Merge pull request #7206 from andyjpaddle/fix_vl

Fix visionlan predict format

doc/doc_ch/algorithm_rec_visionlan.md

@@ -101,7 +101,7 @@ python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pre
 执行如下命令进行模型推理：
 
 ```shell
-python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt'
+python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' --use_space_char=False
 # 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_words_en/'。
 ```
 
@@ -110,7 +110,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png'
 执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
 结果如下：
 ```shell
-Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982)
+Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.9999493)
 ```
 
 **注意**：

doc/doc_en/algorithm_rec_visionlan_en.md

@@ -90,7 +90,7 @@ After the conversion is successful, there are three files in the directory:
 For VisionLAN text recognition model inference, the following commands can be executed:
 
 ```
-python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt'
+python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' --use_space_char=False
 ```
 
 ![](../imgs_words/en/word_2.png)
@@ -98,7 +98,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png'
 After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
 The result is as follows:
 ```shell
-Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982)
+Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.9999493)
 ```
 
 <a name="4-2"></a>

ppocr/losses/__init__.py

@@ -67,7 +67,7 @@ def build_loss(config):
         'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss',
         'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
         'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss',
-        'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss','StrokeFocusLoss'
+        'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss', 'StrokeFocusLoss'
     ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/postprocess/rec_postprocess.py

@@ -780,7 +780,7 @@ class VLLabelDecode(BaseRecLabelDecode):
             ) + length[i])].topk(1)[0][:, 0]
             preds_prob = paddle.exp(
                 paddle.log(preds_prob).sum() / (preds_prob.shape[0] + 1e-6))
-            text.append((preds_text, preds_prob))
+            text.append((preds_text, preds_prob.numpy()[0]))
         if label is None:
             return text
         label = self.decode(label)

tools/program.py

@@ -508,10 +508,10 @@ def eval(model,
                                 1, 2, 0).astype(np.uint8)
                             fm_lr = (lr_img[i].numpy() * 255).transpose(
                                 1, 2, 0).astype(np.uint8)
-                            cv2.imwrite("output/images/{}_{}_sr.jpg".format(sum_images,
-                                                                            i), fm_sr)
-                            cv2.imwrite("output/images/{}_{}_lr.jpg".format(sum_images,
-                                                                            i), fm_lr) 
+                            cv2.imwrite("output/images/{}_{}_sr.jpg".format(
+                                sum_images, i), fm_sr)
+                            cv2.imwrite("output/images/{}_{}_lr.jpg".format(
+                                sum_images, i), fm_lr)
                     else:
                         preds = model(images)
             else:
@@ -529,10 +529,10 @@ def eval(model,
                             1, 2, 0).astype(np.uint8)
                         fm_lr = (lr_img[i].numpy() * 255).transpose(
                             1, 2, 0).astype(np.uint8)
-                        cv2.imwrite("output/images/{}_{}_sr.jpg".format(sum_images,
-                                                                        i), fm_sr)
-                        cv2.imwrite("output/images/{}_{}_lr.jpg".format(sum_images,
-                                                                        i), fm_lr)                    
+                        cv2.imwrite("output/images/{}_{}_sr.jpg".format(
+                            sum_images, i), fm_sr)
+                        cv2.imwrite("output/images/{}_{}_lr.jpg".format(
+                            sum_images, i), fm_lr)
                 else:
                     preds = model(images)