怎么将soft teacher中的单一固定阈值改为像lable Match中的自适应阈值（ACT），代码部分怎么修改

将Soft Teacher中的单一固定阈值改为自适应阈值（如ACT）需要进行以下修改：

1. 首先，需要定义一个新的Soft Teacher类，以包含自适应阈值的计算方法。

class ACTSoftTeacher(nn.Module):
    def __init__(self, teacher_model, temperature, act_freq):
        super(ACTSoftTeacher, self).__init__()
        self.teacher_model = teacher_model
        self.temperature = temperature
        self.act_freq = act_freq
        self.total_steps = 0
        self.threshold = 0.5  # 初始阈值设为0.5

    def calc_threshold(self, step):
        self.total_steps += 1
        if self.total_steps % self.act_freq == 0:
            # 计算最近act_freq个步骤中软标签和硬标签的KL散度
            kl_div = 0.0
            for i in range(self.act_freq):
                # 计算软标签
                teacher_outputs = self.teacher_model(input_ids, attention_mask)
                teacher_logits = teacher_outputs[0] / self.temperature
                teacher_probs = F.softmax(teacher_logits, dim=-1)
                # 计算硬标签
                student_outputs = self.student_model(input_ids, attention_mask)
                student_logits = student_outputs[0] / self.temperature
                student_probs = F.softmax(student_logits, dim=-1)

                kl_div += F.kl_div(teacher_probs.log(), student_probs, reduction='batchmean')

            # 将KL散度作为新的阈值
            self.threshold = kl_div.item()

    def forward(self, input_ids, attention_mask):
        with torch.no_grad():
            teacher_outputs = self.teacher_model(input_ids, attention_mask)
            teacher_logits = teacher_outputs[0] / self.temperature
            teacher_probs = F.softmax(teacher_logits, dim=-1)

        student_outputs = self.student_model(input_ids, attention_mask)
        student_logits = student_outputs[0] / self.temperature
        student_probs = F.softmax(student_logits, dim=-1)

        # 计算软标签和硬标签之间的阈值
        self.calc_threshold(step)
        soft_labels = self.threshold * teacher_probs + (1 - self.threshold) * student_probs
        hard_labels = student_probs

        outputs = (student_logits, soft_labels, hard_labels) + student_outputs[1:]
        return outputs

2. 在使用Soft Teacher进行训练的代码中，需要使用新的ACTSoftTeacher类并传入自适应阈值计算的超参数：

teacher = ACTSoftTeacher(teacher_model, temperature, act_freq)

其中，act_freq是控制阈值计算频率的超参数。

需要注意的是，自适应阈值的计算方法可以根据具体需求进行修改和优化。