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https://github.com/PaddlePaddle/FastDeploy.git
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32047016d6
* Update README.md * Update README.md * Update README.md * Create README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add evaluation calculate time and fix some bugs * Update classification __init__ * Move to ppseg Co-authored-by: Jason <jiangjiajun@baidu.com>
278 lines
10 KiB
C++
278 lines
10 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/ort/ort_backend.h"
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#include <memory>
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#include "fastdeploy/backends/ort/ops/multiclass_nms.h"
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#include "fastdeploy/backends/ort/utils.h"
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#include "fastdeploy/utils/utils.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<OrtCustomOp*> OrtBackend::custom_operators_ =
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std::vector<OrtCustomOp*>();
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void OrtBackend::BuildOption(const OrtBackendOption& option) {
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option_ = option;
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if (option.graph_optimization_level >= 0) {
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session_options_.SetGraphOptimizationLevel(
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GraphOptimizationLevel(option.graph_optimization_level));
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}
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if (option.intra_op_num_threads >= 0) {
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session_options_.SetIntraOpNumThreads(option.intra_op_num_threads);
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}
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if (option.inter_op_num_threads >= 0) {
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session_options_.SetInterOpNumThreads(option.inter_op_num_threads);
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}
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if (option.execution_mode >= 0) {
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session_options_.SetExecutionMode(ExecutionMode(option.execution_mode));
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}
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if (option.use_gpu) {
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auto all_providers = Ort::GetAvailableProviders();
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bool support_cuda = false;
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std::string providers_msg = "";
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for (size_t i = 0; i < all_providers.size(); ++i) {
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providers_msg = providers_msg + all_providers[i] + ", ";
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if (all_providers[i] == "CUDAExecutionProvider") {
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support_cuda = true;
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}
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}
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if (!support_cuda) {
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FDWARNING << "Compiled fastdeploy with onnxruntime doesn't "
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"support GPU, the available providers are "
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<< providers_msg << "will fallback to CPUExecutionProvider."
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<< std::endl;
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option_.use_gpu = false;
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} else {
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OrtCUDAProviderOptions cuda_options;
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cuda_options.device_id = option.gpu_id;
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session_options_.AppendExecutionProvider_CUDA(cuda_options);
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}
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}
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}
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bool OrtBackend::InitFromPaddle(const std::string& model_file,
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const std::string& params_file,
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const OrtBackendOption& option, bool verbose) {
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if (initialized_) {
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FDERROR << "OrtBackend 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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#ifdef ENABLE_PADDLE_FRONTEND
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char* model_content_ptr;
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int model_content_size = 0;
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std::vector<paddle2onnx::CustomOp> custom_ops;
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for (auto& item : option.custom_op_info_) {
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paddle2onnx::CustomOp op;
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strcpy(op.op_name, item.first.c_str());
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strcpy(op.export_op_name, item.second.c_str());
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custom_ops.emplace_back(op);
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}
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if (!paddle2onnx::Export(model_file.c_str(), params_file.c_str(),
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&model_content_ptr, &model_content_size, 11, true,
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verbose, true, true, true, custom_ops.data(),
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custom_ops.size())) {
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FDERROR << "Error occured while export PaddlePaddle to ONNX format."
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<< std::endl;
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return false;
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}
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std::string onnx_model_proto(model_content_ptr,
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model_content_ptr + model_content_size);
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delete[] model_content_ptr;
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model_content_ptr = nullptr;
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return InitFromOnnx(onnx_model_proto, option, true);
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#else
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FDERROR << "Didn't compile with PaddlePaddle frontend, you can try to "
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"call `InitFromOnnx` instead."
