Hi,
I have a little network that is generating 4 values (angle, scale, translation x, translation y) that I want to use to build an affine transformation matrix. I am doing it like this (the forward pass works, but backprop fails)
trans_matrix = torch.stack([
            trans_params[:, 1] * torch.cos(trans_params[:, 0]), trans_params[:, 1] * -1 * torch.sin(trans_params[:, 0]), trans_params[:, 2],
            trans_params[:, 1] * torch.sin(trans_params[:, 0]), trans_params[:, 1] *      torch.cos(trans_params[:, 0]), trans_params[:, 3]
        ]).view(-1, 2, 3)

I get this error:

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

This is the line that fails:
trans_params[:, 1] * torch.sin(trans_params[:, 0]), trans_params[:, 1] *      torch.cos(trans_params[:, 0]), trans_params[:, 3]

I guess that the slicing operations are the problem here. But how can I compose a matrix of sin and cos that are applied to certain dimensions of another tensor correctly?
There is also another location in my code that causes this error message:
affine_params[:, 0] = -1.0 * affine_params[:, 0] # Fails
affine_params[:, 1] = 1 / affine_params[:, 1] # works

This is even more confusion to me. Why does the division work but the multiplication not?
affine_params is the same tensor type as trans_params in the first code example.
Is there maybe even a more elegant way to construct an affine transformation matrix?