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: 0
	output_dir: sweep/ablation3/outputs/c56229d4ba6289b297e84c0302608547
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 166817229
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [0]
	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: 0.5
	lambda2: 0.5
	last_k_epoch: 0.25
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 3
	weight_decay: 0.0
	worst_case_p: 0.3333333333333333
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    
0.1258833922  0.2988082004  0.1245583039  0.1272084806  0.2978823529  0.3182674200  0.3141660320  0.3292682927  0.2599037394  0.2488888889  0.0000000000  4.0834522247  2.3337798119  0             1.5015726089 
0.9673144871  0.8097187004  0.9699646643  0.9646643110  0.7614117647  0.7532956685  0.8796648896  0.8003048780  0.9237319511  0.8755555556  8.4805653710  1.5595593111  2.5947308540  300           0.6407747181 
0.9867491161  0.8274205107  0.9840989399  0.9893992933  0.8117647059  0.7702448211  0.9253617669  0.8216463415  0.9511292114  0.8903703704  16.961130742  1.1974863776  2.5947308540  600           0.6452358468 
0.9889575967  0.8306604221  0.9885159011  0.9893992933  0.8357647059  0.7721280603  0.9485910129  0.8368902439  0.9700111070  0.8829629630  25.441696113  1.0272517029  2.5947308540  900           0.6448718301 
0.9902826850  0.8232951834  0.9911660777  0.9893992933  0.8672941176  0.7532956685  0.9676313785  0.8262195122  0.9781562384  0.8903703704  33.922261484  0.9012108250  2.5947308540  1200          0.6408483028 
0.9898409889  0.8272088249  0.9902826855  0.9893992933  0.8960000000  0.7740112994  0.9782939832  0.8231707317  0.9837097371  0.8844444444  42.402826855  0.8004801897  2.5947308540  1500          0.6425794427 
0.9898409889  0.8252959209  0.9902826855  0.9893992933  0.9190588235  0.7758945386  0.9840060929  0.8155487805  0.9870418364  0.8844444444  50.883392226  0.7660747784  2.5947308540  1800          0.6335362307 
0.9898409889  0.8289581449  0.9902826855  0.9893992933  0.9355294118  0.7777777778  0.9889565880  0.8231707317  0.9888930026  0.8859259259  59.363957597  0.7548513695  2.5947308540  2100          0.6383803248 
0.9898409889  0.8287966788  0.9902826855  0.9893992933  0.9444705882  0.7683615819  0.9935262757  0.8246951220  0.9911144021  0.8933333333  67.844522968  0.7449071334  2.5947308540  2400          0.6355665795 
0.9898409889  0.8236324185  0.9902826855  0.9893992933  0.9600000000  0.7721280603  0.9942878903  0.8246951220  0.9970381340  0.8740740741  76.325088339  0.7302935304  2.5947308540  2700          0.6334865499 
0.9893992928  0.8289449009  0.9893992933  0.9893992933  0.9665882353  0.7702448211  0.9973343488  0.8262195122  0.9959274343  0.8903703704  84.805653710  0.7221719106  2.5947308540  3000          0.6223563655 
0.9889575967  0.8284060470  0.9885159011  0.9893992933  0.9750588235  0.7853107345  0.9969535415  0.8125000000  0.9966679008  0.8874074074  93.286219081  0.7147868248  2.5947308540  3300          0.6249858562 
0.9885159006  0.8312728066  0.9876325088  0.9893992933  0.9783529412  0.7815442561  0.9965727342  0.8307926829  0.9966679008  0.8814814815  101.76678445  0.7128674079  2.5947308540  3600          0.6364120817 
0.9885159006  0.8285256633  0.9876325088  0.9893992933  0.9816470588  0.7871939736  0.9954303123  0.8109756098  0.9981488338  0.8874074074  110.24734982  0.5924309658  5.3918771744  3900          0.6218206970 
0.9889575967  0.8321551475  0.9885159011  0.9893992933  0.9802352941  0.7796610169  0.9973343488  0.8338414634  0.9977786005  0.8829629630  118.72791519  0.4362248406  5.3918771744  4200          0.6288376403 
0.9889575967  0.8332327533  0.9885159011  0.9893992933  0.9840000000  0.7890772128  0.9973343488  0.8246951220  0.9985190670  0.8859259259  127.20848056  0.4254552276  5.3918771744  4500          0.6287330413 
0.9889575967  0.8227071688  0.9885159011  0.9893992933  0.9868235294  0.7740112994  0.9973343488  0.8170731707  0.9981488338  0.8770370370  135.68904593  0.4142003902  5.3918771744  4800          0.6316847078 
0.9889575967  0.8347152938  0.9885159011  0.9893992933  0.9901176471  0.7815442561  0.9988575781  0.8292682927  0.9988893003  0.8933333333  141.34275618  0.4107069878  5.3918771744  5000          0.6254162359 
