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体验新版 GitCode,发现更多精彩内容 >>
提交
577a92d9
编写于
12月 17, 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
use typename DeviceContext. test=develop
上级
0c4acc83
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
103 addition
and
216 deletion
+103
-216
paddle/fluid/operators/yolov3_loss_op.cc
paddle/fluid/operators/yolov3_loss_op.cc
+8
-4
paddle/fluid/operators/yolov3_loss_op.h
paddle/fluid/operators/yolov3_loss_op.h
+92
-209
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
+3
-3
未找到文件。
paddle/fluid/operators/yolov3_loss_op.cc
浏览文件 @
577a92d9
...
...
@@ -204,7 +204,11 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR
(
yolov3_loss
,
ops
::
Yolov3LossOp
,
ops
::
Yolov3LossOpMaker
,
ops
::
Yolov3LossGradMaker
);
REGISTER_OPERATOR
(
yolov3_loss_grad
,
ops
::
Yolov3LossOpGrad
);
REGISTER_OP_CPU_KERNEL
(
yolov3_loss
,
ops
::
Yolov3LossKernel
<
float
>
,
ops
::
Yolov3LossKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
yolov3_loss_grad
,
ops
::
Yolov3LossGradKernel
<
float
>
,
ops
::
Yolov3LossGradKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
yolov3_loss
,
ops
::
Yolov3LossKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
Yolov3LossKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
yolov3_loss_grad
,
ops
::
Yolov3LossGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
Yolov3LossGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/yolov3_loss_op.h
浏览文件 @
577a92d9
...
...
@@ -13,6 +13,7 @@
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -32,183 +33,6 @@ static inline bool isZero(T x) {
return
fabs
(
x
)
<
1e-6
;
}
template
<
typename
T
>
static
inline
void
CalcL1LossWithWeight
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
Tensor
&
weight
,
const
T
loss_weight
,
T
*
loss
)
{
int
n
=
x
.
dims
()[
0
];
int
stride
=
x
.
numel
()
/
n
;
const
T
*
x_data
=
x
.
data
<
T
>
();
const
T
*
y_data
=
y
.
data
<
T
>
();
const
T
*
weight_data
=
weight
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
stride
;
j
++
)
{
loss
[
i
]
+=
fabs
(
y_data
[
j
]
-
x_data
[
j
])
*
weight_data
[
j
]
*
loss_weight
;
}
x_data
+=
stride
;
y_data
+=
stride
;
weight_data
+=
stride
;
}
}
template
<
typename
T
>
static
void
CalcL1LossGradWithWeight
(
const
T
*
loss_grad
,
Tensor
*
grad
,
const
Tensor
&
x
,
const
Tensor
&
y
,
const
Tensor
&
weight
)
{
int
n
=
x
.
dims
()[
0
];
int
stride
=
x
.
numel
()
/
n
;
T
*
grad_data
=
grad
->
data
<
T
>
();
const
T
*
x_data
=
x
.
data
<
T
>
();
const
T
*
y_data
=
y
.
data
<
T
>
();
const
T
*
weight_data
=
weight
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
stride
;
j
++
)
{
grad_data
[
j
]
=
weight_data
[
j
]
*
loss_grad
[
i
];
if
(
x_data
[
j
]
<
y_data
[
j
])
grad_data
[
j
]
*=
-
1.0
;
}
grad_data
+=
stride
;
x_data
+=
stride
;
y_data
+=
stride
;
weight_data
+=
stride
;
}
}
template
<
typename
T
>
static
inline
void
CalcMSEWithWeight
(
const
Tensor
&
x
,
const
Tensor
&
y
,
const
Tensor
&
weight
,
const
T
loss_weight
,
T
*
loss
)
{
int
n
=
x
.
dims
()[
0
];
int
stride
=
x
.
numel
()
/
n
;
const
T
*
x_data
=
x
.
data
<
T
>
();
const
T
*
y_data
=
y
.
data
<
T
>
();
const
T
*
weight_data
=
weight
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
stride
;
j
++
)
{
loss
[
i
]
+=
pow
(
y_data
[
j
]
-
x_data
[
j
],
2
)
*
weight_data
[
j
]
*
loss_weight
;
}
x_data
+=
stride
;
y_data
+=
stride
;
weight_data
+=
stride
;
}
}
template
<
typename
T
>
static
void
CalcMSEGradWithWeight
(
const
T
*
loss_grad
,
Tensor
*
grad
,
const
Tensor
&
x
,
const
Tensor
&
y
,
const
Tensor
&
weight
)
{
int
n
=
x
.
