163 lines
3.8 KiB
Markdown
163 lines
3.8 KiB
Markdown
> 运行示例前请先安装1.2.0或更高版本PaddleSlim
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# 模型量化压缩教程
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压缩结果：
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<table>
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<thead>
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<tr>
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<th>序号</th>
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<th>任务</th>
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<th>模型</th>
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<th>压缩策略</th>
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<th>精度(自建中文数据集)</th>
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<th>耗时(ms)</th>
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<th>整体耗时(ms)</th>
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<th>加速比</th>
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<th>整体模型大小(M)</th>
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<th>压缩比例</th>
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<th>下载链接</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td rowspan="2">0</td>
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<td>检测</td>
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<td>MobileNetV3_DB</td>
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<td>无</td>
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<td>61.7</td>
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<td>224</td>
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<td rowspan="2">375</td>
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<td rowspan="2">-</td>
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<td rowspan="2">8.6</td>
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<td rowspan="2">-</td>
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<td></td>
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</tr>
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<tr>
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<td>识别</td>
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<td>MobileNetV3_CRNN</td>
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<td>无</td>
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<td>62.0</td>
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<td>9.52</td>
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<td></td>
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</tr>
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<tr>
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<td rowspan="2">1</td>
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<td>检测</td>
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<td>SlimTextDet</td>
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<td>PACT量化训练</td>
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<td>62.1</td>
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<td>195</td>
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<td rowspan="2">348</td>
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<td rowspan="2">8%</td>
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<td rowspan="2">2.8</td>
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<td rowspan="2">67.82%</td>
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<td></td>
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</tr>
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<tr>
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<td>识别</td>
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<td>SlimTextRec</td>
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<td>PACT量化训练</td>
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<td>61.48</td>
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<td>8.6</td>
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<td></td>
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</tr>
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<tr>
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<td rowspan="2">2</td>
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<td>检测</td>
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<td>SlimTextDet_quat_pruning</td>
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<td>剪裁+PACT量化训练</td>
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<td>60.86</td>
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<td>142</td>
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<td rowspan="2">288</td>
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<td rowspan="2">30%</td>
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<td rowspan="2">2.8</td>
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<td rowspan="2">67.82%</td>
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<td></td>
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</tr>
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<tr>
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<td>识别</td>
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<td>SlimTextRec</td>
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<td>PACT量化训练</td>
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<td>61.48</td>
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<td>8.6</td>
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<td></td>
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</tr>
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<tr>
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<td rowspan="2">3</td>
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<td>检测</td>
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<td>SlimTextDet_pruning</td>
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<td>剪裁</td>
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<td>61.57</td>
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<td>138</td>
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<td rowspan="2">295</td>
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<td rowspan="2">27%</td>
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<td rowspan="2">2.9</td>
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<td rowspan="2">66.28%</td>
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<td></td>
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</tr>
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<tr>
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<td>识别</td>
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<td>SlimTextRec</td>
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<td>PACT量化训练</td>
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<td>61.48</td>
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<td>8.6</td>
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<td></td>
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</tr>
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</tbody>
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</table>
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## 概述
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复杂的模型有利于提高模型的性能，但也导致模型中存在一定冗余，模型量化将全精度缩减到定点数减少这种冗余，达到减少模型计算复杂度，提高模型推理性能的目的。
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该示例使用PaddleSlim提供的[量化压缩API](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)对OCR模型进行压缩。
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在阅读该示例前，建议您先了解以下内容：
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- [OCR模型的常规训练方法](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)
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- [PaddleSlim使用文档](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)
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## 安装PaddleSlim
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```bash
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git clone https://github.com/PaddlePaddle/PaddleSlim.git
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cd Paddleslim
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python setup.py install
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```
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## 获取预训练模型
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[识别预训练模型下载地址]()
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[检测预训练模型下载地址]()
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## 量化训练
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加载预训练模型后，在定义好量化策略后即可对模型进行量化。量化相关功能的使用具体细节见：[模型量化](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/quantization_api.html)
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进入PaddleOCR根目录，通过以下命令对模型进行量化：
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```bash
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python deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=det_mv3_db/best_accuracy Global.save_model_dir=./output/quant_model
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```
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## 导出模型
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在得到量化训练保存的模型后，我们可以将其导出为inference_model，用于预测部署：
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```bash
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python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
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```
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