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: OfficeHome
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true, "lr": 1e-05, "weight_decay": 0.0003, "batch_size": 25, "rdi_lambda_geo": 0.0, "rdi_lambda_rec": 0.0, "rdi_beta": 0.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/oh/ERMg_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: 25
	class_balanced: False
	data_augmentation: True
	dinov2: False
	freeze_bn: True
	lars: False
	linear_steps: 500
	lr: 1e-05
	nonlinear_classifier: False
	rdi_alpha: 2.0
	rdi_beta: 0.0
	rdi_ema: 0.85
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.0
	rdi_lambda_rec: 0.0
	rdi_proj_dropout: 0.3
	rdi_proj_hidden: 1024
	rdi_proj_out: 512
	rdi_tau: 1.0
	rdi_warmup: 500
	resnet18: False
	resnet50_augmix: False
	resnet_dropout: 0.0
	vit: False
	vit_attn_tune: False
	vit_dropout: 0.0
	weight_decay: 0.0003
/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    
4.4779205322  0.0159629248  0.0082474227  0.0146048110  0.0171821306  0.0168918919  0.0157835400  0.0163511188  0.0080367394  0.0000000000  4.4779205322  6.4339017868  0             1.5404195786 
2.3200796290  0.7832131823  0.7134020619  0.7041809851  0.6403207331  0.6990427928  0.6786922210  0.7928858290  0.7566016073  3.8619979403  2.3200796290  6.6788215637  300           0.1610012086 
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    
1.0163451417  0.8753861998  0.7773195876  0.8112829324  0.7021764032  0.7497184685  0.7350620068  0.8789443488  0.8174512055  7.7239958805  1.5476996061  1.0163451417  8.6326036453  0.2687910658  0.4440549220  600           1.8750579604 
0.6473996037  0.9335736354  0.7773195876  0.8739977090  0.7502863688  0.7719594595  0.7531003382  0.9113597246  0.8243398393  11.585993820  1.5133313096  0.6473996037  8.7171430588  0.2621004613  0.4347606102  900           5.1898334916 
0.4348260480  0.9572605561  0.7835051546  0.9034936999  0.7754868270  0.7674549550  0.7531003382  0.9340218015  0.8220436280  15.447991761  1.4811756770  0.4348260480  8.7175889015  0.2545762769  0.4294438409  1200          5.1692648474 
0.3098330320  0.9752832132  0.7835051546  0.9272623139  0.7823596793  0.7747747748  0.7632468997  0.9549627080  0.8243398393  19.309989701  1.4694504793  0.3098330320  8.7176866531  0.2480882693  0.4343287070  1500          5.1732839115 
0.2268630624  0.9866117405  0.7938144330  0.9435853379  0.7857961054  0.7795608108  0.7553551297  0.9675846242  0.8450057405  23.171987641  1.4604502428  0.2268630624  8.7180781364  0.2420337468  0.4363486722  1800          5.1715384761 
0.1678134673  0.9927909372  0.8020618557  0.9587628866  0.7823596793  0.7809684685  0.7733934611  0.9802065404  0.8254879449  27.033985581  1.4562302848  0.1678134673  8.7180781364  0.2367213184  0.4395775849  2100          5.1557711943 
0.1286950662  0.9927909372  0.7979381443  0.9673539519  0.7961053837  0.7835022523  0.7497181511  0.9836488812  0.8530424799  30.895983522  1.4502696308  0.1286950662  8.7180781364  0.2331392588  0.4413798325  2400          5.1834504183 
0.0993405820  0.9948506694  0.8082474227  0.9659221077  0.8018327606  0.7767454955  0.7722660654  0.9850831899  0.8404133180  34.757981462  1.4462828445  0.0993405820  8.8439407349  0.2291611704  0.4420598436  2700          5.1604831409 
0.0795535068  0.9948506694  0.8041237113  0.9742268041  0.7880870561  0.7854729730  0.7801578354  0.9916810098  0.8335246843  38.619979402  1.4425259757  0.0795535068  8.8439407349  0.2250554151  0.4437403578  3000          5.1662227201 
0.0679576464  0.9943357364  0.8103092784  0.9790950745  0.7995418099  0.7823761261  0.7677564825  0.9919678715  0.8392652124  42.481977342  1.4406705089  0.0679576464  8.8439407349  0.2231615480  0.4497346062  3300          5.1536526227 
0.0600073199  0.9958805355  0.8144329897  0.9782359679  0.7949599084  0.7728040541  0.7621195039  0.9939759036  0.8346727899  46.343975283  1.4379078146  0.0600073199  8.8439407349  0.2195283182  0.4461295787  3600          5.1407451987 
0.0504396712  0.9963954686  0.8000000000  0.9810996564  0.8029782360  0.7719594595  0.7632468997  0.9959839357  0.8369690011  50.205973223  1.4356272046  0.0504396712  8.8439407349  0.2161356023  0.4457870962  3900          5.1508114457 
0.0455311620  0.9953656025  0.8000000000  0.9785223368  0.7961053837  0.7713963964  0.7531003382  0.9934021801  0.8323765786  54.067971163  1.4332657679  0.0455311620  8.8439407349  0.2141388828  0.4531421196  4200          5.1443334762 
0.0428389083  0.9953656025  0.8082474227  0.9810996564  0.8052691867  0.7787162162  0.7632468997  0.9954102123  0.8358208955  57.929969104  1.4323859465  0.0428389083  8.8439407349  0.2143788414  0.4479985459  4500          5.1432742246 
0.0414713973  0.9953656025  0.8020618557  0.9813860252  0.7972508591  0.7840653153  0.7711386697  0.9962707975  0.8358208955  61.791967044  1.4306841815  0.0414713973  8.8448004723  0.2105936639  0.4471318941  4800          5.2007869458 
0.0407896191  0.9963954686  0.8020618557  0.9822451317  0.7995418099  0.7767454955  0.7756482525  0.9959839357  0.8346727899  64.366632337  1.4269723934  0.0407896191  8.8448004723  0.2088417316  0.4443439765  5000          5.1407534707