4c2ef2de74a2b80864591bb50d997da749c6461c,fairseq/models/fconv.py,,LinearizedConv1d,#Any#Any#Any#Any#,438

Before Change

    m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
    std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
    m.weight.data.normal_(mean=0, std=std)
    m.bias.data.zero_()
    return nn.utils.weight_norm(m, dim=2)


def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):

After Change

    m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
    std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
    nn.init.normal(m.weight, mean=0, std=std)
    nn.init.constant(m.bias, 0)
    return nn.utils.weight_norm(m, dim=2)


def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):

Instances