight_{xy} * loss_{xy} + \loss_weight
_{wh} * loss_{wh}
+ \l
oss_weight_{conf_target} * loss_{conf_target
}
+ \l
oss_weight_{conf_notarget} * loss_{conf_notarget
}
+ \l
oss_weight
_{class} * loss_{class}
$$
)DOC"
);
}
...
...
@@ -182,12 +196,14 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op
->
SetType
(
"yolov3_loss_grad"
);
op
->
SetInput
(
"X"
,
Input
(
"X"
));
op
->
SetInput
(
"GTBox"
,
Input
(
"GTBox"
));
op
->
SetInput
(
"GTLabel"
,
Input
(
"GTLabel"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Loss"
),
OutputGrad
(
"Loss"
));
op
->
SetAttrMap
(
Attrs
());
op
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
InputGrad
(
"X"
));
op
->
SetOutput
(
framework
::
GradVarName
(
"GTBox"
),
{});
op
->
SetOutput
(
framework
::
GradVarName
(
"GTLabel"
),
{});
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
op
);
}
};
...
...
paddle/fluid/operators/yolov3_loss_op.h
浏览文件 @
f115eb0d
...
...
@@ -186,15 +186,17 @@ static T CalcBoxIoU(std::vector<T> box1, std::vector<T> box2) {
}
template
<
typename
T
>
static
void
PreProcessGTBox
(
const
Tensor
&
gt_boxes
,
const
float
ignore_thresh
,
std
::
vector
<
int
>
anchors
,
const
int
grid_size
,
Tensor
*
obj_mask
,
Tensor
*
noobj_mask
,
Tensor
*
tx
,
Tensor
*
ty
,
Tensor
*
tw
,
Tensor
*
th
,
Tensor
*
tconf
,
static
void
PreProcessGTBox
(
const
Tensor
&
gt_box
,
const
Tensor
&
gt_label
,
const
float
ignore_thresh
,
std
::
vector
<
int
>
anchors
,
const
int
grid_size
,
Tensor
*
obj_mask
,
Tensor
*
noobj_mask
,
Tensor
*
tx
,
Tensor
*
ty
,
Tensor
*
tw
,
Tensor
*
th
,
Tensor
*
tconf
,
Tensor
*
tclass
)
{
const
int
n
=
gt_box
es
.
dims
()[
0
];
const
int
b
=
gt_box
es
.
dims
()[
1
];
const
int
n
=
gt_box
.
dims
()[
0
];
const
int
b
=
gt_box
.
dims
()[
1
];
const
int
anchor_num
=
anchors
.
size
()
/
2
;
auto
gt_boxes_t
=
EigenTensor
<
T
,
3
>::
From
(
gt_boxes
);
auto
gt_box_t
=
EigenTensor
<
T
,
3
>::
From
(
gt_box
);
auto
gt_label_t
=
EigenTensor
<
int
,
2
>::
From
(
gt_label
);
auto
obj_mask_t
=
EigenTensor
<
int
,
4
>::
From
(
*
obj_mask
).
setConstant
(
0
);
auto
noobj_mask_t
=
EigenTensor
<
int
,
4
>::
From
(
*
noobj_mask
).
setConstant
(
1
);
auto
tx_t
=
EigenTensor
<
T
,
4
>::
From
(
*
tx
).
setConstant
(
0.0
);
...
...
