Merge pull request #2889 from tink2123/pd_serving_2.1
update pdserving for 2.1
This commit is contained in:
xiaoting
GitHub
commit
8eb1f2a320
|
|
@ -30,38 +30,32 @@ The introduction and tutorial of Paddle Serving service deployment framework ref
|
|||
PaddleOCR operating environment and Paddle Serving operating environment are needed.
|
||||
|
||||
1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md).
|
||||
Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.0.1.
|
||||
|
||||
|
||||
2. The steps of PaddleServing operating environment prepare are as follows:
|
||||
|
||||
Install serving which used to start the service
|
||||
```
|
||||
pip3 install paddle-serving-server==0.5.0 # for CPU
|
||||
pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
|
||||
pip3 install paddle-serving-server==0.6.1 # for CPU
|
||||
pip3 install paddle-serving-server-gpu==0.6.1 # for GPU
|
||||
# Other GPU environments need to confirm the environment and then choose to execute the following commands
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
|
||||
pip3 install paddle-serving-server-gpu==0.6.1.post101 # GPU with CUDA10.1 + TensorRT6
|
||||
pip3 install paddle-serving-server-gpu==0.6.1.post11 # GPU with CUDA11 + TensorRT7
|
||||
```
|
||||
|
||||
3. Install the client to send requests to the service
|
||||
```
|
||||
pip3 install paddle-serving-client==0.5.0 # for CPU
|
||||
In [download link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md) find the client installation package corresponding to the python version.
|
||||
The python3.7 version is recommended here:
|
||||
|
||||
pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
|
||||
```
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
|
||||
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
|
||||
```
|
||||
|
||||
4. Install serving-app
|
||||
```
|
||||
pip3 install paddle-serving-app==0.3.0
|
||||
# fix local_predict to support load dynamic model
|
||||
# find the install directoory of paddle_serving_app
|
||||
vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
|
||||
# replace line 85 of local_predict.py config = AnalysisConfig(model_path) with:
|
||||
if os.path.exists(os.path.join(model_path, "__params__")):
|
||||
config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
|
||||
else:
|
||||
config = AnalysisConfig(model_path)
|
||||
pip3 install paddle-serving-app==0.6.1
|
||||
```
|
||||
|
||||
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md).
|
||||
|
|
@ -74,38 +68,38 @@ When using PaddleServing for service deployment, you need to convert the saved i
|
|||
Firstly, download the [inference model](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15) of PPOCR
|
||||
```
|
||||
# Download and unzip the OCR text detection model
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
# Download and unzip the OCR text recognition model
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
|
||||
|
||||
```
|
||||
Then, you can use installed paddle_serving_client tool to convert inference model to server model.
|
||||
Then, you can use installed paddle_serving_client tool to convert inference model to mobile model.
|
||||
```
|
||||
# Detection model conversion
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_det_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_det_server_2.0_serving/ \
|
||||
--serving_client ./ppocr_det_server_2.0_client/
|
||||
--serving_server ./ppocr_det_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_det_mobile_2.0_client/
|
||||
|
||||
# Recognition model conversion
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_rec_server_2.0_serving/ \
|
||||
--serving_client ./ppocr_rec_server_2.0_client/
|
||||
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_rec_mobile_2.0_client/
|
||||
|
||||
```
|
||||
|
||||
After the detection model is converted, there will be additional folders of `ppocr_det_server_2.0_serving` and `ppocr_det_server_2.0_client` in the current folder, with the following format:
|
||||
After the detection model is converted, there will be additional folders of `ppocr_det_mobile_2.0_serving` and `ppocr_det_mobile_2.0_client` in the current folder, with the following format:
|
||||
```
|
||||
|- ppocr_det_server_2.0_serving/
|
||||
|- ppocr_det_mobile_2.0_serving/
|
||||
|- __model__
|
||||
|- __params__
|
||||
|- serving_server_conf.prototxt
|
||||
|- serving_server_conf.stream.prototxt
|
||||
|
||||
|- ppocr_det_server_2.0_client
|
||||
|- ppocr_det_mobile_2.0_client
|
||||
|- serving_client_conf.prototxt
|
||||
|- serving_client_conf.stream.prototxt
|
||||
|
||||
|
|
@ -147,6 +141,80 @@ The recognition model is the same.
