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_beta": 0.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-noRate_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: 0.4
	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    
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             0.7472887039 
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           0.2116200113 
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    
0.0389371643  0.9945088469  0.9559902200  0.9904051173  0.9487179487  0.9970059880  0.9790419162  0.6698473282  0.6598726115  14.371257485  1.2774084193  0.1344014271  8.3915948868  0.5125529887  0.1403776314  600           0.4714717929 
0.0268833381  0.9847467968  0.9339853301  0.9888059701  0.9529914530  0.9992514970  0.9580838323  0.7328244275  0.7273885350  21.556886227  1.2235020940  0.2926514830  8.3915948868  0.4292775702  0.1053386091  900           0.9933011532 
0.0220972726  0.9859670531  0.9242053790  0.9888059701  0.9508547009  1.0000000000  0.9670658683  0.7436386768  0.7121019108  28.742514970  1.1767475772  0.2727724775  8.3915948868  0.3507877558  0.0766284279  1200          0.9924916919 
0.0118451075  0.9975594875  0.9559902200  0.9978678038  0.9529914530  0.9992514970  0.9850299401  0.7891221374  0.7834394904  35.928143712  1.1443677692  0.2531707803  8.3915948868  0.2907386599  0.0622605742  1500          0.9921674450 
0.0159386454  0.9975594875  0.9535452323  0.9989339019  0.9636752137  0.9992514970  0.9700598802  0.7865776081  0.7885350318  43.113772455  1.1129373991  0.2487977970  8.3915948868  0.2677986784  0.0513583439  1800          0.9918730593 
0.0133356096  0.9938987187  0.9095354523  0.9962686567  0.9487179487  0.9977544910  0.9670658683  0.7398218830  0.7350318471  50.299401197  1.0821697740  0.2385192792  8.3915948868  0.2417934407  0.0437485612  2100          0.9915258646 
0.0162886746  0.9981696156  0.9535452323  0.9994669510  0.9487179487  0.9992514970  0.9820359281  0.8113867684  0.8012738854  57.485029940  1.0513270426  0.2341326057  8.3915948868  0.2393471038  0.0378925966  2400          0.9917018517 
0.0092594260  0.9932885906  0.9242053790  0.9973347548  0.9444444444  0.9977544910  0.9790419162  0.7251908397  0.7070063694  64.670658682  1.0136352374  0.2187026522  8.3915948868  0.2082570141  0.0335808799  2700          0.9911501352 
0.0099569210  0.9890176937  0.9217603912  0.9957356077  0.9487179487  0.9947604790  0.9700598802  0.7140585242  0.7235668790  71.856287425  0.9862076082  0.2131992589  8.3915948868  0.2008797278  0.0300040657  3000          0.9911742306 
0.0106460616  0.9957291031  0.9437652812  0.9984008529  0.9508547009  1.0000000000  0.9730538922  0.7945292621  0.7898089172  79.041916167  0.9599481066  0.2080335422  8.3915948868  0.1941064846  0.0269892838  3300          0.9910218279 
0.0081189426  0.9945088469  0.9242053790  0.9978678038  0.9487179487  0.9992514970  0.9790419162  0.8393765903  0.8394904459  86.227544910  0.9169835923  0.1965204444  8.3915948868  0.1825846976  0.0250239021  3600          0.9906727950 
0.0110224409  0.9908480781  0.9315403423  0.9914712154  0.9380341880  0.9970059880  0.9730538922  0.7417302799  0.7515923567  93.413173652  0.8797077497  0.1916064057  8.3915948868  0.1815049558  0.0232120601  3900          0.9907695119 
0.0131349730  0.9987797437  0.9388753056  0.9984008529  0.9401709402  0.9977544910  0.9820359281  0.7862595420  0.7872611465  100.59880239  0.8526756928  0.1878081803  8.3915948868  0.1821533663  0.0206903297  4200          0.9911322935 
0.0069653112  1.0000000000  0.9413202934  0.9994669510  0.9551282051  0.9992514970  0.9730538922  0.7853053435  0.7656050955  107.78443113  0.8211084342  0.1750829531  8.3915948868  0.1665122087  0.0194797628  4500          0.9905703878 
0.0127246481  0.9914582062  0.9119804401  0.9952025586  0.9529914530  1.0000000000  0.9790419162  0.7706743003  0.7707006369  114.97005988  0.7982532018  0.1759796354  8.3915948868  0.1720803912  0.0180217207  4800          0.9907302936 
0.0098673146  0.9969493594  0.9413202934  0.9984008529  0.9572649573  1.0000000000  0.9820359281  0.8215648855  0.8127388535  119.76047904  0.7627973965  0.1657935439  8.3915948868  0.1656389319  0.0168337366  5000          0.9907035518