Jittering code
class Jittering(object):
    def __init__(self, sigma):
        assert isinstance(sigma, (float, tuple))
        self.sigma = sigma

    def __call__(self, sample):
        #print(sample)
        data, label = sample['data'], sample['label']
        if isinstance(self.sigma, float):
            myNoise = np.random.normal(loc=0, scale=self.sigma, size=data.shape)
            data = data+myNoise
        return {'data': data, 'label': label}

Create Dataset instance
dataset = Dataset(data_csv='data/train_data.csv', label_csv='data/train_label.csv',transform=transforms.Compose([Jittering(0.3)]))

Is this common approach?
If I use such augmentation using transforms.Compose, will both the processed data and original data are used for training phase? I am really wondering this.