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733bb82e
编写于
1月 29, 2019
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
downsample -> downsample_ratio. test=develop
上级
ae0b0d5f
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
29 addition
and
26 deletion
+29
-26
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/yolov3_loss_op.cc
paddle/fluid/operators/yolov3_loss_op.cc
+1
-1
paddle/fluid/operators/yolov3_loss_op.h
paddle/fluid/operators/yolov3_loss_op.h
+22
-19
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+5
-5
未找到文件。
paddle/fluid/API.spec
浏览文件 @
733bb82e
...
...
@@ -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'], 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', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample', 'name'], varargs=None, keywords=None, defaults=(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/yolov3_loss_op.cc
浏览文件 @
733bb82e
...
...
@@ -135,7 +135,7 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"The mask index of anchors used in "
"current YOLOv3 loss calculation."
)
.
SetDefault
(
std
::
vector
<
int
>
{});
AddAttr
<
int
>
(
"downsample"
,
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."
)
...
...
paddle/fluid/operators/yolov3_loss_op.h
浏览文件 @
733bb82e
...
...
@@ -32,7 +32,7 @@ static inline bool LessEqualZero(T x) {
}
template
<
typename
T
>
static
T
S
CE
(
T
x
,
T
label
)
{
static
T
S
igmoidCrossEntropy
(
T
x
,
T
label
)
{
return
(
x
>
0
?
x
:
0.0
)
-
x
*
label
+
std
::
log
(
1.0
+
std
::
exp
(
-
std
::
abs
(
x
)));
}
...
...
@@ -42,7 +42,7 @@ static T L2Loss(T x, T y) {
}
template
<
typename
T
>
static
T
S
CE
Grad
(
T
x
,
T
label
)
{
static
T
S
igmoidCrossEntropy
Grad
(
T
x
,
T
label
)
{
return
1.0
/
(
1.0
+
std
::
exp
(
-
x
))
-
label
;
}
...
...
@@ -62,7 +62,7 @@ static int GetMaskIndex(std::vector<int> mask, int val) {
template
<
typename
T
>
struct
Box
{
float
x
,
y
,
w
,
h
;
T
x
,
y
,
w
,
h
;
};
template
<
typename
T
>
...
...
@@ -128,8 +128,8 @@ static void CalcBoxLocationLoss(T* loss, const T* input, Box<T> gt,
T
th
=
std
::
log
(
gt
.
h
*
input_size
/
anchors
[
2
*
an_idx
+
1
]);
T
scale
=
(
2.0
-
gt
.
w
*
gt
.
h
);
loss
[
0
]
+=
S
CE
<
T
>
(
input
[
box_idx
],
tx
)
*
scale
;
loss
[
0
]
+=
S
CE
<
T
>
(
input
[
box_idx
+
stride
],
ty
)
*
scale
;
loss
[
0
]
+=
S
igmoidCrossEntropy
<
T
>
(
input
[
box_idx
],
tx
)
*
scale
;
loss
[
0
]
+=
S
igmoidCrossEntropy
<
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
;
}
...
...
@@ -145,9 +145,10 @@ static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
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
]
=
SCEGrad
<
T
>
(
input
[
box_idx
],
tx
)
*
scale
*
loss
;
input_grad
[
box_idx
]
=
SigmoidCrossEntropyGrad
<
T
>
(
input
[
box_idx
],
tx
)
*
scale
*
loss
;
input_grad
[
box_idx
+
stride
]
=
S
CE
Grad
<
T
>
(
input
[
box_idx
+
stride
],
ty
)
*
scale
*
loss
;
S
igmoidCrossEntropy
Grad
<
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
]
=
...
...
@@ -160,7 +161,7 @@ static inline void CalcLabelLoss(T* loss, const T* input, const int index,
const
int
stride
)
{
for
(
int
i
=
0
;
i
<
class_num
;
i
++
)
{
T
pred
=
input
[
index
+
i
*
stride
];
loss
[
0
]
+=
S
CE
<
T
>
(
pred
,
(
i
==
label
)
?
1.0
:
0.0
);
loss
[
0
]
+=
S
igmoidCrossEntropy
<
T
>
(
pred
,
(
i
==
label
)
?
1.0
:
0.0
);
}
}
...
...
@@ -172,7 +173,7 @@ static inline void CalcLabelLossGrad(T* input_grad, const T loss,
for
(
int
i
=
0
;
i
<
class_num
;
i
++
)
{
T
pred
=
input
[
index
+
i
*
stride
];
input_grad
[
index
+
i
*
stride
]
=
S
CE
Grad
<
T
>
(
pred
,
(
i
==
label
)
?
