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: [1]
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/063bf58eec9c08eaa4ae04f6bda2f678
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1525812371
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [1]
	trial_seed: 2
	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: 0.8194577311903198
	lambda2: 0.5954971958468116
	last_k_epoch: 0.2502561813215683
	lr: 3e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 2.3092434210820203
	weight_decay: 1e-06
	worst_case_p: 0.25
using augment transform
using normal 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    
0.0416559211  0.1181543805  0.1068904594  0.0812720848  0.0456470588  0.0376647834  0.1165270373  0.1265243902  0.1299518697  0.1466666667  0.0000000000  4.6160521507  2.0073552132  0             1.5797889233 
0.6439162509  0.8988341880  0.9991166078  0.9964664311  0.6437647059  0.6440677966  0.8830921554  0.8170731707  0.9211403184  0.8829629630  8.4805653710  1.7963822961  2.2565736771  300           0.1600481383 
0.6625102467  0.9028563199  1.0000000000  0.9964664311  0.6564705882  0.6685499058  0.9105102818  0.8246951220  0.9370603480  0.8874074074  16.961130742  1.1453686806  2.2565736771  600           0.1814389078 
0.6566270075  0.9100735253  1.0000000000  0.9929328622  0.6484705882  0.6647834275  0.9272658035  0.8246951220  0.9477971122  0.9125925926  25.441696113  0.9888104788  2.2565736771  900           0.1808309793 
0.6516845017  0.9080081351  1.0000000000  0.9893992933  0.6442352941  0.6591337100  0.9447829398  0.8353658537  0.9592743428  0.8992592593  33.922261484  0.9180806722  2.2565736771  1200          0.1814296595 
0.6570980389  0.9058049799  1.0000000000  0.9964664311  0.6475294118  0.6666666667  0.9520182788  0.8231707317  0.9670492410  0.8977777778  42.402826855  0.8668476613  2.2565736771  1500          0.1799070509 
0.6554500938  0.9066739468  1.0000000000  0.9929328622  0.6480000000  0.6629001883  0.9668697639  0.8307926829  0.9703813402  0.8962962963  50.883392226  0.8527696276  2.2565736771  1800          0.1805754121 
0.6528614154  0.9073865818  1.0000000000  0.9964664311  0.6447058824  0.6610169492  0.9725818736  0.8338414634  0.9755646057  0.8918518519  59.363957597  0.8178299534  2.2565736771  2100          0.1788763070 
0.6505075880  0.9116980360  1.0000000000  0.9929328622  0.6437647059  0.6572504708  0.9737242955  0.8399390244  0.9781562384  0.9022222222  67.844522968  0.8051191562  2.2565736771  2400          0.1823746530 
0.6507433253  0.9113085933  1.0000000000  0.9964664311  0.6423529412  0.6591337100  0.9775323686  0.8307926829  0.9825990374  0.9066666667  76.325088339  0.7964229369  2.2565736771  2700          0.1810673889 
0.6486247920  0.9086778613  1.0000000000  0.9929328622  0.6418823529  0.6553672316  0.9821020564  0.8338414634  0.9837097371  0.8992592593  84.805653710  0.7906049085  2.2565736771  3000          0.1790670633 
0.6483894978  0.9074151877  1.0000000000  0.9964664311  0.6414117647  0.6553672316  0.9840060929  0.8368902439  0.9840799704  0.8888888889  93.286219081  0.7773574873  2.2565736771  3300          0.1797931902 
0.6486247920  0.9016641606  1.0000000000  0.9929328622  0.6418823529  0.6553672316  0.9870525514  0.8231707317  0.9885227693  0.8888888889  101.76678445  0.7933759411  2.2565736771  3600          0.1820879165 
0.6495664116  0.9113658049  1.0000000000  0.9964664311  0.6418823529  0.6572504708  0.9847677075  0.8368902439  0.9851906701  0.9007407407  110.24734982  0.6010555430  5.3972864151  3900          0.2002709889 
0.6488600861  0.9061944226  1.0000000000  0.9929328622  0.6423529412  0.6553672316  0.9885757807  0.8323170732  0.9870418364  0.8933333333  118.72791519  0.3921012875  5.3972864151  4200          0.2177628517 
0.6490953802  0.9060094848  1.0000000000  1.0000000000  0.6428235294  0.6553672316  0.9881949733  0.8246951220  0.9881525361  0.8933333333  127.20848056  0.3749834916  5.3972864151  4500          0.2193065405 
0.6502727370  0.9112185117  1.0000000000  0.9929328622  0.6414117647  0.6591337100  0.9874333587  0.8414634146  0.9888930026  0.8992592593  135.68904593  0.3652958751  5.3972864151  4800          0.2194428333 
0.6495668547  0.9099887079  1.0000000000  1.0000000000  0.6400000000  0.6591337100  0.9897182026  0.8277439024  0.9907441688  0.9022222222  141.34275618  0.3554207811  5.3972864151  5000          0.2198201287 
