[NNVM] Porting mxnet yolo model

Hello,

I’ve been trying to port an mxnet yolo model that I’ve trained and have had little success.
It would appear that darknyet yolo orders the conv_pred output channels for each cell: [x,y,w,h, obj-score, [cats]]
Whereas mxnet yolo formats the channels as: [[cats], obj-score, x, y, w, h].
I’ve thus reordered the weights and biases in the conv_pred layer to match the expected output format, however the output is not as expected.
From looking at the source code in topi for yolo_region I see that the layer is only performing the activation functions for the region layer, thus to debug I look at what I expect to be the activated obj-score feature map. When I run my model in mxnet I have an example image with a detection with obj-score 0.16. However if I check the obj-score feature maps out of tvm and I find none with a similar obj-score.

Does my approach sound correct? Does anyone have ideas where else to look into?
Next I plan on looking at an unmodified conv_pred output to see if the results are the same as from mxnet.