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: [0]
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: 0
	output_dir: sweep/ablation3/outputs/0c16543dcb63a211d4444b3fb5621688
	save_linear_probed_clip: False
	save_model_every_checkpoint: False
	seed: 1913065099
	skip_model_save: False
	steps: 5001
	sweep: True
	task: domain_generalization
	test_envs: [0]
	trial_seed: 1
	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: 0.5
	lambda2: 0.5
	last_k_epoch: 0.25
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet_dropout: 0
	student_model: resnet
	temperature: 3
	weight_decay: 0.0
	worst_case_p: 0.3333333333333333
using normal transform
using augment 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    
0.1502280148  0.1443226509  0.1513117755  0.1491442543  0.1076759062  0.1260683761  0.1803892216  0.1616766467  0.1374045802  0.1452229299  0.0000000000  6.4224696159  2.3338298798  0             1.4374055862 
0.9132812507  0.9649265786  0.9145820622  0.9119804401  0.9834754797  0.9572649573  0.9970059880  0.9910179641  0.9599236641  0.9464968153  7.1856287425  1.8526837750  2.5978608131  300           0.1585221386 
0.9251899373  0.9708849867  0.9261744966  0.9242053790  0.9914712154  0.9636752137  1.0000000000  0.9910179641  0.9761450382  0.9579617834  14.371257485  0.7162966711  2.5978608131  600           0.1755463179 
0.9300754376  0.9727421196  0.9310555217  0.9290953545  0.9973347548  0.9615384615  0.9992514970  1.0000000000  0.9825063613  0.9566878981  21.556886227  0.6227328927  2.5978608131  900           0.1736411691 
0.9349631755  0.9760040099  0.9334960342  0.9364303178  0.9989339019  0.9679487179  0.9992514970  0.9970059880  0.9853689567  0.9630573248  28.742514970  0.5822773856  2.5978608131  1200          0.1776257920 
0.9328254895  0.9710318648  0.9316656498  0.9339853301  0.9989339019  0.9594017094  0.9992514970  0.9970059880  0.9875954198  0.9566878981  35.928143712  0.5421600360  2.5978608131  1500          0.1762824202 
0.9337429193  0.9741703589  0.9310555217  0.9364303178  0.9978678038  0.9743589744  1.0000000000  0.9940119760  0.9914122137  0.9541401274  43.113772455  0.5356005382  2.5978608131  1800          0.1768983269 
0.9361856694  0.9777161336  0.9334960342  0.9388753056  0.9978678038  0.9743589744  1.0000000000  0.9970059880  0.9923664122  0.9617834395  50.299401197  0.4953323222  2.5978608131  2100          0.1722220405 
0.9361856694  0.9730217389  0.9334960342  0.9388753056  0.9989339019  0.9700854701  1.0000000000  0.9910179641  0.9939567430  0.9579617834  57.485029940  0.5101911831  2.5978608131  2400          0.1741151500 
0.9322153613  0.9754423753  0.9304453935  0.9339853301  0.9989339019  0.9700854701  1.0000000000  0.9970059880  0.9939567430  0.9592356688  64.670658682  0.4866631977  2.5978608131  2700          0.1734649309 
0.9312979315  0.9774285114  0.9310555217  0.9315403423  0.9994669510  0.9722222222  1.0000000000  0.9970059880  0.9955470738  0.9630573248  71.856287425  0.4788744051  2.5978608131  3000          0.1732772096 
0.9322153613  0.9777161336  0.9304453935  0.9339853301  0.9984008529  0.9743589744  1.0000000000  0.9970059880  0.9971374046  0.9617834395  79.041916167  0.4550119009  2.5978608131  3300          0.1741936088 
0.9316052332  0.9771526300  0.9292251373  0.9339853301  0.9989339019  0.9722222222  1.0000000000  1.0000000000  0.9961832061  0.9592356688  86.227544910  0.4515726432  2.5978608131  3600          0.1746907822 
0.9322153613  0.9760058789  0.9304453935  0.9339853301  0.9984008529  0.9722222222  1.0000000000  0.9940119760  0.9968193384  0.9617834395  93.413173652  0.3775850160  5.3948016167  3900          0.1895844793 
0.9346581115  0.9771427581  0.9328859060  0.9364303178  1.0000000000  0.9743589744  1.0000000000  0.9940119760  0.9984096692  0.9630573248  100.59880239  0.2642216057  5.3948016167  4200          0.2053257720 
0.9358783677  0.9762916322  0.9353264185  0.9364303178  0.9994669510  0.9700854701  1.0000000000  0.9970059880  0.9961832061  0.9617834395  107.78443113  0.2474536272  5.3948016167  4500          0.2049473159 
0.9361834318  0.9777142646  0.9359365467  0.9364303178  0.9994669510  0.9700854701  1.0000000000  1.0000000000  0.9961832061  0.9630573248  114.97005988  0.2359863112  5.3948016167  4800          0.2056849662 
0.9349609379  0.9772915052  0.9359365467  0.9339853301  0.9994669510  0.9743589744  1.0000000000  0.9970059880  0.9974554707  0.9605095541  119.76047904  0.2297108765  5.3948016167  5000          0.2077727091 