dims
()[
0
];
int
stride
=
x
.
numel
()
/
n
;
T
*
grad_data
=
grad
->
data
<
T
>
();
const
T
*
x_data
=
x
.
data
<
T
>
();
const
T
*
y_data
=
y
.
data
<
T
>
();
const
T
*
weight_data
=
weight
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
stride
;
j
++
)
{
grad_data
[
j
]
=
2.0
*
weight_data
[
j
]
*
(
x_data
[
j
]
-
y_data
[
j
])
*
loss_grad
[
i
];
}
grad_data
+=
stride
;
x_data
+=
stride
;
y_data
+=
stride
;
weight_data
+=
stride
;
}
}
template
<
typename
T
>
static
inline
void
CalcSCEWithWeight
(
const
Tensor
&
x
,
const
Tensor
&
label
,
const
Tensor
&
weight
,
const
T
loss_weight
,
T
*
loss
)
{
int
n
=
x
.
dims
()[
0
];
int
stride
=
x
.
numel
()
/
n
;
const
T
*
x_data
=
x
.
data
<
T
>
();
const
T
*
label_data
=
label
.
data
<
T
>
();
const
T
*
weight_data
=
weight
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
stride
;
j
++
)
{
T
term1
=
(
x_data
[
j
]
>
0
)
?
x_data
[
j
]
:
0
;
T
term2
=
x_data
[
j
]
*
label_data
[
j
];
T
term3
=
std
::
log
(
1.0
+
std
::
exp
(
-
std
::
abs
(
x_data
[
j
])));
loss
[
i
]
+=
(
term1
-
term2
+
term3
)
*
weight_data
[
j
]
*
loss_weight
;
}
x_data
+=
stride
;
label_data
+=
stride
;
weight_data
+=
stride
;
}
}
template
<
typename
T
>
static
inline
void
CalcSCEGradWithWeight
(
const
T
*
loss_grad
,
Tensor
*
grad
,
const
Tensor
&
x
,
const
Tensor
&
label
,
const
Tensor
&
weight
)
{
int
n
=
x
.
dims
()[
0
];
int
stride
=
x
.
numel
()
/
n
;
T
*
grad_data
=
grad
->
data
<
T
>
();
const
T
*
x_data
=
x
.
data
<
T
>
();
const
T
*
label_data
=
label
.
data
<
T
>
();
const
T
*
weight_data
=
weight
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
stride
;
j
++
)
{
grad_data
[
j
]
=
(
1.0
/
(
1.0
+
std
::
exp
(
-
x_data
[
j
]))
-
label_data
[
j
])
*
weight_data
[
j
]
*
loss_grad
[
i
];
}
grad_data
+=
stride
;
x_data
+=
stride
;
label_data
+=
stride
;
weight_data
+=
stride
;
}
}
// template <typename T>
// static void SplitPredResult(const Tensor& input, Tensor* pred_conf,
// Tensor* pred_class, Tensor* pred_x, Tensor*
// pred_y,
// Tensor* pred_w, Tensor* pred_h,
// const int anchor_num, const int class_num) {
// const int n = input.dims()[0];
// const int h = input.dims()[2];
// const int w = input.dims()[3];
// const int box_attr_num = 5 + class_num;
//
// auto input_t = EigenTensor<T, 4>::From(input);
// auto pred_conf_t = EigenTensor<T, 4>::From(*pred_conf);
// auto pred_class_t = EigenTensor<T, 5>::From(*pred_class);
// auto pred_x_t = EigenTensor<T, 4>::From(*pred_x);
// auto pred_y_t = EigenTensor<T, 4>::From(*pred_y);
// auto pred_w_t = EigenTensor<T, 4>::From(*pred_w);
// auto pred_h_t = EigenTensor<T, 4>::From(*pred_h);
//
// for (int i = 0; i < n; i++) {
// for (int an_idx = 0; an_idx < anchor_num; an_idx++) {
// for (int j = 0; j < h; j++) {
// for (int k = 0; k < w; k++) {
// pred_x_t(i, an_idx, j, k) = input_t(i, box_attr_num * an_idx, j,
// k);
// pred_y_t(i, an_idx, j, k) =
// input_t(i, box_attr_num * an_idx + 1, j, k);
// pred_w_t(i, an_idx, j, k) =
// input_t(i, box_attr_num * an_idx + 2, j, k);
// pred_h_t(i, an_idx, j, k) =
// input_t(i, box_attr_num * an_idx + 3, j, k);
//
// pred_conf_t(i, an_idx, j, k) =
// input_t(i, box_attr_num * an_idx + 4, j, k);
//
// for (int c = 0; c < class_num; c++) {
// pred_class_t(i, an_idx, j, k, c) =
// input_t(i, box_attr_num * an_idx + 5 + c, j, k);
// }
// }
// }
// }
// }
// }
template
<
typename
T
>
static
T
CalcBoxIoU
(
std
::
vector
<
T
>
box1
,
std
::
vector
<
T
>
box2
)
{
T
b1_x1
=
box1
[
0
]
-
box1
[
2
]
/
2
;
...
