Load data/label from dataset

def _get_batch(self):
        """
        Load data/label from dataset
        """
        batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1]))
        batch_label = []
        for i in range(self.batch_size):
            if (self._current + i) >= self._size:
                if not self.is_train:
                    continue
                # use padding from middle in each epoch
                idx = (self._current + i + self._size // 2) % self._size
                index = self._index[idx]
            else:
                index = self._index[self._current + i]
            # index = self.debug_index
            im_path = self._imdb.image_path_from_index(index)
            with open(im_path, 'rb') as fp:
                img_content = fp.read()
            img = mx.img.imdecode(img_content)
            gt = self._imdb.label_from_index(index).copy() if self.is_train else None
            data, label = self._data_augmentation(img, gt)
            batch_data[i] = data
            if self.is_train:
                batch_label.append(label)
        self._data = {'data': batch_data}
        if self.is_train:
            self._label = {'label': mx.nd.array(np.array(batch_label))}
        else:
            self._label = {'label': None}