2025-06-13 16:56:26,017 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"name": "starcraft", "dataset": {"path": "../../datasets/starcraftimage", "image_format": "cifar10", "image_size": 224, "sensor_size": 56}, "cache_directory": null, "output": "./results/mvce-starcraft-dir/", "classifier": "./models/dirichlet-starcraft-cifar10/starcraft-20250613-033643.pklz", "classifier_feature": null, "calibration_map": "", "mask": null, "drop": {"name": "block-dropout"}, "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}
2025-06-13 16:56:26,050 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-06-13 16:56:26,395 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:1
2025-06-13 16:56:26,631 [INFO ] [loader.py:getDatasetSplits:24]	 Loading StarCraft dataset from ../../datasets/starcraftimage
2025-06-13 16:56:26,631 [INFO ] [starcraft.py:createStarCraftDataset:97]	 Using 10 StarCraft targets: ['Acolyte LE at Beginning', 'Acolyte LE at End', 'Abyssal Reef LE at Beginning', 'Abyssal Reef LE at End', 'Ascension to Aiur LE at Beginning', 'Ascension to Aiur LE at End', 'Mech Depot LE at Beginning', 'Mech Depot LE at End', 'Odyssey LE at Beginning', 'Odyssey LE at End']
2025-06-13 16:56:26,690 [INFO ] [baselines.py:getTransform:24]	 Resizing images from 32 to 224
2025-06-13 16:56:26,691 [INFO ] [baselines.py:getTransform:40]	 Applying 0.5 normalization
2025-06-13 16:56:27,747 [INFO ] [torch_utils.py:split:72]	 Split training from 50000 into 35000 training images and 15000 validation images. Found 10000 testing images.
2025-06-13 16:56:27,747 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset starcraft with 10000 test samples and 10 classes
2025-06-13 16:56:27,747 [INFO ] [mvce_dataset.py:<module>:134]	 Including Dirichlet decision maker
2025-06-13 16:56:27,748 [INFO ] [mvce_dataset.py:<module>:192]	 Adding 0 methods with discarded masks.
2025-06-13 16:56:27,748 [INFO ] [mvce_dataset.py:<module>:209]	 Using randon dropping with arguments {'name': 'block-dropout'}
2025-06-13 16:56:27,748 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-06-13 16:56:27,748 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-06-13 16:56:27,748 [INFO ] [calibration.py:__call__:310]	 Started running Dirichlet Network missing block-dropout in zero-one
2025-06-13 16:57:06,812 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.021258452907204628 for Dirichlet Network in 39.06300311698578 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    3,    1,   10,  357,  408,  464,  632, 8123],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1730, 0.2627, 0.3450, 0.4883, 0.5529, 0.6499, 0.7496, 0.8552, 0.9928])
        * Final bucket consistencies: tensor([0.0000, 0.6667, 1.0000, 0.5000, 0.5546, 0.6152, 0.7004, 0.8038, 0.9755])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1294,  706, 1203,  798, 1206,  793, 1067,  933, 1309,  691],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1273,  727, 1193,  807, 1240,  760, 1043,  956, 1307,  694],
       dtype=torch.int32)
        * Average consistency: 0.9214000105857849
    
2025-06-13 16:57:44,871 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.020271319895982742 for Dirichlet Network in 38.05750804604031 seconds with 10 buckets:
        * Non-zero buckets: tensor([3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,   13,  371,  392,  449,  638, 8135], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.3545, 0.4664, 0.5509, 0.6515, 0.7511, 0.8534, 0.9925])
        * Final bucket consistencies: tensor([0.5000, 0.3846, 0.5175, 0.6633, 0.7082, 0.8260, 0.9743])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1294,  706, 1203,  798, 1206,  793, 1067,  933, 1309,  691],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1276,  723, 1184,  817, 1252,  747, 1047,  953, 1318,  683],
       dtype=torch.int32)
        * Average consistency: 0.9229000210762024
    
2025-06-13 16:58:22,929 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.017276836559176445 for Dirichlet Network in 38.0564142530784 seconds with 10 buckets:
        * Non-zero buckets: tensor([3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   1,   15,  350,  413,  468,  638, 8115], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.3680, 0.4864, 0.5491, 0.6501, 0.7493, 0.8525, 0.9923])
        * Final bucket consistencies: tensor([0.0000, 0.4667, 0.5514, 0.6538, 0.7457, 0.8009, 0.9756])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1294,  706, 1203,  798, 1206,  793, 1067,  933, 1309,  691],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1271,  729, 1204,  797, 1246,  752, 1053,  947, 1312,  689],
       dtype=torch.int32)
        * Average consistency: 0.9247000217437744
    
2025-06-13 16:58:56,693 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.021159091964364052 for Dirichlet Network in 33.7628951149527 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    2,    9,  355,  440,  484,  648, 8060], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1605, 0.3165, 0.4801, 0.5525, 0.6506, 0.7500, 0.8515, 0.9924])
        * Final bucket consistencies: tensor([0.5000, 0.0000, 0.7778, 0.5944, 0.6068, 0.7087, 0.8241, 0.9756])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1294,  706, 1203,  798, 1206,  793, 1067,  933, 1309,  691],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1280,  720, 1188,  815, 1226,  771, 1037,  963, 1304,  696],
       dtype=torch.int32)
        * Average consistency: 0.9225999712944031
    
2025-06-13 16:58:56,694 [INFO ] [calibration.py:__call__:336]	 Finished running Dirichlet Network missing block-dropout in zero-one in 148.94531134306453 seconds
2025-06-13 16:58:56,694 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 1/1 experiments.
2025-06-13 16:58:56,694 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-starcraft-dir/starcraft-20250613-165626.csv
2025-06-13 16:58:56,694 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
