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
Eric.Lee2021
handpose_x
提交
ea8f5763
handpose_x
项目概览
Eric.Lee2021
/
handpose_x
通知
4968
Star
366
Fork
139
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
44
列表
看板
标记
里程碑
合并请求
0
DevOps
流水线
流水线任务
计划
Wiki
2
Wiki
分析
仓库
DevOps
项目成员
Pages
handpose_x
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
44
Issue
44
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
DevOps
DevOps
流水线
流水线任务
计划
分析
分析
仓库分析
DevOps
Wiki
2
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
流水线任务
提交
Issue看板
前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
提交
ea8f5763
编写于
4月 17, 2021
作者:
Eric.Lee2021
🚴🏻
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add adaptive_wing_loss
上级
c5c3dbd3
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
46 addition
and
7 deletion
+46
-7
loss/loss.py
loss/loss.py
+39
-2
train.py
train.py
+7
-5
未找到文件。
loss/loss.py
浏览文件 @
ea8f5763
#-*-coding:utf-8-*-
#-*-coding:utf-8-*-
# date:2019-05-20
# date:2019-05-20
# function: wing loss
# function: wing loss
& adaptive wing loss
import
torch
import
torch
import
torch.nn
as
nn
import
torch.nn
as
nn
import
torch.optim
as
optim
import
torch.optim
as
optim
import
os
import
os
import
math
import
math
def
wing_loss
(
landmarks
,
labels
,
w
=
0.06
,
epsilon
=
0.01
):
'''
wing_loss
'''
# def wing_loss(landmarks, labels, w=0.06, epsilon=0.01):
def
wing_loss
(
landmarks
,
labels
,
w
=
10.
,
epsilon
=
2.
):
"""
"""
Arguments:
Arguments:
landmarks, labels: float tensors with shape [batch_size, landmarks]. landmarks means x1,x2,x3,x4...y1,y2,y3,y4 1-D
landmarks, labels: float tensors with shape [batch_size, landmarks]. landmarks means x1,x2,x3,x4...y1,y2,y3,y4 1-D
...
@@ -34,3 +38,36 @@ def wing_loss(landmarks, labels, w=0.06, epsilon=0.01):
...
@@ -34,3 +38,36 @@ def wing_loss(landmarks, labels, w=0.06, epsilon=0.01):
def
got_total_wing_loss
(
output
,
crop_landmarks
):
def
got_total_wing_loss
(
output
,
crop_landmarks
):
loss
=
wing_loss
(
output
,
crop_landmarks
)
loss
=
wing_loss
(
output
,
crop_landmarks
)
return
loss
return
loss
'''
AdaptiveWingLoss
'''
class
AdaptiveWingLoss
(
nn
.
Module
):
def
__init__
(
self
,
omega
=
14
,
theta
=
0.5
,
epsilon
=
1
,
alpha
=
2.1
):
super
(
AdaptiveWingLoss
,
self
).
__init__
()
self
.
omega
=
omega
self
.
theta
=
theta
self
.
epsilon
=
epsilon
self
.
alpha
=
alpha
def
forward
(
self
,
pred
,
target
):
'''
:param pred: BxNxHxH
:param target: BxNxHxH
:return:
'''
y
=
target
y_hat
=
pred
delta_y
=
(
y
-
y_hat
).
abs
()
delta_y1
=
delta_y
[
delta_y
<
self
.
theta
]
delta_y2
=
delta_y
[
delta_y
>=
self
.
theta
]
y1
=
y
[
delta_y
<
self
.
theta
]
y2
=
y
[
delta_y
>=
self
.
theta
]
loss1
=
self
.
omega
*
torch
.
log
(
1
+
torch
.
pow
(
delta_y1
/
self
.
omega
,
self
.
alpha
-
y1
))
A
=
self
.
omega
*
(
1
/
(
1
+
torch
.
pow
(
self
.
theta
/
self
.
epsilon
,
self
.
alpha
-
y2
)))
*
(
self
.
alpha
-
y2
)
*
(
torch
.
pow
(
self
.
theta
/
self
.
epsilon
,
self
.
alpha
-
y2
-
1
))
*
(
1
/
self
.
epsilon
)
C
=
self
.
theta
*
A
-
self
.
omega
*
torch
.
log
(
1
+
torch
.
pow
(
self
.
theta
/
self
.
epsilon
,
self
.
alpha
-
y2
))
loss2
=
A
*
delta_y2
-
C
return
(
loss1
.
sum
()
+
loss2
.
sum
())
/
(
len
(
loss1
)
+
len
(
loss2
))
train.py
浏览文件 @
ea8f5763
...
@@ -99,8 +99,10 @@ def trainer(ops,f_log):
...
