box_nms: Boxes are negative values

[shreyasr-upenn]

Hi,

Thank you so much for this implementation. I was trying to train the magicpoint network in Linux, and I encountered this issue in NMS:

RuntimeError: Trying to create tensor with negative dimension -1459651072: [-1459651072]

On investigating, I see that the boxes have negative values. How do I correct this?

File /workspace/SuperPoint/models/magicpoint.py:39, in MagicPoint.forward(self, x)
     [37](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:37) prob = output['prob']
     [38](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:38) if self.nms is not None:
---> [39](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:39)     prob = [box_nms(p.unsqueeze(dim=0),
     [40](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:40)                     self.nms,
     [41](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:41)                     min_prob=self.threshold,
     [42](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:42)                     keep_top_k=self.top_k).squeeze(dim=0) for p in prob]
     [43](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:43)     prob = torch.stack(prob)
     [45](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:45)     pred = prob[prob>=self.threshold]

File /workspace/SuperPoint/models/magicpoint.py:39, in <listcomp>(.0)
     [37](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:37) prob = output['prob']
     [38](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:38) if self.nms is not None:
---> [39](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:39)     prob = [box_nms(p.unsqueeze(dim=0),
     [40](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:40)                     self.nms,
     [41](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:41)                     min_prob=self.threshold,
     [42](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:42)                     keep_top_k=self.top_k).squeeze(dim=0) for p in prob]
     [43](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:43)     prob = torch.stack(prob)
     [45](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/models/magicpoint.py:45)     pred = prob[prob>=self.threshold]

File /workspace/SuperPoint/utils/nms.py:70, in box_nms(prob, size, iou, min_prob, keep_top_k)
     [67](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/utils/nms.py:67) if boxes.nelement() == 0 or scores.nelement() == 0:
     [68](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/utils/nms.py:68)     print("Error: One of the tensors is empty")
---> [70](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/utils/nms.py:70) indices = torchvision.ops.nms(boxes=boxes, scores=scores, iou_threshold=iou)
     [71](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/utils/nms.py:71) pts = pts[indices,:]
     [72](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/workspace/SuperPoint/utils/nms.py:72) scores = scores[indices]

File /usr/local/lib/python3.8/dist-packages/torchvision/ops/boxes.py:41, in nms(boxes, scores, iou_threshold)
     [39](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torchvision/ops/boxes.py:39)     _log_api_usage_once(nms)
     [40](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torchvision/ops/boxes.py:40) _assert_has_ops()
---> [41](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torchvision/ops/boxes.py:41) return torch.ops.torchvision.nms(boxes, scores, iou_threshold)

File /usr/local/lib/python3.8/dist-packages/torch/_ops.py:442, in OpOverloadPacket.__call__(self, *args, **kwargs)
    [437](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torch/_ops.py:437) def __call__(self, *args, **kwargs):
    [438](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torch/_ops.py:438)     # overloading __call__ to ensure torch.ops.foo.bar()
    [439](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torch/_ops.py:439)     # is still callable from JIT
    [440](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torch/_ops.py:440)     # We save the function ptr as the `op` attribute on
    [441](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torch/_ops.py:441)     # OpOverloadPacket to access it here.
--> [442](https://vscode-remote+attached-002dcontainer-002b7b22636f6e7461696e65724e616d65223a222f7375706572706f696e74227d-0040ssh-002dremote-002b10-002e64-002e60-002e182.vscode-resource.vscode-cdn.net/usr/local/lib/python3.8/dist-packages/torch/_ops.py:442)     return self._op(*args, **kwargs or {})

RuntimeError: Trying to create tensor with negative dimension -1707356657: [-1707356657]