add convert to serving model

deploy/pdserving/det_local_server.py

@@ -23,7 +23,7 @@ from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes
 if sys.argv[1] == 'gpu':
     from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu'
+elif sys.argv[1] == 'cpu':
     from paddle_serving_server.web_service import WebService
 import time
 import re
@@ -67,11 +67,13 @@ class OCRService(WebService):
 
 ocr_service = OCRService(name="ocr")
 ocr_service.load_model_config("ocr_det_model")
+ocr_service.init_det()
 if sys.argv[1] == 'gpu':
     ocr_service.set_gpus("0")
     ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+    ocr_service.run_debugger_service(gpu=True)
 elif sys.argv[1] == 'cpu':
     ocr_service.prepare_server(workdir="workdir", port=9292)
+    ocr_service.run_debugger_service()
 ocr_service.init_det()
-ocr_service.run_debugger_service()
 ocr_service.run_web_service()

deploy/pdserving/ocr_local_server.py

@@ -104,10 +104,11 @@ class OCRService(WebService):
 
 ocr_service = OCRService(name="ocr")
 ocr_service.load_model_config("ocr_rec_model")
-ocr_service.prepare_server(workdir="workdir", port=9292)
 ocr_service.init_det_debugger(det_model_config="ocr_det_model")
 if sys.argv[1] == 'gpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
     ocr_service.run_debugger_service(gpu=True)
 elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
     ocr_service.run_debugger_service()
 ocr_service.run_web_service()

deploy/pdserving/readme.md

@@ -55,6 +55,23 @@ tar -xzvf ocr_det.tar.gz
 ```
 执行上述命令会下载`db_crnn_mobile`的模型，如果想要下载规模更大的`db_crnn_server`模型，可以在下载预测模型并解压之后。参考[如何从Paddle保存的预测模型转为Paddle Serving格式可部署的模型](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md)。
 
+我们以`ch_rec_r34_vd_crnn`模型作为例子，下载链接在：
+
+```
+wget --no-check-certificate https://paddleocr.bj.bcebos.com/ch_models/ch_rec_r34_vd_crnn_infer.tar
+tar xf ch_rec_r34_vd_crnn_infer.tar
+```
+因此我们按照Serving模型转换教程，运行下列python文件。
+```
+from paddle_serving_client.io import inference_model_to_serving
+inference_model_dir = "ch_rec_r34_vd_crnn"
+serving_client_dir = "serving_client_dir"
+serving_server_dir = "serving_server_dir"
+feed_var_names, fetch_var_names = inference_model_to_serving(
+        inference_model_dir, serving_client_dir, serving_server_dir, model_filename="model", params_filename="params")
+```
+最终会在`serving_client_dir`和`serving_server_dir`生成客户端和服务端的模型配置。
+
 ### 3. 启动服务
 启动服务可以根据实际需求选择启动`标准版`或者`快速版`，两种方式的对比如下表：  
 

deploy/pdserving/rec_local_server.py

@@ -22,7 +22,10 @@ from paddle_serving_client import Client
 from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
-from paddle_serving_server_gpu.web_service import WebService
+if sys.argv[1] == 'gpu':
+    from paddle_serving_server_gpu.web_service import WebService
+elif sys.argv[1] == 'cpu':
+    from paddle_serving_server.web_service import WebService
 import time
 import re
 import base64
@@ -65,8 +68,12 @@ class OCRService(WebService):
 
 ocr_service = OCRService(name="ocr")
 ocr_service.load_model_config("ocr_rec_model")
-ocr_service.set_gpus("0")
 ocr_service.init_rec()
-ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-ocr_service.run_debugger_service()
+if sys.argv[1] == 'gpu':
+    ocr_service.set_gpus("0")
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
+    ocr_service.run_debugger_service(gpu=True)
+elif sys.argv[1] == 'cpu':
+    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+    ocr_service.run_debugger_service()
 ocr_service.run_web_service()