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: OfficeHome
	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/63c03f91c4fb42e90d3d016cfc922d0b
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1277831251
	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    
0.0081682232  0.0139080788  0.0139031926  0.0164948454  0.0137457045  0.0126002291  0.0095720721  0.0067643743  0.0088927137  0.0126291619  0.0000000000  8.6302671432  1.7954735756  0             1.4496233463 
0.6708431406  0.6715884844  0.7626158599  0.6865979381  0.6660939290  0.5830469645  0.6742680180  0.6674182638  0.7662076879  0.7451205511  4.9433573635  6.4371684790  2.0943040848  300           0.3055250748 
0.7585929006  0.7575473586  0.9073120494  0.7690721649  0.8313287514  0.6953035510  0.7561936937  0.7609921082  0.8904188181  0.8082663605  9.8867147271  3.8432402595  2.0943040848  600           0.3041408769 
0.7791573983  0.7918633107  0.9490216272  0.7917525773  0.8920389462  0.7388316151  0.7747747748  0.7835400225  0.9294320138  0.8450057405  14.830072090  2.8630549939  2.0943040848  900           0.3188432312 
0.7923976326  0.8007155163  0.9768280124  0.8103092784  0.9243986254  0.7548682703  0.7865990991  0.7981961669  0.9566838784  0.8369690011  19.773429454  2.3794707505  2.0943040848  1200          0.3073242108 
0.8001441933  0.8170760434  0.9871266735  0.8226804124  0.9435853379  0.7674684994  0.7942004505  0.8060879369  0.9678714859  0.8610792193  24.716786817  2.0637853734  2.0943040848  1500          0.3065121643 
0.8078907539  0.8239532794  0.9886714727  0.8226804124  0.9564719359  0.7823596793  0.8018018018  0.8139797069  0.9738955823  0.8668197474  29.660144181  1.8698638892  2.0943040848  1800          0.3034581319 
0.8119748655  0.8277706539  0.9907312049  0.8226804124  0.9616265750  0.7949599084  0.8065878378  0.8173618940  0.9827882960  0.8656716418  34.603501544  1.7639993012  2.0943040848  2100          0.2829203995 
0.8107047997  0.8277724074  0.9943357364  0.8226804124  0.9667812142  0.7926689576  0.8096846847  0.8117249154  0.9856569134  0.8679678530  39.546858908  1.6385444136  2.0943040848  2400          0.2802207136 
0.8150723472  0.8294568217  0.9953656025  0.8185567010  0.9716494845  0.7961053837  0.8116554054  0.8184892897  0.9873780838  0.8737083812  44.490216271  1.5989977821  2.0943040848  2700          0.2869481365 
0.8163392391  0.8361049626  0.9948506694  0.8350515464  0.9762313860  0.7938144330  0.8141891892  0.8184892897  0.9911072863  0.8794489093  49.433573635  1.5589804037  2.0943040848  3000          0.2874435925 
0.8180296978  0.8362506787  0.9953656025  0.8412371134  0.9768041237  0.7972508591  0.8153153153  0.8207440812  0.9899598394  0.8702640643  54.376930999  1.4966291130  2.0943040848  3300          0.2739158765 
0.8201424539  0.8364042855  0.9953656025  0.8371134021  0.9788087056  0.8006872852  0.8172860360  0.8229988726  0.9925415950  0.8714121699  59.320288362  1.3822856724  5.4026646614  3600          0.2760633858 
0.8192972245  0.8306777853  0.9963954686  0.8268041237  0.9793814433  0.7926689576  0.8167229730  0.8218714769  0.9936890419  0.8725602755  64.263645726  1.2025765717  5.4026646614  3900          0.2862413351 
0.8211278143  0.8294568217  0.9953656025  0.8185567010  0.9828178694  0.7961053837  0.8192567568  0.8229988726  0.9922547332  0.8737083812  69.207003089  1.1213595335  5.4026646614  4200          0.2962455360 
0.8205634816  0.8335041680  0.9958805355  0.8329896907  0.9816723940  0.7938144330  0.8203828829  0.8207440812  0.9948364888  0.8737083812  74.150360453  1.0816657474  5.4026646614  4500          0.2769354582 
0.8208450131  0.8313624403  0.9948506694  0.8185567010  0.9813860252  0.8064146621  0.8209459459  0.8207440812  0.9945496271  0.8691159587  79.093717816  1.0345670827  5.4026646614  4800          0.2744899559 
0.8214080762  0.8333479310  0.9948506694  0.8268041237  0.9810996564  0.8041237113  0.8220720721  0.8207440812  0.9948364888  0.8691159587  82.389289392  1.0038366336  5.4026646614  5000          0.2676495874 
