def accuracy(label, val, vocab, r_vocab, lpr_patterns):
pred = decode_beams(val, r_vocab)
label_len = len(label)
acc, acc1 = 0, 0
num = 0
for i in range(label_len):
if not lpr_pattern_check(label[i].decode('utf-8'),
lpr_patterns): # GT label fails
print('GT label fails: ' + label[i].decode('utf-8'))
continue
best = pred[i]
#the edit distance show the number of steps that takes to change a string to the other
edd = edit_distance(encode(label[i].decode('utf-8'), vocab),
encode(best, vocab))
#they return two accuracies
if edd <= 1:
acc1 += 1
if label[i].decode('utf-8') == best:
acc += 1
else:
if label[i].decode('utf-8') not in pred[i]:
print('Check GT label: ' + label[i].decode('utf-8'))
print(label[i].decode('utf-8') + ' -- ' + best +
' Edit Distance: ' + str(edd))
num += 1
return float(acc), float(acc1), num
def accuracy(label, val, vocab, r_vocab):
pred = decode_beams(val, r_vocab)
label_len = len(label)
acc, acc1 = 0, 0
num = 0
for i in range(label_len):
if not lpr_pattern_check(label[i].decode('utf-8')): # GT label fails
print('GT label fails: ' + label[i].decode('utf-8'))
continue
best = pred[i]
edd = edit_distance(encode(label[i].decode('utf-8'), vocab),
encode(best, vocab))
if edd <= 1:
acc1 += 1
if label[i].decode('utf-8') == best:
acc += 1
else:
if label[i].decode('utf-8') not in pred[i]:
print('Check GT label: ' + label[i].decode('utf-8'))
print(label[i].decode('utf-8') + ' -- ' + best +
' Edit Distance: ' + str(edd))
num += 1
return float(acc), float(acc1), num