|
|||
After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
|
||||
|
||||
|
||||
Adjust the number of concurrency in config.yml to get the largest QPS. Generally, the number of concurrent detection and recognition is 2:1
|
||||
|
||||
```
|
||||
det:
|
||||
concurrency: 8
|
||||
...
|
||||
rec:
|
||||
concurrency: 4
|
||||
...
|
||||
```
|
||||
|
||||
Multiple service requests can be sent at the same time if necessary.
|
||||
|
||||
The predicted performance data will be automatically written into the `PipelineServingLogs/pipeline.tracer` file.
|
||||
|
||||
Tested on 200 real pictures, and limited the detection long side to 960. The average QPS on T4 GPU can reach around 23:
|
||||
|
||||
```
|
||||
|
||||
2021-05-13 03:42:36,895 ==================== TRACER ======================
|
||||
2021-05-13 03:42:36,975 Op(rec):
|
||||
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
|
||||
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
|
||||
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
|
||||
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
|
||||
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
|
||||
2021-05-13 03:42:36,976 idle[0.16254943864471572]
|
||||
2021-05-13 03:42:36,976 Op(det):
|
||||
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
|
||||
2021-05-13 03:42:36,976 prep[69.5980625 ms]
|
||||
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
|
||||
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
|
||||
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
|
||||
2021-05-13 03:42:36,977 idle[0.7477961159203756]
|
||||
2021-05-13 03:42:36,977 DAGExecutor:
|
||||
2021-05-13 03:42:36,977 Query count[224]
|
||||
2021-05-13 03:42:36,977 QPS[22.4 q/s]
|
||||
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
|
||||
2021-05-13 03:42:36,977 Error req[169, 170]
|
||||
2021-05-13 03:42:36,977 Latency:
|
||||
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
|
||||
2021-05-13 03:42:36,977 .50[172.651 ms]
|
||||
2021-05-13 03:42:36,977 .60[187.904 ms]
|
||||
2021-05-13 03:42:36,977 .70[245.675 ms]
|
||||
2021-05-13 03:42:36,977 .80[526.684 ms]
|
||||
2021-05-13 03:42:36,977 .90[854.596 ms]
|
||||
2021-05-13 03:42:36,977 .95[1722.728 ms]
|
||||
2021-05-13 03:42:36,977 .99[3990.292 ms]
|
||||
2021-05-13 03:42:36,978 Channel (server worker num[10]):
|
||||
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
|
||||
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
|
||||
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
|
||||
```
|
||||
|
||||
## WINDOWS Users
|
||||
|
||||
Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL.md)
|
||||
|
||||
|
||||
1. Start Server
|
||||
|
||||
```
|
||||
cd win
|
||||
python3 ocr_web_server.py gpu(for gpu user)
|
||||
or
|
||||
python3 ocr_web_server.py cpu(for cpu user)
