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57 lines
1.4 KiB
Markdown
57 lines
1.4 KiB
Markdown
# 如何 Profile 模型
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模型转换结束后，MMDeploy 提供了 `tools/test.py` 做为单测工具。
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## 依赖
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需要参照 [安装说明](../01-how-to-build/build_from_source.md) 完成依赖安装
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，按照 [转换说明](../02-how-to-run/convert_model.md) 转出模型。
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## 用法
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```shell
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python tools/test.py \
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${DEPLOY_CFG} \
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${MODEL_CFG} \
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--model ${BACKEND_MODEL_FILES} \
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[--speed-test] \
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[--warmup ${WARM_UP}] \
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[--log-interval ${LOG_INTERVERL}] \
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[--log2file ${LOG_RESULT_TO_FILE}]
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```
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## 参数详解
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| 参数 | 说明 |
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| ------------ | ------------------------- |
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| deploy_cfg | 部署配置文件 |
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| model_cfg | codebase 中的模型配置文件 |
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| log2file | 保存日志和运行文件的路径 |
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| speed-test | 是否做速度测试 |
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| warm-up | 执行前是否 warm-up |
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| log-interval | 日志打印间隔 |
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## 使用样例
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执行模型推理
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```shell
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python tools/test.py \
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configs/mmcls/classification_onnxruntime_static.py \
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{MMCLS_DIR}/configs/resnet/resnet50_b32x8_imagenet.py \
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--model model.onnx \
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--out out.pkl \
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--device cuda:0
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```
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profile 速度测试
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```shell
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python tools/test.py \
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configs/mmcls/classification_onnxruntime_static.py \
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{MMCLS_DIR}/configs/resnet/resnet50_b32x8_imagenet.py \
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--model model.onnx \
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--speed-test \
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--device cpu
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```
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