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_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: 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    
2.5905101299  0.2213114754  0.2215384615  0.4323009982  0.4454685100  0.2730099502  0.2761194030  0.2405063291  0.2369281046  0.0000000000  2.5905101299  9.1894187927  0             23.167886018 
0.7448039016  0.8703893443  0.8317948718  0.7852572306  0.8079877112  0.5360696517  0.5286069652  0.6819109841  0.6993464052  3.4514925373  0.7448039016  9.3927731514  300           0.2438849394 
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    
0.4660511869  0.9190573770  0.8902564103  0.8580752496  0.8607270865  0.5254975124  0.5012437811  0.7690894243  0.7442810458  6.9029850746  1.5101859440  0.6120275565  9.6873030663  0.5535473496  0.1617462437  600           0.5817067289 
0.3926086235  0.9426229508  0.9097435897  0.8772715639  0.8745519713  0.5914179104  0.5671641791  0.8231931401  0.8047385621  10.354477611  1.4723783720  0.7756443153  9.6873030663  0.3710671919  0.1096664379  900           1.2601751129 
0.3385469253  0.9431352459  0.9158974359  0.8931405170  0.8873527906  0.5715174129  0.5447761194  0.8325847285  0.7957516340  13.805970149  1.4178093247  0.6887043464  9.6873030663  0.2960924124  0.0739389519  1200          1.2624136416 
0.2990401917  0.9526127049  0.9261538462  0.8951881239  0.8883768561  0.5522388060  0.5758706468  0.8701510821  0.8341503268  17.257462686  1.3949253488  0.6333432247  9.7000403404  0.2537981161  0.0574941305  1500          1.2737940931 
0.2612593698  0.9597848361  0.9230769231  0.8995392885  0.9016897081  0.5758706468  0.5410447761  0.8836259698  0.8308823529  20.708955223  1.3879470440  0.5870460180  9.7000403404  0.2222978495  0.0495085155  1800          1.2877545579 
0.2342607014  0.9474897541  0.9128205128  0.8983875096  0.8847926267  0.5379353234  0.5534825871  0.8542262148  0.8210784314  24.160447761  1.3636131124  0.5501075651  9.7002134323  0.2039428982  0.0417759971  2100          1.3010544475 
0.2114754562  0.9725922131  0.9435897436  0.8914768364  0.8689196109  0.5771144279  0.5820895522  0.8999591670  0.8521241830  27.611940298  1.3413444722  0.5191616918  9.7002134323  0.1932429607  0.0347696267  2400          1.2996305633 
0.1929333765  0.9795081967  0.9600000000  0.9326849245  0.9057859703  0.5845771144  0.5547263682  0.8913842385  0.8284313725  31.063432835  1.3131533003  0.4909486515  9.7002711296  0.1760661802  0.0299155991  2700          1.2998592758 
0.1810834819  0.9825819672  0.9548717949  0.9486818531  0.9313876088  0.5792910448  0.5509950249  0.9071049408  0.8594771242  34.514925373  1.2955173425  0.4734894395  9.7002763748  0.1669588564  0.0275288379  3000          1.2990365314 
0.1595266460  0.9805327869  0.9579487179  0.9385717942  0.9201228879  0.5764925373  0.5509950249  0.9154757044  0.8619281046  37.966417910  1.2784488519  0.4457779735  9.7002763748  0.1536326140  0.0250555161  3300          1.3013495088 
0.1542313927  0.9848872951  0.9538461538  0.9523931405  0.9272913466  0.5258084577  0.5472636816  0.9160881993  0.8643790850  41.417910447  1.2604120926  0.4344439246  9.7002763748  0.1427933947  0.0223703266  3600          1.3006213649 
0.1536437314  0.9805327869  0.9446153846  0.9358843102  0.9155145929  0.5718283582  0.5659203980  0.9105757452  0.8488562092  44.869402985  1.2464439535  0.4294199882  9.7002902031  0.1359675613  0.0203077539  3900          1.2937118308 
0.1441727716  0.9877049180  0.9569230769  0.9444586639  0.9211469534  0.5758706468  0.5684079602  0.9218048183  0.8741830065  48.320895522  1.2284473340  0.4148961022  9.7002902031  0.1295628510  0.0186647757  4200          1.2927768890 
0.1338562349  0.9869364754  0.9528205128  0.9512413617  0.9344598054  0.5649875622  0.5646766169  0.9285422621  0.8668300654  51.772388059  1.2161938314  0.4009426477  9.7002902031  0.1247236268  0.0171303285  4500          1.3042711655 
0.1295516964  0.9905225410  0.9600000000  0.9642948554  0.9416282642  0.5447761194  0.5360696517  0.9409963250  0.8807189542  55.223880597  1.2082963602  0.3936966774  9.7130589485  0.1183838203  0.0157604377  4800          1.3070725687 
0.1201795443  0.9802766393  0.9497435897  0.9622472485  0.9359959037  0.5746268657  0.5522388060  0.9303797468  0.8750000000  57.524875621  1.2212607092  0.3859238738  9.7130603790  0.1123889702  0.0154461273  5000          1.3176484919