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}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI_env1_s2
	save_model_every_checkpoint: False
	seed: 2
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [1]
	trial_seed: 2
	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: 0.3
	rdi_ema: 0.9
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.4
	rdi_lambda_rec: 0.4
	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    
2.1131775379  0.2391702257  0.1955990220  0.1700426439  0.1773504274  0.3016467066  0.2814371257  0.1622137405  0.1592356688  0.0000000000  2.1131775379  7.9755177498  0             0.7416520119 
0.1755540796  0.9749847468  0.9144254279  0.7649253731  0.7948717949  0.9962574850  0.9880239521  0.9599236641  0.9108280255  7.1856287425  0.1755540796  8.2003893852  300           0.2031783096 
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    
0.0571987840  0.9932885906  0.9242053790  0.7531982942  0.7820512821  0.9977544910  0.9700598802  0.9799618321  0.9439490446  14.371257485  1.2711850358  0.1736774009  8.3914413452  0.4407989819  0.1280961377  600           0.4705061388 
0.0301175316  0.9951189750  0.9437652812  0.7649253731  0.8098290598  0.9985029940  0.9820359281  0.9917302799  0.9490445860  21.556886227  1.2098891624  0.3269658922  8.3914413452  0.2800261535  0.0643329991  900           0.9927729003 
0.0182065066  0.9993898719  0.9462102689  0.8363539446  0.8568376068  1.0000000000  0.9790419162  0.9961832061  0.9426751592  28.742514970  1.1701888911  0.2886314634  8.3914413452  0.1904007583  0.0391353034  1200          0.9918237003 
0.0178253890  0.9963392312  0.9486552567  0.7755863539  0.8034188034  0.9985029940  0.9760479042  0.9926844784  0.9414012739  35.928143712  1.1387085931  0.2738418904  8.3914413452  0.1513156930  0.0278871223  1500          0.9912839174 
0.0151300209  0.9993898719  0.9462102689  0.7537313433  0.7948717949  1.0000000000  0.9820359281  0.9961832061  0.9401273885  43.113772455  1.1188046686  0.2621421363  8.3914413452  0.1268492613  0.0211189464  1800          0.9910713728 
0.0165874842  0.9865771812  0.9315403423  0.7441364606  0.7756410256  0.9992514970  0.9910179641  0.9955470738  0.9375796178  50.299401197  1.1021191204  0.2576361241  8.3914413452  0.1146535534  0.0171339008  2100          0.9907490635 
0.0138569089  0.9993898719  0.9535452323  0.7697228145  0.7948717949  0.9992514970  0.9670658683  0.9936386768  0.9248407643  57.485029940  1.0843269229  0.2485125867  8.3914413452  0.0993492220  0.0144395328  2400          0.9909306709 
0.0096735828  0.9963392312  0.9388753056  0.8033049041  0.8076923077  1.0000000000  0.9640718563  0.9955470738  0.9350318471  64.670658682  1.0697293695  0.2385439659  8.3914413452  0.0829195939  0.0124328338  2700          0.9907733281 
0.0102576683  0.9914582062  0.9266503667  0.8118336887  0.8333333333  1.0000000000  0.9820359281  0.9891857506  0.9299363057  71.856287425  1.0509060522  0.2339464540  8.3914413452  0.0758503168  0.0106501262  3000          0.9903788630 
0.0176485186  0.9920683344  0.9339853301  0.7899786780  0.8076923077  0.9962574850  0.9790419162  0.9945928753  0.9299363057  79.041916167  1.0360811090  0.2384117434  8.3914413452  0.0786074111  0.0087794415  3300          0.9903543305 
0.0130899659  1.0000000000  0.9462102689  0.8065031983  0.8418803419  1.0000000000  0.9910179641  0.9984096692  0.9452229299  86.227544910  1.0183554506  0.2287782820  8.3914413452  0.0693483479  0.0080748515  3600          0.9905606325 
0.0091144021  0.9969493594  0.9315403423  0.7814498934  0.7948717949  0.9992514970  0.9820359281  0.9971374046  0.9414012739  93.413173652  1.0006556626  0.2197615654  8.3914413452  0.0605885482  0.0071387264  3900          0.9907072608 
0.0100044774  0.9945088469  0.9290953545  0.7787846482  0.8034188034  0.9962574850  0.9640718563  0.9942748092  0.9261146497  100.59880239  0.9822678337  0.2160265936  8.3914413452  0.0552609840  0.0063969920  4200          0.9908356253 
0.0089374456  0.9981696156  0.9290953545  0.8134328358  0.8376068376  0.9985029940  0.9910179641  0.9958651399  0.9452229299  107.78443113  0.9587796750  0.2096550962  8.3914413452  0.0521652156  0.0056846515  4500          0.9907478301 
0.0123611334  0.9987797437  0.9388753056  0.7558635394  0.7756410256  0.9992514970  0.9730538922  0.9952290076  0.9426751592  114.97005988  0.9347480319  0.2083348927  8.3914413452  0.0533158753  0.0051338387  4800          0.9908928108 
0.0084993890  0.9945088469  0.9364303178  0.7393390192  0.7585470085  0.9992514970  0.9700598802  0.9958651399  0.9414012739  119.76047904  0.9147380999  0.1993381665  8.3914413452  0.0464321050  0.0046316943  5000          0.9908922148