2025-05-22 22:40:55,574 [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, "calibration_map": "./results/calibration/celeba/Smiling/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 22:40:55,615 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-05-22 22:40:58,422 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:0
2025-05-22 22:41:00,405 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 22:41:05,954 [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 bottom
2025-05-22 22:41:06,106 [INFO ] [mvce_dataset.py:<module>:134]	 Including Dirichlet decision maker
2025-05-22 22:41:06,106 [INFO ] [mvce_dataset.py:<module>:192]	 Adding 0 methods with discarded masks.
2025-05-22 22:41:06,106 [INFO ] [mvce_dataset.py:<module>:205]	 Using masked dataset with mask {'name': 'bottom'}
2025-05-22 22:41:06,107 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-05-22 22:41:06,107 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 22:41:06,107 [INFO ] [calibration.py:__call__:310]	 Started running Dirichlet Network missing bottom in zero-one
2025-05-22 22:44:26,064 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.10905596613883972 for Dirichlet Network in 199.95420239958912 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([463, 437, 415, 458, 227], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5498, 0.6482, 0.7505, 0.8509, 0.9292])
        * Final bucket consistencies: tensor([0.4514, 0.4416, 0.5880, 0.8362, 0.9648])
        * 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 22:46:43,029 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.11335600167512894 for Dirichlet Network in 136.92250618059188 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([460, 436, 403, 479, 222], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5504, 0.6461, 0.7483, 0.8495, 0.9308])
        * Final bucket consistencies: tensor([0.4326, 0.4472, 0.6005, 0.8163, 0.9775])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 325, 1675], dtype=torch.int32)
        * Average consistency: 0.621999979019165
    
2025-05-22 22:51:04,358 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.10411302000284195 for Dirichlet Network in 261.32681247312576 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([463, 434, 414, 450, 239], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5499, 0.6476, 0.7494, 0.8495, 0.9287])
        * Final bucket consistencies: tensor([0.4622, 0.4447, 0.6135, 0.8178, 0.9665])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 341, 1659], dtype=torch.int32)
        * Average consistency: 0.6299999952316284
    
2025-05-22 22:54:28,593 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.10772705078125 for Dirichlet Network in 204.22409115545452 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([468, 431, 410, 465, 226], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5502, 0.6480, 0.7505, 0.8502, 0.9309])
        * Final bucket consistencies: tensor([0.4615, 0.4501, 0.6098, 0.8086, 0.9823])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1035,  965], dtype=torch.int32)
        * Prediction Action Counts: tensor([ 339, 1661], dtype=torch.int32)
        * Average consistency: 0.6290000081062317
    
2025-05-22 22:54:28,609 [INFO ] [calibration.py:__call__:336]	 Finished running Dirichlet Network missing bottom in zero-one in 802.5021440405399 seconds
2025-05-22 22:54:28,609 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 1/1 experiments.
2025-05-22 22:54:28,610 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-25/Smiling/bottom/celeba-20250522-224055.csv
2025-05-22 22:54:29,390 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
