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: 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: 3
	output_dir: sweep/ablation3/outputs/10f4cb53ade4cb1a5b1c540478689e69
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1641256908
	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: 0.5694511041907044
	lambda2: 0.6621108818243455
	last_k_epoch: 0.20248994529588413
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 4.502579147595572
	weight_decay: 0.0001
	worst_case_p: 0.2
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    
0.0553892215  0.1321936390  0.1201952410  0.1418092910  0.1364605544  0.1324786325  0.0598802395  0.0508982036  0.1170483461  0.1222929936  0.0000000000  10.458591461  1.7945718765  0             1.4944403172 
0.9910179636  0.9391286489  0.9725442343  0.9413202934  0.9770788913  0.9423076923  0.9910179641  0.9910179641  0.9615139949  0.9337579618  7.1856287425  3.0867992270  2.0936598778  300           0.1427724489 
0.9947604785  0.9625647598  0.9981696156  0.9706601467  0.9930703625  0.9743589744  0.9925149701  0.9970059880  0.9799618321  0.9426751592  14.371257485  1.2048143289  2.0936598778  600           0.1598603551 
0.9947604785  0.9623592694  0.9981696156  0.9657701711  0.9946695096  0.9786324786  0.9925149701  0.9970059880  0.9850508906  0.9426751592  21.556886227  1.0308643178  2.0936598778  900           0.1669095437 
0.9951347300  0.9605786614  0.9969493594  0.9608801956  0.9968017058  0.9743589744  0.9932634731  0.9970059880  0.9875954198  0.9464968153  28.742514970  0.9219838683  2.0936598778  1200          0.1655362161 
0.9943862270  0.9648999988  0.9993898719  0.9682151589  0.9973347548  0.9914529915  0.9917664671  0.9970059880  0.9901399491  0.9350318471  35.928143712  0.8900434816  2.0936598778  1500          0.1651300852 
0.9940119756  0.9662836706  0.9993898719  0.9682151589  0.9984008529  0.9764957265  0.9910179641  0.9970059880  0.9939567430  0.9541401274  43.113772455  0.8463920895  2.0936598778  1800          0.1606457988 
0.9940119756  0.9698791474  0.9993898719  0.9755501222  0.9978678038  0.9850427350  0.9910179641  0.9970059880  0.9930025445  0.9490445860  50.299401197  0.7991730843  2.0936598778  2100          0.1628547343 
0.9943862270  0.9657905202  1.0000000000  0.9731051345  0.9989339019  0.9764957265  0.9917664671  0.9970059880  0.9917302799  0.9477707006  57.485029940  0.7663495137  2.0936598778  2400          0.1638733157 
0.9951347300  0.9679957943  0.9993898719  0.9682151589  0.9994669510  0.9829059829  0.9932634731  0.9970059880  0.9949109415  0.9528662420  64.670658682  0.7666994584  2.0936598778  2700          0.1636270150 
0.9947604785  0.9690298905  0.9987797437  0.9755501222  0.9994669510  0.9850427350  0.9925149701  0.9970059880  0.9942748092  0.9464968153  71.856287425  0.7621917685  2.0936598778  3000          0.1672162644 
0.9951347300  0.9639071671  0.9993898719  0.9657701711  0.9989339019  0.9743589744  0.9932634731  0.9970059880  0.9930025445  0.9515923567  79.041916167  0.7738354355  2.0936598778  3300          0.1677373552 
0.9951347300  0.9730706469  1.0000000000  0.9804400978  1.0000000000  0.9871794872  0.9932634731  0.9970059880  0.9971374046  0.9515923567  86.227544910  0.7363459091  2.0936598778  3600          0.1671999931 
0.9951347300  0.9696942704  1.0000000000  0.9779951100  0.9994669510  0.9807692308  0.9932634731  0.9970059880  0.9965012723  0.9503184713  93.413173652  0.7281788422  2.0936598778  3900          0.1645113691 
0.9947604785  0.9689271830  1.0000000000  0.9633251834  0.9994669510  0.9893162393  0.9925149701  0.9970059880  0.9955470738  0.9541401274  100.59880239  0.5429150272  5.3983359337  4200          0.2014152789 
0.9947604785  0.9715297904  0.9993898719  0.9779951100  1.0000000000  0.9786324786  0.9925149701  0.9970059880  0.9968193384  0.9579617834  107.78443113  0.4254287120  5.3983359337  4500          0.2132022921 
0.9947604785  0.9700161536  1.0000000000  0.9755501222  1.0000000000  0.9829059829  0.9925149701  0.9970059880  0.9971374046  0.9515923567  114.97005988  0.3922895401  5.3983359337  4800          0.2144951916 
0.9951347300  0.9686052998  0.9993898719  0.9657701711  1.0000000000  0.9871794872  0.9932634731  0.9970059880  0.9971374046  0.9528662420  119.76047904  0.3772307944  5.3983359337  5000          0.2143620420 
