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_env2_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [2]
	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    
1.9950832129  0.1891397193  0.1760391198  0.2052238806  0.1858974359  0.2028443114  0.2095808383  0.2076972010  0.2152866242  0.0000000000  1.9950832129  7.9755101204  0             0.7301406860 
0.2457256681  0.9560707749  0.9046454768  0.9744136461  0.9423076923  0.9678143713  0.9550898204  0.9449745547  0.9248407643  7.1856287425  0.2457256681  8.2003893852  300           0.2028923972 
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    
0.0627789225  0.9945088469  0.9462102689  0.9936034115  0.9508547009  0.9648203593  0.9670658683  0.9713740458  0.9439490446  14.371257485  1.2736542638  0.0957187663  8.3915619850  0.4822098495  0.1275481946  600           0.4680587562 
0.0383098368  0.9877974375  0.9217603912  0.9936034115  0.9508547009  0.9670658683  0.9520958084  0.9888676845  0.9515923567  21.556886227  1.2191588795  0.1004755553  8.3915619850  0.3224459367  0.0689941299  900           0.9995593325 
0.0285335558  0.9945088469  0.9511002445  0.9968017058  0.9700854701  0.9730538922  0.9670658683  0.9936386768  0.9464968153  28.742514970  1.1854838590  0.0718493210  8.3915619850  0.2333238379  0.0415859403  1200          0.9990850520 
0.0234643083  0.9884075656  0.9070904645  0.9941364606  0.9529914530  0.9678143713  0.9491017964  0.9809160305  0.9312101911  35.928143712  1.1648854852  0.0574212329  8.3915619850  0.1876115587  0.0290759491  1500          0.9988975223 
0.0198441574  0.9993898719  0.9437652812  0.9984008529  0.9700854701  0.9670658683  0.9610778443  0.9825063613  0.9388535032  43.113772455  1.1543592036  0.0480480985  8.3915619850  0.1578752212  0.0226132835  1800          0.9987055572 
0.0140887972  0.9877974375  0.9144254279  0.9973347548  0.9529914530  0.9618263473  0.9461077844  0.9949109415  0.9363057325  50.299401197  1.1425499960  0.0371209687  8.3915619850  0.1288866450  0.0184958696  2100          0.9985156830 
0.0197591771  0.9975594875  0.9413202934  0.9984008529  0.9615384615  0.9655688623  0.9520958084  0.9936386768  0.9566878981  57.485029940  1.1350938670  0.0416992773  8.3915619850  0.1258093208  0.0153435070  2400          0.9985834996 
0.0153432339  0.9975594875  0.9168704156  0.9920042644  0.9358974359  0.9693113772  0.9610778443  0.9926844784  0.9592356688  64.670658682  1.1323959875  0.0342379367  8.3915619850  0.1083771194  0.0131906712  2700          0.9983359106 
0.0129365235  0.9938987187  0.9339853301  0.9989339019  0.9594017094  0.9580838323  0.9491017964  0.9939567430  0.9401273885  71.856287425  1.1262789818  0.0298776983  8.3915619850  0.0974476124  0.0116201613  3000          0.9982000772 
0.0122039111  0.9981696156  0.9364303178  1.0000000000  0.9572649573  0.9663173653  0.9520958084  0.9968193384  0.9617834395  79.041916167  1.1274483005  0.0272425761  8.3915619850  0.0865205670  0.0103028970  3300          0.9983530132 
0.0118171609  0.9554606467  0.8728606357  0.9888059701  0.9401709402  0.9266467066  0.8922155689  0.9952290076  0.9490445860  86.227544910  1.1181037259  0.0255681931  8.3915619850  0.0789307856  0.0095570689  3600          0.9982282861 
0.0113105095  0.9877974375  0.9266503667  0.9968017058  0.9572649573  0.9550898204  0.9520958084  0.9965012723  0.9592356688  93.413173652  1.1174889187  0.0241953997  8.3915619850  0.0747444385  0.0083661199  3900          0.9981971280 
0.0157133874  0.9835265406  0.9144254279  0.9946695096  0.9358974359  0.9461077844  0.9371257485  0.9974554707  0.9503184713  100.59880239  1.1176047722  0.0284228165  8.3915619850  0.0746366178  0.0075696799  4200          0.9984227490 
0.0102882112  0.9951189750  0.9193154034  0.9957356077  0.9487179487  0.9483532934  0.9311377246  0.9917302799  0.9426751592  107.78443113  1.1159694886  0.0216639789  8.3915619850  0.0662671603  0.0071784666  4500          0.9980790393 
0.0081389571  0.9963392312  0.9315403423  1.0000000000  0.9722222222  0.9648203593  0.9431137725  0.9990458015  0.9554140127  114.97005988  1.1147106946  0.0183779539  8.3915619850  0.0594466899  0.0066099650  4800          0.9984200080 
0.0116122022  1.0000000000  0.9242053790  0.9994669510  0.9594017094  0.9610778443  0.9491017964  0.9974554707  0.9592356688  119.76047904  1.1155910403  0.0220464503  8.3915619850  0.0615714412  0.0059926573  5000          0.9986368799