AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy

使用tf.distribute.MirroredStrategy()時，出現警告：

AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Did not find a shardable source, walked to a node which is not a dataset

代碼如下：

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    keras_model = build_model()
train_datagen = ImageDataGenerator()
training_img_generator = train_datagen.flow_from_directory(
    input_path,
    target_size=(image_size, image_size),
    batch_size=batch_size,
    class_mode="categorical",
)
train_dataset = tf.data.Dataset.from_generator(
    lambda: training_img_generator,
    output_types=(tf.float32, tf.float32),
    output_shapes=([None, image_size, image_size, 3], [None, len(image_classes)])
)
# similar for validation_dataset = ...
keras_model.fit(
    train_dataset,
    steps_per_epoch=train_steps_per_epoch,
    epochs=epoch_count,
    validation_data=validation_dataset,
    validation_steps=validation_steps_per_epoch,
)

Now this seem to work, the model is trained as usual. However, during training I get the following warning message, when using a mirrored strategy:

AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Did not find a shardable source, walked to a node which is not a dataset

嘗試解决但無效

So I added the following lines between creating the data sets and calling fit():

options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
train_dataset.with_options(options)
validation_dataset.with_options(options)

However, I still get the same warning.
This leads me to these two questions:

1. What do I need to do in order to get rid of this warning?
2. Even more important: Why is TF not able to split the dataset with the default AutoShardPolicy.FILE policy, since I am using thousands of images per class in the input folder?