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_beta": 1.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-beta1_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: 1.0
	rdi_ema: 0.9
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.4
	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    
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             0.7343597412 
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           0.2024664537 
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    
0.0477893522  0.8682123246  0.8801955990  0.9968017058  0.9615384615  1.0000000000  0.9760479042  0.9856870229  0.9477707006  14.371257485  1.2525881883  0.1996167614  8.3915634155  0.3579228394  0.1074671889  600           0.4596035544 
0.0290235211  0.9005491153  0.8801955990  0.9978678038  0.9551282051  0.9977544910  0.9670658683  0.9844147583  0.9515923567  21.556886227  1.1746907643  0.3605962487  8.3915634155  0.1775882455  0.0392022416  900           0.9916296792 
0.0163713682  0.8438071995  0.8361858191  0.9957356077  0.9465811966  1.0000000000  0.9820359281  0.9850508906  0.9388535032  28.742514970  1.1228195167  0.2977391331  8.3915634155  0.1046940687  0.0222841150  1200          0.9907312242 
0.0146923776  0.8730933496  0.8850855746  0.9994669510  0.9679487179  0.9985029940  0.9820359281  0.9955470738  0.9630573248  35.928143712  1.0944680142  0.2763781972  8.3915634155  0.0794972872  0.0152178566  1500          0.9905570388 
0.0149542159  0.8584502746  0.8704156479  0.9962686567  0.9423076923  0.9985029940  0.9790419162  0.9958651399  0.9464968153  43.113772455  1.0694602720  0.2627826651  8.3915634155  0.0632041357  0.0116716164  1800          0.9905788255 
0.0110772927  0.8730933496  0.8753056235  0.9973347548  0.9551282051  0.9992514970  0.9790419162  0.9965012723  0.9579617834  50.299401197  1.0467987597  0.2483929299  8.3915634155  0.0519832769  0.0098212224  2100          0.9904953527 
0.0149028042  0.8572300183  0.8606356968  0.9930703625  0.9594017094  0.9985029940  0.9760479042  0.9853689567  0.9337579618  57.485029940  1.0184428106  0.2431457509  8.3915634155  0.0459200372  0.0079718134  2400          0.9903902499 
0.0077083728  0.8938377059  0.8875305623  0.9989339019  0.9615384615  1.0000000000  0.9760479042  0.9844147583  0.9324840764  64.670658682  1.0010830724  0.2286993929  8.3915634155  0.0386587768  0.0072250754  2700          0.9905875659 
0.0150429104  0.8352654057  0.8410757946  0.9957356077  0.9572649573  0.9992514970  0.9640718563  0.9904580153  0.9503184713  71.856287425  0.9754377890  0.2306308284  8.3915634155  0.0387174059  0.0057082725  3000          0.9904301039 
0.0088000353  0.8541793777  0.8557457213  0.9989339019  0.9594017094  1.0000000000  0.9820359281  0.9984096692  0.9503184713  79.041916167  0.9529962975  0.2164275119  8.3915634155  0.0319477180  0.0052717782  3300          0.9905661798 
0.0118267594  0.8712629652  0.8630806846  1.0000000000  0.9658119658  1.0000000000  0.9790419162  0.9977735369  0.9643312102  86.227544910  0.9211009383  0.2119222442  8.3915634155  0.0299923379  0.0043956273  3600          0.9900937184 
0.0079428045  0.8438071995  0.8361858191  0.9984008529  0.9615384615  1.0000000000  0.9730538922  0.9977735369  0.9566878981  93.413173652  0.8965973975  0.2016977133  8.3915634155  0.0272688235  0.0040050735  3900          0.9902451237 
0.0107459350  0.8688224527  0.8581907090  1.0000000000  0.9636752137  1.0000000000  0.9730538922  0.9984096692  0.9528662420  100.59880239  0.8600457688  0.1962236173  8.3915634155  0.0255314915  0.0035138995  4200          0.9904706589 
0.0088416950  0.8560097621  0.8435207824  0.9968017058  0.9658119658  1.0000000000  0.9850299401  0.9901399491  0.9452229299  107.78443113  0.8284451632  0.1866304265  8.3915634155  0.0229101928  0.0032229922  4500          0.9906591527 
0.0099526583  0.8114704088  0.8190709046  0.9989339019  0.9508547009  0.9992514970  0.9700598802  0.9990458015  0.9656050955  114.97005988  0.7903284784  0.1804428745  8.3915634155  0.0237392285  0.0027745186  4800          0.9908836333 
0.0075609759  0.8529591214  0.8533007335  0.9994669510  0.9700854701  1.0000000000  0.9730538922  0.9965012723  0.9605095541  119.76047904  0.7500038514  0.1678018979  8.3915634155  0.0194455733  0.0025868078  5000          0.9901661158