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mim_usage.md

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Use mim to run scripts from other OpenMMLab repositories

1. All script calls across libraries are currently not supported and are being fixed. More examples will be added to this document when the fix is complete. 2.
2. mAP plotting and average training speed calculation are fixed in the MMDetection dev-3.x branch, which currently needs to be installed via the source code to be run successfully.

Log Analysis

Curve plotting

tools/analysis_tools/analyze_logs.py plots loss/mAP curves given a training log file. Run pip install seaborn first to install the dependency.

mim run mmdet analyze_logs plot_curve \
    ${LOG} \                                     # path of train log in json format
    [--keys ${KEYS}] \                           # the metric that you want to plot, default to 'bbox_mAP'
    [--start-epoch ${START_EPOCH}]               # the epoch that you want to start, default to 1
    [--eval-interval ${EVALUATION_INTERVAL}] \   # the evaluation interval when training, default to 1
    [--title ${TITLE}] \                         # title of figure
    [--legend ${LEGEND}] \                       # legend of each plot, default to None
    [--backend ${BACKEND}] \                     # backend of plt, default to None
    [--style ${STYLE}] \                         # style of plt, default to 'dark'
    [--out ${OUT_FILE}]                          # the path of output file
# [] stands for optional parameters, when actually entering the command line, you do not need to enter []

Examples:

  • Plot the classification loss of some run.

    mim run mmdet analyze_logs plot_curve \
    yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \
    --keys loss_cls \
    --legend loss_cls

  • Plot the classification and regression loss of some run, and save the figure to a pdf.

    mim run mmdet analyze_logs plot_curve \
    yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \
    --keys loss_cls loss_bbox \
    --legend loss_cls loss_bbox \
    --out losses_yolov5_s.pdf

  • Compare the bbox mAP of two runs in the same figure.

    mim run mmdet analyze_logs plot_curve \
    yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json \
    yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739.log.json \
    --keys bbox_mAP \
    --legend yolov5_s yolov5_n \
    --eval-interval 10 # Note that the evaluation interval must be the same as during training. Otherwise, it will raise an error.

Compute the average training speed

mim run mmdet analyze_logs cal_train_time \
    ${LOG} \                                # path of train log in json format
    [--include-outliers]                    # include the first value of every epoch when computing the average time

Examples:

mim run mmdet analyze_logs cal_train_time \
    yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json

The output is expected to be like the following.

-----Analyze train time of yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700.log.json-----
slowest epoch 278, average time is 0.1705 s/iter
fastest epoch 300, average time is 0.1510 s/iter
time std over epochs is 0.0026
average iter time: 0.1556 s/iter