2025-06-16 12:24:57,248 [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": "./results/calibration/starcraft/starcraft-copy-first.csv", "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}
2025-06-16 12:24:57,310 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-06-16 12:24:57,626 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:1
2025-06-16 12:24:57,848 [INFO ] [loader.py:getDatasetSplits:24]	 Loading StarCraft dataset from ../../datasets/starcraftimage
2025-06-16 12:24:57,848 [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']
2025-06-16 12:24:57,882 [INFO ] [baselines.py:getTransform:24]	 Resizing images from 32 to 224
2025-06-16 12:24:57,882 [INFO ] [baselines.py:getTransform:40]	 Applying 0.5 normalization
2025-06-16 12:24:59,216 [INFO ] [torch_utils.py:split:72]	 Split training from 50000 into 35000 training images and 15000 validation images. Found 10000 testing images.
2025-06-16 12:24:59,216 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset starcraft with 10000 test samples and 10 classes
2025-06-16 12:24:59,218 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/starcraft/starcraft-copy-first.csv: {'Dirichlet Network': 0.5}
2025-06-16 12:24:59,218 [INFO ] [mvce_dataset.py:<module>:134]	 Including Dirichlet decision maker
2025-06-16 12:24:59,218 [INFO ] [mvce_dataset.py:<module>:192]	 Adding 0 methods with discarded masks.
2025-06-16 12:24:59,218 [INFO ] [mvce_dataset.py:<module>:209]	 Using randon dropping with arguments {'name': 'block-dropout'}
2025-06-16 12:24:59,218 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-06-16 12:24:59,218 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-06-16 12:24:59,219 [INFO ] [calibration.py:__call__:310]	 Started running Dirichlet Network missing block-dropout in zero-one
2025-06-16 12:25:32,486 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.009649467654526234 for Dirichlet Network in 33.2661195800174 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    3,    1,   47,  508,  566,  573,  822, 7478],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1740, 0.2423, 0.3220, 0.4817, 0.5528, 0.6516, 0.7507, 0.8511, 0.9897])
        * Final bucket consistencies: tensor([0.0000, 0.6667, 1.0000, 0.4681, 0.5846, 0.6696, 0.7696, 0.8698, 0.9842])
        * 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([1272,  728, 1194,  806, 1238,  762, 1042,  957, 1307,  694],
       dtype=torch.int32)
        * Average consistency: 0.9217000007629395
    
2025-06-16 12:26:05,444 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.012435908429324627 for Dirichlet Network in 32.95675123599358 seconds with 10 buckets:
        * Non-zero buckets: tensor([3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   5,   42,  502,  551,  628,  801, 7471], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.3608, 0.4804, 0.5488, 0.6498, 0.7522, 0.8520, 0.9895])
        * Final bucket consistencies: tensor([0.6000, 0.4048, 0.5876, 0.6624, 0.7994, 0.8739, 0.9833])
        * 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([1277,  722, 1183,  818, 1253,  746, 1046,  954, 1317,  684],
       dtype=torch.int32)
        * Average consistency: 0.9228000044822693
    
2025-06-16 12:26:38,370 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.0120976772159338 for Dirichlet Network in 32.92465076292865 seconds with 10 buckets:
        * Non-zero buckets: tensor([3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,   53,  498,  564,  628,  842, 7413], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.3325, 0.4788, 0.5512, 0.6491, 0.7507, 0.8524, 0.9900])
        * Final bucket consistencies: tensor([0.5000, 0.6038, 0.5763, 0.7074, 0.7882, 0.8670, 0.9856])
        * 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([1269,  731, 1206,  795, 1247,  751, 1053,  947, 1312,  689],
       dtype=torch.int32)
        * Average consistency: 0.925000011920929
    
2025-06-16 12:27:16,765 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.011408179998397827 for Dirichlet Network in 38.39381368900649 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    2,    1,   44,  513,  599,  640,  839, 7360],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1575, 0.2930, 0.3620, 0.4813, 0.5522, 0.6495, 0.7496, 0.8543, 0.9900])
        * Final bucket consistencies: tensor([0.0000, 0.0000, 1.0000, 0.6136, 0.5750, 0.6745, 0.8016, 0.8689, 0.9853])
        * 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([1278,  721, 1189,  814, 1227,  770, 1037,  963, 1306,  695],
       dtype=torch.int32)
        * Average consistency: 0.9221000075340271
    
2025-06-16 12:27:16,765 [INFO ] [calibration.py:__call__:336]	 Finished running Dirichlet Network missing block-dropout in zero-one in 137.54666808410548 seconds
2025-06-16 12:27:16,765 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 1/1 experiments.
2025-06-16 12:27:16,765 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-starcraft-dir/starcraft-20250616-122457.csv
2025-06-16 12:27:16,766 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