@@ -206,28 +208,27 @@ static void PreProcessGTBox(const Tensor& gt_boxes, const float ignore_thresh,
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
b
;
j
++
)
{
if
(
isZero
<
T
>
(
gt_boxes_t
(
i
,
j
,
0
))
&&
isZero
<
T
>
(
gt_boxes_t
(
i
,
j
,
1
))
&&
isZero
<
T
>
(
gt_boxes_t
(
i
,
j
,
2
))
&&
isZero
<
T
>
(
gt_boxes_t
(
i
,
j
,
3
))
&&
isZero
<
T
>
(
gt_boxes_t
(
i
,
j
,
4
)))
{
if
(
isZero
<
T
>
(
gt_box_t
(
i
,
j
,
0
))
&&
isZero
<
T
>
(
gt_box_t
(
i
,
j
,
1
))
&&
isZero
<
T
>
(
gt_box_t
(
i
,
j
,
2
))
&&
isZero
<
T
>
(
gt_box_t
(
i
,
j
,
3
)))
{
continue
;
}
int
gt_label
=
static_cast
<
int
>
(
gt_boxes_t
(
i
,
j
,
0
)
);
T
gx
=
gt_box
es_t
(
i
,
j
,
1
)
*
grid_size
;
T
gy
=
gt_box
es_t
(
i
,
j
,
2
)
*
grid_size
;
T
gw
=
gt_box
es_t
(
i
,
j
,
3
)
*
grid_size
;
T
gh
=
gt_box
es_t
(
i
,
j
,
4
)
*
grid_size
;
int
cur_label
=
gt_label_t
(
i
,
j
);
T
gx
=
gt_box
_t
(
i
,
j
,
0
)
*
grid_size
;
T
gy
=
gt_box
_t
(
i
,
j
,
1
)
*
grid_size
;
T
gw
=
gt_box
_t
(
i
,
j
,
2
)
*
grid_size
;
T
gh
=
gt_box
_t
(
i
,
j
,
3
)
*
grid_size
;
int
gi
=
static_cast
<
int
>
(
gx
);
int
gj
=
static_cast
<
int
>
(
gy
);
T
max_iou
=
static_cast
<
T
>
(
0
);
T
iou
;
int
best_an_index
=
-
1
;
std
::
vector
<
T
>
gt_box
({
0
,
0
,
gw
,
gh
});
std
::
vector
<
T
>
gt_box
_shape
({
0
,
0
,
gw
,
gh
});
for
(
int
an_idx
=
0
;
an_idx
<
anchor_num
;
an_idx
++
)
{
std
::
vector
<
T
>
anchor_shape
({
0
,
0
,
static_cast
<
T
>
(
anchors
[
2
*
an_idx
]),
static_cast
<
T
>
(
anchors
[
2
*
an_idx
+
1
])});
iou
=
CalcBoxIoU
<
T
>
(
gt_box
,
anchor_shape
);
iou
=
CalcBoxIoU
<
T
>
(
gt_box
_shape
,
anchor_shape
);
if
(
iou
>
max_iou
)
{
max_iou
=
iou
;
best_an_index
=
an_idx
;
...
...
@@ -242,7 +243,7 @@ static void PreProcessGTBox(const Tensor& gt_boxes, const float ignore_thresh,
ty_t
(
i
,
best_an_index
,
gj
,
gi
)
=
gy
-
gj
;
tw_t
(
i
,
best_an_index
,
gj
,
gi
)
=
log
(
gw
/
anchors
[
2
*
best_an_index
]);
th_t
(
i
,
best_an_index
,
gj
,
gi
)
=
log
(
gh
/
anchors
[
2
*
best_an_index
+
1
]);
tclass_t
(
i
,
best_an_index
,
gj
,
gi
,
gt
_label
)
=
1
;
tclass_t
(
i
,
best_an_index
,
gj
,
gi
,
cur
_label
)
=
1
;
tconf_t
(
i
,
best_an_index
,
gj
,
gi
)
=
1
;
}
}
...
...
@@ -267,10 +268,10 @@ static void AddAllGradToInputGrad(
Tensor
*
grad
,
T
loss
,
const
Tensor
&
pred_x
,
const
Tensor
&
pred_y
,
const
Tensor
&
pred_conf
,
const
Tensor
&
pred_class
,
const
Tensor
&
grad_x
,
const
Tensor
&
grad_y
,
const
Tensor
&
grad_w
,
const
Tensor
&
grad_h
,
const
Tensor
&
grad_conf_
obj
,
const
Tensor
&
grad_conf_noobj
,
const
Tensor
&
grad_class
,
const
int
class_num
,
const
float
l
ambda
_xy
,
const
float
l
ambda_wh
,
const
float
lambda_conf_obj
,
const
float
l
ambda_conf_noobj
,
const
float
lambda
_class
)
{
const
Tensor
&
grad_conf_
target
,
const
Tensor
&
grad_conf_notarget
,
const
Tensor
&
grad_class
,
const
int
class_num
,
const
float
l
oss_weight
_xy
,
const
float
l
oss_weight_wh
,
const
float
loss_weight_conf_target
,
const
float
l
oss_weight_conf_notarget
,
const
float
loss_weight
_class
)
{
const
int
n
=
pred_x
.
dims
()[
0
];
const
int
an_num
=
pred_x
.
dims
()[
1
];
const
int
h
=
pred_x
.
dims
()[
2
];
...
...
