def xaviermultiplier()

in models/utils.py [0:0]

• 38 lines of code
• 8 McCabe index (conditional complexity)

def xaviermultiplier(m, gain):
    if isinstance(m, nn.Conv1d):
        ksize = m.kernel_size[0]
        n1 = m.in_channels
        n2 = m.out_channels

        std = gain * math.sqrt(2.0 / ((n1 + n2) * ksize))
    elif isinstance(m, nn.ConvTranspose1d):
        ksize = m.kernel_size[0] // m.stride[0]
        n1 = m.in_channels
        n2 = m.out_channels

        std = gain * math.sqrt(2.0 / ((n1 + n2) * ksize))
    elif isinstance(m, nn.Conv2d):
        ksize = m.kernel_size[0] * m.kernel_size[1]
        n1 = m.in_channels
        n2 = m.out_channels

        std = gain * math.sqrt(2.0 / ((n1 + n2) * ksize))
    elif isinstance(m, nn.ConvTranspose2d):
        ksize = m.kernel_size[0] * m.kernel_size[1] // m.stride[0] // m.stride[1]
        n1 = m.in_channels
        n2 = m.out_channels

        std = gain * math.sqrt(2.0 / ((n1 + n2) * ksize))
    elif isinstance(m, nn.Conv3d):
        ksize = m.kernel_size[0] * m.kernel_size[1] * m.kernel_size[2]
        n1 = m.in_channels
        n2 = m.out_channels

        std = gain * math.sqrt(2.0 / ((n1 + n2) * ksize))
    elif isinstance(m, nn.ConvTranspose3d):
        ksize = m.kernel_size[0] * m.kernel_size[1] * m.kernel_size[2] // m.stride[0] // m.stride[1] // m.stride[2]
        n1 = m.in_channels
        n2 = m.out_channels

        std = gain * math.sqrt(2.0 / ((n1 + n2) * ksize))
    elif isinstance(m, nn.Linear):
        n1 = m.in_features
        n2 = m.out_features

        std = gain * math.sqrt(2.0 / (n1 + n2))
    else:
        return None

    return std