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: 2
	output_dir: sweep/ablation3/outputs/a95a9c59758d6081f3ef9199a6ccef90
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1670918098
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [1]
	trial_seed: 0
	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: 0.7419102898692578
	lambda2: 0.5215383105176172
	last_k_epoch: 0.23762453204831346
	lr: 3e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 2.9748462276330736
	weight_decay: 1e-06
	worst_case_p: 0.3
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    
0.0668289331  0.0891595311  0.0685473240  0.0685654008  0.0725381949  0.0611196713  0.1590050378  0.1385390428  0.0635224134  0.0603741497  0.0000000000  4.9692463875  2.1692652702  0             1.5016920567 
0.2693080260  0.6842215463  0.8236224624  0.7974683544  0.2581846193  0.2804314330  0.6117758186  0.6259445844  0.6364988315  0.6292517007  3.0226700252  2.6782520485  2.4375615120  300           0.1624820145 
0.3236191082  0.7594016768  0.8650145004  0.8533755274  0.3087687765  0.3384694402  0.7405541562  0.7267002519  0.7157425112  0.6981292517  6.0453400504  2.2623860530  2.4375615120  600           0.1889546935 
0.3420433830  0.7864836395  0.8950698655  0.8850210970  0.3322634485  0.3518233179  0.7742443325  0.7380352645  0.7554705757  0.7363945578  9.0680100756  2.1296644382  2.4375615120  900           0.1889503638 
0.3624579405  0.7994617628  0.9100975481  0.8924050633  0.3551161895  0.3697996918  0.8054156171  0.7644836272  0.7854259613  0.7414965986  12.090680100  2.0003382667  2.4375615120  1200          0.1894540040 
0.3776084995  0.8251841429  0.9285525969  0.9103375527  0.3700089870  0.3852080123  0.8236775819  0.7871536524  0.8068833652  0.7780612245  15.113350125  1.9275467141  2.4375615120  1500          0.1883604336 
0.3873022170  0.8368198912  0.9388346955  0.9261603376  0.3806650404  0.3939393939  0.8444584383  0.7909319899  0.8177182919  0.7933673469  18.136020151  1.8201645811  2.4375615120  1800          0.1847210852 
0.3972527398  0.8429232907  0.9438439230  0.9251054852  0.3913210939  0.4031843862  0.8664987406  0.8060453401  0.8393881453  0.7976190476  21.158690176  1.7218256728  2.4375615120  2100          0.1831955981 
0.4048922784  0.8510895077  0.9562351701  0.9398734177  0.3978687893  0.4119157678  0.8699622166  0.8098236776  0.8444869344  0.8035714286  24.181360201  1.6746944018  2.4375615120  2400          0.1853061167 
0.4122108844  0.8545523782  0.9583443185  0.9314345992  0.4032610091  0.4211607601  0.8841309824  0.8337531486  0.8616953474  0.7984693878  27.204030226  1.6136210998  2.4375615120  2700          0.1894810875 
0.4165121184  0.8610500979  0.9604534669  0.9419831224  0.4072409809  0.4257832563  0.8926322418  0.8324937028  0.8676439346  0.8086734694  30.226700251  1.5741572249  2.4375615120  3000          0.1865091912 
0.4184380430  0.8706337653  0.9636171896  0.9398734177  0.4090383875  0.4278376990  0.9071158690  0.8463476071  0.8810282558  0.8256802721  33.249370277  1.5445503422  2.4375615120  3300          0.1895343717 
0.4205565470  0.8794605416  0.9670445558  0.9440928270  0.4112209526  0.4298921418  0.9074307305  0.8677581864  0.8840025494  0.8265306122  36.272040302  1.5464323326  2.4375615120  3600          0.1878502170 
0.4213269630  0.8823056912  0.9694173477  0.9462025316  0.4112209526  0.4314329738  0.9175062972  0.8639798489  0.8884639898  0.8367346939  39.294710327  1.4328987960  5.3968682289  3900          0.1995953608 
0.4211343837  0.8836226795  0.9723174268  0.9409282700  0.4108357941  0.4314329738  0.9187657431  0.8740554156  0.8973868706  0.8358843537  42.317380352  1.1280939603  5.3968682289  4200          0.2250048399 
0.4188874277  0.8931955289  0.9752175059  0.9472573840  0.4094235460  0.4283513097  0.9222292191  0.8853904282  0.8965370724  0.8469387755  45.340050377  1.0990990482  5.3968682289  4500          0.2298515852 
0.4175393068  0.8883622577  0.9765357237  0.9504219409  0.4077545256  0.4273240883  0.9304156171  0.8702770781  0.9105587423  0.8443877551  48.362720403  1.0911954075  5.3968682289  4800          0.2273358671 
0.4174109206  0.8922087931  0.9749538624  0.9493670886  0.4074977532  0.4273240883  0.9348236776  0.8828715365  0.9082217973  0.8443877551  50.377833753  1.0730464971  5.3968682289  5000          0.2224079502 
