PyTorch is not only a good deep learning framework, but also a fast tool when it comes to matrix operations and convolutions on large data. A great example is PyTorchWavelets.
In this package I implement basic NMF and NMFD module minimizing beta-divergence using multiplicative update rules,
and is based on torch.nn.Module
so the models can be moved freely among CPU/GPU devices.
Part of the multiplier is obtained via torch.autograd
so the amount of codes is reduced and easy to maintain
(only the denominator is calculated).
The interface is similar to sklearn.decomposition.NMF
with some extra options.
Here is a short example of decompose a spectrogram.
import torch
import librosa
from torchnmf import NMF
from torchnmf.metrics import KL_divergence
y, sr = librosa.load(librosa.util.example_audio_file())
y = torch.from_numpy(y)
windowsize = 2048
S = torch.stft(y, windowsize, window=torch.hann_window(windowsize)).pow(2).sum(2).sqrt().cuda()
R = 8 # number of components
net = NMF(S.shape, n_components=R).cuda()
# run extremely fast on gpu
_, V = net.fit_transform(S) # fit to target matrix S
print(KL_divergence(V, S)) # KL divergence to S
A more detailed version can be found here, which redo this example with NMFD.
The barchart shows the time cost per iteration with different beta-divergence. It is clear that pytorch-based NMF is faster than scipy-based NMF (sklearn) when beta != 2 (Euclidean distance), and run even faster when computation is done on GPU. The test is conducted on a Acer E5 laptop with i5-7200U CPU and GTX 950M GPU.
Using pip:
pip install git+http://github.com/yoyololicon/pytorch-NMFs
Or clone this repo and do:
python setup.py install
- PyTorch == 0.4.1
- If you notice significant slow down when operating on CPU, please flush denormal numbers by
torch.set_flush_denormal(True)
. - Replace zeros in target matrix with small value to avoid overflow.
- Support sparse matrix.
- Regularization.
- NNDSVD initialization.