Closed TMDTom closed 3 weeks ago
Hi @TMDTom, Thanks for being interested in our work.
Our MaskGAN retains the content in the background region by multiplying the background attention with the original input image: https://github.com/HieuPhan33/MaskGAN/blob/55b2cd3b31f3e0dd3e4c4cfcc5ae90a9faefcef6/models/networks.py#L472.
During training, the background mask should have the value of 1: https://github.com/HieuPhan33/MaskGAN/blob/55b2cd3b31f3e0dd3e4c4cfcc5ae90a9faefcef6/models/mask_gan_model.py#L200.
During data loading, it takes the mask with bg = 0, and convert it to the value of 1, as shown here: https://github.com/HieuPhan33/MaskGAN/blob/55b2cd3b31f3e0dd3e4c4cfcc5ae90a9faefcef6/data/augmentations.py#L26.
If your input masks (the raw png format) have background of 0, it is correct. The dataloader will read and convert it to the value of 1 during training the background attention.
Hi @TMDTom, Thanks for being interested in our work.
Our MaskGAN retains the content in the background region by multiplying the background attention with the original input image:
. During training, the background mask should have the value of 1:
. During data loading, it takes the mask with bg = 0, and convert it to the value of 1, as shown here:
. If your input masks (the raw png format) have background of 0, it is correct. The dataloader will read and convert it to the value of 1 during training the background attention.
Thanks for your nice answer. I did't get to the augmentation code before. So I'm confused. Now I get it. Thank you very mush.Thanks a lot!
Hello, thanks for your nice work. I find the mask in your network is kind of different
rom mine.
the background of mine is black 0, and your background is white 1. Can you tell me the differnet. Thanks a lot.