|
||||
```
|
||||
|
||||
2. Client Send Requests
|
||||
|
||||
```
|
||||
python3 ocr_web_client.py
|
||||
```
|
||||
|
||||
<a name="faq"></a>
|
||||
## FAQ
|
||||
**Q1**: No result return after sending the request.
|
||||
|
|
|
|||
|
|
@ -29,41 +29,31 @@ PaddleOCR提供2种服务部署方式：
|
|||
|
||||
需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
|
||||
|
||||
- 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md)
|
||||
- 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
|
||||
根据环境下载对应的paddle whl包，推荐安装2.0.1版本
|
||||
|
||||
- 准备PaddleServing的运行环境，步骤如下
|
||||
|
||||
1. 安装serving，用于启动服务
|
||||
```
|
||||
pip3 install paddle-serving-server==0.5.0 # for CPU
|
||||
pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
|
||||
pip3 install paddle-serving-server==0.6.1 # for CPU
|
||||
pip3 install paddle-serving-server-gpu==0.6.1 # for GPU
|
||||
# 其他GPU环境需要确认环境再选择执行如下命令
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
|
||||
pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
|
||||
pip3 install paddle-serving-server-gpu==0.6.1.post101 # GPU with CUDA10.1 + TensorRT6
|
||||
pip3 install paddle-serving-server-gpu==0.6.1.post11 # GPU with CUDA11 + TensorRT7
|
||||
```
|
||||
|
||||
2. 安装client，用于向服务发送请求
|
||||
```
|
||||
pip3 install paddle-serving-client==0.5.0 # for CPU
|
||||
在[下载链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)中找到对应python版本的client安装包，这里推荐python3.7版本：
|
||||
|
||||
pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
|
||||
```
|
||||
wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
|
||||
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
|
||||
```
|
||||
|
||||
3. 安装serving-app
|
||||
```
|
||||
pip3 install paddle-serving-app==0.3.0
|
||||
```
|
||||
**note:** 安装0.3.0版本的serving-app后，为了能加载动态图模型，需要修改serving_app的源码，具体为：
|
||||
```
|
||||
# 找到paddle_serving_app的安装目录，找到并编辑local_predict.py文件
|
||||
vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
|
||||
# 将local_predict.py 的第85行 config = AnalysisConfig(model_path) 替换为：
|
||||
if os.path.exists(os.path.join(model_path, "__params__")):
|
||||
config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
|
||||
else:
|
||||
config = AnalysisConfig(model_path)
|
||||
pip3 install paddle-serving-app==0.6.1
|
||||
```
|
||||
|
||||
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。
|
||||
|
|
@ -76,38 +66,38 @@ PaddleOCR提供2种服务部署方式：
|
|||
首先，下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15)
|
||||
```
|
||||
# 下载并解压 OCR 文本检测模型
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
|
||||
# 下载并解压 OCR 文本识别模型
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
|
||||
```
|
||||
|
||||
接下来，用安装的paddle_serving_client把下载的inference模型转换成易于server部署的模型格式。
|
||||
|
||||
```
|
||||
# 转换检测模型
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_det_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_det_server_2.0_serving/ \
|
||||
--serving_client ./ppocr_det_server_2.0_client/
|
||||
--serving_server ./ppocr_det_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_det_mobile_2.0_client/
|
||||
|
||||
# 转换识别模型
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
|
||||
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_infer/ \
|
||||
--model_filename inference.pdmodel \
|
||||
--params_filename inference.pdiparams \
|
||||
--serving_server ./ppocr_rec_server_2.0_serving/ \
|
||||
--serving_client ./ppocr_rec_server_2.0_client/
|
||||
--serving_server ./ppocr_rec_mobile_2.0_serving/ \
|
||||
--serving_client ./ppocr_rec_mobile_2.0_client/
|
||||
```
|
||||
|
||||
检测模型转换完成后，会在当前文件夹多出`ppocr_det_server_2.0_serving` 和`ppocr_det_server_2.0_client`的文件夹，具备如下格式：
|
||||
检测模型转换完成后，会在当前文件夹多出`ppocr_det_mobile_2.0_serving` 和`ppocr_det_mobile_2.0_client`的文件夹，具备如下格式：
|
||||
```
|
||||
|- ppocr_det_server_2.0_serving/
|
||||
|- ppocr_det_mobile_2.0_serving/
|
||||
|- __model__
|
||||
|- __params__
|
||||
|- serving_server_conf.prototxt
|
||||
|- serving_server_conf.stream.prototxt
|
||||
|
||||
|- ppocr_det_server_2.0_client
|
||||
|- ppocr_det_mobile_2.0_client
|
||||
|- serving_client_conf.prototxt
|
||||
|- serving_client_conf.stream.prototxt
|
||||
|
||||
|
|
@ -148,6 +138,79 @@ python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_in
|
|||
成功运行后，模型预测的结果会打印在cmd窗口中，结果示例为：
|
||||
|
||||
|
||||
调整 config.yml 中的并发个数获得最大的QPS, 一般检测和识别的并发数为2：1
|
||||
```
|
||||
det:
|
||||
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
|
||||
concurrency: 8
|
||||
...
|
||||
rec:
|
||||
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
|
||||
concurrency: 4
|
||||
...
