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# * Copyright (c) 2025 STMicroelectronics.
# * All rights reserved.
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
import os
import numpy as np
import tensorflow as tf
from typing import Tuple, List
from .utils import load_cifar_batch, get_ds, get_prediction_ds
def _load_cifar_10(training_path: str, num_classes: int = None, input_size: list = None,
interpolation: str = None, aspect_ratio: str = None,
batch_size: int = None, seed: int = None, to_cache: bool = False) -> Tuple:
"""
Loads the CIFAR-10 dataset and returns two TensorFlow datasets for training and validation.
Args:
training_path (str): The path to the CIFAR-10 training data.
num_classes (int, optional): The number of classes in the dataset. Must be 10. Defaults to None.
input_size (list, optional): The size of the input images. Defaults to None.
interpolation (str, optional): The interpolation method to use when resizing images. Defaults to None.
aspect_ratio (bool, optional): Whether to crop images to maintain the aspect ratio. Defaults to None.
batch_size (int, optional): The batch size for the datasets. Defaults to None.
seed (int): seed for random shuffler. Defaults to None.
to_cache (bool, optional): Whether to cache the datasets in memory. Defaults to False.
Returns:
tuple: A tuple of two TensorFlow datasets for training and validation.
"""
# When calling this function using the config file data, some of the arguments
# may be used but equal to None (happens when an attribute is missing in the
# config file or has no value). For this reason, all the arguments in the
# definition of the function defaults to None and we set default values here
# in case the function is called in another context with missing arguments.
# Set default values for optional arguments
interpolation = interpolation if interpolation else "bilinear"
aspect_ratio = aspect_ratio if aspect_ratio else "fit"
batch_size = batch_size if batch_size else 32
input_size = list(input_size)
if num_classes != 10:
raise ValueError('Number of classes must be 10.' f"Received: number of classes={num_classes}.")
num_train_samples = 50000
x_train = np.empty((num_train_samples, 3, 32, 32), dtype="uint8")
y_train = np.empty((num_train_samples,), dtype="uint8")
for i in range(1, 6):
fpath = os.path.join(training_path, "data_batch_" + str(i))
(
x_train[(i - 1) * 10000: i * 10000, :, :, :],
y_train[(i - 1) * 10000: i * 10000],
) = load_cifar_batch(fpath)
fpath = os.path.join(training_path, "test_batch")
x_test, y_test = load_cifar_batch(fpath)
y_train = np.reshape(y_train, (len(y_train),))
y_test = np.reshape(y_test, (len(y_test),))
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
x_test = x_test.astype(np.uint8)
y_test = y_test.astype(np.uint8)
print("Found {} files belonging to {} classes.".format(len(x_train) + len(x_test), num_classes))
print("Using {} files for training.".format(len(x_train)))
print("Using {} files for validation.".format(len(x_test)))
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_ds = train_ds.shuffle(len(x_train), reshuffle_each_iteration=True, seed=seed).batch(batch_size)
valid_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test))
valid_ds = valid_ds.batch(batch_size)
if to_cache:
train_ds = train_ds.cache()
valid_ds = valid_ds.cache()
train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE)
valid_ds = valid_ds.prefetch(buffer_size=tf.data.AUTOTUNE)
if input_size != [32, 32]:
crop_to_aspect_ratio = False if aspect_ratio == "fit" else True
train_ds = train_ds.map(
lambda x, y: (tf.keras.layers.Resizing(
input_size[0], input_size[1],
interpolation=interpolation,
crop_to_aspect_ratio=crop_to_aspect_ratio
)(x), y))
valid_ds = valid_ds.map(
lambda x, y: (tf.keras.layers.Resizing(
input_size[0], input_size[1],
interpolation=interpolation,
crop_to_aspect_ratio=crop_to_aspect_ratio
)(x), y))
return train_ds, valid_ds
def load_cifar10(training_path: str = None,
quantization_path: str = None,
test_path: str = None,
prediction_path: str = None,
quantization_split: float = None,
class_names: list[str] = None,
image_size: tuple[int] = None,
interpolation: str = None,
aspect_ratio: str = None,
color_mode: str = None,
batch_size: int = None,
seed: int = None
) -> Tuple[tf.data.Dataset, tf.data.Dataset, tf.data.Dataset, tf.data.Dataset, tf.data.Dataset]:
"""
Loads the images from the given dataset root directories and returns training,
validation, and test tf.data.Datasets.
The datasets have the following directory structure (checked in parse_config.py):
dataset_root_dir:
class_a:
a_image_1.jpg
a_image_2.jpg
class_b:
b_image_1.jpg
b_image_2.jpg
Args:
training_path (str): Path to the directory containing the training images.
quantization_path (str): Path to the directory containing the quantization images.
test_path (str): Path to the directory containing the test images.
quantization_split (float): Fraction of the data to use for quantization.
class_names (list[str]): List of class names to use for the images.
image_size (tuple[int]): resizing (height, width) of input images
interpolation (str): Interpolation method to use when resizing the images.
aspect_ratio (bool): Whether or not to crop the images to the specified aspect ratio.
color_mode (str): Color mode to use for the images.
batch_size (int): Batch size to use for the datasets.
seed (int): Seed to use for shuffling the data.
Returns:
Tuple[tf.data.Dataset, tf.data.Dataset, tf.data.Dataset, tf.data.Dataset, tf.data.Dataset]: Training, validation,
quantization, test and prediction datasets.
"""
if class_names:
num_classes = len(class_names)
elif quantization_path:
class_names = []
num_classes = 0
else:
return None, None, None, None
# Get training and validation sets
train_ds, val_ds = _load_cifar_10(training_path,
num_classes=num_classes,
input_size=image_size,
interpolation=interpolation,
aspect_ratio=aspect_ratio,
batch_size=batch_size,
seed=seed,
to_cache=False)
# Get quantization set
if quantization_path:
quantization_ds = get_ds(
quantization_path,
class_names=class_names,
image_size=image_size,
interpolation=interpolation,
aspect_ratio=aspect_ratio,
color_mode=color_mode,
batch_size=1,
shuffle=False,
seed=seed)
elif train_ds is not None:
quantization_ds = train_ds
else:
quantization_ds = None
if quantization_ds:
quant_split = quantization_split if quantization_split else 1.0
print(f'[INFO] : Quantizing by using {quant_split * 100} % of the provided dataset...')
quantization_ds = quantization_ds.take(int(len(quantization_ds) * float(quant_split)))
# Get test set
if test_path:
test_ds = get_ds(
test_path,
class_names=class_names,
image_size=image_size,
interpolation=interpolation,
aspect_ratio=aspect_ratio,
color_mode=color_mode,
batch_size=batch_size,
shuffle=False,
seed=seed)
else:
test_ds = None
# Get prediction set
if prediction_path:
predict_ds = get_prediction_ds(
prediction_path,
class_names=class_names,
image_size=image_size,
interpolation=interpolation,
aspect_ratio=aspect_ratio,
color_mode=color_mode,
batch_size=1,
shuffle=False,
seed=seed)
else:
predict_ds = None
return {'train': train_ds, 'valid': val_ds, 'quantization': quantization_ds, 'test': test_ds, 'predict': predict_ds}
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