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: TerraIncognita
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/ti/ERM_env1_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [1]
	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    
0.2095286885  0.2092307692  0.4609674942  0.4792626728  0.2111318408  0.2238805970  0.2276439363  0.2230392157  0.0000000000  2.3732578754  7.9323825836  0             1.4958074093 
0.8627049180  0.8687179487  0.4512413617  0.4628776242  0.7235696517  0.6703980100  0.7213148224  0.7042483660  2.9850746269  0.8099030465  8.1373462677  300           0.1788218745 
0.9039446721  0.8882051282  0.5117737394  0.5232974910  0.7960199005  0.7487562189  0.7890975909  0.7671568627  5.9701492537  0.5102103932  8.1373462677  600           0.1777935942 
0.9290471311  0.8964102564  0.4795239314  0.5048643113  0.8305348259  0.7985074627  0.8011433238  0.7696078431  8.9552238806  0.4327799779  8.1373462677  900           0.1778595479 
0.9213627049  0.8943589744  0.3612746353  0.3650793651  0.8358208955  0.7748756219  0.8221723152  0.7892156863  11.940298507  0.3791089841  8.1373462677  1200          0.1780963143 
0.9459528689  0.9200000000  0.4571282314  0.4659498208  0.8532338308  0.8059701493  0.8378930176  0.7973856209  14.925373134  0.3391918215  8.1373462677  1500          0.1782277155 
0.9574795082  0.9333333333  0.4987202457  0.5192012289  0.8945895522  0.8407960199  0.8511637403  0.8120915033  17.910447761  0.2980826549  8.1373462677  1800          0.1784113034 
0.9533811475  0.9241025641  0.3680573330  0.3799283154  0.8718905473  0.8233830846  0.8527970600  0.8169934641  20.895522388  0.2680188938  8.1373462677  2100          0.1773319880 
0.9651639344  0.9343589744  0.4710775531  0.4787506400  0.8949004975  0.8582089552  0.8685177624  0.8218954248  23.880597014  0.2511450357  8.1373462677  2400          0.1763505832 
0.9654200820  0.9271794872  0.5241873560  0.5483870968  0.8905472637  0.8420398010  0.8850551245  0.8300653595  26.865671641  0.2302758496  8.1373462677  2700          0.1744279544 
0.9641393443  0.9374358974  0.4314051702  0.4464925755  0.9144900498  0.8731343284  0.8844426296  0.8447712418  29.850746268  0.2168913708  8.1373462677  3000          0.1737987558 
0.9728483607  0.9405128205  0.4468901971  0.4639016897  0.9272388060  0.8731343284  0.8989383422  0.8439542484  32.835820895  0.1997177403  8.1373462677  3300          0.1714577587 
0.9713114754  0.9415384615  0.4708216023  0.4859190988  0.9347014925  0.8681592040  0.9001633320  0.8406862745  35.820895522  0.1949121202  8.1373462677  3600          0.1700458431 
0.9743852459  0.9384615385  0.5211159457  0.5340501792  0.9250621891  0.8855721393  0.9017966517  0.8455882353  38.805970149  0.1772785918  8.1373462677  3900          0.1679089371 
0.9731045082  0.9446153846  0.4242385462  0.4295954941  0.9384328358  0.8768656716  0.9087382605  0.8594771242  41.791044776  0.1692232241  8.1373462677  4200          0.1639567622 
0.9789959016  0.9507692308  0.4427949834  0.4454685100  0.9437189055  0.9042288557  0.9175173540  0.8709150327  44.776119403  0.1582996423  8.1373462677  4500          0.1633744550 
0.9756659836  0.9446153846  0.4665984131  0.4690220174  0.9458955224  0.8917910448  0.9060841160  0.8398692810  47.761194029  0.1492759788  8.1373462677  4800          0.1625034761 
0.9792520492  0.9538461538  0.4713335040  0.4751664107  0.9505597015  0.9029850746  0.9191506737  0.8594771242  49.751243781  0.1491167409  8.1373462677  5000          0.1528251433