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: 2
	output_dir: sweep/ablation3/outputs/609d34a8cbfa4560516a3286302fee1d
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 731180883
	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: 0.8194577311903198
	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 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    
0.1796407185  0.1163936481  0.1561928005  0.1540342298  0.0927505330  0.1047008547  0.1766467066  0.1826347305  0.0916030534  0.0904458599  0.0000000000  7.7239060402  2.0074062347  0             1.4485321045 
0.9962574845  0.9486276218  0.9713239780  0.9535452323  0.9722814499  0.9636752137  0.9925149701  1.0000000000  0.9389312977  0.9286624204  7.1856287425  3.1391557948  2.2544541359  300           0.1499637445 
0.9958832330  0.9547436118  0.9871873093  0.9706601467  0.9845415778  0.9572649573  0.9947604790  0.9970059880  0.9602417303  0.9363057325  14.371257485  1.2245176891  2.2544541359  600           0.1740712325 
0.9958832330  0.9569488482  0.9914582062  0.9755501222  0.9941364606  0.9615384615  0.9947604790  0.9970059880  0.9688295165  0.9337579618  21.556886227  1.0452414529  2.2544541359  900           0.1730975898 
0.9958832330  0.9479838930  0.9945088469  0.9486552567  0.9946695096  0.9615384615  0.9947604790  0.9970059880  0.9736005089  0.9337579618  28.742514970  0.9526296002  2.2544541359  1200          0.1712900257 
0.9936377241  0.9581885103  0.9975594875  0.9682151589  0.9962686567  0.9636752137  0.9932634731  0.9940119760  0.9758269720  0.9426751592  35.928143712  0.9150146931  2.2544541359  1500          0.1722863913 
0.9936377241  0.9594760055  0.9945088469  0.9682151589  0.9968017058  0.9700854701  0.9932634731  0.9940119760  0.9777353690  0.9401273885  43.113772455  0.8763512687  2.2544541359  1800          0.1719465987 
0.9936377241  0.9622907474  0.9957291031  0.9706601467  0.9978678038  0.9786324786  0.9932634731  0.9940119760  0.9802798982  0.9375796178  50.299401197  0.8456496896  2.2544541359  2100          0.1722794310 
0.9936377241  0.9595581374  0.9963392312  0.9633251834  0.9968017058  0.9764957265  0.9932634731  0.9940119760  0.9805979644  0.9388535032  57.485029940  0.8347765758  2.2544541359  2400          0.1719516087 
0.9951347300  0.9662287585  0.9987797437  0.9731051345  0.9984008529  0.9829059829  0.9932634731  0.9970059880  0.9866412214  0.9426751592  64.670658682  0.8169365281  2.2544541359  2700          0.1707371386 
0.9947604785  0.9582021579  0.9987797437  0.9584352078  0.9973347548  0.9722222222  0.9925149701  0.9970059880  0.9834605598  0.9439490446  71.856287425  0.8062822203  2.2544541359  3000          0.1716591040 
0.9947604785  0.9569488859  0.9987797437  0.9657701711  0.9973347548  0.9636752137  0.9925149701  0.9970059880  0.9821882952  0.9414012739  79.041916167  0.7953017993  2.2544541359  3300          0.1702246443 
0.9947604785  0.9646536411  0.9987797437  0.9731051345  0.9973347548  0.9743589744  0.9925149701  0.9970059880  0.9853689567  0.9464968153  86.227544910  0.7882083668  2.2544541359  3600          0.1703737720 
0.9947604785  0.9603458758  0.9987797437  0.9755501222  0.9962686567  0.9615384615  0.9925149701  0.9970059880  0.9856870229  0.9439490446  93.413173652  0.6271393426  5.3987078667  3900          0.1882186755 
0.9947604785  0.9636331170  0.9993898719  0.9755501222  0.9994669510  0.9764957265  0.9925149701  0.9970059880  0.9844147583  0.9388535032  100.59880239  0.4225317001  5.3987078667  4200          0.2064983781 
0.9947604785  0.9659068753  0.9981696156  0.9755501222  0.9994669510  0.9807692308  0.9925149701  0.9970059880  0.9850508906  0.9414012739  107.78443113  0.4025272520  5.3987078667  4500          0.2051264898 
0.9947604785  0.9639071294  0.9981696156  0.9755501222  0.9973347548  0.9722222222  0.9925149701  0.9970059880  0.9885496183  0.9439490446  114.97005988  0.3860037190  5.3987078667  4800          0.2062882900 
0.9955089815  0.9596951436  0.9993898719  0.9633251834  0.9978678038  0.9743589744  0.9940119760  0.9970059880  0.9856870229  0.9414012739  119.76047904  0.3695800441  5.3987078667  5000          0.2067667973 
