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# 从训练到推理部署工具链测试方法介绍
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test.sh和params.txt文件配合使用,完成OCR轻量检测和识别模型从训练到预测的流程测试。
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# 安装依赖
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- 安装PaddlePaddle >= 2.0
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- 安装PaddleOCR依赖
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```
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pip3 install -r ../requirements.txt
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```
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- 安装autolog
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```
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git clone https://github.com/LDOUBLEV/AutoLog
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cd AutoLog
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pip3 install -r requirements.txt
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python3 setup.py bdist_wheel
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pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
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cd ../
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```
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# 目录介绍
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```bash
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tests/
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├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件
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├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件
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├── ocr_ppocr_mobile_params.txt # 测试OCR检测+识别模型串联的参数配置文件
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└── prepare.sh # 完成test.sh运行所需要的数据和模型下载
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└── test.sh # 测试主程序
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```
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# 使用方法
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test.sh包含四种运行模式,每种模式的运行数据不同,分别用于测试速度和精度,分别是:
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- 模式1:lite_train_infer,使用少量数据训练,用于快速验证训练到预测的走通流程,不验证精度和速度;
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```shell
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bash tests/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
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bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
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```
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- 模式2:whole_infer,使用少量数据训练,一定量数据预测,用于验证训练后的模型执行预测,预测速度是否合理;
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```shell
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bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
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bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
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```
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- 模式3:infer 不训练,全量数据预测,走通开源模型评估、动转静,检查inference model预测时间和精度;
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```shell
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bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
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# 用法1:
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bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
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# 用法2: 指定GPU卡预测,第三个传入参数为GPU卡号
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bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
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```
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- 模式4:whole_train_infer , CE: 全量数据训练,全量数据预测,验证模型训练精度,预测精度,预测速度;
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```shell
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bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
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bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
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```
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- 模式5:cpp_infer , CE: 验证inference model的c++预测是否走通;
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```shell
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bash tests/prepare.sh ./tests/ocr_det_params.txt 'cpp_infer'
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bash tests/test.sh ./tests/ocr_det_params.txt 'cpp_infer'
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```
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# 日志输出
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最终在```tests/output```目录下生成.log后缀的日志文件
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