package uploaded

Author: akanimax

README.md

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+# pro_gan_pytorch
+Package contains implementation of ProGAN. 
+Paper titled "Progressive growing of GANs for improved 
+Quality, Stability, and Variation". <br>
+link -> https://arxiv.org/abs/1710.10196
+
+# Steps to use:
+1.) Install your appropriate version of PyTorch. 
+The torch dependency in this package uses the most basic
+"cpu" version. follow instructions on 
+<a href="http://pytorch.org/"> http://pytorch.org </a> to 
+install the "gpu" version of PyTorch.<br>
+
+2.)  Install this package using pip:
+    
+    $ workon [your virtual environment]
+    $ pip install pro-gan-pth
+    
+3.) In your code:
+    
+    import pytorch_pro_gan.PRO_GAN as pg
+ 
+ Use the modules `pg.Generator`, `pg.Discriminator` and
+ `pg.ProGAN`.
+ 
+    Help on class ProGAN in module pro_gan_pytorch.PRO_GAN:
+    
+    class ProGAN(builtins.object)
+     |  Wrapper around the Generator and the Discriminator
+     |  
+     |  Methods defined here:
+     |  
+     |  __init__(self, depth=7, latent_size=64, learning_rate=0.001, beta_1=0, beta_2=0.99, eps=1e-08, drift=0.001, n_critic=1, device=device(type='cpu'))
+     |      constructor for the class
+     |      :param depth: depth of the GAN (will be used for each generator and discriminator)
+     |      :param latent_size: latent size of the manifold used by the GAN
+     |      :param learning_rate: learning rate for Adam
+     |      :param beta_1: beta_1 for Adam
+     |      :param beta_2: beta_2 for Adam
+     |      :param eps: epsilon for Adam
+     |      :param n_critic: number of times to update discriminator
+     |      :param device: device to run the GAN on (GPU / CPU)
+     |  
+     |  optimize_discriminator(self, noise, real_batch, depth, alpha)
+     |      performs one step of weight update on discriminator using the batch of data
+     |      :param noise: input noise of sample generation
+     |      :param real_batch: real samples batch
+     |      :param depth: current depth of optimization
+     |      :param alpha: current alpha for fade-in
+     |      :return: current loss (Wasserstein loss)
+     |  
+     |  optimize_generator(self, noise, depth, alpha)
+     |      performs one step of weight update on generator for the given batch_size
+     |      :param noise: input random noise required for generating samples
+     |      :param depth: depth of the network at which optimization is done
+     |      :param alpha: value of alpha for fade-in effect
+     |      :return: current loss (Wasserstein estimate)
+     |  
+     |  ----------------------------------------------------------------------
+     |  Data descriptors defined here:
+     |  
+     |  __dict__
+     |      dictionary for instance variables (if defined)
+     |  
+     |  __weakref__
+     |      list of weak references to the object (if defined)

setup.py

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+from setuptools import setup, find_packages
+
+setup(
+    name='pro-gan-pth',
+    version='1.0',
+    packages=find_packages("."),
+    url='https://github.com/akanimax/pro_gan_pytorch',
+    license='MIT',
+    author='akanimax',
+    author_email='akanimax@gmail.com',
+    description='ProGAN package implemented as an extension of PyTorch nn.Module'
+)