2025-05-22 22:40:55,852 [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, "calibration_map": "./results/calibration/celeba/Smiling/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 22:40:55,878 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 22:40:58,425 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:0
2025-05-22 22:41:00,404 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 22:41:05,903 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Smiling']
2025-05-22 22:41:06,075 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 22:41:06,075 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-22 22:41:06,078 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/celeba/Smiling/celeba.csv: {'Dirichlet Network': 2.5}
2025-05-22 22:41:06,078 [INFO ] [mvce_dataset.py:<module>:124]	 Loading mask top
2025-05-22 22:41:06,114 [INFO ] [mvce_dataset.py:<module>:134]	 Including Dirichlet decision maker
2025-05-22 22:41:06,114 [INFO ] [mvce_dataset.py:<module>:192]	 Adding 0 methods with discarded masks.
2025-05-22 22:41:06,114 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 22:41:06,114 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-22 22:41:06,114 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 22:41:06,114 [INFO ] [calibration.py:__call__:310]	 Started running Dirichlet Network missing top in zero-one
2025-05-22 22:44:26,259 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.02891644276678562 for Dirichlet Network in 200.1426679166034 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  57,   63,   64,  106, 1710], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5555, 0.6443, 0.7551, 0.8533, 0.9942])
        * Final bucket consistencies: tensor([0.6491, 0.8413, 0.9844, 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 22:47:17,438 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.026667101308703423 for Dirichlet Network in 171.16658519674093 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  55,   64,   62,  110, 1709], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5565, 0.6475, 0.7534, 0.8540, 0.9943])
        * Final bucket consistencies: tensor([0.5455, 0.8438, 0.9839, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([1041,  959], dtype=torch.int32)
        * Average consistency: 0.9819999933242798
    
2025-05-22 22:51:04,434 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.028601989150047302 for Dirichlet Network in 226.93584409728646 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  53,   65,   56,  121, 1705], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5499, 0.6467, 0.7475, 0.8534, 0.9944])
        * Final bucket consistencies: tensor([0.6038, 0.8615, 0.9821, 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: 0.984499990940094
    
2025-05-22 22:54:28,785 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.02824144810438156 for Dirichlet Network in 204.3474944634363 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  56,   63,   58,  112, 1711], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5535, 0.6470, 0.7452, 0.8514, 0.9943])
        * Final bucket consistencies: tensor([0.6429, 0.8254, 0.9828, 1.0000, 1.0000])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([1039,  961], dtype=torch.int32)
        * Average consistency: 0.984000027179718
    
2025-05-22 22:54:28,816 [INFO ] [calibration.py:__call__:336]	 Finished running Dirichlet Network missing top in zero-one in 802.7017668904737 seconds
2025-05-22 22:54:28,817 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 1/1 experiments.
2025-05-22 22:54:28,817 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Smiling/top/celeba-20250522-224055.csv
2025-05-22 22:54:29,390 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
