- authors: Luan Tran, Xi Yin, Xiaoming Liu
- CVPR2017: http://cvlab.cse.msu.edu/pdfs/Tran_Yin_Liu_CVPR2017.pdf
- Pytorch implimentation of DR-GAN (updated version in "Representation Learning by Rotating Your Faces")
- Powered by DL HACKS
- python 3.6
- pytorch 0.2.0
- numpy 1.13.1
- scipy 0.18.1
- matplotlib 2.0.0
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modify DataLoader function at main.py to define dataloader which is applicable to your data
- data needs to have ID and pose lables corresponds to each image
- if you don't have, "-random" option allow you to see how the code works with meanless random data.
python main.py -random
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Run main.py to train models
- trained models and Loss_log will be saved at "DR_GAN/snapshot/Single" by default
python main.py -random
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Generate Image with arbitrary pose
- use "-generate" option
- specify leaned model by "-snapshot" option
- generated images will be saved at specified sanpshot directory
python main.py -random -generate -snapshot=snapshot/Single/2017-09-22_20-31-08/epoch1
-
modify DataLoader function at main.py to define dataloader which is applicable to your data
- data needs to have ID and pose lables corresponds to each image
- if you don't have, "-random" option allow you to see how the code works with meanless random data.
python main.py -multi-DRGAN -images-perID=4 -random
-
Run main.py with "-multi-DRGAN" and "-images-perID" option
- Multi-Image DR-GAN assumes input data to have N images per person and in my code, they should be sequentially aligned. So change N depends on your data.
- input data size have to be divisible by batch size
- batch size have to be divisible by images_perID
- trained modles and Loss_log will be saved at "DR_GAN/snapshot/Multi" by default
python main.py -multi-DRGAN -images-perID=4 -random
-
Generate Image with arbitrary pose
- use "-generate" option
- specify leaned model by "-snapshot" option
- generated images will be saved at specified sanpshot directory
python main.py -random -multi-DRGAN -generate -images-perID=4 -snapshot=snapshot/Multi/2017-09-22_23-03-50/epoch5