Forward pass computation for GDAS NAS coding

promach: <p>For <a href="https://gist.github.com/promach/b6f526c56e20f029d68e6f9041c3f5c0#file-gdas-py-L143-L169" rel="noopener nofollow ugc">https://gist.github.com/promach/b6f526c56e20f029d68e6f9041c3f5c0#file-gdas-py-L143-L169</a> , how to do forward pass training computation for architecture <code>weights</code> of each and every edges ?</p>
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      <a href="https://github.com/D-X-Y/AutoDL-Projects/issues/99#issuecomment-835802887" target="_blank" rel="noopener nofollow ugc">Questions about DARTS</a>
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        opened <span class="discourse-local-date" data-format="ll" data-date="2021-05-01" data-time="04:47:17" data-timezone="UTC">04:47AM - 01 May 21 UTC</span>
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    <p class="github-body-container">1. For DARTS complexity analysis, anyone have any idea how to derive the *(k+1)\<span class="show-more-container"><a href="" rel="noopener" class="show-more">…</a></span><span class="excerpt hidden">*k/2* expression ?  Why 2 input nodes ?  How will the calculated value change if graph isomorphism is considered ?  Why "2+3+4+5" learnable edges ?  If there is lack of connection, the paper should not add 1 which does not actually contribute to learnable edges configurations at all ?

2. Why need to train the weights for normal cells and reduction cells **separately** as shown in Figures 4 and 5 below ?

3. How to arrange the nodes such that the NAS search will actually converge with minimum error ?   Note: Not all nodes are connected to each and every other nodes

4. Why is [GDAS](https://arxiv.org/pdf/1910.04465.pdf#page=7) 10 times faster than DARTS ?


![DARTS_normal_reduction_cells](https://user-images.githubusercontent.com/3324659/116770930-e97d4d80-aa79-11eb-8aae-ecf867aabe49.png)

![DARTS_complexity_analysis](https://user-images.githubusercontent.com/3324659/116770928-e2eed600-aa79-11eb-90b9-f00546958b12.png)</span></p>
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