2025-05-22 07:27:32,943 [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/bottom/", "classifier": "./models/dirichlet-celeba/Smiling/celeba-20250513-220427.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 07:27:33,051 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-22 07:27:33,283 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:1
2025-05-22 07:27:33,803 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 07:27:46,563 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Smiling']
2025-05-22 07:27:46,700 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 07:27:46,700 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 07:27:46,700 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask bottom
2025-05-22 07:27:46,742 [INFO ] [mvce_dataset.py:<module>:121]	 Including Dirichlet decision maker
2025-05-22 07:27:46,750 [INFO ] [mvce_dataset.py:<module>:179]	 Adding 0 methods with discarded masks.
2025-05-22 07:27:46,750 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 07:27:46,750 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 07:27:46,751 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 07:27:46,751 [INFO ] [calibration.py:__call__:223]	 Started running Dirichlet Network missing bottom in zero-one
2025-05-22 07:45:35,876 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10518252849578857 for Dirichlet Network in 1069.0963665070012 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([479, 442, 419, 456, 204], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5481, 0.6464, 0.7491, 0.8498, 0.9277])
        * Final bucket consistencies: tensor([0.4593, 0.4434, 0.6062, 0.8311, 0.9755])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 329, 1671], dtype=torch.int32)
        * Average consistency: 0.6240000128746033
    
2025-05-22 08:02:28,682 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10820350050926208 for Dirichlet Network in 1012.7725342782214 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([488, 443, 401, 466, 202], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5506, 0.6470, 0.7485, 0.8487, 0.9285])
        * Final bucket consistencies: tensor([0.4385, 0.4515, 0.6185, 0.8219, 0.9802])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 324, 1676], dtype=torch.int32)
        * Average consistency: 0.6215000152587891
    
2025-05-22 08:19:19,676 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.09900698810815811 for Dirichlet Network in 1010.9545224905014 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([491, 430, 422, 440, 217], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5502, 0.6479, 0.7484, 0.8489, 0.9266])
        * Final bucket consistencies: tensor([0.4684, 0.4419, 0.6351, 0.8205, 0.9677])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 340, 1660], dtype=torch.int32)
        * Average consistency: 0.6294999718666077
    
2025-05-22 08:34:14,841 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.10492247343063354 for Dirichlet Network in 895.1472375625744 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([486, 433, 424, 452, 205], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5490, 0.6460, 0.7489, 0.8499, 0.9287])
        * Final bucket consistencies: tensor([0.4527, 0.4503, 0.6203, 0.8208, 0.9805])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 335, 1665], dtype=torch.int32)
        * Average consistency: 0.625
    
2025-05-22 08:34:14,885 [INFO ] [calibration.py:__call__:249]	 Finished running Dirichlet Network missing bottom in zero-one in 3988.1345431050286 seconds
2025-05-22 08:34:14,886 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 1/1 experiments.
2025-05-22 08:34:14,886 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Smiling/bottom/celeba-20250522-072732.csv
2025-05-22 08:34:14,946 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
