utils.py

import random
import numpy as np
import torch

def seed_worker(worker_id):
    """
    Used in generating seed for the worker of torch.utils.data.Dataloader
    """
    worker_seed = torch.initial_seed() % 2**32
    np.random.seed(worker_seed)
    random.seed(worker_seed)


def set_seed(seed):
    """ 
    set initial seed for reproduction
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

def compute_det_curve(target_scores, nontarget_scores):
    n_scores = target_scores.size + nontarget_scores.size
    all_scores = np.concatenate((target_scores, nontarget_scores))
    labels = np.concatenate((np.ones(target_scores.size), np.zeros(nontarget_scores.size)))

    # Sort labels based on scores
    indices = np.argsort(all_scores, kind='mergesort')
    labels = labels[indices]

    # Compute false rejection and false acceptance rates
    tar_trial_sums = np.cumsum(labels)
    nontarget_trial_sums = nontarget_scores.size - (np.arange(1, n_scores + 1) - tar_trial_sums)

    frr = np.concatenate((np.atleast_1d(0), tar_trial_sums / target_scores.size))  # false rejection rates
    far = np.concatenate((np.atleast_1d(1), nontarget_trial_sums / nontarget_scores.size))  # false acceptance rates
    thresholds = np.concatenate((np.atleast_1d(all_scores[indices[0]] - 0.001), all_scores[indices]))  # Thresholds are the sorted scores

    return frr, far, thresholds

def compute_eer(target_scores, nontarget_scores):
    target_scores = np.array(target_scores).flatten()
    nontarget_scores = np.array(nontarget_scores).flatten()
    frr, far, thresholds = compute_det_curve(target_scores, nontarget_scores)
    abs_diffs = np.abs(frr - far)
    min_index = np.argmin(abs_diffs)
    eer = np.mean((frr[min_index], far[min_index]))
    return eer, thresholds[min_index]