Metadata-Version: 2.1
Name: gans_implementations
Version: 0.1.0
Summary: A bunch of GAN implementations
Home-page: https://github.com/UdbhavPrasad072300/GANs-Implementations
Author: Udbhav Prasad
Author-email: udbhavprasad072300@gmail.com
License: MIT
Description: # GANs-Implementations
        
        <p>
          <a href="https://github.com/UdbhavPrasad072300/GANs-Implementations/blob/main/LICENSE">
                <img alt="License" src="https://img.shields.io/github/license/UdbhavPrasad072300/GANs-Implementations">
          </a>
          <a href="https://pypi.org/project/GANs-Implementations/">
                <img alt="PyPi Version" src="https://img.shields.io/pypi/v/GANs-Implementations">
          </a>
          <a href="https://pypi.org/project/GANs-Implementations/">
                <img alt="PyPi Downloads" src="https://img.shields.io/pypi/dm/GANs-Implementations">
          </a>
          <a href="https://pypi.org/project/GANs-Implementations/">
                <img alt="Package Status" src="https://img.shields.io/pypi/status/GANs-Implementations">
          </a>
        </p>
        
        GANs Implementations and other generative models + Training (in ./notebooks)
        
        Implemented:
        <ul>
            <li>Vanilla GAN</li>
            <li>DCGAN - Deep Convolutional GAN</li>
            <li>WGAN - Wasserstein GAN</li>
            <li>SNGAN - Spectrally Normalized GAN </li>
            <li>SRGAN - Super Resolution GAN </li>
            <li>StyleGAN</li>
            <li>Pix2PixHD</li>
            <li>C-VAE - Convolutional Variational Auto-encoder</li>
        </ul>
        
        ## Installation
        
        <a href="https://pypi.org/project/gans-implementations/">PyPi Installation</a>
        
        ```bash
        $ pip install gans-implementations
        ```
        
        Local Install and Run: 
        
        ```bash
        $ cd {PROJECT_DIRECTORY}
        $ pip install -e .
        ```
        
        ## Example
        
        In notebooks directory there is a notebook on how to use each of these models for their intented use case; such as image 
        generation for StyleGAN and others. Check them out!
        
        ```python
        from gans_package.models import StyleGAN_Generator, StyleGAN_Discriminator
        
        in_channels = 256
        out_channels = 3
        hidden_channels = 512
        z_dim = 128
        mapping_hidden_size = 256
        w_dim = 512
        synthesis_layers = 5
        kernel_size=3
        
        in_size = 3
        d_hidden_size = 16
        
        g = StyleGAN_Generator(in_channels, 
                               out_channels, 
                               hidden_channels, 
                               z_dim, 
                               mapping_hidden_size, 
                               w_dim, 
                               synthesis_layers, 
                               kernel_size, 
                               device=DEVICE).to(DEVICE)
        
        d = StyleGAN_Discriminator(in_size, d_hidden_size).to(DEVICE)
        
        import torch
        
        noise = torch.randn(BATCH_SIZE, z_dim).to(DEVICE)
        
        fake = g(noise)
        pred = d(fake)
        ```
        
        ## Handwritten Digits - MNIST 
        
        <ul>
          <li><a href="https://github.com/UdbhavPrasad072300/GANs-Implementations/blob/main/notebooks/GAN%20with%20BCE%20-%20MNIST.ipynb">GAN with BCELoss</a></li>
          <li><a href="https://github.com/UdbhavPrasad072300/GANs-Implementations/blob/main/notebooks/DCGAN%20with%20BCE%20-%20MNIST.ipynb">DCGAN with BCELoss</a></li>
          <li><a href="https://github.com/UdbhavPrasad072300/GANs-Implementations/blob/main/notebooks/SN-WGAN%20with%20GP%20-%20MNIST.ipynb">SN-WGAN with Wasserstein Loss</a></li>
        </ul>
        
        ## Work Cited
        
        https://arxiv.org/pdf/1609.04802v5.pdf
        
        https://arxiv.org/pdf/1812.04948.pdf
        
        https://www.coursera.org/specializations/generative-adversarial-networks-gans?
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3 :: Only
Description-Content-Type: text/markdown
