Maybe I didn't understand well.
In my opinion, the number of points used in training are determined by event_shape, which is default 256.
And in denosie eval, I found the number of points used in prediction is 1024.
if x_points.shape[0] > 1024:
indices = subsample_indices(n_points_orig, subsample_size=1024, seed=-1)
x_points = x_points[indices, :]
x_points_pp = x_points_pp[indices] if x_points_pp is not None else None
indices_not_selected = np.delete(np.arange(n_points_orig), indices)
x_points_mask_indices_not_selected = np.where(x_points_mask.detach().cpu().numpy())[0][
indices_not_selected]
x_points_mask[x_points_mask_indices_not_selected] = False
How to under the difference between them, Thanks
Maybe I didn't understand well.
In my opinion, the number of points used in training are determined by event_shape, which is default 256.
And in denosie eval, I found the number of points used in prediction is 1024.
How to under the difference between them, Thanks