1.0
:
0.0
)
*
loss
;
S
igmoidCrossEntropy
Grad
<
T
>
(
pred
,
(
i
==
label
)
?
1.0
:
0.0
)
*
loss
;
}
}
...
...
@@ -187,11 +188,11 @@ static inline void CalcObjnessLoss(T* loss, const T* input, const T* objness,
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
T
obj
=
objness
[
k
*
w
+
l
];
if
(
obj
>
1e-5
)
{
// positive sample: obj =
mixup score
loss
[
i
]
+=
S
CE
<
T
>
(
input
[
k
*
w
+
l
],
1.0
);
// positive sample: obj =
1
loss
[
i
]
+=
S
igmoidCrossEntropy
<
T
>
(
input
[
k
*
w
+
l
],
1.0
);
}
else
if
(
obj
>
-
0.5
)
{
// negetive sample: obj = 0
loss
[
i
]
+=
S
CE
<
T
>
(
input
[
k
*
w
+
l
],
0.0
);
loss
[
i
]
+=
S
igmoidCrossEntropy
<
T
>
(
input
[
k
*
w
+
l
],
0.0
);
}
}
}
...
...
@@ -213,9 +214,11 @@ static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
for
(
int
l
=
0
;
l
<
w
;
l
++
)
{
T
obj
=
objness
[
k
*
w
+
l
];
if
(
obj
>
1e-5
)
{
input_grad
[
k
*
w
+
l
]
=
SCEGrad
<
T
>
(
input
[
k
*
w
+
l
],
1.0
)
*
loss
[
i
];
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
]
=
SCEGrad
<
T
>
(
input
[
k
*
w
+
l
],
0.0
)
*
loss
[
i
];
input_grad
[
k
*
w
+
l
]
=
SigmoidCrossEntropyGrad
<
T
>
(
input
[
k
*
w
+
l
],
0.0
)
*
loss
[
i
];
}
}
}
...
...
@@ -256,7 +259,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
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
=
ctx
.
Attr
<
int
>
(
"downsample
"
);
int
downsample
_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio
"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
h
=
input
->
dims
()[
2
];
...
...
@@ -264,7 +267,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
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
*
h
;
int
input_size
=
downsample
_ratio
*
h
;
const
int
stride
=
h
*
w
;
const
int
an_stride
=
(
class_num
+
5
)
*
stride
;
...
...
@@ -308,7 +311,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
}
}
// If best IoU is
great
er then ignore_thresh,
// If best IoU is
bigg
er then ignore_thresh,
// ignore the objectness loss.
if
(
best_iou
>
ignore_thresh
)
{
int
obj_idx
=
(
i
*
mask_num
+
j
)
*
stride
+
k
*
w
+
l
;
...
...
@@ -388,7 +391,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
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
=
ctx
.
Attr
<
int
>
(
"downsample
"
);
int
downsample
_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio
"
);
const
int
n
=
input_grad
->
dims
()[
0
];
const
int
c
=
input_grad
->
dims
()[
1
];
...
...
@@ -396,7 +399,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
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
*
h
;
int
input_size
=
downsample
_ratio
*
h
;
const
int
stride
=
h
*
w
;
const
int
an_stride
=
(
class_num
+
5
)
*
stride
;
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
733bb82e
...
...
@@ -416,7 +416,7 @@ def yolov3_loss(x,
anchor_mask
,
class_num
,
ignore_thresh
,
downsample
,
downsample
_ratio
,
name
=
None
):
"""
${comment}
...
...
@@ -434,7 +434,7 @@ def yolov3_loss(x,
anchor_mask (list|tuple): ${anchor_mask_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
downsample
(int): ${downsample
_comment}
downsample
_ratio (int): ${downsample_ratio
_comment}
name (string): the name of yolov3 loss
Returns:
...
...
@@ -456,8 +456,8 @@ def yolov3_loss(x,
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
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
)
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
())
...
...
@@ -491,7 +491,7 @@ def yolov3_loss(x,
"anchor_mask"
:
anchor_mask
,
"class_num"
:
class_num
,
"ignore_thresh"
:
ignore_thresh
,
"downsample
"
:
downsample
,
"downsample
_ratio"
:
downsample_ratio
,
}
helper
.
append_op
(
...
...
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