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Hyper-parameter Scheduler Visualization

This tool aims to help the user to check the hyper-parameter scheduler of the optimizer(without training), which support the “learning rate” or “momentum”

Introduce the scheduler visualization tool

python tools/visualizations/vis_scheduler.py \
    ${CONFIG_FILE} \
    [-p, --parameter ${PARAMETER_NAME}] \
    [-d, --dataset-size ${DATASET_SIZE}] \
    [-n, --ngpus ${NUM_GPUs}] \
    [-s, --save-path ${SAVE_PATH}] \
    [--title ${TITLE}] \
    [--style ${STYLE}] \
    [--window-size ${WINDOW_SIZE}] \
    [--cfg-options]

Description of all arguments

  • config: The path of a model config file.

  • -p, --parameter: The param to visualize its change curve, choose from “lr” and “momentum”. Default to use “lr”.

  • -d, --dataset-size: The size of the datasets. If set,build_dataset will be skipped and ${DATASET_SIZE} will be used as the size. Default to use the function build_dataset.

  • -n, --ngpus: The number of GPUs used in training, default to be 1.

  • -s, --save-path: The learning rate curve plot save path, default not to save.

  • --title: Title of figure. If not set, default to be config file name.

  • --style: Style of plt. If not set, default to be whitegrid.

  • --window-size: The shape of the display window. If not specified, it will be set to 12*7. If used, it must be in the format 'W*H'.

  • --cfg-options: Modifications to the configuration file, refer to Learn about Configs.

Note

Loading annotations maybe consume much time, you can directly specify the size of the dataset with -d, dataset-size to save time.

How to plot the learning rate curve without training

You can use the following command to plot the step learning rate schedule used in the config configs/resnet/resnet50_b16x8_cifar100.py:

python tools/visualizations/vis_scheduler.py configs/resnet/resnet50_b16x8_cifar100.py

When using ImageNet, directly specify the size of ImageNet, as below:

python tools/visualizations/vis_scheduler.py configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py --dataset-size 1281167 --ngpus 4 --save-path ./repvgg-B3g4_4xb64-lr.jpg
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