@@ -285,8 +286,8 @@ static void AddAllGradToInputGrad(
auto
grad_y_t
=
EigenTensor
<
T
,
4
>::
From
(
grad_y
);
auto
grad_w_t
=
EigenTensor
<
T
,
4
>::
From
(
grad_w
);
auto
grad_h_t
=
EigenTensor
<
T
,
4
>::
From
(
grad_h
);
auto
grad_conf_
obj_t
=
EigenTensor
<
T
,
4
>::
From
(
grad_conf_obj
);
auto
grad_conf_no
obj_t
=
EigenTensor
<
T
,
4
>::
From
(
grad_conf_noobj
);
auto
grad_conf_
target_t
=
EigenTensor
<
T
,
4
>::
From
(
grad_conf_target
);
auto
grad_conf_no
target_t
=
EigenTensor
<
T
,
4
>::
From
(
grad_conf_notarget
);
auto
grad_class_t
=
EigenTensor
<
T
,
5
>::
From
(
grad_class
);
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
...
...
@@ -295,25 +296,26 @@ static void AddAllGradToInputGrad(
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
grad_t
(
i
,
j
*
attr_num
,
k
,
l
)
=
grad_x_t
(
i
,
j
,
k
,
l
)
*
pred_x_t
(
i
,
j
,
k
,
l
)
*
(
1.0
-
pred_x_t
(
i
,
j
,
k
,
l
))
*
loss
*
l
ambda
_xy
;
(
1.0
-
pred_x_t
(
i
,
j
,
k
,
l
))
*
loss
*
l
oss_weight
_xy
;
grad_t
(
i
,
j
*
attr_num
+
1
,
k
,
l
)
=
grad_y_t
(
i
,
j
,
k
,
l
)
*
pred_y_t
(
i
,
j
,
k
,
l
)
*
(
1.0
-
pred_y_t
(
i
,
j
,
k
,
l
))
*
loss
*
l
ambda
_xy
;
(
1.0
-
pred_y_t
(
i
,
j
,
k
,
l
))
*
loss
*
l
oss_weight
_xy
;
grad_t
(
i
,
j
*
attr_num
+
2
,
k
,
l
)
=
grad_w_t
(
i
,
j
,
k
,
l
)
*
loss
*
l
ambda
_wh
;
grad_w_t
(
i
,
j
,
k
,
l
)
*
loss
*
l
oss_weight
_wh
;
grad_t
(
i
,
j
*
attr_num
+
3
,
k
,
l
)
=
grad_h_t
(
i
,
j
,
k
,
l
)
*
loss
*
l
ambda
_wh
;
grad_h_t
(
i
,
j
,
k
,
l
)
*
loss
*
l
oss_weight
_wh
;
grad_t
(
i
,
j
*
attr_num
+
4
,
k
,
l
)
=
grad_conf_
obj
_t
(
i
,
j
,
k
,
l
)
*
pred_conf_t
(
i
,
j
,
k
,
l
)
*
(
1.0
-
pred_conf_t
(
i
,
j
,
k
,
l
))
*
loss
*
l
ambda_conf_obj
;
grad_conf_
target
_t
(
i
,
j
,
k
,
l
)
*
pred_conf_t
(
i
,
j
,
k
,
l
)
*
(
1.0
-
pred_conf_t
(
i
,
j
,
k
,
l
))
*
loss
*
l
oss_weight_conf_target
;
grad_t
(
i
,
j
*
attr_num
+
4
,
k
,
l
)
+=
grad_conf_noobj_t
(
i
,
j
,
k
,
l
)
*
pred_conf_t
(
i
,
j
,
k
,
l
)
*
(
1.0
-
pred_conf_t
(
i
,
j
,
k
,
l
))
*
loss
*
lambda_conf_noobj
;
grad_conf_notarget_t
(
i
,
j
,
k
,
l
)
*
pred_conf_t
(
i
,
j
,
k
,
l
)
*
(
1.0
-
pred_conf_t
(
i
,
j
,
k
,
l
))
*
loss
*
loss_weight_conf_notarget
;
for
(
int
c
=
0
;
c
<
class_num
;
c
++
)
{
grad_t
(
i
,
j
*
attr_num
+
5
+
c
,
k
,
l
)
=
grad_class_t
(
i
,
j
,
k
,
l
,
c
)
*
pred_class_t
(
i
,
j
,
k
,
l
,
c
)
*
(
1.0
-
pred_class_t
(
i
,
j
,
k
,
l
,
c
))
*
loss
*
l
ambda
_class
;
(
1.0
-
pred_class_t
(
i
,
j
,
k
,
l
,
c
))
*
loss
*
l
oss_weight
_class
;
}
}
}
...
...
@@ -326,16 +328,18 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
gt_boxes
=
ctx
.
Input
<
Tensor
>
(
"GTBox"
);
auto
*
gt_box
=
ctx
.
