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https://github.com/didriknielsen/survae_flows
Code for paper "SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows"
https://github.com/didriknielsen/survae_flows
flows survae-flows
Last synced: 3 months ago
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Code for paper "SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows"
- Host: GitHub
- URL: https://github.com/didriknielsen/survae_flows
- Owner: didriknielsen
- License: mit
- Created: 2020-07-03T13:04:08.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-02-01T10:33:24.000Z (almost 4 years ago)
- Last Synced: 2024-07-04T02:15:31.484Z (4 months ago)
- Topics: flows, survae-flows
- Language: Python
- Homepage:
- Size: 4.02 MB
- Stars: 283
- Watchers: 28
- Forks: 34
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SurVAE Flows
> Official code for [SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows](https://arxiv.org/abs/2007.02731)
by Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling.
SurVAE Flows is a framework of composable transformations that extends the framework of normalizing flows.
SurVAE Flows make use of not only **bijective** transformations, but also **surjective** and **stochastic** transformations.
For more details, see the [paper](https://arxiv.org/abs/2007.02731) or check out this [talk](https://www.youtube.com/watch?v=bXp8fk4MRXQ) by Max Welling.
## Contents
* `/survae/`: Code for the SurVAE library. See description below.
* `/examples/`: Runnable examples using the SurVAE library.
* `/experiments/`: Code to reproduce the experiments in the paper.
**Pretrained models** can be downloaded from [releases](https://github.com/didriknielsen/survae_flows/releases/tag/v1.0.0).
## The SurVAE Library
The SurVAE library is a Python package, built on top of [PyTorch](https://pytorch.org/).
The SurVAE library allows straightforward construction of SurVAE flows.
#### Installation
In the folder containing `setup.py`, run
```
pip install .
```
#### Example 1: Normalizing Flow
We can construct a simple *normalizing flow* by stacking **bijective transformations**.
In this case, we model 2d data using a flow of 4 affine coupling layers.
```python
import torch.nn as nn
from survae.flows import Flow
from survae.distributions import StandardNormal
from survae.transforms import AffineCouplingBijection, ActNormBijection, Reverse
from survae.nn.layers import ElementwiseParams
def net():
return nn.Sequential(nn.Linear(1, 200), nn.ReLU(),
nn.Linear(200, 100), nn.ReLU(),
nn.Linear(100, 2), ElementwiseParams(2))
model = Flow(base_dist=StandardNormal((2,)),
transforms=[
AffineCouplingBijection(net()), ActNormBijection(2), Reverse(2),
AffineCouplingBijection(net()), ActNormBijection(2), Reverse(2),
AffineCouplingBijection(net()), ActNormBijection(2), Reverse(2),
AffineCouplingBijection(net()), ActNormBijection(2),
])
```
See [here](https://github.com/didriknielsen/survae_flows/blob/master/examples/toy_flow.py) for a runnable example.
#### Example 2: VAE
We can further build *VAEs* using **stochastic transformations**.
We here construct a simple VAE for binary images of shape (1,28,28), such as binarized MNIST.
We can easily extend this simple VAE by adding more layers to obtain e.g. hierarchical VAEs or VAEs with flow priors.
We can also use conditional flows in the encoder and/or decoder to obtain a more expressive VAE transformation.
```python
from survae.flows import Flow
from survae.transforms import VAE
from survae.distributions import StandardNormal, ConditionalNormal, ConditionalBernoulli
from survae.nn.nets import MLP
encoder = ConditionalNormal(MLP(784, 2*latent_size,
hidden_units=[512,256],
activation='relu',
in_lambda=lambda x: 2 * x.view(x.shape[0], 784).float() - 1))
decoder = ConditionalBernoulli(MLP(latent_size, 784,
hidden_units=[512,256],
activation='relu',
out_lambda=lambda x: x.view(x.shape[0], 1, 28, 28)))
model = Flow(base_dist=StandardNormal((latent_size,)),
transforms=[
VAE(encoder=encoder, decoder=decoder)
])
```
See [here](https://github.com/didriknielsen/survae_flows/blob/master/examples/mnist_vae.py) for a runnable example.
#### Example 3: Multi-Scale Augmented Flow
We can implement e.g. dequantization, augmentation and multi-scale flows using **surjective transformations**.
Here, we use these layers in a *multi-scale augmented flow* for (3,32,32) images such as CIFAR-10.
Notice that this makes use of 3 types of surjective layers:
1. **Generative rounding:** Implemented using `UniformDequantization`. Allows conversion to continuous variables. Useful for training continuous flows on ordinal discrete data.
1. **Generative slicing:** Implemented using `Augment`. Allows increasing dimensionality towards the latent space. Useful for constructing augmented normalizing flows.
1. **Inference slicing:** Implemented using `Slice`. Allows decreasing dimensionality towards the latent space. Useful for constructing multi-scale architectures.
```python
import torch.nn as nn
from survae.flows import Flow
from survae.distributions import StandardNormal, StandardUniform
from survae.transforms import AffineCouplingBijection, ActNormBijection2d, Conv1x1
from survae.transforms import UniformDequantization, Augment, Squeeze2d, Slice
from survae.nn.layers import ElementwiseParams2d
from survae.nn.nets import DenseNet
def net(channels):
return nn.Sequential(DenseNet(in_channels=channels//2,
out_channels=channels,
num_blocks=1,
mid_channels=64,
depth=8,
growth=16,
dropout=0.0,
gated_conv=True,
zero_init=True),
ElementwiseParams2d(2))
model = Flow(base_dist=StandardNormal((24,8,8)),
transforms=[
UniformDequantization(num_bits=8),
Augment(StandardUniform((3,32,32)), x_size=3),
AffineCouplingBijection(net(6)), ActNormBijection2d(6), Conv1x1(6),
AffineCouplingBijection(net(6)), ActNormBijection2d(6), Conv1x1(6),
AffineCouplingBijection(net(6)), ActNormBijection2d(6), Conv1x1(6),
AffineCouplingBijection(net(6)), ActNormBijection2d(6), Conv1x1(6),
Squeeze2d(), Slice(StandardNormal((12,16,16)), num_keep=12),
AffineCouplingBijection(net(12)), ActNormBijection2d(12), Conv1x1(12),
AffineCouplingBijection(net(12)), ActNormBijection2d(12), Conv1x1(12),
AffineCouplingBijection(net(12)), ActNormBijection2d(12), Conv1x1(12),
AffineCouplingBijection(net(12)), ActNormBijection2d(12), Conv1x1(12),
Squeeze2d(), Slice(StandardNormal((24,8,8)), num_keep=24),
AffineCouplingBijection(net(24)), ActNormBijection2d(24), Conv1x1(24),
AffineCouplingBijection(net(24)), ActNormBijection2d(24), Conv1x1(24),
AffineCouplingBijection(net(24)), ActNormBijection2d(24), Conv1x1(24),
AffineCouplingBijection(net(24)), ActNormBijection2d(24), Conv1x1(24),
])
```
See [here](https://github.com/didriknielsen/survae_flows/blob/master/examples/cifar10_aug_flow.py) for a runnable example.
#### Acknowledgements
This code base builds on several other repositories. The biggest sources of inspiration are:
* https://github.com/bayesiains/nsf
* https://github.com/pclucas14/pytorch-glow
* https://github.com/karpathy/pytorch-made
Thanks to the authors of these and the many other useful repositories!