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_env1_s2
	save_model_every_checkpoint: False
	seed: 2
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [1]
	trial_seed: 2
	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    
0.2605247102  0.2371638142  0.1823027719  0.1538461538  0.3450598802  0.3532934132  0.1892493639  0.1987261146  0.0000000000  2.0833289623  7.9323368073  0             0.6123533249 
0.9731543624  0.9486552567  0.7553304904  0.7841880342  0.9917664671  0.9850299401  0.9440203562  0.9121019108  7.1856287425  0.2025915018  8.1372547150  300           0.1838029019 
0.9658328249  0.9266503667  0.7547974414  0.7884615385  0.9932634731  0.9670658683  0.9767811705  0.9273885350  14.371257485  0.0626212611  8.1372547150  600           0.1831407595 
0.9932885906  0.9413202934  0.7777185501  0.7905982906  0.9985029940  0.9790419162  0.9891857506  0.9426751592  21.556886227  0.0404389992  8.1372547150  900           0.1834581741 
0.9987797437  0.9486552567  0.7164179104  0.7414529915  1.0000000000  0.9940119760  0.9856870229  0.9414012739  28.742514970  0.0294299272  8.1372547150  1200          0.1834239252 
0.9920683344  0.9364303178  0.8001066098  0.8119658120  0.9992514970  0.9850299401  0.9920483461  0.9414012739  35.928143712  0.0321432179  8.1372547150  1500          0.1834311215 
0.9920683344  0.9486552567  0.7057569296  0.7329059829  0.9992514970  0.9790419162  0.9936386768  0.9401273885  43.113772455  0.0227843719  8.1372547150  1800          0.1834461625 
0.9701037218  0.9095354523  0.7702558635  0.7905982906  0.9992514970  0.9850299401  0.9939567430  0.9248407643  50.299401197  0.0192412413  8.1372547150  2100          0.1833295059 
0.9926784625  0.9364303178  0.7963752665  0.8354700855  0.9977544910  0.9730538922  0.9926844784  0.9350318471  57.485029940  0.0196117226  8.1372547150  2400          0.1835284623 
0.9902379500  0.9339853301  0.6865671642  0.7264957265  0.9985029940  0.9850299401  0.9920483461  0.9324840764  64.670658682  0.0195049724  8.1372547150  2700          0.1831097213 
0.9951189750  0.9315403423  0.8171641791  0.8247863248  0.9970059880  0.9670658683  0.9984096692  0.9452229299  71.856287425  0.0158305508  8.1372547150  3000          0.1829749020 
0.9975594875  0.9388753056  0.7697228145  0.7841880342  0.9977544910  0.9820359281  0.9949109415  0.9375796178  79.041916167  0.0182567289  8.1372547150  3300          0.1829086415 
0.9926784625  0.9242053790  0.7654584222  0.8098290598  0.9977544910  0.9850299401  0.9952290076  0.9439490446  86.227544910  0.0147668212  8.1372547150  3600          0.1829585576 
0.9993898719  0.9559902200  0.8160980810  0.8376068376  0.9992514970  0.9820359281  0.9933206107  0.9541401274  93.413173652  0.0111655592  8.1372547150  3900          0.1832366276 
0.9865771812  0.9168704156  0.7361407249  0.7158119658  0.9962574850  0.9760479042  0.9895038168  0.9235668790  100.59880239  0.0199612743  8.1372547150  4200          0.1832591256 
0.9957291031  0.9315403423  0.8272921109  0.8354700855  0.9992514970  0.9700598802  0.9971374046  0.9452229299  107.78443113  0.0162654828  8.1372547150  4500          0.1829727546 
0.9957291031  0.9339853301  0.7963752665  0.8183760684  1.0000000000  0.9730538922  0.9980916031  0.9464968153  114.97005988  0.0172264165  8.1372547150  4800          0.1829599897 
0.9938987187  0.9168704156  0.7819829424  0.7905982906  0.9985029940  0.9820359281  0.9977735369  0.9439490446  119.76047904  0.0135695327  8.1372547150  5000          0.1820597589