代码问题

[wiluen]

for i in range(task_num):

        # 1. run the i-th task and compute loss for k=0
        logits = self.net(x_spt[i], vars=None, bn_training=True)
        loss = F.cross_entropy(logits, y_spt[i])
        grad = torch.autograd.grad(loss, self.net.parameters())
        fast_weights = list(map(lambda p: p[1] - self.update_lr * p[0], zip(grad, self.net.parameters())))

        # this is the loss and accuracy before first update
        with torch.no_grad():
            # [setsz, nway]
            logits_q = self.net(x_qry[i], self.net.parameters(), bn_training=True)
            loss_q = F.cross_entropy(logits_q, y_qry[i])
            losses_q[0] += loss_q

            pred_q = F.softmax(logits_q, dim=1).argmax(dim=1)
            correct = torch.eq(pred_q, y_qry[i]).sum().item()
            corrects[0] = corrects[0] + correct

在每个任务的第一轮， logits_q = self.net(x_qry[i], self.net.parameters(), bn_training=True)为什么用的网络参数是 self.net.parameters()而不是fast_weights呢？这两个数据的值并不一样。而后续的更新用的一直是fast_weights