错误：需要grad并且没有grad_fn，并且叶子变量已移至图形内部

Question

每个人 我正在尝试训练一个pytorch模型，该模型包含一个自定义层，没有可学习的参数，并且该模型没有grad_fn错误。

import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
import torch.utils.data as Data

# custom layer
class Filter(nn.Module):
    def __init__(self, filter_size=10, filter_step=1):
        super(Filter, self).__init__()
        self.filter_size = filter_size
        self.filter_step = filter_step

    def Output_filter(self, x):
        frame_indx = 0
        while 1:
            if frame_indx + self.filter_size <= len(x):
                if sum(x[frame_indx: frame_indx + self.filter_size]) >= self.filter_size / 2:
                    x[frame_indx: frame_indx + self.filter_size] = 1

            else:
                if sum(x[frame_indx:]) >= len(x[frame_indx:]) / 2:
                    x[frame_indx:] = 1

                break
            frame_indx = frame_indx + self.filter_step
        return x

    def forward(self, x):

        x = F.relu(self.Output_filter(x))

        return x
# model
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(7,2)

        self.filter = Filter(3,1)
    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(x)
        x = torch.argmax(x, 1).float()
        x = self.filter(x)
        return x
if __name__ == "__main__":

    net = Net()
    optimizer = torch.optim.Adam(net.parameters())
    train_x = torch.rand((10,7),dtype=torch.float)
    train_y = torch.tensor([1,1,1,1,0,1,0,0,1,0], dtype=torch.float)
    train_dataset = Data.TensorDataset(train_x, train_y)
    train_loader = Data.DataLoader(dataset=train_dataset, batch_size=10, shuffle=False, num_workers=2)
    loss_func = nn.BCELoss()

    net.train()
    for step, (b_x, b_y) in enumerate(train_loader, 1):

        pred_train = net(b_x)
        loss_ = loss_func(pred_train, b_y)

        optimizer.zero_grad()
        loss_.backward()
        optimizer.step()

我的猜测是torch.argmax(x, 1)操作是不可区分的，因此我附加了require_grad_（True），例如  torch.argmax(x, 1).float().requires_grad_(True)，  但是这次模型抛出“ 叶变量已移入图形内部” 错误。

在pycharm调试模式下，x.is_leaf操作后torch.argmax为TRUE。 我的问题是，还有其他可区分的函数可以用来代替torch.argmax还是有其他方法可以使我的代码正常工作