Training command:
python main.py --primary_samples=100 --backup_samples=100 --num_embeddings=32 --outf=100_32 --num_epochs=2000 --image_dir=./data/kodim01.png
| Argument | Possible values |
|---|---|
--primary_samples |
the number of primary samples |
--backup_samples |
the number of backup samples |
--num_embeddings |
the number of embeddings |
--outf |
the output directory |
--num_epochs |
the number of epochs for training |
--image_dir |
the directory of the image |
Alternatively, you can follow the settings in the train_all.py file.
During training, the best model will be saved.
Test command:
python main.py --eval=True --primary_samples=100 --backup_samples=100 --num_embeddings=32 --outf=100_32 --num_epochs=2000 --image_dir=./data/kodim01.png
PS:
- I'll be very happy if you could accelerate it or convert it into CUDA language. Try it!
- I've exported the environment.yml file from my Conda environment for reference purposes.
- Please feel free to contact me if you have any problem.