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mmpretrain/docs/en/useful_tools/scheduler_visualization.md

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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/visualization/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.
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/visualization/vis_scheduler.py configs/resnet/resnet50_b16x8_cifar100.py

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

python tools/visualization/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