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Train

1. Modify the configuration file ("cfgs/MX/train.yaml")

You can set these values by giving command-line arguments like argparse, not modifying this configuration file directly. For the detailed description, please refer here.

  • decomposition: The location of decomposition rule file.

  • n_primals: The number of the entire primals.

  • trainer: (leave blank)

    • resume: Path to the checkpoint to resume from.
    • work_dir: Path to save the checkpoints, the validation images, and log.

  • gen: (leave blank)

    • n_experts: number of the experts.

2. Run training

python train_MX.py cfgs/MX/train.yaml cfgs/data/train/custom.yaml -g(optional) 2 -n(optional) 2 -nr(optional) -p(optional) 12241 0 --work_dir(optional) path/to/save/outputs

-g, -n, -nr, -p are arguments for the DistributedDataParallel training. You do not need to give these arguments if you are using a single GPU.

  • arguments
    • path/to/config (first argument): path to configration file.
      • Multiple values are allowed but the first one should locate in cfgs/MX.
    • -g : number of gpus to use for the training.
    • -n : number of nodes to use for the training.
    • -nr : the ranking of current node within the nodes.
    • -p : the port to use for the DistributedDataParallel training.
    • --work_dir : path to save outputs. The trainer.work_dir in the configuration file will be overwrited to this value.

Evaluate

Run evaluation

python inference.py cfgs/MX/eval.yaml cfgs/data/eval/kor_ttf.yaml \
--model MX \
--weight weights/MX_chn.pth \
--result_dir ./result/MX
  • arguments
    • path/to/config (first argument, multiple values are allowed): path to configration file.
    • --model : The model to evaluate. DM, LF, MX and FUNIT are available.
    • --weight: The weight to evaluate.
    • --result_dir: Path to save generated images.
    • --n_ref: The number of reference characters to use for the generation.