2025-05-22 07:27:21,665 [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": ["Smiling"]}, "cache_directory": null, "output": "./results/mvce-25/Smiling/top/", "classifier": "./models/dirichlet-celeba/Smiling/celeba-20250513-220427.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 07:27:21,697 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-22 07:27:22,012 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:1
2025-05-22 07:27:23,631 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 07:27:35,937 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Smiling']
2025-05-22 07:27:36,194 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 07:27:36,194 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 07:27:36,194 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask top
2025-05-22 07:27:36,216 [INFO ] [mvce_dataset.py:<module>:121]	 Including Dirichlet decision maker
2025-05-22 07:27:36,216 [INFO ] [mvce_dataset.py:<module>:179]	 Adding 0 methods with discarded masks.
2025-05-22 07:27:36,217 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 07:27:36,217 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 07:27:36,218 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 07:27:36,218 [INFO ] [calibration.py:__call__:223]	 Started running Dirichlet Network missing top in zero-one
2025-05-22 07:45:22,180 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.037494365125894547 for Dirichlet Network in 1065.8882024884224 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  70,   64,   92,  130, 1644], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5539, 0.6476, 0.7552, 0.8601, 0.9930])
        * Final bucket consistencies: tensor([0.6857, 0.8750, 0.9891, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([1042,  958], dtype=torch.int32)
        * Average consistency: 0.984499990940094
    
2025-05-22 08:02:10,160 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.035212498158216476 for Dirichlet Network in 1007.8976558409631 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  65,   69,   94,  119, 1653], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5529, 0.6450, 0.7557, 0.8592, 0.9927])
        * Final bucket consistencies: tensor([0.5846, 0.8841, 1.0000, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([1038,  962], dtype=torch.int32)
        * Average consistency: 0.9825000166893005
    
2025-05-22 08:19:01,753 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03680652007460594 for Dirichlet Network in 1011.4962936742231 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  65,   68,   90,  128, 1649], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5503, 0.6444, 0.7544, 0.8574, 0.9928])
        * Final bucket consistencies: tensor([0.6000, 0.9265, 0.9889, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([1037,  963], dtype=torch.int32)
        * Average consistency: 0.984000027179718
    
2025-05-22 08:34:14,842 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03611244261264801 for Dirichlet Network in 913.0448130173609 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  69,   67,   87,  125, 1652], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5566, 0.6489, 0.7536, 0.8583, 0.9928])
        * Final bucket consistencies: tensor([0.6377, 0.8955, 0.9885, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([1040,  960], dtype=torch.int32)
        * Average consistency: 0.9835000038146973
    
2025-05-22 08:34:14,873 [INFO ] [calibration.py:__call__:249]	 Finished running Dirichlet Network missing top in zero-one in 3998.6555176200345 seconds
2025-05-22 08:34:14,874 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 1/1 experiments.
2025-05-22 08:34:14,874 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Smiling/top/celeba-20250522-072721.csv
2025-05-22 08:34:15,001 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
