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/ca03811ddbcdcdc744d70963fc0e1d20
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 441485224
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [2]
	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: 0.6171767606394463
	lambda2: 0.5293911814442921
	last_k_epoch: 0.3102645027326114
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 2.022572617872769
	weight_decay: 0.0001
	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    
0.1026670443  0.0910729139  0.1007067138  0.0777385159  0.0710588235  0.0621468927  0.1016755522  0.1036585366  0.1299518697  0.1333333333  0.0000000000  3.7575719357  1.7945408821  0             1.5245020390 
0.7672367550  0.8590857969  0.9964664311  0.9964664311  0.7844705882  0.7363465160  0.7707539985  0.7637195122  0.9037393558  0.8444444444  8.4805653710  1.7717422124  2.0935831070  300           0.6247768195 
0.7895279146  0.8608495498  1.0000000000  1.0000000000  0.8000000000  0.7306967985  0.7787509520  0.8003048780  0.9529803776  0.8518518519  16.961130742  1.3190292813  2.0935831070  600           0.6232292080 
0.8017189731  0.8719313661  1.0000000000  1.0000000000  0.8155294118  0.7476459510  0.7894135567  0.8140243902  0.9655683080  0.8681481481  25.441696113  1.1823201634  2.0935831070  900           0.6118603047 
0.8007652133  0.8722912740  1.0000000000  1.0000000000  0.8376470588  0.7457627119  0.7920792079  0.8094512195  0.9766753054  0.8711111111  33.922261484  1.0650602933  2.0935831070  1200          0.6227330963 
0.7952400243  0.8715296084  1.0000000000  1.0000000000  0.8607058824  0.7419962335  0.7901751714  0.8003048780  0.9829692706  0.8725925926  42.402826855  0.9670759183  2.0935831070  1500          0.6264699284 
0.7940987634  0.8678049798  1.0000000000  1.0000000000  0.8856470588  0.7382297552  0.7848438690  0.8033536585  0.9896334691  0.8651851852  50.883392226  0.9846647535  2.0935831070  1800          0.6178701878 
0.7946705548  0.8721071351  1.0000000000  1.0000000000  0.9035294118  0.7570621469  0.7844630617  0.8048780488  0.9911144021  0.8592592593  59.363957597  0.9132437162  2.0935831070  2100          0.6223401189 
0.7899075610  0.8738503920  1.0000000000  0.9964664311  0.9209411765  0.7495291902  0.7825590251  0.7972560976  0.9940762680  0.8755555556  67.844522968  0.9057779982  2.0935831070  2400          0.6390799705 
0.7902889488  0.8764595101  1.0000000000  1.0000000000  0.9261176471  0.7627118644  0.7817974105  0.7987804878  0.9974083673  0.8666666667  76.325088339  0.8853663278  2.0935831070  2700          0.6404583740 
0.7893363500  0.8719313661  1.0000000000  1.0000000000  0.9378823529  0.7476459510  0.7814166032  0.7972560976  0.9966679008  0.8681481481  84.805653710  0.8907534375  2.0935831070  3000          0.6355098573 
0.7883849122  0.8751058848  1.0000000000  0.9964664311  0.9463529412  0.7532956685  0.7779893374  0.7987804878  0.9974083673  0.8755555556  93.286219081  0.9120494167  2.0935831070  3300          0.6422316845 
0.7880041049  0.8717974469  1.0000000000  1.0000000000  0.9595294118  0.7457627119  0.7772277228  0.7987804878  0.9959274343  0.8696296296  101.76678445  0.6333335781  5.3995943069  3600          0.6480460540 
0.7860989073  0.8768194180  1.0000000000  1.0000000000  0.9595294118  0.7608286252  0.7764661081  0.7957317073  0.9981488338  0.8696296296  110.24734982  0.3541077996  5.3995943069  3900          0.6402702610 
0.7847655013  0.8734546973  1.0000000000  1.0000000000  0.9623529412  0.7551789077  0.7753236862  0.7942073171  0.9985190670  0.8651851852  118.72791519  0.3413361120  5.3995943069  4200          0.6236303139 
0.7828608842  0.8672548699  1.0000000000  0.9964664311  0.9661176471  0.7401129944  0.7730388423  0.7926829268  0.9988893003  0.8651851852  127.20848056  0.3357602253  5.3995943069  4500          0.6345497529 
0.7834320952  0.8759656829  1.0000000000  1.0000000000  0.9694117647  0.7627118644  0.7741812643  0.7926829268  0.9981488338  0.8651851852  135.68904593  0.3283398588  5.3995943069  4800          0.6387242667 
0.7826693195  0.8735467668  1.0000000000  1.0000000000  0.9717647059  0.7495291902  0.7757044935  0.7896341463  0.9977786005  0.8711111111  141.34275618  0.3258018115  5.3995943069  5000          0.6296271133 
