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: [0]
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/6405a99d1322fe827c121881a760e110
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 932942206
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [0]
	trial_seed: 1
	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: 0.5439617198173775
	lambda2: 0.5509403872292429
	last_k_epoch: 0.38252238504986713
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 4.920147743151585
	weight_decay: 1e-06
	worst_case_p: 0.3
using normal transform
using augment transform
using augment 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    
0.2420494698  0.4292781553  0.2367491166  0.2473498233  0.5425882353  0.5536723164  0.3606245240  0.3978658537  0.3398741207  0.3362962963  0.0000000000  2.8784415722  2.1691374779  0             1.6344006062 
0.9814487628  0.8047333861  0.9805653710  0.9823321555  0.7882352941  0.7438794727  0.8640517898  0.8140243902  0.9192891522  0.8562962963  8.4805653710  1.6443112429  2.4372453690  300           0.6363629731 
0.9845406356  0.8217328605  0.9832155477  0.9858657244  0.7910588235  0.7419962335  0.9127951257  0.8506097561  0.9503887449  0.8725925926  16.961130742  1.2950882471  2.4372453690  600           0.6546103040 
0.9818904589  0.8192340598  0.9849823322  0.9787985866  0.8192941176  0.7495291902  0.9463061691  0.8429878049  0.9648278415  0.8651851852  25.441696113  1.1654712637  2.4372453690  900           0.6409846282 
0.9867491161  0.8259799220  0.9876325088  0.9858657244  0.8470588235  0.7457627119  0.9596344250  0.8536585366  0.9788967049  0.8785185185  33.922261484  1.0154394470  2.4372453690  1200          0.6401575534 
0.9889575967  0.8255014568  0.9885159011  0.9893992933  0.8752941176  0.7382297552  0.9702970297  0.8597560976  0.9825990374  0.8785185185  42.402826855  0.8971632036  2.4372453690  1500          0.6522446974 
0.9911660772  0.8307197518  0.9858657244  0.9964664311  0.9040000000  0.7570621469  0.9821020564  0.8521341463  0.9870418364  0.8829629630  50.883392226  0.8340525275  2.4372453690  1800          0.6442548958 
0.9911660772  0.8301481581  0.9858657244  0.9964664311  0.9247058824  0.7627118644  0.9866717441  0.8536585366  0.9918548686  0.8740740741  59.363957597  0.8047050951  2.4372453690  2100          0.6368036572 
0.9911660772  0.8293160369  0.9858657244  0.9964664311  0.9425882353  0.7514124294  0.9912414318  0.8506097561  0.9959274343  0.8859259259  67.844522968  0.7764073034  2.4372453690  2400          0.6442440955 
0.9911660772  0.8277424859  0.9858657244  0.9964664311  0.9520000000  0.7570621469  0.9920030465  0.8506097561  0.9966679008  0.8755555556  76.325088339  0.7825494138  2.4372453690  2700          0.6474163628 
0.9911660772  0.8284428059  0.9858657244  0.9964664311  0.9689411765  0.7514124294  0.9946686976  0.8628048780  0.9959274343  0.8711111111  84.805653710  0.8083880913  2.4372453690  3000          0.6477609444 
0.9911660772  0.8256527539  0.9858657244  0.9964664311  0.9712941176  0.7570621469  0.9946686976  0.8384146341  0.9974083673  0.8814814815  93.286219081  0.5823773055  5.3972330093  3300          0.6438260182 
0.9911660772  0.8272957014  0.9858657244  0.9964664311  0.9736470588  0.7664783427  0.9954303123  0.8368902439  0.9985190670  0.8785185185  101.76678445  0.4717315877  5.3972330093  3600          0.6481910102 
0.9911660772  0.8312268229  0.9858657244  0.9964664311  0.9760000000  0.7645951036  0.9950495050  0.8490853659  0.9970381340  0.8800000000  110.24734982  0.4492215451  5.3972330093  3900          0.6435857797 
0.9916077734  0.8293864925  0.9867491166  0.9964664311  0.9849411765  0.7589453861  0.9950495050  0.8536585366  0.9977786005  0.8755555556  118.72791519  0.4392987477  5.3972330093  4200          0.6339330872 
0.9911660772  0.8240311012  0.9858657244  0.9964664311  0.9788235294  0.7608286252  0.9965727342  0.8475609756  0.9977786005  0.8637037037  127.20848056  0.4328479723  5.3972330093  4500          0.6304531701 
0.9907243811  0.8359063660  0.9849823322  0.9964664311  0.9877647059  0.7589453861  0.9973343488  0.8643292683  0.9988893003  0.8844444444  135.68904593  0.4272511048  5.3972330093  4800          0.6385527070 
0.9907243811  0.8231498193  0.9849823322  0.9964664311  0.9863529412  0.7551789077  0.9984767708  0.8490853659  0.9988893003  0.8651851852  141.34275618  0.4225330599  5.3972330093  5000          0.6303042603 
