eval with alphanumeric and case sensitive setting

dataset.py

@@ -122,7 +122,7 @@ def hierarchical_dataset(root, opt, select_data='/'):
                 dataset_list.append(dataset)
 
     concatenated_dataset = ConcatDataset(dataset_list)
-    
+
     return concatenated_dataset, dataset_log
 
 

test.py

@@ -2,6 +2,7 @@
 import time
 import string
 import argparse
+import re
 
 import torch
 import torch.backends.cudnn as cudnn
@@ -133,20 +134,28 @@ def validation(model, criterion, evaluation_loader, converter, opt):
                 pred = pred[:pred_EOS]  # prune after "end of sentence" token ([s])
                 pred_max_prob = pred_max_prob[:pred_EOS]
 
+            # To evaluate the model with 'alphanumeric and case insensitve setting'
+            if opt.sensitive and opt.data_filtering_off:
+                pred = pred.lower()
+                gt = gt.lower()
+                alphanumeric_case_insensitve = '0123456789abcdefghijklmnopqrstuvwxyz'
+                out_of_alphanumeric_case_insensitve = f'[^{alphanumeric_case_insensitve}]'
+                pred = re.sub(out_of_alphanumeric_case_insensitve, '', pred)
+
             if pred == gt:
                 n_correct += 1
 
             '''
             (old version) ICDAR2017 DOST Normalized Edit Distance https://rrc.cvc.uab.es/?ch=7&com=tasks
-            "For each word we calculate the normalized edit distance to the length of the ground truth transcription." 
+            "For each word we calculate the normalized edit distance to the length of the ground truth transcription."
             if len(gt) == 0:
                 norm_ED += 1
             else:
                 norm_ED += edit_distance(pred, gt) / len(gt)
             '''
-            
-            # ICDAR2019 Normalized Edit Distance 
-            if len(gt) == 0 or len(pred) ==0:
+
+            # ICDAR2019 Normalized Edit Distance
+            if len(gt) == 0 or len(pred) == 0:
                 norm_ED += 0
             elif len(gt) > len(pred):
                 norm_ED += 1 - edit_distance(pred, gt) / len(gt)
@@ -162,7 +171,7 @@ def validation(model, criterion, evaluation_loader, converter, opt):
             # print(pred, gt, pred==gt, confidence_score)
 
     accuracy = n_correct / float(length_of_data) * 100
-    norm_ED = norm_ED / float(length_of_data) # ICDAR2019 Normalized Edit Distance
+    norm_ED = norm_ED / float(length_of_data)  # ICDAR2019 Normalized Edit Distance
 
     return valid_loss_avg.val(), accuracy, norm_ED, preds_str, confidence_score_list, labels, infer_time, length_of_data