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/39e713f0ae4df93d5957b27920c13990
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1351236746
	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: 0.8194577311903198
	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 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    
0.1766260273  0.1225523506  0.1043319097  0.0953545232  0.1780383795  0.1752136752  0.0875748503  0.0748502994  0.1844783715  0.1974522293  0.0000000000  8.2495937347  2.0074062347  0             2.1909418106 
0.8249867873  0.9565238604  0.9823062843  0.9511002445  0.8123667377  0.8376068376  0.9992514970  1.0000000000  0.9446564885  0.9184713376  7.1856287425  2.6796893442  2.2544541359  300           0.1509517701 
0.8239161332  0.9647766025  0.9890176937  0.9682151589  0.8144989339  0.8333333333  0.9985029940  1.0000000000  0.9685114504  0.9261146497  14.371257485  1.0525001719  2.2544541359  600           0.1715069898 
0.8329893568  0.9639616066  0.9932885906  0.9657701711  0.8219616205  0.8440170940  0.9992514970  1.0000000000  0.9736005089  0.9261146497  21.556886227  0.9621494955  2.2544541359  900           0.1723387559 
0.8447323816  0.9649722588  0.9938987187  0.9633251834  0.8304904051  0.8589743590  1.0000000000  0.9940119760  0.9764631043  0.9375796178  28.742514970  0.8900971180  2.2544541359  1200          0.1731157605 
0.8441947766  0.9646621164  0.9951189750  0.9657701711  0.8336886994  0.8547008547  0.9992514970  0.9970059880  0.9793256997  0.9312101911  35.928143712  0.8755995226  2.2544541359  1500          0.1744127003 
0.8473976268  0.9659260921  0.9932885906  0.9755501222  0.8358208955  0.8589743590  1.0000000000  0.9910179641  0.9793256997  0.9312101911  43.113772455  0.8172173956  2.2544541359  1800          0.1774411662 
0.8503316745  0.9698721439  0.9975594875  0.9682151589  0.8395522388  0.8611111111  0.9985029940  1.0000000000  0.9780534351  0.9414012739  50.299401197  0.8076304990  2.2544541359  2100          0.1768876934 
0.8492632985  0.9671756262  0.9969493594  0.9682151589  0.8395522388  0.8589743590  0.9992514970  0.9970059880  0.9805979644  0.9363057325  57.485029940  0.7880594265  2.2544541359  2400          0.1755869174 
0.8471242683  0.9688398790  0.9981696156  0.9706601467  0.8416844350  0.8525641026  1.0000000000  0.9970059880  0.9802798982  0.9388535032  64.670658682  0.7736662646  2.2544541359  2700          0.1738152965 
0.8468577438  0.9669682667  0.9963392312  0.9633251834  0.8411513859  0.8525641026  0.9985029940  1.0000000000  0.9796437659  0.9375796178  71.856287425  0.7727276973  2.2544541359  3000          0.1742445072 
0.8489899400  0.9723171317  0.9981696156  0.9755501222  0.8454157783  0.8525641026  1.0000000000  1.0000000000  0.9866412214  0.9414012739  79.041916167  0.7548074883  2.2544541359  3300          0.1732317758 
0.8484568909  0.9676129028  0.9975594875  0.9657701711  0.8443496802  0.8525641026  1.0000000000  0.9880239521  0.9837786260  0.9490445860  86.227544910  0.7498980262  2.2544541359  3600          0.1757112781 
0.8521927902  0.9703310334  0.9975594875  0.9657701711  0.8475479744  0.8568376068  0.9992514970  1.0000000000  0.9844147583  0.9452229299  93.413173652  0.5589045762  5.3987078667  3900          0.1935799003 
0.8537942153  0.9709287603  0.9993898719  0.9731051345  0.8486140725  0.8589743590  0.9992514970  0.9970059880  0.9875954198  0.9426751592  100.59880239  0.3596216512  5.3987078667  4200          0.2144391934 
0.8545960669  0.9632737450  0.9993898719  0.9633251834  0.8480810235  0.8611111111  0.9992514970  0.9940119760  0.9844147583  0.9324840764  107.78443113  0.3391614761  5.3987078667  4500          0.2174159590 
0.8564640165  0.9711703806  0.9993898719  0.9755501222  0.8496801706  0.8632478632  1.0000000000  0.9940119760  0.9860050891  0.9439490446  114.97005988  0.3246604039  5.3987078667  4800          0.2146920013 
0.8588672931  0.9714678748  0.9975594875  0.9755501222  0.8502132196  0.8675213675  1.0000000000  1.0000000000  0.9831424936  0.9388535032  119.76047904  0.3191606579  5.3987078667  5000          0.2148539209 
