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_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: 0.4
	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    
1.9950832129  0.1891397193  0.1760391198  0.2052238806  0.1858974359  0.2028443114  0.2095808383  0.2076972010  0.2152866242  0.0000000000  1.9950832129  7.9755101204  0             0.7526581287 
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           0.1874537945 
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    
0.0633716393  0.9914582062  0.9486552567  0.9920042644  0.9508547009  0.9625748503  0.9700598802  0.9793256997  0.9388535032  14.371257485  1.2771930447  0.1727466043  8.3915619850  0.2866420165  0.1322060867  600           0.4602897263 
0.0424062019  0.9926784625  0.9535452323  0.9957356077  0.9487179487  0.9745508982  0.9670658683  0.9831424936  0.9375796178  21.556886227  1.2154665323  0.3311399372  8.3915619850  0.1996630851  0.0784548054  900           0.9921231063 
0.0275278712  0.9957291031  0.9462102689  0.9941364606  0.9636752137  0.9738023952  0.9580838323  0.9869592875  0.9401273885  28.742514970  1.1752695505  0.2941423146  8.3915619850  0.1446464939  0.0493177771  1200          0.9911052195 
0.0227632905  0.9963392312  0.9266503667  0.9962686567  0.9551282051  0.9655688623  0.9550898204  0.9901399491  0.9515923567  35.928143712  1.1433907672  0.2758906276  8.3915619850  0.1164771650  0.0348880261  1500          0.9911276436 
0.0201143045  0.9926784625  0.9290953545  0.9925373134  0.9487179487  0.9685628743  0.9670658683  0.9952290076  0.9503184713  43.113772455  1.1224499174  0.2650395138  8.3915619850  0.0996148484  0.0274649731  1800          0.9907863959 
0.0191627160  0.9938987187  0.9388753056  0.9946695096  0.9594017094  0.9505988024  0.9431137725  0.9872773537  0.9375796178  50.299401197  1.0978636567  0.2563847167  8.3915619850  0.0890332808  0.0214713680  2100          0.9908968027 
0.0127324573  0.9969493594  0.9339853301  0.9957356077  0.9423076923  0.9685628743  0.9520958084  0.9968193384  0.9668789809  57.485029940  1.0806211551  0.2438784979  8.3915619850  0.0754925931  0.0184895836  2400          0.9907409199 
0.0163490708  0.9993898719  0.9413202934  0.9989339019  0.9636752137  0.9610778443  0.9520958084  0.9936386768  0.9617834395  64.670658682  1.0652515566  0.2432913683  8.3915619850  0.0718661757  0.0155602852  2700          0.9906123805 
0.0147215039  0.9920683344  0.9364303178  0.9978678038  0.9508547009  0.9723053892  0.9610778443  0.9949109415  0.9464968153  71.856287425  1.0493608876  0.2371329883  8.3915619850  0.0659074627  0.0132659167  3000          0.9906590748 
0.0159257406  0.9987797437  0.9437652812  0.9994669510  0.9764957265  0.9588323353  0.9550898204  0.9977735369  0.9605095541  79.041916167  1.0411882977  0.2358303660  8.3915619850  0.0625406950  0.0114292877  3300          0.9913081551 
0.0118256752  0.9975594875  0.9339853301  0.9978678038  0.9401709402  0.9558383234  0.9371257485  0.9965012723  0.9503184713  86.227544910  1.0193689950  0.2259756180  8.3915619850  0.0545045567  0.0105022872  3600          0.9908483179 
0.0121166425  0.9902379500  0.9266503667  0.9936034115  0.9594017094  0.9505988024  0.9041916168  0.9828244275  0.9337579618  93.413173652  1.0008894066  0.2220077761  8.3915619850  0.0523548249  0.0093001258  3900          0.9913215852 
0.0150536859  0.9957291031  0.9168704156  0.9973347548  0.9423076923  0.9595808383  0.9461077844  0.9952290076  0.9554140127  100.59880239  0.9872410405  0.2221123495  8.3915619850  0.0529233610  0.0083597367  4200          0.9914405767 
0.0111512057  0.9975594875  0.9217603912  0.9989339019  0.9551282051  0.9565868263  0.9311377246  0.9965012723  0.9388535032  107.78443113  0.9668453592  0.2132204233  8.3915619850  0.0479116297  0.0075669925  4500          0.9911498253 
0.0111559104  0.9981696156  0.9193154034  1.0000000000  0.9636752137  0.9423652695  0.9401197605  0.9984096692  0.9630573248  114.97005988  0.9488377452  0.2091433620  8.3915619850  0.0456559192  0.0068575592  4800          0.9913188140 
0.0119056295  0.9981696156  0.9242053790  0.9989339019  0.9487179487  0.9565868263  0.9341317365  0.9984096692  0.9490445860  119.76047904  0.9324971989  0.2064831983  8.3915619850  0.0454749891  0.0062843903  5000          0.9912380946