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_env3_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [3]
	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    
1.8969436884  0.1854789506  0.1760391198  0.1950959488  0.1752136752  0.2582335329  0.2634730539  0.2073791349  0.2114649682  0.0000000000  1.8969436884  7.9755215645  0             0.7397091389 
0.1621012442  0.9707138499  0.9046454768  0.9818763326  0.9508547009  0.9977544910  0.9820359281  0.6653944020  0.6598726115  7.1856287425  0.1621012442  8.2003893852  300           0.1880563625 
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    
0.0383517850  0.9853569250  0.9413202934  0.9946695096  0.9551282051  0.9977544910  0.9790419162  0.6695292621  0.6828025478  14.371257485  1.2456801327  0.6554173721  8.3915948868  0.4335825222  0.1193927054  600           0.4619244321 
0.0265789301  0.9969493594  0.9584352078  0.9973347548  0.9594017094  1.0000000000  0.9760479042  0.7515903308  0.7414012739  21.556886227  1.1357527765  1.6705652952  8.3915948868  0.2841652312  0.0565385832  900           0.9929942346 
0.0180199568  0.9951189750  0.9413202934  0.9962686567  0.9487179487  1.0000000000  0.9850299401  0.8171119593  0.8178343949  28.742514970  0.9957175036  1.4480797021  8.3915948868  0.1985930878  0.0313314982  1200          0.9923719684 
0.0108160895  0.9926784625  0.9193154034  0.9957356077  0.9529914530  0.9985029940  0.9520958084  0.7881679389  0.7757961783  35.928143712  0.8605660494  1.2430589116  8.3915948868  0.1537161846  0.0219647386  1500          0.9923768123 
0.0165965067  0.9859670531  0.9290953545  0.9994669510  0.9487179487  1.0000000000  0.9730538922  0.7547709924  0.7439490446  43.113772455  0.7270122568  1.0588082812  8.3915948868  0.1405735815  0.0165429688  1800          0.9922987588 
0.0141928674  0.9920683344  0.9290953545  0.9968017058  0.9551282051  0.9985029940  0.9760479042  0.7833969466  0.7821656051  50.299401197  0.6013217515  0.8764175930  8.3915948868  0.1189044905  0.0126930625  2100          0.9924849939 
0.0129500217  0.9993898719  0.9413202934  0.9973347548  0.9423076923  0.9992514970  0.9670658683  0.6720737913  0.6726114650  57.485029940  0.4991363208  0.7300629544  8.3915948868  0.1086408001  0.0101298724  2400          0.9925963338 
0.0091777063  0.9993898719  0.9437652812  0.9989339019  0.9529914530  1.0000000000  0.9760479042  0.7592239186  0.7808917197  64.670658682  0.4186436034  0.6105397884  8.3915948868  0.0901947584  0.0086591684  2700          0.9925246429 
0.0100543896  0.9902379500  0.9242053790  0.9989339019  0.9465811966  0.9962574850  0.9520958084  0.7611323155  0.7707006369  71.856287425  0.3453495296  0.5083028216  8.3915948868  0.0885615486  0.0073743281  3000          0.9927419567 
0.0129273059  0.9969493594  0.9437652812  0.9994669510  0.9380341880  0.9992514970  0.9730538922  0.7366412214  0.7464968153  79.041916167  0.2833246637  0.4233363855  8.3915948868  0.0835783018  0.0060890526  3300          0.9930184285 
0.0094700228  1.0000000000  0.9559902200  1.0000000000  0.9594017094  1.0000000000  0.9790419162  0.7757633588  0.7783439490  86.227544910  0.2339471955  0.3498397403  8.3915948868  0.0783750997  0.0054369140  3600          0.9927053833 
0.0083747294  0.9945088469  0.9486552567  0.9984008529  0.9551282051  0.9992514970  0.9760479042  0.7814885496  0.7821656051  93.413173652  0.1871895271  0.2823639729  8.3915948868  0.0733337947  0.0046191997  3900          0.9927969837 
0.0107571588  0.9969493594  0.9388753056  0.9978678038  0.9423076923  1.0000000000  0.9820359281  0.8209287532  0.8280254777  100.59880239  0.1597247736  0.2463747915  8.3915948868  0.0745953439  0.0040682516  4200          0.9931819320 
0.0107022274  0.9981696156  0.9486552567  0.9962686567  0.9508547009  0.9992514970  0.9640718563  0.7293256997  0.7503184713  107.78443113  0.1316177473  0.2059023577  8.3915948868  0.0681559728  0.0035594488  4500          0.9930201817 
0.0077760833  0.9975594875  0.9437652812  0.9968017058  0.9529914530  1.0000000000  0.9730538922  0.7375954198  0.7324840764  114.97005988  0.1137370620  0.1768833820  8.3915948868  0.0612298806  0.0034546609  4800          0.9930522863 
0.0129476412  0.9993898719  0.9388753056  1.0000000000  0.9551282051  0.9992514970  0.9610778443  0.7967557252  0.8000000000  119.76047904  0.0984939494  0.1615036856  8.3915948868  0.0674572917  0.0027296180  5000          0.9928677130