evaluators.py 文件源码

def pairwise_distance(features, query=None, gallery=None, metric=None):
    if query is None and gallery is None:
        n = len(features)
        x = torch.cat(list(features.values()))
        x = x.view(n, -1)
        if metric is not None:
            x = metric.transform(x)
        dist = torch.pow(x, 2).sum(dim=1, keepdim=True) * 2
        dist = dist.expand(n, n) - 2 * torch.mm(x, x.t())
        return dist

    x = torch.cat([features[f].unsqueeze(0) for f, _, _ in query], 0)
    y = torch.cat([features[f].unsqueeze(0) for f, _, _ in gallery], 0)
    m, n = x.size(0), y.size(0)
    x = x.view(m, -1)
    y = y.view(n, -1)
    if metric is not None:
        x = metric.transform(x)
        y = metric.transform(y)
    dist = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(m, n) + \
           torch.pow(y, 2).sum(dim=1, keepdim=True).expand(n, m).t()
    dist.addmm_(1, -2, x, y.t())
    return dist