// Copyright (c) 2019 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. #include "lite/kernels/host/reshape_compute.h" #include #include "lite/operators/reshape_op.h" namespace paddle { namespace lite { namespace kernels { namespace host { void ReshapeCompute::Run() { auto& param = Param(); auto x = param.x; auto actual_shape = param.actual_shape; auto output = param.output; bool inplace = param.inplace; auto x_dims = x->dims(); auto output_dims = output->dims(); if (actual_shape) { auto actual_shape_dims = actual_shape->dims(); auto* actual_shape_data = actual_shape->data(); #ifdef LITE_WITH_CUDA lite::Tensor cpu_actual_shape; if (actual_shape->target() == TARGET(kCUDA)) { cpu_actual_shape.CopyDataFrom(*actual_shape); actual_shape_data = cpu_actual_shape.data(); } #endif auto shape = std::vector( actual_shape_data, actual_shape_data + actual_shape_dims.production()); output_dims = lite::operators::ValidateShape(shape, x_dims); output->Resize(output_dims); } if (inplace) { output->ShareDataWith(*x); } else { output->CopyDataFrom(*x); } output->Resize(output_dims); } } // namespace host } // namespace kernels } // namespace lite } // namespace paddle REGISTER_LITE_KERNEL(reshape, kHost, kAny, kAny, paddle::lite::kernels::host::ReshapeCompute, def) .BindInput("X", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindInput("ShapeTensor", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindInput("Shape", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindOutput("Out", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .Finalize(); REGISTER_LITE_KERNEL(reshape2, kHost, kAny, kAny, paddle::lite::kernels::host::ReshapeCompute, def) .BindInput("X", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindInput("Shape", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindInput("ShapeTensor", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindOutput("Out", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindOutput("XShape", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .Finalize(); REGISTER_LITE_KERNEL(flatten, kHost, kAny, kAny, paddle::lite::kernels::host::ReshapeCompute, def) .BindInput("X", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindInput("Shape", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindOutput("Out", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .Finalize(); REGISTER_LITE_KERNEL(flatten2, kHost, kAny, kAny, paddle::lite::kernels::host::ReshapeCompute, def) .BindInput("X", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindInput("Shape", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindOutput("Out", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .BindOutput("XShape", {LiteType::GetTensorTy( TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny), -1)}) .Finalize();