class_docstr: Class performing adversarial training following TRADES protocol.
  
  | Paper link: https://proceedings.mlr.press/v97/zhang19p.html
class_docstr_tok: ["Class","performing","adversarial","training","following","TRADES","protocol",".","|","Paper","link",":","https",":","\/\/proceedings.mlr.press\/v97\/zhang19p.html"]
docstr: Train a model adversarially with TRADES protocol using a data generator.
  See class documentation for more information on the exact procedure.
  
  :param generator: Data generator.
  :param nb_epochs: Number of epochs to use for trainings.
  :param scheduler: Learning rate scheduler to run at the end of every epoch.
  :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
                            the target classifier.
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code: def fit_generator(
      self,
      generator: DataGenerator,
      nb_epochs: int = 20,
      scheduler: Optional[
          "torch.optim.lr_scheduler._LRScheduler"
      ] = None,
      **kwargs,
  ):  # pylint: disable=W0221
      """
      Train a model adversarially with TRADES protocol using a data generator.
      See class documentation for more information on the exact procedure.
  
      :param generator: Data generator.
      :param nb_epochs: Number of epochs to use for trainings.
      :param scheduler: Learning rate scheduler to run at the end of every epoch.
      :param kwargs: Dictionary of framework-specific arguments. These will be passed as such to the `fit` function of
                                the target classifier.
      """
      import torch
  
      logger.info(
          "Performing adversarial training with TRADES protocol"
      )
  
      # pylint: disable=W0212
      if (scheduler is not None) and (
          not isinstance(
              scheduler, torch.optim.lr_scheduler._LRScheduler
          )
      ):  # pylint: enable=W0212
          raise ValueError(
              "Invalid Pytorch scheduler is provided for adversarial training."
          )
  
      size = generator.size
      batch_size = generator.batch_size
      if size is not None:
          nb_batches = int(np.ceil(size / batch_size))
      else:
          raise ValueError("Size is None.")
  
      logger.info("Adversarial Training TRADES")
  
      for i_epoch in trange(
          nb_epochs, desc="Adversarial Training TRADES - Epochs"
      ):
          start_time = time.time()
          train_loss = 0.0
          train_acc = 0.0
          train_n = 0.0
  
          for batch_id in range(nb_batches):  # pylint: disable=W0612
              # Create batch data
              x_batch, y_batch = generator.get_batch()
              x_batch = x_batch.copy()
  
              _train_loss, _train_acc, _train_n = self._batch_process(
                  x_batch, y_batch
              )
  
              train_loss += _train_loss
              train_acc += _train_acc
              train_n += _train_n
  
          if scheduler:
              scheduler.step()
  
          train_time = time.time()
          logger.info(
              "epoch: %s time(s): %.1f loss: %.4f acc: %.4f",
              i_epoch,
              train_time - start_time,
              train_loss / train_n,
              train_acc / train_n,
          )
  
file_path: adversarial-robustness-toolbox/art/defences/trainer/adversarial_trainer_trades_pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import time
  from typing import Optional, Tuple, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.defences.trainer.adversarial_trainer_trades import AdversarialTrainerTRADES
  from art.estimators.classification.pytorch import PyTorchClassifier
  from art.data_generators import DataGenerator
  from art.attacks.attack import EvasionAttack
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 3 for calling the AdversarialTrainerTRADESPyTorch obj's underlying member method code with example usage: obj.fit_generator(generator, nb_epochs, scheduler) without return types