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, "rdi_tau": 1.0}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/pacs/RDI-tau1_env0_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [0]
	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: 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: 1.0
	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    
2.0235202312  0.1879194631  0.1613691932  0.1876332623  0.1709401709  0.2582335329  0.2574850299  0.2258269720  0.2203821656  0.0000000000  2.0235202312  7.9755101204  0             1.5007953644 
0.1832763110  0.8395363026  0.8484107579  0.9744136461  0.9316239316  0.9947604790  0.9790419162  0.9653307888  0.9312101911  7.1856287425  0.1832763110  8.2003893852  300           0.1875085155 
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    
0.0488215264  0.8523489933  0.8704156479  0.9824093817  0.9572649573  0.9985029940  0.9580838323  0.9866412214  0.9515923567  14.371257485  1.2845486284  0.1580057587  8.3915634155  0.2680221104  0.1359827440  600           0.4724650081 
0.0358631262  0.8370957901  0.8557457213  0.9946695096  0.9572649573  0.9955089820  0.9700598802  0.9866412214  0.9503184713  21.556886227  1.2163567829  0.3219836278  8.3915634155  0.1813431803  0.0782383285  900           0.9932214236 
0.0223465154  0.7535082367  0.7334963325  0.9786780384  0.9188034188  0.9925149701  0.9461077844  0.9726463104  0.9375796178  28.742514970  1.1626274137  0.2840969619  8.3915634155  0.1285105748  0.0497418749  1200          0.9914012814 
0.0209993844  0.8419768151  0.8508557457  0.9877398721  0.9465811966  0.9940119760  0.9550898204  0.9885496183  0.9490445860  35.928143712  1.1317004772  0.2703346909  8.3915634155  0.1072079265  0.0345700946  1500          0.9920884562 
0.0153697240  0.8505186089  0.8679706601  0.9984008529  0.9572649573  1.0000000000  0.9850299401  0.9942748092  0.9605095541  43.113772455  1.1109358704  0.2562628566  8.3915634155  0.0887276492  0.0269840405  1800          0.9917438873 
0.0134002617  0.7230018304  0.7628361858  0.9866737740  0.9316239316  0.9992514970  0.9670658683  0.9904580153  0.9541401274  50.299401197  1.0868488407  0.2467255629  8.3915634155  0.0772659444  0.0218281852  2100          0.9919594479 
0.0113952646  0.8065893838  0.8044009780  0.9978678038  0.9636752137  0.9992514970  0.9820359281  0.9974554707  0.9630573248  57.485029940  1.0718611431  0.2395526111  8.3915634155  0.0677391721  0.0181211884  2400          0.9919088308 
0.0122063895  0.8157413057  0.8068459658  0.9941364606  0.9294871795  1.0000000000  0.9820359281  0.9961832061  0.9528662420  64.670658682  1.0569605855  0.2362759798  8.3915634155  0.0636022061  0.0156856924  2700          0.9923552004 
0.0117767846  0.8419768151  0.8361858191  0.9968017058  0.9551282051  0.9977544910  0.9820359281  0.9863231552  0.9426751592  71.856287425  1.0383884497  0.2308492472  8.3915634155  0.0582165827  0.0133114097  3000          0.9921710626 
0.0093201557  0.8761439902  0.8606356968  0.9994669510  0.9679487179  0.9992514970  0.9730538922  0.9980916031  0.9490445860  79.041916167  1.0180445272  0.2230443925  8.3915634155  0.0516622824  0.0118299314  3300          0.9923682157 
0.0132878040  0.8120805369  0.8117359413  0.9877398721  0.9123931624  0.9985029940  0.9491017964  0.9945928753  0.9554140127  86.227544910  1.0025114703  0.2235412283  8.3915634155  0.0514396104  0.0101759276  3600          0.9924119465 
0.0112026040  0.8187919463  0.8312958435  0.9941364606  0.9487179487  1.0000000000  0.9700598802  0.9952290076  0.9464968153  93.413173652  0.9885919921  0.2176346930  8.3915634155  0.0458154221  0.0092068698  3900          0.9928624320 
0.0086106131  0.8590604027  0.8606356968  1.0000000000  0.9722222222  1.0000000000  0.9700598802  0.9987277354  0.9643312102  100.59880239  0.9679095316  0.2102964331  8.3915634155  0.0429261728  0.0083249276  4200          0.9930050985 
0.0094246318  0.8688224527  0.8655256724  0.9968017058  0.9636752137  0.9992514970  0.9760479042  0.9971374046  0.9464968153  107.78443113  0.9416699054  0.2053084504  8.3915634155  0.0405132305  0.0073642459  4500          0.9928880827 
0.0063104738  0.7956070775  0.8019559902  0.9994669510  0.9572649573  0.9992514970  0.9700598802  0.9971374046  0.9554140127  114.97005988  0.9172194445  0.1964085610  8.3915634155  0.0353440090  0.0067629658  4800          0.9928948371 
0.0093887649  0.8517388652  0.8508557457  0.9989339019  0.9658119658  0.9970059880  0.9580838323  0.9977735369  0.9592356688  119.76047904  0.8998408753  0.1961286063  8.3915634155  0.0368545990  0.0062173659  5000          0.9913683391