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: 4
	output_dir: sweep/ablation3/outputs/a14970369aeaf3c9acd3e44e2bba8454
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1592157070
	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: 0.6171767606394463
	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 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    
0.1321822163  0.1672596516  0.3495913525  0.3586497890  0.1297984337  0.1345659990  0.0462846348  0.0453400504  0.1380922031  0.0977891156  0.0000000000  4.5154852867  1.7946186066  0             1.6729001999 
0.1702507644  0.5573808643  0.6604271026  0.6635021097  0.1740916677  0.1664098613  0.4921284635  0.4559193955  0.5818992989  0.5527210884  3.0226700252  2.6182627710  2.0937752724  300           0.1508276089 
0.2919691109  0.7146661615  0.8439230161  0.8132911392  0.2891256901  0.2948125321  0.6993073048  0.6725440806  0.7061822817  0.6581632653  6.0453400504  2.2956299384  2.0937752724  600           0.1749497223 
0.3472425290  0.7473313548  0.8803058265  0.8470464135  0.3503659006  0.3441191577  0.7150503778  0.7027707809  0.7416613554  0.6921768707  9.0680100756  2.1776443311  2.0937752724  900           0.1729044779 
0.3615586437  0.7772517793  0.8958607962  0.8734177215  0.3625625883  0.3605546995  0.7477959698  0.7329974811  0.7686424474  0.7253401361  12.090680100  2.1005214596  2.0937752724  1200          0.1733541902 
0.3762600489  0.7868606436  0.9029791722  0.8829113924  0.3734754140  0.3790446841  0.7777078086  0.7506297229  0.7917994476  0.7270408163  15.113350125  2.0305451600  2.0937752724  1500          0.1720750705 
0.3770944932  0.8118368964  0.9193250725  0.9029535865  0.3761715239  0.3780174628  0.8041561713  0.7808564232  0.8022094752  0.7517006803  18.136020151  1.9671763802  2.0937752724  1800          0.1732285134 
0.3821660113  0.8209003949  0.9306617453  0.9040084388  0.3822056747  0.3821263482  0.8268261965  0.7984886650  0.8251540259  0.7602040816  21.158690176  1.9050685998  2.0937752724  2100          0.1773170328 
0.3872374634  0.8368131491  0.9414711310  0.9229957806  0.3892669149  0.3852080123  0.8387909320  0.8110831234  0.8370512003  0.7763605442  24.181360201  1.8421470499  2.0937752724  2400          0.1765313935 
0.3930152704  0.8408603647  0.9472712892  0.9177215190  0.3941455899  0.3918849512  0.8583123426  0.8123425693  0.8449118334  0.7925170068  27.204030226  1.7858623640  2.0937752724  2700          0.1753957979 
0.3959683505  0.8483915989  0.9533350910  0.9251054852  0.3969700860  0.3949666153  0.8762594458  0.8198992443  0.8551094115  0.8001700680  30.226700251  1.7376304289  2.0937752724  3000          0.1773852181 
0.3994992343  0.8582263675  0.9586079620  0.9398734177  0.3999229683  0.3990755008  0.8816120907  0.8312342569  0.8659443382  0.8035714286  33.249370277  1.6897620404  2.0937752724  3300          0.1794952989 
0.4005905828  0.8636372145  0.9649354073  0.9388185654  0.4010784440  0.4001027221  0.8866498741  0.8425692695  0.8733800722  0.8095238095  36.272040302  1.3896360823  5.3998093605  3600          0.2007344007 
0.3998201998  0.8659516211  0.9651990509  0.9335443038  0.4005648992  0.3990755008  0.9077455919  0.8513853904  0.8829403017  0.8129251701  39.294710327  1.0710729831  5.3998093605  3900          0.2220723534 
0.4013610318  0.8751045197  0.9670445558  0.9504219409  0.4005648992  0.4021571649  0.9134130982  0.8551637280  0.8846398980  0.8197278912  42.317380352  1.0429096196  5.3998093605  4200          0.2212935948 
0.4009758403  0.8746487709  0.9709992091  0.9409282700  0.4003081268  0.4016435542  0.9108942065  0.8513853904  0.8903760357  0.8316326531  45.340050377  1.0167373838  5.3998093605  4500          0.2203689416 
0.4009758073  0.8846882685  0.9728447139  0.9419831224  0.4008216716  0.4011299435  0.9159319899  0.8727959698  0.8997238156  0.8392857143  48.362720403  1.0123469164  5.3998093605  4800          0.2201403594 
0.4012968387  0.8836333077  0.9733720011  0.9472573840  0.4004365130  0.4021571649  0.9159319899  0.8677581864  0.9001487147  0.8358843537  50.377833753  1.0032126799  5.3998093605  5000          0.2188600981 
