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: 2
	output_dir: sweep/ablation3/outputs/8269844104bf8b56416d312003bcf85b
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 695483037
	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: 0.8194577311903198
	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 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    
0.0867443325  0.0297011863  0.0100184550  0.0116033755  0.0190011555  0.0179763739  0.0828085642  0.0906801008  0.0756320374  0.0595238095  0.0000000000  4.4424209595  2.0074815750  0             1.5043184757 
0.4056989922  0.6615145936  0.6754547851  0.6719409283  0.7534985236  0.7462763225  0.4096347607  0.4017632242  0.5717017208  0.5663265306  3.0226700252  2.8862904612  2.2580189705  300           0.1573124321 
0.4565491182  0.7618270047  0.8336409175  0.8090717300  0.8030555912  0.7765793529  0.4697732997  0.4433249370  0.6887614192  0.6998299320  6.0453400504  2.4905474607  2.2580189705  600           0.1816166035 
0.4724496219  0.7788495081  0.8710783021  0.8322784810  0.8225702914  0.7976373909  0.4826826196  0.4622166247  0.7117059698  0.7066326531  9.0680100756  2.3600969585  2.2580189705  900           0.1831096037 
0.4940176320  0.8106882511  0.8911152122  0.8713080169  0.8411862884  0.8269131998  0.5018891688  0.4861460957  0.7439983004  0.7338435374  12.090680100  2.2575565688  2.2580189705  1200          0.1806005716 
0.5067695212  0.8216453177  0.9106248352  0.8723628692  0.8535113622  0.8289676425  0.5147984887  0.4987405542  0.7701295942  0.7636054422  15.113350125  2.1683652174  2.2580189705  1500          0.1787268599 
0.5105478587  0.8370660494  0.9256525178  0.8892405063  0.8649377327  0.8489984592  0.5198362720  0.5012594458  0.7928616953  0.7729591837  18.136020151  2.1004543996  2.2580189705  1800          0.1810722907 
0.5130667504  0.8468670055  0.9340891115  0.9008438819  0.8764924894  0.8659476117  0.5286523929  0.4974811083  0.8081580625  0.7738095238  21.158690176  2.0228320098  2.2580189705  2100          0.1803675771 
0.5116498738  0.8534215677  0.9417347746  0.9103375527  0.8831685711  0.8608115049  0.5245591940  0.4987405542  0.8211174846  0.7891156463  24.181360201  1.9631314150  2.2580189705  2400          0.1806087462 
0.5179471030  0.8633192874  0.9464803586  0.9166666667  0.8913852869  0.8705701079  0.5295969773  0.5062972292  0.8304652645  0.8027210884  27.204030226  1.8895555715  2.2580189705  2700          0.1804242436 
0.5173173801  0.8722785221  0.9543896652  0.9251054852  0.8967775067  0.8813559322  0.5321158690  0.5025188917  0.8440620353  0.8103741497  30.226700251  1.8354923709  2.2580189705  3000          0.1808836381 
0.5244017630  0.8771396351  0.9543896652  0.9293248945  0.9012710232  0.8798151002  0.5374685139  0.5113350126  0.8546845124  0.8222789116  33.249370277  1.7828605807  2.2580189705  3300          0.1831694198 
0.5269206546  0.8822365898  0.9609807540  0.9335443038  0.9060213121  0.8823831536  0.5399874055  0.5138539043  0.8633949437  0.8307823129  36.272040302  1.7500697740  2.2580189705  3600          0.1788238525 
0.5275503776  0.8799746938  0.9625626153  0.9335443038  0.9051226088  0.8849512070  0.5412468514  0.5138539043  0.8670065859  0.8214285714  39.294710327  1.5456738373  5.3947548866  3900          0.1997512023 
0.5300692693  0.8882060038  0.9683627735  0.9335443038  0.9139812556  0.8900873138  0.5425062972  0.5176322418  0.8695559805  0.8409863946  42.317380352  1.2909530838  5.3947548866  4200          0.2168426728 
0.5327455917  0.8875397760  0.9670445558  0.9282700422  0.9155218898  0.8967642527  0.5428211587  0.5226700252  0.8782664117  0.8375850340  45.340050377  1.2662255498  5.3947548866  4500          0.2194917448 
0.5340050375  0.8873300118  0.9673081993  0.9367088608  0.9147515727  0.8885464818  0.5428211587  0.5251889169  0.8810282558  0.8367346939  48.362720403  1.2535182595  5.3947548866  4800          0.2222024568 
0.5318010073  0.8898862842  0.9694173477  0.9251054852  0.9157786622  0.8967642527  0.5396725441  0.5239294710  0.8869768430  0.8477891156  50.377833753  1.2388003302  5.3947548866  5000          0.2201825988 
