啥时候增加一下最近邻插值算子的二阶导算子,paddle

pieyvz9o  于 2023-02-04  发布在  其他
关注(0)|答案(4)|浏览(205)
def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01):
    noise = paddle.randn(fake_img.shape) / math.sqrt(
        fake_img.shape[2] * fake_img.shape[3])
    grad = paddle.grad(outputs=(fake_img * noise).sum(),
                       inputs=latents,
                       create_graph=True)[0]
    path_lengths = paddle.sqrt((grad * grad).sum(2).mean(1))

    path_mean = mean_path_length + decay * (path_lengths.mean() -
                                            mean_path_length)

    path_penalty = ((path_lengths - path_mean) *
                    (path_lengths - path_mean)).mean()

    return path_penalty, path_lengths.detach().mean(), path_mean.detach()

w8ntj3qf

w8ntj3qf1#

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pvcm50d1

pvcm50d12#

把代码里的 True 改成 False

create_graph=True)[0]
gc0ot86w

gc0ot86w3#

为了,梯度惩罚实现,而不是仅仅是让这份代码跑

lsmepo6l

lsmepo6l4#

把代码里的 True 改成 False

create_graph=True)[0]

请你可以去看看paddlegan stylegan2 model里面梯度惩罚的实现,而不是仅仅跑通,我解释的清楚了吗

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