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/99922712e9518bb1f376647de4a3c706
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1278746636
	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    
0.1177581863  0.0584080895  0.0487740575  0.0400843882  0.0454487097  0.0416024653  0.1196473552  0.1158690176  0.0864669641  0.0935374150  0.0000000000  4.0905418396  1.7946186066  0             1.7945525646 
0.3425692694  0.6178427589  0.7519114158  0.7531645570  0.6551547054  0.6445814073  0.3350125945  0.3501259446  0.4746122796  0.4557823129  3.0226700252  2.7846101101  2.0933327675  300           0.1537432734 
0.4033375313  0.7554895484  0.8246770366  0.8364978903  0.7993323918  0.8007190550  0.3860201511  0.4206549118  0.6649670703  0.6292517007  6.0453400504  2.4361060699  2.0933327675  600           0.1721591131 
0.4308879091  0.7770260420  0.8639599262  0.8544303797  0.8212864296  0.8125321007  0.4247481108  0.4370277078  0.7136180157  0.6641156463  9.0680100756  2.3313516736  2.0933327675  900           0.1725737405 
0.4386020149  0.8029466193  0.8808331136  0.8618143460  0.8341250481  0.8335901387  0.4313602015  0.4458438287  0.7403866582  0.7134353741  12.090680100  2.2359124478  2.0933327675  1200          0.1765797520 
0.4500944582  0.8135793291  0.8955971526  0.8786919831  0.8518423418  0.8418079096  0.4417506297  0.4584382872  0.7699171447  0.7202380952  15.113350125  2.1768488832  2.0933327675  1500          0.1735425647 
0.4529282114  0.8220678977  0.9066701819  0.9029535865  0.8599306715  0.8438623523  0.4448992443  0.4609571788  0.7879753559  0.7193877551  18.136020151  2.0918599280  2.0933327675  1800          0.1742587034 
0.4645780854  0.8342051344  0.9201160032  0.8945147679  0.8664783669  0.8623523369  0.4581234257  0.4710327456  0.7996600807  0.7457482993  21.158690176  2.0375269345  2.0933327675  2100          0.1761595917 
0.4693010073  0.8505549750  0.9264434485  0.9156118143  0.8752086276  0.8715973292  0.4663098237  0.4722921914  0.8175058424  0.7644557823  24.181360201  1.9729083014  2.0933327675  2400          0.1753071698 
0.4719773297  0.8508364426  0.9414711310  0.9092827004  0.8791885993  0.8762198254  0.4741813602  0.4697732997  0.8300403654  0.7670068027  27.204030226  1.9050697664  2.0933327675  2700          0.1715908321 
0.4782745590  0.8620133350  0.9499077248  0.9261603376  0.8889459494  0.8741653826  0.4767002519  0.4798488665  0.8440620353  0.7857142857  30.226700251  1.8523517048  2.0933327675  3000          0.1725428168 
0.4864609569  0.8655455252  0.9543896652  0.9208860759  0.8965207344  0.8823831536  0.4867758186  0.4861460957  0.8470363289  0.7933673469  33.249370277  1.8049098849  2.0933327675  3300          0.1769077746 
0.4919710325  0.8725077241  0.9588716056  0.9345991561  0.8997303890  0.8870056497  0.4914987406  0.4924433249  0.8593584024  0.7959183673  36.272040302  1.4846056755  5.3967728615  3600          0.1972241108 
0.4966939544  0.8769270957  0.9620353282  0.9335443038  0.9037103608  0.8911145352  0.4959068010  0.4974811083  0.8695559805  0.8061224490  39.294710327  1.1379240243  5.3967728615  3900          0.2149465617 
0.5015743071  0.8786518743  0.9649354073  0.9356540084  0.9088458082  0.8916281459  0.5006297229  0.5025188917  0.8765668154  0.8086734694  42.317380352  1.1122001386  5.3967728615  4200          0.2202845875 
0.5053526446  0.8858544094  0.9720537833  0.9409282700  0.9171909103  0.8926553672  0.5044080605  0.5062972292  0.8829403017  0.8239795918  45.340050377  1.0939183738  5.3967728615  4500          0.2201380801 
0.5064546597  0.8908171677  0.9702082784  0.9419831224  0.9177044550  0.9013867488  0.5066120907  0.5062972292  0.8895262375  0.8290816327  48.362720403  1.0826429685  5.3967728615  4800          0.2169298569 
0.5097607050  0.8894553619  0.9688900606  0.9409282700  0.9175760688  0.9060092450  0.5094458438  0.5100755668  0.8910133843  0.8214285714  50.377833753  1.0689875913  5.3967728615  5000          0.2229538381 
