import os
import numpy as np
from torch.utils.data import Dataset, DataLoader

class Cifar10Dataset(Dataset):
    def __init__(self, data_dir):
        self.imgs = []
        self.labels = []
        for file in os.listdir(data_dir):
            if 'data_batch' in file:
                batch = self.unpickle(f'{data_dir}/{file}')

                length = len(batch[b'data'])
                self.labels += batch[b'labels']

                # read image data
                values = np.array(batch[b'data']) / 255.0
                imgs = np.zeros((length, 3, 32, 32))
                for index in range(length):
                    for channel in range(3):
                        imgs[index][channel] = values[index][32*32*channel : 32*32*(channel+1)].reshape((32, 32))
                self.imgs.append(imgs)
        self.imgs = np.concatenate(self.imgs)
        print(f"load images : {self.imgs.shape}")
        print(f"load labels : {len(self.labels)}")

    def unpickle(self, file):
        import pickle
        with open(file, 'rb') as fo:
            dict = pickle.load(fo, encoding='bytes')
        return dict

    def __getitem__(self, index):
        return self.imgs[index], self.labels[index]

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