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: 4
	output_dir: sweep/ablation3/outputs/76f7b67a8fb6ab7abe49384450f6f7c7
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1488626115
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [2]
	trial_seed: 0
	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: 0.8001163740830898
	lambda2: 0.7340462818445315
	last_k_epoch: 0.3149495809125332
	lr: 3e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 2.8284464949429635
	weight_decay: 1e-06
	worst_case_p: 0.2
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    
0.2034294716  0.2475800709  0.1289752650  0.1201413428  0.3402352941  0.3559322034  0.2010662605  0.2057926829  0.2558311736  0.2666666667  0.0000000000  4.5813083649  1.7945408821  0             1.5244009495 
0.7600002550  0.8756415617  0.9982332155  0.9964664311  0.7637647059  0.7608286252  0.7593297791  0.7606707317  0.8937430581  0.8696296296  8.4805653710  2.1046166070  2.0935831070  300           0.6387453254 
0.7788525389  0.8808058220  0.9991166078  0.9964664311  0.7896470588  0.7570621469  0.7909367860  0.7667682927  0.9396519807  0.8888888889  16.961130742  1.4806185333  2.0935831070  600           0.6389883931 
0.7801859450  0.8756141449  1.0000000000  1.0000000000  0.8004705882  0.7438794727  0.7920792079  0.7682926829  0.9537208441  0.8829629630  25.441696113  1.3792672839  2.0935831070  900           0.6501646566 
0.7832341450  0.8816237704  1.0000000000  1.0000000000  0.8240000000  0.7589453861  0.7936024372  0.7728658537  0.9637171418  0.8859259259  33.922261484  1.2671156436  2.0935831070  1200          0.6331821203 
0.7859009572  0.8812220127  1.0000000000  1.0000000000  0.8343529412  0.7532956685  0.7958872810  0.7759146341  0.9711218067  0.8903703704  42.402826855  1.1929648016  2.0935831070  1500          0.6465036281 
0.7834239682  0.8842268254  1.0000000000  1.0000000000  0.8494117647  0.7608286252  0.7955064737  0.7713414634  0.9748241392  0.8918518519  50.883392226  1.1082364698  2.0935831070  1800          0.6475719754 
0.7784699901  0.8749863985  1.0000000000  1.0000000000  0.8592941176  0.7419962335  0.7947448591  0.7621951220  0.9766753054  0.8829629630  59.363957597  1.0882345808  2.0935831070  2100          0.6440143402 
0.7733273499  0.8827453439  1.0000000000  1.0000000000  0.8644705882  0.7608286252  0.7890327494  0.7576219512  0.9844502036  0.8874074074  67.844522968  1.0506079030  2.0935831070  2400          0.6485495973 
0.7727561390  0.8818497591  1.0000000000  1.0000000000  0.8729411765  0.7551789077  0.7878903275  0.7576219512  0.9851906701  0.8903703704  76.325088339  1.0396222075  2.0935831070  2700          0.6410669390 
0.7706599573  0.8769617072  1.0000000000  1.0000000000  0.8875294118  0.7419962335  0.7882711348  0.7530487805  0.9892632358  0.8888888889  84.805653710  1.0302137854  2.0935831070  3000          0.6362542192 
0.7706599573  0.8799665199  1.0000000000  1.0000000000  0.9002352941  0.7495291902  0.7882711348  0.7530487805  0.9900037023  0.8903703704  93.286219081  1.0395765716  2.0935831070  3300          0.6465706404 
0.7740895451  0.8806361160  1.0000000000  1.0000000000  0.9011764706  0.7589453861  0.7890327494  0.7591463415  0.9911144021  0.8829629630  101.76678445  0.7226302770  5.4007902145  3600          0.6330663586 
0.7731375268  0.8859761453  1.0000000000  1.0000000000  0.9058823529  0.7645951036  0.7871287129  0.7591463415  0.9929655683  0.8933333333  110.24734982  0.4525615339  5.4007902145  3900          0.6382937908 
0.7710425061  0.8771374762  1.0000000000  1.0000000000  0.9138823529  0.7514124294  0.7844630617  0.7576219512  0.9944465013  0.8800000000  118.72791519  0.4316570402  5.4007902145  4200          0.6404880571 
0.7693282927  0.8749863985  1.0000000000  1.0000000000  0.9152941176  0.7419962335  0.7825590251  0.7560975610  0.9937060348  0.8829629630  127.20848056  0.4143659313  5.4007902145  4500          0.6385318406 
0.7695192768  0.8817158398  1.0000000000  1.0000000000  0.9204705882  0.7532956685  0.7814166032  0.7576219512  0.9944465013  0.8918518519  135.68904593  0.4059056266  5.4007902145  4800          0.6356159321 
0.7685666780  0.8807281855  1.0000000000  1.0000000000  0.9181176471  0.7532956685  0.7810357959  0.7560975610  0.9940762680  0.8888888889  141.34275618  0.3978431316  5.4007902145  5000          0.6291059101 
