how to code the following graph structure ?
which kind of data structure is suitable in this case for both forward inference and backward propagation ?
Would Tutorial — NetworkX 2.5 documentation or PyTorch Geometric Documentation — pytorch_geometric 1.7.0 documentation or https://www.dgl.ai/ be suitable ?

image976×1015 285 KB


  
      github.com/D-X-Y/AutoDL-Projects
  
  
    
  
	  
  

  
    
      Questions about DARTS
    

    
      
        opened 
        
          
        
        May 1, 2021
      
      


      
        
          
          promach
        
      
    

    
        question
    
  


  
    1. For DARTS complexity analysis, anyone have any idea how to derive the *(k+1)\…*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)
  

  
  
    
    
  
  



  
      gist.github.com
  
  
    https://gist.github.com/promach/b6f526c56e20f029d68e6f9041c3f5c0#file-gdas-py-L106-L107 19
gdas.py
# https://github.com/D-X-Y/AutoDL-Projects/issues/99

import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

import torchvision
import torchvision.transforms as transforms
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