I’m currently implementing a DETR head for my model, to replace a semantic segmentation head. However, I’m getting strange results for the bboxes when in model.eval() where they’re all clustered closely together around the middle of the image, whereas in model.train() they seem reasonably spread out and actually detecting things.
Gradients on/off has no effect. There are no branches in the model code for module.training or anything like that, so I’m unsure why this occuring and where to look to resolve this; I’ve never had this issue before…
Ubuntu20.04 + Pytorch 1.7.1 + RTX3090 (CUDA 11.1)