Local feature dimensions

[wildcat5566]

Hello!

Thank you for the well-organised code repository.
I am currently working on (modifying) embedded feature dimensions based on the original paper, and the model generating code here.
https://github.com/huanghoujing/AlignedReID-Re-Production-Pytorch/blob/master/aligned_reid/model/Model.py#L23

According to original paper, the dimensions of feature map generated by Resnet50 are:
self.base[N,C,H,W] = N(images)x2048x7x7
and that of the local features are [N,c,H]=Nx128x7.

However, when I run train.py (with training), the aforementioned dimensions seem to be:
self.base[N,C,H,W] = Nx2048x8x4
and feat_local[N,c,H]=Nx128x8.

I wonder where could I modify this? (specifically the 'H' value, i.e. number of local features per image).
Should I set pretrained=False and modify it in the Bottleneck class?
https://github.com/huanghoujing/AlignedReID-Re-Production-Pytorch/blob/master/aligned_reid/model/resnet.py#L55

Thank you very much! :)