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_env0_s2
	save_model_every_checkpoint: False
	seed: 2
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [0]
	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    
0.2599145821  0.2469437653  0.1961620469  0.1773504274  0.3121257485  0.3293413174  0.1898854962  0.2050955414  0.0000000000  2.0998075008  7.9323368073  0             0.5847871304 
0.7077486272  0.7163814181  0.9339019190  0.9273504274  0.9775449102  0.9550898204  0.9277989822  0.8942675159  7.1856287425  0.2067586746  8.1372547150  300           0.1839775276 
0.8035387431  0.8215158924  0.9797441365  0.9337606838  0.9932634731  0.9700598802  0.9748727735  0.9159235669  14.371257485  0.0622528433  8.1372547150  600           0.1832265027 
0.7901159243  0.7921760391  0.9797441365  0.9401709402  0.9940119760  0.9760479042  0.9726463104  0.9057324841  21.556886227  0.0386066197  8.1372547150  900           0.1833546368 
0.8615009152  0.8508557457  0.9968017058  0.9700854701  0.9970059880  0.9730538922  0.9879134860  0.9286624204  28.742514970  0.0282550458  8.1372547150  1200          0.1832685065 
0.8175716901  0.8435207824  0.9904051173  0.9465811966  0.9977544910  0.9880239521  0.9875954198  0.9222929936  35.928143712  0.0235177290  8.1372547150  1500          0.1833536235 
0.8279438682  0.8557457213  0.9925373134  0.9508547009  0.9970059880  0.9820359281  0.9904580153  0.9503184713  43.113772455  0.0237456096  8.1372547150  1800          0.1833719842 
0.7583892617  0.7750611247  0.9909381663  0.9551282051  0.9902694611  0.9730538922  0.9761450382  0.9146496815  50.299401197  0.0190173898  8.1372547150  2100          0.1834505240 
0.8047589994  0.7897310513  0.9957356077  0.9551282051  0.9992514970  0.9760479042  0.9939567430  0.9414012739  57.485029940  0.0149132433  8.1372547150  2400          0.1833727376 
0.8017083588  0.8068459658  0.9957356077  0.9636752137  0.9947604790  0.9491017964  0.9965012723  0.9426751592  64.670658682  0.0151878891  8.1372547150  2700          0.1830720369 
0.8486882245  0.8630806846  1.0000000000  0.9764957265  0.9992514970  0.9790419162  0.9971374046  0.9554140127  71.856287425  0.0181518111  8.1372547150  3000          0.1828976679 
0.8389261745  0.8606356968  0.9946695096  0.9636752137  0.9985029940  0.9730538922  0.9977735369  0.9554140127  79.041916167  0.0135308190  8.1372547150  3300          0.1830514876 
0.8456375839  0.8630806846  0.9882729211  0.9551282051  0.9992514970  0.9730538922  0.9863231552  0.9248407643  86.227544910  0.0156484562  8.1372547150  3600          0.1829001594 
0.7693715680  0.7701711491  0.9957356077  0.9529914530  0.9970059880  0.9760479042  0.9930025445  0.9401273885  93.413173652  0.0143930741  8.1372547150  3900          0.1831276035 
0.8401464308  0.7995110024  0.9925373134  0.9700854701  0.9992514970  0.9730538922  0.9942748092  0.9426751592  100.59880239  0.0168711618  8.1372547150  4200          0.1831784821 
0.8114704088  0.7995110024  0.9962686567  0.9529914530  0.9970059880  0.9670658683  0.9910941476  0.9363057325  107.78443113  0.0109523671  8.1372547150  4500          0.1828214343 
0.8486882245  0.8361858191  0.9968017058  0.9508547009  0.9970059880  0.9730538922  0.9961832061  0.9439490446  114.97005988  0.0135287121  8.1372547150  4800          0.1826331194 
0.8407565589  0.8215158924  0.9978678038  0.9700854701  0.9977544910  0.9820359281  0.9955470738  0.9566878981  119.76047904  0.0107419249  8.1372547150  5000          0.1819012749