Source code for ada.models.layers

import torch
from torch.autograd import Function


def weight_init_glorot_uniform(m):
    classname = m.__class__.__name__
    if classname.find("Linear") != -1:
        torch.nn.init.xavier_uniform_(m.weight)
        m.bias.data.fill_(0.01)
    if classname.find("Conv") != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find("BatchNorm") != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)


class ReverseLayerF(Function):
    @staticmethod
    def forward(ctx, x, alpha):
        ctx.alpha = alpha

        return x.view_as(x)

    @staticmethod
    def backward(ctx, grad_output):
        output = grad_output.neg() * ctx.alpha
        return output, None