# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals """L2 normalize in python""" import numpy as np def l2_normalize_python(a_np, eps, axis=None): """L2 normalize operator in NCHW layout. Parameters ---------- a_np : numpy.ndarray 4-D with shape [batch, in_channel, in_height, in_width] eps : float epsilon constant value axis : list of int axis over the normalization applied Returns ------- l2_normalize_out : np.ndarray 4-D with shape [batch, out_channel, out_height, out_width] """ dot_value = np.power(a_np, 2.0) sqr_sum = np.sum(dot_value, axis, keepdims=True) sqrt_sum = np.sqrt(np.maximum(np.broadcast_to(sqr_sum, a_np.shape), eps)) l2_normalize_out = np.divide(a_np, sqrt_sum) return l2_normalize_out