// 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/cuda/layout_compute.h" #include "lite/backends/cuda/math/transpose.h" #include "lite/core/op_registry.h" namespace paddle { namespace lite { namespace kernels { namespace cuda { template void NCHWToNHWCCompute::Run() { auto& param = this->template Param(); auto& ctx = this->ctx_->template As(); auto input = param.x->template data(); auto input_dim = param.x->dims(); CHECK(input_dim.size() == 4) << "NCHW to NHWC should guarantee that the input dims should be 4"; auto output = param.y->template mutable_data(TARGET(kCUDA)); int n = input_dim[0]; int c = input_dim[1]; int h = input_dim[2]; int w = input_dim[3]; lite::cuda::math::NCHW2NHWC(n, c, h * w, input, output, &ctx); } template void NHWCToNCHWCompute::Run() { auto& param = this->template Param(); auto& ctx = this->ctx_->template As(); auto input = param.x->template data(); auto output = param.y->template mutable_data(TARGET(kCUDA)); auto input_dim = param.x->dims(); CHECK(input_dim.size() == 4) << "NHWC to NCHW should guarantee that the input dims should be 4"; int n = input_dim[0]; int h = input_dim[1]; int w = input_dim[2]; int c = input_dim[3]; lite::cuda::math::NHWC2NCHW(n, c, h * w, input, output, &ctx); } } // namespace cuda } // namespace kernels } // namespace lite } // namespace paddle REGISTER_LITE_KERNEL(layout, kCUDA, kFloat, kNCHW, paddle::lite::kernels::cuda::NCHWToNHWCCompute, nchw2nhwc) .BindInput("X", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kFloat), DATALAYOUT(kNCHW))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kFloat), DATALAYOUT(kNHWC))}) .Finalize(); REGISTER_LITE_KERNEL(layout, kCUDA, kFloat, kNHWC, paddle::lite::kernels::cuda::NHWCToNCHWCompute, nhwc2nchw) .BindInput("X", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kFloat), DATALAYOUT(kNHWC))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kFloat), DATALAYOUT(kNCHW))}) .Finalize(); REGISTER_LITE_KERNEL(layout, kCUDA, kInt8, kNCHW, paddle::lite::kernels::cuda::NCHWToNHWCCompute, nchw2nhwc) .BindInput("X", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kInt8), DATALAYOUT(kNCHW))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kInt8), DATALAYOUT(kNHWC))}) .Finalize(); REGISTER_LITE_KERNEL(layout, kCUDA, kInt8, kNHWC, paddle::lite::kernels::cuda::NHWCToNCHWCompute, nhwc2nchw) .BindInput("X", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kInt8), DATALAYOUT(kNHWC))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kCUDA), PRECISION(kInt8), DATALAYOUT(kNCHW))}) .Finalize();