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: PACS
	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: 1
	output_dir: sweep/ablation3/outputs/498fdc6fcebcde5e79580b08170064ef
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 469549013
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [2]
	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: 0.6109302038716249
	lambda2: 0.5117744391412766
	last_k_epoch: 0.31317984427858747
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 4.002688876693512
	weight_decay: 1e-06
	worst_case_p: 0.25
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    
0.2758233532  0.1066816710  0.1415497254  0.1442542787  0.1194029851  0.1324786325  0.2881736527  0.2634730539  0.0502544529  0.0433121019  0.0000000000  7.0261559486  2.0074062347  0             1.4550778866 
0.9891467061  0.9439158415  0.9804758999  0.9633251834  0.9733475480  0.9423076923  0.9842814371  0.9940119760  0.9608778626  0.9261146497  7.1856287425  2.5336604689  2.2566514015  300           0.1550894324 
0.9947604785  0.9622565241  0.9932885906  0.9633251834  0.9893390192  0.9807692308  0.9925149701  0.9970059880  0.9783715013  0.9426751592  14.371257485  1.0121358915  2.2566514015  600           0.1686699351 
0.9943862270  0.9686943974  0.9957291031  0.9779951100  0.9978678038  0.9764957265  0.9917664671  0.9970059880  0.9837786260  0.9515923567  21.556886227  0.8787226673  2.2566514015  900           0.1679260135 
0.9958832330  0.9639550379  0.9987797437  0.9633251834  0.9962686567  0.9807692308  0.9917664671  1.0000000000  0.9872773537  0.9477707006  28.742514970  0.8139004928  2.2566514015  1200          0.1688604363 
0.9958832330  0.9701874208  0.9981696156  0.9731051345  0.9984008529  0.9807692308  0.9917664671  1.0000000000  0.9901399491  0.9566878981  35.928143712  0.7781232935  2.2566514015  1500          0.1677274052 
0.9962574845  0.9621059081  0.9981696156  0.9633251834  0.9989339019  0.9764957265  0.9925149701  1.0000000000  0.9910941476  0.9464968153  43.113772455  0.7657929512  2.2566514015  1800          0.1675552503 
0.9943862270  0.9638729061  0.9987797437  0.9682151589  0.9989339019  0.9743589744  0.9917664671  0.9970059880  0.9907760814  0.9490445860  50.299401197  0.7273001883  2.2566514015  2100          0.1675409079 
0.9940119756  0.9634618875  0.9981696156  0.9682151589  0.9984008529  0.9807692308  0.9910179641  0.9970059880  0.9910941476  0.9414012739  57.485029940  0.6922328103  2.2566514015  2400          0.1685635599 
0.9932634726  0.9728172856  1.0000000000  0.9779951100  1.0000000000  0.9850427350  0.9895209581  0.9970059880  0.9955470738  0.9554140127  64.670658682  0.6808701437  2.2566514015  2700          0.1684391149 
0.9936377241  0.9657562970  1.0000000000  0.9657701711  0.9994669510  0.9786324786  0.9902694611  0.9970059880  0.9923664122  0.9528662420  71.856287425  0.6790689731  2.2566514015  3000          0.1690946515 
0.9932634726  0.9739199038  0.9993898719  0.9804400978  0.9994669510  0.9871794872  0.9895209581  0.9970059880  0.9939567430  0.9541401274  79.041916167  0.6601870972  2.2566514015  3300          0.1684252278 
0.9925149696  0.9650576561  1.0000000000  0.9657701711  0.9984008529  0.9829059829  0.9880239521  0.9970059880  0.9952290076  0.9464968153  86.227544910  0.5505965707  5.3973727226  3600          0.1897746420 
0.9925149696  0.9662151487  0.9993898719  0.9731051345  0.9989339019  0.9764957265  0.9880239521  0.9970059880  0.9942748092  0.9490445860  93.413173652  0.4000255753  5.3973727226  3900          0.2066978677 
0.9928892211  0.9707626653  1.0000000000  0.9731051345  1.0000000000  0.9850427350  0.9887724551  0.9970059880  0.9942748092  0.9541401274  100.59880239  0.3704183659  5.3973727226  4200          0.2061649911 
0.9913922151  0.9640713930  1.0000000000  0.9657701711  0.9994669510  0.9850427350  0.9887724551  0.9940119760  0.9961832061  0.9414012739  107.78443113  0.3512865564  5.3973727226  4500          0.2056730143 
0.9913922151  0.9687286584  1.0000000000  0.9755501222  0.9994669510  0.9764957265  0.9887724551  0.9940119760  0.9949109415  0.9541401274  114.97005988  0.3402543604  5.3973727226  4800          0.2062484980 
0.9913922151  0.9640578209  0.9993898719  0.9559902200  0.9989339019  0.9807692308  0.9887724551  0.9940119760  0.9965012723  0.9554140127  119.76047904  0.3335424684  5.3973727226  5000          0.2046372402 
