VQ-VAE | VQ-VAE implementation / pytorch | Dataset library

by nakosung Python Version: Current License: No License

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kandi X-RAY | VQ-VAE Summary

VQ-VAE is a Python library typically used in Artificial Intelligence, Dataset, Deep Learning, Pytorch applications. VQ-VAE has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

This is a repro of Vector Quantisation VAE from Deepmind. Authors had applied VQ-VAE for various tasks, but this repo is a slight modification of yunjey's VAE-GAN(CelebA dataset) to replace VAE with VQ-VAE.

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Support

VQ-VAE has a low active ecosystem.

It has 176 star(s) with 25 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 2 have been closed. On average issues are closed in 0 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of VQ-VAE is current.

Quality

VQ-VAE has 0 bugs and 0 code smells.

Security

VQ-VAE has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

VQ-VAE code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

VQ-VAE does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

VQ-VAE releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed VQ-VAE and discovered the below as its top functions. This is intended to give you an instant insight into VQ-VAE implemented functionality, and help decide if they suit your requirements.

Train the model
Show images summary
Convert x into a Variable object
Normalize x
Update the learning rate of the optimizer
Bwd backward
Resets gradient
Add a scalar summary
Loads the trained model
Load state from file
Sample the model
Decode a binary quadratic array
Forward forward computation
Detach from a Variable object
Infer the model
Get data loader
Save the current state of the model
Decodes the given array
Load the state from a file

Get all kandi verified functions for this library.

VQ-VAE Key Features

No Key Features are available at this moment for VQ-VAE.

VQ-VAE Examples and Code Snippets

No Code Snippets are available at this moment for VQ-VAE.

Community Discussions

Trending Discussions on VQ-VAE

ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors on a VQVAE

Implementation of VQ-VAE-2 paper

QUESTION

ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors on a VQVAE

Asked 2022-Mar-21 at 06:09

I am training a VQVAE with this dataset (64x64x3). I have downloaded it locally and loaded it with keras in Jupyter notebook. The problem is that when I ran fit() to train the model I get this error: ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors. Inputs received: [, ] . I have taken most of the code from here and adapted it myself. But for some reason I can't make it work for other datasets. You can ignore most of the code here and check it in the page, help is much appreciated.

The code I have so far:

...

ANSWER

Answered 2022-Mar-21 at 06:09

This kind of model does not work with labels. Try running:

Source https://stackoverflow.com/questions/71540034

QUESTION

Implementation of VQ-VAE-2 paper

Asked 2020-Apr-01 at 15:29

I am trying to build a 2 stage VQ-VAE-2 + PixelCNN as shown in the paper: "Generating Diverse High-Fidelity Images with VQ-VAE-2" (https://arxiv.org/pdf/1906.00446.pdf). I have 3 implementation questions:

The paper mentions:

We allow each level in the hierarchy to separately depend on pixels.

I understand the second latent space in the VQ-VAE-2 must be conditioned on a concatenation of the 1st latent space and a downsampled version of the image. Is that correct ?

The paper "Conditional Image Generation with PixelCNN Decoders" (https://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders.pdf) says:

h is a one-hot encoding that specifies a class this is equivalent to adding a class dependent bias at every layer.

As I understand it, the condition is entered as a 1D tensor that is injected into the bias through a convolution. Now for a 2 stage conditional PixelCNN, one needs to condition on the class vector but also on the latent code of the previous stage. A possibility I see is to append them and feed a 3D tensor. Does anyone see another way to do this ?

The loss and optimization are unchanged in 2 stages. One simply adds the loss of each stage into a final loss that is optimized. Is that correct ?

...

ANSWER

Answered 2020-Apr-01 at 15:29

Discussing with one of the author of the paper, I received answers to all those questions and shared them below.

Question 1

This is correct, but the downsampling of the image is implemented with strided convolution rather than a non-parametric resize. This can be absorbed as part of the encoder architecture in something like this (the number after each variable indicates their spatial dim, so for example h64 is [B, 64, 64, D] and so on).

Source https://stackoverflow.com/questions/60884274

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install VQ-VAE

You can download it from GitHub.
You can use VQ-VAE like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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