GitHub - chaiyujin/glow-pytorch: pytorch implementation of openai paper "Gl...
source link: https://github.com/chaiyujin/glow-pytorch
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
readme.md
Glow
This is pytorch implementation of paper "Glow: Generative Flow with Invertible 1x1 Convolutions". Most modules are adapted from the offical TensorFlow version openai/glow.
TODO
- Glow model. The model is coded as described in original paper, some functions are adapted from offical TF version. Most modules are tested.
- Trainer, builder and hparams loaded from json.
- Infer after training
- Test LU_decomposed 1x1 conv2d
Scripts
- Train a model with
train.py <hparams> <dataset> <dataset_root>
- Generate
z_delta
and manipulate attributes withinfer_celeba.py <hparams> <dataset_root> <z_dir>
Training result
Currently, I trained model for 45,000 batches with hparams/celeba.json
using CelebA dataset. In short, I trained with follwing parameters
- Download pre-trained model from Dropbox
Reconstruction
Following are some samples at training phase. Row 1: reconstructed, Row 2: original.
Manipulate attribute
Use the method decribed in paper to calculate z_pos
and z_neg
for a given attribute.
And z_delta = z_pos - z_neg
is the direction to manipulate the original image.
-
manipulate
Smiling
(from negative to positive): -
manipulate
Young
(from negative to positive): -
manipulate
Pale_Skin
(from negative to positive): -
manipulate
Male
(from negative to positive):
Issues
There might be some errors in my codes. Please help me to figure out.
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK