questions regarding multiplicative constant for flow & correlation max_displacement in multi-levels

[InnovArul]

I am trying to understand the code and the design decisions made while designing this model.
In this regard, I have two doubts as below:

1. The correlation max_displacement is kept constant at 4 for all the levels of feature maps.
   

   back2future.pytorch/back2future.py

   Line 73 in a3b619a

   self.corr = Correlation(pad_size=4, kernel_size=1, max_displacement=4, stride1=1, stride2=1, corr_multiply=1)

In your opinion, do you think we have to have different max_displacement for different levels of the network?

2. I would like to get an intuition regarding the multiplication factors used for flow:
   0.625 for level-6 flow
   

   back2future.pytorch/back2future.py

   Lines 178 to 179 in a3b619a

   	feat5b_warped = self.warp(feat5b, 0.625*flow6_fwd_up)
   	feat5c_warped = self.warp(feat5c, -0.625*flow6_bwd_up)

1.25 for level-5 flow

back2future.pytorch/back2future.py

Lines 196 to 197 in a3b619a

	feat4b_warped = self.warp(feat4b, 1.25*flow5_fwd_up)
	feat4c_warped = self.warp(feat4c, -1.25*flow5_bwd_up)

2.5 for level-4 flow

back2future.pytorch/back2future.py

Lines 213 to 214 in a3b619a

	feat3b_warped = self.warp(feat3b, 2.5*flow4_fwd_up)
	feat3c_warped = self.warp(feat3c, -2.5*flow4_bwd_up)

5 for level-3 flow

back2future.pytorch/back2future.py

Lines 230 to 231 in a3b619a

	feat2b_warped = self.warp(feat2b, 5.0*flow3_fwd_up)
	feat2c_warped = self.warp(feat2c, -5.0*flow3_bwd_up)

Could you please let me know why do we need these multiplicative factors?
I am sorry if this is a basic question to be asked. As the network is learnable, isn't it possible that the network learns this multiplicative factor automatically as well?

Thanks in advance!