|
||||
```
|
||||
有需要的话可以同时发送多个服务请求
|
||||
|
||||
预测性能数据会被自动写入 `PipelineServingLogs/pipeline.tracer` 文件中。
|
||||
|
||||
在200张真实图片上测试，把检测长边限制为960。T4 GPU 上 QPS 均值可达到23左右：
|
||||
|
||||
```
|
||||
2021-05-13 03:42:36,895 ==================== TRACER ======================
|
||||
2021-05-13 03:42:36,975 Op(rec):
|
||||
2021-05-13 03:42:36,976 in[14.472382882882883 ms]
|
||||
2021-05-13 03:42:36,976 prep[9.556855855855856 ms]
|
||||
2021-05-13 03:42:36,976 midp[59.921905405405404 ms]
|
||||
2021-05-13 03:42:36,976 postp[15.345945945945946 ms]
|
||||
2021-05-13 03:42:36,976 out[1.9921216216216215 ms]
|
||||
2021-05-13 03:42:36,976 idle[0.16254943864471572]
|
||||
2021-05-13 03:42:36,976 Op(det):
|
||||
2021-05-13 03:42:36,976 in[315.4468035714286 ms]
|
||||
2021-05-13 03:42:36,976 prep[69.5980625 ms]
|
||||
2021-05-13 03:42:36,976 midp[18.989535714285715 ms]
|
||||
2021-05-13 03:42:36,976 postp[18.857803571428573 ms]
|
||||
2021-05-13 03:42:36,977 out[3.1337544642857145 ms]
|
||||
2021-05-13 03:42:36,977 idle[0.7477961159203756]
|
||||
2021-05-13 03:42:36,977 DAGExecutor:
|
||||
2021-05-13 03:42:36,977 Query count[224]
|
||||
2021-05-13 03:42:36,977 QPS[22.4 q/s]
|
||||
2021-05-13 03:42:36,977 Succ[0.9910714285714286]
|
||||
2021-05-13 03:42:36,977 Error req[169, 170]
|
||||
2021-05-13 03:42:36,977 Latency:
|
||||
2021-05-13 03:42:36,977 ave[535.1678348214285 ms]
|
||||
2021-05-13 03:42:36,977 .50[172.651 ms]
|
||||
2021-05-13 03:42:36,977 .60[187.904 ms]
|
||||
2021-05-13 03:42:36,977 .70[245.675 ms]
|
||||
2021-05-13 03:42:36,977 .80[526.684 ms]
|
||||
2021-05-13 03:42:36,977 .90[854.596 ms]
|
||||
2021-05-13 03:42:36,977 .95[1722.728 ms]
|
||||
2021-05-13 03:42:36,977 .99[3990.292 ms]
|
||||
2021-05-13 03:42:36,978 Channel (server worker num[10]):
|
||||
2021-05-13 03:42:36,978 chl0(In: ['@DAGExecutor'], Out: ['det']) size[0/0]
|
||||
2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
|
||||
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
|
||||
```
|
||||
|
||||
## WINDOWS用户
|
||||
|
||||
Windows用户不能使用上述的启动方式，需要使用Web Service，详情参见[Windows平台使用Paddle Serving指导](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL_CN.md)
|
||||
|
||||
|
||||
1. 启动服务端程序
|
||||
|
||||
```
|
||||
cd win
|
||||
python3 ocr_web_server.py gpu(使用gpu方式)
|
||||
或者
|
||||
python3 ocr_web_server.py cpu(使用cpu方式)
|
||||
```
|
||||
|
||||
2. 发送服务请求
|
||||
|
||||
```
|
||||
python3 ocr_web_client.py
|
||||
```
|
||||
|
||||
|
||||
<a name="FAQ"></a>
|
||||
## FAQ
|
||||
|
|
|
|||
|
|
@ -1,32 +1,32 @@
|
|||
#rpc端口, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时，会自动将rpc_port设置为http_port+1
|
||||
rpc_port: 18090
|
||||
rpc_port: 18091
|
||||
|
||||
#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时，不自动生成http_port
|
||||
http_port: 9999
|
||||
http_port: 9998
|
||||
|
||||
#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程，每个进程内构建grpcSever和DAG
|
||||
##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
|
||||
worker_num: 20
|
||||
worker_num: 10
|
||||
|
||||
#build_dag_each_worker, False，框架在进程内创建一条DAG；True，框架会每个进程内创建多个独立的DAG
|
||||
build_dag_each_worker: false
|
||||
build_dag_each_worker: False
|
||||
|
||||
dag:
|
||||
#op资源类型, True, 为线程模型；False，为进程模型
|
