polish code

tools/infer/predict_rec.py

@@ -196,15 +196,15 @@ class TextRecognizer(object):
                     norm_img_batch.append(norm_img[0])
 
             norm_img_batch = np.concatenate(norm_img_batch, axis=0)
+            norm_img_batch = norm_img_batch.copy()
 
+            if self.loss_type == "srn":
                 encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
-
                 gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
-
-            gsrm_slf_attn_bias1_list = np.concatenate(gsrm_slf_attn_bias1_list)
-
-            gsrm_slf_attn_bias2_list = np.concatenate(gsrm_slf_attn_bias2_list)
-
+                gsrm_slf_attn_bias1_list = np.concatenate(
+                    gsrm_slf_attn_bias1_list)
+                gsrm_slf_attn_bias2_list = np.concatenate(
+                    gsrm_slf_attn_bias2_list)
                 starttime = time.time()
 
                 norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
@@ -217,11 +217,15 @@ class TextRecognizer(object):
                     gsrm_slf_attn_bias2_list)
 
                 inputs = [
-                norm_img_batch, encoder_word_pos_list, gsrm_slf_attn_bias1_list,
-                gsrm_slf_attn_bias2_list, gsrm_word_pos_list
+                    norm_img_batch, encoder_word_pos_list,
+                    gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list,
+                    gsrm_word_pos_list
                 ]
 
                 self.predictor.run(inputs)
+            else:
+                self.input_tensor.copy_from_cpu(norm_img_batch)
+                self.predictor.zero_copy_run()
 
             if self.loss_type == "ctc":
                 rec_idx_batch = self.output_tensors[0].copy_to_cpu()

tools/infer/utility.py

@@ -59,10 +59,10 @@ def parse_args():
     parser.add_argument("--det_sast_polygon", type=bool, default=False)
 
     #params for text recognizer
-    parser.add_argument("--rec_algorithm", type=str, default='SRN')
+    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
     parser.add_argument("--rec_model_dir", type=str)
-    parser.add_argument("--rec_image_shape", type=str, default="3, 64, 256")
-    parser.add_argument("--rec_char_type", type=str, default='en')
+    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
+    parser.add_argument("--rec_char_type", type=str, default='ch')
     parser.add_argument("--rec_batch_num", type=int, default=30)
     parser.add_argument("--max_text_length", type=int, default=25)
     parser.add_argument(
@@ -107,11 +107,9 @@ def create_predictor(args, mode):
 
     # use zero copy
     config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
-    #config.switch_use_feed_fetch_ops(False)
-    config.switch_use_feed_fetch_ops(True)
+    config.switch_use_feed_fetch_ops(False)
     predictor = create_paddle_predictor(config)
     input_names = predictor.get_input_names()
-    print(input_names)
     for name in input_names:
         input_tensor = predictor.get_input_tensor(name)
     output_names = predictor.get_output_names()

tools/infer_rec.py

@@ -162,10 +162,7 @@ def main():
 
     fluid.io.save_inference_model(
         "./output/",
-        feeded_var_names=[
-            'image', 'encoder_word_pos', 'gsrm_slf_attn_bias1',
-            'gsrm_slf_attn_bias2', 'gsrm_word_pos'
-        ],
+        feeded_var_names=['image'],
         target_vars=target_var,
         executor=exe,
         main_program=eval_prog,