mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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254 lines
9.4 KiB
C++
254 lines
9.4 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/openvino/ov_backend.h"
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#ifdef ENABLE_PADDLE_FRONTEND
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#include "paddle2onnx/converter.h"
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#endif
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namespace fastdeploy {
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std::vector<int64_t> PartialShapeToVec(const ov::PartialShape& shape) {
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std::vector<int64_t> res;
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for (int i = 0; i < shape.size(); ++i) {
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auto dim = shape[i];
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if (dim.is_dynamic()) {
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res.push_back(-1);
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} else {
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res.push_back(dim.get_length());
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}
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}
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return res;
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}
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FDDataType OpenVINODataTypeToFD(const ov::element::Type& type) {
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if (type == ov::element::f32) {
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return FDDataType::FP32;
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} else if (type == ov::element::f64) {
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return FDDataType::FP64;
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} else if (type == ov::element::i8) {
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return FDDataType::INT8;
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} else if (type == ov::element::i32) {
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return FDDataType::INT32;
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} else if (type == ov::element::i64) {
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return FDDataType::INT64;
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} else {
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FDASSERT(false, "Only support float/double/int8/int32/int64 now.");
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}
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return FDDataType::FP32;
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}
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ov::element::Type FDDataTypeToOV(const FDDataType& type) {
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if (type == FDDataType::FP32) {
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return ov::element::f32;
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} else if (type == FDDataType::FP64) {
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return ov::element::f64;
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} else if (type == FDDataType::INT8) {
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return ov::element::i8;
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} else if (type == FDDataType::INT32) {
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return ov::element::i32;
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} else if (type == FDDataType::INT64) {
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return ov::element::i64;
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}
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FDASSERT(false, "Only support float/double/int8/int32/int64 now.");
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return ov::element::f32;
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}
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void OpenVINOBackend::InitTensorInfo(
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const std::vector<ov::Output<ov::Node>>& ov_outputs,
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std::map<std::string, TensorInfo>* tensor_infos) {
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for (size_t i = 0; i < ov_outputs.size(); ++i) {
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TensorInfo info;
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auto partial_shape = PartialShapeToVec(ov_outputs[i].get_partial_shape());
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info.shape.assign(partial_shape.begin(), partial_shape.end());
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info.name = ov_outputs[i].get_any_name();
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info.dtype = OpenVINODataTypeToFD(ov_outputs[i].get_element_type());
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tensor_infos->insert(std::make_pair(info.name, info));
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}
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}
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bool OpenVINOBackend::InitFromPaddle(const std::string& model_file,
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const std::string& params_file,
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const OpenVINOBackendOption& option) {
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if (initialized_) {
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FDERROR << "OpenVINOBackend 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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option_ = option;
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ov::AnyMap properties;
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if (option_.cpu_thread_num > 0) {
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properties["INFERENCE_NUM_THREADS"] = option_.cpu_thread_num;
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}
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std::shared_ptr<ov::Model> model = core_.read_model(model_file, params_file);
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// Get inputs/outputs information from loaded model
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const std::vector<ov::Output<ov::Node>> inputs = model->inputs();
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std::map<std::string, TensorInfo> input_infos;
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InitTensorInfo(inputs, &input_infos);
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const std::vector<ov::Output<ov::Node>> outputs = model->outputs();
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std::map<std::string, TensorInfo> output_infos;
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InitTensorInfo(outputs, &output_infos);
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// OpenVINO model may not keep the same order with original model
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// So here will reorder it's inputs and outputs
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std::string model_content;
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ReadBinaryFromFile(model_file, &model_content);
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auto reader = paddle2onnx::PaddleReader(model_content.c_str(), model_content.size());
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if (reader.num_inputs != input_infos.size()) {
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FDERROR << "The number of inputs from PaddleReader:" << reader.num_inputs << " not equal to the number of inputs from OpenVINO:" << input_infos.size() << "." << std::endl;
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return false;
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}
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if (reader.num_outputs != output_infos.size()) {
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FDERROR << "The number of outputs from PaddleReader:" << reader.num_outputs << " not equal to the number of outputs from OpenVINO:" << output_infos.size() << "." << std::endl;
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return false;
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}
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for (int i = 0; i < reader.num_inputs; ++i) {
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auto iter = input_infos.find(std::string(reader.inputs[i].name));
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if (iter == input_infos.end()) {
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FDERROR << "Cannot find input name:" << reader.inputs[i].name << " from OpenVINO model." << std::endl;
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return false;
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}
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input_infos_.push_back(iter->second);
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}
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for (int i = 0; i < reader.num_outputs; ++i) {
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auto iter = output_infos.find(std::string(reader.outputs[i].name));
