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: RDI
	checkpoint_freq: None
	data_dir: /home/kaustubh/Datasets
	dataset: TerraIncognita
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true, "lr": 3e-05, "weight_decay": 0.0002, "batch_size": 37}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/ti/RDI_env3_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [3]
	trial_seed: 0
	uda_holdout_fraction: 0
HParams:
	batch_size: 37
	class_balanced: False
	data_augmentation: True
	dinov2: False
	freeze_bn: True
	lars: False
	linear_steps: 500
	lr: 3e-05
	nonlinear_classifier: False
	rdi_alpha: 2.0
	rdi_beta: 0.3
	rdi_ema: 0.85
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.4
	rdi_lambda_rec: 0.6
	rdi_proj_dropout: 0.3
	rdi_proj_hidden: 1024
	rdi_proj_out: 512
	rdi_tau: 2.0
	rdi_warmup: 500
	resnet18: False
	resnet50_augmix: False
	resnet_dropout: 0.0
	vit: False
	vit_attn_tune: False
	vit_dropout: 0.0
	weight_decay: 0.0002
/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)
ce            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    
2.5793292522  0.2156762295  0.2143589744  0.4253903251  0.4383000512  0.2472014925  0.2611940299  0.2184565129  0.2148692810  0.0000000000  2.5793292522  9.1894111633  0             20.438196420 
0.6969349070  0.8883196721  0.8605128205  0.8117481444  0.8172043011  0.7549751244  0.7139303483  0.4001633320  0.4101307190  3.4514925373  0.6969349070  9.3927693367  300           0.2438703307 
ce            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         geo           loss          mem_gb        rate          rec           step          step_time    
0.4270250927  0.9218750000  0.8994871795  0.8566675198  0.8586789555  0.8258706468  0.8258706468  0.4205798285  0.4305555556  6.9029850746  1.5000963471  0.5732248542  9.6719341278  0.5654578277  0.1647291891  600           0.5809990613 
0.3468845195  0.9339139344  0.9035897436  0.8533401587  0.8535586278  0.8563432836  0.7960199005  0.4595753369  0.4624183007  10.354477611  1.4387248782  0.7261442655  9.6719408035  0.3920653089  0.1090165431  900           1.2601567872 
0.2936338506  0.9546618852  0.9364102564  0.8876375736  0.8817204301  0.8572761194  0.7997512438  0.4293589220  0.4460784314  13.805970149  1.3855295018  0.6396511567  9.6719450951  0.3051861069  0.0771116007  1200          1.2614500419 
0.2477317618  0.9654200820  0.9333333333  0.8971077553  0.8970814132  0.9070273632  0.8706467662  0.4120049000  0.4207516340  17.257462686  1.3596667123  0.5771820245  9.6856040955  0.2663208691  0.0585626076  1500          1.2731374590 
0.2203976942  0.9679815574  0.9446153846  0.8950601485  0.8934971838  0.9151119403  0.8694029851  0.4152715394  0.4076797386  20.708955223  1.3526430285  0.5396534651  9.6996073723  0.2322809301  0.0462833873  1800          1.2881661669 
0.1995118978  0.9567110656  0.9179487179  0.9083695930  0.8955453149  0.9157338308  0.8495024876  0.3726010617  0.3635620915  24.160447761  1.3650777268  0.5149213563  9.6996312141  0.2056214641  0.0385022907  2100          1.3010471145 
0.1756077862  0.9692622951  0.9333333333  0.9093933965  0.8965693804  0.9303482587  0.8731343284  0.4173131891  0.4142156863  27.611940298  1.3417177494  0.4827909655  9.6996312141  0.1924529813  0.0332389208  2400          1.2986101079 
0.1637386035  0.9508196721  0.9230769231  0.9297414896  0.9144905274  0.9179104478  0.8557213930  0.3715802368  0.3790849673  31.063432835  1.3209568445  0.4632654609  9.7002844810  0.1771534772  0.0292082009  2700          1.2995885746 
0.1473898719  0.9743852459  0.9487179487  0.9410033274  0.9103942652  0.9203980100  0.8669154229  0.4026133116  0.3921568627  34.514925373  1.3067560124  0.4409655444  9.7002844810  0.1639523614  0.0254386944  3000          1.2987232558 
0.1383642048  0.9800204918  0.9579487179  0.9379319171  0.9150025602  0.9449626866  0.8955223881  0.3693344222  0.3888888889  37.966417910  1.2875619864  0.4259912271  9.7002873421  0.1530156696  0.0238742229  3300          1.3010774350 
0.1263963055  0.9661885246  0.9476923077  0.9243665216  0.9006656426  0.9393656716  0.8868159204  0.4009799918  0.4027777778  41.417910447  1.2709914184  0.4083683127  9.7002873421  0.1419288570  0.0216146291  3600          1.3002536178 
0.1216767257  0.9828381148  0.9610256410  0.9518812388  0.9303635433  0.9458955224  0.9004975124  0.3317680686  0.3259803922  44.869402985  1.2574346602  0.3995593014  9.7002944946  0.1370841468  0.0194433848  3900          1.2943382510 
0.1166384772  0.9838627049  0.9548717949  0.9541847965  0.9267793139  0.9552238806  0.8955223881  0.3452429563  0.3472222222  48.320895522  1.2405596137  0.3889582972  9.7002944946  0.1263147733  0.0175355854  4200          1.2926387016 
0.1072368894  0.9795081967  0.9466666667  0.9511133862  0.9308755760  0.9378109453  0.8917910448  0.3121682319  0.3039215686  51.772388059  1.2526538813  0.3808044343  9.7132930756  0.1206390036  0.0164697138  4500          1.3039864675 
0.1009943642  0.9784836066  0.9610256410  0.9503455337  0.9231950845  0.9480721393  0.8905472637  0.4148632095  0.4240196078  55.223880597  1.2448653162  0.3716777649  9.7140293121  0.1147641809  0.0149856874  4800          1.3075826844 
0.1001082745  0.9882172131  0.9610256410  0.9632710520  0.9441884281  0.9601990050  0.9203980100  0.4585545120  0.4607843137  57.524875621  1.2321935105  0.3672781922  9.7140293121  0.1103387359  0.0139346733  5000          1.3178869605