bug and question about hdmapnet

[Francis777]

Hi @liqi17thu, I find the two adaptations you mentioned in 3.(1) very interesting to think about.

1. Using softmax instead of sigmoid as activation for prediction
   I understand this as you still want to encourage the network to predict only 1 class(with high likelihood), rather than in typical multi-label classification setting where the prediction for each class is treated as independent variables. Is that correct?
2. Normalising the labels
   I assume you're doing standard normalisation rather than softmax, so this won't change the BCE loss for the classes with label 0, but will change the BCE loss of the 2 classes with label 1 from NLL(red curve below) to (blue curve below), this would mean that the averaged loss is lowest when the network predicts probability 0.5 for both classes with label 1, but relatively high when it predicts probability 1 for one of the 2 correct classes. This seems opposite to the motivation of 1, so I'd love to hear more about your thoughts on the design of this loss, thanks!
   

[liqi0126]

1. Thanks for pointing out this! We have fixed this on the main branch.
2. Your modification looks great! Is this only a visualization change or did you also modify the labels? Also, feel free to create a pull request for this modification.
3. (1) We use BCE loss and one adaptation is that we also "softmax" the label, that is if there are two 1s, then we will normalize them to 0.5. But I guess it would be fine to directly use sigmoid as the activation function.
   (2) This configuration is used throughout the whole paper. For the second question, a larger perception range should result in worse numerical results because it could be harder for sensors to perceive roads at a long distance. As for the resolution, I think a larger resolution would result in heavier computation costs. Also the solution should not be too small because a road divider won't have a width, say 0.3m.

[Vishal1711]

How you visualize generated label?