Extension of input dim

[StrivedTye]

Hi Authors,

Thanks for your great work!
During training, I want to utilize other geometric features (e.g. normal) except the coordinates (x, y, z). This is, the input shape will become (B, N, 6) instead of (B, N, 3). After doing this extension, there is some problems about the calculation of the jacobi matrix. I still only calculate gradient with respect to x, y, and z: A1 = self.mlp1[0].weight[:, :3, :] . Does it make sense?

Thanks!

[Lilac-Lee]

Hi @StrivedTye, you could consider the ReLU-MLP as ReLU(Ax+b) (see eq. 8). Therefore, you are computing the partial derivative of the network of the input. If your input has size 6, you will get a gradient of size 6, and each dimension is in correspondence with your input. So, your understanding should be correct.

Cheers.