mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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df8dd3e3ac
* add horizon backend and PPYOLOE examples * 更改horizon头文件编码规范 * 更改horizon头文件编码规范 * 更改horizon头文件编码规范 * 增加horizon packages下载及自动安装 * Add UseHorizonNPUBackend Method * 删除编译FD SDK后多余的头文件,同时更改部分规范 * Update horizon.md * Update horizon.md --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
399 lines
13 KiB
C++
399 lines
13 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/runtime/backends/horizon/horizon_backend.h"
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namespace fastdeploy{
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HorizonBackend::~HorizonBackend() {
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int ret = -1;
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// Release memory uniformly here
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if (input_properties_ != nullptr){
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free(input_properties_);
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}
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if (output_properties_ != nullptr){
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free(output_properties_);
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}
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if(input_mems_ == nullptr){
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return;
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}
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for(int i = 0; i < NumInputs(); i++){
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ret = hbSysFreeMem(&(input_mems_[i].sysMem[0]));
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if(ret != 0){
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FDERROR << "release input mem fail! ret=" << ret << std::endl;
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}
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if(input_mems_ != nullptr){
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free(input_mems_);
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}
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}
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for(int i = 0; i < NumOutputs(); i++){
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ret = hbSysFreeMem(&(output_mems_[i].sysMem[0]));
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if(ret != 0){
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FDERROR << "release output mem fail! ret=" << ret << std::endl;
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}
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if(output_mems_ != nullptr){
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free(output_mems_);
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}
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}
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ret = hbDNNRelease(packed_dnn_handle_);
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if(ret != 0){
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FDERROR << "hbDNNRelease fail! ret=" << ret << std::endl;
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}
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}
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bool HorizonBackend::GetModelInputOutputInfos(){
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const char **model_name_list;
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int model_count = 0;
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int ret;
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// get model name
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ret = hbDNNGetModelNameList(&model_name_list, &model_count, packed_dnn_handle_);
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if(ret != 0){
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FDERROR << "get model name fail! ret=" << ret << std::endl;
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return false;
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}
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// get dnn handle
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ret = hbDNNGetModelHandle(&dnn_handle_, packed_dnn_handle_, model_name_list[0]);
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if(ret != 0){
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FDERROR << "get dnn handle fail! ret=" << ret << std::endl;
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return false;
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}
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// get input infos
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// Get detailed input parameters
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int input_count = 0;
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ret = hbDNNGetInputCount(&input_count, dnn_handle_);
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if(ret != 0){
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FDERROR << "get input count fail! ret=" << ret << std::endl;
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return false;
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}
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input_properties_ = (hbDNNTensorProperties*)malloc(sizeof(hbDNNTensorProperties) * input_count);
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memset(input_properties_, 0, input_count * sizeof(hbDNNTensorProperties));
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inputs_desc_.resize(input_count);
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// get input info and copy to input tensor info
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for (uint32_t i = 0; i < input_count; i++) {
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ret = hbDNNGetInputTensorProperties(&input_properties_[i], dnn_handle_, i);
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if(ret != 0){
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FDERROR << "get input tensor properties fail! ret=" << ret << std::endl;
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return false;
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}
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if ((input_properties_[i].tensorLayout != HB_DNN_LAYOUT_NHWC)) {
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FDERROR << "horizon_backend only support input layout is NHWC"
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<< std::endl;
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}
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if(input_properties_[i].tensorType!= HB_DNN_IMG_TYPE_RGB){
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FDERROR << "horizon_backend only support input format is RGB"
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<< std::endl;
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}
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const char *name;
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ret = hbDNNGetInputName(&name, dnn_handle_, i);
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if(ret != 0){
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FDERROR << "get input tensor name fail! ret=" << ret << std::endl;
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return false;
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}
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// copy input proper to input tensor info
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std::string temp_name = name;
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std::vector<int> temp_shape{};
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int n_dims = input_properties_[i].validShape.numDimensions;
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temp_shape.resize(n_dims);
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for (int j = 0; j < n_dims; j++) {
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temp_shape[j] = (int)input_properties_[i].validShape.dimensionSize[j];
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}
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// Only support RGB format, so input type is UINT8
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FDDataType temp_dtype = FDDataType::UINT8;
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TensorInfo temp_input_info = {temp_name, temp_shape, temp_dtype};
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inputs_desc_[i] = temp_input_info;
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}
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// get output infos
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// Get detailed output parameters
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int output_count = 0;
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ret = hbDNNGetOutputCount(&output_count, dnn_handle_);
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if(ret != 0){
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FDERROR << "get output count fail! ret=" << ret << std::endl;
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return false;
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}
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output_properties_ = (hbDNNTensorProperties*)malloc(sizeof(hbDNNTensorProperties) * output_count);
