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_env3_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [3]
	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    
4.4087882042  0.0159629248  0.0061855670  0.0146048110  0.0183276060  0.0174549550  0.0191657272  0.0166379805  0.0091848450  0.0000000000  4.4087882042  6.4339017868  0             1.2282419205 
2.3542184714  0.7554067971  0.7216494845  0.7007445590  0.6300114548  0.8122184685  0.7914317926  0.7355134825  0.7347876005  3.8619979403  2.3542184714  6.6788215637  300           0.1615759198 
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    
1.0250577343  0.8650875386  0.7649484536  0.8015463918  0.7044673540  0.8918918919  0.8342728298  0.7851405622  0.7646383467  7.7239958805  1.5636272348  1.0250577343  8.6326632500  0.2718308513  0.4456614760  600           1.8687241006 
0.6413648758  0.9294541710  0.7793814433  0.8765750286  0.7468499427  0.9253941441  0.8568207441  0.7980493402  0.7807118255  11.585993820  1.5290426826  0.6413648758  8.8020563126  0.2636212588  0.4352662639  900           5.1893143686 
0.4322482926  0.9572605561  0.7876288660  0.9037800687  0.7709049255  0.9524211712  0.8692220970  0.8043602983  0.7956371986  15.447991761  1.4957623204  0.4322482926  8.8036084175  0.2543927963  0.4283522999  1200          5.1656607008 
0.3036213346  0.9768280124  0.7793814433  0.9341351661  0.7938144330  0.9676238739  0.8703494927  0.8037865749  0.7944890930  19.309989701  1.4810766164  0.3036213346  8.8036084175  0.2471779819  0.4330661862  1500          5.1760770535 
0.2192378367  0.9871266735  0.7814432990  0.9427262314  0.7743413517  0.9800112613  0.8850056370  0.8043602983  0.8048220436  23.171987641  1.4704631050  0.2192378367  8.8036084175  0.2426021699  0.4335479957  1800          5.1668071659 
0.1634171794  0.9933058702  0.7773195876  0.9619129439  0.7903780069  0.9850788288  0.8940248027  0.8017785427  0.7990815155  27.033985581  1.4646381462  0.1634171794  8.8036084175  0.2369838792  0.4391200214  2100          5.1572654812 
0.1208341629  0.9933058702  0.7773195876  0.9644902635  0.7903780069  0.9881756757  0.8951521984  0.8066551922  0.7956371986  30.895983522  1.4584662398  0.1208341629  8.8036084175  0.2322246055  0.4427382397  2400          5.1820601296 
0.0968969146  0.9943357364  0.7896907216  0.9739404353  0.7972508591  0.9898648649  0.9041713641  0.7977624785  0.8013777268  34.757981462  1.4544968673  0.0968969146  8.8036084175  0.2287291247  0.4447605832  2700          5.1547476927 
0.0787663757  0.9963954686  0.7835051546  0.9725085911  0.7812142039  0.9932432432  0.8951521984  0.8080895009  0.7876004592  38.619979402  1.4507090100  0.0787663757  8.8036084175  0.2247979685  0.4477087355  3000          5.1612549504 
0.0671605860  0.9963954686  0.8041237113  0.9776632302  0.7846506300  0.9949324324  0.8917700113  0.8052208835  0.7944890930  42.481977342  1.4479085120  0.0671605860  8.8036084175  0.2228328792  0.4518860222  3300          5.1397449724 
0.0560287803  0.9953656025  0.7938144330  0.9796678121  0.8041237113  0.9946509009  0.9019165727  0.8040734366  0.7944890930  46.343975283  1.4440783354  0.0560287803  8.8036084175  0.2189887947  0.4504411531  3600          5.1354398139 
0.0488436959  0.9958805355  0.7793814433  0.9831042383  0.7857961054  0.9946509009  0.8951521984  0.8032128514  0.8002296211  50.205973223  1.4404614830  0.0488436959  8.8036084175  0.2148756221  0.4527196474  3900          5.1500176779 
0.0453446561  0.9979402678  0.7835051546  0.9825315006  0.7938144330  0.9949324324  0.9019165727  0.8023522662  0.7898966705  54.067971163  1.4391739515  0.0453446561  8.8036084175  0.2130761211  0.4524831986  4200          5.1617135477 
0.0434222807  0.9953656025  0.7876288660  0.9825315006  0.8087056128  0.9963400901  0.9064261556  0.8078026391  0.7921928817  57.929969104  1.4389421089  0.0434222807  8.8036084175  0.2132644856  0.4509070799  4500          5.1325409500 
0.0399314424  0.9969104016  0.7896907216  0.9836769759  0.7823596793  0.9960585586  0.8917700113  0.8037865749  0.7830080367  61.791967044  1.4392283662  0.0399314424  8.8036084175  0.2093956477  0.4474612481  4800          5.1917024549 
0.0374306518  0.9953656025  0.7938144330  0.9848224513  0.7938144330  0.9954954955  0.8974069899  0.8043602983  0.7898966705  64.366632337  1.4341197491  0.0374306518  8.8036084175  0.2071565448  0.4451996206  5000          5.1365053093