如何添加新的评测算法
~~~~~~~~~~~~~~~~~~~~~~~~

攻击算法存储位置
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::

  ├── EvalBox
  │   ├── **Attack**
  │   │   ├──__init__.py
  │   │   ├──attack.py
  │   │   ├──fgsm.py
  │   │   ├──pgd.py
  │   │   ├──....py
  ├── Models
  ├── utils
  ├── test
  ├── Datasets

扩展实例——FGSM算法
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FGSM算法路径为：

::

  ~/AI-Testing/audio/EvalBox/Attack/fgsm.py

FGSM算法源代码:

::

  import torch
  import torch.nn as nn
  from audio.EvalBox.Attack.attack import Attacker
  from audio.EvalBox.Attack.utils import target_sentence_to_label


  class FGSMAttacker(Attacker):
      def __init__(self, model, device, **kwargs):
          super(FGSMAttacker, self).__init__(model, device)
          self._parse_params(**kwargs)
          self.criterion = nn.CTCLoss()

      def _parse_params(self, **kwargs):
          self.eps = kwargs.get("eps", 0.025)

      def generate(self, sounds, targets):
          targets = target_sentence_to_label(targets)
          targets = targets.view(1, -1).to(self.device).detach()
          target_lengths = torch.IntTensor([targets.shape[1]]).view(1, -1)
          advs = sounds.clone().detach().to(self.device).requires_grad_(True)
          self.model.zero_grad()
          with torch.backends.cudnn.flags(enabled=False):
              out, output_sizes = self.model(advs)
              out = out.transpose(0, 1).log()
              loss = self.criterion(out, targets, output_sizes, target_lengths)
              loss.backward()
              data_grad = advs.grad.data.nan_to_num(nan=0)
              advs = advs - self.eps * data_grad.sign()
              advs = advs.detach().requires_grad_(True)
          advs = torch.clamp(advs, min=-1, max=1)
          return advs

扩展说明
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1. 用户需要实现个人攻击算法，并继承基础的Attack类

2. 用户需要将待扩展的攻击算法对应文件，如new_attack_method.py，放置于以下路径中

::

  ~/AI-Testing/audio/EvalBox/Attack/

3. 用户需要在2中路径下的__init__.py文件中，添加用户攻击算法类的引用：

::

  from .attack import Attack
  from .fgsm import FGSM
  ...
  from .new_attack_method import NEW_ATTACK_METHOD