small fix

Author: HaoyangLee

tools/infer/text/predict_cls.py

@@ -38,7 +38,7 @@ def __init__(self, args):
         self.visualize_output = args.visualize_output
         # self.batch_mode = args.cls_batch_mode and (self.batch_num > 1)
         _logger.info(
-            "recognize in {} mode {}".format(
+            "classify text direction in {} mode {}".format(
                 "batch" if self.batch_mode else "serial",
                 "batch_size: " + str(self.batch_num) if self.batch_mode else "",
             )
@@ -99,7 +99,7 @@ def __call__(self, img_or_path_list: list):
         assert isinstance(
             img_or_path_list, list
         ), "Input for text direction classification must be list of images or image paths."
-        _logger.info("num images for cls: ", len(img_or_path_list))
+        _logger.info(f"num images for cls: {len(img_or_path_list)}")
         if self.batch_mode:
             cls_res_all, all_rotated_imgs = self.run_batchwise(img_or_path_list)
         else:
@@ -263,4 +263,4 @@ def save_cls_res(cls_res_all, img_paths, include_score=False, save_path="./cls_r
     save_cls_res(cls_res_all, img_paths, include_score=True, save_path=save_fp)
     # print('All cls res: ', cls_res_all)
     print("Done! Text direction classification results saved in ", save_dir)
-    print("Time cost: ", t, "FPS: ", len(img_paths) / t)
+    print("CLS time: ", t, "FPS: ", len(img_paths) / t)

tools/infer/text/predict_system.py

@@ -97,6 +97,7 @@ def __call__(self, img_or_path: Union[str, np.ndarray], do_visualize=True):
             cls_start = time()
             _, crops = self.direction_classify(crops)
             time_profile["cls"] = time() - cls_start
+            _logger.info(f"\nCls time: {time_profile['cls']}")
 
         # recognize cropped images
         rs = time()

tools/infer/text/preprocess.py

@@ -117,21 +117,19 @@ def __init__(self, task="det", algo="DB", **kwargs):
                     f"batch mode = {batch_mode}."
                 )
 
-            _logger.info(
-                f"Pick optimal preprocess hyper-params for {task} algo {algo}:\n",
-                "\n".join(
-                    [
-                        f"{k}:\t{str(v)}"
-                        for k, v in dict(
-                            target_height=target_height,
-                            target_width=target_width,
-                            padding=padding,
-                            keep_ratio=keep_ratio,
-                            norm_before_pad=norm_before_pad,
-                        ).items()
-                    ]
-                ),
+            hparam = "\n".join(
+                [
+                    f"{k}:\t{str(v)}"
+                    for k, v in dict(
+                        target_height=target_height,
+                        target_width=target_width,
+                        padding=padding,
+                        keep_ratio=keep_ratio,
+                        norm_before_pad=norm_before_pad,
+                    ).items()
+                ]
             )
+            _logger.info(f"Pick optimal preprocess hyper-params for {task} algo {algo}:\n{hparam}")
 
             pipeline = [
                 {"DecodeImage": {"img_mode": "RGB", "keep_ori": True, "to_float32": False}},