mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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105 lines
3.7 KiB
C++
105 lines
3.7 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/backends/paddle/paddle_backend.h"
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namespace fastdeploy {
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void PaddleBackend::BuildOption(const PaddleBackendOption& option) {
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if (option.use_gpu) {
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config_.EnableUseGpu(option.gpu_mem_init_size, option.gpu_id);
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} else {
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config_.DisableGpu();
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if (option.enable_mkldnn) {
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config_.EnableMKLDNN();
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config_.SetMkldnnCacheCapacity(option.mkldnn_cache_size);
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}
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}
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if (!option.enable_log_info) {
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config_.DisableGlogInfo();
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}
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config_.SetCpuMathLibraryNumThreads(option.cpu_thread_num);
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}
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bool PaddleBackend::InitFromPaddle(const std::string& model_file,
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const std::string& params_file,
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const PaddleBackendOption& option) {
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if (initialized_) {
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FDERROR << "PaddleBackend is already initlized, cannot initialize again."
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<< std::endl;
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return false;
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}
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config_.SetModel(model_file, params_file);
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BuildOption(option);
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predictor_ = paddle_infer::CreatePredictor(config_);
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std::vector<std::string> input_names = predictor_->GetInputNames();
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std::vector<std::string> output_names = predictor_->GetOutputNames();
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for (size_t i = 0; i < input_names.size(); ++i) {
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auto handle = predictor_->GetInputHandle(input_names[i]);
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TensorInfo info;
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auto shape = handle->shape();
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info.shape.assign(shape.begin(), shape.end());
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info.dtype = PaddleDataTypeToFD(handle->type());
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info.name = input_names[i];
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inputs_desc_.emplace_back(info);
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}
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for (size_t i = 0; i < output_names.size(); ++i) {
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auto handle = predictor_->GetOutputHandle(output_names[i]);
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TensorInfo info;
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auto shape = handle->shape();
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info.shape.assign(shape.begin(), shape.end());
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info.dtype = PaddleDataTypeToFD(handle->type());
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info.name = output_names[i];
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outputs_desc_.emplace_back(info);
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}
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initialized_ = true;
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return true;
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}
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TensorInfo PaddleBackend::GetInputInfo(int index) {
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FDASSERT(index < NumInputs(), "The index: %d should less than the number of inputs: %d.", index, NumInputs());
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return inputs_desc_[index];
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}
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TensorInfo PaddleBackend::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, NumOutputs());
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return outputs_desc_[index];
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}
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bool PaddleBackend::Infer(std::vector<FDTensor>& inputs,
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std::vector<FDTensor>* outputs) {
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if (inputs.size() != inputs_desc_.size()) {
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FDERROR << "[PaddleBackend] Size of 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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for (size_t i = 0; i < inputs.size(); ++i) {
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auto handle = predictor_->GetInputHandle(inputs[i].name);
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ShareTensorFromCpu(handle.get(), inputs[i]);
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}
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predictor_->Run();
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outputs->resize(outputs_desc_.size());
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for (size_t i = 0; i < outputs_desc_.size(); ++i) {
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auto handle = predictor_->GetOutputHandle(outputs_desc_[i].name);
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CopyTensorToCpu(handle, &((*outputs)[i]));
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}
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return true;
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}
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} // namespace fastdeploy
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