rm some useless code

OCR/MGP-STR/test_final.py

@@ -239,12 +239,7 @@ def validation(model, criterion, evaluation_loader, converter, opt):
                 if out_pred == gt:
                     out_n_correct += 1
 
-                # calculate confidence score (= multiply of pred_max_prob)
-                try:
-                    confidence_score = char_preds_max_prob[index].cumprod(dim=0)[-1]
-                except:
-                    confidence_score = 0  # for empty pred case, when prune after "end of sentence" token ([s])
-                confidence_score_list.append(confidence_score)
+                confidence_score_list.append(char_confidence_score)
 
         elif opt.Transformer in ["char-str"]:
             attens, char_preds = model(image, is_eval=True) # final
@@ -393,8 +388,8 @@ def test(opt):
             _, accuracy_by_best_model, _, _, _, _, _, _ = validation(
                 model, criterion, evaluation_loader, converter, opt)
             log.write(eval_data_log)
-            print(f'{accuracy_by_best_model:0.3f}')
-            log.write(f'{accuracy_by_best_model:0.3f}\n')
+            print(f'{accuracy_by_best_model[0]:0.3f}')
+            log.write(f'{accuracy_by_best_model[0]:0.3f}\n')
             log.close()
 
 # https://github.com/clovaai/deep-text-recognition-benchmark/issues/125

OCR/MGP-STR/train_final_dist.py

@@ -79,10 +79,7 @@ def train(opt):
 
     if opt.saved_model != '':
         print(f'loading pretrained model from {opt.saved_model}')
-        if opt.FT:
-            model.load_state_dict(torch.load(opt.saved_model, map_location='cpu'), strict=True)
-        else:
-            model.load_state_dict(torch.load(opt.saved_model, map_location='cpu'), strict=True)
+        model.load_state_dict(torch.load(opt.saved_model, map_location='cpu'), strict=True)
 
     """ setup loss """
     criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device)  # ignore [GO] token = ignore index 0