forked from alibaba/EasyCV
-
Notifications
You must be signed in to change notification settings - Fork 0
/
swin_tiny_patch4_window7_224_b64x16_300e_jpg.py
88 lines (81 loc) · 2.3 KB
/
swin_tiny_patch4_window7_224_b64x16_300e_jpg.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
_base_ = '../common/dataset/imagenet_classification.py'
num_classes = 1000
# model settings
model = dict(
type='Classification',
train_preprocess=['mixUp'],
mixup_cfg=dict(
mixup_alpha=0.8,
cutmix_alpha=1.0,
prob=0.5,
mode='batch',
label_smoothing=0.1,
num_classes=num_classes),
backbone=dict(
type='PytorchImageModelWrapper',
model_name='swin_tiny_patch4_window7_224',
num_classes=num_classes,
),
head=dict(
type='ClsHead',
loss_config={
'type': 'SoftTargetCrossEntropy',
},
with_fc=False))
image_size2 = 224
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
dict(type='RandomResizedCrop', size=image_size2),
dict(type='RandomHorizontalFlip'),
dict(
type='MMRandAugment',
num_policies=2,
total_level=10,
magnitude_level=9,
magnitude_std=0.5,
hparams=dict(
pad_val=[round(x * 255) for x in img_norm_cfg['mean'][::-1]],
interpolation='bicubic')),
dict(
type='MMRandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=[x * 255 for x in img_norm_cfg['mean'][::-1]],
fill_std=[x * 255 for x in img_norm_cfg['std'][::-1]]),
dict(type='ToTensor'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Collect', keys=['img', 'gt_labels'])
]
data = dict(
imgs_per_gpu=64, # total 256
workers_per_gpu=8,
train=dict(pipeline=train_pipeline))
# optimizer
paramwise_options = {
'norm': dict(weight_decay=0.),
'bias': dict(weight_decay=0.),
'absolute_pos_embed': dict(weight_decay=0.),
'relative_position_bias_table': dict(weight_decay=0.)
}
optimizer = dict(
type='AdamW',
lr=5e-4 * 1024 / 512,
weight_decay=0.05,
eps=1e-8,
betas=(0.9, 0.999),
paramwise_options=paramwise_options)
optimizer_config = dict(grad_clip=dict(max_norm=5.0), update_interval=2)
# learning policy
lr_config = dict(
policy='CosineAnnealing',
by_epoch=False,
min_lr_ratio=1e-2,
warmup='linear',
warmup_ratio=1e-3,
warmup_iters=20,
warmup_by_epoch=True)
checkpoint_config = dict(interval=30)
# runtime settings
total_epochs = 300