250 lines
13 KiB
Markdown
250 lines
13 KiB
Markdown
English | [简体中文](readme.md)
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- [Service deployment based on PaddleHub Serving](#service-deployment-based-on-paddlehub-serving)
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- [1. Update](#1-update)
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- [2. Quick start service](#2-quick-start-service)
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- [2.1 Install PaddleHub](#21-install-paddlehub)
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- [2.2 Download inference model](#22-download-inference-model)
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- [2.3 Install Service Module](#23-install-service-module)
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- [2.4 Start service](#24-start-service)
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- [2.4.1 Start with command line parameters (CPU only)](#241-start-with-command-line-parameters-cpu-only)
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- [2.4.2 Start with configuration file（CPU and GPU）](#242-start-with-configuration-filecpugpu)
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- [3. Send prediction requests](#3-send-prediction-requests)
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- [4. Returned result format](#4-returned-result-format)
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- [5. User-defined service module modification](#5-user-defined-service-module-modification)
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PaddleOCR provides 2 service deployment methods:
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- Based on **PaddleHub Serving**: Code path is `./deploy/hubserving`. Please follow this tutorial.
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- Based on **PaddleServing**: Code path is `./deploy/pdserving`. Please refer to the [tutorial](../../deploy/pdserving/README.md) for usage.
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# Service deployment based on PaddleHub Serving
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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:
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```
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deploy/hubserving/
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└─ ocr_det text detection module service package
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└─ ocr_cls text angle class module service package
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└─ ocr_rec text recognition module service package
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└─ ocr_system text detection+text angle class+text recognition three-stage series connection service package
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└─ structure_layout layout analysis service package
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└─ structure_table table recognition service package
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└─ structure_system PP-Structure service package
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└─ kie_ser KIE(SER) service package
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└─ kie_ser_re KIE(SER+RE) service package
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```
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Each service pack contains 3 files. Take the 2-stage series connection service package as an example, the directory is as follows:
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```
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deploy/hubserving/ocr_system/
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└─ __init__.py Empty file, required
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└─ config.json Configuration file, optional, passed in as a parameter when using configuration to start the service
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└─ module.py Main module file, required, contains the complete logic of the service
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└─ params.py Parameter file, required, including parameters such as model path, pre and post-processing parameters
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```
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## 1. Update
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* 2022.10.09 add KIE services.
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* 2022.08.23 add layout analysis services.
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* 2022.03.30 add PP-Structure and table recognition services.
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* 2022.05.05 add PP-OCRv3 text detection and recognition services.
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## 2. Quick start service
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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.
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### 2.1 Install PaddleHub
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```bash
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pip3 install paddlehub==2.1.0 --upgrade
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```
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### 2.2 Download inference model
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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:
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| Model | Path |
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| ------- | - |
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| text detection model | ./inference/ch_PP-OCRv3_det_infer/ |
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| text recognition model | ./inference/ch_PP-OCRv3_rec_infer/ |
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| text angle classifier | ./inference/ch_ppocr_mobile_v2.0_cls_infer/ |
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| layout parse model | ./inference/picodet_lcnet_x1_0_fgd_layout_infer/ |
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| tanle recognition | ./inference/ch_ppstructure_mobile_v2.0_SLANet_infer/ |
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| KIE(SER) | ./inference/ser_vi_layoutxlm_xfund_infer/ |
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| KIE(SER+RE) | ./inference/re_vi_layoutxlm_xfund_infer/ |
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**The model path can be found and modified in `params.py`.**
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More models provided by PaddleOCR can be obtained from the [model library](../../doc/doc_en/models_list_en.md). You can also use models trained by yourself.
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### 2.3 Install Service Module
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PaddleOCR provides 5 kinds of service modules, install the required modules according to your needs.
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* On the Linux platform(replace `/` with `\` if using Windows), the examples are as the following table:
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| Service model | Command |
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| text detection | `hub install deploy/hubserving/ocr_det` |
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| text angle class: | `hub install deploy/hubserving/ocr_cls` |
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| text recognition: | `hub install deploy/hubserving/ocr_rec` |
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| 2-stage series: | `hub install deploy/hubserving/ocr_system` |
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| table recognition | `hub install deploy/hubserving/structure_table` |
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| PP-Structure | `hub install deploy/hubserving/structure_system` |
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| KIE(SER) | `hub install deploy/hubserving/kie_ser` |
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| KIE(SER+RE) | `hub install deploy/hubserving/kie_ser_re` |
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### 2.4 Start service
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#### 2.4.1 Start with command line parameters (CPU only)
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**start command:**
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```bash
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hub serving start --modules Module1==Version1, Module2==Version2, ... \
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--port 8866 \
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--use_multiprocess \
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--workers \
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```
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**Parameters:**
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|parameters|usage|
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|---|---|
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|`--modules`/`-m`|PaddleHub Serving pre-installed model, listed in the form of multiple Module==Version key-value pairs<br>**When Version is not specified, the latest version is selected by default**|
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|`--port`/`-p`|Service port, default is 8866|
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|`--use_multiprocess`|Enable concurrent mode, by default using the single-process mode, this mode is recommended for multi-core CPU machines<br>**Windows operating system only supports single-process mode**|
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|`--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|
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For example, start the 2-stage series service:
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```bash
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hub serving start -m ocr_system
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```
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This completes the deployment of a service API, using the default port number 8866.
