2025-05-22 10:54:07,329 [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", "targets": ["Eyeglasses"]}, "output": "./results/ece/celeba/Eyeglasses/", "classifier": "./models/celeba/Eyeglasses/celeba-20250508-134104.pklz", "cuda_index": 0, "batch_size": 100, "buckets": 10, "seed": 1337, "verbose": 1}
2025-05-22 10:54:07,362 [INFO ] [util.py:selectDevice:87]	 Using cuda:0 for tensor calculations
2025-05-22 10:54:07,524 [INFO ] [ece_dataset.py:<module>:61]	 Running on cuda:0
2025-05-22 10:54:07,524 [INFO ] [loader.py:getDatasetSplits:26]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 10:54:12,454 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Eyeglasses']
2025-05-22 10:54:12,509 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 10:54:12,509 [INFO ] [ece_dataset.py:<module>:69]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-22 10:54:12,745 [INFO ] [ece_dataset.py:<module>:80]	 Setting classifier activation function to sigmoid for single class
2025-05-22 10:54:16,584 [INFO ] [calibration.py:computeECE:245]	 
        Computed ECE 0.005266542546451092 in 3.8375077177770436 seconds with 10 buckets:
        * Non-zero buckets: tensor([6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,    3,    6, 1989], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.6477, 0.7684, 0.8314, 0.9990])
        * Final bucket accuracy: tensor([0.5000, 0.6667, 0.5000, 0.9950])
        * Label counts: tensor([1898,  102], dtype=torch.int32)
        * Prediction Action Counts: tensor([1909,   91], dtype=torch.int32)
        * Average accuracy: 0.9925000071525574
    