||||
is_thread_op: False
|
||||
|
||||
#重试次数
|
||||
retry: 1
|
||||
retry: 10
|
||||
|
||||
#使用性能分析, True，生成Timeline性能数据，对性能有一定影响；False为不使用
|
||||
use_profile: False
|
||||
use_profile: True
|
||||
|
||||
tracer:
|
||||
interval_s: 10
|
||||
op:
|
||||
det:
|
||||
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
|
||||
concurrency: 4
|
||||
concurrency: 8
|
||||
|
||||
#当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
|
||||
local_service_conf:
|
||||
|
|
@ -34,18 +34,18 @@ op:
|
|||
client_type: local_predictor
|
||||
|
||||
#det模型路径
|
||||
model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model
|
||||
model_config: ./ppocr_det_mobile_2.0_serving
|
||||
|
||||
#Fetch结果列表，以client_config中fetch_var的alias_name为准
|
||||
fetch_list: ["save_infer_model/scale_0.tmp_1"]
|
||||
|
||||
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
|
||||
devices: "2"
|
||||
devices: "0"
|
||||
|
||||
ir_optim: True
|
||||
rec:
|
||||
#并发数，is_thread_op=True时，为线程并发；否则为进程并发
|
||||
concurrency: 1
|
||||
concurrency: 4
|
||||
|
||||
#超时时间, 单位ms
|
||||
timeout: -1
|
||||
|
|
@ -60,12 +60,12 @@ op:
|
|||
client_type: local_predictor
|
||||
|
||||
#rec模型路径
|
||||
model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model
|
||||
model_config: ./ppocr_rec_mobile_2.0_serving
|
||||
|
||||
#Fetch结果列表，以client_config中fetch_var的alias_name为准
|
||||
fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
|
||||
fetch_list: ["save_infer_model/scale_0.tmp_1"]
|
||||
|
||||
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
|
||||
devices: "2"
|
||||
devices: "0"
|
||||
|
||||
ir_optim: True
|
||||
|
|
|
|||
|
|
@ -21,7 +21,6 @@ import sys
|
|||
import argparse
|
||||
import string
|
||||
from copy import deepcopy
|
||||
import paddle
|
||||
|
||||
|
||||
class DetResizeForTest(object):
|
||||
|
|
@ -34,12 +33,12 @@ class DetResizeForTest(object):
|
|||
elif 'limit_side_len' in kwargs:
|
||||
self.limit_side_len = kwargs['limit_side_len']
|
||||
self.limit_type = kwargs.get('limit_type', 'min')
|
||||
elif 'resize_long' in kwargs:
|
||||
self.resize_type = 2
|
||||
self.resize_long = kwargs.get('resize_long', 960)
|
||||
else:
|
||||
elif 'resize_short' in kwargs:
|
||||
self.limit_side_len = 736
|
||||
self.limit_type = 'min'
|
||||
else:
|
||||
self.resize_type = 2
|
||||
self.resize_long = kwargs.get('resize_long', 960)
|
||||
|
||||
def __call__(self, data):
|
||||
img = deepcopy(data)
|
||||
|
|
@ -227,8 +226,6 @@ class CTCLabelDecode(BaseRecLabelDecode):
|
|||
super(CTCLabelDecode, self).__init__(config)
|
||||
|
||||
def __call__(self, preds, label=None, *args, **kwargs):
|
||||
if isinstance(preds, paddle.Tensor):
|
||||
preds = preds.numpy()
|
||||
preds_idx = preds.argmax(axis=2)
|
||||
preds_prob = preds.max(axis=2)
|
||||
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
|
||||
|
|
|
|||
|
|
@ -23,8 +23,8 @@ def cv2_to_base64(image):
|
|||
return base64.b64encode(image).decode('utf8')
|
||||
|
||||
|
||||
url = "http://127.0.0.1:9999/ocr/prediction"
|
||||
test_img_dir = "../doc/imgs/"
|
||||
url = "http://127.0.0.1:9998/ocr/prediction"
|
||||
test_img_dir = "../../doc/imgs/"
|
||||
for idx, img_file in enumerate(os.listdir(test_img_dir)):
|
