2025-05-22 07:24:52,196 [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/top/", "classifier": "./models/dirichlet-celeba/Blond_Hair/celeba-20250514-005354.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 07:24:52,208 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-05-22 07:24:52,383 [INFO ] [mvce_dataset.py:<module>:90]	 Running on cuda:1
2025-05-22 07:24:53,820 [INFO ] [loader.py:getDatasetSplits:27]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 07:25:01,295 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-22 07:25:01,384 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 07:25:01,384 [INFO ] [mvce_dataset.py:<module>:102]	 Using dataset celeba with 2000 test samples
2025-05-22 07:25:01,384 [INFO ] [mvce_dataset.py:<module>:111]	 Loading mask top
2025-05-22 07:25:01,391 [INFO ] [mvce_dataset.py:<module>:121]	 Including Dirichlet decision maker
2025-05-22 07:25:01,391 [INFO ] [mvce_dataset.py:<module>:179]	 Adding 0 methods with discarded masks.
2025-05-22 07:25:01,391 [INFO ] [mvce_dataset.py:<module>:192]	 Using masked dataset with mask {'name': 'top'}
2025-05-22 07:25:01,391 [INFO ] [mvce_dataset.py:<module>:208]	 Considering action space zero-one
2025-05-22 07:25:01,392 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-05-22 07:25:01,392 [INFO ] [calibration.py:__call__:223]	 Started running Dirichlet Network missing top in zero-one
2025-05-22 07:35:39,366 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.030028510838747025 for Dirichlet Network in 637.9238908067346 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 103,   89,  137,  227, 1444], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5514, 0.6465, 0.7528, 0.8528, 0.9847])
        * Final bucket consistencies: tensor([0.5534, 0.7191, 0.8540, 0.9427, 0.9979])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1722,  278], dtype=torch.int32)
        * Average consistency: 0.9465000033378601
    
2025-05-22 07:53:05,236 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03129265829920769 for Dirichlet Network in 1045.8476417427883 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 106,   91,  135,  225, 1443], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5515, 0.6488, 0.7554, 0.8535, 0.9847])
        * Final bucket consistencies: tensor([0.5849, 0.7033, 0.8593, 0.9511, 0.9972])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1722,  278], dtype=torch.int32)
        * Average consistency: 0.9474999904632568
    
2025-05-22 08:10:33,596 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03324849531054497 for Dirichlet Network in 1048.234052758664 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 105,   90,  130,  233, 1442], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5506, 0.6453, 0.7552, 0.8516, 0.9848])
        * Final bucket consistencies: tensor([0.6095, 0.7000, 0.8538, 0.9528, 0.9979])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1718,  282], dtype=torch.int32)
        * Average consistency: 0.9495000243186951
    
2025-05-22 08:28:00,943 [INFO ] [calibration.py:computeMVCE:154]	 
        Computed MVCE 0.03248503804206848 for Dirichlet Network in 1047.3074300633743 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 100,   97,  130,  233, 1440], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5468, 0.6427, 0.7539, 0.8524, 0.9850])
        * Final bucket consistencies: tensor([0.5800, 0.7216, 0.8615, 0.9442, 0.9979])
        * Actions: tensor([0, 1], dtype=torch.int32)
        * Best Action Counts: tensor([1709,  291], dtype=torch.int32)
        * Prediction Action Counts: tensor([1720,  280], dtype=torch.int32)
        * Average consistency: 0.9484999775886536
    
2025-05-22 08:28:01,153 [INFO ] [calibration.py:__call__:249]	 Finished running Dirichlet Network missing top in zero-one in 3779.7617862066254 seconds
2025-05-22 08:28:01,154 [INFO ] [mvce_dataset.py:<module>:222]	 Finished running 1/1 experiments.
2025-05-22 08:28:01,154 [INFO ] [mvce_dataset.py:<module>:227]	 Saving results to ./results/mvce-25/Blond_Hair/top/celeba-20250522-072452.csv
2025-05-22 08:28:02,250 [INFO ] [mvce_dataset.py:<module>:237]	 Finished saving results