Input
<
Tensor
>
(
"GTBox"
);
auto
*
gt_label
=
ctx
.
Input
<
Tensor
>
(
"GTLabel"
);
auto
*
loss
=
ctx
.
Output
<
Tensor
>
(
"Loss"
);
auto
anchors
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"anchors"
);
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
float
ignore_thresh
=
ctx
.
Attr
<
float
>
(
"ignore_thresh"
);
float
lambda_xy
=
ctx
.
Attr
<
float
>
(
"lambda_xy"
);
float
lambda_wh
=
ctx
.
Attr
<
float
>
(
"lambda_wh"
);
float
lambda_conf_obj
=
ctx
.
Attr
<
float
>
(
"lambda_conf_obj"
);
float
lambda_conf_noobj
=
ctx
.
Attr
<
float
>
(
"lambda_conf_noobj"
);
float
lambda_class
=
ctx
.
Attr
<
float
>
(
"lambda_class"
);
float
loss_weight_xy
=
ctx
.
Attr
<
float
>
(
"loss_weight_xy"
);
float
loss_weight_wh
=
ctx
.
Attr
<
float
>
(
"loss_weight_wh"
);
float
loss_weight_conf_target
=
ctx
.
Attr
<
float
>
(
"loss_weight_conf_target"
);
float
loss_weight_conf_notarget
=
ctx
.
Attr
<
float
>
(
"loss_weight_conf_notarget"
);
float
loss_weight_class
=
ctx
.
Attr
<
float
>
(
"loss_weight_class"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
h
=
input
->
dims
()[
2
];
...
...
@@ -363,7 +367,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
th
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
tconf
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
tclass
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
,
class_num
},
ctx
.
GetPlace
());
PreProcessGTBox
<
T
>
(
*
gt_box
es
,
ignore_thresh
,
anchors
,
h
,
&
obj_mask
,
PreProcessGTBox
<
T
>
(
*
gt_box
,
*
gt_label
,
ignore_thresh
,
anchors
,
h
,
&
obj_mask
,
&
noobj_mask
,
&
tx
,
&
ty
,
&
tw
,
&
th
,
&
tconf
,
&
tclass
);
Tensor
obj_mask_expand
;
...
...
@@ -375,15 +379,16 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
T
loss_y
=
CalcMSEWithMask
<
T
>
(
pred_y
,
ty
,
obj_mask
);
T
loss_w
=
CalcMSEWithMask
<
T
>
(
pred_w
,
tw
,
obj_mask
);
T
loss_h
=
CalcMSEWithMask
<
T
>
(
pred_h
,
th
,
obj_mask
);
T
loss_conf_
obj
=
CalcBCEWithMask
<
T
>
(
pred_conf
,
tconf
,
obj_mask
);
T
loss_conf_no
obj
=
CalcBCEWithMask
<
T
>
(
pred_conf
,
tconf
,
noobj_mask
);
T
loss_conf_
target
=
CalcBCEWithMask
<
T
>
(
pred_conf
,
tconf
,
obj_mask
);
T
loss_conf_no
target
=
CalcBCEWithMask
<
T
>
(
pred_conf
,
tconf
,
noobj_mask
);
T
loss_class
=
CalcBCEWithMask
<
T
>
(
pred_class
,
tclass
,
obj_mask_expand
);
auto
*
loss_data
=
loss
->
mutable_data
<
T
>
({
1
},
ctx
.
GetPlace
());
loss_data
[
0
]
=
lambda_xy
*
(
loss_x
+
loss_y
)
+
lambda_wh
*
(
loss_w
+
loss_h
)
+
lambda_conf_obj
*
loss_conf_obj
+
lambda_conf_noobj
*
loss_conf_noobj
+
lambda_class
*
loss_class
;
loss_data
[
0
]
=
loss_weight_xy
*
(
loss_x
+
loss_y
)
+
loss_weight_wh
*
(
loss_w
+
loss_h
)
+
loss_weight_conf_target
*
loss_conf_target
+
loss_weight_conf_notarget
*
loss_conf_notarget
+
loss_weight_class
*
loss_class
;
}
};
...
...
@@ -392,18 +397,20 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
gt_boxes
=
ctx
.
Input
<
Tensor
>
(
"GTBox"
);
auto
*
gt_box
=
ctx
.
Input
<
Tensor
>
(
"GTBox"
);
auto
*
gt_label
=
ctx
.
Input
<
Tensor
>
(
"GTLabel"
);
auto
anchors
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"anchors"
);
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
float
ignore_thresh
=
ctx
.
