// 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/funcs/math_function.h" namespace phi { using Tensor = DenseTensor; template inline void ResizeToChannelFirst(const DeviceContext& context, const Tensor* input, Tensor* transformed_input) { int dim = input->dims().size() - 2; if (dim == 3) { // input transformed_input->Resize(input->dims()); auto in_dims_vec = vectorize(input->dims()); in_dims_vec[1] = input->dims()[4]; in_dims_vec[2] = input->dims()[1]; in_dims_vec[3] = input->dims()[2]; in_dims_vec[4] = input->dims()[3]; transformed_input->Resize(make_ddim(in_dims_vec)); context.template Alloc(transformed_input); } else if (dim == 2) { // input transformed_input->Resize(input->dims()); auto in_dims_vec = vectorize(input->dims()); in_dims_vec[1] = input->dims()[3]; in_dims_vec[2] = input->dims()[1]; in_dims_vec[3] = input->dims()[2]; transformed_input->Resize(make_ddim(in_dims_vec)); context.template Alloc(transformed_input); } else if (dim == 1) { transformed_input->Resize(input->dims()); auto in_dims_vec = vectorize(input->dims()); in_dims_vec[1] = input->dims()[2]; in_dims_vec[2] = input->dims()[1]; transformed_input->Resize(make_ddim(in_dims_vec)); context.template Alloc(transformed_input); } } template inline void ResizeToChannelLast(const DeviceContext& context, const Tensor* input, Tensor* transformed_input) { int dim = input->dims().size() - 2; if (dim == 3) { // input transformed_input->Resize(input->dims()); auto in_dims_vec = vectorize(input->dims()); in_dims_vec[1] = input->dims()[2]; in_dims_vec[2] = input->dims()[3]; in_dims_vec[3] = input->dims()[4]; in_dims_vec[4] = input->dims()[1]; transformed_input->Resize(make_ddim(in_dims_vec)); context.template Alloc(transformed_input); } else if (dim == 2) { // input transformed_input->Resize(input->dims()); auto in_dims_vec = vectorize(input->dims()); in_dims_vec[1] = input->dims()[2]; in_dims_vec[2] = input->dims()[3]; in_dims_vec[3] = input->dims()[1]; transformed_input->Resize(make_ddim(in_dims_vec)); context.template Alloc(transformed_input); } else if (dim == 1) { transformed_input->Resize(input->dims()); auto in_dims_vec = vectorize(input->dims()); in_dims_vec[1] = input->dims()[2]; in_dims_vec[2] = input->dims()[1]; transformed_input->Resize(make_ddim(in_dims_vec)); context.template Alloc(transformed_input); } } template inline void TransToChannelFirst(const DeviceContext& context, const Tensor* input, Tensor* transformed_input) { VLOG(5) << "Why am I called?"; int dim = input->dims().size() - 2; if (dim == 3) { std::vector axis{0, 4, 1, 2, 3}; phi::funcs::Transpose trans5; trans5(context, *input, transformed_input, axis); } else if (dim == 2) { std::vector axis{0, 3, 1, 2}; phi::funcs::Transpose trans4; trans4(context, *input, transformed_input, axis); } else if (dim == 1) { std::vector axis{0, 2, 1}; phi::funcs::Transpose trans3; trans3(context, *input, transformed_input, axis); } } template inline void TransToChannelLast(const DeviceContext& context, const Tensor* input, Tensor* transformed_input) { int dim = input->dims().size() - 2; if (dim == 3) { std::vector axis{0, 2, 3, 4, 1}; phi::funcs::Transpose trans5; trans5(context, *input, transformed_input, axis); } else if (dim == 2) { std::vector axis{0, 2, 3, 1}; phi::funcs::Transpose trans4; trans4(context, *input, transformed_input, axis); } else if (dim == 1) { std::vector axis{0, 2, 1}; phi::funcs::Transpose trans3; trans3(context, *input, transformed_input, axis); } } } // namespace phi