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mmocr/docs/en/tools.md

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Useful Tools

We provide some useful tools under mmocr/tools directory.

Publish a Model

Before you upload a model to AWS, you may want to (1) convert the model weights to CPU tensors, (2) delete the optimizer states and (3) compute the hash of the checkpoint file and append the hash id to the filename. These functionalities could be achieved by tools/publish_model.py.

python tools/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}

For example,

python tools/publish_model.py work_dirs/psenet/latest.pth psenet_r50_fpnf_sbn_1x_20190801.pth

The final output filename will be psenet_r50_fpnf_sbn_1x_20190801-{hash id}.pth.

Convert txt annotation to lmdb format

Sometimes, loading a large txt annotation file with multiple workers can cause OOM (out of memory) error. You can convert the file into lmdb format using tools/data/utils/txt2lmdb.py and use LmdbLoader in your config to avoid this issue.

python tools/data/utils/txt2lmdb.py -i <txt_label_path> -o <lmdb_label_path>

For example,

python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mixture/Syn90k/label.lmdb

Log Analysis

You can use tools/analyze_logs.py to plot loss/hmean curves given a training log file. Run pip install seaborn first to install the dependency.

python tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]
Arguments Type Description
--keys str The metric that you want to plot. Defaults to loss.
--title str Title of figure.
--legend str Legend of each plot.
--backend str Backend of the plot. more info
--style str Style of the plot. Defaults to dark. more info
--out str Path of output figure.

Examples:

Download the following DBNet and CRNN training logs to run demos.

wget https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_r18_fpnc_sbn_1200e_icdar2015_20210329-ba3ab597.log.json -O DBNet_log.json

wget https://download.openmmlab.com/mmocr/textrecog/crnn/20210326_111035.log.json -O CRNN_log.json

Please specify an output path if you are running the codes on systems without a GUI.

  • Plot loss metric.

    python tools/analyze_logs.py plot_curve DBNet_log.json --keys loss --legend loss

  • Plot hmean-iou:hmean metric of text detection.

    python tools/analyze_logs.py plot_curve DBNet_log.json --keys hmean-iou:hmean --legend hmean-iou:hmean

  • Plot 0_1-N.E.D metric of text recognition.

    python tools/analyze_logs.py plot_curve CRNN_log.json --keys 0_1-N.E.D --legend 0_1-N.E.D

  • Compute the average training speed.

    python tools/analyze_logs.py cal_train_time CRNN_log.json --include-outliers

    The output is expected to be like the following.

    -----Analyze train time of CRNN_log.json-----
slowest epoch 4, average time is 0.3464
fastest epoch 5, average time is 0.2365
time std over epochs is 0.0356
average iter time: 0.2906 s/iter