[MXNet Forum] [Gluon] Understanding and modifying Faster RCNN

[Cézar Bieniek Lemos via MXNet Forum <mx...@discoursemail.com.INVALID>]

Hi,

I am using MxNet 1.51 and GluonCv 0.4. 
Faster RCNN model from GluonCV model zoo returns by default the arrays `ids, scores, bboxes`. But instead of getting  only the id and the score of the best scoring class, I want to get the the whole array with the scores for all classes.

By inspecting the source code and in an ideal scenario, I would create a class that inherits from the `FasterRCNN` class and implement a method that is equal to hybrid_forward, but with a modified `nms` algorithm that would propagate the respective rows from `F.softmax(cls_pred, axis=-1)`.

Since I do not have enough time, I am trying to add this function directly on the source code.
But I have a problem, the hybrid_forward is defined as:

`def hybrid_forward(self, F, x, gt_box=None):`

There is this extra `F` parameter, that seems to be a module.

What I do not understand is that, being `net` an instance of `FasterRCNN`  and `x`  an input tensor, I can get the output as:
`ids, scores, bboxes = net(x)`  
With no extra parameter.
Also, I have not found a modified `__call__` function for the FasterRCNN class that would deal with the `F`.

Is this the right way to approach this problem?





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