utils.py

import torch
import math
import numpy as np
from sklearn.metrics import roc_auc_score


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()

        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        #print(target, pred, batch_size, correct, res)
        return res

def save_model(model, optimizer, conf, epoch, save_file):
    print('==> Saving...',file=conf.log_writter)
    state = {
        'model': model.state_dict(),
        'optimizer': optimizer.state_dict(),
        'epoch': epoch,
    }
    torch.save(state, save_file)
    del state



def computeAUROC(dataGT, dataPRED, classCount=14):
    outAUROC = []

    datanpGT = dataGT.cpu().numpy()
    datanpPRED = dataPRED.cpu().numpy()

    for i in range(classCount):
        outAUROC.append(roc_auc_score(datanpGT[:, i], datanpPRED[:, i]))

    return outAUROC