||||
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
|
||||
image_data1 = file.read()
|
||||
|
|
@ -36,5 +36,5 @@ for idx, img_file in enumerate(os.listdir(test_img_dir)):
|
|||
r = requests.post(url=url, data=json.dumps(data))
|
||||
print(r.json())
|
||||
|
||||
test_img_dir = "../doc/imgs/"
|
||||
test_img_dir = "../../doc/imgs/"
|
||||
print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ import base64
|
|||
import os
|
||||
|
||||
client = PipelineClient()
|
||||
client.connect(['127.0.0.1:18090'])
|
||||
client.connect(['127.0.0.1:18091'])
|
||||
|
||||
|
||||
def cv2_to_base64(image):
|
||||
|
|
@ -39,4 +39,3 @@ for img_file in os.listdir(test_img_dir):
|
|||
for i in range(1):
|
||||
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
|
||||
print(ret)
|
||||
#print(ret)
|
||||
|
|
|
|||
|
|
@ -11,10 +11,7 @@
|
|||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
try:
|
||||
from paddle_serving_server_gpu.web_service import WebService, Op
|
||||
except ImportError:
|
||||
from paddle_serving_server.web_service import WebService, Op
|
||||
from paddle_serving_server.web_service import WebService, Op
|
||||
|
||||
import logging
|
||||
import numpy as np
|
||||
|
|
@ -48,28 +45,24 @@ class DetOp(Op):
|
|||
def preprocess(self, input_dicts, data_id, log_id):
|
||||
(_, input_dict), = input_dicts.items()
|
||||
data = base64.b64decode(input_dict["image"].encode('utf8'))
|
||||
self.raw_im = data
|
||||
data = np.fromstring(data, np.uint8)
|
||||
# Note: class variables(self.var) can only be used in process op mode
|
||||
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
|
||||
self.im = im
|
||||
self.ori_h, self.ori_w, _ = im.shape
|
||||
|
||||
det_img = self.det_preprocess(self.im)
|
||||
det_img = self.det_preprocess(im)
|
||||
_, self.new_h, self.new_w = det_img.shape
|
||||
print("det image shape", det_img.shape)
|
||||
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
|
||||
|
||||
def postprocess(self, input_dicts, fetch_dict, log_id):
|
||||
print("input_dicts: ", input_dicts)
|
||||
det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
|
||||
ratio_list = [
|
||||
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
|
||||
]
|
||||
dt_boxes_list = self.post_func(det_out, [ratio_list])
|
||||
dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
|
||||
out_dict = {"dt_boxes": dt_boxes, "image": self.im}
|
||||
out_dict = {"dt_boxes": dt_boxes, "image": self.raw_im}
|
||||
|
||||
print("out dict", out_dict["dt_boxes"])
|
||||
return out_dict, None, ""
|
||||
|
||||
|
||||
|
|
@ -83,35 +76,75 @@ class RecOp(Op):
|
|||
|
||||
def preprocess(self, input_dicts, data_id, log_id):
|
||||
(_, input_dict), = input_dicts.items()
|
||||
im = input_dict["image"]
|
||||
raw_im = input_dict["image"]
|
||||
data = np.frombuffer(raw_im, np.uint8)
|
||||
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
|
||||
dt_boxes = input_dict["dt_boxes"]
|
||||
dt_boxes = self.sorted_boxes(dt_boxes)
|
||||
feed_list = []
|
||||
img_list = []
|
||||
max_wh_ratio = 0
|
||||
for i, dtbox in enumerate(dt_boxes):
|
||||
boximg = self.get_rotate_crop_image(im, dt_boxes[i])
|
||||
img_list.append(boximg)
|
||||
h, w = boximg.shape[0:2]
