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_tau": 1.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-tau1_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: 0.4
	rdi_lambda_rec: 0.4
	rdi_proj_dropout: 0.3
	rdi_proj_hidden: 1024
	rdi_proj_out: 512
	rdi_tau: 1.0
	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             0.7200605869 
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           0.1878550394 
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    
0.0381003849  0.9932885906  0.9462102689  0.9978678038  0.9615384615  0.9970059880  0.9850299401  0.7003816794  0.7006369427  14.371257485  1.2670124616  0.1451518948  8.3915948868  0.2601794348  0.1277268425  600           0.4613136371 
0.0270908369  0.9853569250  0.9290953545  0.9936034115  0.9465811966  0.9977544910  0.9700598802  0.7337786260  0.7197452229  21.556886227  1.2130539366  0.3107423752  8.3915948868  0.1787801939  0.0711185940  900           0.9930660423 
0.0216996150  0.9914582062  0.9339853301  0.9941364606  0.9508547009  0.9992514970  0.9790419162  0.8018447837  0.7821656051  28.742514970  1.1660679750  0.2827505314  8.3915948868  0.1284128085  0.0428769781  1200          0.9924274278 
0.0103130477  0.9987797437  0.9486552567  0.9984008529  0.9529914530  0.9992514970  0.9910179641  0.7961195929  0.7745222930  35.928143712  1.1401908429  0.2589791587  8.3915948868  0.0955698095  0.0314623346  1500          0.9924420826 
0.0156312681  0.9902379500  0.9144254279  0.9925373134  0.9337606838  0.9970059880  0.9700598802  0.7786259542  0.7757961783  43.113772455  1.1127795382  0.2561269433  8.3915948868  0.0873214141  0.0242077594  1800          0.9923348920 
0.0130653196  0.9938987187  0.9242053790  0.9957356077  0.9423076923  0.9992514970  0.9790419162  0.7611323155  0.7745222930  50.299401197  1.0952919797  0.2472929256  8.3915948868  0.0750392389  0.0195665978  2100          0.9925285284 
0.0165168107  0.9963392312  0.9486552567  0.9957356077  0.9551282051  1.0000000000  0.9850299401  0.8015267176  0.7961783439  57.485029940  1.0770151862  0.2459736479  8.3915948868  0.0715721932  0.0165898357  2400          0.9926680120 
0.0104689569  0.9847467968  0.9119804401  0.9946695096  0.9166666667  0.9977544910  0.9580838323  0.7305979644  0.7388535032  64.670658682  1.0578411198  0.2336159614  8.3915948868  0.0585264340  0.0139990596  2700          0.9925781353 
0.0113720593  0.9926784625  0.9290953545  0.9978678038  0.9465811966  0.9985029940  0.9670658683  0.7738549618  0.7859872611  71.856287425  1.0410661248  0.2303649510  8.3915948868  0.0556834333  0.0121357389  3000          0.9927846098 
0.0116110986  0.9969493594  0.9584352078  0.9989339019  0.9508547009  1.0000000000  0.9820359281  0.7388676845  0.7477707006  79.041916167  1.0262797399  0.2269523389  8.3915948868  0.0527573268  0.0108584491  3300          0.9925390132 
0.0042367725  0.9877974375  0.8924205379  0.9914712154  0.9166666667  0.9985029940  0.9580838323  0.7970737913  0.7821656051  86.227544910  1.0017649194  0.2123290238  8.3915948868  0.0384404022  0.0098660195  3600          0.9927670058 
0.0167473530  0.9877974375  0.9217603912  0.9941364606  0.9487179487  0.9947604790  0.9700598802  0.7522264631  0.7605095541  93.413173652  0.9843123790  0.2227130236  8.3915948868  0.0495816009  0.0083297570  3900          0.9926466266 
0.0115108919  0.9969493594  0.9290953545  0.9994669510  0.9572649573  1.0000000000  0.9790419162  0.7973918575  0.7936305732  100.59880239  0.9705096787  0.2141111326  8.3915948868  0.0463793298  0.0077070148  4200          0.9930527409 
0.0044126559  0.9981696156  0.9388753056  1.0000000000  0.9615384615  1.0000000000  0.9700598802  0.7929389313  0.8050955414  107.78443113  0.9414460464  0.1993537732  8.3915948868  0.0346966111  0.0072370630  4500          0.9929601351 
0.0098133321  0.9963392312  0.9535452323  0.9989339019  0.9658119658  0.9992514970  0.9760479042  0.7595419847  0.7490445860  114.97005988  0.9176831748  0.2000415820  8.3915948868  0.0361812043  0.0063221512  4800          0.9933285006 
0.0097051675  0.9987797437  0.9388753056  0.9978678038  0.9615384615  1.0000000000  0.9820359281  0.7958015267  0.7974522293  119.76047904  0.9026725414  0.1970450852  8.3915948868  0.0373555074  0.0060104018  5000          0.9925568366