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_env0_s2
	save_model_every_checkpoint: False
	seed: 2
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [0]
	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    
2.1189849377  0.2605247102  0.2322738386  0.1689765458  0.1688034188  0.3211077844  0.2994011976  0.1615776081  0.1656050955  0.0000000000  2.1189849377  7.9755177498  0             0.7363588810 
0.1850724105  0.8596705308  0.8753056235  0.9818763326  0.9658119658  0.9955089820  0.9850299401  0.9634223919  0.9273885350  7.1856287425  0.1850724105  8.2003893852  300           0.2035324208 
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    
0.0465903661  0.8602806589  0.8606356968  0.9898720682  0.9444444444  0.9925149701  0.9491017964  0.9799618321  0.9388535032  14.371257485  1.2797327797  0.1639071260  8.3915438652  0.4441465145  0.1294854039  600           0.4701690626 
0.0304663734  0.8547895058  0.8410757946  0.9914712154  0.9423076923  0.9970059880  0.9730538922  0.9812340967  0.9312101911  21.556886227  1.2082196049  0.3281524182  8.3915438652  0.2871227856  0.0648685020  900           0.9926779445 
0.0189895738  0.8352654057  0.8484107579  0.9893390192  0.9337606838  0.9970059880  0.9790419162  0.9767811705  0.9273885350  28.742514970  1.1616093806  0.2883972582  8.3915438652  0.1961447119  0.0383204772  1200          0.9918967803 
0.0173932056  0.8578401464  0.8655256724  0.9978678038  0.9636752137  1.0000000000  0.9790419162  0.9955470738  0.9464968153  35.928143712  1.1310359228  0.2726693874  8.3915438652  0.1572969594  0.0273722496  1500          0.9913470554 
0.0138872938  0.8730933496  0.8704156479  0.9978678038  0.9615384615  1.0000000000  0.9820359281  0.9977735369  0.9503184713  43.113772455  1.1064808786  0.2585355345  8.3915438652  0.1274901746  0.0211426717  1800          0.9911875534 
0.0098087345  0.8688224527  0.8655256724  0.9984008529  0.9700854701  1.0000000000  0.9940119760  0.9955470738  0.9401273885  50.299401197  1.0846495628  0.2459407524  8.3915438652  0.1048535985  0.0173703130  2100          0.9908200645 
0.0145122707  0.8785845027  0.8630806846  0.9962686567  0.9679487179  0.9985029940  0.9790419162  0.9971374046  0.9503184713  57.485029940  1.0625053851  0.2448681811  8.3915438652  0.1006586816  0.0137801347  2400          0.9903377732 
0.0165891681  0.8200122026  0.8068459658  0.9989339019  0.9679487179  1.0000000000  0.9790419162  0.9974554707  0.9490445860  64.670658682  1.0455157642  0.2424376616  8.3915438652  0.0961342747  0.0116259828  2700          0.9910214233 
0.0087725200  0.8596705308  0.8630806846  0.9936034115  0.9615384615  0.9962574850  0.9670658683  0.9904580153  0.9261146497  71.856287425  1.0270910068  0.2280708175  8.3915438652  0.0790053790  0.0101464286  3000          0.9906668917 
0.0092890269  0.8346552776  0.7970660147  0.9984008529  0.9658119658  0.9962574850  0.9760479042  0.9930025445  0.9503184713  79.041916167  1.0085817442  0.2238327651  8.3915438652  0.0738801319  0.0087268256  3300          0.9906193964 
0.0112421938  0.8804148871  0.8459657702  0.9962686567  0.9487179487  0.9992514970  0.9610778443  0.9901399491  0.9401273885  86.227544910  0.9898252050  0.2208569786  8.3915438652  0.0672793620  0.0077891771  3600          0.9913208882 
0.0097079673  0.8230628432  0.8288508557  0.9989339019  0.9679487179  1.0000000000  0.9760479042  0.9977735369  0.9490445860  93.413173652  0.9689819185  0.2144887386  8.3915438652  0.0637971772  0.0070740355  3900          0.9909490196 
0.0084119915  0.8444173276  0.8459657702  0.9957356077  0.9700854701  0.9992514970  0.9850299401  0.9990458015  0.9554140127  100.59880239  0.9468853650  0.2073630830  8.3915438652  0.0553203908  0.0063797813  4200          0.9909161258 
0.0052094066  0.8352654057  0.8239608802  0.9978678038  0.9743589744  1.0000000000  0.9850299401  0.9977735369  0.9477707006  107.78443113  0.9170781708  0.1972184278  8.3915438652  0.0496509547  0.0057287046  4500          0.9908290688 
0.0091715538  0.8877364246  0.8801955990  0.9989339019  0.9722222222  0.9992514970  0.9730538922  0.9971374046  0.9503184713  114.97005988  0.8881677439  0.1953816616  8.3915438652  0.0504064889  0.0050779075  4800          0.9909509063 
0.0067950516  0.8547895058  0.8386308068  0.9914712154  0.9508547009  0.9925149701  0.9670658683  0.9834605598  0.9184713376  119.76047904  0.8560862646  0.1854701893  8.3915438652  0.0431538897  0.0049239953  5000          0.9910426247