I’m using the same code as in the link.

  
      github.com
  
  
    meetshah1995/pytorch-semseg/blob/master/ptsemseg/metrics.py 29
# Adapted from score written by wkentaro
# https://github.com/wkentaro/pytorch-fcn/blob/master/torchfcn/utils.py

import numpy as np


class runningScore(object):
    def __init__(self, n_classes):
        self.n_classes = n_classes
        self.confusion_matrix = np.zeros((n_classes, n_classes))

    def _fast_hist(self, label_true, label_pred, n_class):
        mask = (label_true >= 0) & (label_true < n_class)
        hist = np.bincount(
            n_class * label_true[mask].astype(int) + label_pred[mask], minlength=n_class ** 2
        ).reshape(n_class, n_class)
        return hist

    def update(self, label_trues, label_preds):
        for lt, lp in zip(label_trues, label_preds):


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// Code To Calculate Accuarcy.
import numpy as np
class runningScore(object):
def init(self, n_classes):
self.n_classes = n_classes
self.confusion_matrix = np.zeros((n_classes, n_classes))
def _fast_hist(self, label_true, label_pred, n_class):
    mask = (label_true >= 0) & (label_true < n_class)
    hist = np.bincount(
        n_class * label_true[mask].astype(int) + label_pred[mask], minlength=n_class ** 2
    ).reshape(n_class, n_class)
    return hist

def update(self, label_trues, label_preds):
    for lt, lp in zip(label_trues, label_preds):
        self.confusion_matrix += self._fast_hist(lt.flatten(), lp.flatten(), self.n_classes)

def get_scores(self):
    """Returns accuracy score evaluation result.
        - overall accuracy
        - mean accuracy
        - mean IU
        - fwavacc
    """
    hist = self.confusion_matrix
    acc = np.diag(hist).sum() / hist.sum()
    acc_cls = np.diag(hist) / hist.sum(axis=1)
    acc_cls = np.nanmean(acc_cls)
    iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))
    mean_iu = np.nanmean(iu)
    freq = hist.sum(axis=1) / hist.sum()
    fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
    cls_iu = dict(zip(range(self.n_classes), iu))

    return (
        {
            "Overall Acc: \t": acc,
            "Mean Acc : \t": acc_cls,
            "FreqW Acc : \t": fwavacc,
            "Mean IoU : \t": mean_iu,
        },
        cls_iu,
    )

def reset(self):
    self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))

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