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, "rdi_lambda_rec": 0.0, "rdi_beta": 0.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/ERMg_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: 0.0
	rdi_ema: 0.9
	rdi_gamma: 0.5
	rdi_k: 32
	rdi_lambda_geo: 0.0
	rdi_lambda_rec: 0.0
	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             0.7828505039 
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           0.2111944087 
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    
0.0391688776  0.9682733374  0.9119804401  0.9626865672  0.9123931624  0.9940119760  0.9730538922  0.6215012723  0.6305732484  14.371257485  1.2936795218  0.0391688776  8.3915948868  0.5330512842  0.1532263498  600           0.4728488294 
0.0246108968  0.9871873093  0.9339853301  0.9952025586  0.9487179487  0.9947604790  0.9640718563  0.7350508906  0.7159235669  21.556886227  1.2899181843  0.0246108968  8.3915948868  0.5143151789  0.1499352854  900           1.0004271197 
0.0260396508  0.9701037218  0.9095354523  0.9824093817  0.9209401709  0.9970059880  0.9610778443  0.7363231552  0.7286624204  28.742514970  1.2751804872  0.0260396508  8.3915948868  0.4850446149  0.1400574191  1200          0.9995598292 
0.0174713034  0.9725442343  0.9070904645  0.9877398721  0.9358974359  0.9970059880  0.9730538922  0.7732188295  0.7681528662  35.928143712  1.2694509264  0.0174713034  8.3915948868  0.4624587824  0.1386267952  1500          1.0000082994 
0.0141091563  0.9731543624  0.9144254279  0.9898720682  0.9337606838  0.9955089820  0.9610778443  0.7697201018  0.7796178344  43.113772455  1.2654215662  0.0141091563  8.3915948868  0.4449426766  0.1327621487  1800          0.9997080382 
0.0138544949  0.9823062843  0.9168704156  0.9941364606  0.9508547009  0.9970059880  0.9610778443  0.7827608142  0.7885350318  50.299401197  1.2645576000  0.0138544949  8.3915948868  0.4259342205  0.1295825661  2100          0.9995927739 
0.0133478744  0.9877974375  0.9217603912  0.9930703625  0.9316239316  0.9835329341  0.9700598802  0.7274173028  0.7184713376  57.485029940  1.2629756896  0.0133478744  8.3915948868  0.4152802931  0.1287383018  2400          0.9996883345 
0.0086139558  0.9963392312  0.9462102689  0.9984008529  0.9487179487  0.9992514970  0.9640718563  0.8062977099  0.7987261146  64.670658682  1.2565052223  0.0086139558  8.3915948868  0.4033624570  0.1239326954  2700          1.0000450397 
0.0148096025  0.9981696156  0.9364303178  0.9904051173  0.9465811966  0.9955089820  0.9790419162  0.8164758270  0.8178343949  71.856287425  1.2552072962  0.0148096025  8.3915948868  0.3953746205  0.1135583925  3000          0.9996819337 
0.0102474479  0.9987797437  0.9364303178  0.9978678038  0.9529914530  0.9992514970  0.9670658683  0.7274173028  0.7388535032  79.041916167  1.2537945231  0.0102474479  8.3915948868  0.3809914038  0.1146007248  3300          0.9995400612 
0.0081127345  0.9945088469  0.9315403423  0.9946695096  0.9487179487  0.9962574850  0.9790419162  0.6628498728  0.6891719745  86.227544910  1.2533019328  0.0081127345  8.3915948868  0.3690432620  0.1144868047  3600          0.9993288247 
0.0109873971  0.9969493594  0.9388753056  0.9957356077  0.9572649573  1.0000000000  0.9790419162  0.7172391858  0.7235668790  93.413173652  1.2446320128  0.0109873971  8.3915948868  0.3713489173  0.1077457964  3900          0.9997554040 
0.0099833432  0.9938987187  0.9168704156  0.9962686567  0.9444444444  0.9992514970  0.9760479042  0.7687659033  0.7605095541  100.59880239  1.2407455293  0.0099833432  8.3915948868  0.3562657483  0.1034178962  4200          0.9993561729 
0.0092453794  0.9890176937  0.9168704156  0.9930703625  0.9401709402  0.9970059880  0.9550898204  0.7986641221  0.8203821656  107.78443113  1.2404440240  0.0092453794  8.3915948868  0.3464355937  0.1000323607  4500          0.9995148476 
0.0115778370  0.9981696156  0.9535452323  0.9962686567  0.9679487179  0.9977544910  0.9820359281  0.7843511450  0.7898089172  114.97005988  1.2334181352  0.0115778370  8.3915948868  0.3447353095  0.0961449660  4800          0.9993881726 
0.0074774235  0.9969493594  0.9388753056  0.9957356077  0.9465811966  0.9970059880  0.9550898204  0.7531806616  0.7337579618  119.76047904  1.2371621269  0.0074774235  8.3915948868  0.3283689186  0.0958956323  5000          0.9978516746