update serving (#3801) (#3816)

deploy/serving/README.md

@@ -10,7 +10,7 @@ python tools/infer.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml --infer_i
 ```
 
 ## 2. 安装 paddle serving
-请参考[PaddleServing](https://github.com/PaddlePaddle/Serving/tree/v0.5.0) 中安装教程安装
+请参考[PaddleServing](https://github.com/PaddlePaddle/Serving/tree/v0.6.0) 中安装教程安装（版本>=0.6.0）。
 
 ## 3. 导出模型
 PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/deploy/EXPORT_MODEL.md)
@@ -87,7 +87,7 @@ fetch_var {
 cd output_inference/yolov3_darknet53_270e_coco/
 
 # GPU
-python -m paddle_serving_server_gpu.serve --model serving_server --port 9393 --gpu_ids 0
+python -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_ids 0
 
 # CPU
 python -m paddle_serving_server.serve --model serving_server --port 9393

deploy/serving/test_client.py

@@ -33,8 +33,12 @@ im = preprocess(sys.argv[1])
 fetch_map = client.predict(
     feed={
         "image": im,
-        "im_size": np.array(list(im.shape[1:])),
+        "im_shape": np.array(list(im.shape[1:])).reshape(-1),
+        "scale_factor": np.array([1.0, 1.0]).reshape(-1),
     },
-    fetch=["multiclass_nms_0.tmp_0"])
+    fetch=["save_infer_model/scale_0.tmp_1"],
+    batch=False)
+print(fetch_map)
 fetch_map["image"] = sys.argv[1]
 postprocess(fetch_map)
+