read_preprocess.py 文件源码

def apply(self, data, copy=False):
        if copy:
            data = np.copy(data)
        data_shape = data.shape
        if len(data.shape) > 2:
            data = data.reshape(data.shape[0], np.product(data.shape[1:]))
        assert len(data.shape) == 2, 'Contrast norm on flattened data'
#        assert np.min(data) >= 0.
#        assert np.max(data) <= 1.
        data -= data.mean(axis=1)[:, np.newaxis]
        norms = np.sqrt(np.sum(data ** 2, axis=1)) / self.scale
        norms[norms < self.epsilon] = self.epsilon
        data /= norms[:, np.newaxis]
        if data_shape != data.shape:
            data = data.reshape(data_shape)
        return data