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
s920243400
PaddleDetection
提交
0d1a9996
P
PaddleDetection
项目概览
s920243400
/
PaddleDetection
与 Fork 源项目一致
Fork自
PaddlePaddle / PaddleDetection
通知
2
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
PaddleDetection
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
提交
0d1a9996
编写于
3月 05, 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
fix unittest for yolov3_loss. test=develop
上级
f0804433
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
86 addition
and
36 deletion
+86
-36
paddle/fluid/operators/detection/yolov3_loss_op.cc
paddle/fluid/operators/detection/yolov3_loss_op.cc
+9
-0
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+29
-10
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+10
-2
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
+38
-24
未找到文件。
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @
0d1a9996
...
@@ -223,6 +223,15 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -223,6 +223,15 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
loss = (loss_{xy} + loss_{wh}) * weight_{box}
loss = (loss_{xy} + loss_{wh}) * weight_{box}
+ loss_{conf} + loss_{class}
+ loss_{conf} + loss_{class}
$$
$$
While :attr:`use_label_smooth` is set to be :attr:`True`, the classification
target will be smoothed when calculating classification loss, target of
positive samples will be smoothed to $$1.0 - 1.0/class_num$$ and target of
negetive samples will be smoothed to $$1.0/class_num$$.
While :attr:`GTScore` is given, which means the mixup score of ground truth
boxes, all looses incured by a ground truth box will be multiplied by its
mixup score.
)DOC"
);
)DOC"
);
}
}
};
};
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
0d1a9996
...
@@ -515,7 +515,9 @@ def yolov3_loss(x,
...
@@ -515,7 +515,9 @@ def yolov3_loss(x,
class_num
,
class_num
,
ignore_thresh
,
ignore_thresh
,
downsample_ratio
,
downsample_ratio
,
name
=
None
):
name
=
None
,
gtscore
=
None
,
use_label_smooth
=
True
):
"""
"""
${comment}
${comment}
...
@@ -534,27 +536,34 @@ def yolov3_loss(x,
...
@@ -534,27 +536,34 @@ def yolov3_loss(x,
ignore_thresh (float): ${ignore_thresh_comment}
ignore_thresh (float): ${ignore_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
downsample_ratio (int): ${downsample_ratio_comment}
name (string): the name of yolov3 loss
name (string): the name of yolov3 loss
gtscore (Variable): mixup score of ground truth boxes, shoud be in shape
of [N, B].
use_label_smooth (bool): ${use_label_smooth_comment}
Returns:
Returns:
Variable: A 1-D tensor with shape [
1
], the value of yolov3 loss
Variable: A 1-D tensor with shape [
N
], the value of yolov3 loss
Raises:
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox 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: Input gtlabel of yolov3_loss must be Variable"
TypeError: Input gtscore of yolov3_loss must be Variable"
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
TypeError: Attr use_label_smooth of yolov3_loss must be a bool value
Examples:
Examples:
.. code-block:: python
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], 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')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
gtscore = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask = [0, 1, 2]
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel, anchors=anchors,
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel,
gtscore=gtscore, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
ignore_thresh=0.7, downsample_ratio=32)
"""
"""
...
@@ -566,6 +575,8 @@ def yolov3_loss(x,
...
@@ -566,6 +575,8 @@ def yolov3_loss(x,
raise
TypeError
(
"Input gtbox of yolov3_loss must be Variable"
)
raise
TypeError
(
"Input gtbox of yolov3_loss must be Variable"
)
if
not
isinstance
(
gtlabel
,
Variable
):
if
not
isinstance
(
gtlabel
,
Variable
):
raise
TypeError
(
"Input gtlabel of yolov3_loss must be Variable"
)
raise
TypeError
(
"Input gtlabel of yolov3_loss must be Variable"
)
if
not
isinstance
(
gtscore
,
Variable
):
raise
TypeError
(
"Input gtscore of yolov3_loss must be Variable"
)
if
not
isinstance
(
anchors
,
list
)
and
not
isinstance
(
anchors
,
tuple
):
if
not
isinstance
(
anchors
,
list
)
and
not
isinstance
(
anchors
,
tuple
):
raise
TypeError
(
"Attr anchors of yolov3_loss must be list or 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
):
if
not
isinstance
(
anchor_mask
,
list
)
and
not
isinstance
(
anchor_mask
,
tuple
):
...
