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Train

1. Modify the configuration file ("cfgs/DM/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.

  • use_ddp: Whether to use DataDistributedParallel. Set True to this to use multi-gpu.

  • port: The port for the DataDistributedParallel training.

  • 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.

2. Run training

python train_DM.py cfgs/DM/train.yaml cfgs/data/train/custom.yaml --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/DM.
    • -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

1. Modify the configuration file ("cfgs/DM/eval.yaml")

All the arguments should be identical to the arguments used for the training the weight to evaluate.

  • decomposition: The location of decomposition rule file.
  • n_primals: The number of the entire primals.

2. Run evaluation

python inference.py cfgs/DM/eval.yaml cfgs/data/eval/kor_ttf.yaml \
--model DM \
--weight weights/DM_kor.pth \
--result_dir ./result/DM
  • arguments
    • path/to/config (first argument, multiple values are allowed): path to configration file.
      • Multiple values are allowed but the first one should locate in cfgs/DM.
    • --model : The model to evaluate. DM, LF, MX and FUNIT are available.
    • --weight: The weight to evaluate.
    • --result_dir: Path to save generated images.