Weight of layer as a result of dot operation

Hi, I’m trying to implement a network, in which the weights of the layers should be calculated as a result of a tensor operation. This is the code I have:

class NOWANet(nn.Module):
    def __init__(self, V, Wstacked):
        super(NOWANet, self).__init__()

        # first conv layer
        # define layer
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5)

        # define tensors that will be used to calculate weights and biases
        self.conv1_W_V = nn.Parameter(V[0], requires_grad=True)
        self.conv1_B_V = nn.Parameter(V[1], requires_grad=True)
        self.conv1_W_stack = nn.Parameter(Wstacked[0], requires_grad=False)
        self.conv1_B_stack = nn.Parameter(Wstacked[1], requires_grad=False)
        # set layer weights and bias
        self.conv1.weight = nn.Parameter(torch.tensordot(self.conv1_W_V, 
                                                         dims=1, out=None),
        self.conv1.bias = nn.Parameter(torch.tensordot(self.conv1_B_V, 
                                                       dims=1, out=None),

The idea is to perform a tensordot between the V and W_stack tensors, which then should be used as weights and bias (one tensordot each). The catch is that I only want to optimize V so that W_stack remains the intact. The way it is currently written initializes the weights correctly, but backprop optimizes over the actual weights of the layer, there is no change to the V vector.

Any ideas or suggestions on how to do it?

Use the functionals instead of the convolutional module. Functionals takes weights as inputs.