support tensorrt

tools/infer/predict_rec.py

@@ -42,6 +42,7 @@ class TextRecognizer(object):
         self.rec_algorithm = args.rec_algorithm
         self.text_len = args.max_text_length
         self.use_zero_copy_run = args.use_zero_copy_run
+        self.benchmark = args.enable_benchmark
         char_ops_params = {
             "character_type": args.rec_char_type,
             "character_dict_path": args.rec_char_dict_path,
@@ -62,8 +63,8 @@ class TextRecognizer(object):
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
         assert imgC == img.shape[2]
-        #if self.character_type == "ch":
-            #imgW = int((32 * max_wh_ratio))
+        if self.character_type == "ch" and not self.benchmark:
+            imgW = int((32 * max_wh_ratio))
         h, w = img.shape[:2]
         ratio = w / float(h)
         if math.ceil(imgH * ratio) > imgW:
@@ -313,13 +314,17 @@ def main(args):
             continue
         valid_image_file_list.append(image_file)
         img_list.append(img)
-
-    rec_res, predict_time = text_recognizer(img_list)
-    """
+    try:
+        rec_res, predict_time = text_recognizer(img_list)
     except Exception as e:
         print(e)
+        logger.info(
+            "ERROR!!!! \n"
+            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
+            "If your model has tps module:  "
+            "TPS does not support variable shape.\n"
+            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
         exit()
-    """
     for ino in range(len(img_list)):
         print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
     print("Total predict time for %d images:%.3f" %

tools/infer/predict_system.py

@@ -154,11 +154,7 @@ def main(args):
                 scores = [rec_res[i][1] for i in range(len(rec_res))]
 
                 draw_img = draw_ocr(
-                    image,
-                    boxes,
-                    txts,
-                    scores,
-                    drop_score=drop_score)
+                    image, boxes, txts, scores, drop_score=drop_score)
                 draw_img_save = "./inference_results/"
                 if not os.path.exists(draw_img_save):
                     os.makedirs(draw_img_save)
@@ -171,20 +167,20 @@ def main(args):
         test_num = 10
         test_time = 0.0
         for i in range(0, test_num + 10):
-            #inputs = np.random.rand(640, 640, 3).astype(np.float32)
-            #print(image_file_list)
             image_file = image_file_list[0]
             inputs = cv2.imread(image_file)
             inputs = cv2.resize(inputs, (int(640), int(640)))
             start_time = time.time()
-            dt_boxes,rec_res = text_sys(inputs)
+            dt_boxes, rec_res = text_sys(inputs)
             if i >= 10:
                 test_time += time.time() - start_time
             time.sleep(0.01)
         fp_message = "FP16" if args.use_fp16 else "FP32"
         trt_msg = "using tensorrt" if args.use_tensorrt else "not using tensorrt"
-        print("model\t{0}\t{1}\tbatch size: {2}\ttime(ms): {3}".format(
-        trt_msg, fp_message, args.max_batch_size, 1000 *
-        test_time / test_num))
+        print("Benchmark\t{0}\t{1}\tbatch size: {2}\ttime(ms): {3}".format(
+            trt_msg, fp_message, args.max_batch_size, 1000 * test_time /
+            test_num))
+
+
 if __name__ == "__main__":
     main(utility.parse_args())

tools/infer/utility.py

@@ -37,8 +37,8 @@ def parse_args():
     parser.add_argument("--use_tensorrt", type=str2bool, default=False)
     parser.add_argument("--gpu_mem", type=int, default=8000)
     parser.add_argument("--use_fp16", type=str2bool, default=False)
-    parser.add_argument("--max_batch_size", type=int, default=10)
-    parser.add_argument("--enable_benchmark", type=str2bool, default=True)
+    parser.add_argument("--max_batch_size", type=int, default=1)
+    parser.add_argument("--enable_benchmark", type=str2bool, default=False)
     #params for text detector
     parser.add_argument("--image_dir", type=str)
     parser.add_argument("--det_algorithm", type=str, default='DB')