最简单，最明显的方法是修改Keras代码(您可以找到此处).但是，尽管没有很好的记录，Keras确实提供了一个优雅的API 来处理此问题.

The easiest and most obvious way is to modify the Keras code (you can find full implementation of ImageDataGenerator in here). However, Keras does provide an elegant API to deal with this, although it is not very well documented.

我们需要编写一个新的Keras生成器，该生成器继承自 Iterator 类. Iterator 类本身只是来自 Sequence 的便捷子类，有关详细教程，请参见这里.

We would need to write a new Keras generator, inheriting from Iterator class. The Iterator class itself is just a convenient child class from Sequence, a detailed tutorial of which can be found here.

from keras.preprocessing.image import Iterator, ImageDataGenerator


class MyIterator(Iterator):
  """This is a toy example of a wrapper around ImageDataGenerator"""

  def __init__(self, n, batch_size, shuffle, seed, **kwargs):
    super().__init__(n, batch_size, shuffle, seed)

    # Load any data you need here (CSV, HDF5, raw stuffs). The code
    # below is just a pseudo-code for demonstration purpose.
    input_images = ...
    ground_truth = ...

    # Here is our beloved image augmentator <3
    self.generator = ImageDataGenerator(**kwargs)
    
  def _get_batches_of_transformed_samples(self, index_array):
    """Gets a batch of transformed samples from array of indices"""

    # Get a batch of image data
    batch_x = input_images[index_array].copy()
    batch_y = ground_truth[index_array].copy()

    # Transform the inputs and correct the outputs accordingly
    for i, (x, y) in enumerate(zip(batch_x, batch_y)):
      transform_params = self.generator.get_random_transform(x.shape)
      batch_x[i] = self.generator.apply_transform(x, transform_params)
      batch_y[i] = process_outputs_accordingly(y, transform_params)

    return batch_x, batch_y

为什么我们要从Keras迭代器继承?

建议让您的生成器继承自 keras.utils.Sequence (也是其他类的基类).它允许在多个线程之间并行加载数据.

Why we inherit from Keras Iterator?

It is recommended to have your generators inherited from keras.utils.Sequence (also the base class of other classes). It allows the data be loaded in parallel between several threads.

您可以使用方法flow，flow_from_directory和flow_from_dataframe编写自定义生成器-Keras API的核心功能.

You can write a custom generator with methods flow, flow_from_directory, and flow_from_dataframe --- core functions of Keras API.