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memset(output_properties_, 0, output_count * sizeof(hbDNNTensorProperties));
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outputs_desc_.resize(output_count);
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for (uint32_t i = 0; i < output_count; i++){
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// get model output size
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ret = hbDNNGetOutputTensorProperties(&output_properties_[i], dnn_handle_, i);
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const char *name;
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ret = hbDNNGetOutputName(&name, dnn_handle_, i);
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if(ret != 0){
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FDERROR << "get output tensor name fail! ret=" << ret << std::endl;
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return false;
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}
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// copy output proper to output tensor info
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std::string temp_name = name;
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std::vector<int> temp_shape{};
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int n_dims = output_properties_[i].validShape.numDimensions;
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if ((n_dims == 4) && (output_properties_[i].validShape.dimensionSize[3] == 1)) {
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n_dims--;
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}
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temp_shape.resize(n_dims);
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for (int j = 0; j < n_dims; j++) {
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temp_shape[j] = (int)output_properties_[i].validShape.dimensionSize[j];
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}
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FDDataType temp_dtype = HorizonTensorTypeToFDDataType(output_properties_[i].tensorType);
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TensorInfo temp_input_info = {temp_name, temp_shape, temp_dtype};
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outputs_desc_[i] = temp_input_info;
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}
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return true;
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}
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TensorInfo HorizonBackend::GetInputInfo(int index){
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FDASSERT(index < NumInputs(),
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"The index: %d should less than the number of inputs: %d.", index,
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NumInputs());
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return inputs_desc_[index];
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}
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std::vector<TensorInfo> HorizonBackend::GetInputInfos(){
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return inputs_desc_;
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}
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TensorInfo HorizonBackend::GetOutputInfo(int index){
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FDASSERT(index < NumOutputs(),
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"The index: %d should less than the number of outputs %d.", index,
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NumOutputs());
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return outputs_desc_[index];
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}
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std::vector<TensorInfo> HorizonBackend::GetOutputInfos(){
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return outputs_desc_;
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}
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bool HorizonBackend::LoadModel(const char *model){
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int ret = -1;
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ret = hbDNNInitializeFromFiles(&packed_dnn_handle_, &model , 1);
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if(ret != 0){
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FDERROR << "horizon_init fail! ret=" << ret << std::endl;
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return false;
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}
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return true;
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}
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bool HorizonBackend::Init(const RuntimeOption& runtime_option){
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// Init model from file
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if (!LoadModel((char*)runtime_option.model_file.data())) {
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FDERROR << "load model failed" << std::endl;
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return false;
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}
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// GetModelInputOutputInfos
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if (!GetModelInputOutputInfos()) {
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FDERROR << "get model input output infos failed" << std::endl;
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return false;
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}
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return true;
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}
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bool HorizonBackend::Infer(std::vector<FDTensor>& inputs,
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std::vector<FDTensor>* outputs, bool copy_to_fd){
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// Judge whether the input and output size are the same
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if (inputs.size() != inputs_desc_.size()) {
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FDERROR << "[HorizonBackend] Size of the inputs(" << inputs.size()
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<< ") should keep same with the inputs of this model("
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<< inputs_desc_.size() << ")." << std::endl;
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return false;
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}
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RUNTIME_PROFILE_LOOP_H2D_D2H_BEGIN
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int ret = -1;
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if(!infer_init_){
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// Create input tensor memory
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int input_count = NumInputs();
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int output_count = NumOutputs();
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input_mems_ = (hbDNNTensor*)malloc(sizeof(hbDNNTensor) * input_count);
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output_mems_ = (hbDNNTensor*)malloc(sizeof(hbDNNTensor) * output_count);
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for(uint32_t i = 0; i < input_count; i++){
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input_mems_[i].properties = input_properties_[i];
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input_mems_[i].properties.alignedShape = input_mems_[i].properties.validShape;
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auto current_shape = GetInputInfo(i).shape;
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auto &mem = input_mems_[i].sysMem[0];
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int intput_memSize = input_properties_[i].alignedByteSize;
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ret = hbSysAllocCachedMem(&mem, intput_memSize);
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if(ret != 0){
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FDERROR << "hbSysAllocCachedMem fails." << std::endl;
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return false;
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}
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}
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for(uint32_t i = 0; i < output_count; i++){
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output_mems_[i].properties = output_properties_[i];
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auto current_shape = GetOutputInfo(i).shape;
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auto &mem = output_mems_[i].sysMem[0];
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int output_memSize = output_properties_[i].alignedByteSize;
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ret = hbSysAllocCachedMem(&mem, output_memSize);
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if(ret != 0){
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FDERROR << "hbSysAllocCachedMem fails." << std::endl;
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return false;
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}
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}
