2025-05-22 10:52:53,534 [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": ["Blond_Hair"]}, "output": "./results/ece/celeba/Blond_Hair/", "classifier": "./models/celeba/Blond_Hair/celeba-20250508-133714.pklz", "cuda_index": 0, "batch_size": 100, "buckets": 10, "seed": 1337, "verbose": 1}
2025-05-22 10:52:53,561 [INFO ] [util.py:selectDevice:87]	 Using cuda:0 for tensor calculations
2025-05-22 10:52:53,666 [INFO ] [ece_dataset.py:<module>:61]	 Running on cuda:0
2025-05-22 10:52:53,666 [INFO ] [loader.py:getDatasetSplits:26]	 Loading CelebA dataset from ../../datasets/CelebAMask/256/img
2025-05-22 10:52:58,585 [INFO ] [celeba.py:createCelebADataset:139]	 Found 1 CelebA attributes for 30000 images: ['Blond_Hair']
2025-05-22 10:52:58,640 [INFO ] [celeba.py:createCelebADataset:149]	 Loading 26000 training images, 2000 validation images, and 2000 testing images
2025-05-22 10:52:58,640 [INFO ] [ece_dataset.py:<module>:69]	 Using dataset celeba with 2000 test samples and 1 classes
2025-05-22 10:52:58,884 [INFO ] [ece_dataset.py:<module>:80]	 Setting classifier activation function to sigmoid for single class
2025-05-22 10:53:02,549 [INFO ] [calibration.py:computeECE:245]	 
        Computed ECE 0.03970574960112572 in 3.6639034040272236 seconds with 10 buckets:
        * Non-zero buckets: tensor([5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  22,   32,   28,   46, 1872], dtype=torch.int32) totaling 2000 samples
        * Final bucket confidences: tensor([0.5517, 0.6517, 0.7550, 0.8500, 0.9948])
        * Final bucket accuracy: tensor([0.4091, 0.5938, 0.6786, 0.5652, 0.9631])
        * Label counts: tensor([1668,  332], dtype=torch.int32)
        * Prediction Action Counts: tensor([1750,  250], dtype=torch.int32)
        * Average accuracy: 0.9380000233650208
    
