Environment:
	Python: 3.10.11
	PyTorch: 2.0.1
	Torchvision: 0.15.2
	CUDA: 11.7
	CUDNN: 8500
	NumPy: 1.24.3
	PIL: 9.4.0
	Testing environment: [2]
Args:
	algorithm: Selective_KD
	checkpoint_freq: 300
	data_dir: ./domainbed/data
	dataset: VLCS
	holdout_fraction: 0.2
	hparams: {
    "resnet18": false,
    "resnet_dropout": 0,
    "nonlinear_classifier": false,
    "data_augmentation": true,
    "clip_backbone": "ViT-B/32",
    "student_model": "resnet",
    "SMA": true,
    "batch_size": 32
}
	hparams_seed: 0
	output_dir: sweep/ablation3/outputs/2b8642daaff8a751eb584e227984afba
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1152585904
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [2]
	trial_seed: 2
	uda_holdout_fraction: 0
	visualize: False
Not saving models
HParams:
	SMA: True
	batch_size: 32
	class_balanced: False
	clip_backbone: ViT-B/32
	data_augmentation: True
	lambda1: 0.5
	lambda2: 0.5
	last_k_epoch: 0.25
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 3
	weight_decay: 0.0
	worst_case_p: 0.3333333333333333
using augment transform
using augment transform
using normal transform
using augment transform
using device:  cuda
Using ViT-B/32...
constructing student model
using resnet 50
Using SMA
n_steps 5001
checkpoint_freq 300
agg_test_acc  agg_val_acc   env0_in_acc   env0_out_acc  env1_in_acc   env1_out_acc  env2_in_acc   env2_out_acc  env3_in_acc   env3_out_acc  epoch         loss          mem_gb        step          step_time    
0.0417152351  0.0682849658  0.0212014134  0.0212014134  0.0588235294  0.0414312618  0.0392231531  0.0442073171  0.1366160681  0.1422222222  0.0000000000  4.0198202133  2.3337798119  0             1.6597263813 
0.7721826060  0.8783366862  1.0000000000  0.9964664311  0.7849411765  0.7570621469  0.7928408225  0.7515243902  0.9189189189  0.8814814815  8.4805653710  1.4760480795  2.5977830887  300           0.6521391074 
0.7834222267  0.8847982890  1.0000000000  0.9964664311  0.8277647059  0.7645951036  0.8000761615  0.7667682927  0.9566827101  0.8933333333  16.961130742  1.0036339309  2.5977830887  600           0.6505490239 
0.7820911426  0.8906739953  1.0000000000  0.9964664311  0.8724705882  0.7777777778  0.7928408225  0.7713414634  0.9718622732  0.8977777778  25.441696113  0.8575695602  2.5977830887  900           0.6581064812 
0.7782813280  0.8905963588  1.0000000000  1.0000000000  0.8983529412  0.7740112994  0.7897943641  0.7667682927  0.9788967049  0.8977777778  33.922261484  0.7810236609  2.5977830887  1200          0.6342541607 
0.7761868878  0.8880853732  1.0000000000  1.0000000000  0.9256470588  0.7664783427  0.7856054836  0.7667682927  0.9837097371  0.8977777778  42.402826855  0.7216879656  2.5977830887  1500          0.6362872616 
0.7727584610  0.8829150499  1.0000000000  0.9964664311  0.9378823529  0.7589453861  0.7817974105  0.7637195122  0.9885227693  0.8933333333  50.883392226  0.6940080935  2.5977830887  1800          0.6427473585 
0.7712346512  0.8944488435  1.0000000000  0.9964664311  0.9562352941  0.7683615819  0.7802741813  0.7621951220  0.9925953351  0.9185185185  59.363957597  0.6643497658  2.5977830887  2100          0.6528983625 
0.7700922293  0.8879096042  1.0000000000  1.0000000000  0.9656470588  0.7570621469  0.7779893374  0.7621951220  0.9937060348  0.9066666667  67.844522968  0.6637177653  2.5977830887  2400          0.6384588552 
0.7662824147  0.8891650970  1.0000000000  1.0000000000  0.9741176471  0.7608286252  0.7749428789  0.7576219512  0.9933358016  0.9066666667  76.325088339  0.6574093095  2.5977830887  2700          0.6282369351 
0.7662824147  0.8914500939  1.0000000000  1.0000000000  0.9802352941  0.7721280603  0.7749428789  0.7576219512  0.9962976675  0.9022222222  84.805653710  0.6672164237  2.5977830887  3000          0.6344085685 
0.7666638025  0.8835846460  1.0000000000  0.9964664311  0.9792941176  0.7683615819  0.7741812643  0.7591463415  0.9981488338  0.8859259259  93.286219081  0.6476509742  2.5977830887  3300          0.6385527794 
0.7657112037  0.8857083069  1.0000000000  1.0000000000  0.9835294118  0.7608286252  0.7738004570  0.7576219512  0.9970381340  0.8962962963  101.76678445  0.6424463177  2.5977830887  3600          0.6534960953 
0.7619013891  0.8852144797  1.0000000000  1.0000000000  0.9858823529  0.7608286252  0.7707539985  0.7530487805  0.9974083673  0.8948148148  110.24734982  0.4875756518  5.3943490982  3900          0.6445346236 
0.7613290171  0.8848127220  1.0000000000  1.0000000000  0.9863529412  0.7551789077  0.7726580350  0.7500000000  0.9977786005  0.8992592593  118.72791519  0.3211143985  5.3943490982  4200          0.6485873254 
0.7619008086  0.8876836155  1.0000000000  1.0000000000  0.9872941176  0.7608286252  0.7722772277  0.7515243902  0.9974083673  0.9022222222  127.20848056  0.3104461724  5.3943490982  4500          0.6423206623 
0.7588526086  0.8910901859  1.0000000000  1.0000000000  0.9934117647  0.7740112994  0.7707539985  0.7469512195  0.9988893003  0.8992592593  135.68904593  0.3031979268  5.3943490982  4800          0.6353208264 
0.7598052074  0.8859761453  1.0000000000  1.0000000000  0.9934117647  0.7645951036  0.7711348058  0.7484756098  0.9977786005  0.8933333333  141.34275618  0.2978938619  5.3943490982  5000          0.6301443112 
