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_env3_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: False
	steps: None
	task: domain_generalization
	test_envs: [3]
	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: 0.3
	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    
1.8969436884  0.1854789506  0.1760391198  0.1950959488  0.1752136752  0.2582335329  0.2634730539  0.2073791349  0.2114649682  0.0000000000  1.8969436884  7.9755215645  0             0.9315516949 
0.1621012442  0.9707138499  0.9046454768  0.9818763326  0.9508547009  0.9977544910  0.9820359281  0.6653944020  0.6598726115  7.1856287425  0.1621012442  8.2003893852  300           0.1883191387 
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    
0.0378845278  0.9932885906  0.9559902200  0.9941364606  0.9658119658  0.9992514970  0.9850299401  0.7299618321  0.7312101911  14.371257485  1.2621811888  0.1531634155  8.3915948868  0.4378551124  0.1214901184  600           0.4656056054 
0.0244338432  0.9847467968  0.9462102689  0.9856076759  0.9337606838  0.9947604790  0.9640718563  0.6886132316  0.6535031847  21.556886227  1.2059075475  0.3202322637  8.3915948868  0.2830683501  0.0607832603  900           0.9935446127 
0.0222106537  0.9951189750  0.9511002445  0.9962686567  0.9444444444  0.9992514970  0.9790419162  0.7611323155  0.7605095541  28.742514970  1.1595264999  0.2908293775  8.3915948868  0.1996089601  0.0338603756  1200          0.9930840309 
0.0130083461  0.9981696156  0.9437652812  0.9984008529  0.9444444444  0.9992514970  0.9880239521  0.7687659033  0.7617834395  35.928143712  1.1301387020  0.2666533621  8.3915948868  0.1522227055  0.0239193261  1500          0.9931226945 
0.0147292276  0.9981696156  0.9682151589  0.9984008529  0.9572649573  0.9992514970  0.9790419162  0.7525445293  0.7414012739  43.113772455  1.1064079579  0.2591857608  8.3915948868  0.1301320222  0.0182756736  1800          0.9925127602 
0.0113432848  0.9957291031  0.9364303178  0.9973347548  0.9487179487  0.9970059880  0.9730538922  0.7477735369  0.7439490446  50.299401197  1.0884893767  0.2480772067  8.3915948868  0.1067397967  0.0151253677  2100          0.9930227629 
0.0118070430  0.9926784625  0.9437652812  0.9968017058  0.9423076923  0.9970059880  0.9700598802  0.7617684478  0.7477707006  57.485029940  1.0632575647  0.2413730543  8.3915948868  0.0959705970  0.0125945265  2400          0.9919129809 
0.0109742477  0.9975594875  0.9437652812  0.9989339019  0.9465811966  0.9992514970  0.9760479042  0.7280534351  0.7146496815  64.670658682  1.0447841398  0.2348129303  8.3915948868  0.0851201621  0.0105691373  2700          0.9916952109 
0.0071405228  0.9957291031  0.9413202934  0.9994669510  0.9465811966  0.9985029940  0.9760479042  0.7506361323  0.7579617834  71.856287425  1.0256257951  0.2248805338  8.3915948868  0.0715477199  0.0094134520  3000          0.9916943916 
0.0079687979  0.9951189750  0.9242053790  0.9984008529  0.9487179487  1.0000000000  0.9820359281  0.7900763359  0.8038216561  79.041916167  0.9977736066  0.2189782710  8.3915948868  0.0656071345  0.0080683918  3300          0.9916357565 
0.0135816664  0.9932885906  0.9290953545  0.9936034115  0.9465811966  0.9977544910  0.9730538922  0.7452290076  0.7439490446  86.227544910  0.9801949092  0.2212356945  8.3915948868  0.0677653837  0.0072511828  3600          0.9916985774 
0.0093496791  0.9902379500  0.9388753056  0.9973347548  0.9209401709  0.9970059880  0.9700598802  0.8136132316  0.8165605096  93.413173652  0.9580869085  0.2110132139  8.3915948868  0.0580923015  0.0066615287  3900          0.9915086826 
0.0111303627  0.9981696156  0.9242053790  0.9962686567  0.9358974359  1.0000000000  0.9850299401  0.7048346056  0.6942675159  100.59880239  0.9320629615  0.2074198060  8.3915948868  0.0579177568  0.0059459216  4200          0.9916437499 
0.0080117348  0.9987797437  0.9486552567  0.9989339019  0.9508547009  1.0000000000  0.9670658683  0.7935750636  0.7834394904  107.78443113  0.9006606317  0.1967456044  8.3915948868  0.0499365986  0.0055562514  4500          0.9915028040 
0.0086217819  0.9920683344  0.9290953545  0.9962686567  0.9594017094  0.9977544910  0.9610778443  0.7436386768  0.7171974522  114.97005988  0.8664174626  0.1898655074  8.3915948868  0.0464437059  0.0049683691  4800          0.9916492240 
0.0056667218  1.0000000000  0.9437652812  1.0000000000  0.9679487179  1.0000000000  0.9760479042  0.7897582697  0.7808917197  119.76047904  0.8445184523  0.1817033788  8.3915948868  0.0412169392  0.0047521119  5000          0.9919274974