Save detection inference logs to file (#12042)

* Save detection inference logs to file

* Minor fix on save_log_path

* Formatted with black

* If logger is None: get_logger()

tools/infer/predict_det.py

@@ -32,11 +32,11 @@ from ppocr.data import create_operators, transform
 from ppocr.postprocess import build_post_process
 import json
 
-logger = get_logger()
-
 
 class TextDetector(object):
-    def __init__(self, args):
+    def __init__(self, args, logger=None):
+        if logger is None:
+            logger = get_logger()
         self.args = args
         self.det_algorithm = args.det_algorithm
         self.use_onnx = args.use_onnx
@@ -158,7 +158,7 @@ class TextDetector(object):
                 model_precision=args.precision,
                 batch_size=1,
                 data_shape="dynamic",
-                save_path=None,
+                save_path=None,  # not used if logger is not None
                 inference_config=self.config,
                 pids=pid,
                 process_name=None,
@@ -398,11 +398,21 @@ class TextDetector(object):
 if __name__ == "__main__":
     args = utility.parse_args()
     image_file_list = get_image_file_list(args.image_dir)
-    text_detector = TextDetector(args)
     total_time = 0
     draw_img_save_dir = args.draw_img_save_dir
     os.makedirs(draw_img_save_dir, exist_ok=True)
 
+    # logger
+    log_file = args.save_log_path
+    if os.path.isdir(args.save_log_path) or (
+        not os.path.exists(args.save_log_path) and args.save_log_path.endswith("/")
+    ):
+        log_file = os.path.join(log_file, "benchmark_detection.log")
+    logger = get_logger(log_file=log_file)
+
+    # create text detector
+    text_detector = TextDetector(args, logger)
+
     if args.warmup:
         img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
         for i in range(2):