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_tau": 1.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-tau1_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: 0.3
	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: 1.0
	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             0.8984687328 
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           0.1898398813 
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    
0.0499898531  0.9871873093  0.9437652812  0.7425373134  0.7585470085  0.9970059880  0.9760479042  0.9608778626  0.9108280255  14.371257485  1.2786353638  0.1587648976  8.3912892342  0.2657786054  0.1375016173  600           0.4649677666 
0.0311557052  0.9829164124  0.9364303178  0.7883795309  0.8183760684  0.9790419162  0.9431137725  0.9545165394  0.9019108280  21.556886227  1.2125870311  0.3159577104  8.3912940025  0.1774024200  0.0783711634  900           1.0008686479 
0.0228300262  0.9945088469  0.9290953545  0.7659914712  0.7927350427  1.0000000000  0.9880239521  0.9958651399  0.9490445860  28.742514970  1.1668883419  0.2847649714  8.3912944794  0.1271749081  0.0474051870  1200          0.9998547101 
0.0216375162  0.9969493594  0.9437652812  0.7995735608  0.8269230769  0.9970059880  0.9820359281  0.9856870229  0.9375796178  35.928143712  1.1377810943  0.2717115712  8.3913035393  0.1043989070  0.0342899825  1500          1.0002531838 
0.0144349486  0.9951189750  0.9388753056  0.7963752665  0.8076923077  0.9992514970  0.9850299401  0.9936386768  0.9541401274  43.113772455  1.1159562437  0.2558586798  8.3913207054  0.0855281328  0.0270163017  1800          1.0001344450 
0.0163745395  0.9963392312  0.9413202934  0.7505330490  0.7820512821  0.9992514970  0.9670658683  0.9945928753  0.9592356688  50.299401197  1.0934355577  0.2508955284  8.3913207054  0.0773442035  0.0211612175  2100          1.0001916448 
0.0172872546  0.9957291031  0.9413202934  0.7196162047  0.7564102564  0.9985029940  0.9730538922  0.9866412214  0.9414012739  57.485029940  1.0737534487  0.2461442998  8.3913207054  0.0702080185  0.0178757426  2400          1.0004003819 
0.0125177169  0.9969493594  0.9462102689  0.7638592751  0.7884615385  0.9985029940  0.9790419162  0.9917302799  0.9477707006  64.670658682  1.0594454396  0.2365426325  8.3913207054  0.0602619761  0.0154826384  2700          1.0006820973 
0.0131610713  0.9993898719  0.9339853301  0.7995735608  0.8141025641  0.9992514970  0.9610778443  0.9968193384  0.9439490446  71.856287425  1.0402357332  0.2324287877  8.3913207054  0.0568521279  0.0134637365  3000          1.0006726392 
0.0156918989  0.9957291031  0.9388753056  0.8166311301  0.8482905983  0.9925149701  0.9580838323  0.9923664122  0.9452229299  79.041916167  1.0244282007  0.2311402750  8.3913207054  0.0548867928  0.0116485724  3300          1.0007521963 
0.0165608320  0.9920683344  0.9266503667  0.7905117271  0.7948717949  0.9977544910  0.9640718563  0.9961832061  0.9375796178  86.227544910  1.0157084018  0.2297785338  8.3913207054  0.0531907748  0.0104870065  3600          1.0010603849 
0.0093116040  0.9993898719  0.9462102689  0.8027718550  0.8162393162  0.9985029940  0.9730538922  0.9984096692  0.9605095541  93.413173652  0.9958416371  0.2170459818  8.3913207054  0.0443431545  0.0095728704  3900          1.0011132725 
0.0084985557  0.9969493594  0.9388753056  0.8246268657  0.8461538462  1.0000000000  0.9610778443  0.9974554707  0.9490445860  100.59880239  0.9735292242  0.2108935000  8.3913207054  0.0396598464  0.0087006001  4200          1.0003151933 
0.0116856435  0.9975594875  0.9486552567  0.8400852878  0.8696581197  0.9992514970  0.9700598802  0.9971374046  0.9490445860  107.78443113  0.9543356744  0.2103257827  8.3913207054  0.0413814275  0.0078289374  4500          1.0009034260 
0.0116942578  0.9993898719  0.9242053790  0.8139658849  0.8226495726  1.0000000000  0.9760479042  0.9987277354  0.9490445860  114.97005988  0.9356790298  0.2060589994  8.3913207054  0.0385036662  0.0072669045  4800          1.0012197955 
0.0081227699  0.9987797437  0.9266503667  0.7990405117  0.8098290598  0.9992514970  0.9730538922  0.9980916031  0.9554140127  119.76047904  0.9061173528  0.1958897285  8.3913207054  0.0346863851  0.0067026355  5000          1.0005765748