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: 4
	output_dir: sweep/ablation3/outputs/cc455f2fd8b2016afdc383ada341dbb0
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1549172261
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [1]
	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: 0.5122476832929141
	lambda2: 0.9892716333303577
	last_k_epoch: 0.2899069879248226
	lr: 3e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 2.19589595208549
	weight_decay: 0.0001
	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    
0.1811523882  0.1776824001  0.1519219036  0.1735941320  0.1913646055  0.1709401709  0.2140718563  0.2065868263  0.1663486005  0.1528662420  0.0000000000  7.4370293617  2.1691875458  0             1.4238286018 
0.8108450418  0.9633080059  0.9737644905  0.9608801956  0.7990405117  0.8226495726  0.9962574850  0.9940119760  0.9459287532  0.9350318471  7.1856287425  2.5615972068  2.4361939430  300           0.1536535660 
0.8297887846  0.9642032270  0.9896278218  0.9682151589  0.8176972281  0.8418803419  1.0000000000  0.9970059880  0.9701017812  0.9273885350  14.371257485  1.0180486957  2.4361939430  600           0.1735994045 
0.8327251103  0.9674974717  0.9902379500  0.9755501222  0.8192963753  0.8461538462  1.0000000000  0.9970059880  0.9751908397  0.9299363057  21.556886227  0.9058574345  2.4361939430  900           0.1698822602 
0.8353903555  0.9656484172  0.9920683344  0.9559902200  0.8246268657  0.8461538462  1.0000000000  0.9970059880  0.9790076336  0.9439490446  28.742514970  0.8387502962  2.4361939430  1200          0.1706205495 
0.8375225517  0.9631249979  0.9963392312  0.9633251834  0.8288912580  0.8461538462  1.0000000000  0.9910179641  0.9812340967  0.9350318471  35.928143712  0.7977324339  2.4361939430  1500          0.1696830328 
0.8396570258  0.9699406659  0.9975594875  0.9633251834  0.8310234542  0.8482905983  0.9992514970  1.0000000000  0.9863231552  0.9464968153  43.113772455  0.7584301700  2.4361939430  1800          0.1704966871 
0.8412584509  0.9670954010  0.9963392312  0.9633251834  0.8320895522  0.8504273504  1.0000000000  0.9940119760  0.9844147583  0.9439490446  50.299401197  0.7308770023  2.4361939430  2100          0.1696821896 
0.8439259741  0.9639742548  0.9969493594  0.9633251834  0.8352878465  0.8525641026  1.0000000000  0.9910179641  0.9879134860  0.9375796178  57.485029940  0.7253384530  2.4361939430  2400          0.1703724464 
0.8487302494  0.9705383928  0.9993898719  0.9706601467  0.8384861407  0.8589743590  1.0000000000  0.9970059880  0.9860050891  0.9439490446  64.670658682  0.7044854796  2.4361939430  2700          0.1714246202 
0.8500605940  0.9707114912  0.9987797437  0.9779951100  0.8432835821  0.8568376068  1.0000000000  0.9940119760  0.9882315522  0.9401273885  71.856287425  0.6917233145  2.4361939430  3000          0.1708412997 
0.8495252670  0.9679906221  0.9981696156  0.9706601467  0.8443496802  0.8547008547  0.9992514970  0.9970059880  0.9914122137  0.9363057325  79.041916167  0.6992769697  2.4361939430  3300          0.1730385669 
0.8511289701  0.9689670132  0.9981696156  0.9706601467  0.8432835821  0.8589743590  1.0000000000  0.9910179641  0.9888676845  0.9452229299  86.227544910  0.6256909180  5.3954119682  3600          0.1770358062 
0.8521950682  0.9736595390  1.0000000000  0.9706601467  0.8454157783  0.8589743590  1.0000000000  1.0000000000  0.9904580153  0.9503184713  93.413173652  0.3707001878  5.3954119682  3900          0.2068251586 
0.8532611663  0.9684837725  0.9987797437  0.9657701711  0.8475479744  0.8589743590  0.9992514970  0.9970059880  0.9936386768  0.9426751592  100.59880239  0.3418408167  5.3954119682  4200          0.2059503977 
0.8537964933  0.9671756262  0.9987797437  0.9682151589  0.8464818763  0.8611111111  1.0000000000  0.9970059880  0.9923664122  0.9363057325  107.78443113  0.3304404976  5.3954119682  4500          0.2053993559 
0.8543295423  0.9685982586  0.9969493594  0.9682151589  0.8475479744  0.8611111111  1.0000000000  1.0000000000  0.9933206107  0.9375796178  114.97005988  0.3162153400  5.3954119682  4800          0.2051618958 
0.8553979184  0.9708827961  0.9987797437  0.9657701711  0.8475479744  0.8632478632  0.9992514970  0.9940119760  0.9926844784  0.9528662420  119.76047904  0.3088139460  5.3954119682  5000          0.2083891571 