...
@@ -242,30 +66,36 @@ static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
Tensor
*
tconf
,
Tensor
*
tclass
)
{
const
int
n
=
gt_box
.
dims
()[
0
];
const
int
b
=
gt_box
.
dims
()[
1
];
const
int
anchor_num
=
anchors
.
size
()
/
2
;
auto
gt_box_t
=
EigenTensor
<
T
,
3
>::
From
(
gt_box
);
auto
gt_label_t
=
EigenTensor
<
int
,
2
>::
From
(
gt_label
);
auto
conf_mask_t
=
EigenTensor
<
T
,
4
>::
From
(
*
conf_mask
).
setConstant
(
1.0
);
auto
obj_mask_t
=
EigenTensor
<
T
,
4
>::
From
(
*
obj_mask
).
setConstant
(
0.0
);
auto
tx_t
=
EigenTensor
<
T
,
4
>::
From
(
*
tx
).
setConstant
(
0.0
);
auto
ty_t
=
EigenTensor
<
T
,
4
>::
From
(
*
ty
).
setConstant
(
0.0
);
auto
tw_t
=
EigenTensor
<
T
,
4
>::
From
(
*
tw
).
setConstant
(
0.0
);
auto
th_t
=
EigenTensor
<
T
,
4
>::
From
(
*
th
).
setConstant
(
0.0
);
auto
tweight_t
=
EigenTensor
<
T
,
4
>::
From
(
*
tweight
).
setConstant
(
0.0
);
auto
tconf_t
=
EigenTensor
<
T
,
4
>::
From
(
*
tconf
).
setConstant
(
0.0
);
auto
tclass_t
=
EigenTensor
<
T
,
5
>::
From
(
*
tclass
).
setConstant
(
0.0
);
const
int
an_num
=
anchors
.
size
()
/
2
;
const
int
h
=
tclass
->
dims
()[
2
];
const
int
w
=
tclass
->
dims
()[
3
];
const
int
class_num
=
tclass
->
dims
()[
4
];
const
T
*
gt_box_data
=
gt_box
.
data
<
T
>
();
const
int
*
gt_label_data
=
gt_label
.
data
<
int
>
();
T
*
conf_mask_data
=
conf_mask
->
data
<
T
>
();
T
*
obj_mask_data
=
obj_mask
->
data
<
T
>
();
T
*
tx_data
=
tx
->
data
<
T
>
();
T
*
ty_data
=
ty
->
data
<
T
>
();
T
*
tw_data
=
tw
->
data
<
T
>
();
T
*
th_data
=
th
->
data
<
T
>
();
T
*
tweight_data
=
tweight
->
data
<
T
>
();
T
*
tconf_data
=
tconf
->
data
<
T
>
();
T
*
tclass_data
=
tclass
->
data
<
T
>
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
for
(
int
j
=
0
;
j
<
b
;
j
++
)
{
if
(
isZero
<
T
>
(
gt_box_t
(
i
,
j
,
2
))
&&
isZero
<
T
>
(
gt_box_t
(
i
,
j
,
3
)))
{
int
box_idx
=
(
i
*
b
+
j
)
*
4
;
if
(
isZero
<
T
>
(
gt_box_data
[
box_idx
+
2
])
&&
isZero
<
T
>
(
gt_box_data
[
box_idx
+
3
]))
{
continue
;
}
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
)
*
input_size
;
T
gh
=
gt_box_
t
(
i
,
j
,
3
)
*
input_size
;
int
cur_label
=
gt_label_
data
[
i
*
b
+
j
]
;
T
gx
=
gt_box_
data
[
box_idx
]
*
grid_size
;
T
gy
=
gt_box_
data
[
box_idx
+
1
]
*
grid_size
;
T
gw
=
gt_box_
data
[
box_idx
+
2
]
*
input_size
;
T
gh
=
gt_box_
data
[
box_idx
+
3
]
*
input_size
;
int
gi
=
static_cast
<
int
>
(
gx
);
int
gj
=
static_cast
<
int
>
(
gy
);
...