@@ -99,8 +99,10 @@ def trainer(ops,f_log):
print
(
'/**********************************************/'
)
print
(
'/**********************************************/'
)
# 损失函数
# 损失函数
if
ops
.
loss_define
!=
'wing
_loss'
:
if
ops
.
loss_define
==
'mse
_loss'
:
criterion
=
nn
.
MSELoss
(
reduce
=
True
,
reduction
=
'mean'
)
criterion
=
nn
.
MSELoss
(
reduce
=
True
,
reduction
=
'mean'
)
elif
ops
.
loss_define
==
'adaptive_wing_loss'
:
criterion
=
AdaptiveWingLoss
()
step
=
0
step
=
0
idx
=
0
idx
=
0
...
@@ -153,7 +155,7 @@ def trainer(ops,f_log):
...
@@ -153,7 +155,7 @@ def trainer(ops,f_log):
print
(
' %s - %s - epoch [%s/%s] (%s/%s):'
%
(
loc_time
,
ops
.
model
,
epoch
,
ops
.
epochs
,
i
,
int
(
dataset
.
__len__
()
/
ops
.
batch_size
)),
\
print
(
' %s - %s - epoch [%s/%s] (%s/%s):'
%
(
loc_time
,
ops
.
model
,
epoch
,
ops
.
epochs
,
i
,
int
(
dataset
.
__len__
()
/
ops
.
batch_size
)),
\
'Mean Loss : %.6f - Loss: %.6f'
%
(
loss_mean
/
loss_idx
,
loss
.
item
()),
\
'Mean Loss : %.6f - Loss: %.6f'
%
(
loss_mean
/
loss_idx
,
loss
.
item
()),
\
' lr : %.8f'
%
init_lr
,
' bs :'
,
ops
.
batch_size
,
\
' lr : %.8f'
%
init_lr
,
' bs :'
,
ops
.
batch_size
,
\
' img_size: %s x %s'
%
(
ops
.
img_size
[
0
],
ops
.
img_size
[
1
]),
' best_loss: %.6f'
%
best_loss
)
' img_size: %s x %s'
%
(
ops
.
img_size
[
0
],
ops
.
img_size
[
1
]),
' best_loss: %.6f'
%
best_loss
,
" {}"
.
format
(
ops
.
loss_define
)
)
# 计算梯度
# 计算梯度
loss
.
backward
()
loss
.
backward
()
# 优化器对模型参数更新
# 优化器对模型参数更新
...
@@ -162,7 +164,7 @@ def trainer(ops,f_log):
...
@@ -162,7 +164,7 @@ def trainer(ops,f_log):
optimizer
.
zero_grad
()
optimizer
.
zero_grad
()
step
+=
1
step
+=
1
torch
.
save
(
model_
.
state_dict
(),
ops
.
model_exp
+
'{}-size-{}-
model_epoch-{}.pth'
.
format
(
ops
.
model
,
ops
.
img_size
[
0
]
,
epoch
))
torch
.
save
(
model_
.
state_dict
(),
ops
.
model_exp
+
'{}-size-{}-
loss-{}-model_epoch-{}.pth'
.
format
(
ops
.
model
,
ops
.
img_size
[
0
],
ops
.
loss_define
,
epoch
))
except
Exception
as
e
:
except
Exception
as
e
:
print
(
'Exception : '
,
e
)
# 打印异常
print
(
'Exception : '
,
e
)
# 打印异常
...
@@ -192,8 +194,8 @@ if __name__ == "__main__":
...
@@ -192,8 +194,8 @@ if __name__ == "__main__":
help
=
'imageNet_Pretrain'
)
# 初始化学习率
help
=
'imageNet_Pretrain'
)
# 初始化学习率
parser
.
add_argument
(
'--fintune_model'
,
type
=
str
,
default
=
'None'
,
parser
.
add_argument
(
'--fintune_model'
,
type
=
str
,
default
=
'None'
,
help
=
'fintune_model'
)
# fintune model
help
=
'fintune_model'
)
# fintune model
parser
.
add_argument
(
'--loss_define'
,
type
=
str
,
default
=
'
wing
_loss'
,
parser
.
add_argument
(
'--loss_define'
,
type
=
str
,
default
=
'
mse
_loss'
,
help
=
'define_loss'
)
# 损失函数定义
help
=
'define_loss
: wing_loss, mse_loss ,adaptive_wing_loss
'
)
# 损失函数定义
parser
.
add_argument
(
'--init_lr'
,
type
=
float
,
default
=
1e-3
,
parser
.
add_argument
(
'--init_lr'
,
type
=
float
,
default
=
1e-3
,
help
=
'init learning Rate'
)
# 初始化学习率
help
=
'init learning Rate'
)
# 初始化学习率
parser
.
add_argument
(
'--lr_decay'
,
type
=
float
,
default
=
0.1
,
parser
.
add_argument
(
'--lr_decay'
,
type
=
float
,
default
=
0.1
,
...
...
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