import darklight as dl
import torch
from vit import VisionTransformer
import vitconfigs as vcfg

net=VisionTransformer(vcfg.base)
dm=dl.ImageNetManager('/sfnvme/imagenet/', size=[224,224], bsize=128)

opt_params={
	'optimizer': torch.optim.AdamW,
	'okwargs': {'lr': 1e-4, 'weight_decay':0.05},
	'scheduler':torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
	'skwargs': {'T_0':10,'T_mult':2},
	'amplevel': None
	}
trainer=dl.StudentTrainer(net, dm, None, opt_params=opt_params)
trainer.train(epochs=300, save='vitbase_{}.pth')
98f3348b7f31a79362ede005ac79ee5c7f3e23eb
<mask token>

<mask token>
trainer.train(epochs=300, save='vitbase_{}.pth')

<mask token>
net = VisionTransformer(vcfg.base)
dm = dl.ImageNetManager('/sfnvme/imagenet/', size=[224, 224], bsize=128)
opt_params = {'optimizer': torch.optim.AdamW, 'okwargs': {'lr': 0.0001,
    'weight_decay': 0.05}, 'scheduler': torch.optim.lr_scheduler.
    CosineAnnealingWarmRestarts, 'skwargs': {'T_0': 10, 'T_mult': 2},
    'amplevel': None}
trainer = dl.StudentTrainer(net, dm, None, opt_params=opt_params)
trainer.train(epochs=300, save='vitbase_{}.pth')

import darklight as dl
import torch
from vit import VisionTransformer
import vitconfigs as vcfg
net = VisionTransformer(vcfg.base)
dm = dl.ImageNetManager('/sfnvme/imagenet/', size=[224, 224], bsize=128)
opt_params = {'optimizer': torch.optim.AdamW, 'okwargs': {'lr': 0.0001,
    'weight_decay': 0.05}, 'scheduler': torch.optim.lr_scheduler.
    CosineAnnealingWarmRestarts, 'skwargs': {'T_0': 10, 'T_mult': 2},
    'amplevel': None}
trainer = dl.StudentTrainer(net, dm, None, opt_params=opt_params)
trainer.train(epochs=300, save='vitbase_{}.pth')

import darklight as dl
import torch
from vit import VisionTransformer
import vitconfigs as vcfg

net=VisionTransformer(vcfg.base)
dm=dl.ImageNetManager('/sfnvme/imagenet/', size=[224,224], bsize=128)

opt_params={
	'optimizer': torch.optim.AdamW,
	'okwargs': {'lr': 1e-4, 'weight_decay':0.05},
	'scheduler':torch.optim.lr_scheduler.CosineAnnealingWarmRestarts,
	'skwargs': {'T_0':10,'T_mult':2},
	'amplevel': None
	}
trainer=dl.StudentTrainer(net, dm, None, opt_params=opt_params)
trainer.train(epochs=300, save='vitbase_{}.pth')