Tensorflow 2.0 implementation of Sinusodial Representation networks (SIREN).
Project description
Tensorflow Sinusodial Representation Networks (SIREN)
Tensorflow 2.0 implementation of Sinusodial Representation networks (SIREN) from the paper Implicit Neural Representations with Periodic Activation Functions.
Installation
 Pip install
$ pip install upgrade tf_siren
 Pip install (test support)
$ pip install upgrade tf_siren[tests]
Usage
For general usage equivalent to the paper, import and use either SinusodialRepresentationDense
or SIRENModel
.
from tf_siren import SinusodialRepresentationDense
from tf_siren import SIRENModel
# You can use SinusodialRepresentationDense exactly like you ordinarily use Dense layers.
ip = tf.keras.layers.Input(shape=[2])
x = SinusodialRepresentationDense(32,
activation='sine', # default activation function
w0=1.0)(ip) # w0 represents sin(w0 * x) where x is the input.
model = tf.keras.Model(inputs=ip, outputs=x)
# Or directly use the model class to build a multi layer SIREN
model = SIRENModel(units=256, final_units=3, final_activation='sigmoid',
num_layers=5, w0=1.0, w0_initial=30.0)
For the (experimental) kernel scaled variants, import and use either ScaledSinusodialRepresentationDense
or ScaledSIRENModel
.
from tf_siren import ScaledSinusodialRepresentationDense
from tf_siren import ScaledSIRENModel
# You can use SinusodialRepresentationDense exactly like you ordinarily use Dense layers.
ip = tf.keras.layers.Input(shape=[2])
x = ScaledSinusodialRepresentationDense(32,
scale=1.0 # scale value should be carefully chosen in range [1, 2]
activation='sine', # default activation function
w0=1.0)(ip) # w0 represents sin(w0 * x) where x is the input.
model = tf.keras.Model(inputs=ip, outputs=x)
# Or directly use the model class to build a multi layer Scaled SIREN
model = ScaledSIRENModel(units=256, final_units=3, final_activation='sigmoid', scale=1.0,
num_layers=5, w0=1.0, w0_initial=30.0)
Results on Image Inpainting task
A partial implementation of the image inpainting task is available as the train_inpainting_siren.py
and eval_inpainting_siren.py
scripts inside the scripts
directory.
Weight files are made available in the repository under the Release
tab of the project. Extract the weights and place the checkpoints
folder at the scripts directory
These weights generates the following output after 5000 epochs of training with batch size 8192 while using only 10% of the available pixels in the image during training phase.
If we train for using only 20% of the available pixels in the image during training phase 
If we train for using only 30% of the available pixels in the image during training phase 
SIREN Hyper Network
We can use a Hyper Network in order to encode an entire dataset into the weights of a SIREN model. The weights for the SIREN model are generated by this hyper network, which computes these weights based on an encoded representation.
Support for the Hyper Network is available by using NeuralProcessHyperNet
, which uses the SetEncoder
from the paper as the encoder.
Training on the CIFAR 10 dataset is available inside the scripts
directory  train_cifar_inpainting_siren.py
and eval_cifar_inpainting_siren.py
.
Pretrained weights are available in the Release
tab under assets
.
On evaluating on the test set with 1000 context pixels, this model gets an average MSE of 0.009
. Using 100 context pixels, the MSE increases to 0.019
.
The following image is using 1000 context pixels on the test set :
(Experimental) Comparison of convergence between original and kernel scaled SIRENs
The kernel scaled variants of the model converge faster than the original SIREN under certain circumstances. All the models below are trained with Adam optimizer with constant learning rate of 5e5 for 5000 epochs and batch size of 8192 on the same image pixels (10% of the celtic spiral image).
The tensorboard logs can be found here 
Citation
@inproceedings{sitzmann2019siren,
author = {Sitzmann, Vincent
and Martel, Julien N.P.
and Bergman, Alexander W.
and Lindell, David B.
and Wetzstein, Gordon},
title = {Implicit Neural Representations
with Periodic Activation Functions},
booktitle = {arXiv},
year={2020}
}
Requirements
 Tensorflow 2.0+
 Matplotlib to visualize eval result
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for tf_siren0.0.5py2.py3noneany.whl
Algorithm  Hash digest  

SHA256  fd174ca5a932b3ae04597bb2d1d8da41b6b899cd5e2caa90ef970e4b6306679a 

MD5  d758abea48e0a7e90ed3729a1e532217 

BLAKE2b256  f949e27fd34d6c4ff58a2dc0cf758faf3d734d7b00d91030caa9548d8589971b 