添加静态图的create_predictor

tools/infer/utility.py

@@ -14,11 +14,14 @@
 
 import argparse
 import os
+import sys
 import cv2
 import numpy as np
 import json
 from PIL import Image, ImageDraw, ImageFont
 import math
+from paddle.fluid.core import AnalysisConfig
+from paddle.fluid.core import create_paddle_predictor
 
 
 def parse_args():
@@ -71,6 +74,59 @@ def parse_args():
     return parser.parse_args()
 
 
+def create_predictor(args, mode, logger):
+    if mode == "det":
+        model_dir = args.det_model_dir
+    elif mode == 'cls':
+        model_dir = args.cls_model_dir
+    else:
+        model_dir = args.rec_model_dir
+
+    if model_dir is None:
+        logger.info("not find {} model file path {}".format(mode, model_dir))
+        sys.exit(0)
+    model_file_path = model_dir + "/__model__"
+    params_file_path = model_dir + "/__variables__"
+    if not os.path.exists(model_file_path):
+        logger.info("not find model file path {}".format(model_file_path))
+        sys.exit(0)
+    if not os.path.exists(params_file_path):
+        logger.info("not find params file path {}".format(params_file_path))
+        sys.exit(0)
+
+    config = AnalysisConfig(model_file_path, params_file_path)
+
+    if args.use_gpu:
+        config.enable_use_gpu(args.gpu_mem, 0)
+    else:
+        config.disable_gpu()
+        config.set_cpu_math_library_num_threads(6)
+        if args.enable_mkldnn:
+            # cache 10 different shapes for mkldnn to avoid memory leak
+            config.set_mkldnn_cache_capacity(10)
+            config.enable_mkldnn()
+
+    # config.enable_memory_optim()
+    config.disable_glog_info()
+
+    if args.use_zero_copy_run:
+        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
+        config.switch_use_feed_fetch_ops(False)
+    else:
+        config.switch_use_feed_fetch_ops(True)
+
+    predictor = create_paddle_predictor(config)
+    input_names = predictor.get_input_names()
+    for name in input_names:
+        input_tensor = predictor.get_input_tensor(name)
+    output_names = predictor.get_output_names()
+    output_tensors = []
+    for output_name in output_names:
+        output_tensor = predictor.get_output_tensor(output_name)
+        output_tensors.append(output_tensor)
+    return predictor, input_tensor, output_tensors
+
+
 def draw_text_det_res(dt_boxes, img_path):
     src_im = cv2.imread(img_path)
     for box in dt_boxes: