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_beta": 0.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-noRate_env2_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [2]
	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: 0.0
	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    
1.9950832129  0.1891397193  0.1760391198  0.2052238806  0.1858974359  0.2028443114  0.2095808383  0.2076972010  0.2152866242  0.0000000000  1.9950832129  7.9755101204  0             0.7405021191 
0.2457256681  0.9560707749  0.9046454768  0.9744136461  0.9423076923  0.9678143713  0.9550898204  0.9449745547  0.9248407643  7.1856287425  0.2457256681  8.2003893852  300           0.2116950146 
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    
0.0645764473  0.9914582062  0.9462102689  0.9946695096  0.9487179487  0.9738023952  0.9730538922  0.9828244275  0.9464968153  14.371257485  1.2872846268  0.1608725345  8.3915619850  0.5471576246  0.1428552359  600           0.4695278915 
0.0404387567  0.9762050031  0.9413202934  0.9930703625  0.9508547009  0.9648203593  0.9580838323  0.9907760814  0.9515923567  21.556886227  1.2319134581  0.3089135897  8.3915619850  0.4555665422  0.1104606913  900           0.9930727386 
0.0324902666  0.9884075656  0.9486552567  0.9957356077  0.9401709402  0.9790419162  0.9700598802  0.9936386768  0.9439490446  28.742514970  1.1852198943  0.2860514272  8.3915619850  0.3814361998  0.0825859010  1200          0.9925622646 
0.0254065666  0.9963392312  0.9413202934  0.9952025586  0.9508547009  0.9693113772  0.9610778443  0.9850508906  0.9273885350  35.928143712  1.1466654253  0.2678067073  8.3915619850  0.3259487369  0.0653352674  1500          0.9922415940 
0.0208432007  0.9963392312  0.9413202934  0.9989339019  0.9679487179  0.9670658683  0.9580838323  0.9930025445  0.9503184713  43.113772455  1.1078815718  0.2533868984  8.3915619850  0.2904501438  0.0548369043  1800          0.9920307215 
0.0181878367  0.9859670531  0.9242053790  0.9914712154  0.9594017094  0.9610778443  0.9461077844  0.9926844784  0.9337579618  50.299401197  1.0697584832  0.2414470317  8.3915619850  0.2674234099  0.0465374796  2100          0.9915773940 
0.0175460022  0.9981696156  0.9486552567  0.9973347548  0.9487179487  0.9700598802  0.9640718563  0.9904580153  0.9541401274  57.485029940  1.0459616538  0.2347605690  8.3915619850  0.2488280420  0.0401111626  2400          0.9918199054 
0.0170699190  0.9932885906  0.9486552567  0.9930703625  0.9444444444  0.9625748503  0.9520958084  0.9879134860  0.9312101911  64.670658682  1.0134026903  0.2269975790  8.3915619850  0.2362158891  0.0362355985  2700          0.9912810246 
0.0160684302  0.9987797437  0.9559902200  1.0000000000  0.9658119658  0.9633233533  0.9341317365  0.9961832061  0.9566878981  71.856287425  0.9799970647  0.2186117511  8.3915619850  0.2242815752  0.0327195252  3000          0.9907715670 
0.0141374523  0.9957291031  0.9168704156  0.9984008529  0.9465811966  0.9565868263  0.9191616766  0.9930025445  0.9477707006  79.041916167  0.9549859113  0.2108658564  8.3915619850  0.2066265225  0.0286560970  3300          0.9908155251 
0.0169909609  0.9908480781  0.9339853301  0.9962686567  0.9551282051  0.9565868263  0.9371257485  0.9920483461  0.9388535032  86.227544910  0.9271475182  0.2076040498  8.3915619850  0.2082155166  0.0259179147  3600          0.9907600292 
0.0138840510  0.9963392312  0.9364303178  0.9968017058  0.9594017094  0.9573353293  0.9371257485  0.9971374046  0.9452229299  93.413173652  0.9003756416  0.1988531969  8.3915619850  0.2007784595  0.0244700748  3900          0.9908181373 
0.0145945782  0.9957291031  0.9193154034  0.9984008529  0.9636752137  0.9663173653  0.9431137725  0.9990458015  0.9452229299  100.59880239  0.8600446558  0.1912154223  8.3915619850  0.1931050630  0.0230595503  4200          0.9908565370 
0.0090098136  0.9993898719  0.9462102689  0.9994669510  0.9615384615  0.9558383234  0.9520958084  0.9984096692  0.9528662420  107.78443113  0.8004154297  0.1734726102  8.3915619850  0.1797899573  0.0218985391  4500          0.9907609661 
0.0120725656  0.9932885906  0.9095354523  0.9984008529  0.9572649573  0.9603293413  0.9491017964  0.9945928753  0.9541401274  114.97005988  0.7580678155  0.1676981528  8.3915619850  0.1817626553  0.0200601031  4800          0.9908039077 
0.0158232052  0.9914582062  0.9022004890  0.9696162047  0.9145299145  0.9625748503  0.9550898204  0.9910941476  0.9171974522  119.76047904  0.7420401111  0.1679016864  8.3915619850  0.1853588633  0.0183522814  5000          0.9902039075