This is a Python3 / Tensorflow implementation of PixelCNN++, as described in the following paper:
PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications, by Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma, and Yaroslav Bulatov.
Our work builds on PixelCNNs that were originally proposed in van der Oord et al. in June 2016. PixelCNNs are a class of powerful generative models with tractable likelihood that are also easy to sample from. The core convolutional neural network computes a probability distribution over a value of one pixel conditioned on the values of pixels to the left and above it. Below are example samples from a model trained on CIFAR-10 that achieves 2.92 bits per dimension (compared to 3.03 of the PixelCNN in van der Oord et al.):
Samples from the model (left) and samples from a model that is conditioned on the CIFAR-10 class labels (right):
This code supports multi-GPU training of our improved PixelCNN on CIFAR-10 and Small ImageNet, but is easy to adapt for additional datasets. Training on a machine with 8 Maxwell TITAN X GPUs achieves 3.0 bits per dimension in about 10 hours and it takes approximately 5 days to converge to 2.92.
To run this code you need the following:
- a machine with multiple GPUs
- Python3
- Numpy, TensorFlow and imageio packages:
pip install numpy tensorflow-gpu imageio
Use the train.py script to train the model. To train the default model on
CIFAR-10 simply use:
python3 train.py
You might want to at least change the --data_dir and --save_dir which
point to paths on your system to download the data to (if not available), and
where to save the checkpoints.
I want to train on fewer GPUs. To train on fewer GPUs we recommend using CUDA_VISIBLE_DEVICES
to narrow the visibility of GPUs to only a few and then run the script. Don't forget to modulate
the flag --nr_gpu accordingly.
I want to train on my own dataset. Have a look at the DataLoader classes
in the data/ folder. You have to write an analogous data iterator object for
your own dataset and the code should work well from there.
You can download our pretrained (TensorFlow) model that achieves 2.92 bpd on CIFAR-10 here (656MB).
If you find this code useful please cite us in your work:
@inproceedings{Salimans2017PixeCNN,
title={PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications},
author={Tim Salimans and Andrej Karpathy and Xi Chen and Diederik P. Kingma},
booktitle={ICLR},
year={2017}
}