@@ -575,6 +586,9 @@ def yolov3_loss(x,
...
@@ -575,6 +586,9 @@ def yolov3_loss(x,
if
not
isinstance
(
ignore_thresh
,
float
):
if
not
isinstance
(
ignore_thresh
,
float
):
raise
TypeError
(
raise
TypeError
(
"Attr ignore_thresh of yolov3_loss must be a float number"
)
"Attr ignore_thresh of yolov3_loss must be a float number"
)
if
not
isinstance
(
use_label_smooth
,
bool
):
raise
TypeError
(
"Attr use_label_smooth of yolov3_loss must be a bool value"
)
if
name
is
None
:
if
name
is
None
:
loss
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
loss
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
...
@@ -585,21 +599,26 @@ def yolov3_loss(x,
...
@@ -585,21 +599,26 @@ def yolov3_loss(x,
objectness_mask
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
objectness_mask
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
gt_match_mask
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
gt_match_mask
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
inputs
=
{
"X"
:
x
,
"GTBox"
:
gtbox
,
"GTLabel"
:
gtlabel
,
}
if
gtscore
:
inputs
[
"GTScore"
]
=
gtscore
attrs
=
{
attrs
=
{
"anchors"
:
anchors
,
"anchors"
:
anchors
,
"anchor_mask"
:
anchor_mask
,
"anchor_mask"
:
anchor_mask
,
"class_num"
:
class_num
,
"class_num"
:
class_num
,
"ignore_thresh"
:
ignore_thresh
,
"ignore_thresh"
:
ignore_thresh
,
"downsample_ratio"
:
downsample_ratio
,
"downsample_ratio"
:
downsample_ratio
,
"use_label_smooth"
:
use_label_smooth
,
}
}
helper
.
append_op
(
helper
.
append_op
(
type
=
'yolov3_loss'
,
type
=
'yolov3_loss'
,
inputs
=
{
inputs
=
inputs
,
"X"
:
x
,
"GTBox"
:
gtbox
,
"GTLabel"
:
gtlabel
,
},
outputs
=
{
outputs
=
{
'Loss'
:
loss
,
'Loss'
:
loss
,
'ObjectnessMask'
:
objectness_mask
,
'ObjectnessMask'
:
objectness_mask
,
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
0d1a9996
...
@@ -476,8 +476,16 @@ class TestYoloDetection(unittest.TestCase):
...
@@ -476,8 +476,16 @@ class TestYoloDetection(unittest.TestCase):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
30
,
7
,
7
],
dtype
=
'float32'
)
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
30
,
7
,
7
],
dtype
=
'float32'
)
gtbox
=
layers
.
data
(
name
=
'gtbox'
,
shape
=
[
10
,
4
],
dtype
=
'float32'
)
gtbox
=
layers
.
data
(
name
=
'gtbox'
,
shape
=
[
10
,
4
],
dtype
=
'float32'
)
gtlabel
=
layers
.
data
(
name
=
'gtlabel'
,
shape
=
[
10
],
dtype
=
'int32'
)
gtlabel
=
layers
.
data
(
name
=
'gtlabel'
,
shape
=
[
10
],
dtype
=
'int32'
)
loss
=
layers
.
yolov3_loss
(
x
,
gtbox
,
gtlabel
,
[
10
,
13
,
30
,
13
],
gtscore
=
layers
.
data
(
name
=
'gtscore'
,
shape
=
[
10
],
dtype
=
'int32'
)
[
0
,
1
],
10
,
0.7
,
32
)
loss
=
layers
.
yolov3_loss
(
x
,
gtbox
,
gtlabel
,
[
10
,
13
,
30
,
13
],
[
0
,
1
],
10
,
0.7
,
32
,
gtscore
=
gtscore
,
use_label_smooth
=
False
)
self
.
assertIsNotNone
(
loss
)
self
.
assertIsNotNone
(
loss
)
...
...
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
浏览文件 @
0d1a9996
...
@@ -23,8 +23,8 @@ from op_test import OpTest
...
@@ -23,8 +23,8 @@ from op_test import OpTest
from
paddle.fluid
import
core
from
paddle.fluid
import
core
def
l
2
loss
(
x
,
y
):
def
l
1
loss
(
x
,
y
):
return
0.5
*
(
y
-
x
)
*
(
y
-
x
)
return
abs
(
x
-
y
)
def
sce
(
x
,
label
):
def
sce
(
x
,
label
):
...
@@ -66,7 +66,7 @@ def batch_xywh_box_iou(box1, box2):
...
@@ -66,7 +66,7 @@ def batch_xywh_box_iou(box1, box2):
return
inter_area
/
union
return
inter_area
/
union
def
YOLOv3Loss
(
x
,
gtbox
,
gtlabel
,
attrs
):
def
YOLOv3Loss
(
x
,
gtbox
,
gtlabel
,
gtscore
,
attrs
):
n
,
c
,
h
,
w
=
x
.
shape
n
,
c
,
h
,
w
=
x
.
shape
b
=
gtbox
.
shape
[
1
]
b
=
gtbox
.
shape
[
1
]
anchors
=
attrs
[
'anchors'
]
anchors
=
attrs
[
'anchors'
]
...
@@ -75,21 +75,21 @@ def YOLOv3Loss(x, gtbox, gtlabel, attrs):
...
@@ -75,21 +75,21 @@ def YOLOv3Loss(x, gtbox, gtlabel, attrs):
mask_num
=
len
(
anchor_mask
)
mask_num
=
len
(
anchor_mask
)
class_num
=
attrs
[
"class_num"
]
class_num
=
attrs
[
"class_num"
]
ignore_thresh
=
attrs
[
'ignore_thresh'
]
ignore_thresh
=
attrs
[
'ignore_thresh'
]
downsample
=
attrs
[
'downsample'
]
downsample_ratio
=
attrs
[
'downsample_ratio'
]
input_size
=
downsample
*
h
use_label_smooth
=
attrs
[
'use_label_smooth'
]
input_size
=
downsample_ratio
*
h
x
=
x
.
reshape
((
n
,
mask_num
,
5
+
class_num
,
h
,
w
)).
transpose
((
0
,
1
,
3
,
4
,
2
))
x
=
x
.
reshape
((
n
,
mask_num
,
5
+
class_num
,
h
,
w
)).
transpose
((
0
,
1
,
3
,
4
,
2
))
loss
=
np
.
zeros
((
n
)).
astype
(
'float32'
)
loss
=
np
.
zeros
((
n
)).
astype
(
'float32'
)
label_pos
=
1.0
-
1.0
/
class_num
if
use_label_smooth
else
1.0
label_neg
=
1.0
/
class_num
if
use_label_smooth
else
0.0
pred_box
=
x
[:,
:,
:,
:,
:
4
].
copy
()
pred_box
=
x
[:,
:,
:,
:,
:
4
].
copy
()
grid_x
=
np
.
tile
(
np
.
arange
(
w
).
reshape
((
1
,
w
)),
(
h
,
1
))
grid_x
=
np
.
tile
(
np
.
arange
(
w
).
reshape
((
1
,
w
)),
(
h
,
1
))
grid_y
=
np
.
tile
(
np
.
arange
(
h
).
reshape
((
h
,
1
)),
(
1
,
w
))
grid_y
=
np
.
tile
(
np
.
arange
(
h
).
reshape
((
h
,
1
)),
(
1
,
w
))
pred_box
[:,
:,
:,
:,
0
]
=
(
grid_x
+
sigmoid
(
pred_box
[:,
:,
:,
:,
0
]))
/
w
pred_box
[:,
:,
:,
:,
0
]
=
(
grid_x
+
sigmoid
(
pred_box
[:,
:,
:,
:,
0
]))
/
w
pred_box
[:,
:,
:,
:,
1
]
=
(
grid_y
+
sigmoid
(
pred_box
[:,
:,
:,
:,
1
]))
/
h
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
=
[]
mask_anchors
=
[]
for
m
in
anchor_mask
:
for
m
in
anchor_mask
:
mask_anchors
.
append
((
anchors
[
2
*
m
],
anchors
[
2
*
m
+
1
]))
mask_anchors
.
append
((
anchors
[
2
*
m
],
anchors
[
2
*
m
+
1
]))
...
@@ -138,21 +138,22 @@ def YOLOv3Loss(x, gtbox, gtlabel, attrs):
...
@@ -138,21 +138,22 @@ def YOLOv3Loss(x, gtbox, gtlabel, attrs):
ty
=
gtbox
[
i
,
j
,
1
]
*
w
-
gj
ty
=
gtbox
[
i
,
j
,
1
]
*
w
-
gj
tw
=
np
.
log
(
gtbox
[
i
,
j
,
2
]
*
input_size
/
mask_anchors
[
an_idx
][
0
])
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
])
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
])
scale
=
(
2.0
-
gtbox
[
i
,
j
,
2
]
*
gtbox
[
i
,
j
,
3
])
*
gtscore
[
i
,
j
]
loss
[
i
]
+=
sce
(
x
[
i
,
an_idx
,
gj
,
gi
,
0
],
tx
)
*
scale
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
]
+=
sce
(
x
[
i
,
an_idx
,
gj
,
gi
,
1
],
ty
)
*
scale
loss
[
i
]
+=
l
2
loss
(
x
[
i
,
an_idx
,
gj
,
gi
,
2
],
tw
)
*
scale
loss
[
i
]
+=
l
1
loss
(
x
[
i
,
an_idx
,
gj
,
gi
,
2
],
tw
)
*
scale
loss
[
i
]
+=
l
2
loss
(
x
[
i
,
an_idx
,
gj
,
gi
,
3
],
th
)
*
scale
loss
[
i
]
+=
l
1
loss
(
x
[
i
,
an_idx
,
gj
,
gi
,
3
],
th
)
*
scale
objness
[
i
,
an_idx
*
h
*
w
+
gj
*
w
+
gi
]
=
1.0
objness
[
i
,
an_idx
*
h
*
w
+
gj
*
w
+
gi
]
=
gtscore
[
i
,
j
]
for
label_idx
in
range
(
class_num
):
for
label_idx
in
range
(
class_num
):
loss
[
i
]
+=
sce
(
x
[
i
,
an_idx
,
gj
,
gi
,
5
+
label_idx
],
loss
[
i
]
+=
sce
(
x
[
i
,
an_idx
,
gj
,
gi
,
5
+
label_idx
],
label_pos
float
(
label_idx
==
gtlabel
[
i
,
j
]))
if
label_idx
==
gtlabel
[
i
,
j
]
else
label_neg
)
*
gtscore
[
i
,
j
]
for
j
in
range
(
mask_num
*
h
*
w
):
for
j
in
range
(
mask_num
*
h
*
w
):
if
objness
[
i
,
j
]
>
0
:
if
objness
[
i
,
j
]
>
0
:
loss
[
i
]
+=
sce
(
pred_obj
[
i
,
j
],
1.0
)
loss
[
i
]
+=
sce
(
pred_obj
[
i
,
j
],
1.0
)
*
objness
[
i
,
j
]
elif
objness
[
i
,
j
]
==
0
:
elif
objness
[
i
,
j
]
==
0
:
loss
[
i
]
+=
sce
(
pred_obj
[
i
,
j
],
0.0
)
loss
[
i
]
+=
sce
(
pred_obj
[
i
,
j
],
0.0
)
...
@@ -167,6 +168,7 @@ class TestYolov3LossOp(OpTest):
...
@@ -167,6 +168,7 @@ class TestYolov3LossOp(OpTest):
x
=
logit
(
np
.
random
.
uniform
(
0
,
1
,
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'
)
gtbox
=
np
.
random
.
random
(
size
=
self
.
gtbox_shape
).
astype
(
'float32'
)
gtlabel
=
np
.
random
.
randint
(
0
,
self
.
class_num
,
self
.
gtbox_shape
[:
2
])
gtlabel
=
np
.
random
.
randint
(
0
,
self
.
class_num
,
self
.
gtbox_shape
[:
2
])
gtscore
=
np
.
random
.
random
(
self
.
gtbox_shape
[:
2
]).
astype
(
'float32'
)
gtmask
=
np
.
random
.
randint
(
0
,
2
,
self
.
gtbox_shape
[:
2
])
gtmask
=
np
.
random
.
randint
(
0
,
2
,
self
.
gtbox_shape
[:
2
])
gtbox
=
gtbox
*
gtmask
[:,
:,
np
.
newaxis
]
gtbox
=
gtbox
*
gtmask
[:,
:,
np
.
newaxis
]
gtlabel
=
gtlabel
*
gtmask
gtlabel
=
gtlabel
*
gtmask
...
@@ -176,15 +178,18 @@ class TestYolov3LossOp(OpTest):
...
@@ -176,15 +178,18 @@ class TestYolov3LossOp(OpTest):
"anchor_mask"
:
self
.
anchor_mask
,
"anchor_mask"
:
self
.
anchor_mask
,
"class_num"
:
self
.
class_num
,
"class_num"
:
self
.
class_num
,
"ignore_thresh"
:
self
.
ignore_thresh
,
"ignore_thresh"
:
self
.
ignore_thresh
,
"downsample"
:
self
.
downsample
,
"downsample_ratio"
:
self
.
downsample_ratio
,
"use_label_smooth"
:
self
.
use_label_smooth
,
}
}
self
.
inputs
=
{
self
.
inputs
=
{
'X'
:
x
,
'X'
:
x
,
'GTBox'
:
gtbox
.
astype
(
'float32'
),
'GTBox'
:
gtbox
.
astype
(
'float32'
),
'GTLabel'
:
gtlabel
.
astype
(
'int32'
),
'GTLabel'
:
gtlabel
.
astype
(
'int32'
),
'GTScore'
:
gtscore
.
astype
(
'float32'
)
}
}
loss
,
objness
,
gt_matches
=
YOLOv3Loss
(
x
,
gtbox
,
gtlabel
,
self
.
attrs
)
loss
,
objness
,
gt_matches
=
YOLOv3Loss
(
x
,
gtbox
,
gtlabel
,
gtscore
,
self
.
attrs
)
self
.
outputs
=
{
self
.
outputs
=
{
'Loss'
:
loss
,
'Loss'
:
loss
,
'ObjectnessMask'
:
objness
,
'ObjectnessMask'
:
objness
,
...
@@ -193,24 +198,33 @@ class TestYolov3LossOp(OpTest):
...
@@ -193,24 +198,33 @@ class TestYolov3LossOp(OpTest):
def
test_check_output
(
self
):
def
test_check_output
(
self
):
place
=
core
.
CPUPlace
()
place
=
core
.
CPUPlace
()
self
.
check_output_with_place
(
place
,
atol
=
1
e-3
)
self
.
check_output_with_place
(
place
,
atol
=
2
e-3
)
def
test_check_grad_ignore_gtbox
(
self
):
def
test_check_grad_ignore_gtbox
(
self
):
place
=
core
.
CPUPlace
()
place
=
core
.
CPUPlace
()
self
.
check_grad_with_place
(
self
.
check_grad_with_place
(
place
,
[
'X'
],
place
,
[
'X'
],
'Loss'
,
'Loss'
,
no_grad_set
=
set
([
"GTBox"
,
"GTLabel"
]),
no_grad_set
=
set
([
"GTBox"
,
"GTLabel"
,
"GTScore"
]),
max_relative_error
=
0.
3
)
max_relative_error
=
0.
2
)
def
initTestCase
(
self
):
def
initTestCase
(
self
):
self
.
anchors
=
[
10
,
13
,
16
,
30
,
33
,
23
]
self
.
anchors
=
[
self
.
anchor_mask
=
[
1
,
2
]
10
,
13
,
16
,
30
,
33
,
23
,
30
,
61
,
62
,
45
,
59
,
119
,
116
,
90
,
156
,
198
,
373
,
326
]
self
.
anchor_mask
=
[
0
,
1
,
2
]
self
.
class_num
=
5
self
.
class_num
=
5
self
.
ignore_thresh
=
0.5
self
.
ignore_thresh
=
0.5
self
.
downsample
=
32
self
.
downsample
_ratio
=
32
self
.
x_shape
=
(
3
,
len
(
self
.
anchor_mask
)
*
(
5
+
self
.
class_num
),
5
,
5
)
self
.
x_shape
=
(
3
,
len
(
self
.
anchor_mask
)
*
(
5
+
self
.
class_num
),
5
,
5
)
self
.
gtbox_shape
=
(
3
,
5
,
4
)
self
.
gtbox_shape
=
(
3
,
5
,
4
)
self
.
use_label_smooth
=
True
class
TestYolov3LossWithoutLabelSmooth
(
TestYolov3LossOp
):
def
set_label_smooth
(
self
):
self
.
use_label_smooth
=
False
if
__name__
==
"__main__"
:
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