2025-06-16 12:28:07,025 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"name": "cifar10", "dataset": {"path": "../../datasets/cifar10", "image_size": 224, "sensor_size": 56}, "cache_directory": null, "output": "./results/mvce-cifar10-dir/", "classifier": "./models/dirichlet-cifar10/cifar10-20250611-062342.pklz", "classifier_feature": null, "calibration_map": "./results/calibration/cifar10/cifar10-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 12:28:07,055 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-06-16 12:28:07,162 [INFO ] [mvce_dataset.py:<module>:93]	 Running on cuda:1
2025-06-16 12:28:07,421 [INFO ] [loader.py:getDatasetSplits:21]	 Loading CIFAR10 dataset from ../../datasets/cifar10
2025-06-16 12:28:07,421 [INFO ] [baselines.py:createCIFAR10Dataset:69]	 Using 10 CIFAR10 targets: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
2025-06-16 12:28:07,474 [INFO ] [baselines.py:getTransform:24]	 Resizing images from 32 to 224
2025-06-16 12:28:07,475 [INFO ] [baselines.py:getTransform:37]	 Applying CIFAR10 standard normalization
2025-06-16 12:28:08,621 [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 12:28:08,621 [INFO ] [mvce_dataset.py:<module>:106]	 Using dataset cifar10 with 10000 test samples and 10 classes
2025-06-16 12:28:08,623 [INFO ] [mvce_dataset.py:<module>:119]	 Found calibration map at ./results/calibration/cifar10/cifar10-copy-first.csv: {'Dirichlet Network': 1.0}
2025-06-16 12:28:08,623 [INFO ] [mvce_dataset.py:<module>:134]	 Including Dirichlet decision maker
2025-06-16 12:28:08,623 [INFO ] [mvce_dataset.py:<module>:192]	 Adding 0 methods with discarded masks.
2025-06-16 12:28:08,623 [INFO ] [mvce_dataset.py:<module>:209]	 Using randon dropping with arguments {'name': 'block-dropout'}
2025-06-16 12:28:08,623 [INFO ] [mvce_dataset.py:<module>:220]	 Considering action space zero-one
2025-06-16 12:28:08,623 [INFO ] [threading_utils.py:distributeTasks:72]	 Running a single task on the main thread
2025-06-16 12:28:08,623 [INFO ] [calibration.py:__call__:310]	 Started running Dirichlet Network missing block-dropout in zero-one
2025-06-16 12:29:06,427 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.050118207931518555 for Dirichlet Network in 57.802447609137744 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  65,  396,  626,  669,  673,  542,  556,  649, 5824],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1770, 0.2562, 0.3486, 0.4501, 0.5490, 0.6468, 0.7514, 0.8521, 0.9904])
        * Final bucket consistencies: tensor([0.4154, 0.4167, 0.4665, 0.5830, 0.6389, 0.7159, 0.7734, 0.8182, 0.9686])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1162,  891,  821,  906,  955,  898, 1309,  837, 1176, 1045],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1156,  879,  829,  776,  921,  859, 1558,  805, 1162, 1055],
       dtype=torch.int32)
        * Average consistency: 0.8294000029563904
    
2025-06-16 12:29:53,450 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.047317419201135635 for Dirichlet Network in 47.022037660935894 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  49,  426,  612,  649,  621,  595,  571,  644, 5833],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1795, 0.2573, 0.3498, 0.4504, 0.5477, 0.6489, 0.7491, 0.8526, 0.9908])
        * Final bucket consistencies: tensor([0.2653, 0.3873, 0.4788, 0.5855, 0.6522, 0.7210, 0.7443, 0.8137, 0.9717])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1162,  891,  821,  906,  955,  898, 1309,  837, 1176, 1045],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1187,  865,  785,  797,  924,  845, 1585,  786, 1151, 1075],
       dtype=torch.int32)
        * Average consistency: 0.8302000164985657
    
2025-06-16 12:30:26,675 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.04708549752831459 for Dirichlet Network in 33.22328256396577 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  69,  434,  620,  650,  635,  560,  563,  648, 5821],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1783, 0.2550, 0.3521, 0.4517, 0.5474, 0.6479, 0.7490, 0.8528, 0.9906])
        * Final bucket consistencies: tensor([0.2754, 0.3571, 0.4935, 0.5908, 0.6252, 0.7268, 0.7638, 0.8210, 0.9701])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1162,  891,  821,  906,  955,  898, 1309,  837, 1176, 1045],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1120,  879,  789,  810,  930,  860, 1563,  794, 1202, 1053],
       dtype=torch.int32)
        * Average consistency: 0.8277000188827515
    
2025-06-16 12:30:59,554 [INFO ] [calibration.py:computeMVCE:153]	 
        Computed MVCE 0.038359057158231735 for Dirichlet Network in 32.87733400496654 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  62,  393,  633,  671,  671,  595,  584,  641, 5750],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1791, 0.2554, 0.3513, 0.4500, 0.5471, 0.6474, 0.7513, 0.8520, 0.9909])
        * Final bucket consistencies: tensor([0.3387, 0.4097, 0.4597, 0.5544, 0.6066, 0.6992, 0.7671, 0.8565, 0.9750])
        * Actions: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)
        * Best Action Counts: tensor([1162,  891,  821,  906,  955,  898, 1309,  837, 1176, 1045],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1182,  890,  793,  765,  928,  852, 1587,  793, 1145, 1065],
       dtype=torch.int32)
        * Average consistency: 0.8270999789237976
    
2025-06-16 12:30:59,554 [INFO ] [calibration.py:__call__:336]	 Finished running Dirichlet Network missing block-dropout in zero-one in 170.93034038110636 seconds
2025-06-16 12:30:59,554 [INFO ] [mvce_dataset.py:<module>:234]	 Finished running 1/1 experiments.
2025-06-16 12:30:59,554 [INFO ] [mvce_dataset.py:<module>:239]	 Saving results to ./results/mvce-cifar10-dir/cifar10-20250616-122807.csv
2025-06-16 12:30:59,554 [INFO ] [mvce_dataset.py:<module>:249]	 Finished saving results
