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_env0_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [0]
	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    
2.0235202312  0.1879194631  0.1613691932  0.1876332623  0.1709401709  0.2582335329  0.2574850299  0.2258269720  0.2203821656  0.0000000000  2.0235202312  7.9755101204  0             0.8995540142 
0.1832763110  0.8395363026  0.8484107579  0.9744136461  0.9316239316  0.9947604790  0.9790419162  0.9653307888  0.9312101911  7.1856287425  0.1832763110  8.2003893852  300           0.2118183168 
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    
0.0490659027  0.8438071995  0.8581907090  0.9850746269  0.9551282051  0.9977544910  0.9640718563  0.9866412214  0.9503184713  14.371257485  1.2951457701  0.1462184899  8.3915634155  0.5236891080  0.1477143930  600           0.4745294833 
0.0327979275  0.8486882245  0.8508557457  0.9920042644  0.9444444444  0.9977544910  0.9760479042  0.9812340967  0.9439490446  21.556886227  1.2318455088  0.3017189474  8.3915634155  0.4363267688  0.1127595698  900           0.9933565950 
0.0200703230  0.8133007932  0.7946210269  0.9909381663  0.9230769231  0.9992514970  0.9700598802  0.9764631043  0.9350318471  28.742514970  1.1738518103  0.2715059956  8.3915634155  0.3521135344  0.0833265380  1200          0.9924149164 
0.0198783497  0.8096400244  0.8288508557  0.9968017058  0.9444444444  0.9970059880  0.9700598802  0.9847328244  0.9375796178  35.928143712  1.1364023348  0.2601918966  8.3915634155  0.3112785018  0.0651653852  1500          0.9922149285 
0.0193504340  0.8413666870  0.8459657702  0.9893390192  0.9401709402  1.0000000000  0.9790419162  0.9958651399  0.9528662420  43.113772455  1.1063145824  0.2514827846  8.3915634155  0.2782737193  0.0543471586  1800          0.9920524518 
0.0153604882  0.7284929835  0.7652811736  0.9802771855  0.9358974359  0.9872754491  0.9461077844  0.9885496183  0.9426751592  50.299401197  1.0751024922  0.2396961715  8.3915634155  0.2588088050  0.0465759066  2100          0.9917714405 
0.0177748202  0.8370957901  0.8606356968  0.9973347548  0.9594017094  0.9985029940  0.9850299401  0.9958651399  0.9566878981  57.485029940  1.0439380860  0.2345893434  8.3915634155  0.2456807059  0.0401345146  2400          0.9916420364 
0.0111134651  0.8291641245  0.8361858191  0.9792110874  0.9123931624  0.9947604790  0.9580838323  0.9901399491  0.9477707006  64.670658682  1.0146872556  0.2215285780  8.3915634155  0.2176801045  0.0373882934  2700          0.9914709791 
0.0113817143  0.8663819402  0.8581907090  0.9989339019  0.9401709402  1.0000000000  0.9700598802  0.9965012723  0.9541401274  71.856287425  0.9901295118  0.2157860881  8.3915634155  0.2091154210  0.0318923424  3000          0.9914345654 
0.0117519428  0.8419768151  0.8606356968  0.9941364606  0.9636752137  0.9985029940  0.9640718563  0.9936386768  0.9439490446  79.041916167  0.9588356465  0.2092419609  8.3915634155  0.2015358902  0.0286144296  3300          0.9914444065 
0.0120263358  0.8523489933  0.8264058680  0.9989339019  0.9465811966  0.9977544910  0.9580838323  0.9923664122  0.9350318471  86.227544910  0.9202581054  0.2013455443  8.3915634155  0.1970716406  0.0263379232  3600          0.9918445841 
0.0117844592  0.7852348993  0.8190709046  0.9968017058  0.9572649573  0.9970059880  0.9520958084  0.9939567430  0.9464968153  93.413173652  0.8808141667  0.1928185174  8.3915634155  0.1948856591  0.0243561078  3900          0.9917584538 
0.0070565362  0.8010982306  0.8092909535  0.9968017058  0.9487179487  0.9992514970  0.9640718563  0.9965012723  0.9515923567  100.59880239  0.8223203697  0.1761678878  8.3915634155  0.1745653057  0.0232363739  4200          0.9917452367 
0.0077670259  0.8798047590  0.8728606357  0.9909381663  0.9508547009  1.0000000000  0.9670658683  0.9936386768  0.9439490446  107.78443113  0.7764740084  0.1673708600  8.3915634155  0.1704195980  0.0215451499  4500          0.9915682324 
0.0113981596  0.8450274558  0.8484107579  0.9936034115  0.9658119658  0.9962574850  0.9580838323  0.9958651399  0.9515923567  114.97005988  0.7424620225  0.1637767550  8.3915634155  0.1763187549  0.0194309362  4800          0.9917681766 
0.0091890353  0.8492983527  0.8557457213  0.9994669510  0.9594017094  1.0000000000  0.9670658683  0.9984096692  0.9656050955  119.76047904  0.7178873220  0.1564049170  8.3915634155  0.1731064682  0.0181920735  5000          0.9916833484