utils.py

import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import torch.nn.functional as F
import pdb
from sklearn.metrics import confusion_matrix

def Entropy(input_):
    bs = input_.size(0)
    epsilon = 1e-5
    entropy = -input_ * torch.log(input_ + epsilon)
    entropy = torch.sum(entropy, dim=1)
    return entropy 


def cal_acc(loader, netF, netB, netC, flag=False):
    start_test = True
    with torch.no_grad():
        iter_test = iter(loader)
        for i in range(len(loader)):
            data = iter_test.next()
            inputs = data[0]
            labels = data[1]
            inputs = inputs.cuda()
            outputs = netC(netB(netF(inputs)))
            if start_test:
                all_output = outputs.float().cpu()
                all_label = labels.float()
                start_test = False
            else:
                all_output = torch.cat((all_output, outputs.float().cpu()), 0)
                all_label = torch.cat((all_label, labels.float()), 0)
    _, predict = torch.max(all_output, 1)
    accuracy = torch.sum(torch.squeeze(predict).float() == all_label).item() / float(all_label.size()[0])
    matrix = confusion_matrix(all_label, torch.squeeze(predict).float())
    acc = matrix.diagonal()/matrix.sum(axis=1) * 100
    aacc = acc.mean()
    aa = [str(np.round(i, 2)) for i in acc]
    acc = ' '.join(aa)
    return aacc, acc