Implementing DIoU, calculate grad explicitly

vision
1

Hi,

I’ve implemented the DIoU loss as described in this paper. Unfortunately I’m getting the error grad can be implicitly created only for scalar outputs.

Following this thread I suppose this is because my returned loss has the shape (B x Loss) (so one loss for each element in Batch). My loss function returns the following 1 - self.bbox_iou(y, y_hat) + self.bbox_diou_penalty(y, y_hat).

I could use something like torch.mean() or torch.sum() to solve this problem. However, this would presumable result in bad predictions. From my rudimentary understanding I guess there is a way to calculate the grad explicitly, using the requires_grad Flag, but how exactly do I do this?

Thanks

2

If you are reducing the loss and call .backward() on it implicitly a torch.ones(1) gradient will be passed as the gradient argument to backward. Since you are dealing with multiple elements in your loss, you could use e.g. loss.backward(gradient=torch.ones_like(loss)) instead.