2025-05-22 21:35:55,710 [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/bottom/", "classifier": "./models/dirichlet-celeba/Blond_Hair/celeba-20250514-005354.pklz", "classifier_feature": null, "calibration_map": "./results/calibration/celeba/Blond_Hair/celeba.csv", "mask": {"name": "bottom"}, "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 21:35:55,741 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 21:35:55,898 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:0
2025-05-22 21:35:56,446 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 21:36:01,864 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-22 21:36:01,922 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 21:36:01,922 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-22 21:36:01,924 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/celeba/Blond_Hair/celeba.csv: {'Dirichlet Network': 7.5}
2025-05-22 21:36:01,924 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask bottom
2025-05-22 21:36:01,925 [INFO ] [mvce_dataset.py:<module>:134]	 Including Dirichlet decision maker
2025-05-22 21:36:01,925 [INFO ] [mvce_dataset.py:<module>:192]	 Adding 0 methods with discarded masks.
2025-05-22 21:36:01,925 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 21:36:01,925 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-22 21:36:01,925 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 21:36:01,925 [INFO ] [calibration.py:__call__:310]	 Started running Dirichlet Network missing bottom in zero-one
2025-05-22 21:42:12,288 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.006211059633642435 for Dirichlet Network in 370.3615206135437 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  34,   32,   39,   70, 1825], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5374, 0.6415, 0.7621, 0.8549, 0.9960])
        * Final bucket consistencies: tensor([0.5000, 0.6875, 0.8462, 0.9000, 0.9978])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1705,  295], dtype=torch.int32)
        * Average consistency: 0.9779999852180481
    
2025-05-22 21:52:37,776 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.00698450393974781 for Dirichlet Network in 625.4536738935858 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  32,   32,   37,   72, 1827], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5416, 0.6347, 0.7498, 0.8544, 0.9958])
        * Final bucket consistencies: tensor([0.4688, 0.5938, 0.8378, 0.9028, 0.9978])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1700,  300], dtype=torch.int32)
        * Average consistency: 0.9764999747276306
    
2025-05-22 22:03:13,701 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.005376992281526327 for Dirichlet Network in 635.9026366192847 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  34,   32,   36,   73, 1825], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5404, 0.6431, 0.7551, 0.8549, 0.9960])
        * Final bucket consistencies: tensor([0.5588, 0.6562, 0.8333, 0.9178, 0.9973])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1700,  300], dtype=torch.int32)
        * Average consistency: 0.9785000085830688
    
2025-05-22 22:13:57,033 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.00523705780506134 for Dirichlet Network in 643.299344365485 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  32,   35,   37,   72, 1824], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5437, 0.6458, 0.7564, 0.8573, 0.9959])
        * Final bucket consistencies: tensor([0.5312, 0.6571, 0.8378, 0.9028, 0.9978])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1701,  299], dtype=torch.int32)
        * Average consistency: 0.9779999852180481
    
2025-05-22 22:13:57,063 [INFO ] [calibration.py:__call__:336]	 Finished running Dirichlet Network missing bottom in zero-one in 2275.1380863767117 seconds
2025-05-22 22:13:57,064 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 1/1 experiments.
2025-05-22 22:13:57,064 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Blond_Hair/bottom/celeba-20250522-213555.csv
2025-05-22 22:13:57,783 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
