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": 2.8}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-sumgeo_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: 0.3
	rdi_ema: 0.9
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 2.8
	rdi_lambda_rec: 0.4
	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             0.8949773312 
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           0.1874229916 
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    
0.0628307843  0.9908480781  0.9486552567  0.9920042644  0.9508547009  0.9648203593  0.9700598802  0.9815521628  0.9477707006  14.371257485  1.2601338649  0.6891136028  8.3915619850  0.4734271237  0.1252227656  600           0.4593911179 
0.0409026790  0.9920683344  0.9364303178  0.9973347548  0.9529914530  0.9708083832  0.9700598802  0.9891857506  0.9490445860  21.556886227  1.1630208806  1.7287638946  8.3915619850  0.3126348400  0.0636839014  900           0.9923728299 
0.0284360041  0.9926784625  0.9462102689  0.9968017058  0.9487179487  0.9760479042  0.9700598802  0.9949109415  0.9528662420  28.742514970  1.0750959508  1.5742185660  8.3915619850  0.2226234270  0.0362736947  1200          0.9911704381 
0.0228255835  0.9884075656  0.9290953545  0.9829424307  0.9465811966  0.9633233533  0.9670658683  0.9882315522  0.9363057325  35.928143712  0.9894626478  1.4395294277  8.3915619850  0.1764325246  0.0249563943  1500          0.9913762538 
0.0176293493  0.9963392312  0.9559902200  0.9994669510  0.9594017094  0.9730538922  0.9640718563  0.9961832061  0.9605095541  43.113772455  0.9003443994  1.3047969715  8.3915619850  0.1530539266  0.0186369403  1800          0.9910402354 
0.0162461078  0.9926784625  0.9437652812  0.9941364606  0.9444444444  0.9655688623  0.9610778443  0.9885496183  0.9401273885  50.299401197  0.7813999240  1.1336214987  8.3915619850  0.1351456333  0.0157183497  2100          0.9909607943 
0.0152365913  0.9969493594  0.9364303178  0.9994669510  0.9551282051  0.9678143713  0.9610778443  0.9971374046  0.9707006369  57.485029940  0.6763123592  0.9834779018  8.3915619850  0.1255014438  0.0128940359  2400          0.9908333906 
0.0182821810  0.9987797437  0.9559902200  0.9952025586  0.9551282051  0.9565868263  0.9401197605  0.9904580153  0.9490445860  64.670658682  0.5570434288  0.8178828174  8.3915619850  0.1165872536  0.0112588042  2700          0.9908712387 
0.0175112643  0.9969493594  0.9486552567  0.9952025586  0.9572649573  0.9633233533  0.9461077844  0.9860050891  0.9452229299  71.856287425  0.4516611310  0.6692859741  8.3915619850  0.1166383018  0.0097669454  3000          0.9907253671 
0.0097267798  0.9975594875  0.9315403423  0.9978678038  0.9679487179  0.9618263473  0.9281437126  0.9974554707  0.9592356688  79.041916167  0.3408323025  0.5035648857  8.3915619850  0.0991150972  0.0090281234  3300          0.9911858145 
0.0119748677  0.9920683344  0.9486552567  0.9952025586  0.9658119658  0.9648203593  0.9550898204  0.9961832061  0.9426751592  86.227544910  0.2450153473  0.3709483047  8.3915619850  0.0962017698  0.0076084487  3600          0.9909360083 
0.0149335991  0.9914582062  0.9193154034  0.9973347548  0.9487179487  0.9483532934  0.9281437126  0.9920483461  0.9490445860  93.413173652  0.1888317727  0.2954025225  8.3915619850  0.0991088554  0.0061905844  3900          0.9913111051 
0.0151746144  0.9957291031  0.9413202934  0.9984008529  0.9658119658  0.9610778443  0.9461077844  0.9987277354  0.9477707006  100.59880239  0.1560399810  0.2488735811  8.3915619850  0.0943663723  0.0054402015  4200          0.9914505410 
0.0085077446  0.9987797437  0.9559902200  1.0000000000  0.9786324786  0.9655688623  0.9520958084  0.9980916031  0.9528662420  107.78443113  0.1340076518  0.2092653936  8.3915619850  0.0808816322  0.0050734700  4500          0.9911965013 
0.0133874319  0.9975594875  0.9364303178  0.9973347548  0.9529914530  0.9446107784  0.9221556886  0.9945928753  0.9490445860  114.97005988  0.1152014554  0.1877349374  8.3915619850  0.0810772951  0.0045193779  4800          0.9914621488 
0.0140047065  0.9993898719  0.9119804401  0.9989339019  0.9465811966  0.9595808383  0.9491017964  0.9945928753  0.9337579618  119.76047904  0.1108905765  0.1824557744  8.3915619850  0.0824792957  0.0041618445  5000          0.9911470890