famos | Adversarial Framework for Parametric Image | Machine Learning library

by zalandoresearch Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | famos Summary

famos is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. famos has no vulnerabilities, it has a Permissive License and it has low support. However famos has 1 bugs and it build file is not available. You can download it from GitHub.

Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization" available at This code allows to generate image stylisation using an adversarial approach combining parametric and non-parametric elements. Tested to work on Ubuntu 16.04, Pytorch 0.4, Python 3.6. Nvidia GPU p100. It is recommended to have a GPU with 12, 16GB, or more of VRAM.

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Quality

Security

License

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Support

famos has a low active ecosystem.

It has 96 star(s) with 16 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of famos is current.

Quality

famos has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 124 code smells.

Security

famos has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

famos code analysis shows 0 unresolved vulnerabilities.

There are 4 security hotspots that need review.

License

famos is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

famos releases are not available. You will need to build from source code and install.

famos has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

famos saves you 713 person hours of effort in developing the same functionality from scratch.

It has 1647 lines of code, 63 functions and 9 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

This function is used to generate a famos
Compute the mix image for a given list of templates
Blend two scales
Cross border border
Get N templates
Gets the flow of a tensor
Reflect the object
Draw a random tile
Split the image into split ratios
Splits the image H
Generate GAN
Compute the derivative of the d block
Perform a random crop overfitting
Multiply RGB channels
Calculate the value of the gradient of the gradient
This function is used to set noise
Compute the gram matrix of x y
Get an image
Generate a Gaussian kernel
Calculate the cost of a two or more network
Function to plot statistics
Compute the eigenvalue of x

Get all kandi verified functions for this library.

famos Key Features

No Key Features are available at this moment for famos.

famos Examples and Code Snippets

No Code Snippets are available at this moment for famos.

Community Discussions

Trending Discussions on famos

How to implement fractionally strided convolution layers in pytorch?

How to lock an element to the bottom of a div that is in a container (square)?

QUESTION

How to implement fractionally strided convolution layers in pytorch?

Asked 2021-Feb-20 at 10:51

Before everything, I searched google and StackOverflow but I do not find any similar questions so here I propose a new one.

I'm interested in this paper and want to implement this SGAN for my project. The paper mentioned that its generator network is composed of "a stack of fractionally strided convolution layers", I found two different ways of implementing this in pytorch, one is:

...

ANSWER

Answered 2021-Feb-20 at 10:51

tldr; There are some shape constraints but both perform the same operations.

The output shape of nn.ConvTranspose2d is given by y = (x − 1)s - 2p + d(k-1) + p_out + 1, where x and y are the input and ouput shape, respectively, k is the kernel size, s the stride, d the dilation, p and p_out the padding and padding out. Here we keep things simple with s=1, p=0, p_out=0, d=1.

Therefore, the output shape of the transposed convolution is:

Source https://stackoverflow.com/questions/66287552

QUESTION

How to lock an element to the bottom of a div that is in a container (square)?

Asked 2017-Aug-11 at 12:35

...

ANSWER

Answered 2017-Aug-11 at 12:29

Based on your question, I suppose you want to absolutely position the "link" at the bottom of its parent. For that to wor, simply use:

Source https://stackoverflow.com/questions/45635146

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

Vulnerabilities

No vulnerabilities reported

Install famos

You can download it from GitHub.
You can use famos 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

mosaicGAN.py can now render a whole folder of test images with the trained model. Example videos: lion video with Münich and Berlin. with your myFolder and all images from that folder will be rendered by the generator of the GAN. Best to use the same test folder as content folder for training. To use in a video editing pipeline, save all video frames as images with a tool like AVIDEMUX, train FAMOS and save rendered frames, assemble again as video. Note: this my take some time to render thousands of images, you can edit in the code VIDEO_SAVE_FREQ to render the test image folder less frequently.

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