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if (iter == output_infos.end()) {
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FDERROR << "Cannot find output name:" << reader.outputs[i].name << " from OpenVINO model." << std::endl;
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return false;
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}
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output_infos_.push_back(iter->second);
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}
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compiled_model_ = core_.compile_model(model, "CPU", properties);
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request_ = compiled_model_.create_infer_request();
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initialized_ = true;
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return true;
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}
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TensorInfo OpenVINOBackend::GetInputInfo(int index) {
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FDASSERT(index < NumInputs(),
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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 input_infos_[index];
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}
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TensorInfo OpenVINOBackend::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 output_infos_[index];
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}
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bool OpenVINOBackend::InitFromOnnx(const std::string& model_file,
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const OpenVINOBackendOption& option) {
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if (initialized_) {
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FDERROR << "OpenVINOBackend 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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option_ = option;
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ov::AnyMap properties;
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if (option_.cpu_thread_num > 0) {
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properties["INFERENCE_NUM_THREADS"] = option_.cpu_thread_num;
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}
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std::shared_ptr<ov::Model> model = core_.read_model(model_file);
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// Get inputs/outputs information from loaded model
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const std::vector<ov::Output<ov::Node>> inputs = model->inputs();
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std::map<std::string, TensorInfo> input_infos;
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InitTensorInfo(inputs, &input_infos);
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const std::vector<ov::Output<ov::Node>> outputs = model->outputs();
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std::map<std::string, TensorInfo> output_infos;
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InitTensorInfo(outputs, &output_infos);
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// OpenVINO model may not keep the same order with original model
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// So here will reorder it's inputs and outputs
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std::string model_content;
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ReadBinaryFromFile(model_file, &model_content);
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auto reader = paddle2onnx::OnnxReader(model_content.c_str(), model_content.size());
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if (reader.num_inputs != input_infos.size()) {
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FDERROR << "The number of inputs from OnnxReader:" << reader.num_inputs << " not equal to the number of inputs from OpenVINO:" << input_infos.size() << "." << std::endl;
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return false;
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}
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if (reader.num_outputs != output_infos.size()) {
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FDERROR << "The number of outputs from OnnxReader:" << reader.num_outputs << " not equal to the number of outputs from OpenVINO:" << output_infos.size() << "." << std::endl;
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return false;
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}
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for (int i = 0; i < reader.num_inputs; ++i) {
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auto iter = input_infos.find(std::string(reader.inputs[i].name));
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if (iter == input_infos.end()) {
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FDERROR << "Cannot find input name:" << reader.inputs[i].name << " from OpenVINO model." << std::endl;
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return false;
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}
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input_infos_.push_back(iter->second);
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}
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for (int i = 0; i < reader.num_outputs; ++i) {
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auto iter = output_infos.find(std::string(reader.outputs[i].name));
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if (iter == output_infos.end()) {
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FDERROR << "Cannot find output name:" << reader.outputs[i].name << " from OpenVINO model." << std::endl;
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return false;
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}
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output_infos_.push_back(iter->second);
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}
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compiled_model_ = core_.compile_model(model, "CPU", properties);
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request_ = compiled_model_.create_infer_request();
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initialized_ = true;
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return true;
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}
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int OpenVINOBackend::NumInputs() const { return input_infos_.size(); }
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int OpenVINOBackend::NumOutputs() const { return output_infos_.size(); }
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bool OpenVINOBackend::Infer(std::vector<FDTensor>& inputs,
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std::vector<FDTensor>* outputs) {
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if (inputs.size() != input_infos_.size()) {
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FDERROR << "[OpenVINOBackend] Size of the inputs(" << inputs.size()
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<< ") should keep same with the inputs of this model("
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<< input_infos_.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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ov::Shape shape(inputs[i].shape.begin(), inputs[i].shape.end());
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ov::Tensor ov_tensor(FDDataTypeToOV(inputs[i].dtype), shape,
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inputs[i].Data());
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request_.set_tensor(inputs[i].name, ov_tensor);
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}
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request_.infer();
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outputs->resize(output_infos_.size());
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for (size_t i = 0; i < output_infos_.size(); ++i) {
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auto out_tensor = request_.get_output_tensor(i);
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auto out_tensor_shape = out_tensor.get_shape();
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std::vector<int64_t> shape(out_tensor_shape.begin(),
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out_tensor_shape.end());
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(*outputs)[i].Allocate(shape,
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OpenVINODataTypeToFD(out_tensor.get_element_type()),
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output_infos_[i].name);
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memcpy((*outputs)[i].MutableData(), out_tensor.data(),
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(*outputs)[i].Nbytes());
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}
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return true;
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}
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} // namespace fastdeploy
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