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_env2_s2
	save_model_every_checkpoint: False
	seed: 2
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [2]
	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    
2.1825447083  0.2397803539  0.2102689487  0.1727078891  0.1752136752  0.2627245509  0.2485029940  0.1434478372  0.1503184713  0.0000000000  2.1825447083  7.9755177498  0             1.3508307934 
0.2413536594  0.9701037218  0.9095354523  0.9648187633  0.9487179487  0.9678143713  0.9700598802  0.9421119593  0.9057324841  7.1856287425  0.2413536594  8.2003893852  300           0.1983741808 
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    
0.0658115754  0.9804758999  0.9168704156  0.9829424307  0.9380341880  0.9618263473  0.9670658683  0.9790076336  0.9286624204  14.371257485  1.2694885813  0.1837095779  8.3914217949  0.4750183588  0.1252080295  600           0.4616381725 
0.0410089486  0.9884075656  0.9290953545  0.9829424307  0.9508547009  0.9535928144  0.9401197605  0.9777353690  0.9261146497  21.556886227  1.2148548261  0.3441970371  8.3914217949  0.3166485725  0.0635991647  900           0.9939135242 
0.0244237515  0.9945088469  0.9462102689  0.9968017058  0.9658119658  0.9648203593  0.9670658683  0.9917302799  0.9375796178  28.742514970  1.1740229674  0.3003808174  8.3914217949  0.2214929642  0.0396426219  1200          0.9931166267 
0.0238570540  0.9920683344  0.9413202934  0.9904051173  0.9380341880  0.9588323353  0.9520958084  0.9872773537  0.9337579618  35.928143712  1.1512483641  0.2865548514  8.3914217949  0.1793724253  0.0277112865  1500          0.9929094481 
0.0164686828  0.9945088469  0.9413202934  0.9989339019  0.9615384615  0.9663173653  0.9640718563  0.9949109415  0.9566878981  43.113772455  1.1292381839  0.2686793689  8.3914217949  0.1477208589  0.0210245850  1800          0.9926448409 
0.0131667785  1.0000000000  0.9535452323  0.9989339019  0.9551282051  0.9700598802  0.9640718563  0.9965012723  0.9426751592  50.299401197  1.1126733943  0.2578288947  8.3914217949  0.1243725250  0.0173577757  2100          0.9923039484 
0.0151209643  0.9902379500  0.9413202934  0.9989339019  0.9594017094  0.9633233533  0.9491017964  0.9949109415  0.9515923567  57.485029940  1.0965458481  0.2546039206  8.3914217949  0.1150410897  0.0145880982  2400          0.9923570625 
0.0185442364  0.9920683344  0.9413202934  0.9957356077  0.9508547009  0.9633233533  0.9550898204  0.9961832061  0.9401273885  64.670658682  1.0873627528  0.2549267929  8.3914217949  0.1099910966  0.0120566890  2700          0.9917432229 
0.0145493153  0.9987797437  0.9364303178  0.9984008529  0.9764957265  0.9670658683  0.9700598802  0.9984096692  0.9579617834  71.856287425  1.0741430668  0.2463093995  8.3914217949  0.0988034944  0.0105547154  3000          0.9918800847 
0.0115830261  0.9908480781  0.9315403423  0.9989339019  0.9594017094  0.9558383234  0.9281437126  0.9974554707  0.9375796178  79.041916167  1.0572143050  0.2376480886  8.3914217949  0.0852503325  0.0091732406  3300          0.9918689601 
0.0099087114  0.9981696156  0.9364303178  0.9973347548  0.9658119658  0.9640718563  0.9640718563  0.9980916031  0.9426751592  86.227544910  1.0421777312  0.2310029493  8.3914217949  0.0734018181  0.0082420799  3600          0.9919792525 
0.0135489479  0.9951189750  0.9584352078  0.9957356077  0.9679487179  0.9543413174  0.9520958084  0.9974554707  0.9490445860  93.413173652  1.0267614794  0.2313981342  8.3914217949  0.0735580237  0.0073159162  3900          0.9918584752 
0.0107430030  0.9938987187  0.8997555012  0.9941364606  0.9444444444  0.9528443114  0.9491017964  0.9815521628  0.9222929936  100.59880239  1.0069025612  0.2235783650  8.3914217949  0.0676499137  0.0065367971  4200          0.9916395497 
0.0150040680  0.9920683344  0.9242053790  0.9824093817  0.9230769231  0.9528443114  0.9520958084  0.9926844784  0.9414012739  107.78443113  0.9935183060  0.2251412137  8.3914217949  0.0683589045  0.0058982264  4500          0.9915730206 
0.0113705178  0.9963392312  0.9388753056  0.9989339019  0.9529914530  0.9513473054  0.9670658683  0.9965012723  0.9464968153  114.97005988  0.9755982792  0.2163261153  8.3914217949  0.0581730046  0.0055499379  4800          0.9915007703 
0.0061013713  0.9975594875  0.9511002445  0.9978678038  0.9529914530  0.9588323353  0.9431137725  0.9974554707  0.9426751592  119.76047904  0.9450118330  0.2037365618  8.3914217949  0.0508777281  0.0050058091  5000          0.9914085221