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_env0_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [0]
	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    
2.0235202312  0.1879194631  0.1613691932  0.1876332623  0.1709401709  0.2582335329  0.2574850299  0.2258269720  0.2203821656  0.0000000000  2.0235202312  7.9755101204  0             0.6743135452 
0.1832763110  0.8395363026  0.8484107579  0.9744136461  0.9316239316  0.9947604790  0.9790419162  0.9653307888  0.9312101911  7.1856287425  0.1832763110  8.2003893852  300           0.1958260671 
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    
0.0488823708  0.8474679683  0.8435207824  0.9904051173  0.9508547009  0.9970059880  0.9730538922  0.9812340967  0.9464968153  14.371257485  1.3099385406  0.0488823708  8.3915634155  0.5422654677  0.1616943147  600           0.4601055781 
0.0347119464  0.8578401464  0.8753056235  0.9850746269  0.9487179487  0.9977544910  0.9790419162  0.9767811705  0.9452229299  21.556886227  1.2974901084  0.0347119464  8.3915634155  0.5163174221  0.1578760555  900           0.9957153265 
0.0240712014  0.7504575961  0.7212713936  0.9802771855  0.9294871795  0.9880239521  0.9461077844  0.9513358779  0.9146496815  28.742514970  1.2889286423  0.0240712014  8.3915634155  0.4883513137  0.1545242898  1200          0.9949495387 
0.0204210541  0.8322147651  0.8459657702  0.9952025586  0.9337606838  0.9985029940  0.9760479042  0.9904580153  0.9452229299  35.928143712  1.2790697491  0.0204210541  8.3915634155  0.4680755252  0.1456368981  1500          0.9952874462 
0.0120056062  0.8389261745  0.8435207824  0.9952025586  0.9679487179  0.9992514970  0.9640718563  0.9965012723  0.9592356688  43.113772455  1.2774633082  0.0120056062  8.3915634155  0.4476423761  0.1443688025  1800          0.9946605833 
0.0225634969  0.7693715680  0.7823960880  0.9957356077  0.9529914530  0.9970059880  0.9580838323  0.9850508906  0.9350318471  50.299401197  1.2683940184  0.0225634969  8.3915634155  0.4436950752  0.1341689660  2100          0.9960109433 
0.0140088932  0.8547895058  0.8361858191  0.9984008529  0.9679487179  1.0000000000  0.9700598802  0.9895038168  0.9528662420  57.485029940  1.2659151709  0.0140088932  8.3915634155  0.4268999663  0.1328270305  2400          0.9943997685 
0.0107915380  0.8712629652  0.8630806846  0.9973347548  0.9658119658  1.0000000000  0.9760479042  0.9955470738  0.9503184713  64.670658682  1.2637922176  0.0107915380  8.3915634155  0.4023531494  0.1305486347  2700          0.9949850869 
0.0134381538  0.7742525930  0.7750611247  0.9968017058  0.9551282051  0.9985029940  0.9670658683  0.9888676845  0.9503184713  71.856287425  1.2587631512  0.0134381538  8.3915634155  0.3986227759  0.1182794770  3000          0.9949131370 
0.0112227584  0.8230628432  0.8312958435  0.9984008529  0.9679487179  1.0000000000  0.9760479042  0.9930025445  0.9375796178  79.041916167  1.2561350373  0.0112227584  8.3915634155  0.3772288296  0.1200766097  3300          0.9949989676 
0.0109820256  0.7284929835  0.7481662592  0.9973347548  0.9594017094  0.9872754491  0.9491017964  0.9895038168  0.9337579618  86.227544910  1.2526239312  0.0109820256  8.3915634155  0.3772124646  0.1123043570  3600          0.9940724834 
0.0130161141  0.8834655278  0.8753056235  0.9978678038  0.9636752137  0.9992514970  0.9700598802  0.9968193384  0.9490445860  93.413173652  1.2521338173  0.0130161141  8.3915634155  0.3728733424  0.1084275394  3900          0.9947185373 
0.0109488430  0.7693715680  0.7628361858  0.9946695096  0.9508547009  0.9970059880  0.9431137725  0.9952290076  0.9439490446  100.59880239  1.2460781999  0.0109488430  8.3915634155  0.3568610292  0.1074294961  4200          0.9942023349 
0.0083666033  0.8450274558  0.8606356968  0.9973347548  0.9743589744  0.9992514970  0.9730538922  0.9965012723  0.9401273885  107.78443113  1.2407033471  0.0083666033  8.3915634155  0.3485527490  0.1008520722  4500          1.0053725203 
0.0086024369  0.7919463087  0.8215158924  0.9994669510  0.9529914530  0.9985029940  0.9550898204  0.9977735369  0.9630573248  114.97005988  1.2408695122  0.0086024369  8.3915634155  0.3384572906  0.0979040930  4800          0.9942917323 
0.0078823274  0.8285539963  0.8435207824  0.9946695096  0.9551282051  0.9992514970  0.9520958084  0.9961832061  0.9452229299  119.76047904  1.2345783913  0.0078823274  8.3915634155  0.3343545060  0.0937398084  5000          0.9934033859