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_env1_s1
	save_model_every_checkpoint: False
	seed: 1
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [1]
	trial_seed: 1
	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    
2.1769599915  0.1793776693  0.1882640587  0.2270788913  0.2478632479  0.3413173653  0.2844311377  0.1902035623  0.2076433121  0.0000000000  2.1769599915  7.9755082130  0             1.0115971565 
0.1858797896  0.9768151312  0.9388753056  0.7404051173  0.7264957265  0.9925149701  0.9700598802  0.9624681934  0.9350318471  7.1856287425  0.1858797896  8.2003893852  300           0.1876836538 
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    
0.0491263611  0.9908480781  0.9266503667  0.7793176972  0.7307692308  0.9977544910  0.9880239521  0.9879134860  0.9668789809  14.371257485  1.2686907525  0.1653843375  8.3914384842  0.4431994684  0.1255132092  600           0.4627673467 
0.0278632075  0.9975594875  0.9388753056  0.7755863539  0.7628205128  0.9970059880  0.9940119760  0.9895038168  0.9656050955  21.556886227  1.2068789391  0.3248140758  8.3914384842  0.2826781482  0.0658667640  900           0.9930190325 
0.0249763100  0.9975594875  0.9559902200  0.7931769723  0.7927350427  0.9992514970  0.9790419162  0.9866412214  0.9439490446  28.742514970  1.1661720538  0.2962113544  8.3914384842  0.2025359094  0.0381012133  1200          0.9921791673 
0.0189999671  0.9890176937  0.9144254279  0.7729211087  0.7606837607  0.9955089820  0.9790419162  0.9879134860  0.9452229299  35.928143712  1.1419730341  0.2766674965  8.3914384842  0.1587080391  0.0273335631  1500          0.9918999338 
0.0137538387  0.9957291031  0.9535452323  0.7595948827  0.7264957265  0.9985029940  0.9880239521  0.9939567430  0.9541401274  43.113772455  1.1199186762  0.2617058041  8.3914384842  0.1313449998  0.0213323842  1800          0.9914581792 
0.0156541593  0.9896278218  0.9339853301  0.8139658849  0.7884615385  0.9970059880  0.9670658683  0.9910941476  0.9464968153  50.299401197  1.0996664532  0.2562526024  8.3914384842  0.1148610878  0.0171799317  2100          0.9914461851 
0.0127911061  0.9938987187  0.9217603912  0.8033049041  0.8098290598  0.9992514970  0.9850299401  0.9961832061  0.9694267516  57.485029940  1.0783085640  0.2465699241  8.3914384842  0.1019354612  0.0141339120  2400          0.9906090792 
0.0114436153  0.9969493594  0.9413202934  0.8214285714  0.7991452991  0.9992514970  0.9970059880  0.9968193384  0.9592356688  64.670658682  1.0593395865  0.2392470739  8.3914384842  0.0900537188  0.0121373980  2700          0.9906040208 
0.0126938942  0.9981696156  0.9584352078  0.8081023454  0.7799145299  1.0000000000  0.9880239521  0.9984096692  0.9719745223  71.856287425  1.0462310914  0.2361767648  8.3914384842  0.0807629499  0.0106110289  3000          0.9904114159 
0.0105597356  0.9969493594  0.9437652812  0.8294243070  0.8226495726  1.0000000000  0.9910179641  0.9968193384  0.9617834395  79.041916167  1.0223552728  0.2276072044  8.3914384842  0.0714540523  0.0092915199  3300          0.9904220573 
0.0099998354  0.9993898719  0.9339853301  0.8038379531  0.7713675214  1.0000000000  0.9880239521  0.9958651399  0.9668789809  86.227544910  1.0071265676  0.2232270149  8.3914384842  0.0678372780  0.0081313607  3600          0.9903947361 
0.0148267341  0.9987797437  0.9388753056  0.8134328358  0.8012820513  0.9992514970  0.9850299401  0.9958651399  0.9579617834  93.413173652  0.9920488805  0.2249448838  8.3914384842  0.0687310935  0.0069935295  3900          0.9904813353 
0.0072932761  0.9975594875  0.9144254279  0.8054371002  0.7970085470  0.9992514970  0.9910179641  0.9968193384  0.9719745223  100.59880239  0.9718454268  0.2113324893  8.3914384842  0.0559177376  0.0064123208  4200          0.9906314969 
0.0113913355  0.9981696156  0.9364303178  0.7500000000  0.7094017094  1.0000000000  0.9850299401  0.9914122137  0.9401273885  107.78443113  0.9402691815  0.2088757692  8.3914384842  0.0552206662  0.0057374696  4500          0.9905341212 
0.0083702053  0.9993898719  0.9413202934  0.8219616205  0.8141025641  0.9985029940  0.9820359281  0.9971374046  0.9605095541  114.97005988  0.9194886712  0.2007499571  8.3914384842  0.0498261594  0.0050404537  4800          0.9905684725 
0.0155662805  0.9920683344  0.9193154034  0.7675906183  0.7670940171  0.9955089820  0.9670658683  0.9856870229  0.9592356688  119.76047904  0.8966694453  0.2036920822  8.3914384842  0.0523618175  0.0046881828  5000          0.9905284631