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: PACS
	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/9693332f56647ba3ebe08f052121bfe2
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1695073963
	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    
0.1787559455  0.1965301856  0.1708358755  0.1784841076  0.1844349680  0.1730769231  0.2020958084  0.1856287425  0.2375954198  0.2254777070  0.0000000000  7.3823475838  2.1691875458  0             1.4990987778 
0.8151117121  0.9529790306  0.9810860281  0.9388753056  0.8054371002  0.8247863248  0.9977544910  0.9850299401  0.9481552163  0.9350318471  7.1856287425  2.5025067015  2.4373230934  300           0.1576323883 
0.8372514712  0.9701822863  0.9926784625  0.9657701711  0.8326226013  0.8418803419  1.0000000000  0.9970059880  0.9589694656  0.9477707006  14.371257485  1.0146692594  2.4373230934  600           0.1708683252 
0.8396501918  0.9691383181  0.9963392312  0.9584352078  0.8374200426  0.8418803419  1.0000000000  0.9910179641  0.9710559796  0.9579617834  21.556886227  0.8870611658  2.4373230934  900           0.1691745210 
0.8420511905  0.9643285677  0.9938987187  0.9486552567  0.8400852878  0.8440170940  1.0000000000  0.9940119760  0.9739185751  0.9503184713  28.742514970  0.8274871087  2.4373230934  1200          0.1720114326 
0.8452563187  0.9734648278  0.9957291031  0.9633251834  0.8400852878  0.8504273504  0.9992514970  0.9940119760  0.9751908397  0.9630573248  35.928143712  0.7787377696  2.4373230934  1500          0.1734734758 
0.8468577438  0.9701020611  0.9957291031  0.9608801956  0.8411513859  0.8525641026  1.0000000000  0.9940119760  0.9761450382  0.9554140127  43.113772455  0.7436145474  2.4373230934  1800          0.1731840356 
0.8495275450  0.9720421953  0.9975594875  0.9633251834  0.8422174840  0.8568376068  0.9992514970  0.9910179641  0.9799618321  0.9617834395  50.299401197  0.7166859263  2.4373230934  2100          0.1715330354 
0.8487279714  0.9760928236  0.9969493594  0.9682151589  0.8406183369  0.8568376068  1.0000000000  0.9970059880  0.9825063613  0.9630573248  57.485029940  0.7123301522  2.4373230934  2400          0.1741488004 
0.8508624456  0.9756339342  0.9987797437  0.9706601467  0.8427505330  0.8589743590  1.0000000000  0.9970059880  0.9844147583  0.9592356688  64.670658682  0.7031104758  2.4373230934  2700          0.1733316342 
0.8538010493  0.9681060532  0.9975594875  0.9608801956  0.8422174840  0.8653846154  1.0000000000  0.9880239521  0.9825063613  0.9554140127  71.856287425  0.6758117579  2.4373230934  3000          0.1720464365 
0.8516642971  0.9773667090  0.9987797437  0.9682151589  0.8422174840  0.8611111111  0.9992514970  0.9970059880  0.9872773537  0.9668789809  79.041916167  0.6554058767  2.4373230934  3300          0.1727695664 
0.8543340983  0.9750948196  0.9987797437  0.9682151589  0.8432835821  0.8653846154  1.0000000000  0.9940119760  0.9847328244  0.9630573248  86.227544910  0.6516528192  2.4373230934  3600          0.1725711115 
0.8527326732  0.9760486530  0.9987797437  0.9804400978  0.8422174840  0.8632478632  0.9992514970  0.9910179641  0.9869592875  0.9566878981  93.413173652  0.5705613867  5.3973193169  3900          0.1828655481 
0.8514000506  0.9742455627  0.9987797437  0.9682151589  0.8395522388  0.8632478632  1.0000000000  0.9940119760  0.9901399491  0.9605095541  100.59880239  0.3672446149  5.3973193169  4200          0.2060445603 
0.8500651500  0.9738551952  0.9987797437  0.9657701711  0.8390191898  0.8611111111  1.0000000000  0.9940119760  0.9888676845  0.9617834395  107.78443113  0.3360959342  5.3973193169  4500          0.2067921193 
0.8497963475  0.9710198400  0.9969493594  0.9559902200  0.8406183369  0.8589743590  1.0000000000  0.9940119760  0.9879134860  0.9630573248  114.97005988  0.3236389681  5.3973193169  4800          0.2051125137 
0.8511312481  0.9741310766  0.9987797437  0.9657701711  0.8411513859  0.8611111111  1.0000000000  0.9910179641  0.9860050891  0.9656050955  119.76047904  0.3148174964  5.3973193169  5000          0.2069714797 
