我设计了一个定制的损失：

class CustomIndicesEdgeAccuracyLoss(torch.nn.Module):
    def __init__(self, num_classes: int, selected_indices: list):
        super(CustomIndicesEdgeAccuracyLoss, self).__init__()
        self.num_classes = num_classes
        self.selected_indices = selected_indices

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        batch_size, num_classes, feature_size = input.shape
        selected_input = input[::, ::, self.selected_indices]
        selected_target = target[::, self.selected_indices]
        selected_preds = torch.argmax(selected_input, dim=1)
        edge_acc = torch.eq(selected_preds, selected_target).sum()/torch.numel(selected_preds)
        loss = 1 – edge_acc
        loss.requires_grad = True

        return loss

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但是loss不会反向传播到模型参数，换句话说，模型参数的梯度总是为0，模型参数不能更新。可能的原因是什么？我应该如何修改代码？
下面是forward()的局部变量的一些信息：

input.shape: torch.Size([64, 3, 5])
target.shape:torch.Size([64, 5])
selected_input.shape: torch.Size([64, 3, 2]) 
selected_target.shape:torch.Size([64, 2])

型
PS.我在here中问了同样的问题，所以我会把答案从这篇文章复制到那里，反之亦然。