This did double the training data. Thanks. However, I have a question. Since I am using these transforms:
transform_img = transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

So shouldn’t the increase be 4x?
Also, I made a change to the code because it was returning PIL Image type but non-transformed images:
class dataload(Dataset):

    def __init__(self, x, transform=None):
        self.data = [(el, 'none') for el in x]
        self.data.extend([(el, 'augm1') for el in x])
        self.transform = transform

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

    def __getitem__(self, i):


        img = mpimg.imread(self.data[i][0]) - [103.939, 116.779, 123.68]
        img = img.transpose((2, 0, 1))
        img = torch.from_numpy(img).float()
        tmp = np.int32(filenames[i].split('/')[-1].split('_')[0][1])
        label = np.zeros(67)
        label[tmp] = 1
        label = torch.from_numpy(label).float()
    
        if self.data[i][1] == 'augm1' and self.transform:
            img = Image.open(self.data[i][0])
            img = self.transform(img)

        return img,label

Let me know what you think.