mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
79890f42c2
* add ppl install doc and how to test a model doc * remove mmdet model list in ppl.md * resolve comments * fix typo * add mmocr doc * mv configs as the final column * fix typo and align tables * fix lint
1.6 KiB
1.6 KiB
How to test model
After we convert a PyTorch model to a backend model, we may need to test the speed of the model before using it. In MMDeploy, we provide a tool to test the speed of backend models in tools/test.py
Prerequisite
Before test the speed of a model of a specific backend, you should install the plugins of the backend and convert the model to the backend with our deploy tools.
Usage
python tools/test.py \
${DEPLOY_CFG} \
${MODEL_CFG} \
--model ${BACKEND_MODEL_FILES} \
[--speed-test] \
[--warmup ${WARM_UP}] \
[--log-interval ${LOG_INTERVERL}] \
[--log2file ${LOG_RESULT_TO_FILE}] \
Description of all arguments
deploy_cfg: The config for deployment.model_cfg: The config of the model in OpenMMLab codebases.--model: The backend model files. For example, if we convert a model to ncnn, we need to pass a ".param" file and a ".bin" file. If we convert a model to TensorRT, we need to pass the model file with ".engine" suffix.--speed-test: Whether to activate speed test.--warmup: warmup before counting inference elapse, require setting speed-test first.--log-interval: The interval between each log, require setting speed-test first.--log2file: Log speed test result in file format, need speed-test first.
* Other arguments in tools/test.py are used for performance test. They have no concern with speed test.
Example
python tools/test.py \
configs/mmcls/classification_onnxruntime_static.py \
{MMCLS_DIR}/configs/resnet/resnet50_b32x8_imagenet.py \
--model model.onnx \
--speed-test \
--device cpu \