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#### 2.4.2 Start with configuration file（CPU and GPU）
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**start command:**
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```bash
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hub serving start --config/-c config.json
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```
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In which the format of `config.json` is as follows:
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```json
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{
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"modules_info": {
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"ocr_system": {
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"init_args": {
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"version": "1.0.0",
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"use_gpu": true
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},
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"predict_args": {
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}
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}
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},
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"port": 8868,
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"use_multiprocess": false,
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"workers": 2
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}
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```
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- The configurable parameters in `init_args` are consistent with the `_initialize` function interface in `module.py`.
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**When `use_gpu` is `true`, it means that the GPU is used to start the service**.
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- The configurable parameters in `predict_args` are consistent with the `predict` function interface in `module.py`.
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**Note:**
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- When using the configuration file to start the service, other parameters will be ignored.
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- If you use GPU prediction (that is, `use_gpu` is set to `true`), you need to set the environment variable CUDA_VISIBLE_DEVICES before starting the service, such as:
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```bash
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export CUDA_VISIBLE_DEVICES=0
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```
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- **`use_gpu` and `use_multiprocess` cannot be `true` at the same time.**
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For example, use GPU card No. 3 to start the 2-stage series service:
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```bash
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export CUDA_VISIBLE_DEVICES=3
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hub serving start -c deploy/hubserving/ocr_system/config.json
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```
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## 3. Send prediction requests
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After the service starts, you can use the following command to send a prediction request to obtain the prediction result:
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```bash
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python tools/test_hubserving.py --server_url=server_url --image_dir=image_path
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```
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Two parameters need to be passed to the script:
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- **server_url**:service address, the format of which is
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`http://[ip_address]:[port]/predict/[module_name]`
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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,
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also modified the port for each service, then the `server_url` to send the request will be:
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```
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http://127.0.0.1:8865/predict/ocr_det
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http://127.0.0.1:8866/predict/ocr_cls
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http://127.0.0.1:8867/predict/ocr_rec
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http://127.0.0.1:8868/predict/ocr_system
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http://127.0.0.1:8869/predict/structure_table
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http://127.0.0.1:8870/predict/structure_system
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http://127.0.0.1:8870/predict/structure_layout
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http://127.0.0.1:8871/predict/kie_ser
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http://127.0.0.1:8872/predict/kie_ser_re
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```
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- **image_dir**:Test image path, which can be a single image path or an image directory path
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- **visualize**:Whether to visualize the results, the default value is False
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- **output**:The folder to save the Visualization result, the default value is `./hubserving_result`
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Example:
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```bash
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python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir=./doc/imgs/ --visualize=false`
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```
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## 4. Returned result format
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The returned result is a list. Each item in the list is a dictionary which may contain three fields. The information is as follows:
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|field name|data type|description|
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|----|----|----|
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|angle|str|angle|
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|text|str|text content|
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|confidence|float|text recognition confidence|
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|text_region|list|text location coordinates|
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|html|str|table HTML string|
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|regions|list|The result of layout analysis + table recognition + OCR, each item is a list<br>including `bbox` indicating area coordinates, `type` of area type and `res` of area results|
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|layout|list|The result of layout analysis, each item is a dict, including `bbox` indicating area coordinates, `label` of area type|
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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:
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|field name/module name |ocr_det |ocr_cls |ocr_rec |ocr_system |structure_table |structure_system |structure_layout |kie_ser |kie_re |
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|--- |--- |--- |--- |--- |--- |--- |--- |--- |--- |
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|angle | |✔ | |✔ | | | |
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|text | | |✔ |✔ | |✔ | |✔ |✔ |
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|confidence | |✔ |✔ |✔ | |✔ | |✔ |✔ |
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|text_region |✔ | | |✔ | |✔ | |✔ |✔ |
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|html | | | | |✔ |✔ | | | |
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|regions | | | | |✔ |✔ | | | |
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|layout | | | | | | |✔ | | |
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|ser_res | | | | | | | |✔ | |
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|re_res | | | | | | | | |✔ |
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**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.
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## 5. User-defined service module modification
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If you need to modify the service logic, the following steps are generally required (take the modification of `deploy/hubserving/ocr_system` for example):
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1. Stop service:
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```bash
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hub serving stop --port/-p XXXX
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```
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2. Modify the code in the corresponding files under `deploy/hubserving/ocr_system`, such as `module.py` and `params.py`, to your actual needs.
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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`.
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Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation.
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**It is suggested to run `module.py` directly for debugging after modification before starting the service test.**
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**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.
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3. (Optional) If you want to rename the module, the following lines should be modified:
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- [`ocr_system` within `from deploy.hubserving.ocr_system.params import read_params`](https://github.com/PaddlePaddle/PaddleOCR/blob/a923f35de57b5e378f8dd16e54d0a3e4f51267fd/deploy/hubserving/ocr_system/module.py#L35)
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- [`ocr_system` within `name="ocr_system",`](https://github.com/PaddlePaddle/PaddleOCR/blob/a923f35de57b5e378f8dd16e54d0a3e4f51267fd/deploy/hubserving/ocr_system/module.py#L39)
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4. (Optional) It may require you to delete the directory `__pycache__` to force flush build cache of CPython:
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```bash
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find deploy/hubserving/ocr_system -name '__pycache__' -exec rm -r {} \;
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```
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5. Install modified service module:
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```bash
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hub install deploy/hubserving/ocr_system/
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```
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6. Restart service:
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```bash
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hub serving start -m ocr_system
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```
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