def calc_gradient_penalty(self, netD, real_data, fake_data): alpha = torch.rand(1, 1) alpha = alpha.expand(real_data.size()) alpha = alpha.cuda() interpolates = alpha * real_data + ((1 - alpha) * fake_data) interpolates = interpolates.cuda() interpolates = Variable(interpolates, requires_grad=True) disc_interpolates, s = netD.forward(interpolates) s = torch.autograd.Variable(torch.tensor(0.0), requires_grad=True).cuda() gradients1 = autograd.grad(outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones(disc_interpolates.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradients2 = autograd.grad(outputs=s, inputs=interpolates, grad_outputs=torch.ones(s.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] if gradients2 is None: return None gradient_penalty = (((gradients1.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) + \ (((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) return gradient_penalty def get_loss(self, net,fakeB, realB): self.D_fake, x = net.forward(fakeB.detach()) self.D_fake = self.D_fake.mean() self.D_fake = (self.D_fake + x).mean() # Real self.D_real, x = net.forward(realB) self.D_real = (self.D_real+x).mean() # Combined loss self.loss_D = self.D_fake - self.D_real gradient_penalty = self.calc_gradient_penalty(net, realB.data, fakeB.data) return self.loss_D + gradient_penalty，return self.loss_D + gradient_penalty出现错误：TypeError: unsupported operand type(s) for +: 'Tensor' and 'NoneType'

这个错误是因为在 calc_gradient_penalty 函数中，如果 gradients2 是 None，则会返回 None。在 get_loss 函数中，如果 gradient_penalty 是 None，则返回的就是 self.loss_D + None，这导致了 TypeError 错误。

为了解决这个问题，你可以在 calc_gradient_penalty 函数中添加一个条件判断，如果 gradients2 是 None，则返回 0.0，而不是 None。这样，当计算 gradient_penalty 时，即使 gradients2 是 None，也不会导致错误。修改后的代码如下：

def calc_gradient_penalty(self, netD, real_data, fake_data):
    alpha = torch.rand(1, 1)
    alpha = alpha.expand(real_data.size())
    alpha = alpha.cuda()
    interpolates = alpha * real_data + ((1 - alpha) * fake_data)
    interpolates = interpolates.cuda()
    interpolates = Variable(interpolates, requires_grad=True)
    disc_interpolates, s = netD.forward(interpolates)
    s = torch.autograd.Variable(torch.tensor(0.0), requires_grad=True).cuda()
    gradients1 = autograd.grad(outputs=disc_interpolates, inputs=interpolates,
                               grad_outputs=torch.ones(disc_interpolates.size()).cuda(),
                               create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0]
    gradients2 = autograd.grad(outputs=s, inputs=interpolates,
                               grad_outputs=torch.ones(s.size()).cuda(),
                               create_graph=True, retain_graph=True,
                               only_inputs=True, allow_unused=True)[0]
    if gradients2 is None:
        return 0.0
    gradient_penalty = (((gradients1.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) + \
                        (((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA)
    return gradient_penalty

def get_loss(self, net,fakeB, realB):
    self.D_fake, x = net.forward(fakeB.detach())
    self.D_fake = self.D_fake.mean()
    self.D_fake = (self.D_fake + x).mean()
    # Real
    self.D_real, x = net.forward(realB)
    self.D_real = (self.D_real+x).mean()
    # Combined loss
    self.loss_D = self.D_fake - self.D_real
    gradient_penalty = self.calc_gradient_penalty(net, realB.data, fakeB.data)
    if gradient_penalty == None:
        gradient_penalty = 0.0
    return self.loss_D + gradient_penalty