image_net_dataset.py

# -*- mode: python -*-
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"""
Creates data-loader for Image-Net dataset
"""
import os
import tensorflow as tf
from tensorflow.keras.preprocessing import image_dataset_from_directory

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"


class ImageNetDataset:
    """ Dataset for ImageNet with Keras """

    def __init__(self, dataset_dir: str, image_size: int = 224, batch_size: int = 128, is_training: bool = False):
        """
        :param dataset_dir: The path to the dataset's directory
        :param image_size: Required size for images. Images will be resized to image_size x image_size
        :param batch_size: The batch size to use for training and validation
        :param is_training: If the dataset returned should be training or validation dataset
        """

        if not dataset_dir:
            raise ValueError("dataset_dir cannot be None")

        self._dataset = image_dataset_from_directory(directory=dataset_dir,
                                                     labels="inferred",
                                                     label_mode="categorical",
                                                     batch_size=batch_size,
                                                     shuffle=is_training,
                                                     image_size=(image_size, image_size))

        self._batch_size = batch_size

    @property
    def batch_size(self) -> int:
        """
        Returns the batch size
        :return: a value representing batch size
        """
        return self._batch_size

    @property
    def dataset(self) -> tf.data.Dataset:
        """
        Returns the dataset
        :return: a tf.data.Dataset representing dataset
        """
        return self._dataset