model.py 文件源码

def get_attr_loss(output, attributes, flip, params):
    """
    Compute attributes loss.
    """
    assert type(flip) is bool
    k = 0
    loss = 0
    for (_, n_cat) in params.attr:
        # categorical
        x = output[:, k:k + n_cat].contiguous()
        y = attributes[:, k:k + n_cat].max(1)[1].view(-1)
        if flip:
            # generate different categories
            shift = torch.LongTensor(y.size()).random_(n_cat - 1) + 1
            y = (y + Variable(shift.cuda())) % n_cat
        loss += F.cross_entropy(x, y)
        k += n_cat
    return loss