|
||||
wh_ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||||
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
|
||||
max_wh_ratio).shape
|
||||
## Many mini-batchs, the type of feed_data is list.
|
||||
max_batch_size = 6 # len(dt_boxes)
|
||||
|
||||
imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
|
||||
for id, img in enumerate(img_list):
|
||||
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
|
||||
imgs[id] = norm_img
|
||||
print("rec image shape", imgs.shape)
|
||||
feed = {"x": imgs.copy()}
|
||||
return feed, False, None, ""
|
||||
# If max_batch_size is 0, skipping predict stage
|
||||
if max_batch_size == 0:
|
||||
return {}, True, None, ""
|
||||
boxes_size = len(dt_boxes)
|
||||
batch_size = boxes_size // max_batch_size
|
||||
rem = boxes_size % max_batch_size
|
||||
for bt_idx in range(0, batch_size + 1):
|
||||
imgs = None
|
||||
boxes_num_in_one_batch = 0
|
||||
if bt_idx == batch_size:
|
||||
if rem == 0:
|
||||
continue
|
||||
else:
|
||||
boxes_num_in_one_batch = rem
|
||||
elif bt_idx < batch_size:
|
||||
boxes_num_in_one_batch = max_batch_size
|
||||
else:
|
||||
_LOGGER.error("batch_size error, bt_idx={}, batch_size={}".
|
||||
format(bt_idx, batch_size))
|
||||
break
|
||||
|
||||
def postprocess(self, input_dicts, fetch_dict, log_id):
|
||||
rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
|
||||
res_lst = []
|
||||
for res in rec_res:
|
||||
res_lst.append(res[0])
|
||||
res = {"res": str(res_lst)}
|
||||
start = bt_idx * max_batch_size
|
||||
end = start + boxes_num_in_one_batch
|
||||
img_list = []
|
||||
for box_idx in range(start, end):
|
||||
boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx])
|
||||
img_list.append(boximg)
|
||||
h, w = boximg.shape[0:2]
|
||||
wh_ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||||
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
|
||||
max_wh_ratio).shape
|
||||
|
||||
imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32')
|
||||
for id, img in enumerate(img_list):
|
||||
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
|
||||
imgs[id] = norm_img
|
||||
feed = {"x": imgs.copy()}
|
||||
feed_list.append(feed)
|
||||
|
||||
return feed_list, False, None, ""
|
||||
|
||||
def postprocess(self, input_dicts, fetch_data, log_id):
|
||||
res_list = []
|
||||
if isinstance(fetch_data, dict):
|
||||
if len(fetch_data) > 0:
|
||||
rec_batch_res = self.ocr_reader.postprocess(
|
||||
fetch_data, with_score=True)
|
||||
for res in rec_batch_res:
|
||||
res_list.append(res[0])
|
||||
elif isinstance(fetch_data, list):
|
||||
for one_batch in fetch_data:
|
||||
one_batch_res = self.ocr_reader.postprocess(
|
||||
one_batch, with_score=True)
|
||||
for res in one_batch_res:
|
||||
res_list.append(res[0])
|
||||
|
||||
res = {"res": str(res_list)}
|
||||
return res, None, ""
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,13 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import requests
|
||||
import json
|
||||
import cv2
|
||||
import base64
|
||||
import os, sys
|
||||
import time
|
||||
|
||||
|
||||
def cv2_to_base64(image):
|
||||
#data = cv2.imencode('.jpg', image)[1]
|
||||
return base64.b64encode(image).decode(
|
||||
'utf8') #data.tostring()).decode('utf8')
|
||||
|
||||
|
||||
headers = {"Content-type": "application/json"}
|
||||
url = "http://127.0.0.1:9292/ocr/prediction"
|
||||
|
||||
test_img_dir = "../../../doc/imgs/"
|
||||
for idx, img_file in enumerate(os.listdir(test_img_dir)):
|
||||
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
|
||||
image_data1 = file.read()
|
||||
|
||||
image = cv2_to_base64(image_data1)
|
||||
for i in range(1):
|
||||
data = {
|
||||
"feed": [{
|
||||
"image": image
|
||||
}],
|
||||
"fetch": ["save_infer_model/scale_0.tmp_1"]
|
||||
}
|
||||
r = requests.post(url=url, headers=headers, data=json.dumps(data))
|
||||
print(r.json())
|
||||
|
||||
test_img_dir = "../../../doc/imgs/"
