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: 2
	output_dir: sweep/ablation3/outputs/08a0b2a0fed089369b9debfac2b6f070
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 118227030
	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: 0.8194577311903198
	lambda2: 0.5954971958468116
	last_k_epoch: 0.2502561813215683
	lr: 3e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 2.3092434210820203
	weight_decay: 1e-06
	worst_case_p: 0.25
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    
0.2706715675  0.1793043327  0.0865724382  0.0848056537  0.2988235294  0.2975517891  0.2578065499  0.2835365854  0.1728989263  0.1555555556  0.0000000000  4.4913454056  2.0073552132  0             1.5317420959 
0.7297080783  0.8834088770  1.0000000000  0.9964664311  0.7929411765  0.7589453861  0.7460015232  0.7134146341  0.9048500555  0.8948148148  8.4805653710  1.9120404979  2.2543764114  300           0.6360584124 
0.7773258267  0.8803119948  1.0000000000  0.9964664311  0.7731764706  0.7570621469  0.7970297030  0.7576219512  0.9440947797  0.8874074074  16.961130742  1.3602002692  2.2543764114  600           0.6504430731 
0.7838024535  0.8830634021  1.0000000000  1.0000000000  0.7920000000  0.7514124294  0.8023610053  0.7652439024  0.9544613106  0.8977777778  25.441696113  1.2361561833  2.2543764114  900           0.6405362121 
0.7862788620  0.8894747852  1.0000000000  1.0000000000  0.8117647059  0.7721280603  0.8042650419  0.7682926829  0.9592743428  0.8962962963  33.922261484  1.1295232385  2.2543764114  1200          0.6543419377 
0.7843748255  0.8897784104  1.0000000000  0.9964664311  0.8216470588  0.7721280603  0.8004569688  0.7682926829  0.9689004073  0.9007407407  42.402826855  1.0550749902  2.2543764114  1500          0.6423319189 
0.7813272060  0.8823793730  1.0000000000  0.9964664311  0.8376470588  0.7514124294  0.7974105103  0.7652439024  0.9729729730  0.8992592593  50.883392226  1.0218195645  2.2543764114  1800          0.6350079489 
0.7820899816  0.8880853732  1.0000000000  1.0000000000  0.8545882353  0.7664783427  0.7958872810  0.7682926829  0.9763050722  0.8977777778  59.363957597  0.9960909198  2.2543764114  2100          0.6372135433 
0.7805667523  0.8856162374  1.0000000000  1.0000000000  0.8743529412  0.7664783427  0.7928408225  0.7682926829  0.9803776379  0.8903703704  67.844522968  0.9788400455  2.2543764114  2400          0.6403819370 
0.7794231694  0.8871897884  1.0000000000  1.0000000000  0.8752941176  0.7608286252  0.7936024372  0.7652439024  0.9859311366  0.9007407407  76.325088339  0.9755909862  2.2543764114  2700          0.6286747932 
0.7790423621  0.8841705427  1.0000000000  0.9964664311  0.8837647059  0.7627118644  0.7928408225  0.7652439024  0.9855609034  0.8933333333  84.805653710  0.9627656297  2.2543764114  3000          0.6323391803 
0.7813295280  0.8867735977  1.0000000000  0.9964664311  0.9030588235  0.7645951036  0.7913175933  0.7713414634  0.9866716031  0.8992592593  93.286219081  0.9546045003  2.2543764114  3300          0.6430636319 
0.7788531194  0.8845867333  1.0000000000  1.0000000000  0.9040000000  0.7589453861  0.7894135567  0.7682926829  0.9888930026  0.8948148148  101.76678445  0.9405356656  2.2543764114  3600          0.6322627012 
0.7786627158  0.8906884282  1.0000000000  1.0000000000  0.9101176471  0.7683615819  0.7890327494  0.7682926829  0.9911144021  0.9037037037  110.24734982  0.6983413815  5.3986215591  3900          0.6373747643 
0.7782819085  0.8898346932  1.0000000000  1.0000000000  0.9190588235  0.7702448211  0.7882711348  0.7682926829  0.9885227693  0.8992592593  118.72791519  0.4144716747  5.3986215591  4200          0.6509873056 
0.7779011012  0.8840929062  1.0000000000  1.0000000000  0.9209411765  0.7589453861  0.7875095202  0.7682926829  0.9896334691  0.8933333333  127.20848056  0.4037442254  5.3986215591  4500          0.6469515030 
0.7769485024  0.8868298804  1.0000000000  1.0000000000  0.9190588235  0.7627118644  0.7871287129  0.7667682927  0.9925953351  0.8977777778  135.68904593  0.3905263793  5.3986215591  4800          0.6512312357 
0.7761863073  0.8917955688  1.0000000000  0.9964664311  0.9303529412  0.7796610169  0.7871287129  0.7652439024  0.9903739356  0.8992592593  141.34275618  0.3911702432  5.3986215591  5000          0.6346734905 