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infer_init_ = true;
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}
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// Copy input data to input tensor memory
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for (uint32_t i = 0; i < NumInputs(); i++) {
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if (inputs[i].Data() == nullptr) {
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FDERROR << "inputs[i].Data is NULL." << std::endl;
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return false;
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}
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auto &mem = input_mems_[i].sysMem[0];
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memcpy(mem.virAddr, inputs[i].Data(), inputs[i].Nbytes());
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ret = hbSysFlushMem(&mem, HB_SYS_MEM_CACHE_CLEAN);
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if(ret != 0){
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FDERROR << "hbSysFlushMem fails." << std::endl;
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return false;
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}
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}
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hbDNNTaskHandle_t task_handle = nullptr;
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hbDNNInferCtrlParam infer_ctrl_param;
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HB_DNN_INITIALIZE_INFER_CTRL_PARAM(&infer_ctrl_param);
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RUNTIME_PROFILE_LOOP_BEGIN(1)
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ret = hbDNNInfer(&task_handle,
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&output_mems_,
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input_mems_,
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dnn_handle_,
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&infer_ctrl_param);
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RUNTIME_PROFILE_LOOP_END
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if(ret != 0){
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FDERROR << "hbDNNInference fails." << std::endl;
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return false;
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}
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ret = hbDNNWaitTaskDone(task_handle, 0);
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if(ret !=0){
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FDERROR << "hbDNNWaitTaskDone fails." << std::endl;
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return false;
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}
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ret = hbDNNReleaseTask(task_handle);
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if(ret !=0){
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FDERROR << "hbDNNReleaseTask fails." << std::endl;
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return false;
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}
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// get result
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outputs->resize(outputs_desc_.size());
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std::vector<int64_t> temp_shape(4);
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for (size_t i = 0; i < outputs_desc_.size(); ++i) {
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temp_shape.resize(outputs_desc_[i].shape.size());
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for (int j = 0; j < outputs_desc_[i].shape.size(); ++j) {
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temp_shape[j] = outputs_desc_[i].shape[j];
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}
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(*outputs)[i].Resize(temp_shape, outputs_desc_[i].dtype,
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outputs_desc_[i].name);
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hbSysFlushMem(&(output_mems_[i].sysMem[0]), HB_SYS_MEM_CACHE_INVALIDATE);
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auto data = (float *)(output_mems_[i].sysMem[0].virAddr);
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auto shift = output_mems_[i].properties.shift.shiftData;
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auto scale = output_mems_[i].properties.scale.scaleData;
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for(int j = 0; j < (*outputs)[i].Nbytes(); j++){
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if (output_mems_[i].properties.quantiType == SHIFT) {
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data[j] = data[j] / (1 << shift[j]);
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} else if (output_mems_[i].properties.quantiType == SCALE) {
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data[j] = data[j] * scale[j];
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}
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}
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memcpy((*outputs)[i].MutableData(), (float*)output_mems_[i].sysMem[0].virAddr,
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(*outputs)[i].Nbytes());
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}
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RUNTIME_PROFILE_LOOP_H2D_D2H_END
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return true;
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}
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FDDataType HorizonBackend::HorizonTensorTypeToFDDataType(int32_t type){
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if (type == hbDNNDataType::HB_DNN_TENSOR_TYPE_F16) {
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return FDDataType::FP16;
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}
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if (type == hbDNNDataType::HB_DNN_TENSOR_TYPE_F32) {
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return FDDataType::FP32;
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}
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if (type == hbDNNDataType::HB_DNN_TENSOR_TYPE_S8) {
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return FDDataType::INT8;
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}
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if (type == hbDNNDataType::HB_DNN_TENSOR_TYPE_S16) {
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return FDDataType::INT16;
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}
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if (type == hbDNNDataType::HB_DNN_TENSOR_TYPE_S32) {
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return FDDataType::INT32;
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}
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if (type == hbDNNDataType::HB_DNN_TENSOR_TYPE_U8) {
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return FDDataType::UINT8;
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}
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FDERROR << "FDDataType don't support this type" << std::endl;
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return FDDataType::UNKNOWN1;
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}
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hbDNNDataType HorizonBackend::FDDataTypeToHorizonTensorType(FDDataType type){
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if (type == FDDataType::FP16) {
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return hbDNNDataType::HB_DNN_TENSOR_TYPE_F16;
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}
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if (type == FDDataType::FP32) {
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return hbDNNDataType::HB_DNN_TENSOR_TYPE_F32;
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}
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if (type == FDDataType::INT8) {
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return hbDNNDataType::HB_DNN_TENSOR_TYPE_S8;
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}
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if (type == FDDataType::INT16) {
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return hbDNNDataType::HB_DNN_TENSOR_TYPE_S16;
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}
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if (type == FDDataType::INT32) {
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return hbDNNDataType::HB_DNN_TENSOR_TYPE_S32;
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}
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if (type == FDDataType::UINT8) {
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return hbDNNDataType::HB_DNN_TENSOR_TYPE_U8;
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}
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FDERROR << "horizon_tensor_type don't support this type" << std::endl;
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return hbDNNDataType::HB_DNN_TENSOR_TYPE_MAX;
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}
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} //namespace fastdeploy
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