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_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: 0.3
	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    
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             0.8012001514 
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           0.1878983537 
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    
0.0488607032  0.8499084808  0.8826405868  0.9856076759  0.9679487179  0.9970059880  0.9610778443  0.9831424936  0.9490445860  14.371257485  1.2643262006  0.6756797772  8.3915634155  0.4410339328  0.1281354406  600           0.4747345996 
0.0317915015  0.8468578401  0.8777506112  0.9962686567  0.9508547009  1.0000000000  0.9790419162  0.9891857506  0.9566878981  21.556886227  1.1344965072  1.6734463636  8.3915634155  0.2736044778  0.0615955378  900           0.9932703797 
0.0207965397  0.8145210494  0.8337408313  0.9722814499  0.9102564103  0.9962574850  0.9640718563  0.9548346056  0.9184713376  28.742514970  0.9764158640  1.4228925900  8.3915634155  0.1870158061  0.0353074698  1200          0.9920503418 
0.0181309278  0.8383160464  0.8386308068  0.9978678038  0.9551282051  0.9992514970  0.9790419162  0.9952290076  0.9579617834  35.928143712  0.8479047028  1.2324404093  8.3915634155  0.1485614116  0.0247935187  1500          0.9920064298 
0.0115576927  0.8157413057  0.8312958435  0.9941364606  0.9444444444  0.9992514970  0.9820359281  0.9965012723  0.9617834395  43.113772455  0.7180358313  1.0387645421  8.3915634155  0.1186047149  0.0208299481  1800          0.9917357969 
0.0183539566  0.7925564369  0.8044009780  0.9946695096  0.9465811966  0.9992514970  0.9730538922  0.9904580153  0.9477707006  50.299401197  0.6174227403  0.9032980271  8.3915634155  0.1158958320  0.0158393520  2100          0.9921089268 
0.0111723192  0.8602806589  0.8630806846  0.9994669510  0.9722222222  1.0000000000  0.9880239521  0.9980916031  0.9630573248  57.485029940  0.5138180470  0.7466350275  8.3915634155  0.0884788989  0.0142280881  2400          0.9917275016 
0.0092296827  0.8486882245  0.8459657702  0.9978678038  0.9636752137  0.9992514970  0.9700598802  0.9977735369  0.9605095541  64.670658682  0.4320847178  0.6276053743  8.3915634155  0.0744997457  0.0114106557  2700          0.9922981572 
0.0172318779  0.8505186089  0.8288508557  0.9994669510  0.9423076923  0.9977544910  0.9790419162  0.9968193384  0.9528662420  71.856287425  0.3780269927  0.5592864799  8.3915634155  0.0738954125  0.0086625414  3000          0.9920644903 
0.0126867877  0.8584502746  0.8704156479  0.9978678038  0.9743589744  1.0000000000  0.9790419162  0.9974554707  0.9426751592  79.041916167  0.3208769352  0.4733369011  8.3915634155  0.0662796011  0.0074023452  3300          0.9927428071 
0.0093846884  0.7901159243  0.8215158924  0.9973347548  0.9487179487  0.9977544910  0.9730538922  0.9930025445  0.9503184713  86.227544910  0.2683872830  0.3952354778  8.3915634155  0.0592746486  0.0060869990  3600          0.9924891853 
0.0114899222  0.7803538743  0.7555012225  0.9957356077  0.9465811966  0.9977544910  0.9461077844  0.9910941476  0.9477707006  93.413173652  0.2423719576  0.3600872971  8.3915634155  0.0550701096  0.0050806138  3900          0.9927050368 
0.0115936076  0.7675411836  0.7775061125  0.9946695096  0.9465811966  0.9985029940  0.9700598802  0.9952290076  0.9503184713  100.59880239  0.2145085414  0.3212378598  8.3915634155  0.0563772868  0.0043785185  4200          0.9926608944 
0.0112987186  0.8407565589  0.8459657702  0.9978678038  0.9679487179  0.9985029940  0.9700598802  0.9968193384  0.9566878981  107.78443113  0.1916910697  0.2882983734  8.3915634155  0.0523247614  0.0039172400  4500          0.9928714450 
0.0102806345  0.8523489933  0.8533007335  1.0000000000  0.9700854701  0.9992514970  0.9820359281  0.9965012723  0.9592356688  114.97005988  0.1704475640  0.2565976393  8.3915634155  0.0465045116  0.0035737057  4800          0.9929969088 
0.0054768103  0.8657718121  0.8655256724  0.9989339019  0.9615384615  0.9992514970  0.9880239521  0.9996819338  0.9617834395  119.76047904  0.1537450503  0.2273336950  8.3915634155  0.0398616537  0.0031728478  5000          0.9913836074