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: TerraIncognita
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true, "lr": 3e-05, "weight_decay": 0.0002, "batch_size": 37}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/ti/RDI_env1_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [1]
	trial_seed: 0
	uda_holdout_fraction: 0
HParams:
	batch_size: 37
	class_balanced: False
	data_augmentation: True
	dinov2: False
	freeze_bn: True
	lars: False
	linear_steps: 500
	lr: 3e-05
	nonlinear_classifier: False
	rdi_alpha: 2.0
	rdi_beta: 0.3
	rdi_ema: 0.85
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.4
	rdi_lambda_rec: 0.6
	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: 0.0002
/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    
2.4673378468  0.2210553279  0.2225641026  0.4488098285  0.4644137225  0.2388059701  0.2450248756  0.2474479379  0.2393790850  0.0000000000  2.4673378468  9.1894187927  0             16.159489154 
0.7796545684  0.8821721311  0.8615384615  0.3722805221  0.3758320533  0.7658582090  0.7338308458  0.7247856268  0.7303921569  3.4514925373  0.7796545684  9.3927693367  300           0.2184680915 
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    
0.4762664367  0.9282786885  0.8943589744  0.3954440747  0.3952892985  0.8324004975  0.8059701493  0.8023683136  0.7892156863  6.9029850746  1.5289598987  0.6219388301  9.7011981010  0.5027816266  0.1716034710  600           0.5891345414 
0.3924795857  0.9336577869  0.8994871795  0.4811876120  0.4869431644  0.8585199005  0.7786069652  0.8278889343  0.7867647059  10.354477611  1.4816723053  0.7729632606  9.7140097618  0.3448915895  0.1080515507  900           1.2993081689 
0.3258922503  0.9497950820  0.9200000000  0.3917327873  0.3911930364  0.8616293532  0.7947761194  0.8450387913  0.8178104575  13.805970149  1.4319659944  0.6766970289  9.7140097618  0.2748553520  0.0772775666  1200          1.2954126358 
0.2850248085  0.9590163934  0.9343589744  0.3849500896  0.3850486431  0.9014303483  0.8308457711  0.8621886484  0.8276143791  17.257462686  1.4056375631  0.6201360012  9.7140097618  0.2393323830  0.0602793902  1500          1.2936194515 
0.2532724212  0.9643954918  0.9282051282  0.4847709240  0.4818228367  0.9154228856  0.8805970149  0.8701510821  0.8325163399  20.708955223  1.3724938826  0.5750144775  9.7140097618  0.2153321756  0.0498114795  1800          1.2945645388 
0.2221014719  0.9690061475  0.9435897436  0.3994113130  0.4091141833  0.9368781095  0.8905472637  0.8985300122  0.8406862745  24.160447761  1.3474860585  0.5343206264  9.7140097618  0.1982937179  0.0432595889  2100          1.3000432499 
0.2081016472  0.9751536885  0.9507692308  0.4320450474  0.4423963134  0.9238184080  0.8582089552  0.8973050225  0.8562091503  27.611940298  1.3240258308  0.5111116689  9.7140097618  0.1795570430  0.0375709716  2400          1.3062211927 
0.1791880753  0.9813012295  0.9558974359  0.4051702073  0.4142345110  0.9337686567  0.8917910448  0.9046549612  0.8537581699  31.063432835  1.2999311189  0.4736048859  9.7140235901  0.1632456076  0.0331457944  2700          1.2975166297 
0.1659327792  0.9777151639  0.9548717949  0.4035065267  0.4152585765  0.9340796020  0.8818407960  0.9136382197  0.8651960784  34.514925373  1.2806304801  0.4533067230  9.7140235901  0.1512256632  0.0285466440  3000          1.2988866488 
0.1566596911  0.9838627049  0.9558974359  0.4403634502  0.4531490015  0.9412313433  0.9029850746  0.9207839935  0.8619281046  37.966417910  1.2588627946  0.4367079607  9.7140250206  0.1372196885  0.0256424962  3300          1.2951427476 
0.1480777317  0.9846311475  0.9579487179  0.4490657794  0.4685099846  0.9539800995  0.8980099502  0.9193548387  0.8562091503  41.417910447  1.2454537499  0.4238649920  9.7140250206  0.1307813067  0.0235977008  3600          1.2963187496 
0.1449138641  0.9743852459  0.9558974359  0.3551318147  0.3722478239  0.9477611940  0.8967661692  0.9230298081  0.8611111111  44.869402985  1.2373897000  0.4175608343  9.7140250206  0.1261291682  0.0208321639  3900          1.2926302791 
0.1313123676  0.9848872951  0.9579487179  0.3698489890  0.3824884793  0.9536691542  0.9067164179  0.9328297264  0.8799019608  48.320895522  1.2233955141  0.3997304467  9.7140250206  0.1190520171  0.0196039031  4200          1.2905288315 
0.1237981049  0.9823258197  0.9589743590  0.3898131559  0.4014336918  0.9539800995  0.9054726368  0.9291547570  0.8651960784  51.772388059  1.2139630401  0.3890734022  9.7140250206  0.1138109408  0.0180368138  4500          1.2896226406 
0.1229211289  0.9887295082  0.9589743590  0.3908369593  0.4014336918  0.9375000000  0.8743781095  0.9026133116  0.8455882353  55.223880597  1.2081468316  0.3853176629  9.7140250206  0.1049987981  0.0167244815  4800          1.2905133963 
0.1115772702  0.9761782787  0.9558974359  0.4319170719  0.4336917563  0.9589552239  0.9216417910  0.9236423030  0.8578431373  57.524875621  1.2004459447  0.3717844695  9.7140250206  0.1014837649  0.0163181437  5000          1.2816148317