Network graph view - tensorboard is only partly functional?

username: xvdp
cooked: <p>I wanted to understand StyleGAN2, and I do pytorch, so I grabbed a pytorch implementation and  ran tensorboard - but I didn’t like what I saw; So I did the same for the original tensorflow repo.</p>
<ul>
<li>Are there ways to get the cleanliness that one gets with tensorflow, using pytorch?</li>
<li>StyleGAN2 has a discriminator and generator, can I see both graphs in one environment?</li>
<li>is there an alternative to tensorboard that will make good graphs in pytorch, for more complex networks?<br>
I forked both repos, so this is all reproducible</li>
</ul>
<h2>StyleGAN2 Tensorflow Tensorboard</h2>
<p>                    <a href="https://raw.githubusercontent.com/xvdp/stylegan2/899c69c69418fb7f4daf227301e575a66085d4d8/jupyter/StyleGAN2.png" target="_blank" rel="nofollow noopener" class="onebox">
            <img src="https://raw.githubusercontent.com/xvdp/stylegan2/899c69c69418fb7f4daf227301e575a66085d4d8/jupyter/StyleGAN2.png" width="654" height="500">
          </a>

</p>
<h2>stylegan2-pytorch</h2>
<p>As you see the graphs are not nearly as neat - I only forked this implementation, I suppose I should build my own, as it has a few other problems that I havent yet sussed out, however if I do I would like to end up building a graph that looks as neat as what i see w tf. With tensorboard or other setup.<br>
To be fair, I dont think that the tensorboard representation is clean enough either. I would like to see a functional graph - so I can evaluate or read the code of any of the nodes. I dont want to forgo the cleanliness of code, but to be able to view and encapsulate the code in a funcional graph would be grand.</p>
<p>                    <a href="https://raw.githubusercontent.com/xvdp/stylegan2-pytorch/master/jupyter/StyleGAN2_Generator.png" target="_blank" rel="nofollow noopener" class="onebox">
            <img src="https://raw.githubusercontent.com/xvdp/stylegan2-pytorch/master/jupyter/StyleGAN2_Generator.png" width="465" height="500">
          </a>

<br>
                    <a href="https://raw.githubusercontent.com/xvdp/stylegan2-pytorch/master/jupyter/StyleGAN2_Discriminator.png" target="_blank" rel="nofollow noopener" class="onebox">
            <img src="https://raw.githubusercontent.com/xvdp/stylegan2-pytorch/master/jupyter/StyleGAN2_Discriminator.png" width="584" height="500">
          </a>

</p>
<h1>moreinfo</h1>
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      <a href="https://github.com/xvdp/stylegan2/tree/master/jupyter" target="_blank" rel="nofollow noopener">GitHub</a>
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    <img src="https://avatars2.githubusercontent.com/u/40925114?s=400&amp;v=4" class="thumbnail onebox-avatar" width="420" height="420">

<h3><a href="https://github.com/xvdp/stylegan2/tree/master/jupyter" target="_blank" rel="nofollow noopener">xvdp/stylegan2</a></h3>

<p>StyleGAN2 - Official TensorFlow Implementation - Modified for analysis of opportunities of extending the work - xvdp/stylegan2</p>


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      <a href="https://github.com/xvdp/stylegan2-pytorch/tree/master/jupyter" target="_blank" rel="nofollow noopener">GitHub</a>
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    <img src="https://avatars2.githubusercontent.com/u/40925114?s=400&amp;v=4" class="thumbnail onebox-avatar" width="420" height="420">

<h3><a href="https://github.com/xvdp/stylegan2-pytorch/tree/master/jupyter" target="_blank" rel="nofollow noopener">xvdp/stylegan2-pytorch</a></h3>

<p>StyleGAN2  PyTorch Implementation - Modified for Comparison with Official version and Graph Visualization - xvdp/stylegan2-pytorch</p>


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