mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +08:00
64 lines
2.1 KiB
C++
64 lines
2.1 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.h"
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namespace fd = fastdeploy;
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int main(int argc, char* argv[]) {
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std::string model_file = "mobilenetv2.onnx";
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// setup option
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fd::RuntimeOption runtime_option;
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runtime_option.SetModelPath(model_file, "", fd::ModelFormat::ONNX);
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runtime_option.UseOrtBackend();
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runtime_option.SetCpuThreadNum(12);
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// **** GPU ****
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// To use GPU, use the following commented code
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// runtime_option.UseGpu(0);
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// init runtime
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std::unique_ptr<fd::Runtime> runtime =
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std::unique_ptr<fd::Runtime>(new fd::Runtime());
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if (!runtime->Init(runtime_option)) {
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std::cerr << "--- Init FastDeploy Runitme Failed! "
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<< "\n--- Model: " << model_file << std::endl;
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return -1;
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} else {
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std::cout << "--- Init FastDeploy Runitme Done! "
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<< "\n--- Model: " << model_file << std::endl;
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}
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// init input tensor shape
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fd::TensorInfo info = runtime->GetInputInfo(0);
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info.shape = {1, 3, 224, 224};
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std::vector<fd::FDTensor> input_tensors(1);
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std::vector<fd::FDTensor> output_tensors(1);
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std::vector<float> inputs_data;
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inputs_data.resize(1 * 3 * 224 * 224);
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for (size_t i = 0; i < inputs_data.size(); ++i) {
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inputs_data[i] = std::rand() % 1000 / 1000.0f;
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}
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input_tensors[0].SetExternalData({1, 3, 224, 224}, fd::FDDataType::FP32, inputs_data.data());
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//get input name
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input_tensors[0].name = info.name;
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runtime->Infer(input_tensors, &output_tensors);
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output_tensors[0].PrintInfo();
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return 0;
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} |