Merge branch 'dygraph' into dygraph

tools/infer/predict_rec.py

@@ -119,6 +119,10 @@ class TextRecognizer(object):
             resized_w = imgW
         else:
             resized_w = int(math.ceil(imgH * ratio))
+        if self.rec_algorithm == 'RARE':
+            if resized_w > self.rec_image_shape[2]:
+                resized_w = self.rec_image_shape[2]
+            imgW = self.rec_image_shape[2]
         resized_image = cv2.resize(img, (resized_w, imgH))
         resized_image = resized_image.astype('float32')
         resized_image = resized_image.transpose((2, 0, 1)) / 255

tools/infer/utility.py

@@ -312,12 +312,26 @@ def create_predictor(args, mode, logger):
         input_names = predictor.get_input_names()
         for name in input_names:
             input_tensor = predictor.get_input_handle(name)
+        output_tensors = get_output_tensors(args, mode, predictor)
+        return predictor, input_tensor, output_tensors, config
+
+
+def get_output_tensors(args, mode, predictor):
     output_names = predictor.get_output_names()
     output_tensors = []
+    if mode == "rec" and args.rec_algorithm == "CRNN":
+        output_name = 'softmax_0.tmp_0'
+        if output_name in output_names:
+            return [predictor.get_output_handle(output_name)]
+        else:
             for output_name in output_names:
                 output_tensor = predictor.get_output_handle(output_name)
                 output_tensors.append(output_tensor)
-        return predictor, input_tensor, output_tensors, config
+    else:
+        for output_name in output_names:
+            output_tensor = predictor.get_output_handle(output_name)
+            output_tensors.append(output_tensor)
+    return output_tensors
 
 
 def get_infer_gpuid():