Keras: Could Keras prefetch data like tensorflow Dataset?

[Diyago]

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If your issue is an implementation question, please ask your question on StackOverflow or on the Keras Slack channel instead of opening a GitHub issue.

System information

• Have I written custom code (as opposed to using example directory):
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  Ubuntu 18.04
• TensorFlow backend (yes / no): yes
• TensorFlow version: 1.13
• Keras version: 2.2.4
• Python version: 3.6
• CUDA/cuDNN version: 9.0
• GPU model and memory: 1070ti

You can obtain the TensorFlow version with:
python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
You can obtain the Keras version with:
python -c 'import keras as k; print(k.__version__)'

Describe the current behavior
Current approach - Data is generated only then next batch is proceeded. Even If I use custom datagen. I have something like this:

class DataGenerator(Sequence):
'''
Sample usage:
test_generator = DataGenerator(x_train, y_train, 1, 
                           image_sizes, image_sizes, 1, True)
Xtest, ytest = test_generator.__getitem__(1)
plt.imshow(Xtest[0])
plt.show()
plt.imshow(ytest[0, :,:,0])
plt.show()
'''
def __init__(self, X, y, batch_size,  height,width, nb_y_features, augmentation = True):
    'Initialization'
    self.batch_size = batch_size
    self.X = X
    self.y = y
    self.indexes = None
    self.currentIndex = 0
    self.augmentation = augmentation
    self.on_epoch_end()
    self.height = height
    self.width = width
    self.nb_y_features = nb_y_features

def __len__(self):

    'Denotes the number of batches per epoch'
    return int(np.ceil(len(self.X) / self.batch_size))

def __getitem__(self, index):
    'Generate one batch of data'
    # Generate indexes of the batch
    data_index_min = int(index*self.batch_size)
    data_index_max = int(min((index+1)*self.batch_size, len(self.indexes)))
    indexes = self.indexes[data_index_min:data_index_max]

    this_batch_size = len(indexes) # The last batch can be smaller than the others

    X = np.empty((this_batch_size, self.width, self.height, 3)) #, dtype=int)
    y = np.empty((this_batch_size, self.width, self.height, self.nb_y_features), dtype=int)

    for i, sample_index in enumerate(indexes):
        data_index = self.indexes[index * self.batch_size + i]
        X_sample, y_sample = self.X[data_index].copy(), self.y[data_index].copy()
        if self.augmentation:
            augmented = aug()(image=X_sample, mask=y_sample)

            image_augm = augmented['image']
            mask_augm = augmented['mask']#.reshape(self.width, self.height, self.nb_y_features)
            X[i, ...] = image_augm
            y[i, ...] = mask_augm

        else:
            X[i, ...] = X_sample
            y[i, ...] = y_sample

    return X, y

def on_epoch_end(self):
    'Updates indexes after each epoch'
    self.indexes = list(range(len(self.X)))
    np.random.shuffle(self.indexes)

Describe the expected behavior
In TensorFlow's Dataset API, we can use dataset.prefetch(buffer_size=xxx) to preload other batches' data while GPU is processing the current batch's data, therefore, I can make full use of GPU. How to modify current code to get it start working with preloading batches behavior.
Code to reproduce the issue
Calling fit predict in keras

[Dref360]

If you call fit_generator with workers > 1, use_multiprocessing=True, we will prefetch queue_size batches.