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: 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: 3
	output_dir: sweep/ablation3/outputs/bfc13b1b5dabc5f42d1679fb827a07cd
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1564735604
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [1]
	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: 0.8610090196552951
	lambda2: 0.5777772877463595
	last_k_epoch: 0.32350558703299503
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 2.866557071912062
	weight_decay: 0.0001
	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    
0.0156000425  0.1221871056  0.0975481149  0.0991561181  0.0137373219  0.0174627632  0.1442065491  0.1687657431  0.0926280008  0.0986394558  0.0000000000  4.5713472366  2.0074815750  0             1.4908866882 
0.2233421449  0.6159635418  0.6981281308  0.7004219409  0.2227500321  0.2239342578  0.5925692695  0.5717884131  0.5882727852  0.5756802721  3.0226700252  2.7536212118  2.2545704842  300           0.1569420783 
0.3668867364  0.7317488259  0.8399683628  0.8270042194  0.3721915522  0.3615819209  0.7222921914  0.6964735516  0.7214786488  0.6717687075  6.0453400504  2.3929577001  2.2545704842  600           0.1814125641 
0.3950692515  0.7743211158  0.8737147377  0.8586497890  0.4013352163  0.3888032871  0.7534634761  0.7644836272  0.7501593372  0.6998299320  9.0680100756  2.2960568662  2.2545704842  900           0.1818256068 
0.4072027022  0.7851854585  0.9021882415  0.8713080169  0.4107074079  0.4036979969  0.7827455919  0.7682619647  0.7788400255  0.7159863946  12.090680100  2.2088009671  2.2545704842  1200          0.1841212241 
0.4111830037  0.8021062863  0.9224887951  0.8945147679  0.4135319040  0.4088341037  0.8051007557  0.7745591940  0.7894625027  0.7372448980  15.113350125  2.1250000628  2.2545704842  1500          0.1805712986 
0.4152918232  0.8154574191  0.9261798049  0.9050632911  0.4145589935  0.4160246533  0.8221032746  0.7947103275  0.8090078606  0.7465986395  18.136020151  2.0461588669  2.2545704842  1800          0.1843787130 
0.4135584778  0.8266882189  0.9372528342  0.9029535865  0.4131467454  0.4139702106  0.8372166247  0.8211586902  0.8209050351  0.7559523810  21.158690176  1.9679680590  2.2545704842  2100          0.1832712452 
0.4122744841  0.8385391618  0.9477985763  0.9240506329  0.4126332007  0.4119157678  0.8463476071  0.8211586902  0.8374760994  0.7704081633  24.181360201  1.8942736018  2.2545704842  2400          0.1835543434 
0.4133657996  0.8485640095  0.9491167941  0.9261603376  0.4143022211  0.4124293785  0.8699622166  0.8287153652  0.8440620353  0.7908163265  27.204030226  1.8308678544  2.2545704842  2700          0.1835302687 
0.4142645359  0.8576488729  0.9557078829  0.9314345992  0.4155860829  0.4129429892  0.8750000000  0.8387909320  0.8538347143  0.8027210884  30.226700251  1.7754242074  2.2545704842  3000          0.1846576254 
0.4142003098  0.8549614568  0.9580806749  0.9335443038  0.4159712415  0.4124293785  0.8916876574  0.8362720403  0.8640322923  0.7950680272  33.249370277  1.7301788541  2.2545704842  3300          0.1798628887 
0.4124027713  0.8569923354  0.9609807540  0.9345991561  0.4144306073  0.4103749358  0.9011335013  0.8413098237  0.8706182282  0.7950680272  36.272040302  1.3801504755  5.3988366127  3600          0.2090114808 
0.4089360806  0.8701818756  0.9662536251  0.9356540084  0.4116061112  0.4062660503  0.9001889169  0.8551637280  0.8701933291  0.8197278912  39.294710327  1.2122728248  5.3988366127  3900          0.2217116984 
0.4069460287  0.8660196834  0.9657263380  0.9430379747  0.4086532289  0.4052388290  0.9096347607  0.8463476071  0.8727427236  0.8086734694  42.317380352  1.1841431932  5.3988366127  4200          0.2208934005 
0.4054052297  0.8719400441  0.9694173477  0.9345991561  0.4081396842  0.4026707756  0.9099496222  0.8589420655  0.8825154026  0.8222789116  45.340050377  1.1781223583  5.3988366127  4500          0.2203492181 
0.4036719172  0.8782586111  0.9667809122  0.9409282700  0.4062138914  0.4011299435  0.9112090680  0.8690176322  0.8886764393  0.8248299320  48.362720403  1.1553413777  5.3988366127  4800          0.2201848833 
0.4034793380  0.8847778313  0.9733720011  0.9451476793  0.4058287328  0.4011299435  0.9184508816  0.8664987406  0.8876141916  0.8426870748  50.377833753  1.1563119724  5.3988366127  5000          0.2193048084 
