# 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 """TVM operator for local response norm compute.""" from .. import cpp def lrn(data, size, axis=1, alpha=0.0001, beta=0.75, bias=2): """Perform the across channels local response normalisation on the input data. sum_sqr_up^i{x, y} = (bias+((alpha/size)* \ {sum_{j=max(0, i-size/2)}^{min(N-1,i+size/2)} \ (data^j{x,y})^2}))^beta output^i{x, y} = data^i{x, y}/sum_sqr_up^i{x, y} N is the number for input channels Parameters ---------- data : tvm.te.Tensor 4-D with shape [batch, channel, height, width] size : int normalisation window size axis : int input data layout channel axis default value is 1 for NCHW format bias : float offset to avoid dividing by 0 alpha : float to be divided beta : float exponent Returns ------- output : tvm.te.Tensor 4-D output with same shape """ return cpp.nn.lrn(data, size, axis, alpha, beta, bias)