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_env0_s1
	save_model_every_checkpoint: False
	seed: 1
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [0]
	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    
2.1441533566  0.1836485662  0.2029339853  0.2547974414  0.2841880342  0.3226047904  0.2485029940  0.1927480916  0.2127388535  0.0000000000  2.1441533566  7.9755082130  0             1.1485891342 
0.1861163665  0.8608907871  0.8630806846  0.9712153518  0.9230769231  0.9925149701  0.9880239521  0.9691475827  0.9401273885  7.1856287425  0.1861163665  8.2003893852  300           0.2095948418 
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    
0.0491784375  0.8596705308  0.8337408313  0.9909381663  0.9615384615  0.9992514970  0.9850299401  0.9796437659  0.9579617834  14.371257485  1.2708112804  0.1658162410  8.3915724754  0.4457585941  0.1271143290  600           0.4619885604 
0.0292554487  0.8541793777  0.8606356968  0.9941364606  0.9572649573  0.9992514970  0.9880239521  0.9917302799  0.9630573248  21.556886227  1.2029041954  0.3266418061  8.3915724754  0.2927948562  0.0644314250  900           0.9945553621 
0.0208500046  0.8535692495  0.8728606357  0.9946695096  0.9529914530  0.9970059880  0.9790419162  0.9596055980  0.9273885350  28.742514970  1.1578160497  0.2906373813  8.3915724754  0.2042052906  0.0379668441  1200          0.9932675751 
0.0177265870  0.8608907871  0.8581907090  0.9973347548  0.9465811966  0.9992514970  0.9850299401  0.9936386768  0.9694267516  35.928143712  1.1295613809  0.2734091269  8.3915724754  0.1630229535  0.0265840918  1500          0.9932533145 
0.0141229802  0.8572300183  0.8533007335  0.9941364606  0.9508547009  0.9962574850  0.9760479042  0.9939567430  0.9617834395  43.113772455  1.1058416013  0.2593218616  8.3915724754  0.1323412483  0.0208968474  1800          0.9927937555 
0.0136256214  0.8767541184  0.8581907090  0.9989339019  0.9508547009  1.0000000000  0.9880239521  0.9939567430  0.9605095541  50.299401197  1.0885982021  0.2524612386  8.3915724754  0.1188867895  0.0164147779  2100          0.9924367873 
0.0125015302  0.7919463087  0.7555012225  0.9925373134  0.9508547009  0.9977544910  0.9790419162  0.9860050891  0.9554140127  57.485029940  1.0714982196  0.2452098320  8.3915724754  0.1041668964  0.0139181012  2400          0.9925573572 
0.0107561656  0.8871262965  0.8704156479  0.9978678038  0.9551282051  1.0000000000  0.9850299401  0.9980916031  0.9694267516  64.670658682  1.0512363493  0.2371976157  8.3915724754  0.0922813573  0.0117598643  2700          0.9922104828 
0.0100034808  0.8456375839  0.8386308068  0.9989339019  0.9508547009  0.9985029940  0.9850299401  0.9965012723  0.9579617834  71.856287425  1.0385294878  0.2318305851  8.3915724754  0.0804777415  0.0102477078  3000          0.9921582421 
0.0103741403  0.8139109213  0.8215158924  0.9930703625  0.9572649573  0.9985029940  0.9910179641  0.9914122137  0.9630573248  79.041916167  1.0190888004  0.2273796448  8.3915724754  0.0762781248  0.0087301139  3300          0.9924636316 
0.0097252854  0.8926174497  0.8777506112  0.9984008529  0.9401709402  0.9992514970  0.9850299401  0.9980916031  0.9668789809  86.227544910  1.0010692638  0.2217206256  8.3915724754  0.0681138183  0.0078220589  3600          0.9922324133 
0.0070278498  0.8846857840  0.8679706601  0.9994669510  0.9508547009  1.0000000000  0.9910179641  0.9990458015  0.9617834395  93.413173652  0.9784444821  0.2134354231  8.3915724754  0.0622193221  0.0069288741  3900          0.9925553767 
0.0108965949  0.8578401464  0.8435207824  0.9989339019  0.9572649573  0.9992514970  0.9820359281  0.9961832061  0.9617834395  100.59880239  0.9566603593  0.2128288534  8.3915724754  0.0627114356  0.0059673401  4200          0.9926842403 
0.0079532308  0.7962172056  0.7750611247  0.9968017058  0.9465811966  1.0000000000  0.9850299401  0.9977735369  0.9439490446  107.78443113  0.9350140200  0.2040701943  8.3915724754  0.0535096025  0.0054385765  4500          0.9923162969 
0.0109253839  0.8462477120  0.8117359413  0.9930703625  0.9487179487  0.9932634731  0.9700598802  0.9904580153  0.9579617834  114.97005988  0.9163261175  0.2040782552  8.3915724754  0.0595355461  0.0047865636  4800          0.9924979687 
0.0105514623  0.8712629652  0.8386308068  1.0000000000  0.9615384615  0.9992514970  0.9790419162  0.9968193384  0.9592356688  119.76047904  0.8912085876  0.1978304265  8.3915724754  0.0544555116  0.0043445865  5000          0.9908623958