How to evaluate model

After converting a PyTorch model to a backend model, you may evaluate backend models with tools/test.py

Prerequisite

Install MMDeploy according to get-started instructions. And convert the PyTorch model or ONNX model to the backend model by following the guide.

Usage

python tools/test.py \
${DEPLOY_CFG} \
${MODEL_CFG} \
--model ${BACKEND_MODEL_FILES} \
[--out ${OUTPUT_PKL_FILE}] \
[--format-only] \
[--metrics ${METRICS}] \
[--show] \
[--show-dir ${OUTPUT_IMAGE_DIR}] \
[--show-score-thr ${SHOW_SCORE_THR}] \
--device ${DEVICE} \
[--cfg-options ${CFG_OPTIONS}] \
[--metric-options ${METRIC_OPTIONS}]
[--log2file work_dirs/output.txt]
[--batch-size ${BATCH_SIZE}]
[--speed-test] \
[--warmup ${WARM_UP}] \
[--log-interval ${LOG_INTERVERL}] \

Description of all arguments

• deploy_cfg: The config for deployment.

• model_cfg: The config of the model in OpenMMLab codebases.

• --model: The backend model file. For example, if we convert a model to TensorRT, we need to pass the model file with “.engine” suffix.

• --out: The path to save output results in pickle format. (The results will be saved only if this argument is given)

• --format-only: Whether format the output results without evaluation or not. It is useful when you want to format the result to a specific format and submit it to the test server

• --metrics: The metrics to evaluate the model defined in OpenMMLab codebases. e.g. “segm”, “proposal” for COCO in mmdet, “precision”, “recall”, “f1_score”, “support” for single label dataset in mmpretrain.

• --show: Whether to show the evaluation result on the screen.

• --show-dir: The directory to save the evaluation result. (The results will be saved only if this argument is given)

• --show-score-thr: The threshold determining whether to show detection bounding boxes.

• --device: The device that the model runs on. Note that some backends restrict the device. For example, TensorRT must run on cuda.

• --cfg-options: Extra or overridden settings that will be merged into the current deploy config.

• --metric-options: Custom options for evaluation. The key-value pair in xxx=yyy format will be kwargs for dataset.evaluate() function.

• --log2file: log evaluation results (and speed) to file.

• --batch-size: the batch size for inference, which would override samples_per_gpu in data config. Default is 1. Note that not all models support batch_size>1.

• --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.

* Other arguments in tools/test.py are used for speed test. They have no concern with evaluation.

Example

python tools/test.py \
    configs/mmpretrain/classification_onnxruntime_static.py \
    {MMPRETRAIN_DIR}/configs/resnet/resnet50_b32x8_imagenet.py \
    --model model.onnx \
    --out out.pkl \
    --device cpu \
    --speed-test

Note

• The performance of each model in OpenMMLab codebases can be found in the document of each codebase.