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, "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/pacs/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: 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: 0.0
	rdi_ema: 0.9
	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: 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    
1.9950832129  0.1891397193  0.1760391198  0.2052238806  0.1858974359  0.2028443114  0.2095808383  0.2076972010  0.2152866242  0.0000000000  1.9950832129  7.9755101204  0             0.7699849606 
0.2457256681  0.9560707749  0.9046454768  0.9744136461  0.9423076923  0.9678143713  0.9550898204  0.9449745547  0.9248407643  7.1856287425  0.2457256681  8.2003893852  300           0.2109215029 
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    
0.0647344293  0.9871873093  0.9388753056  0.9920042644  0.9423076923  0.9595808383  0.9640718563  0.9847328244  0.9439490446  14.371257485  1.3003476941  0.0647344293  8.3915619850  0.5687830631  0.1557863399  600           0.4705841271 
0.0420009285  0.9810860281  0.9144254279  0.9909381663  0.9358974359  0.9685628743  0.9670658683  0.9891857506  0.9477707006  21.556886227  1.2897466163  0.0420009285  8.3915619850  0.5323750808  0.1523054439  900           0.9997814624 
0.0310010458  0.9902379500  0.9413202934  0.9845415778  0.9487179487  0.9655688623  0.9520958084  0.9748727735  0.9324840764  28.742514970  1.2826586076  0.0310010458  8.3915619850  0.5057585456  0.1474835584  1200          0.9999991632 
0.0270803235  0.9945088469  0.9462102689  0.9957356077  0.9551282051  0.9633233533  0.9610778443  0.9872773537  0.9388535032  35.928143712  1.2779127955  0.0270803235  8.3915619850  0.4807799525  0.1408682206  1500          0.9999362024 
0.0168423811  0.9877974375  0.9266503667  0.9968017058  0.9679487179  0.9610778443  0.9431137725  0.9802798982  0.9388535032  43.113772455  1.2752087120  0.0168423811  8.3915619850  0.4592038409  0.1383592005  1800          1.0002286140 
0.0199048026  0.9969493594  0.9486552567  0.9973347548  0.9487179487  0.9633233533  0.9640718563  0.9910941476  0.9439490446  50.299401197  1.2644785349  0.0199048026  8.3915619850  0.4430885035  0.1275708782  2100          0.9997237078 
0.0178471697  0.9914582062  0.9511002445  0.9984008529  0.9551282051  0.9558383234  0.9491017964  0.9980916031  0.9541401274  57.485029940  1.2565809488  0.0178471697  8.3915619850  0.4276824591  0.1248611068  2400          0.9996403019 
0.0194386864  0.9896278218  0.9168704156  0.9941364606  0.9465811966  0.9655688623  0.9491017964  0.9920483461  0.9515923567  64.670658682  1.2559261894  0.0194386864  8.3915619850  0.4207051933  0.1207600949  2700          0.9997749861 
0.0143256549  0.9957291031  0.9486552567  0.9984008529  0.9679487179  0.9685628743  0.9431137725  0.9939567430  0.9528662420  71.856287425  1.2562855359  0.0143256549  8.3915619850  0.4047257710  0.1193140180  3000          0.9997654748 
0.0126817905  0.9871873093  0.9144254279  0.9984008529  0.9508547009  0.9423652695  0.9071856287  0.9914122137  0.9350318471  79.041916167  1.2512111561  0.0126817905  8.3915619850  0.3921535781  0.1147840862  3300          0.9997549788 
0.0148322768  0.9932885906  0.9168704156  0.9957356077  0.9529914530  0.9618263473  0.9461077844  0.9942748092  0.9401273885  86.227544910  1.2466944782  0.0148322768  8.3915619850  0.3897028776  0.1089452208  3600          0.9995635827 
0.0127486760  0.9914582062  0.9315403423  0.9984008529  0.9529914530  0.9498502994  0.9221556886  0.9965012723  0.9566878981  93.413173652  1.2477078307  0.0127486760  8.3915619850  0.3779355947  0.1050354142  3900          0.9995420702 
0.0127939776  0.9932885906  0.9315403423  0.9989339019  0.9529914530  0.9670658683  0.9401197605  0.9977735369  0.9592356688  100.59880239  1.2429708219  0.0127939776  8.3915619850  0.3694781078  0.1015604106  4200          0.9994905893 
0.0170311788  0.9951189750  0.9388753056  0.9952025586  0.9572649573  0.9670658683  0.9371257485  0.9901399491  0.9426751592  107.78443113  1.2413307297  0.0170311788  8.3915619850  0.3716271098  0.0994425221  4500          0.9994194754 
0.0133515391  0.9981696156  0.9339853301  1.0000000000  0.9679487179  0.9610778443  0.9221556886  0.9977735369  0.9656050955  114.97005988  1.2361872172  0.0133515391  8.3915619850  0.3654535351  0.0952226939  4800          0.9995595670 
0.0054201363  0.9993898719  0.9364303178  0.9989339019  0.9700854701  0.9498502994  0.9221556886  0.9977735369  0.9464968153  119.76047904  1.2378822893  0.0054201363  8.3915619850  0.3446059617  0.1007434236  5000          0.9984995925