batch size > 1

[facundolazcano]

Hi, thanks for sharing your research.

I need to stack vertices and faces in a batch, but I note that all your experiments in the repository have batch size=None. Have you tried stacking vertices and faces in a batch, adding padding like pytorch3d to avoid shape differences? Can batch size and padding produce an error or bad behavior in diffusion-net?

[nmwsharp]

Hi!

I believe that all of the methods in this codebase are set up to take [B,V,C] tensors (etc), where the leading dimension is a padded rectangular batch like you describe, so I think it should all "just work".

(However, that is off the top of my head, and none of the examples in this repo uses padded batches, so be wary :) If you try it and run in to any issues please write back here to let us know.)

[MatteoGioia]

Hi!

I believe that all of the methods in this codebase are set up to take [B,V,C] tensors (etc), where the leading dimension is a padded rectangular batch like you describe, so I think it should all "just work".

(However, that is off the top of my head, and none of the examples in this repo uses padded batches, so be wary :) If you try it and run in to any issues please write back here to let us know.)

Hi @nmwsharp! Sorry for replying on an old issue but I thought it would be best to use one that was already open.

I am trying to work with padded triangular mesh in batches (e.g. with positions BxNxV and faces BxNxF). I essentially implemented padding with zeros to make sure each mesh has the same number of faces and vertices.

When I try to run the code, however, I get a bunch of errors regarding dimensions. For instance, I had to modify the face_coords method to something like

def face_coords(verts, faces):

    coords_tensor = None
    coords = None
    if verts.dim() > 2:
        coords_tensor = torch.zeros((verts.shape[0], faces.shape[1], 3,3))
        for index in range(verts.shape[0]):
            coords_tensor[index] = verts[index][faces[index]]
        coords = coords_tensor
    else:
        coords = verts[faces]

    return coords

I was initially trying to use default torch_geometric batching as you also mention in #10, however it forces me to recompute operators for each batch every time as the vertices/faces included in the matrix are shuffled by the dataloader.

Would it be possible to make batched training work? It's a shame as the model is really light and efficient and I really want to take advantage of batching. Thank you in advance :)

Edit: I just noticed that the error is only when computing operators. If I compute them separately and then concatenate them the rest of the code works, so the fix might be simpler than I initially thought