2025-05-22 21:35:45,798 [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": ["Blond_Hair"]}, "cache_directory": null, "output": "./results/mvce-25/Blond_Hair/top/", "classifier": "./models/dirichlet-celeba/Blond_Hair/celeba-20250514-005354.pklz", "classifier_feature": null, "calibration_map": "./results/calibration/celeba/Blond_Hair/celeba.csv", "mask": {"name": "top"}, "drop": null, "threads": 4, "generator_samples": [], "decision_samples": 1000, "cuda_index": 0, "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 21:35:45,805 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 21:35:45,993 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:0
2025-05-22 21:35:46,303 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 21:35:51,699 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-22 21:35:51,760 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 21:35:51,761 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-22 21:35:51,836 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/celeba/Blond_Hair/celeba.csv: {'Dirichlet Network': 7.5}
2025-05-22 21:35:51,837 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask top
2025-05-22 21:35:51,842 [INFO ] [mvce_dataset.py:<module>:134]	 Including Dirichlet decision maker
2025-05-22 21:35:51,842 [INFO ] [mvce_dataset.py:<module>:192]	 Adding 0 methods with discarded masks.
2025-05-22 21:35:51,842 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 21:35:51,843 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-22 21:35:51,843 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 21:35:51,843 [INFO ] [calibration.py:__call__:310]	 Started running Dirichlet Network missing top in zero-one
2025-05-22 21:42:11,781 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.008054574951529503 for Dirichlet Network in 379.9089024597779 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  64,   77,   61,  105, 1693], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5492, 0.6505, 0.7570, 0.8634, 0.9928])
        * Final bucket consistencies: tensor([0.5781, 0.6883, 0.6721, 0.8762, 0.9900])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1718,  282], dtype=torch.int32)
        * Average consistency: 0.9495000243186951
    
2025-05-22 21:52:37,696 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.006407084409147501 for Dirichlet Network in 625.888752464205 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  67,   69,   59,  109, 1696], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5522, 0.6471, 0.7483, 0.8571, 0.9927])
        * Final bucket consistencies: tensor([0.5970, 0.6232, 0.7119, 0.8532, 0.9894])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1721,  279], dtype=torch.int32)
        * Average consistency: 0.9480000138282776
    
2025-05-22 22:03:12,113 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.004464970901608467 for Dirichlet Network in 634.4007100397721 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  62,   72,   64,  106, 1696], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5493, 0.6478, 0.7480, 0.8589, 0.9928])
        * Final bucket consistencies: tensor([0.5806, 0.6528, 0.7344, 0.8491, 0.9900])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1716,  284], dtype=torch.int32)
        * Average consistency: 0.9495000243186951
    
2025-05-22 22:13:57,472 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.0056575993075966835 for Dirichlet Network in 645.3049102853984 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  66,   70,   58,  115, 1691], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5510, 0.6485, 0.7459, 0.8604, 0.9930])
        * Final bucket consistencies: tensor([0.5455, 0.6857, 0.6897, 0.8522, 0.9905])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1720,  280], dtype=torch.int32)
        * Average consistency: 0.9484999775886536
    
2025-05-22 22:13:57,524 [INFO ] [calibration.py:__call__:336]	 Finished running Dirichlet Network missing top in zero-one in 2285.680932207033 seconds
2025-05-22 22:13:57,524 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 1/1 experiments.
2025-05-22 22:13:57,524 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Blond_Hair/top/celeba-20250522-213545.csv
2025-05-22 22:13:57,783 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
