Environment:
	Python: 3.10.19
	PyTorch: 2.8.0+cu128
	Torchvision: 0.23.0+cu128
	CUDA: 12.8
	CUDNN: 91002
	NumPy: 2.2.6
	PIL: 12.3.0
Args:
	algorithm: ERM
	checkpoint_freq: None
	data_dir: /home/kaustubh/Datasets
	dataset: OfficeHome
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/oh/ERM_env2_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [2]
	trial_seed: 0
	uda_holdout_fraction: 0
HParams:
	batch_size: 32
	class_balanced: False
	data_augmentation: True
	dinov2: False
	freeze_bn: True
	lars: False
	linear_steps: 500
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet50_augmix: False
	resnet_dropout: 0.0
	vit: False
	vit_attn_tune: False
	vit_dropout: 0.0
	weight_decay: 0.0
/home/kaustubh/miniconda3/envs/torch_128/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
  warnings.warn(
/home/kaustubh/miniconda3/envs/torch_128/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
  warnings.warn(msg)
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    
0.0370751802  0.0247422680  0.0214776632  0.0148911798  0.0076013514  0.0078917700  0.0186460126  0.0172215844  0.0000000000  4.3036947250  7.9332218170  0             0.8758473396 
0.8877445932  0.7422680412  0.8210194731  0.6884306987  0.7091779279  0.6820744081  0.8714859438  0.7807118255  4.9433573635  1.0922462263  8.1390247345  300           0.1829272699 
0.9495365602  0.7051546392  0.8831615120  0.7296678121  0.7080518018  0.7080045096  0.9228341939  0.7749712974  9.8867147271  0.3353705085  8.1390247345  600           0.1831546640 
0.9701338826  0.7278350515  0.9272623139  0.7560137457  0.7367680180  0.7181510710  0.9495123351  0.7933409874  14.830072090  0.2041423323  8.1390247345  900           0.1844953227 
0.9778578785  0.7422680412  0.9458762887  0.7697594502  0.7263513514  0.7091319053  0.9652897303  0.7967853042  19.773429454  0.1467513349  8.1390247345  1200          0.1848772049 
0.9665293512  0.7298969072  0.9538946163  0.7594501718  0.7218468468  0.6956031567  0.9635685600  0.7898966705  24.716786817  0.1215750682  8.1390247345  1500          0.1842083549 
0.9840370752  0.7546391753  0.9616265750  0.7663230241  0.7398648649  0.7339346110  0.9764773379  0.7933409874  29.660144181  0.1052218755  8.1390247345  1800          0.1833491747 
0.9742533471  0.7113402062  0.9518900344  0.7548682703  0.7243806306  0.6967305524  0.9698795181  0.8013777268  34.603501544  0.0830696426  8.1390247345  2100          0.1824680289 
0.9881565396  0.7319587629  0.9690721649  0.7605956472  0.7291666667  0.7125140924  0.9859437751  0.8140068886  39.546858908  0.0881410259  8.1390247345  2400          0.1829089657 
0.9891864058  0.7567010309  0.9710767468  0.7651775487  0.7212837838  0.7125140924  0.9827882960  0.7933409874  44.490216271  0.0762894852  8.1390247345  2700          0.1820755442 
0.9819773429  0.7381443299  0.9590492554  0.7514318442  0.7404279279  0.7181510710  0.9784853701  0.7979334099  49.433573635  0.0771656617  8.1390247345  3000          0.1819151982 
0.9824922760  0.7525773196  0.9613402062  0.7663230241  0.7280405405  0.7294250282  0.9759036145  0.7990815155  54.376930999  0.0739750193  8.1390247345  3300          0.1802207367 
0.9938208033  0.7608247423  0.9773768614  0.7766323024  0.7345157658  0.7192784667  0.9876649455  0.8036739380  59.320288362  0.0606539060  8.1390247345  3600          0.1791446249 
0.9902162719  0.7402061856  0.9782359679  0.7926689576  0.7240990991  0.7001127396  0.9839357430  0.8094144661  64.263645726  0.0612206246  8.1390247345  3900          0.1790890479 
0.9933058702  0.7463917526  0.9773768614  0.7651775487  0.7477477477  0.7282976325  0.9890992542  0.8071182549  69.207003089  0.0580773551  8.1390247345  4200          0.1784280849 
0.9778578785  0.6969072165  0.9667812142  0.7709049255  0.7156531532  0.6944757610  0.9753298910  0.7910447761  74.150360453  0.0617632043  8.1390247345  4500          0.1765969284 
0.9881565396  0.7319587629  0.9702176403  0.7605956472  0.7395833333  0.7125140924  0.9842226047  0.7990815155  79.093717816  0.0547552003  8.1390247345  4800          0.1782350675 
0.9902162719  0.7257731959  0.9733676976  0.7628865979  0.7215653153  0.7136414882  0.9873780838  0.8048220436  82.389289392  0.0548606041  8.1390247345  5000          0.1687621224