hbcbh1999 / pggan-pytorch Goto Github PK

PyTorch implementation of PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION
YOUR CONTRIBUTION IS INVALUABLE FOR THIS PROJECT :)

• to be updated...

[step 1.] Prepare dataset
CelebA-HQ dataset is not available yet, so I used 100,000 generated PNGs of CelebA-HQ released by the author.
The quality of the generated image was good enough for training and verifying the preformance of the code.
If the CelebA-HQ dataset is releasted in then near future, I will update the experimental result.
[download]

• CAUTION: loading 1024 x 1024 image and resizing every forward process makes training slow. I recommend you to use normal CelebA dataset until the output resolution converges to 256x256.

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The training data folder should look like : 
<train_data_root>
                |--classA
                        |--image1A
                        |--image2A ...
                |--classB
                        |--image1B
                        |--image2B ...
---------------------------------------------

[step 2.] Prepare environment using virtualenv

• you can easily set PyTorch and TensorFlow evnironment using virtualenv.
• CAUTION: if you have trouble installing PyTorch, install it mansually using pip. [PyTorch Install]

$ virtualenv --python=python2.7 venv
$ . venv/bin/activate
$ pip install -r requirements.txt

[step 3.] Run training

• edit config.py to change parameters. (don't forget to change path to training images)
• specify which gpu devices to be used, and change "n_gpu" option in config.py to support Multi-GPU training.
• run and enjoy!

  (example)
  If using Single-GPU (device_id = 0):
  $ vim config.py   -->   change "n_gpu=1"
  $ CUDA_VISIBLE_DEVICES=0 python trainer.py
  
  If using Multi-GPUs (device id = 1,3,7):
  $ vim config.py   -->   change "n_gpu=3"
  $ CUDA_VISIBLE_DEVICES=1,3,7 python trainer.py

[step 4.] Display on tensorboard

• you can check the results on tensorboard.

$ tensorboard --logdir repo/tensorboard --port 8888
$ <host_ip>:8888 at your browser.

The model is still being trained at this moment.
The result of higher resolution will be updated soon.

• Equalized learning rate (weight normalization)
• Pixel-wise normalization
• Support WGAN-GP loss

• tkarras/progressive_growing_of_gans

MinchulShin, @nashory