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Commits
f1c63e27
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f1c63e27
authored
9月 13, 2020
作者:
Glenn Jocher
浏览文件
操作
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下载
电子邮件补丁
差异文件
add mosaic and warmup to hyperparameters (#931)
上级
806e75f2
隐藏空白字符变更
内嵌
并排
正在显示
4 个修改的文件
包含
22 行增加
和
9 行删除
+22
-9
hyp.finetune.yaml
data/hyp.finetune.yaml
+4
-0
hyp.scratch.yaml
data/hyp.scratch.yaml
+4
-0
train.py
train.py
+11
-7
datasets.py
utils/datasets.py
+3
-2
没有找到文件。
data/hyp.finetune.yaml
浏览文件 @
f1c63e27
...
@@ -12,6 +12,9 @@ lr0: 0.0032
...
@@ -12,6 +12,9 @@ lr0: 0.0032
lrf
:
0.12
lrf
:
0.12
momentum
:
0.843
momentum
:
0.843
weight_decay
:
0.00036
weight_decay
:
0.00036
warmup_epochs
:
2.0
warmup_momentum
:
0.5
warmup_bias_lr
:
0.05
giou
:
0.0296
giou
:
0.0296
cls
:
0.243
cls
:
0.243
cls_pw
:
0.631
cls_pw
:
0.631
...
@@ -31,4 +34,5 @@ shear: 0.602
...
@@ -31,4 +34,5 @@ shear: 0.602
perspective
:
0.0
perspective
:
0.0
flipud
:
0.00856
flipud
:
0.00856
fliplr
:
0.5
fliplr
:
0.5
mosaic
:
1.0
mixup
:
0.243
mixup
:
0.243
data/hyp.scratch.yaml
浏览文件 @
f1c63e27
...
@@ -7,6 +7,9 @@ lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
...
@@ -7,6 +7,9 @@ lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf
:
0.2
# final OneCycleLR learning rate (lr0 * lrf)
lrf
:
0.2
# final OneCycleLR learning rate (lr0 * lrf)
momentum
:
0.937
# SGD momentum/Adam beta1
momentum
:
0.937
# SGD momentum/Adam beta1
weight_decay
:
0.0005
# optimizer weight decay 5e-4
weight_decay
:
0.0005
# optimizer weight decay 5e-4
warmup_epochs
:
3.0
# warmup epochs (fractions ok)
warmup_momentum
:
0.8
# warmup initial momentum
warmup_bias_lr
:
0.1
# warmup initial bias lr
giou
:
0.05
# box loss gain
giou
:
0.05
# box loss gain
cls
:
0.5
# cls loss gain
cls
:
0.5
# cls loss gain
cls_pw
:
1.0
# cls BCELoss positive_weight
cls_pw
:
1.0
# cls BCELoss positive_weight
...
@@ -26,4 +29,5 @@ shear: 0.0 # image shear (+/- deg)
...
@@ -26,4 +29,5 @@ shear: 0.0 # image shear (+/- deg)
perspective
:
0.0
# image perspective (+/- fraction), range 0-0.001
perspective
:
0.0
# image perspective (+/- fraction), range 0-0.001
flipud
:
0.0
# image flip up-down (probability)
flipud
:
0.0
# image flip up-down (probability)
fliplr
:
0.5
# image flip left-right (probability)
fliplr
:
0.5
# image flip left-right (probability)
mosaic
:
1.0
# image mosaic (probability)
mixup
:
0.0
# image mixup (probability)
mixup
:
0.0
# image mixup (probability)
train.py
浏览文件 @
f1c63e27
...
@@ -202,7 +202,7 @@ def train(hyp, opt, device, tb_writer=None):
...
@@ -202,7 +202,7 @@ def train(hyp, opt, device, tb_writer=None):
# Start training
# Start training
t0
=
time
.
time
()
t0
=
time
.
time
()
nw
=
max
(
3
*
nb
,
1e3
)
# number of warmup iterations, max(3 epochs, 1k iterations)
nw
=
max
(
round
(
hyp
[
'warmup_epochs'
]
*
nb
)
,
1e3
)
# number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps
=
np
.
zeros
(
nc
)
# mAP per class
maps
=
np
.
zeros
(
nc
)
# mAP per class
results
=
(
0
,
0
,
0
,
0
,
0
,
0
,
0
)
# 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
results
=
(
0
,
0
,
0
,
0
,
0
,
0
,
0
)
# 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
...
@@ -250,9 +250,9 @@ def train(hyp, opt, device, tb_writer=None):
...
@@ -250,9 +250,9 @@ def train(hyp, opt, device, tb_writer=None):
accumulate
=
max
(
1
,
np
.
interp
(
ni
,
xi
,
[
1
,
nbs
/
total_batch_size
])
.
round
())
accumulate
=
max
(
1
,
np
.
interp
(
ni
,
xi
,
[
1
,
nbs
/
total_batch_size
])
.
round
())
for
j
,
x
in
enumerate
(
optimizer
.
param_groups
):
for
j
,
x
in
enumerate
(
optimizer
.
param_groups
):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x
[
'lr'
]
=
np
.
interp
(
ni
,
xi
,
[
0.1
if
j
==
2
else
0.0
,
x
[
'initial_lr'
]
*
lf
(
epoch
)])
x
[
'lr'
]
=
np
.
interp
(
ni
,
xi
,
[
hyp
[
'warmup_bias_lr'
]
if
j
==
2
else
0.0
,
x
[
'initial_lr'
]
*
lf
(
epoch
)])
if
'momentum'
in
x
:
if
'momentum'
in
x
:
x
[
'momentum'
]
=
np
.
interp
(
ni
,
xi
,
[
0.9
,
hyp
[
'momentum'
]])
x
[
'momentum'
]
=
np
.
interp
(
ni
,
xi
,
[
hyp
[
'warmup_momentum'
]
,
hyp
[
'momentum'
]])
# Multi-scale
# Multi-scale
if
opt
.
multi_scale
:
if
opt
.
multi_scale
:
...
@@ -460,8 +460,11 @@ if __name__ == '__main__':
...
@@ -460,8 +460,11 @@ if __name__ == '__main__':
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
meta
=
{
'lr0'
:
(
1
,
1e-5
,
1e-1
),
# initial learning rate (SGD=1E-2, Adam=1E-3)
meta
=
{
'lr0'
:
(
1
,
1e-5
,
1e-1
),
# initial learning rate (SGD=1E-2, Adam=1E-3)
'lrf'
:
(
1
,
0.01
,
1.0
),
# final OneCycleLR learning rate (lr0 * lrf)
'lrf'
:
(
1
,
0.01
,
1.0
),
# final OneCycleLR learning rate (lr0 * lrf)
'momentum'
:
(
0.
1
,
0.6
,
0.98
),
# SGD momentum/Adam beta1
'momentum'
:
(
0.
3
,
0.6
,
0.98
),
# SGD momentum/Adam beta1
'weight_decay'
:
(
1
,
0.0
,
0.001
),
# optimizer weight decay
'weight_decay'
:
(
1
,
0.0
,
0.001
),
# optimizer weight decay
'warmup_epochs'
:
(
1
,
0.0
,
5.0
),
# warmup epochs (fractions ok)
'warmup_momentum'
:
(
1
,
0.0
,
0.95
),
# warmup initial momentum
'warmup_bias_lr'
:
(
1
,
0.0
,
0.2
),
# warmup initial bias lr
'giou'
:
(
1
,
0.02
,
0.2
),
# GIoU loss gain
'giou'
:
(
1
,
0.02
,
0.2
),
# GIoU loss gain
'cls'
:
(
1
,
0.2
,
4.0
),
# cls loss gain
'cls'
:
(
1
,
0.2
,
4.0
),
# cls loss gain
'cls_pw'
:
(
1
,
0.5
,
2.0
),
# cls BCELoss positive_weight
'cls_pw'
:
(
1
,
0.5
,
2.0
),
# cls BCELoss positive_weight
...
@@ -469,7 +472,7 @@ if __name__ == '__main__':
...
@@ -469,7 +472,7 @@ if __name__ == '__main__':
'obj_pw'
:
(
1
,
0.5
,
2.0
),
# obj BCELoss positive_weight
'obj_pw'
:
(
1
,
0.5
,
2.0
),
# obj BCELoss positive_weight
'iou_t'
:
(
0
,
0.1
,
0.7
),
# IoU training threshold
'iou_t'
:
(
0
,
0.1
,
0.7
),
# IoU training threshold
'anchor_t'
:
(
1
,
2.0
,
8.0
),
# anchor-multiple threshold
'anchor_t'
:
(
1
,
2.0
,
8.0
),
# anchor-multiple threshold
'anchors'
:
(
1
,
2.0
,
10.0
),
# anchors per output grid (0 to ignore)
'anchors'
:
(
2
,
2.0
,
10.0
),
# anchors per output grid (0 to ignore)
'fl_gamma'
:
(
0
,
0.0
,
2.0
),
# focal loss gamma (efficientDet default gamma=1.5)
'fl_gamma'
:
(
0
,
0.0
,
2.0
),
# focal loss gamma (efficientDet default gamma=1.5)
'hsv_h'
:
(
1
,
0.0
,
0.1
),
# image HSV-Hue augmentation (fraction)
'hsv_h'
:
(
1
,
0.0
,
0.1
),
# image HSV-Hue augmentation (fraction)
'hsv_s'
:
(
1
,
0.0
,
0.9
),
# image HSV-Saturation augmentation (fraction)
'hsv_s'
:
(
1
,
0.0
,
0.9
),
# image HSV-Saturation augmentation (fraction)
...
