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# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Input reader to load segmentation dataset."""
import tensorflow as tf
_NUM_INPUTS_PROCESSED_CONCURRENTLY = 32
_SHUFFLE_BUFFER_SIZE = 1000
class InputReader(object):
"""Input function that creates a dataset from files."""
def __init__(self,
file_pattern,
decoder_fn,
generator_fn=None,
is_training=False):
"""Initializes the input reader.
Args:
file_pattern: The file pattern for the data example, in TFRecord format
decoder_fn: A callable that takes a serialized tf.Example and produces
parsed (and potentially processed / augmented) tensors.
generator_fn: An optional `callable` that takes the decoded raw tensors
dict and generates a ground-truth dictionary that can be consumed by
the model. It will be executed after decoder_fn (default: None).
is_training: If this dataset is used for training or not (default: False).
"""
self._file_pattern = file_pattern
self._is_training = is_training
self._decoder_fn = decoder_fn
self._generator_fn = generator_fn
def __call__(self, batch_size=1, max_num_examples=-1):
"""Provides tf.data.Dataset object.
Args:
batch_size: Expected batch size input data.
max_num_examples: Positive integer or -1. If positive, the returned
dataset will only take (at most) this number of examples and raise
tf.errors.OutOfRangeError after that (default: -1).
Returns:
tf.data.Dataset object.
"""
dataset = tf.data.Dataset.list_files(self._file_pattern)
if self._is_training:
# File level shuffle.
dataset = dataset.shuffle(dataset.cardinality(),
reshuffle_each_iteration=True)
dataset = dataset.repeat()
# During training, interleave TFRecord conversion for maximum efficiency.
# During evaluation, read input in consecutive order for tasks requiring
# such behavior.
dataset = dataset.interleave(
map_func=tf.data.TFRecordDataset,
cycle_length=(_NUM_INPUTS_PROCESSED_CONCURRENTLY
if self._is_training else 1),
num_parallel_calls=tf.data.experimental.AUTOTUNE,
deterministic=not self._is_training)
if self._is_training:
dataset = dataset.shuffle(_SHUFFLE_BUFFER_SIZE)
if max_num_examples > 0:
dataset = dataset.take(max_num_examples)
# Parses the fetched records to input tensors for model function.
dataset = dataset.map(
self._decoder_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
if self._generator_fn is not None:
dataset = dataset.map(
self._generator_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size, drop_remainder=True)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
return dataset
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