2025-05-22 07:26:46,877 [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/top/", "classifier": "./models/dirichlet-celeba/Eyeglasses/celeba-20250514-004153.pklz", "classifier_feature": null, "mask": {"name": "top"}, "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 07:26:46,903 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-22 07:26:47,104 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:1
2025-05-22 07:26:47,586 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 07:26:56,871 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Eyeglasses']
2025-05-22 07:26:56,987 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 07:26:56,988 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 07:26:56,988 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask top
2025-05-22 07:26:57,018 [INFO ] [mvce_dataset.py:<module>:121]	 Including Dirichlet decision maker
2025-05-22 07:26:57,018 [INFO ] [mvce_dataset.py:<module>:179]	 Adding 0 methods with discarded masks.
2025-05-22 07:26:57,019 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 07:26:57,019 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 07:26:57,020 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 07:26:57,020 [INFO ] [calibration.py:__call__:223]	 Started running Dirichlet Network missing top in zero-one
2025-05-22 07:44:47,021 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0020495906937867403 for Dirichlet Network in 1069.9692626614124 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   5,    1,    7,    7, 1980], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5336, 0.6000, 0.7317, 0.8570, 0.9993])
        * Final bucket consistencies: tensor([0.4000, 0.0000, 0.7143, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1901,   99], dtype=torch.int32)
        * Average consistency: 0.9955000281333923
    
2025-05-22 08:01:40,071 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.001909526064991951 for Dirichlet Network in 1013.0333860395476 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   6,    3,    4,    6, 1981], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5433, 0.6827, 0.7630, 0.8512, 0.9993])
        * Final bucket consistencies: tensor([0.3333, 0.6667, 0.7500, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1901,   99], dtype=torch.int32)
        * Average consistency: 0.9955000281333923
    
2025-05-22 08:18:56,038 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0025004534982144833 for Dirichlet Network in 1035.9461315879598 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   6,    3,    4,    7, 1980], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5385, 0.6860, 0.7575, 0.8591, 0.9993])
        * Final bucket consistencies: tensor([0.1667, 0.6667, 0.7500, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Average consistency: 0.9950000047683716
    
2025-05-22 08:34:14,486 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.0024694944731891155 for Dirichlet Network in 918.4381374372169 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   6,    3,    4,    6, 1981], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5408, 0.6850, 0.7697, 0.8530, 0.9993])
        * Final bucket consistencies: tensor([0.1667, 0.6667, 0.7500, 1.0000, 0.9985])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Prediction Action Counts: tensor([1902,   98], dtype=torch.int32)
        * Average consistency: 0.9950000047683716
    
2025-05-22 08:34:14,501 [INFO ] [calibration.py:__call__:249]	 Finished running Dirichlet Network missing top in zero-one in 4037.4810249991715 seconds
2025-05-22 08:34:14,502 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 1/1 experiments.
2025-05-22 08:34:14,502 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Eyeglasses/top/celeba-20250522-072646.csv
2025-05-22 08:34:14,640 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