Attr
<
float
>
(
"ignore_thresh"
);
auto
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Loss"
));
const
T
loss
=
output_grad
->
data
<
T
>
()[
0
];
float
lambda_xy
=
ctx
.
Attr
<
float
>
(
"lambda_xy"
);
float
lambda_wh
=
ctx
.
Attr
<
float
>
(
"lambda_wh"
);
float
lambda_conf_obj
=
ctx
.
Attr
<
float
>
(
"lambda_conf_obj"
);
float
lambda_conf_noobj
=
ctx
.
Attr
<
float
>
(
"lambda_conf_noobj"
);
float
lambda_class
=
ctx
.
Attr
<
float
>
(
"lambda_class"
);
float
loss_weight_xy
=
ctx
.
Attr
<
float
>
(
"loss_weight_xy"
);
float
loss_weight_wh
=
ctx
.
Attr
<
float
>
(
"loss_weight_wh"
);
float
loss_weight_conf_target
=
ctx
.
Attr
<
float
>
(
"loss_weight_conf_target"
);
float
loss_weight_conf_notarget
=
ctx
.
Attr
<
float
>
(
"loss_weight_conf_notarget"
);
float
loss_weight_class
=
ctx
.
Attr
<
float
>
(
"loss_weight_class"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
c
=
input
->
dims
()[
1
];
...
...
@@ -432,7 +439,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
th
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
tconf
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
tclass
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
,
class_num
},
ctx
.
GetPlace
());
PreProcessGTBox
<
T
>
(
*
gt_box
es
,
ignore_thresh
,
anchors
,
h
,
&
obj_mask
,
PreProcessGTBox
<
T
>
(
*
gt_box
,
*
gt_label
,
ignore_thresh
,
anchors
,
h
,
&
obj_mask
,
&
noobj_mask
,
&
tx
,
&
ty
,
&
tw
,
&
th
,
&
tconf
,
&
tclass
);
Tensor
obj_mask_expand
;
...
...
@@ -441,13 +448,13 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
ExpandObjMaskByClassNum
(
&
obj_mask_expand
,
obj_mask
);
Tensor
grad_x
,
grad_y
,
grad_w
,
grad_h
;
Tensor
grad_conf_
obj
,
grad_conf_noobj
,
grad_class
;
Tensor
grad_conf_
target
,
grad_conf_notarget
,
grad_class
;
grad_x
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_y
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_w
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_h
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_conf_
obj
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_conf_no
obj
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_conf_
target
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_conf_no
target
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
},
ctx
.
GetPlace
());
grad_class
.
mutable_data
<
T
>
({
n
,
an_num
,
h
,
w
,
class_num
},
ctx
.
GetPlace
());
T
obj_mf
=
CalcMaskPointNum
<
int
>
(
obj_mask
);
T
noobj_mf
=
CalcMaskPointNum
<
int
>
(
noobj_mask
);
...
...
@@ -456,8 +463,9 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
CalcMSEGradWithMask
<
T
>
(
&
grad_y
,
pred_y
,
ty
,
obj_mask
,
obj_mf
);
CalcMSEGradWithMask
<
T
>
(
&
grad_w
,
pred_w
,
tw
,
obj_mask
,
obj_mf
);
CalcMSEGradWithMask
<
T
>
(
&
grad_h
,
pred_h
,
th
,
obj_mask
,
obj_mf
);
CalcBCEGradWithMask
<
T
>
(
&
grad_conf_obj
,
pred_conf
,
tconf
,
obj_mask
,
obj_mf
);
CalcBCEGradWithMask
<
T
>
(
&
grad_conf_noobj
,
pred_conf
,
tconf
,
noobj_mask
,
CalcBCEGradWithMask
<
T
>
(
&
grad_conf_target
,
pred_conf
,
tconf
,
obj_mask
,
obj_mf
);
CalcBCEGradWithMask
<
T
>
(
&
grad_conf_notarget
,
pred_conf
,
tconf
,
noobj_mask
,
noobj_mf
);
CalcBCEGradWithMask
<
T
>
(
&
grad_class
,
pred_class
,
tclass
,
obj_mask_expand
,
obj_expand_mf
);
...
...
@@ -465,8 +473,9 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
input_grad
->
mutable_data
<
T
>
({
n
,
c
,
h
,
w
},
ctx
.
