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a509dd8ec1
* add smoke model * add 3d vis * update code * update doc * mv paddle3d from detection to perception * update result for velocity * update code for CI * add set input data for TRT backend * add serving support for smoke model * update code * update code * update code --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
121 lines
4.9 KiB
C++
Executable File
121 lines
4.9 KiB
C++
Executable File
// 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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#pragma once
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#include "fastdeploy/core/fd_type.h"
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#include <iostream>
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#include <map>
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#include <string>
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#include <vector>
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namespace fastdeploy {
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/*! @brief Option object to configure TensorRT backend
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*/
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struct TrtBackendOption {
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/// `max_batch_size`, it's deprecated in TensorRT 8.x
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size_t max_batch_size = 32;
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/// `max_workspace_size` for TensorRT
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size_t max_workspace_size = 1 << 30;
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/// Enable log while converting onnx model to tensorrt
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bool enable_log_info = false;
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/// Enable half precison inference, on some device not support half precision,
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/// it will fallback to float32 mode
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bool enable_fp16 = false;
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/** \brief Set shape range of input tensor for the model that contain dynamic input shape while using TensorRT backend
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*
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* \param[in] tensor_name The name of input for the model which is dynamic shape
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* \param[in] min The minimal shape for the input tensor
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* \param[in] opt The optimized shape for the input tensor, just set the most common shape, if set as default value, it will keep same with min_shape
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* \param[in] max The maximum shape for the input tensor, if set as default value, it will keep same with min_shape
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*/
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void SetShape(const std::string& tensor_name,
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const std::vector<int32_t>& min,
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const std::vector<int32_t>& opt = std::vector<int32_t>(),
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const std::vector<int32_t>& max = std::vector<int32_t>()) {
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min_shape[tensor_name].clear();
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max_shape[tensor_name].clear();
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opt_shape[tensor_name].clear();
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min_shape[tensor_name].assign(min.begin(), min.end());
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if (opt.size() == 0) {
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opt_shape[tensor_name].assign(min.begin(), min.end());
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} else {
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opt_shape[tensor_name].assign(opt.begin(), opt.end());
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}
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if (max.size() == 0) {
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max_shape[tensor_name].assign(min.begin(), min.end());
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} else {
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max_shape[tensor_name].assign(max.begin(), max.end());
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}
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}
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/** \brief Set Input data for input tensor for the model while using TensorRT backend
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*
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* \param[in] tensor_name The name of input for the model which is dynamic shape
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* \param[in] min_data The input data for minimal shape for the input tensor
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* \param[in] opt_data The input data for optimized shape for the input tensor
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* \param[in] max_data The input data for maximum shape for the input tensor, if set as default value, it will keep same with min_data
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*/
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void SetInputData(const std::string& tensor_name,
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const std::vector<float> min_data,
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const std::vector<float> opt_data = std::vector<float>(),
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const std::vector<float> max_data = std::vector<float>()) {
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max_input_data[tensor_name].clear();
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min_input_data[tensor_name].clear();
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opt_input_data[tensor_name].clear();
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min_input_data[tensor_name].assign(min_data.begin(), min_data.end());
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if (opt_data.empty()) {
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opt_input_data[tensor_name].assign(min_data.begin(), min_data.end());
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} else {
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opt_input_data[tensor_name].assign(opt_data.begin(), opt_data.end());
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}
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if (max_data.empty()) {
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max_input_data[tensor_name].assign(min_data.begin(), min_data.end());
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} else {
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max_input_data[tensor_name].assign(max_data.begin(), max_data.end());
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}
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}
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/// Set cache file path while use TensorRT backend.
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/// Loadding a Paddle/ONNX model and initialize TensorRT will
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/// take a long time,
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/// by this interface it will save the tensorrt engine to `cache_file_path`,
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/// and load it directly while execute the code again
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std::string serialize_file = "";
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std::map<std::string, std::vector<float>> max_input_data;
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std::map<std::string, std::vector<float>> min_input_data;
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std::map<std::string, std::vector<float>> opt_input_data;
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// The below parameters may be removed in next version, please do not
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// visit or use them directly
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std::map<std::string, std::vector<int32_t>> max_shape;
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std::map<std::string, std::vector<int32_t>> min_shape;
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std::map<std::string, std::vector<int32_t>> opt_shape;
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bool enable_pinned_memory = false;
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void* external_stream_ = nullptr;
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int gpu_id = 0;
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std::string model_file = ""; // Path of model file
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std::string params_file = ""; // Path of parameters file, can be empty
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// format of input model
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ModelFormat model_format = ModelFormat::AUTOREC;
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};
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} // namespace fastdeploy
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