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: PACS
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true, "lr": 4e-05, "weight_decay": 0.01, "batch_size": 32, "rdi_lambda_geo": 0.0, "rdi_lambda_rec": 0.0, "rdi_beta": 0.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/ERMg_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: 4e-05
	nonlinear_classifier: False
	rdi_alpha: 0.5
	rdi_beta: 0.0
	rdi_ema: 0.9
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.0
	rdi_lambda_rec: 0.0
	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: 0.01
/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    
1.9995113611  0.1708358755  0.1735941320  0.1796375267  0.1709401709  0.2522455090  0.2514970060  0.2296437659  0.2229299363  0.0000000000  1.9995113611  7.9755101204  0             0.7521677017 
0.1710804016  0.9701037218  0.9242053790  0.7761194030  0.7820512821  0.9910179641  0.9640718563  0.9570610687  0.9363057325  7.1856287425  0.1710804016  8.2003893852  300           0.1957844639 
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    
0.0506766152  0.9847467968  0.9339853301  0.7766524520  0.7927350427  0.9887724551  0.9640718563  0.9669211196  0.9184713376  14.371257485  1.3101397092  0.0506766152  8.3912892342  0.5437619621  0.1660917944  600           0.4619443464 
0.0320420577  0.9914582062  0.9413202934  0.7441364606  0.7692307692  0.9925149701  0.9610778443  0.9707379135  0.9146496815  21.556886227  1.3007427641  0.0320420577  8.3912940025  0.5220328173  0.1642024924  900           0.9954456274 
0.0246109208  0.9945088469  0.9486552567  0.8001066098  0.8098290598  1.0000000000  0.9790419162  0.9930025445  0.9439490446  28.742514970  1.2930654947  0.0246109208  8.3912944794  0.4914075581  0.1543655955  1200          0.9949227778 
0.0240397985  0.9914582062  0.9388753056  0.8017057569  0.8162393162  0.9977544910  0.9730538922  0.9901399491  0.9490445860  35.928143712  1.2807578671  0.0240397985  8.3913035393  0.4683634188  0.1430143667  1500          0.9953479203 
0.0181592403  0.9987797437  0.9559902200  0.7862473348  0.8055555556  0.9992514970  0.9730538922  0.9895038168  0.9414012739  43.113772455  1.2752796666  0.0181592403  8.3913207054  0.4473036786  0.1432720395  1800          0.9947071465 
0.0149131104  0.9963392312  0.9266503667  0.8075692964  0.8141025641  0.9940119760  0.9670658683  0.9942748092  0.9477707006  50.299401197  1.2693156683  0.0149131104  8.3913207054  0.4361356168  0.1391340625  2100          0.9950775425 
0.0176615940  0.9975594875  0.9437652812  0.7921108742  0.8162393162  0.9970059880  0.9700598802  0.9898218830  0.9426751592  57.485029940  1.2645776808  0.0176615940  8.3913207054  0.4265179186  0.1345865450  2400          0.9948369805 
0.0156134404  0.9914582062  0.9217603912  0.7633262260  0.7692307692  1.0000000000  0.9760479042  0.9901399491  0.9452229299  64.670658682  1.2591878792  0.0156134404  8.3913207054  0.4161956284  0.1278984674  2700          0.9948048933 
0.0129098744  0.9969493594  0.9388753056  0.8022388060  0.8183760684  0.9992514970  0.9730538922  0.9980916031  0.9566878981  71.856287425  1.2573746816  0.0129098744  8.3913207054  0.4018934816  0.1260954194  3000          0.9939644535 
0.0143245825  0.9847467968  0.9193154034  0.7585287846  0.7991452991  0.9977544910  0.9760479042  0.9971374046  0.9490445860  79.041916167  1.2531417640  0.0143245825  8.3913207054  0.3940872863  0.1183828790  3300          0.9948077059 
0.0123565157  0.9969493594  0.9315403423  0.7947761194  0.8098290598  0.9992514970  0.9820359281  0.9965012723  0.9541401274  86.227544910  1.2497997534  0.0123565157  8.3913207054  0.3750736805  0.1166949299  3600          0.9944564994 
0.0119430298  0.9969493594  0.9217603912  0.7425373134  0.7500000000  0.9992514970  0.9700598802  0.9945928753  0.9477707006  93.413173652  1.2453519980  0.0119430298  8.3913207054  0.3663103584  0.1135148821  3900          0.9946058512 
0.0102879762  0.9957291031  0.9266503667  0.8043710021  0.8183760684  0.9970059880  0.9520958084  0.9939567430  0.9426751592  100.59880239  1.2461755983  0.0102879762  8.3913207054  0.3559992652  0.1113025698  4200          0.9943109981 
0.0144068040  0.9993898719  0.9633251834  0.8342217484  0.8696581197  1.0000000000  0.9730538922  0.9955470738  0.9554140127  107.78443113  1.2470331947  0.0144068040  8.3913207054  0.3628122805  0.1031016934  4500          1.0047220031 
0.0073633469  0.9981696156  0.9193154034  0.7590618337  0.7649572650  1.0000000000  0.9640718563  0.9961832061  0.9490445860  114.97005988  1.2419656364  0.0073633469  8.3913207054  0.3458272232  0.1089187473  4800          0.9941286071 
0.0124705843  0.9969493594  0.9364303178  0.8214285714  0.8205128205  0.9985029940  0.9670658683  0.9971374046  0.9464968153  119.76047904  1.2391236657  0.0124705843  8.3913207054  0.3414666240  0.1023908772  5000          0.9933109915