Started a pytorch port. Infos on training procedure

[lejeunel]

I am currently trying to reproduce the method on pyTorch. See here:
cobnet
FYI, a code (with python bindings) for the computation of the hierarchical merge tree is available in the higra package

I would therefore appreciate a more formal description of the training procedure as the paper is rather unclear on that matter.
From my understanding it goes like this.

phase 1:

• Regress a bunch of dimensionality reduction (conv1x1) layer that connect to output of resNet blocks. The outputs you call "side activations" are 5 in total.
• Regress two sets of parameters (conv1x1) for fine and coarse scales. These take as inputs tensors of 4 channels (concatenate 4 lowest and 4 highest side activations, respectively, to give Yfine and Ycoarse.
  It is unclear what you mean by " To train the two sets of weights of the linear combinations, we freeze the pre-trained weights...".
  Does that also apply to the side activation losses (eq. 1)? In that case, the parameters of the backbone would never be modified. My intuition is that phase 1 is in fact divided in two steps (1) sum all losses of eq. 1 and backprop through both dimensionality reduction and resNet. (2) freeze resNet and aforementioned dim reduction, compute sum of loss on Yfine and Ycoarse, backprop.
  Can you confirm this please?

phase 2:

• Freeze everything except the orientation layers to regress the oriented boundaries.

Thanks a lot in advance for your help.
Also, any help on the porting would be appreciated.