This code means you just choose first label of 128 rois in each batch as the unique label, and you only use object attentive vectors just for this label's class ?
Why do you choose only the first one instead of all the labels of 128 rois?
or from your paper, I thought the code should be
" proposal_labels = rois_label[b 128:(b + 1) 128].data.cpu().numpy()"
Hello! Thank you for releasing code, I have some questions about the details in meta training.
In ./model/faster_rcnn/faster_rcnn.py Line 122,
" proposal_labels = rois_label[b 128:(b + 1) 128].data.cpu().numpy()[0] "
This code means you just choose first label of 128 rois in each batch as the unique label, and you only use object attentive vectors just for this label's class ? Why do you choose only the first one instead of all the labels of 128 rois?
or from your paper, I thought the code should be " proposal_labels = rois_label[b 128:(b + 1) 128].data.cpu().numpy()"