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_env0_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [0]
	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    
2.0235202312  0.1879194631  0.1613691932  0.1876332623  0.1709401709  0.2582335329  0.2574850299  0.2258269720  0.2203821656  0.0000000000  2.0235202312  7.9755101204  0             0.7634465694 
0.1832763110  0.8395363026  0.8484107579  0.9744136461  0.9316239316  0.9947604790  0.9790419162  0.9653307888  0.9312101911  7.1856287425  0.1832763110  8.2003893852  300           0.2038739649 
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    
0.0486401943  0.8596705308  0.8704156479  0.9850746269  0.9508547009  0.9970059880  0.9640718563  0.9853689567  0.9515923567  14.371257485  1.2810357054  0.0802298198  8.3915634155  0.4506567838  0.1311602523  600           0.4746264370 
0.0308643040  0.8352654057  0.8288508557  0.9936034115  0.9508547009  0.9947604790  0.9670658683  0.9783715013  0.9477707006  21.556886227  1.2168567435  0.0878943101  8.3915634155  0.2887475768  0.0685893375  900           0.9948067935 
0.0207413777  0.7962172056  0.7897310513  0.9760127932  0.9358974359  1.0000000000  0.9790419162  0.9640585242  0.9146496815  28.742514970  1.1772561077  0.0589581056  8.3915634155  0.1997019282  0.0413071866  1200          0.9938676858 
0.0167249122  0.8633312996  0.8581907090  0.9973347548  0.9615384615  0.9992514970  0.9700598802  0.9882315522  0.9503184713  35.928143712  1.1577079467  0.0467471782  8.3915634155  0.1613621732  0.0290896944  1500          0.9937937053 
0.0158238915  0.7608297743  0.7848410758  0.9893390192  0.9551282051  0.9970059880  0.9670658683  0.9917302799  0.9592356688  43.113772455  1.1444855487  0.0408577433  8.3915634155  0.1369670324  0.0224439795  1800          0.9935383320 
0.0160527866  0.8529591214  0.8435207824  0.9968017058  0.9401709402  1.0000000000  0.9670658683  0.9920483461  0.9605095541  50.299401197  1.1328527880  0.0378225193  8.3915634155  0.1212601321  0.0179035606  2100          0.9932777349 
0.0097598217  0.8785845027  0.8679706601  1.0000000000  0.9722222222  1.0000000000  0.9850299401  0.9980916031  0.9694267516  57.485029940  1.1257176431  0.0273987063  8.3915634155  0.0965731061  0.0157645905  2400          0.9934049145 
0.0118036214  0.8590604027  0.8410757946  0.9984008529  0.9679487179  1.0000000000  0.9730538922  0.9971374046  0.9515923567  64.670658682  1.1214088964  0.0280417157  8.3915634155  0.0898551530  0.0137991037  2700          0.9933113241 
0.0140018537  0.8102501525  0.8288508557  0.9962686567  0.9380341880  0.9977544910  0.9520958084  0.9939567430  0.9452229299  71.856287425  1.1146863166  0.0294106773  8.3915634155  0.0875023034  0.0114173877  3000          0.9935217627 
0.0094503963  0.8737034777  0.8801955990  0.9994669510  0.9764957265  1.0000000000  0.9730538922  0.9965012723  0.9464968153  79.041916167  1.1152178343  0.0230241897  8.3915634155  0.0761673513  0.0107434510  3300          0.9936530232 
0.0079798853  0.7730323368  0.7897310513  0.9946695096  0.9636752137  0.9910179641  0.9401197605  0.9961832061  0.9528662420  86.227544910  1.1100622376  0.0196278665  8.3915634155  0.0650111287  0.0094815574  3600          0.9929577573 
0.0096909117  0.8078096400  0.8288508557  0.9984008529  0.9487179487  1.0000000000  0.9790419162  0.9984096692  0.9579617834  93.413173652  1.1041464980  0.0213140542  8.3915634155  0.0662397170  0.0084359229  3900          0.9932374215 
0.0098760039  0.8663819402  0.8826405868  0.9973347548  0.9551282051  1.0000000000  0.9700598802  0.9971374046  0.9656050955  100.59880239  1.1026895316  0.0203687636  8.3915634155  0.0594469554  0.0078785801  4200          0.9929868253 
0.0069719213  0.8755338621  0.8728606357  0.9984008529  0.9615384615  1.0000000000  0.9640718563  0.9996819338  0.9566878981  107.78443113  1.0993074509  0.0161555180  8.3915634155  0.0517759851  0.0070859934  4500          0.9932656240 
0.0085156181  0.8535692495  0.8606356968  0.9957356077  0.9700854701  0.9985029940  0.9790419162  0.9977735369  0.9541401274  114.97005988  1.0956487056  0.0182195863  8.3915634155  0.0560842291  0.0064566676  4800          0.9932167260 
0.0118311527  0.8285539963  0.8312958435  0.9957356077  0.9465811966  0.9970059880  0.9790419162  0.9977735369  0.9605095541  119.76047904  1.0969914609  0.0213351675  8.3915634155  0.0557654057  0.0056960175  5000          0.9926409614