@@ -481,6 +484,7 @@ if __name__ == '__main__':
...
@@ -481,6 +484,7 @@ if __name__ == '__main__':
'perspective'
:
(
0
,
0.0
,
0.001
),
# image perspective (+/- fraction), range 0-0.001
'perspective'
:
(
0
,
0.0
,
0.001
),
# image perspective (+/- fraction), range 0-0.001
'flipud'
:
(
1
,
0.0
,
1.0
),
# image flip up-down (probability)
'flipud'
:
(
1
,
0.0
,
1.0
),
# image flip up-down (probability)
'fliplr'
:
(
0
,
0.0
,
1.0
),
# image flip left-right (probability)
'fliplr'
:
(
0
,
0.0
,
1.0
),
# image flip left-right (probability)
'mosaic'
:
(
1
,
0.0
,
1.0
),
# image mixup (probability)
'mixup'
:
(
1
,
0.0
,
1.0
)}
# image mixup (probability)
'mixup'
:
(
1
,
0.0
,
1.0
)}
# image mixup (probability)
assert
opt
.
local_rank
==
-
1
,
'DDP mode not implemented for --evolve'
assert
opt
.
local_rank
==
-
1
,
'DDP mode not implemented for --evolve'
...
@@ -490,7 +494,7 @@ if __name__ == '__main__':
...
@@ -490,7 +494,7 @@ if __name__ == '__main__':
if
opt
.
bucket
:
if
opt
.
bucket
:
os
.
system
(
'gsutil cp gs://
%
s/evolve.txt .'
%
opt
.
bucket
)
# download evolve.txt if exists
os
.
system
(
'gsutil cp gs://
%
s/evolve.txt .'
%
opt
.
bucket
)
# download evolve.txt if exists
for
_
in
range
(
1
):
# generations to evolve
for
_
in
range
(
300
):
# generations to evolve
if
os
.
path
.
exists
(
'evolve.txt'
):
# if evolve.txt exists: select best hyps and mutate
if
os
.
path
.
exists
(
'evolve.txt'
):
# if evolve.txt exists: select best hyps and mutate
# Select parent(s)
# Select parent(s)
parent
=
'single'
# parent selection method: 'single' or 'weighted'
parent
=
'single'
# parent selection method: 'single' or 'weighted'
...
@@ -505,7 +509,7 @@ if __name__ == '__main__':
...
@@ -505,7 +509,7 @@ if __name__ == '__main__':
x
=
(
x
*
w
.
reshape
(
n
,
1
))
.
sum
(
0
)
/
w
.
sum
()
# weighted combination
x
=
(
x
*
w
.
reshape
(
n
,
1
))
.
sum
(
0
)
/
w
.
sum
()
# weighted combination
# Mutate
# Mutate
mp
,
s
=
0.
9
,
0.2
# mutation probability, sigma
mp
,
s
=
0.
8
,
0.2
# mutation probability, sigma
npr
=
np
.
random
npr
=
np
.
random
npr
.
seed
(
int
(
time
.
time
()))
npr
.
seed
(
int
(
time
.
time
()))
g
=
np
.
array
([
x
[
0
]
for
x
in
meta
.
values
()])
# gains 0-1
g
=
np
.
array
([
x
[
0
]
for
x
in
meta
.
values
()])
# gains 0-1
...
...
utils/datasets.py
浏览文件 @
f1c63e27
...
@@ -516,7 +516,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
...
@@ -516,7 +516,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
index
=
self
.
indices
[
index
]
index
=
self
.
indices
[
index
]
hyp
=
self
.
hyp
hyp
=
self
.
hyp
if
self
.
mosaic
:
mosaic
=
self
.
mosaic
and
random
.
random
()
<
hyp
[
'mosaic'
]
if
mosaic
:
# Load mosaic
# Load mosaic
img
,
labels
=
load_mosaic
(
self
,
index
)
img
,
labels
=
load_mosaic
(
self
,
index
)
shapes
=
None
shapes
=
None
...
@@ -550,7 +551,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
...
@@ -550,7 +551,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if
self
.
augment
:
if
self
.
augment
:
# Augment imagespace
# Augment imagespace
if
not
self
.
mosaic
:
if
not
mosaic
:
img
,
labels
=
random_perspective
(
img
,
labels
,
img
,
labels
=
random_perspective
(
img
,
labels
,
degrees
=
hyp
[
'degrees'
],
degrees
=
hyp
[
'degrees'
],
translate
=
hyp
[
'translate'
],
translate
=
hyp
[
'translate'
],
...
...
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