perform data augmentations: crop, mirror, resize, sub mean, swap channels...

def _data_augmentation(self, data, label):
        """
        perform data augmentations: crop, mirror, resize, sub mean, swap channels...
        """
        if self.is_train and self._rand_samplers:
            rand_crops = []
            for rs in self._rand_samplers:
                rand_crops += rs.sample(label)
            num_rand_crops = len(rand_crops)
            # randomly pick up one as input data
            if num_rand_crops > 0:
                index = int(np.random.uniform(0, 1) * num_rand_crops)
                width = data.shape[1]
                height = data.shape[0]
                crop = rand_crops[index][0]
                xmin = int(crop[0] * width)
                ymin = int(crop[1] * height)
                xmax = int(crop[2] * width)
                ymax = int(crop[3] * height)
                if xmin >= 0 and ymin >= 0 and xmax <= width and ymax <= height:
                    data = mx.img.fixed_crop(data, xmin, ymin, xmax-xmin, ymax-ymin)
                else:
                    # padding mode
                    new_width = xmax - xmin
                    new_height = ymax - ymin
                    offset_x = 0 - xmin
                    offset_y = 0 - ymin
                    data_bak = data
                    data = mx.nd.full((new_height, new_width, 3), 128, dtype='uint8')
                    data[offset_y:offset_y+height, offset_x:offset_x + width, :] = data_bak
                label = rand_crops[index][1]
        if self.is_train:
            interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, \
                              cv2.INTER_NEAREST, cv2.INTER_LANCZOS4]
        else:
            interp_methods = [cv2.INTER_LINEAR]
        interp_method = interp_methods[int(np.random.uniform(0, 1) * len(interp_methods))]
        data = mx.img.imresize(data, self._data_shape[1], self._data_shape[0], interp_method)
        if self.is_train and self._rand_mirror:
            if np.random.uniform(0, 1) > 0.5:
                data = mx.nd.flip(data, axis=1)
                valid_mask = np.where(label[:, 0] > -1)[0]
                tmp = 1.0 - label[valid_mask, 1]
                label[valid_mask, 1] = 1.0 - label[valid_mask, 3]
                label[valid_mask, 3] = tmp
        data = mx.nd.transpose(data, (2,0,1))
        data = data.astype('float32')
        data = data - self._mean_pixels
        return data, label