GetPlace
());
AddAllGradToInputGrad
<
T
>
(
input_grad
,
loss
,
pred_x
,
pred_y
,
pred_conf
,
pred_class
,
grad_x
,
grad_y
,
grad_w
,
grad_h
,
grad_conf_obj
,
grad_conf_noobj
,
grad_class
,
class_num
,
lambda_xy
,
lambda_wh
,
lambda_conf_obj
,
lambda_conf_noobj
,
lambda_class
);
grad_w
,
grad_h
,
grad_conf_target
,
grad_conf_notarget
,
grad_class
,
class_num
,
loss_weight_xy
,
loss_weight_wh
,
loss_weight_conf_target
,
loss_weight_conf_notarget
,
loss_weight_class
);
}
};
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
f115eb0d
...
...
@@ -409,32 +409,36 @@ def polygon_box_transform(input, name=None):
@
templatedoc
(
op_type
=
"yolov3_loss"
)
def
yolov3_loss
(
x
,
gtbox
,
gtlabel
,
anchors
,
class_num
,
ignore_thresh
,
l
ambda
_xy
=
None
,
l
ambda
_wh
=
None
,
l
ambda_conf_obj
=
None
,
l
ambda_conf_noobj
=
None
,
l
ambda
_class
=
None
,
l
oss_weight
_xy
=
None
,
l
oss_weight
_wh
=
None
,
l
oss_weight_conf_target
=
None
,
l
oss_weight_conf_notarget
=
None
,
l
oss_weight
_class
=
None
,
name
=
None
):
"""
${comment}
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, shoulb be in shape of [N, B, 5],
in the third dimenstion, class_id, x, y, w, h should
be stored and x, y, w, h should be relative valud of
input image.
gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
in the third dimenstion, x, y, w, h should be stored
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 ins shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
l
ambda_xy (float|None): ${lambda
_xy_comment}
l
ambda_wh (float|None): ${lambda
_wh_comment}
l
ambda_conf_obj (float|None): ${lambda_conf_obj
_comment}
l
ambda_conf_noobj (float|None): ${lambda_conf_noobj
_comment}
l
ambda_class (float|None): ${lambda
_class_comment}
l
oss_weight_xy (float|None): ${loss_weight
_xy_comment}
l
oss_weight_wh (float|None): ${loss_weight
_wh_comment}
l
oss_weight_conf_target (float|None): ${loss_weight_conf_target
_comment}
l
oss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget
_comment}
l
oss_weight_class (float|None): ${loss_weight
_class_comment}
name (string): the name of yolov3 loss
Returns:
...
...
@@ -443,6 +447,7 @@ def yolov3_loss(x,
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable"
TypeError: Input gtlabel of yolov3_loss must be Variable"
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
...
...
@@ -450,8 +455,9 @@ def yolov3_loss(x,
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10, 255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[10, 6, 5], dtype='float32')
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)
...
...
@@ -462,6 +468,8 @@ def yolov3_loss(x,
raise
TypeError
(
"Input x of yolov3_loss must be Variable"
)
if
not
isinstance
(
gtbox
,
Variable
):
raise
TypeError
(
"Input gtbox of yolov3_loss must be Variable"
)
if
not
isinstance
(
gtlabel
,
Variable
):
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
(
class_num
,
int
):
...
...
@@ -482,21 +490,24 @@ def yolov3_loss(x,
"ignore_thresh"
:
ignore_thresh
,
}
if
lambda_xy
is
not
None
and
isinstance
(
lambda_xy
,
float
):
self
.
attrs
[
'lambda_xy'
]
=
lambda_xy
if
lambda_wh
is
not
None
and
isinstance
(
lambda_wh
,
float
):
self
.
attrs
[
'lambda_wh'
]
=
lambda_wh
if
lambda_conf_obj
is
not
None
and
isinstance
(
lambda_conf_obj
,
float
):
self
.
attrs
[
'lambda_conf_obj'
]
=
lambda_conf_obj
if
lambda_conf_noobj
is
not
None
and
isinstance
(
lambda_conf_noobj
,
float
):
self
.
attrs
[
'lambda_conf_noobj'
]
=
lambda_conf_noobj
if
lambda_class
is
not
None
and
isinstance
(
lambda_class
,
float
):
self
.
attrs
[
'lambda_class'
]
=
lambda_class
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
},
inputs
=
{
"X"
:
x
,
"GTBox"
:
gtbox
,
"GTLabel"
:
gtlabel
},
outputs
=
{
'Loss'
:
loss
},
attrs
=
attrs
)
return
loss
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
f115eb0d
...
...
@@ -366,5 +366,18 @@ class TestGenerateProposals(unittest.TestCase):
print
(
rpn_rois
.
shape
)
class
TestYoloDetection
(
unittest
.
