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}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-noDist_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: 0.3
	rdi_ema: 0.9
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.0
	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             0.8612906933 
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           0.2041927640 
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    
0.0500130004  0.9829164124  0.9388753056  0.7462686567  0.7606837607  0.9925149701  0.9670658683  0.9618320611  0.9095541401  14.371257485  1.2759702395  0.0814918486  8.3912892342  0.4477636369  0.1316849026  600           0.4751543164 
0.0277436181  0.9908480781  0.9511002445  0.7574626866  0.7841880342  0.9955089820  0.9700598802  0.9666030534  0.9184713376  21.556886227  1.2148384424  0.0839144922  8.3912940025  0.2831772994  0.0684713851  900           0.9950479237 
0.0267612177  0.9932885906  0.9511002445  0.7686567164  0.7777777778  0.9985029940  0.9790419162  0.9907760814  0.9503184713  28.742514970  1.1793841847  0.0653069277  8.3912944794  0.2050976750  0.0389052862  1200          0.9938720393 
0.0172769813  0.9884075656  0.9315403423  0.7921108742  0.8205128205  0.9970059880  0.9730538922  0.9882315522  0.9414012739  35.928143712  1.1632873607  0.0469577590  8.3913035393  0.1591430060  0.0290466287  1500          0.9937762618 
0.0177547819  0.9987797437  0.9511002445  0.8006396588  0.8205128205  0.9962574850  0.9790419162  0.9869592875  0.9452229299  43.113772455  1.1516690377  0.0426395249  8.3913207054  0.1357921517  0.0225795961  1800          0.9936403386 
0.0165750039  0.9987797437  0.9584352078  0.8139658849  0.8311965812  0.9985029940  0.9700598802  0.9926844784  0.9515923567  50.299401197  1.1408542569  0.0378801495  8.3913207054  0.1184606168  0.0176802618  2100          0.9933391587 
0.0123104228  0.9987797437  0.9462102689  0.7921108742  0.8162393162  1.0000000000  0.9910179641  0.9971374046  0.9464968153  57.485029940  1.1345979830  0.0306982176  8.3913207054  0.1016696924  0.0156867009  2400          0.9932059455 
0.0113719333  0.9987797437  0.9388753056  0.8171641791  0.8354700855  0.9992514970  0.9820359281  0.9930025445  0.9554140127  64.670658682  1.1277004560  0.0271363142  8.3913207054  0.0869116996  0.0136381269  2700          0.9930331222 
0.0153560526  0.9981696156  0.9364303178  0.8086353945  0.8162393162  1.0000000000  0.9790419162  0.9980916031  0.9528662420  71.856287425  1.1237047283  0.0307546764  8.3913207054  0.0871091714  0.0116612375  3000          0.9925235613 
0.0116876822  0.9981696156  0.9511002445  0.8150319829  0.8461538462  0.9992514970  0.9730538922  0.9984096692  0.9503184713  79.041916167  1.1201013450  0.0252959606  8.3913207054  0.0764803301  0.0106811424  3300          0.9939238008 
0.0111494052  0.9969493594  0.9437652812  0.8123667377  0.8119658120  0.9985029940  0.9610778443  0.9955470738  0.9503184713  86.227544910  1.1208026218  0.0233691603  8.3913207054  0.0683631779  0.0098263896  3600          0.9935141412 
0.0105279266  0.9987797437  0.9364303178  0.8027718550  0.8226495726  1.0000000000  0.9730538922  0.9955470738  0.9605095541  93.413173652  1.1152939153  0.0216607847  8.3913207054  0.0626095434  0.0087071313  3900          0.9935712202 
0.0115862736  0.9932885906  0.9388753056  0.7372068230  0.7692307692  0.9992514970  0.9640718563  0.9990458015  0.9414012739  100.59880239  1.1162949375  0.0223782592  8.3913207054  0.0615543541  0.0077941600  4200          0.9929926713 
0.0078791944  0.9981696156  0.9511002445  0.8086353945  0.8205128205  1.0000000000  0.9760479042  0.9961832061  0.9503184713  107.78443113  1.1164474960  0.0173810662  8.3913207054  0.0534820425  0.0073978257  4500          0.9930257495 
0.0074992697  1.0000000000  0.9486552567  0.8230277186  0.8311965812  0.9992514970  0.9760479042  0.9990458015  0.9592356688  114.97005988  1.1111764272  0.0161307814  8.3913207054  0.0481266583  0.0070625633  4800          0.9929801846 
0.0171079807  0.9969493594  0.9266503667  0.8315565032  0.8568376068  0.9992514970  0.9820359281  0.9958651399  0.9503184713  119.76047904  1.1084858960  0.0264404919  8.3913207054  0.0545099555  0.0057800874  5000          0.9929817975