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}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI_env3_s2
	save_model_every_checkpoint: False
	seed: 2
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [3]
	trial_seed: 2
	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: 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    
2.2069647312  0.2464917633  0.2127139364  0.1764392324  0.1730769231  0.3263473054  0.3053892216  0.1342239186  0.1388535032  0.0000000000  2.2069647312  7.9755139351  0             1.3512508869 
0.1632645813  0.9615619280  0.9144254279  0.9669509595  0.9145299145  0.9857784431  0.9910179641  0.7261450382  0.7171974522  7.1856287425  0.1632645813  8.2003893852  300           0.1981396699 
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    
0.0403375341  0.9816961562  0.9339853301  0.9904051173  0.9572649573  0.9977544910  0.9700598802  0.7363231552  0.7286624204  14.371257485  1.2664856014  0.1558353239  8.3916163445  0.4372371668  0.1209002025  600           0.4622715600 
0.0218429916  0.9847467968  0.9364303178  0.9893390192  0.9337606838  0.9955089820  0.9820359281  0.6065521628  0.6458598726  21.556886227  1.1989986853  0.3146529541  8.3916163445  0.2750607538  0.0587555342  900           0.9938666590 
0.0172735108  0.9896278218  0.9462102689  0.9973347548  0.9487179487  0.9932634731  0.9730538922  0.7512722646  0.7490445860  28.742514970  1.1564989094  0.2837857474  8.3916163445  0.1907256071  0.0330180469  1200          0.9930632353 
0.0192314579  0.9975594875  0.9437652812  0.9936034115  0.9551282051  0.9985029940  0.9850299401  0.7500000000  0.7528662420  35.928143712  1.1280097342  0.2729437137  8.3916163445  0.1566476798  0.0230657649  1500          0.9928391703 
0.0131357801  0.9926784625  0.9437652812  0.9968017058  0.9487179487  0.9985029940  0.9820359281  0.7401399491  0.7401273885  43.113772455  1.1075679406  0.2573824365  8.3916163445  0.1272359566  0.0182383623  1800          0.9925692709 
0.0098916123  0.9969493594  0.9559902200  0.9994669510  0.9508547009  0.9970059880  0.9850299401  0.7490458015  0.7630573248  50.299401197  1.0877296205  0.2462564077  8.3916163445  0.1056125726  0.0148849083  2100          0.9922352441 
0.0139273218  0.9908480781  0.9339853301  0.9962686567  0.9572649573  0.9992514970  0.9760479042  0.6437659033  0.6675159236  57.485029940  1.0650348560  0.2434428425  8.3916163445  0.0940453030  0.0120087511  2400          0.9923008800 
0.0115071609  0.9945088469  0.9437652812  0.9962686567  0.9487179487  1.0000000000  0.9910179641  0.7557251908  0.7503184713  64.670658682  1.0504708230  0.2366954817  8.3916163445  0.0869324350  0.0102714401  2700          0.9925406535 
0.0110757574  0.9987797437  0.9413202934  0.9978678038  0.9572649573  0.9992514970  1.0000000000  0.7849872774  0.7719745223  71.856287425  1.0310638920  0.2309475606  8.3916163445  0.0792155869  0.0088834215  3000          0.9918023133 
0.0128817206  0.9987797437  0.9486552567  1.0000000000  0.9679487179  1.0000000000  0.9790419162  0.8091603053  0.7949044586  79.041916167  1.0133953700  0.2282867994  8.3916163445  0.0743621959  0.0078583613  3300          0.9919776948 
0.0053168712  0.9914582062  0.9388753056  0.9978678038  0.9508547009  0.9977544910  0.9760479042  0.8066157761  0.7936305732  86.227544910  0.9929586999  0.2135962541  8.3916163445  0.0549674472  0.0072126116  3600          0.9919102112 
0.0099955789  0.9902379500  0.9559902200  0.9973347548  0.9551282051  0.9977544910  0.9520958084  0.7067430025  0.7388535032  93.413173652  0.9741145925  0.2149921603  8.3916163445  0.0596203830  0.0061530114  3900          0.9917755175 
0.0078321088  0.9920683344  0.9290953545  0.9973347548  0.9529914530  0.9992514970  0.9610778443  0.7751272265  0.7681528662  100.59880239  0.9526578025  0.2075999590  8.3916163445  0.0540893214  0.0056144416  4200          0.9915661772 
0.0104340367  0.9993898719  0.9437652812  0.9994669510  0.9679487179  1.0000000000  0.9820359281  0.7665394402  0.7656050955  107.78443113  0.9279882455  0.2050856847  8.3916163445  0.0536700669  0.0050174269  4500          0.9914955258 
0.0057398669  0.9975594875  0.9315403423  0.9994669510  0.9615384615  1.0000000000  0.9760479042  0.7986641221  0.7987261146  114.97005988  0.8936567760  0.1915108249  8.3916163445  0.0406975662  0.0046748199  4800          0.9914267929 
0.0134701710  0.9951189750  0.9266503667  0.9973347548  0.9658119658  0.9985029940  0.9700598802  0.6692111959  0.6777070064  119.76047904  0.8729477924  0.1965602788  8.3916163445  0.0512078529  0.0040968412  5000          0.9913238108