...
@@ -273,7 +103,7 @@ static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
T
iou
;
int
best_an_index
=
-
1
;
std
::
vector
<
T
>
gt_box_shape
({
0
,
0
,
gw
,
gh
});
for
(
int
an_idx
=
0
;
an_idx
<
an
chor
_num
;
an_idx
++
)
{
for
(
int
an_idx
=
0
;
an_idx
<
an_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_shape
,
anchor_shape
);
...
...
@@ -282,19 +112,22 @@ static void PreProcessGTBox(const Tensor& gt_box, const Tensor& gt_label,
best_an_index
=
an_idx
;
}
if
(
iou
>
ignore_thresh
)
{
conf_mask_t
(
i
,
an_idx
,
gj
,
gi
)
=
static_cast
<
T
>
(
0.0
);
int
conf_idx
=
((
i
*
an_num
+
an_idx
)
*
h
+
gj
)
*
w
+
gi
;
conf_mask_data
[
conf_idx
]
=
static_cast
<
T
>
(
0.0
);
}
}
conf_mask_t
(
i
,
best_an_index
,
gj
,
gi
)
=
static_cast
<
T
>
(
1.0
);
obj_mask_t
(
i
,
best_an_index
,
gj
,
gi
)
=
static_cast
<
T
>
(
1.0
);
tx_t
(
i
,
best_an_index
,
gj
,
gi
)
=
gx
-
gi
;
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
]);
tweight_t
(
i
,
best_an_index
,
gj
,
gi
)
=
2.0
-
gt_box_t
(
i
,
j
,
2
)
*
gt_box_t
(
i
,
j
,
3
);
tclass_t
(
i
,
best_an_index
,
gj
,
gi
,
cur_label
)
=
1
;
tconf_t
(
i
,
best_an_index
,
gj
,
gi
)
=
1
;
int
obj_idx
=
((
i
*
an_num
+
best_an_index
)
*
h
+
gj
)
*
w
+
gi
;
conf_mask_data
[
obj_idx
]
=
static_cast
<
T
>
(
1.0
);
obj_mask_data
[
obj_idx
]
=
static_cast
<
T
>
(
1.0
);
tx_data
[
obj_idx
]
=
gx
-
gi
;
ty_data
[
obj_idx
]
=
gy
-
gj
;
tw_data
[
obj_idx
]
=
log
(
gw
/
anchors
[
2
*
best_an_index
]);
th_data
[
obj_idx
]
=
log
(
gh
/
anchors
[
2
*
best_an_index
+
1
]);
tweight_data
[
obj_idx
]
=
2.0
-
gt_box_data
[
box_idx
+
2
]
*
gt_box_data
[
box_idx
+
3
];
tconf_data
[
obj_idx
]
=
static_cast
<
T
>
(
1.0
);
tclass_data
[
obj_idx
*
class_num
+
cur_label
]
=
static_cast
<
T
>
(
1.0
);
}
}
}
...
...
