Source code for akida_models.detection.coco.data

#!/usr/bin/env python
# ******************************************************************************
# Copyright 2024 Brainchip Holdings Ltd.
#
# 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.
# ******************************************************************************
"""
Load COCO dataset
"""

__all__ = ["get_coco_dataset"]

import os

try:
    import tensorflow_datasets as tfds
except ImportError:
    tfds = None

from ..data_utils import remove_empty_objects, get_dataset_length


[docs]def get_coco_dataset(data_path, training=False): """ Loads coco dataset and builds a tf.dataset out of it. Args: data_path (str): path to the folder containing coco tfrecords. training (bool, optional): True to retrieve training data, False for validation. Defaults to False. Returns: tf.dataset, list, int: the requested dataset (train or validation), labels and the dataset size. """ assert tfds is not None, "To load coco dataset, tensorflow-datasets module must\ be installed." write_dir = os.path.join(data_path, 'tfds', 'data') download_and_prepare_kwargs = { 'download_dir': os.path.join(write_dir, 'downloaded'), 'download_config': tfds.download.DownloadConfig(manual_dir=data_path) } split = 'train' if training else 'validation' dataset, infos = tfds.load( 'coco/2017', data_dir=write_dir, split=split, shuffle_files=training, download=True, download_and_prepare_kwargs=download_and_prepare_kwargs, with_info=True ) dataset = dataset.filter(remove_empty_objects) labels = infos.features['objects']['label'].names dataset_length = get_dataset_length(dataset) return dataset, labels, dataset_length