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<< std::endl;
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#endif
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return false;
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}
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bool OrtBackend::InitFromOnnx(const std::string& model_file,
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const OrtBackendOption& option,
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bool from_memory_buffer) {
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if (initialized_) {
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FDERROR << "OrtBackend 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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BuildOption(option);
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InitCustomOperators();
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if (from_memory_buffer) {
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session_ = {env_, model_file.data(), model_file.size(), session_options_};
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} else {
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#ifdef _WIN32
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session_ = {env_,
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std::wstring(model_file.begin(), model_file.end()).c_str(),
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session_options_};
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#else
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session_ = {env_, model_file.c_str(), session_options_};
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#endif
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}
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binding_ = std::make_shared<Ort::IoBinding>(session_);
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Ort::MemoryInfo memory_info("Cpu", OrtDeviceAllocator, 0, OrtMemTypeDefault);
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Ort::Allocator allocator(session_, memory_info);
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size_t n_inputs = session_.GetInputCount();
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for (size_t i = 0; i < n_inputs; ++i) {
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auto input_name = session_.GetInputName(i, allocator);
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auto type_info = session_.GetInputTypeInfo(i);
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std::vector<int64_t> shape =
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type_info.GetTensorTypeAndShapeInfo().GetShape();
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ONNXTensorElementDataType data_type =
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type_info.GetTensorTypeAndShapeInfo().GetElementType();
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inputs_desc_.emplace_back(OrtValueInfo{input_name, shape, data_type});
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allocator.Free(input_name);
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}
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size_t n_outputs = session_.GetOutputCount();
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for (size_t i = 0; i < n_outputs; ++i) {
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auto output_name = session_.GetOutputName(i, allocator);
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auto type_info = session_.GetOutputTypeInfo(i);
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std::vector<int64_t> shape =
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type_info.GetTensorTypeAndShapeInfo().GetShape();
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ONNXTensorElementDataType data_type =
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type_info.GetTensorTypeAndShapeInfo().GetElementType();
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outputs_desc_.emplace_back(OrtValueInfo{output_name, shape, data_type});
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Ort::MemoryInfo out_memory_info("Cpu", OrtDeviceAllocator, 0,
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OrtMemTypeDefault);
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binding_->BindOutput(output_name, out_memory_info);
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allocator.Free(output_name);
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}
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initialized_ = true;
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return true;
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}
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void OrtBackend::CopyToCpu(const Ort::Value& value, FDTensor* tensor) {
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const auto info = value.GetTensorTypeAndShapeInfo();
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const auto data_type = info.GetElementType();
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size_t numel = info.GetElementCount();
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tensor->shape = info.GetShape();
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if (data_type == ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT) {
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tensor->data.resize(numel * sizeof(float));
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memcpy(static_cast<void*>(tensor->Data()), value.GetTensorData<void*>(),
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numel * sizeof(float));
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tensor->dtype = FDDataType::FP32;
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} else if (data_type == ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32) {
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tensor->data.resize(numel * sizeof(int32_t));
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memcpy(static_cast<void*>(tensor->Data()), value.GetTensorData<void*>(),
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numel * sizeof(int32_t));
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tensor->dtype = FDDataType::INT32;
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} else if (data_type == ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64) {
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tensor->data.resize(numel * sizeof(int64_t));
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memcpy(static_cast<void*>(tensor->Data()), value.GetTensorData<void*>(),
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numel * sizeof(int64_t));
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tensor->dtype = FDDataType::INT64;
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} else if (data_type == ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE) {
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tensor->data.resize(numel * sizeof(double));
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memcpy(static_cast<void*>(tensor->Data()), value.GetTensorData<void*>(),
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numel * sizeof(double));
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tensor->dtype = FDDataType::FP64;
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} else {
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FDASSERT(false, "Unrecognized data type of " + std::to_string(data_type) +
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" while calling OrtBackend::CopyToCpu().");
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}
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}
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bool OrtBackend::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 << "[OrtBackend] 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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// from FDTensor to Ort Inputs
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for (size_t i = 0; i < inputs.size(); ++i) {
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auto ort_value = CreateOrtValue(inputs[i], option_.use_gpu);
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binding_->BindInput(inputs[i].name.c_str(), ort_value);
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}
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for (size_t i = 0; i < outputs_desc_.size(); ++i) {
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Ort::MemoryInfo memory_info("Cpu", OrtDeviceAllocator, 0,
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OrtMemTypeDefault);
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binding_->BindOutput(outputs_desc_[i].name.c_str(), memory_info);
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}
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// Inference with inputs
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try {
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session_.Run({}, *(binding_.get()));
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} catch (const std::exception& e) {
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FDERROR << "Failed to Infer: " << e.what() << std::endl;
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return false;
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}
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// Copy result after inference
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std::vector<Ort::Value> ort_outputs = binding_->GetOutputValues();
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outputs->resize(ort_outputs.size());
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for (size_t i = 0; i < ort_outputs.size(); ++i) {
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(*outputs)[i].name = outputs_desc_[i].name;
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CopyToCpu(ort_outputs[i], &((*outputs)[i]));
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}
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return true;
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}
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TensorInfo OrtBackend::GetInputInfo(int index) {
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FDASSERT(index < NumInputs(), "The index:" + std::to_string(index) +
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" should less than the number of inputs:" +
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std::to_string(NumInputs()) + ".");
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TensorInfo info;
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info.name = inputs_desc_[index].name;
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info.shape.assign(inputs_desc_[index].shape.begin(),
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inputs_desc_[index].shape.end());
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info.dtype = GetFdDtype(inputs_desc_[index].dtype);
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return info;
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}
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TensorInfo OrtBackend::GetOutputInfo(int index) {
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FDASSERT(index < NumOutputs(),
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"The index:" + std::to_string(index) +
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" should less than the number of outputs:" +
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std::to_string(NumOutputs()) + ".");
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TensorInfo info;
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info.name = outputs_desc_[index].name;
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info.shape.assign(outputs_desc_[index].shape.begin(),
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outputs_desc_[index].shape.end());
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info.dtype = GetFdDtype(outputs_desc_[index].dtype);
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return info;
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}
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void OrtBackend::InitCustomOperators() {
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#ifndef NON_64_PLATFORM
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if (custom_operators_.size() == 0) {
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MultiClassNmsOp* custom_op = new MultiClassNmsOp{};
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custom_operators_.push_back(custom_op);
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}
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for (size_t i = 0; i < custom_operators_.size(); ++i) {
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custom_op_domain_.Add(custom_operators_[i]);
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}
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session_options_.Add(custom_op_domain_);
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#endif
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}
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} // namespace fastdeploy
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