@@ -427,18 +260,26 @@ static void CalcYolov3Loss(T* loss_data, const Tensor& input, const Tensor& tx,
const
int
class_num
=
tclass
.
dims
()[
4
];
const
int
grid_num
=
h
*
w
;
// T l = 0.0;
CalcSCE
<
T
>
(
loss_data
,
input_data
,
tx_data
,
tweight_data
,
obj_mask_data
,
n
,
an_num
,
grid_num
,
class_num
,
1
);
CalcSCE
<
T
>
(
loss_data
,
input_data
+
grid_num
,
ty_data
,
tweight_data
,
obj_mask_data
,
n
,
an_num
,
grid_num
,
class_num
,
1
);
// LOG(ERROR) << "C++ xy: " << loss_data[0] - l;
// l = loss_data[0];
CalcL1Loss
<
T
>
(
loss_data
,
input_data
+
2
*
grid_num
,
tw_data
,
tweight_data
,
obj_mask_data
,
n
,
an_num
,
grid_num
,
class_num
);
CalcL1Loss
<
T
>
(
loss_data
,
input_data
+
3
*
grid_num
,
th_data
,
tweight_data
,
obj_mask_data
,
n
,
an_num
,
grid_num
,
class_num
);
// LOG(ERROR) << "C++ wh: " << loss_data[0] - l;
// l = loss_data[0];
CalcSCE
<
T
>
(
loss_data
,
input_data
+
4
*
grid_num
,
tconf_data
,
conf_mask_data
,
conf_mask_data
,
n
,
an_num
,
grid_num
,
class_num
,
1
);
// LOG(ERROR) << "C++ conf: " << loss_data[0] - l;
// l = loss_data[0];
CalcSCE
<
T
>
(
loss_data
,
input_data
+
5
*
grid_num
,
tclass_data
,
obj_mask_data
,
obj_mask_data
,
n
,
an_num
,
grid_num
,
class_num
,
class_num
);
// LOG(ERROR) << "C++ class: " << loss_data[0] - l;
}
template
<
typename
T
>
...
...
@@ -488,7 +329,7 @@ static void CalcYolov3LossGrad(T* input_grad_data, const Tensor& loss_grad,
obj_mask_data
,
n
,
an_num
,
grid_num
,
class_num
,
class_num
);
}
template
<
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
Yolov3LossKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
...
...
@@ -517,6 +358,27 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
tweight
.
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
());
math
::
SetConstant
<
DeviceContext
,
T
>
constant
;
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
conf_mask
,
static_cast
<
T
>
(
1.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
obj_mask
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tx
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
ty
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tw
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
th
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tweight
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tconf
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tclass
,
static_cast
<
T
>
(
0.0
));
PreProcessGTBox
<
T
>
(
*
gt_box
,
*
gt_label
,
ignore_thresh
,
anchors
,
input_size
,
h
,
&
conf_mask
,
&
obj_mask
,
&
tx
,
&
ty
,
&
tw
,
&
th
,
&
tweight
,
&
tconf
,
&
tclass
);
...
...
@@ -528,7 +390,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
}
};
template
<
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
Yolov3LossGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
...
...
@@ -559,6 +421,27 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
tweight
.
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
());
math
::
SetConstant
<
DeviceContext
,
T
>
constant
;
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
conf_mask
,
static_cast
<
T
>
(
1.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
obj_mask
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tx
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
ty
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tw
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
th
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tweight
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tconf
,
static_cast
<
T
>
(
0.0
));
constant
(
ctx
.
template
device_context
<
DeviceContext
>(),
&
tclass
,
static_cast
<
T
>
(
0.0
));
PreProcessGTBox
<
T
>
(
*
gt_box
,
*
gt_label
,
ignore_thresh
,
anchors
,
input_size
,
h
,
&
conf_mask
,
&
obj_mask
,
&
tx
,
&
ty
,
&
tw
,
&
th
,
&
tweight
,
&
tconf
,
&
tclass
);
...
...
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @
577a92d9
...
...
@@ -197,12 +197,12 @@ class TestYolov3LossOp(OpTest):
max_relative_error
=
0.31
)
def
initTestCase
(
self
):
self
.
anchors
=
[
12
,
12
,
11
,
13
]
self
.
anchors
=
[
12
,
12
]
self
.
class_num
=
5
self
.
ignore_thresh
=
0.5
self
.
input_size
=
416
self
.
x_shape
=
(
3
,
len
(
self
.
anchors
)
//
2
*
(
5
+
self
.
class_num
),
5
,
5
)
self
.
gtbox_shape
=
(
3
,
5
,
4
)
self
.
x_shape
=
(
1
,
len
(
self
.
anchors
)
//
2
*
(
5
+
self
.
class_num
),
3
,
3
)
self
.
gtbox_shape
=
(
1
,
5
,
4
)
if
__name__
==
"__main__"
:
...
...
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