TestCase
):
def
test_yolov3_loss
(
self
):
program
=
Program
()
with
program_guard
(
program
):
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
)
self
.
assertIsNotNone
(
loss
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
f115eb0d
...
...
@@ -911,15 +911,6 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
data_1
)
print
(
str
(
program
))
def
test_yolov3_loss
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
30
,
7
,
7
],
dtype
=
'float32'
)
gtbox
=
layers
.
data
(
name
=
'gtbox'
,
shape
=
[
10
,
5
],
dtype
=
'float32'
)
loss
=
layers
.
yolov3_loss
(
x
,
gtbox
,
[
10
,
13
,
30
,
13
],
10
,
0.5
)
self
.
assertIsNotNone
(
loss
)
def
test_bilinear_tensor_product_layer
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @
f115eb0d
...
...
@@ -66,7 +66,7 @@ def box_iou(box1, box2):
return
inter_area
/
(
b1_area
+
b2_area
+
inter_area
)
def
build_target
(
gtboxs
,
attrs
,
grid_size
):
def
build_target
(
gtboxs
,
gtlabel
,
attrs
,
grid_size
):
n
,
b
,
_
=
gtboxs
.
shape
ignore_thresh
=
attrs
[
"ignore_thresh"
]
anchors
=
attrs
[
"anchors"
]
...
...
@@ -87,11 +87,11 @@ def build_target(gtboxs, attrs, grid_size):
if
gtboxs
[
i
,
j
,
:].
sum
()
==
0
:
continue
gt_label
=
int
(
gtboxs
[
i
,
j
,
0
])
gx
=
gtboxs
[
i
,
j
,
1
]
*
grid_size
gy
=
gtboxs
[
i
,
j
,
2
]
*
grid_size
gw
=
gtboxs
[
i
,
j
,
3
]
*
grid_size
gh
=
gtboxs
[
i
,
j
,
4
]
*
grid_size
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
)
...
...
@@ -121,7 +121,7 @@ def build_target(gtboxs, attrs, grid_size):
return
(
tx
,
ty
,
tw
,
th
,
tconf
,
tcls
,
obj_mask
,
noobj_mask
)
def
YoloV3Loss
(
x
,
gtbox
,
attrs
):
def
YoloV3Loss
(
x
,
gtbox
,
gtlabel
,
attrs
):
n
,
c
,
h
,
w
=
x
.
shape
an_num
=
len
(
attrs
[
'anchors'
])
//
2
class_num
=
attrs
[
"class_num"
]
...
...
@@ -134,7 +134,7 @@ def YoloV3Loss(x, gtbox, attrs):
pred_cls
=
sigmoid
(
x
[:,
:,
:,
:,
5
:])
tx
,
ty
,
tw
,
th
,
tconf
,
tcls
,
obj_mask
,
noobj_mask
=
build_target
(
gtbox
,
attrs
,
x
.
shape
[
2
])
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'
])))
...
...
@@ -142,73 +142,73 @@ def YoloV3Loss(x, gtbox, attrs):
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_
obj
=
bce
(
pred_conf
*
obj_mask
,
tconf
*
obj_mask
,
obj_mask
)
loss_conf_no
obj
=
bce
(
pred_conf
*
noobj_mask
,
tconf
*
noobj_mask
,
loss_conf_
target
=
bce
(
pred_conf
*
obj_mask
,
tconf
*
obj_mask
,
obj_mask
)
loss_conf_no
target
=
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
[
'l
ambda
_xy'
]
*
(
loss_x
+
loss_y
)
\
+
attrs
[
'l
ambda
_wh'
]
*
(
loss_w
+
loss_h
)
\
+
attrs
[
'l
ambda_conf_obj'
]
*
loss_conf_obj
\
+
attrs
[
'l
ambda_conf_noobj'
]
*
loss_conf_noobj
\
+
attrs
[
'l
ambda
_class'
]
*
loss_class
return
attrs
[
'l
oss_weight
_xy'
]
*
(
loss_x
+
loss_y
)
\
+
attrs
[
'l
oss_weight
_wh'
]
*
(
loss_w
+
loss_h
)
\
+
attrs
[
'l
oss_weight_conf_target'
]
*
loss_conf_target
\
+
attrs
[
'l
oss_weight_conf_notarget'
]
*
loss_conf_notarget
\
+
attrs
[
'l
oss_weight
_class'
]
*
loss_class
class
TestYolov3LossOp
(
OpTest
):
def
setUp
(
self
):
self
.
