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_s1
	save_model_every_checkpoint: False
	seed: 1
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [2]
	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    
2.2041718960  0.1934106162  0.1955990220  0.2633262260  0.2884615385  0.2687125749  0.2514970060  0.1962468193  0.2114649682  0.0000000000  2.2041718960  7.9755082130  0             1.1175787449 
0.2498419652  0.9725442343  0.9462102689  0.9712153518  0.9166666667  0.9715568862  0.9730538922  0.9529262087  0.9375796178  7.1856287425  0.2498419652  8.2003893852  300           0.1903975765 
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    
0.0634500532  0.9835265406  0.9364303178  0.9888059701  0.9188034188  0.9603293413  0.9580838323  0.9770992366  0.9592356688  14.371257485  1.2657715082  0.1805918646  8.3915367126  0.4697700329  0.1216307940  600           0.4774672222 
0.0391429136  0.9951189750  0.9364303178  0.9957356077  0.9615384615  0.9595808383  0.9730538922  0.9882315522  0.9477707006  21.556886227  1.2073275594  0.3416461742  8.3915367126  0.3198578201  0.0652953493  900           1.0013067118 
0.0293109062  0.9865771812  0.9364303178  0.9898720682  0.9594017094  0.9513473054  0.9790419162  0.9885496183  0.9541401274  28.742514970  1.1578130424  0.3032630023  8.3915367126  0.2314121157  0.0383883339  1200          1.0006477157 
0.0250529499  0.9871873093  0.9217603912  0.9957356077  0.9316239316  0.9446107784  0.9670658683  0.9837786260  0.9426751592  35.928143712  1.1310168759  0.2846556120  8.3915367126  0.1871568072  0.0266288056  1500          1.0005629889 
0.0185136418  0.9969493594  0.9388753056  0.9984008529  0.9615384615  0.9708083832  0.9760479042  0.9990458015  0.9617834395  43.113772455  1.1137460013  0.2685541133  8.3915367126  0.1538874432  0.0210407468  1800          1.0029528332 
0.0154622385  0.9951189750  0.9462102689  0.9989339019  0.9487179487  0.9498502994  0.9670658683  0.9955470738  0.9630573248  50.299401197  1.0977469726  0.2580133196  8.3915367126  0.1307711703  0.0169300380  2100          0.9998074341 
0.0158334069  0.9926784625  0.9266503667  0.9978678038  0.9423076923  0.9476047904  0.9550898204  0.9952290076  0.9541401274  57.485029940  1.0771653712  0.2522265236  8.3915367126  0.1209150690  0.0141138917  2400          0.9994703420 
0.0183013206  0.9938987187  0.9266503667  0.9914712154  0.9380341880  0.9693113772  0.9700598802  0.9958651399  0.9605095541  64.670658682  1.0612059029  0.2490021143  8.3915367126  0.1071876486  0.0119073109  2700          0.9994957066 
0.0121682103  0.9871873093  0.9266503667  0.9877398721  0.9230769231  0.9296407186  0.9610778443  0.9879134860  0.9503184713  71.856287425  1.0477785647  0.2381958763  8.3915367126  0.0958487654  0.0104731706  3000          0.9994761475 
0.0125262330  0.9987797437  0.9315403423  0.9984008529  0.9508547009  0.9565868263  0.9730538922  0.9942748092  0.9554140127  79.041916167  1.0325165669  0.2339334311  8.3915367126  0.0871242049  0.0091762543  3300          0.9994826166 
0.0139232797  0.9932885906  0.9315403423  0.9973347548  0.9594017094  0.9453592814  0.9610778443  0.9971374046  0.9617834395  86.227544910  1.0177905921  0.2312035569  8.3915367126  0.0805800321  0.0081757550  3600          0.9993997169 
0.0121902263  0.9987797437  0.9315403423  0.9989339019  0.9508547009  0.9663173653  0.9670658683  0.9965012723  0.9745222930  93.413173652  0.9945258689  0.2241213783  8.3915367126  0.0773328489  0.0071302348  3900          0.9994982012 
0.0110736774  0.9969493594  0.9168704156  0.9989339019  0.9487179487  0.9371257485  0.9550898204  0.9990458015  0.9757961783  100.59880239  0.9746124919  0.2178652604  8.3915367126  0.0706358626  0.0063685090  4200          0.9994042810 
0.0086478345  0.9975594875  0.9486552567  0.9994669510  0.9529914530  0.9476047904  0.9670658683  0.9993638677  0.9643312102  107.78443113  0.9563239352  0.2104474646  8.3915367126  0.0623796980  0.0058894233  4500          0.9994269490 
0.0103214375  0.9975594875  0.9168704156  0.9930703625  0.9209401709  0.9431137725  0.9431137725  0.9863231552  0.9363057325  114.97005988  0.9349151770  0.2075284945  8.3915367126  0.0610942987  0.0052993649  4800          0.9993229000 
0.0133054420  1.0000000000  0.9315403423  1.0000000000  0.9679487179  0.9595808383  0.9610778443  0.9996819338  0.9643312102  119.76047904  0.9174503940  0.2072981146  8.3915367126  0.0637028413  0.0047358191  5000          0.9989980686