@MariosOreo Thanks for the catch! I’ve fixed it in my post. 
@Deb_Prakash_Chatterj You could count it manually of create a confusion matrix first.
Based on the confusion matrix you could then calculate the stats.
Here is a small example. I tried to validate the results, but you should definitely have another look at it:
nb_samples = 20
nb_classes = 4
output = torch.randn(nb_samples, nb_classes)
pred = torch.argmax(output, 1)
target = torch.randint(0, nb_classes, (nb_samples,))

conf_matrix = torch.zeros(nb_classes, nb_classes)
for t, p in zip(target, pred):
    conf_matrix[t, p] += 1

print('Confusion matrix\n', conf_matrix)

TP = conf_matrix.diag()
for c in range(nb_classes):
    idx = torch.ones(nb_classes).byte()
    idx[c] = 0
    # all non-class samples classified as non-class
    TN = conf_matrix[idx.nonzero()[:, None], idx.nonzero()].sum() #conf_matrix[idx[:, None], idx].sum() - conf_matrix[idx, c].sum()
    # all non-class samples classified as class
    FP = conf_matrix[idx, c].sum()
    # all class samples not classified as class
    FN = conf_matrix[c, idx].sum()
    
    print('Class {}\nTP {}, TN {}, FP {}, FN {}'.format(
        c, TP[c], TN, FP, FN))