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: ERM
	checkpoint_freq: None
	data_dir: /home/kaustubh/Datasets
	dataset: PACS
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/ERM_env3_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [3]
	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: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet50_augmix: False
	resnet_dropout: 0.0
	vit: False
	vit_attn_tune: False
	vit_dropout: 0.0
	weight_decay: 0.0
/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)
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    
0.2355094570  0.2444987775  0.2681236674  0.2500000000  0.3660179641  0.3532934132  0.0696564885  0.0547770701  0.0000000000  2.0500514507  7.9323368073  0             0.6606760025 
0.9731543624  0.9559902200  0.9845415778  0.9380341880  0.9910179641  0.9610778443  0.7172391858  0.7095541401  7.1856287425  0.1935507521  8.1372547150  300           0.1834677807 
0.9749847468  0.9388753056  0.9840085288  0.9423076923  0.9985029940  0.9820359281  0.7264631043  0.7414012739  14.371257485  0.0504239016  8.1372547150  600           0.1846386337 
0.9865771812  0.9144254279  0.9941364606  0.9658119658  0.9970059880  0.9670658683  0.6943384224  0.6891719745  21.556886227  0.0377946829  8.1372547150  900           0.1844797381 
0.9896278218  0.9535452323  0.9930703625  0.9700854701  0.9955089820  0.9580838323  0.7480916031  0.7605095541  28.742514970  0.0230917360  8.1372547150  1200          0.1844900688 
0.9768151312  0.9193154034  0.9797441365  0.9380341880  0.9940119760  0.9670658683  0.7519083969  0.7312101911  35.928143712  0.0244161962  8.1372547150  1500          0.1845442208 
0.9896278218  0.9217603912  0.9978678038  0.9551282051  0.9985029940  0.9760479042  0.7694020356  0.7783439490  43.113772455  0.0242775579  8.1372547150  1800          0.1844853210 
0.9963392312  0.9339853301  0.9930703625  0.9551282051  0.9992514970  0.9760479042  0.7779898219  0.7757961783  50.299401197  0.0207218374  8.1372547150  2100          0.1847278023 
0.9932885906  0.9388753056  0.9957356077  0.9529914530  0.9977544910  0.9790419162  0.7487277354  0.7490445860  57.485029940  0.0142825035  8.1372547150  2400          0.1843793337 
0.9859670531  0.9242053790  0.9946695096  0.9615384615  0.9947604790  0.9640718563  0.7827608142  0.7630573248  64.670658682  0.0100727416  8.1372547150  2700          0.1845519741 
0.9957291031  0.9242053790  0.9973347548  0.9487179487  0.9992514970  0.9700598802  0.8132951654  0.8254777070  71.856287425  0.0190189292  8.1372547150  3000          0.1843435629 
0.9749847468  0.9144254279  0.9850746269  0.9316239316  0.9857784431  0.9491017964  0.7337786260  0.7159235669  79.041916167  0.0181752635  8.1372547150  3300          0.1844742846 
0.9938987187  0.9119804401  0.9957356077  0.9230769231  0.9985029940  0.9580838323  0.6905216285  0.7222929936  86.227544910  0.0154223986  8.1372547150  3600          0.1841763179 
0.9877974375  0.9119804401  0.9946695096  0.9615384615  0.9947604790  0.9610778443  0.7853053435  0.7898089172  93.413173652  0.0111033921  8.1372547150  3900          0.1844059396 
0.9975594875  0.9290953545  0.9978678038  0.9658119658  0.9992514970  0.9760479042  0.7741730280  0.7808917197  100.59880239  0.0196496727  8.1372547150  4200          0.1841907970 
0.9963392312  0.9315403423  0.9978678038  0.9572649573  0.9992514970  0.9670658683  0.7958015267  0.8101910828  107.78443113  0.0143533888  8.1372547150  4500          0.1841769576 
0.9932885906  0.9119804401  0.9973347548  0.9508547009  0.9985029940  0.9520958084  0.7417302799  0.7184713376  114.97005988  0.0150660873  8.1372547150  4800          0.1843904368 
0.9920683344  0.9290953545  0.9866737740  0.9380341880  0.9992514970  0.9670658683  0.8031170483  0.8025477707  119.76047904  0.0180701467  8.1372547150  5000          0.1857755709