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_env1_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: False
	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: 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    
1.9995113611  0.1708358755  0.1735941320  0.1796375267  0.1709401709  0.2522455090  0.2514970060  0.2296437659  0.2229299363  0.0000000000  1.9995113611  7.9755101204  0             1.2349781990 
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           0.1901022093 
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    
0.0499687555  0.9865771812  0.9486552567  0.7569296375  0.7799145299  0.9955089820  0.9790419162  0.9694656489  0.9171974522  14.371257485  1.2728753054  0.1670097031  8.3912892342  0.4454667899  0.1312564519  600           0.4774596524 
0.0275115688  0.9829164124  0.9339853301  0.7622601279  0.7991452991  0.9865269461  0.9461077844  0.9437022901  0.8917197452  21.556886227  1.2035030055  0.3238521251  8.3912940025  0.2815097524  0.0670674320  900           1.0012672806 
0.0231416094  0.9945088469  0.9266503667  0.7921108742  0.8205128205  1.0000000000  0.9790419162  0.9936386768  0.9554140127  28.742514970  1.1563609147  0.2917210024  8.3912944794  0.1991632078  0.0371636198  1200          1.0004759049 
0.0204207285  0.9963392312  0.9339853301  0.8176972281  0.8376068376  0.9962574850  0.9790419162  0.9936386768  0.9503184713  35.928143712  1.1282705224  0.2754994018  8.3913035393  0.1611381864  0.0262691874  1500          1.0004258442 
0.0157121407  0.9951189750  0.9486552567  0.8294243070  0.8611111111  1.0000000000  0.9760479042  0.9898218830  0.9452229299  43.113772455  1.1085054962  0.2611865103  8.3913207054  0.1310706817  0.0205633251  1800          1.0037080805 
0.0133643577  0.9963392312  0.9511002445  0.8091684435  0.8354700855  0.9992514970  0.9820359281  0.9945928753  0.9554140127  50.299401197  1.0912571589  0.2516449977  8.3913207054  0.1110171103  0.0168831906  2100          1.0002886589 
0.0150650531  0.9908480781  0.9315403423  0.7953091684  0.8183760684  1.0000000000  0.9700598802  0.9977735369  0.9617834395  57.485029940  1.0683281708  0.2471733432  8.3913207054  0.1048773333  0.0135552607  2400          0.9995980453 
0.0149302908  0.9981696156  0.9315403423  0.7990405117  0.8205128205  0.9985029940  0.9760479042  0.9977735369  0.9490445860  64.670658682  1.0563551311  0.2420826174  8.3913207054  0.0903929432  0.0116117758  2700          0.9994615324 
0.0116949276  0.9938987187  0.9413202934  0.7953091684  0.7991452991  1.0000000000  0.9640718563  0.9856870229  0.9286624204  71.856287425  1.0356658947  0.2330208513  8.3913207054  0.0812192101  0.0100492960  3000          0.9995328776 
0.0116872351  0.9981696156  0.9535452323  0.7905117271  0.8290598291  0.9992514970  0.9670658683  0.9933206107  0.9388535032  79.041916167  1.0188207223  0.2283601176  8.3913207054  0.0742436036  0.0088609743  3300          0.9995403393 
0.0119376337  0.9945088469  0.9193154034  0.7846481876  0.8098290598  0.9992514970  0.9610778443  0.9965012723  0.9350318471  86.227544910  1.0045762169  0.2246028463  8.3913207054  0.0676540215  0.0080093063  3600          0.9992646472 
0.0128014142  0.9981696156  0.9413202934  0.7713219616  0.8141025641  1.0000000000  0.9730538922  0.9987277354  0.9541401274  93.413173652  0.9818023493  0.2205049452  8.3913207054  0.0666004623  0.0067649415  3900          0.9995050780 
0.0077491474  0.9957291031  0.9168704156  0.7734541578  0.7735042735  0.9992514970  0.9670658683  0.9936386768  0.9528662420  100.59880239  0.9560056394  0.2083189394  8.3913207054  0.0540405541  0.0063128894  4200          0.9993406423 
0.0146970259  0.9987797437  0.9511002445  0.7926439232  0.8247863248  1.0000000000  0.9730538922  0.9971374046  0.9439490446  107.78443113  0.9399123708  0.2128325739  8.3913207054  0.0602714115  0.0055617935  4500          0.9994036110 
0.0073971560  0.9993898719  0.9388753056  0.7958422175  0.8205128205  1.0000000000  0.9730538922  0.9990458015  0.9490445860  114.97005988  0.9111933776  0.1980059243  8.3913207054  0.0487351247  0.0052991028  4800          0.9993694361 
0.0125254850  0.9932885906  0.9095354523  0.7292110874  0.7435897436  0.9992514970  0.9730538922  0.9920483461  0.9324840764  119.76047904  0.8817300275  0.1976563731  8.3913207054  0.0522530312  0.0047346244  5000          1.0002176833