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": 0.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-noRate_env1_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [1]
	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: 0.4
	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    
1.9995113611  0.1708358755  0.1735941320  0.1796375267  0.1709401709  0.2522455090  0.2514970060  0.2296437659  0.2229299363  0.0000000000  1.9995113611  7.9755101204  0             0.7987685204 
0.1710804016  0.9701037218  0.9242053790  0.7761194030  0.7820512821  0.9910179641  0.9640718563  0.9570610687  0.9363057325  7.1856287425  0.1710804016  8.2003893852  300           0.2119553582 
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    
0.0501527109  0.9896278218  0.9437652812  0.7675906183  0.7799145299  0.9962574850  0.9850299401  0.9751908397  0.9426751592  14.371257485  1.2899863059  0.1472151773  8.3912892342  0.5189762222  0.1515354346  600           0.4745827166 
0.0321210832  0.9841366687  0.9388753056  0.7675906183  0.8098290598  0.9723053892  0.9461077844  0.9564249364  0.9146496815  21.556886227  1.2245867570  0.3001280774  8.3912940025  0.4334364491  0.1154482015  900           0.9932955917 
0.0227498315  0.9810860281  0.9290953545  0.7841151386  0.7927350427  0.9970059880  0.9760479042  0.9926844784  0.9554140127  28.742514970  1.1729617838  0.2742958264  8.3912944794  0.3491370444  0.0847681712  1200          0.9922851626 
0.0225723519  0.9975594875  0.9462102689  0.7803837953  0.8183760684  0.9985029940  0.9760479042  0.9926844784  0.9414012739  35.928143712  1.1426902445  0.2644970997  8.3913035393  0.3067818198  0.0669334792  1500          0.9919666044 
0.0172802719  0.9945088469  0.9437652812  0.7675906183  0.7777777778  0.9992514970  0.9640718563  0.9965012723  0.9554140127  43.113772455  1.1159602849  0.2518349241  8.3913207054  0.2681847142  0.0568129592  1800          0.9918052069 
0.0148274251  0.9957291031  0.9388753056  0.8235607676  0.8397435897  0.9955089820  0.9760479042  0.9872773537  0.9363057325  50.299401197  1.0880905179  0.2422285784  8.3913207054  0.2435880212  0.0489152325  2100          0.9916692742 
0.0143819935  0.9981696156  0.9462102689  0.7766524520  0.7927350427  1.0000000000  0.9880239521  0.9965012723  0.9541401274  57.485029940  1.0646904576  0.2359200824  8.3913207054  0.2283653169  0.0429999681  2400          0.9915320841 
0.0180380203  0.9932885906  0.9290953545  0.7478678038  0.7905982906  0.9970059880  0.9730538922  0.9936386768  0.9464968153  64.670658682  1.0411070557  0.2337947498  8.3913207054  0.2221807753  0.0376765750  2700          0.9914215088 
0.0153864182  0.9951189750  0.9168704156  0.6551172708  0.6987179487  0.9970059880  0.9700598802  0.9805979644  0.9159235669  71.856287425  1.0056724523  0.2233885141  8.3913207054  0.2084318931  0.0343380095  3000          0.9913392329 
0.0159984086  0.9841366687  0.9315403423  0.8192963753  0.8333333333  0.9947604790  0.9520958084  0.9882315522  0.9210191083  79.041916167  0.9674102434  0.2157093198  8.3913207054  0.1979173587  0.0311442966  3300          0.9915746538 
0.0083451286  1.0000000000  0.9559902200  0.8097014925  0.8354700855  1.0000000000  0.9820359281  0.9974554707  0.9503184713  86.227544910  0.9322402227  0.2006822040  8.3913207054  0.1838099569  0.0294451396  3600          0.9908858180 
0.0130252874  0.9969493594  0.9315403423  0.7659914712  0.8012820513  1.0000000000  0.9760479042  0.9971374046  0.9592356688  93.413173652  0.8993750133  0.1980756858  8.3913207054  0.1779576227  0.0258769639  3900          0.9916692877 
0.0104741872  0.9865771812  0.9193154034  0.7217484009  0.7521367521  0.9985029940  0.9580838323  0.9952290076  0.9337579618  100.59880239  0.8792612805  0.1911132710  8.3913207054  0.1743936316  0.0239341244  4200          0.9916355356 
0.0120402203  0.9993898719  0.9511002445  0.7846481876  0.8076923077  1.0000000000  0.9730538922  0.9984096692  0.9617834395  107.78443113  0.8519236302  0.1867739712  8.3913207054  0.1714624002  0.0217451079  4500          0.9914751307 
0.0094271289  0.9975594875  0.9290953545  0.7670575693  0.7884615385  0.9992514970  0.9730538922  0.9942748092  0.9490445860  114.97005988  0.8052290356  0.1745171512  8.3913207054  0.1622186703  0.0202210573  4800          0.9918681383 
0.0146130698  0.9975594875  0.9168704156  0.7478678038  0.7841880342  0.9985029940  0.9760479042  0.9977735369  0.9401273885  119.76047904  0.7575102893  0.1698622411  8.3913207054  0.1683379612  0.0187355537  5000          0.9915431643