gumbel-softmax(替代argmax)

由于argmax操作不可导，可用gumbel-softmax操作来替代

用它获得的onehot向量作为mask可以实现argmax效果

torch.nn.functional.gumbel_softmax(logits, tau=1, hard=False, eps=1e-10, dim=- 1)

Gumbel-Softmax 分布中与 logits 形状相同的采样张量。如果是 hard=True ，则返回的样本将为 one-hot，否则它们将是在 dim 中总和为 1 的概率分布。

Gumbel-Softmax Trick - 知乎 (zhihu.com)

#code is from GroupViT
def gumbel_softmax(logits: torch.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> torch.Tensor:
    # _gumbels = (-torch.empty_like(
    #     logits,
    #     memory_format=torch.legacy_contiguous_format).exponential_().log()
    #             )  # ~Gumbel(0,1)
    # more stable https://github.com/pytorch/pytorch/issues/41663
    gumbel_dist = torch.distributions.gumbel.Gumbel(
        torch.tensor(0., device=logits.device, dtype=logits.dtype),
        torch.tensor(1., device=logits.device, dtype=logits.dtype))
    gumbels = gumbel_dist.sample(logits.shape)

    gumbels = (logits + gumbels) / tau  # ~Gumbel(logits,tau)
    y_soft = gumbels.softmax(dim)

    if hard:
        # Straight through.
        index = y_soft.max(dim, keepdim=True)[1]
        y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
        ret = y_hard - y_soft.detach() + y_soft
    else:
        # Reparametrization trick.
        ret = y_soft
    return ret