|
||||
print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
from paddle_serving_client import Client
|
||||
import cv2
|
||||
import sys
|
||||
import numpy as np
|
||||
import os
|
||||
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 ocr_reader import OCRReader
|
||||
from ..ocr_reader import OCRReader
|
||||
try:
|
||||
from paddle_serving_server_gpu.web_service import WebService
|
||||
except ImportError:
|
||||
from paddle_serving_server.web_service import WebService
|
||||
from paddle_serving_app.local_predict import LocalPredictor
|
||||
import time
|
||||
import re
|
||||
import base64
|
||||
|
||||
|
||||
class OCRService(WebService):
|
||||
def init_det_debugger(self, det_model_config):
|
||||
self.det_preprocess = Sequential([
|
||||
ResizeByFactor(32, 960), Div(255),
|
||||
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
|
||||
(2, 0, 1))
|
||||
])
|
||||
self.det_client = LocalPredictor()
|
||||
if sys.argv[1] == 'gpu':
|
||||
self.det_client.load_model_config(
|
||||
det_model_config, use_gpu=True, gpu_id=0)
|
||||
elif sys.argv[1] == 'cpu':
|
||||
self.det_client.load_model_config(det_model_config)
|
||||
self.ocr_reader = OCRReader(
|
||||
char_dict_path="../../../ppocr/utils/ppocr_keys_v1.txt")
|
||||
|
||||
def preprocess(self, feed=[], fetch=[]):
|
||||
data = base64.b64decode(feed[0]["image"].encode('utf8'))
|
||||
data = np.fromstring(data, np.uint8)
|
||||
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
|
||||
ori_h, ori_w, _ = im.shape
|
||||
det_img = self.det_preprocess(im)
|
||||
_, new_h, new_w = det_img.shape
|
||||
det_img = det_img[np.newaxis, :]
|
||||
det_img = det_img.copy()
|
||||
det_out = self.det_client.predict(
|
||||
feed={"x": det_img},
|
||||
fetch=["save_infer_model/scale_0.tmp_1"],
|
||||
batch=True)
|
||||
filter_func = FilterBoxes(10, 10)
|
||||
post_func = DBPostProcess({
|
||||
"thresh": 0.3,
|
||||
"box_thresh": 0.5,
|
||||
"max_candidates": 1000,
|
||||
"unclip_ratio": 1.5,
|
||||
"min_size": 3
|
||||
})
|
||||
sorted_boxes = SortedBoxes()
|
||||
ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
|
||||
dt_boxes_list = post_func(det_out["save_infer_model/scale_0.tmp_1"],
|
||||
[ratio_list])
|
||||
dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
|
||||
dt_boxes = sorted_boxes(dt_boxes)
|
||||
get_rotate_crop_image = GetRotateCropImage()
|
||||
img_list = []
|
||||
max_wh_ratio = 0
|
||||
for i, dtbox in enumerate(dt_boxes):
|
||||
boximg = get_rotate_crop_image(im, dt_boxes[i])
|
||||
img_list.append(boximg)
|
||||
h, w = boximg.shape[0:2]
|
||||
wh_ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||||
if len(img_list) == 0:
|
||||
return [], []
|
||||
_, w, h = self.ocr_reader.resize_norm_img(img_list[0],
|
||||
max_wh_ratio).shape
|
||||
imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
|
||||
for id, img in enumerate(img_list):
|
||||
norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
|
||||
imgs[id] = norm_img
|
||||
feed = {"x": imgs.copy()}
|
||||
fetch = ["save_infer_model/scale_0.tmp_1"]
|
||||
return feed, fetch, True
|
||||
|
||||
def postprocess(self, feed={}, fetch=[], fetch_map=None):
|
||||
rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
|
||||
res_lst = []
|
||||
for res in rec_res:
|
||||
res_lst.append(res[0])
|
||||
res = {"res": res_lst}
|
||||
return res
|
||||
|
||||
|
||||
ocr_service = OCRService(name="ocr")
|
||||
ocr_service.load_model_config("../ppocr_rec_mobile_2.0_serving")
|
||||
ocr_service.prepare_server(workdir="workdir", port=9292)
|
||||
ocr_service.init_det_debugger(
|
||||
det_model_config="../ppocr_det_mobile_2.0_serving")
|
||||
if sys.argv[1] == 'gpu':
|
||||
ocr_service.set_gpus("0")
|
||||
ocr_service.run_debugger_service(gpu=True)
|
||||
elif sys.argv[1] == 'cpu':
|
||||
ocr_service.run_debugger_service()
|
||||
ocr_service.run_web_service()
|
||||
Loading…
Reference in New Issue