Hi,
I am currently experimenting with an approach of using a single Unet network for 2 predictions for image segmentation task. The model output is [32, 7, 256, 256] to be matched with pathology and lunglobes grondtruth i.e [32, [0,1], 256, 256] --> [32, 2, 256, 256] and [32, [2,3,4,5,6], 256, 256] --> [32, 5, 256, 256]. However, I am stuck on efficiently backpropagating individual losses i,e loss1.backward() and loss2.backward() and getting the evaluation metric after each epoch.
Your suggestions are well appreciated