modules/dataset.py

"""This file contains functions to download and transform the CIFAR10 dataset"""
# Needed for image transformations
import albumentations as A
import modules.config as config

# # Needed for padding issues in albumentations
# import cv2
import numpy as np
from albumentations.pytorch.transforms import ToTensorV2
from torch.utils.data import Dataset

# Use precomputed values for mean and standard deviation of the dataset
CIFAR_MEAN = config.CIFAR_MEAN
CIFAR_STD = config.CIFAR_STD
CUTOUT_SIZE = config.CUTOUT_SIZE

# Create class labels and convert to tuple
CIFAR_CLASSES = config.CIFAR_CLASSES


class CIFAR10Transforms(Dataset):
    """Apply albumentations augmentations to CIFAR10 dataset"""

    # Given a dataset and transformations,
    # apply the transformations and return the dataset
    def __init__(self, dataset, transforms):
        self.dataset = dataset
        self.transforms = transforms

    def __getitem__(self, idx):
        # Get the image and label from the dataset
        image, label = self.dataset[idx]

        # Apply transformations on the image
        image = self.transforms(image=np.array(image))["image"]

        return image, label

    def __len__(self):
        return len(self.dataset)

    def __repr__(self):
        return f"CIFAR10Transforms(dataset={self.dataset}, transforms={self.transforms})"

    def __str__(self):
        return f"CIFAR10Transforms(dataset={self.dataset}, transforms={self.transforms})"


def apply_cifar_image_transformations(mean=CIFAR_MEAN, std=CIFAR_STD, cutout_size=CUTOUT_SIZE):
    """
    Function to apply the required transformations to the MNIST dataset.
    """
    # Apply the required transformations to the MNIST dataset
    train_transforms = A.Compose(
        [
            # normalize the images with mean and standard deviation from the whole dataset
            # https://albumentations.ai/docs/api_reference/augmentations/transforms/#albumentations.augmentations.transforms.Normalize
            # # transforms.Normalize(cifar_mean, cifar_std),
            A.Normalize(mean=list(mean), std=list(std)),
            # RandomCrop 32, 32 (after padding of 4)
            # https://albumentations.ai/docs/api_reference/augmentations/geometric/transforms/#albumentations.augmentations.geometric.transforms.PadIfNeeded
            # MinHeight and MinWidth are set to 36 to ensure that the image is padded to 36x36 after padding
            # border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
            # cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
            # Default: cv2.BORDER_REFLECT_101
            A.PadIfNeeded(min_height=36, min_width=36),
            # https://albumentations.ai/docs/api_reference/augmentations/crops/transforms/#albumentations.augmentations.crops.transforms.RandomCrop
            A.RandomCrop(32, 32),
            # CutOut(8, 8)
            # # https://albumentations.ai/docs/api_reference/augmentations/dropout/cutout/#albumentations.augmentations.dropout.cutout.Cutout
            # # Because we normalized the images with mean and standard deviation from the whole dataset, the fill_value is set to the mean of the dataset
            # A.Cutout(
            #     num_holes=1, max_h_size=cutout_size, max_w_size=cutout_size, p=1.0
            # ),
            # https://albumentations.ai/docs/api_reference/augmentations/dropout/coarse_dropout/#coarsedropout-augmentation-augmentationsdropoutcoarse_dropout
            A.CoarseDropout(
                max_holes=1,
                max_height=cutout_size,
                max_width=cutout_size,
                min_holes=1,
                min_height=cutout_size,
                min_width=cutout_size,
                p=1.0,
            ),
            # Convert the images to tensors
            # # transforms.ToTensor(),
            ToTensorV2(),
        ]
    )

    # Test data transformations
    test_transforms = A.Compose(
        # Convert the images to tensors
        # normalize the images with mean and standard deviation from the whole dataset
        [
            A.Normalize(mean=list(mean), std=list(std)),
            # Convert the images to tensors
            ToTensorV2(),
        ]
    )

    return train_transforms, test_transforms


def calculate_mean_std(dataset):
    """Function to calculate the mean and standard deviation of CIFAR dataset"""
    data = dataset.data.astype(np.float32) / 255.0
    mean = np.mean(data, axis=(0, 1, 2))
    std = np.std(data, axis=(0, 1, 2))
    return mean, std