record.py

import numpy as np
import torch
from operator import truediv

def evaluate_accuracy(data_iter, net, loss, device):
    acc_sum, n = 0.0, 0
    with torch.no_grad():
        for X, y in data_iter:
            test_l_sum, test_num = 0, 0
            #X = X.permute(0, 3, 1, 2)
            X = X.to(device)
            y = y.to(device)
            net.eval() 
            y_hat = net(X)
            l = loss(y_hat, y.long())
            acc_sum += (y_hat.argmax(dim=1) == y.to(device)).float().sum().cpu().item()
            test_l_sum += l
            test_num += 1
            net.train() 
            n += y.shape[0]
    return [acc_sum / n, test_l_sum] # / test_num]


def aa_and_each_accuracy(confusion_matrix):
    list_diag = np.diag(confusion_matrix)
    list_raw_sum = np.sum(confusion_matrix, axis=1)
    each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum))
    average_acc = np.mean(each_acc)
    return each_acc, average_acc



def record_output(oa_ae, aa_ae, kappa_ae, element_acc_ae, path):
    f = open(path, 'w')
    sentence0 = 'OAs for each iteration are:' + str(oa_ae) + '\n'
    f.write(sentence0)
    sentence1 = 'AAs for each iteration are:' + str(aa_ae) + '\n'
    f.write(sentence1)
    sentence2 = 'KAPPAs for each iteration are:' + str(kappa_ae) + '\n' + '\n'
    f.write(sentence2)
    sentence3 = 'mean_OA ± std_OA is: ' + str(np.mean(oa_ae)) + ' ± ' + str(np.std(oa_ae)) + '\n'
    f.write(sentence3)
    sentence4 = 'mean_AA ± std_AA is: ' + str(np.mean(aa_ae)) + ' ± ' + str(np.std(aa_ae)) + '\n'
    f.write(sentence4)
    sentence5 = 'mean_KAPPA ± std_KAPPA is: ' + str(np.mean(kappa_ae)) + ' ± ' + str(np.std(kappa_ae)) + '\n' + '\n'
    f.write(sentence5)
    
    element_mean = np.mean(element_acc_ae, axis=0)
    element_std = np.std(element_acc_ae, axis=0)
    sentence8 = "Mean of all elements in confusion matrix: " + str(element_mean) + '\n'
    f.write(sentence8)
    sentence9 = "Standard deviation of all elements in confusion matrix: " + str(element_std) + '\n'
    f.write(sentence9)
    f.close()