# 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. """Instance normalization operator.""" from .. import cpp def instance_norm(data, gamma, beta, channel_axis, axis, epsilon=1e-5): """Instance normalization operator. Parameters ---------- data : tvm.te.Tensor N-D with shape (d_0, d_1, ..., d_{N-1}) gamma: tvm.te.Tensor K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k beta: tvm.te.Tensor Optional, K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k axis : list of int Axis over the normalization applied (the axis along which the mean and variance are computed) epsilon : float The epsilon value to avoid division by zero. Returns ------- result : tvm.te.Tensor N-D with shape (d_0, d_1, ..., d_{N-1}) """ return cpp.nn.instance_norm(data, gamma, beta, channel_axis, axis, epsilon)