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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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"""
General dataset implementations for Keras
"""
from abc import ABCMeta, abstractmethod
import tensorflow
__all__ = [
"Dataset",
]
[docs]class Dataset(metaclass=ABCMeta):
"""
Generic dataset implementation for Keras.
Expected to work with the tensorflow.data APIs
"""
@abstractmethod
def __len__(self):
raise NotImplementedError()
[docs] def build(
self,
batch_size: int,
repeat_count: int = None,
shuffle_buffer_size: int = None,
prefetch_buffer_size: int = None,
num_parallel_calls: int = None,
) -> tensorflow.data.Dataset:
"""
Create the dataset in the current graph using tensorflow.data APIs
:param batch_size: the batch size to create the dataset for
:param repeat_count: the number of times to repeat the dataset,
if unset or None, will repeat indefinitely
:param shuffle_buffer_size: None if not shuffling,
otherwise the size of the buffer to use for shuffling data
:param prefetch_buffer_size: None if not prefetching,
otherwise the size of the buffer to use for buffering
:param num_parallel_calls: the number of parallel calls to run the
processor function with
:return: a tensorflow.data.Dataset instance
"""
dataset = self.creator()
if shuffle_buffer_size and shuffle_buffer_size > 0:
dataset = dataset.shuffle(
shuffle_buffer_size, reshuffle_each_iteration=True
)
dataset = dataset.map(self.processor, num_parallel_calls=num_parallel_calls)
# Together with shuffling above, putting batch after repeat yields
# batches that straddle epoch boundaries
dataset = dataset.repeat(repeat_count)
dataset = dataset.batch(batch_size)
if prefetch_buffer_size and prefetch_buffer_size > 0:
dataset = dataset.prefetch(prefetch_buffer_size)
return dataset
[docs] @abstractmethod
def creator(self) -> tensorflow.data.Dataset:
"""
Implemented by sub classes to create a tensorflow.data dataset for the given
impl.
:return: a created tensorflow.data dataset
"""
raise NotImplementedError()
[docs] @abstractmethod
def processor(self, *args, **kwargs):
"""
Implemented by sub classes to parallelize and map processing functions
for loading the data of the dataset into memory.
:param args: generic inputs for processing
:param kwargs: generic inputs for processing
:return: the processed tensors
"""
raise NotImplementedError()