// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed 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. #pragma once #include "paddle/phi/kernels/nanmedian_kernel.h" #include "paddle/phi/kernels/funcs/math_function.h" namespace phi { template void PreprocessMedianKernel(const Context& dev_ctx, const DenseTensor& input, const IntArray& raw_axes, DenseTensor* x) { auto input_dim = input.dims(); auto rank = input_dim.size(); std::vector perm; std::vector reshape; std::vector axes = raw_axes.GetData(); int64_t axes_size = static_cast(axes.size()); for (int64_t i = 0; i < axes_size; i++) { if (axes[i] < 0) { axes[i] += rank; } } for (int64_t i = 0; i < rank; i++) { if (std::find(axes.begin(), axes.end(), i) == axes.end()) { perm.push_back(i); reshape.push_back(input_dim[i]); } } int64_t post_numel = 1; for (int64_t i = 0; i < rank; i++) { if (std::find(axes.begin(), axes.end(), i) != axes.end()) { perm.push_back(i); post_numel *= input_dim[i]; } } reshape.push_back(post_numel); DDim trans_dim(input_dim); int ndims = perm.size(); for (int i = 0; i < ndims; i++) { trans_dim[i] = input_dim[perm[i]]; } x->Resize(trans_dim); dev_ctx.template Alloc(x); funcs::TransCompute(ndims, dev_ctx, input, x, perm); x->Resize(make_ddim(reshape)); } } // namespace phi