def initmod()

in models/utils.py [0:0]

• 22 lines of code
• 17 McCabe index (conditional complexity)

def initmod(m, gain=1.0, weightinitfunc=xavier_uniform_):
    validclasses = [nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d]
    if any([isinstance(m, x) for x in validclasses]):
        weightinitfunc(m, gain)
        if hasattr(m, 'bias') and isinstance(m.bias, torch.Tensor):
            m.bias.data.zero_()

    # blockwise initialization for transposed convs
    if isinstance(m, nn.ConvTranspose2d):
        # hardcoded for stride=2 for now
        m.weight.data[:, :, 0::2, 1::2] = m.weight.data[:, :, 0::2, 0::2]
        m.weight.data[:, :, 1::2, 0::2] = m.weight.data[:, :, 0::2, 0::2]
        m.weight.data[:, :, 1::2, 1::2] = m.weight.data[:, :, 0::2, 0::2]

    if isinstance(m, nn.ConvTranspose3d):
        # hardcoded for stride=2 for now
        m.weight.data[:, :, 0::2, 0::2, 1::2] = m.weight.data[:, :, 0::2, 0::2, 0::2]
        m.weight.data[:, :, 0::2, 1::2, 0::2] = m.weight.data[:, :, 0::2, 0::2, 0::2]
        m.weight.data[:, :, 0::2, 1::2, 1::2] = m.weight.data[:, :, 0::2, 0::2, 0::2]
        m.weight.data[:, :, 1::2, 0::2, 0::2] = m.weight.data[:, :, 0::2, 0::2, 0::2]
        m.weight.data[:, :, 1::2, 0::2, 1::2] = m.weight.data[:, :, 0::2, 0::2, 0::2]
        m.weight.data[:, :, 1::2, 1::2, 0::2] = m.weight.data[:, :, 0::2, 0::2, 0::2]
        m.weight.data[:, :, 1::2, 1::2, 1::2] = m.weight.data[:, :, 0::2, 0::2, 0::2]

    if isinstance(m, Conv2dWNUB) or isinstance(m, Conv2dWN) or isinstance(m, ConvTranspose2dWN) or \
            isinstance(m, ConvTranspose2dWNUB) or isinstance(m, LinearWN):
        norm = np.sqrt(torch.sum(m.weight.data[:] ** 2))
        m.g.data[:] = norm