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_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: False
	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: 0.4
	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    
2.0235202312  0.1879194631  0.1613691932  0.1876332623  0.1709401709  0.2582335329  0.2574850299  0.2258269720  0.2203821656  0.0000000000  2.0235202312  7.9755101204  0             1.1701586246 
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           0.2094289033 
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    
0.0484803186  0.8419768151  0.8606356968  0.9829424307  0.9508547009  0.9932634731  0.9550898204  0.9834605598  0.9464968153  14.371257485  1.2787320331  0.1660642059  8.3915634155  0.4491921725  0.1306691511  600           0.4605474901 
0.0313465839  0.8688224527  0.8728606357  0.9925373134  0.9551282051  1.0000000000  0.9820359281  0.9821882952  0.9452229299  21.556886227  1.2069379842  0.3291971978  8.3915634155  0.2865826811  0.0673780553  900           0.9942840950 
0.0191695549  0.8309945088  0.8068459658  0.9973347548  0.9529914530  1.0000000000  0.9760479042  0.9907760814  0.9490445860  28.742514970  1.1543770818  0.2879671815  8.3915634155  0.1998709988  0.0397077782  1200          0.9935704843 
0.0181166598  0.8352654057  0.8337408313  0.9952025586  0.9636752137  0.9992514970  0.9880239521  0.9856870229  0.9426751592  35.928143712  1.1268413369  0.2732420844  8.3915634155  0.1616520072  0.0275467612  1500          0.9931662734 
0.0175963277  0.8627211714  0.8606356968  0.9978678038  0.9572649573  1.0000000000  0.9760479042  0.9958651399  0.9503184713  43.113772455  1.1046005662  0.2632645531  8.3915634155  0.1374211318  0.0206746973  1800          0.9931565706 
0.0127579626  0.8755338621  0.8850855746  0.9989339019  0.9700854701  0.9992514970  0.9820359281  0.9977735369  0.9515923567  50.299401197  1.0841310644  0.2502616551  8.3915634155  0.1155042706  0.0167591788  2100          0.9927679435 
0.0100489027  0.8676021965  0.8655256724  0.9962686567  0.9679487179  1.0000000000  0.9820359281  0.9961832061  0.9566878981  57.485029940  1.0632539046  0.2399073481  8.3915634155  0.0960830239  0.0139760338  2400          0.9926377575 
0.0118555528  0.8474679683  0.8435207824  0.9968017058  0.9423076923  0.9985029940  0.9730538922  0.9974554707  0.9592356688  64.670658682  1.0506674262  0.2378756095  8.3915634155  0.0894460535  0.0123483002  2700          0.9923737017 
0.0113993411  0.8560097621  0.8630806846  0.9962686567  0.9594017094  1.0000000000  0.9700598802  0.9942748092  0.9528662420  71.856287425  1.0307704675  0.2319798604  8.3915634155  0.0826409938  0.0101513681  3000          0.9925169762 
0.0125638442  0.8749237340  0.8704156479  0.9957356077  0.9508547009  0.9985029940  0.9760479042  0.9885496183  0.9388535032  79.041916167  1.0157687197  0.2291040101  8.3915634155  0.0774828222  0.0088199758  3300          0.9923683397 
0.0084136089  0.8669920683  0.8630806846  0.9994669510  0.9700854701  1.0000000000  0.9820359281  0.9980916031  0.9681528662  86.227544910  0.9962490211  0.2195157975  8.3915634155  0.0686290885  0.0077900922  3600          0.9925131369 
0.0114569832  0.8389261745  0.8386308068  0.9957356077  0.9465811966  0.9970059880  0.9610778443  0.9898218830  0.9401273885  93.413173652  0.9790274103  0.2179841751  8.3915634155  0.0622990053  0.0068842763  3900          0.9922699165 
0.0115493326  0.9005491153  0.8875305623  1.0000000000  0.9700854701  0.9992514970  0.9760479042  0.9971374046  0.9579617834  100.59880239  0.9613649493  0.2145178179  8.3915634155  0.0631336067  0.0061272510  4200          0.9927223333 
0.0069497635  0.8700427090  0.8704156479  0.9978678038  0.9722222222  1.0000000000  0.9730538922  0.9968193384  0.9515923567  107.78443113  0.9365309107  0.2036314515  8.3915634155  0.0550940011  0.0055570130  4500          0.9923562948 
0.0032876855  0.8828553996  0.8753056235  0.9984008529  0.9679487179  1.0000000000  0.9730538922  0.9984096692  0.9566878981  114.97005988  0.9114472804  0.1930365552  8.3915634155  0.0428275557  0.0051763841  4800          0.9925097982 
0.0103623318  0.8657718121  0.8704156479  0.9994669510  0.9679487179  1.0000000000  0.9880239521  0.9987277354  0.9566878981  119.76047904  0.8914655373  0.1976770014  8.3915634155  0.0542738826  0.0044023825  5000          0.9920697689