71 lines
2.4 KiB
Markdown
71 lines
2.4 KiB
Markdown
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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config文件夹下的txt文件配合使用，完成Clas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PaddleClass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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├── config # 测试模型的参数配置文件
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| |--- *.txt
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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/config/ResNet50_vd.txt 'lite_train_infer'
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bash tests/test.sh ./tests/config/ResNet50_vd.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/config/ResNet50_vd.txt 'whole_infer'
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bash tests/test.sh ./tests/config/ResNet50_vd.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/config/ResNet50_vd.txt 'infer'
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# 用法1:
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bash tests/test.sh ./tests/config/ResNet50_vd.txt 'infer'
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```
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需注意的是，模型的离线量化需使用`infer`模式进行测试
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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/config/ResNet50_vd.txt 'whole_train_infer'
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bash tests/test.sh ./tests/config/ResNet50_vd.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/config/ResNet50_vd.txt 'cpp_infer'
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bash tests/test.sh ./tests/config/ResNet50_vd.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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