deploy/hubserving/readme.md
和doc/doc_ch/models_list.md
(#9110)
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PaddleOCR provides 2 service deployment methods:
./deploy/hubserving
. Please follow this tutorial../deploy/pdserving
. Please refer to the tutorial for usage.The hubserving service deployment directory includes seven service packages: text detection, text angle class, text recognition, text detection+text angle class+text recognition three-stage series connection, layout analysis, table recognition, and PP-Structure. Please select the corresponding service package to install and start the service according to your needs. The directory is as follows:
deploy/hubserving/
└─ ocr_det text detection module service package
└─ ocr_cls text angle class module service package
└─ ocr_rec text recognition module service package
└─ ocr_system text detection+text angle class+text recognition three-stage series connection service package
└─ structure_layout layout analysis service package
└─ structure_table table recognition service package
└─ structure_system PP-Structure service package
└─ kie_ser KIE(SER) service package
└─ kie_ser_re KIE(SER+RE) service package
Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows:
deploy/hubserving/ocr_system/
└─ __init__.py Empty file, required
└─ config.json Configuration file, optional, passed in as a parameter when using configuration to start the service
└─ module.py Main module file, required, contains the complete logic of the service
└─ params.py Parameter file, required, including parameters such as model path, pre and post-processing parameters
The following steps take the 2-stage series service as an example. If only the detection service or recognition service is needed, replace the corresponding file path.
pip3 install paddlehub==2.1.0 --upgrade
Before installing the service module, you need to prepare the inference model and put it in the correct path. By default, the PP-OCRv3 models are used, and the default model path is:
Model | Path |
---|---|
text detection model | ./inference/ch_PP-OCRv3_det_infer/ |
text recognition model | ./inference/ch_PP-OCRv3_rec_infer/ |
text angle classifier | ./inference/ch_ppocr_mobile_v2.0_cls_infer/ |
layout parse model | ./inference/picodet_lcnet_x1_0_fgd_layout_infer/ |
tanle recognition | ./inference/ch_ppstructure_mobile_v2.0_SLANet_infer/ |
KIE(SER) | ./inference/ser_vi_layoutxlm_xfund_infer/ |
KIE(SER+RE) | ./inference/re_vi_layoutxlm_xfund_infer/ |
The model path can be found and modified in params.py
.
More models provided by PaddleOCR can be obtained from the model library. You can also use models trained by yourself.
PaddleOCR provides 5 kinds of service modules, install the required modules according to your needs.
/
with \
if using Windows), the examples are as the following table:
| Service model | Command |
| text detection | hub install deploy/hubserving/ocr_det
|
| text angle class: | hub install deploy/hubserving/ocr_cls
|
| text recognition: | hub install deploy/hubserving/ocr_rec
|
| 2-stage series: | hub install deploy/hubserving/ocr_system
|
| table recognition | hub install deploy/hubserving/structure_table
|
| PP-Structure | hub install deploy/hubserving/structure_system
|
| KIE(SER) | hub install deploy/hubserving/kie_ser
|
| KIE(SER+RE) | hub install deploy/hubserving/kie_ser_re
|start command:
hub serving start --modules Module1==Version1, Module2==Version2, ... \
--port 8866 \
--use_multiprocess \
--workers \
Parameters:
parameters | usage |
---|---|
--modules /-m
|
PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs When Version is not specified, the latest version is selected by default |
--port /-p
|
Service port, default is 8866 |
--use_multiprocess |
Enable concurrent mode, by default using the single-process mode, this mode is recommended for multi-core CPU machines Windows operating system only supports single-process mode |
--workers |
The number of concurrent tasks specified in concurrent mode, the default is 2*cpu_count-1 , where cpu_count is the number of CPU cores |
For example, start the 2-stage series service:
hub serving start -m ocr_system
This completes the deployment of a service API, using the default port number 8866.
start command:
hub serving start --config/-c config.json
In which the format of config.json
is as follows:
{
"modules_info": {
"ocr_system": {
"init_args": {
"version": "1.0.0",
"use_gpu": true
},
"predict_args": {
}
}
},
"port": 8868,
"use_multiprocess": false,
"workers": 2
}
The configurable parameters in init_args
are consistent with the _initialize
function interface in module.py
.
When use_gpu
is true
, it means that the GPU is used to start the service.
