Merge pull request #3269 from littletomatodonkey/dyg/fix_infer_bug

fix bug of inference

deploy/hubserving/readme.md

@@ -29,7 +29,7 @@ deploy/hubserving/ocr_system/
 ### 1. 准备环境
 ```shell
 # 安装paddlehub  
-pip3 install paddlehub==1.8.3 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ### 2. 下载推理模型

deploy/hubserving/readme_en.md

@@ -30,7 +30,7 @@ The following steps take the 2-stage series service as an example. If only the d
 ### 1. Prepare the environment
 ```shell
 # Install paddlehub  
-pip3 install paddlehub==1.8.3 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ### 2. Download inference model

tools/infer/predict_det.py

@@ -175,7 +175,7 @@ class TextDetector(object):
 
         st = time.time()
 
-        if args.benchmark:
+        if self.args.benchmark:
             self.autolog.times.start()
 
         data = transform(data, self.preprocess_op)
@@ -186,7 +186,7 @@ class TextDetector(object):
         shape_list = np.expand_dims(shape_list, axis=0)
         img = img.copy()
 
-        if args.benchmark:
+        if self.args.benchmark:
             self.autolog.times.stamp()
 
         self.input_tensor.copy_from_cpu(img)
@@ -195,7 +195,7 @@ class TextDetector(object):
         for output_tensor in self.output_tensors:
             output = output_tensor.copy_to_cpu()
             outputs.append(output)
-        if args.benchmark:
+        if self.args.benchmark:
             self.autolog.times.stamp()
 
         preds = {}
@@ -220,7 +220,7 @@ class TextDetector(object):
         else:
             dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
 
-        if args.benchmark:
+        if self.args.benchmark:
             self.autolog.times.end(stamp=True)
         et = time.time()
         return dt_boxes, et - st

tools/infer/predict_system.py

@@ -174,8 +174,6 @@ def main(args):
     logger.info("The predict total time is {}".format(time.time() - _st))
     logger.info("\nThe predict total time is {}".format(total_time))
 
-    img_num = text_sys.text_detector.det_times.img_num
-
 
 if __name__ == "__main__":
     args = utility.parse_args()