2025-05-22 07:26:34,463 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"name": "celeba", "dataset": {"path": "../../datasets/CelebAMask/256/img", "lists_root": "datasets/celeba", "attributes_path": "../../datasets/CelebAMask/1024/CelebAMask-HQ-attribute-anno.txt", "return_index": true, "targets": ["Eyeglasses"]}, "cache_directory": null, "output": "./results/mvce-25/Eyeglasses/bottom/", "classifier": "./models/dirichlet-celeba/Eyeglasses/celeba-20250514-004153.pklz", "classifier_feature": null, "mask": {"name": "bottom"}, "drop": null, "threads": 4, "generator_samples": [], "decision_samples": 1000, "cuda_index": 1, "zero_imputation": false, "zero_variance": false, "beta_variance_scales": [], "action_spaces": [{"name": "zero-one"}], "class_count": 1, "batch_size": 100, "batch_mean_imputation": [], "buckets": 10, "trials": 4, "seed": 1337, "verbose": 1}
2025-05-22 07:26:34,474 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-22 07:26:34,629 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:1
2025-05-22 07:26:35,882 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 07:26:44,399 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Eyeglasses']
2025-05-22 07:26:44,501 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 07:26:44,502 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 07:26:44,502 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask bottom
2025-05-22 07:26:44,523 [INFO ] [mvce_dataset.py:<module>:121]	 Including Dirichlet decision maker
2025-05-22 07:26:44,524 [INFO ] [mvce_dataset.py:<module>:179]	 Adding 0 methods with discarded masks.
2025-05-22 07:26:44,524 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 07:26:44,524 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 07:26:44,524 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 07:26:44,524 [INFO ] [calibration.py:__call__:223]	 Started running Dirichlet Network missing bottom in zero-one
2025-05-22 07:44:38,993 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.003405175171792507 for Dirichlet Network in 1074.4654632480815 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   4,    3,   10,    9, 1974], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5570, 0.6220, 0.7470, 0.8742, 0.9986])
        * Final bucket consistencies: tensor([1.0000, 0.6667, 0.6000, 0.8889, 0.9970])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1904,   96], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 08:01:40,240 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0032674584072083235 for Dirichlet Network in 1021.1984976697713 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   4,    3,   10,    8, 1975], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5533, 0.6453, 0.7461, 0.8631, 0.9985])
        * Final bucket consistencies: tensor([1.0000, 0.6667, 0.6000, 0.8750, 0.9970])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1904,   96], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 08:18:53,716 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.003203529864549637 for Dirichlet Network in 1033.4546867553145 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   4,    4,    8,    8, 1976], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5410, 0.6478, 0.7466, 0.8561, 0.9985])
        * Final bucket consistencies: tensor([1.0000, 0.5000, 0.7500, 0.7500, 0.9970])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1904,   96], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 08:34:14,697 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0033635178115218878 for Dirichlet Network in 920.9476886251941 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   4,    3,    8,   10, 1975], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5525, 0.6383, 0.7350, 0.8505, 0.9986])
        * Final bucket consistencies: tensor([1.0000, 0.6667, 0.7500, 0.7000, 0.9970])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1904,   96], dtype=torch.int32)
        * Average consistency: 0.9940000176429749
    
2025-05-22 08:34:14,738 [INFO ] [calibration.py:__call__:249]	 Finished running Dirichlet Network missing bottom in zero-one in 4050.2134996363893 seconds
2025-05-22 08:34:14,739 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 1/1 experiments.
2025-05-22 08:34:14,739 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Eyeglasses/bottom/celeba-20250522-072634.csv
2025-05-22 08:34:14,924 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
