Hello Forum!
I am experimenting with torchvision’s Mask R-CNN model, and it issues
a UserWarning during the forward pass:
UserWarning: The default behavior for interpolate/upsample with float
scale_factor changed in 1.6.0 to align with other frameworks/libraries,
and uses scale_factor directly, instead of relying on the computed
output size. If you wish to keep the old behavior, please set
recompute_scale_factor=True. See the documentation of nn.Upsample
for details.

First, can I safely ignore this warning?
If not, what would be the most practical way to address it?
Some context:
This is something of a known issue.  See for example:

  
      github.com/pytorch/vision
  
  
    
  
	  
  

  
    
      "This overload of nonzero is deprecated" 9
    

    
      
        opened 
        
          
        
        Apr 29, 2020
      
      

        
          closed 
        
          
        
        Sep 24, 2020
      
        

      
        
          
          LexRillo
        
      
    
  



  🐛 Bug
A change in torch\nn\functional might have change usability of the nonzero function used in fasterrcnn_resnet50_fpn. This produces a warning and...



    enhancement
    help wanted
    high priority
    module: models
    topic: object detection


  
  
    
    
  
  


(Here, the “interpolate/upsample” issue is mentioned along side the
more central “overload of nonzero” issue.)
I see this issue running, for example, pytorch/torchvision version
1.8.0.dev20201203/0.9.0.dev20201203 (as well as on a stable
version).
Note that recompute_scale_factor isn’t mentioned in the
documentation for nn.Upsample 2, although it is in
nn.functional.interpolate() 3.
Thanks for any advice.
K. Frank