The configurable parameters in predict_args
are consistent with the predict
function interface in module.py
.
Note:
use_gpu
is set to true
), you need to set the environment variable CUDA_VISIBLE_DEVICES before starting the service, such as:
export CUDA_VISIBLE_DEVICES=0
use_gpu
and use_multiprocess
cannot be true
at the same time.For example, use GPU card No. 3 to start the 2-stage series service:
export CUDA_VISIBLE_DEVICES=3
hub serving start -c deploy/hubserving/ocr_system/config.json
After the service starts, you can use the following command to send a prediction request to obtain the prediction result:
python tools/test_hubserving.py --server_url=server_url --image_dir=image_path
Two parameters need to be passed to the script:
server_url:service address, the format of which is
http://[ip_address]:[port]/predict/[module_name]
For example, if using the configuration file to start the text angle classification, text detection, text recognition, detection+classification+recognition 3 stages, table recognition and PP-Structure service,
also modified the port for each service, then the server_url
to send the request will be:
http://127.0.0.1:8865/predict/ocr_det
http://127.0.0.1:8866/predict/ocr_cls
http://127.0.0.1:8867/predict/ocr_rec
http://127.0.0.1:8868/predict/ocr_system
http://127.0.0.1:8869/predict/structure_table
http://127.0.0.1:8870/predict/structure_system
http://127.0.0.1:8870/predict/structure_layout
http://127.0.0.1:8871/predict/kie_ser
http://127.0.0.1:8872/predict/kie_ser_re
image_dir:Test image path, which can be a single image path or an image directory path
visualize:Whether to visualize the results, the default value is False
output:The folder to save the Visualization result, the default value is ./hubserving_result
Example:
python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir=./doc/imgs/ --visualize=false`
The returned result is a list. Each item in the list is a dictionary which may contain three fields. The information is as follows:
field name | data type | description |
---|---|---|
angle | str | angle |
text | str | text content |
confidence | float | text recognition confidence |
text_region | list | text location coordinates |
html | str | table HTML string |
regions | list | The result of layout analysis + table recognition + OCR, each item is a list including bbox indicating area coordinates, type of area type and res of area results |
layout | list | The result of layout analysis, each item is a dict, including bbox indicating area coordinates, label of area type |
The fields returned by different modules are different. For example, the results returned by the text recognition service module do not contain text_region
, detailed table is as follows:
field name/module name | ocr_det | ocr_cls | ocr_rec | ocr_system | structure_table | structure_system | structure_layout | kie_ser | kie_re |
---|---|---|---|---|---|---|---|---|---|
angle | ✔ | ✔ | |||||||
text | ✔ | ✔ | ✔ | ✔ | ✔ | ||||
confidence | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |||
text_region | ✔ | ✔ | ✔ | ✔ | ✔ | ||||
html | ✔ | ✔ | |||||||
regions | ✔ | ✔ | |||||||
layout | ✔ | ||||||||
ser_res | ✔ | ||||||||
re_res | ✔ |
Note: If you need to add, delete or modify the returned fields, you can modify the file module.py
of the corresponding module. For the complete process, refer to the user-defined modification service module in the next section.
If you need to modify the service logic, the following steps are generally required (take the modification of deploy/hubserving/ocr_system
for example):
hub serving stop --port/-p XXXX
Modify the code in the corresponding files under deploy/hubserving/ocr_system
, such as module.py
and params.py
, to your actual needs.
For example, if you need to replace the model used by the deployed service, you need to modify model path parameters det_model_dir
and rec_model_dir
in params.py
. If you want to turn off the text direction classifier, set the parameter use_angle_cls
to False
.
Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation.
It is suggested to run module.py
directly for debugging after modification before starting the service test.
Note The image input shape used by the PPOCR-v3 recognition model is 3, 48, 320
, so you need to modify cfg.rec_image_shape = "3, 48, 320"
in params.py
, if you do not use the PPOCR-v3 recognition model, then there is no need to modify this parameter.
(Optional) If you want to rename the module, the following lines should be modified:
(Optional) It may require you to delete the directory __pycache__
to force flush build cache of CPython:
find deploy/hubserving/ocr_system -name '__pycache__' -exec rm -r {} \;
Install modified service module:
hub install deploy/hubserving/ocr_system/
Restart service:
hub serving start -m ocr_system
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