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: TerraIncognita
	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/da4f0a78d6aec3fe1dce6e9f160ec88b
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 816260572
	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    
0.0650188917  0.0464917511  0.0234642763  0.0221518987  0.0234946720  0.0220852594  0.0670654912  0.0629722922  0.0905035054  0.0952380952  0.0000000000  4.7264499664  1.7946186066  0             1.4829397202 
0.3792506295  0.6219418203  0.5860796204  0.6044303797  0.7185774811  0.7180277350  0.4083753149  0.3501259446  0.5355852985  0.5433673469  3.0226700252  3.0931669545  2.0937752724  300           0.1497727847 
0.3882241812  0.7098489888  0.7853941471  0.7542194093  0.7590191295  0.7673343606  0.4061712846  0.3702770781  0.5982579137  0.6079931973  6.0453400504  2.7181127667  2.0937752724  600           0.1758811084 
0.4146725439  0.7573008630  0.8317954126  0.8164556962  0.7954808063  0.7955829481  0.4326196474  0.3967254408  0.6439345655  0.6598639456  9.0680100756  2.5735747059  2.0937752724  900           0.1728640151 
0.4387594456  0.7707347313  0.8555233325  0.8301687764  0.8117858518  0.8043143297  0.4530856423  0.4244332494  0.6847248778  0.6777210884  12.090680100  2.4916218694  2.0937752724  1200          0.1711811479 
0.4549748109  0.7926547633  0.8729238070  0.8502109705  0.8251380151  0.8253723677  0.4716624685  0.4382871537  0.7210537497  0.7023809524  15.113350125  2.4036017545  2.0937752724  1500          0.1727972809 
0.4672544078  0.8078302772  0.8966517269  0.8734177215  0.8338682758  0.8315356959  0.4861460957  0.4483627204  0.7365625664  0.7185374150  18.136020151  2.3438334155  2.0937752724  1800          0.1716220148 
0.4748110829  0.8204502209  0.8984972317  0.8797468354  0.8443959430  0.8418079096  0.4962216625  0.4534005038  0.7531336308  0.7397959184  21.158690176  2.2870806793  2.0937752724  2100          0.1723545623 
0.4889798486  0.8305928267  0.9151067756  0.8945147679  0.8522275003  0.8438623523  0.5044080605  0.4735516373  0.7805396218  0.7534013605  24.181360201  2.2273789815  2.0937752724  2400          0.1734864481 
0.5028337529  0.8426500011  0.9230160823  0.9061181435  0.8615996919  0.8556753980  0.5157430730  0.4899244332  0.8034841725  0.7661564626  27.204030226  2.1525039748  2.0937752724  2700          0.1729871504 
0.5105478587  0.8552664121  0.9338254680  0.9113924051  0.8723841315  0.8695428865  0.5223551637  0.4987405542  0.8145315488  0.7848639456  30.226700251  2.1359482094  2.0937752724  3000          0.1743139466 
0.5165302264  0.8531474971  0.9390983390  0.9040084388  0.8698164078  0.8705701079  0.5255037783  0.5075566751  0.8245166773  0.7848639456  33.249370277  2.0548957813  2.0937752724  3300          0.1749351859 
0.5245591937  0.8656442680  0.9412074875  0.9219409283  0.8854795224  0.8705701079  0.5302267003  0.5188916877  0.8353516040  0.8044217687  36.272040302  1.6717295945  5.4010052681  3600          0.2007956783 
0.5299118385  0.8710134799  0.9448984972  0.9187763713  0.8865066119  0.8787878788  0.5333753149  0.5264483627  0.8442744848  0.8154761905  39.294710327  1.3634220886  5.4010052681  3900          0.2149390030 
0.5369962214  0.8638677602  0.9522805167  0.9145569620  0.8897162665  0.8726245506  0.5412468514  0.5327455919  0.8485234757  0.8044217687  42.317380352  1.3218607187  5.4010052681  4200          0.2174456867 
0.5421914355  0.8725917448  0.9538623781  0.9272151899  0.8890743356  0.8844375963  0.5440806045  0.5403022670  0.8578712556  0.8061224490  45.340050377  1.2963814700  5.4010052681  4500          0.2176286864 
0.5483312340  0.8804990683  0.9530714474  0.9293248945  0.8980613686  0.8864920390  0.5488035264  0.5478589421  0.8638198428  0.8256802721  48.362720403  1.2828404160  5.4010052681  4800          0.2151486667 
0.5505352642  0.8793488399  0.9525441603  0.9335443038  0.8985749133  0.8839239856  0.5519521411  0.5491183879  0.8650945400  0.8205782313  50.377833753  1.2674422026  5.4010052681  5000          0.2176638973 
