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_env2_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: False
	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: 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    
1.9950832129  0.1891397193  0.1760391198  0.2052238806  0.1858974359  0.2028443114  0.2095808383  0.2076972010  0.2152866242  0.0000000000  1.9950832129  7.9755101204  0             0.9947850704 
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           0.1877603499 
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    
0.0630569173  0.9896278218  0.9462102689  0.9914712154  0.9572649573  0.9678143713  0.9670658683  0.9805979644  0.9464968153  14.371257485  1.2720193214  0.1815960443  8.3915619850  0.4816073743  0.1272571935  600           0.4632261841 
0.0382823814  0.9829164124  0.9535452323  0.9936034115  0.9529914530  0.9670658683  0.9670658683  0.9764631043  0.9312101911  21.556886227  1.2091960243  0.3421581769  8.3915619850  0.3225578898  0.0682645093  900           0.9928591228 
0.0282343665  0.9920683344  0.9315403423  0.9930703625  0.9465811966  0.9670658683  0.9550898204  0.9875954198  0.9375796178  28.742514970  1.1692823474  0.3046734548  8.3915619850  0.2318571477  0.0390202085  1200          0.9921102357 
0.0252904408  0.9969493594  0.9511002445  0.9968017058  0.9679487179  0.9693113772  0.9760479042  0.9926844784  0.9630573248  35.928143712  1.1410746849  0.2870477004  8.3915619850  0.1866766839  0.0277040781  1500          0.9919838977 
0.0154022412  0.9914582062  0.9559902200  0.9962686567  0.9551282051  0.9565868263  0.9520958084  0.9955470738  0.9439490446  43.113772455  1.1193611391  0.2660226111  8.3915619850  0.1498550192  0.0213494241  1800          0.9916944679 
0.0205910583  0.9896278218  0.9413202934  0.9968017058  0.9572649573  0.9550898204  0.9311377246  0.9904580153  0.9541401274  50.299401197  1.0967972751  0.2637204701  8.3915619850  0.1364550104  0.0165085059  2100          0.9912131174 
0.0203360810  0.9957291031  0.9559902200  0.9968017058  0.9444444444  0.9648203593  0.9341317365  0.9977735369  0.9566878981  57.485029940  1.0818942161  0.2581299345  8.3915619850  0.1241847222  0.0139364917  2400          0.9904091438 
0.0142536233  0.9975594875  0.9388753056  0.9994669510  0.9465811966  0.9558383234  0.9431137725  0.9993638677  0.9554140127  64.670658682  1.0683710452  0.2460274969  8.3915619850  0.1045993475  0.0120487977  2700          0.9903154365 
0.0090339318  0.9963392312  0.9413202934  0.9989339019  0.9572649573  0.9648203593  0.9550898204  0.9961832061  0.9605095541  71.856287425  1.0484982749  0.2339293547  8.3915619850  0.0874021499  0.0104272092  3000          0.9903384431 
0.0149463551  0.9969493594  0.9290953545  0.9994669510  0.9658119658  0.9693113772  0.9491017964  0.9949109415  0.9592356688  79.041916167  1.0340427140  0.2366990047  8.3915619850  0.0880588209  0.0086764023  3300          0.9903917742 
0.0172110641  0.9969493594  0.9535452323  0.9968017058  0.9594017094  0.9565868263  0.9401197605  0.9971374046  0.9477707006  86.227544910  1.0190474943  0.2355244828  8.3915619850  0.0862770703  0.0078117799  3600          0.9905198145 
0.0093344732  0.9981696156  0.9486552567  0.9994669510  0.9594017094  0.9640718563  0.9491017964  0.9933206107  0.9452229299  93.413173652  0.9997691586  0.2213119215  8.3915619850  0.0706782252  0.0071093997  3900          0.9906082010 
0.0117440979  0.9987797437  0.9388753056  0.9989339019  0.9529914530  0.9678143713  0.9341317365  0.9984096692  0.9515923567  100.59880239  0.9809941318  0.2195976881  8.3915619850  0.0693767003  0.0062412785  4200          0.9905433702 
0.0110491112  0.9987797437  0.9437652812  1.0000000000  0.9722222222  0.9580838323  0.9401197605  0.9968193384  0.9554140127  107.78443113  0.9566573697  0.2128911729  8.3915619850  0.0624592534  0.0057084824  4500          0.9902656587 
0.0112025145  0.9981696156  0.9290953545  0.9994669510  0.9615384615  0.9655688623  0.9550898204  0.9971374046  0.9414012739  114.97005988  0.9342198235  0.2086573226  8.3915619850  0.0639739684  0.0050737215  4800          0.9904773696 
0.0089295807  0.9951189750  0.9388753056  0.9968017058  0.9380341880  0.9528443114  0.9431137725  0.9936386768  0.9375796178  119.76047904  0.9182422712  0.2022253501  8.3915619850  0.0579340088  0.0047860530  5000          0.9903930354