2025-05-22 07:25:09,624 [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, "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 07:25:09,634 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-22 07:25:09,794 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:1
2025-05-22 07:25:10,131 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 07:25:17,806 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-22 07:25:17,881 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 07:25:17,881 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 07:25:17,881 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask bottom
2025-05-22 07:25:17,915 [INFO ] [mvce_dataset.py:<module>:121]	 Including Dirichlet decision maker
2025-05-22 07:25:17,915 [INFO ] [mvce_dataset.py:<module>:179]	 Adding 0 methods with discarded masks.
2025-05-22 07:25:17,916 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 07:25:17,916 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 07:25:17,916 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 07:25:17,916 [INFO ] [calibration.py:__call__:223]	 Started running Dirichlet Network missing bottom in zero-one
2025-05-22 07:40:20,319 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.025735510513186455 for Dirichlet Network in 902.3553300350904 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  56,   62,   84,  112, 1686], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5421, 0.6551, 0.7540, 0.8536, 0.9938])
        * Final bucket consistencies: tensor([0.6071, 0.8065, 0.9167, 1.0000, 0.9988])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1704,  296], dtype=torch.int32)
        * Average consistency: 0.9785000085830688
    
2025-05-22 07:57:39,593 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.026144573464989662 for Dirichlet Network in 1039.208785770461 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  59,   53,   83,  118, 1687], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5468, 0.6514, 0.7496, 0.8497, 0.9937])
        * Final bucket consistencies: tensor([0.5932, 0.8113, 0.9398, 0.9915, 0.9988])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1703,  297], dtype=torch.int32)
        * Average consistency: 0.9789999723434448
    
2025-05-22 08:14:38,550 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.02557852678000927 for Dirichlet Network in 1018.854179603979 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  61,   52,   90,  114, 1683], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5459, 0.6543, 0.7506, 0.8533, 0.9939])
        * Final bucket consistencies: tensor([0.5902, 0.8269, 0.9222, 0.9912, 0.9988])
        * 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 08:31:28,840 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.02526109479367733 for Dirichlet Network in 1010.1809826623648 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  62,   59,   78,  118, 1683], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5505, 0.6602, 0.7556, 0.8533, 0.9939])
        * Final bucket consistencies: tensor([0.5968, 0.7797, 0.9744, 0.9831, 0.9988])
        * 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 08:31:28,852 [INFO ] [calibration.py:__call__:249]	 Finished running Dirichlet Network missing bottom in zero-one in 3970.9359701154754 seconds
2025-05-22 08:31:28,853 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 1/1 experiments.
2025-05-22 08:31:28,853 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Blond_Hair/bottom/celeba-20250522-072509.csv
2025-05-22 08:31:29,238 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
