Comments (4)
Hello Abraham,
I'm also trying to do the same thing and find a way to train our own images.
The dataset structure required here should be like:
The way to make this kind of dataset you can refer to the image pre-processing method offered by the author @junyanz here: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/datasets.md
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Create folder /path/to/data with subfolders A and B. A and B should each have their own subfolders train, val, test, etc. In /path/to/data/A/train, put training images in style A. In /path/to/data/B/train, put the corresponding images in style B. Repeat same for other data splits (val, test, etc).
( Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g., /path/to/data/A/train/1.jpg is considered to correspond to /path/to/data/B/train/1.jpg. ) -
Once the data is formatted this way, call:
python path/to/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data
This will combine each pair of images (A,B) into a single image file, which is the dataset ready for training the BicycleGAN.
combine_A_and_B.py file can be found here:
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/datasets/combine_A_and_B.py
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Thanks @twentyfiveYang. This post helped. I guess many would keep the same directory structure as in CycleGAN as nothing is mentioned in the official readme.
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Thanks for the reply. It follows the pix2pix directory structure rather than CycleGAN's.
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