l
ambda
_xy
=
1.0
self
.
l
ambda
_wh
=
1.0
self
.
l
ambda_conf_obj
=
1.0
self
.
l
ambda_conf_noobj
=
1.0
self
.
l
ambda
_class
=
1.0
self
.
l
oss_weight
_xy
=
1.0
self
.
l
oss_weight
_wh
=
1.0
self
.
l
oss_weight_conf_target
=
1.0
self
.
l
oss_weight_conf_notarget
=
1.0
self
.
l
oss_weight
_class
=
1.0
self
.
initTestCase
()
self
.
op_type
=
'yolov3_loss'
x
=
np
.
random
.
random
(
size
=
self
.
x_shape
).
astype
(
'float32'
)
gtbox
=
np
.
random
.
random
(
size
=
self
.
gtbox_shape
).
astype
(
'float32'
)
gt
box
[:,
:,
0
]
=
np
.
random
.
randint
(
0
,
self
.
class_num
,
self
.
gtbox_shape
[:
2
]
)
gt
label
=
np
.
random
.
randint
(
0
,
self
.
class_num
,
self
.
gtbox_shape
[:
2
]).
astype
(
'int32'
)
self
.
attrs
=
{
"anchors"
:
self
.
anchors
,
"class_num"
:
self
.
class_num
,
"ignore_thresh"
:
self
.
ignore_thresh
,
"l
ambda_xy"
:
self
.
lambda
_xy
,
"l
ambda_wh"
:
self
.
lambda
_wh
,
"l
ambda_conf_obj"
:
self
.
lambda_conf_obj
,
"l
ambda_conf_noobj"
:
self
.
lambda_conf_noobj
,
"l
ambda_class"
:
self
.
lambda
_class
,
"l
oss_weight_xy"
:
self
.
loss_weight
_xy
,
"l
oss_weight_wh"
:
self
.
loss_weight
_wh
,
"l
oss_weight_conf_target"
:
self
.
loss_weight_conf_target
,
"l
oss_weight_conf_notarget"
:
self
.
loss_weight_conf_notarget
,
"l
oss_weight_class"
:
self
.
loss_weight
_class
,
}
self
.
inputs
=
{
'X'
:
x
,
'GTBox'
:
gtbox
}
self
.
inputs
=
{
'X'
:
x
,
'GTBox'
:
gtbox
,
'GTLabel'
:
gtlabel
}
self
.
outputs
=
{
'Loss'
:
np
.
array
([
YoloV3Loss
(
x
,
gtbox
,
self
.
attrs
)]).
astype
(
'float32'
)
'Loss'
:
np
.
array
(
[
YoloV3Loss
(
x
,
gtbox
,
gtlabel
,
self
.
attrs
)]).
astype
(
'float32'
)
}
def
test_check_output
(
self
):
place
=
core
.
CPUPlace
()
self
.
check_output_with_place
(
place
,
atol
=
1e-3
)
#
def test_check_grad_ignore_gtbox(self):
#
place = core.CPUPlace()
#
self.check_grad_with_place(
#
place, ['X'],
#
'Loss',
#
no_grad_set=set("GTBox"),
#
max_relative_error=0.06)
def
test_check_grad_ignore_gtbox
(
self
):
place
=
core
.
CPUPlace
()
self
.
check_grad_with_place
(
place
,
[
'X'
],
'Loss'
,
no_grad_set
=
set
(
"GTBox"
),
max_relative_error
=
0.06
)
def
initTestCase
(
self
):
self
.
anchors
=
[
10
,
13
,
12
,
12
]
self
.
class_num
=
10
self
.
ignore_thresh
=
0.5
self
.
x_shape
=
(
5
,
len
(
self
.
anchors
)
//
2
*
(
5
+
self
.
class_num
),
7
,
7
)
self
.
gtbox_shape
=
(
5
,
5
,
5
)
self
.
l
ambda
_xy
=
2.5
self
.
l
ambda
_wh
=
0.8
self
.
l
ambda_conf_obj
=
1.5
self
.
l
ambda_conf_noobj
=
0.5
self
.
l
ambda
_class
=
1.2
self
.
gtbox_shape
=
(
5
,
10
,
4
)
self
.
l
oss_weight
_xy
=
2.5
self
.
l
oss_weight
_wh
=
0.8
self
.
l
oss_weight_conf_target
=
1.5
self
.
l
oss_weight_conf_notarget
=
0.5
self
.
l
oss_weight
_class
=
1.2
if
__name__
==
"__main__"
:
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录