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_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: 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    
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             0.9688813686 
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           0.2103828311 
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    
0.0378933457  0.9884075656  0.9462102689  0.9957356077  0.9508547009  0.9970059880  0.9820359281  0.7064249364  0.6993630573  14.371257485  1.2412902872  0.1870766060  8.3915948868  0.3505747475  0.0978657997  600           0.4618289026 
0.0203510054  0.9938987187  0.9388753056  0.9936034115  0.9465811966  0.9992514970  0.9730538922  0.7019720102  0.7171974522  21.556886227  1.1772560223  0.3469932819  8.3915948868  0.1684163966  0.0349143607  900           0.9928335174 
0.0173198194  0.9969493594  0.9462102689  0.9973347548  0.9423076923  1.0000000000  0.9790419162  0.8479643766  0.8509554140  28.742514970  1.1335505501  0.2991465986  8.3915948868  0.1029354737  0.0182446549  1200          0.9925234636 
0.0089305036  0.9963392312  0.9486552567  0.9978678038  0.9529914530  1.0000000000  0.9880239521  0.7942111959  0.8025477707  35.928143712  1.1016677547  0.2670760703  8.3915948868  0.0701444274  0.0136989952  1500          0.9924404240 
0.0125076177  0.9981696156  0.9364303178  0.9984008529  0.9465811966  0.9992514970  0.9880239521  0.7942111959  0.7834394904  43.113772455  1.0742092017  0.2591380984  8.3915948868  0.0594214689  0.0103895113  1800          0.9924478801 
0.0141830410  0.9908480781  0.9168704156  0.9936034115  0.9294871795  0.9992514970  0.9730538922  0.7827608142  0.7847133758  50.299401197  1.0539739211  0.2522146184  8.3915948868  0.0511123159  0.0084031592  2100          0.9915162873 
0.0105988119  0.9938987187  0.9486552567  0.9930703625  0.9465811966  0.9985029940  0.9700598802  0.7589058524  0.7566878981  57.485029940  1.0301118286  0.2390077485  8.3915948868  0.0418429812  0.0073253871  2400          0.9912762396 
0.0077438845  0.9987797437  0.9388753056  0.9978678038  0.9487179487  0.9992514970  0.9730538922  0.7557251908  0.7464968153  64.670658682  1.0056186072  0.2278485426  8.3915948868  0.0355061236  0.0061393627  2700          0.9915738273 
0.0105981132  0.9993898719  0.9364303178  0.9994669510  0.9636752137  1.0000000000  0.9730538922  0.7954834606  0.8076433121  71.856287425  0.9736908648  0.2230899281  8.3915948868  0.0334940420  0.0050330850  3000          0.9915882635 
0.0102066293  0.9981696156  0.9364303178  0.9962686567  0.9358974359  0.9992514970  0.9670658683  0.7086513995  0.6955414013  79.041916167  0.9458153973  0.2150930634  8.3915948868  0.0296876376  0.0043976669  3300          0.9917030700 
0.0089583801  0.9981696156  0.9315403423  0.9994669510  0.9529914530  1.0000000000  0.9700598802  0.7783078880  0.7681528662  86.227544910  0.9145421161  0.2059915845  8.3915948868  0.0266463352  0.0040080540  3600          0.9920151520 
0.0099244008  0.9969493594  0.9413202934  0.9962686567  0.9529914530  0.9977544910  0.9610778443  0.7522264631  0.7719745223  93.413173652  0.8902152658  0.2021477687  8.3915948868  0.0270054567  0.0033879227  3900          0.9917360473 
0.0087883295  0.9987797437  0.9413202934  0.9989339019  0.9423076923  0.9985029940  0.9790419162  0.8193384224  0.8038216561  100.59880239  0.8559293807  0.1919527557  8.3915948868  0.0227053244  0.0031294266  4200          0.9921844880 
0.0095640054  0.9981696156  0.9437652812  0.9994669510  0.9615384615  0.9977544910  0.9700598802  0.7770356234  0.7847133758  107.78443113  0.8116377340  0.1833142893  8.3915948868  0.0217381838  0.0027682116  4500          0.9919961516 
0.0096448677  1.0000000000  0.9486552567  0.9994669510  0.9529914530  1.0000000000  0.9730538922  0.8266539440  0.8216560510  114.97005988  0.7687684816  0.1745261046  8.3915948868  0.0212842835  0.0024269796  4800          0.9921973491 
0.0081749330  0.9957291031  0.9290953545  0.9861407249  0.9401709402  0.9985029940  0.9520958084  0.7245547074  0.7324840764  119.76047904  0.7272605908  0.1634644588  8.3915948868  0.0187602312  0.0022864457  5000          0.9916884553