添加RFL CNT分支infer支持

ppocr/modeling/heads/rec_rfl_head.py

@@ -103,7 +103,6 @@ class RFLHead(nn.Layer):
             else:
                 seq_outputs = self.seq_head(seq_inputs, None,
                                             self.batch_max_legnth)
+            return cnt_outputs, seq_outputs
         else:
-            seq_outputs = None
-
-        return cnt_outputs, seq_outputs
+            return cnt_outputs

ppocr/postprocess/rec_postprocess.py

@@ -287,9 +287,8 @@ class RFLLabelDecode(BaseRecLabelDecode):
         return result_list
 
     def __call__(self, preds, label=None, *args, **kwargs):
-        cnt_pred, preds = preds
-        if preds is not None:
-
+        if len(preds) == 2:
+            cnt_pred, preds = preds
             if isinstance(preds, paddle.Tensor):
                 preds = preds.numpy()
             preds_idx = preds.argmax(axis=2)
@@ -302,9 +301,12 @@ class RFLLabelDecode(BaseRecLabelDecode):
             return text, label
 
         else:
+            cnt_pred = preds
+            if isinstance(cnt_pred, paddle.Tensor):
+                cnt_pred = cnt_pred.numpy()
             cnt_length = []
             for lens in cnt_pred:
-                length = round(paddle.sum(lens).item())
+                length = round(np.sum(lens))
                 cnt_length.append(length)
             if label is None:
                 return cnt_length

tools/infer_rec.py

@@ -97,7 +97,8 @@ def main():
             elif config['Architecture']['algorithm'] == "SAR":
                 op[op_name]['keep_keys'] = ['image', 'valid_ratio']
             elif config['Architecture']['algorithm'] == "RobustScanner":
-                op[op_name]['keep_keys'] = ['image', 'valid_ratio', 'word_positons']
+                op[op_name][
+                    'keep_keys'] = ['image', 'valid_ratio', 'word_positons']
             else:
                 op[op_name]['keep_keys'] = ['image']
         transforms.append(op)
@@ -136,9 +137,10 @@ def main():
             if config['Architecture']['algorithm'] == "RobustScanner":
                 valid_ratio = np.expand_dims(batch[1], axis=0)
                 word_positons = np.expand_dims(batch[2], axis=0)
-                img_metas = [paddle.to_tensor(valid_ratio),
-                            paddle.to_tensor(word_positons),
-                            ]
+                img_metas = [
+                    paddle.to_tensor(valid_ratio),
+                    paddle.to_tensor(word_positons),
+                ]
             images = np.expand_dims(batch[0], axis=0)
             images = paddle.to_tensor(images)
             if config['Architecture']['algorithm'] == "SRN":
@@ -160,6 +162,10 @@ def main():
                             "score": float(post_result[key][0][1]),
                         }
                 info = json.dumps(rec_info, ensure_ascii=False)
+            elif isinstance(post_result, list) and isinstance(post_result[0],
+                                                              int):
+                # for RFLearning CNT branch 
+                info = str(post_result[0])
             else:
                 if len(post_result[0]) >= 2:
                     info = post_result[0][0] + "\t" + str(post_result[0][1])