API_loss for multi_classes semantic segmentation

[DAgalaxy]

Hello,

I would like to apply your API_loss to my 3_classes semantic segmentation task, but i am not sure it will be work or not after some modification?
Do you have any advice? Look forward to your reply~

Thanks, best wish!

[Karel911]

Hello @DAgalaxy,
I cannot be definitely sure but, perhaps it can work with your task.
The logit of your network returns the shape of X \in R^(B, 3, H, W)?

[DAgalaxy]

Thanks for your reply！ Yes, my net's output is the shape as you said. As you see, can the loss be modified to Sum C(i=0~2) ... directly? | | ***@***.*** | | ***@***.*** |

…

---- Replied Message ---- | From | ***@***.***> | | Date | 10/11/2022 20:45 | | To | ***@***.***> | | Cc | ***@***.******@***.***> | | Subject | Re: [Karel911/TRACER] API_loss for multi_classes semantic segmentation (Issue #27) | Hello @DAgalaxy, I cannot be definitely sure but, perhaps it can work with your task. The logit of your network returns the shape of X \in R^(B, 3, H, W)? — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: ***@***.***>

[Karel911]

How about the ground truth? It was encoded with 0, 1, and 2?
The API loss highly depends on the shape of GTs.
If I'm right, maybe you used nn.CrossEntropyLoss().

[DAgalaxy]

My data's GT are encoded with 0,1,2 ids default, but I can transform it to one-hot with 3 channels, if so, can I use region_based loss like iou_loss or Dice_loss?

[Karel911]

Yes, it is possible. Just calculate the API loss per each channel.
The example form of loss is L = CELoss(logit, original GT) + API(map with prob_class0, encoded channel 0) + API(map with prob_class1, encoded channel 1) + API(map with prob_class2, encoded channel 2)

[DAgalaxy]

Got it! Thank you very much!