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}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-noDist_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: 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: 0.0
	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             0.8249628544 
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           0.2028251823 
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    
0.0380907374  0.9926784625  0.9486552567  0.9930703625  0.9572649573  0.9985029940  0.9820359281  0.6793893130  0.6675159236  14.371257485  1.2648776061  0.0684881650  8.3915948868  0.4392945052  0.1219760488  600           0.4694857979 
0.0273992860  0.9871873093  0.9437652812  0.9898720682  0.9444444444  0.9985029940  0.9700598802  0.7690839695  0.7375796178  21.556886227  1.2176334703  0.0832957735  8.3915948868  0.2905308893  0.0615842604  900           1.0002055025 
0.0238454640  0.9725442343  0.9095354523  0.9850746269  0.9273504274  0.9940119760  0.9670658683  0.7773536896  0.7656050955  28.742514970  1.1795019138  0.0612834295  8.3915948868  0.2021799386  0.0355548662  1200          0.9987400564 
0.0124226213  0.9957291031  0.9242053790  0.9989339019  0.9423076923  0.9992514970  0.9760479042  0.7595419847  0.7541401274  35.928143712  1.1632731152  0.0405750108  8.3915948868  0.1531543899  0.0258961501  1500          0.9986107580 
0.0095380880  0.9920683344  0.9388753056  0.9850746269  0.9273504274  0.9992514970  0.9760479042  0.7280534351  0.7261146497  43.113772455  1.1479862618  0.0317540336  8.3915948868  0.1205800378  0.0206446950  1800          0.9985230223 
0.0130104556  0.9945088469  0.9462102689  0.9978678038  0.9487179487  0.9992514970  0.9670658683  0.7452290076  0.7197452229  50.299401197  1.1374182419  0.0326469452  8.3915948868  0.1092648408  0.0162338142  2100          0.9982547665 
0.0120552260  0.9932885906  0.9266503667  0.9968017058  0.9465811966  0.9992514970  0.9730538922  0.8167938931  0.7987261146  57.485029940  1.1277335763  0.0293794788  8.3915948868  0.0967105402  0.0140883559  2400          0.9983703407 
0.0135256747  0.9975594875  0.9388753056  1.0000000000  0.9594017094  1.0000000000  0.9760479042  0.8031170483  0.8089171975  64.670658682  1.1198498726  0.0294725110  8.3915948868  0.0903826059  0.0119472238  2700          0.9980663180 
0.0082767614  0.9926784625  0.9388753056  0.9962686567  0.9594017094  0.9977544910  0.9700598802  0.7261450382  0.7146496815  71.856287425  1.1147295288  0.0210797154  8.3915948868  0.0709933564  0.0107697497  3000          0.9980920823 
0.0129529281  0.9914582062  0.9119804401  0.9936034115  0.9294871795  0.9992514970  0.9670658683  0.7595419847  0.7668789809  79.041916167  1.1089293182  0.0257361833  8.3915948868  0.0729619445  0.0091948155  3300          0.9980852358 
0.0106982466  0.9969493594  0.9315403423  0.9834754797  0.9380341880  0.9992514970  0.9790419162  0.8346055980  0.8509554140  86.227544910  1.1064167221  0.0222514640  8.3915948868  0.0651445026  0.0089077079  3600          0.9981868259 
0.0159923127  0.9957291031  0.9217603912  0.9920042644  0.9444444444  0.9985029940  0.9640718563  0.6987913486  0.6904458599  93.413173652  1.1038429217  0.0276563333  8.3915948868  0.0674345187  0.0077442122  3900          0.9982541641 
0.0091342941  0.9938987187  0.9217603912  0.9984008529  0.9444444444  1.0000000000  0.9790419162  0.7560432570  0.7388535032  100.59880239  1.1013488893  0.0188042852  8.3915948868  0.0541984052  0.0077011500  4200          0.9982029875 
0.0032864427  0.9957291031  0.9290953545  0.9989339019  0.9636752137  1.0000000000  0.9640718563  0.8097964377  0.7847133758  107.78443113  1.0992914208  0.0111318285  8.3915948868  0.0423380560  0.0074733855  4500          0.9979428307 
0.0089653277  0.9987797437  0.9413202934  0.9994669510  0.9658119658  0.9992514970  0.9700598802  0.8152035623  0.8407643312  114.97005988  1.0970118209  0.0177911293  8.3915948868  0.0505308456  0.0062308724  4800          0.9982841643 
0.0073229918  1.0000000000  0.9437652812  0.9994669510  0.9551282051  0.9992514970  0.9790419162  0.7926208651  0.7936305732  119.76047904  1.0980089551  0.0152244864  8.3915948868  0.0448361944  0.0058803262  5000          0.9965669930