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93 lines
2.8 KiB
93 lines
2.8 KiB
// Copyright (c) 2019 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 <glog/logging.h>
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#include <algorithm>
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#include <fstream>
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#include <iostream>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "gflags/gflags.h"
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#include "paddle/include/paddle_inference_api.h"
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DEFINE_string(modeldir, "", "Directory of the inference model.");
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DEFINE_bool(use_gpu, false, "Whether use gpu.");
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namespace paddle {
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namespace demo {
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void RunAnalysis() {
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// 1. create AnalysisConfig
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AnalysisConfig config;
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if (FLAGS_modeldir.empty()) {
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LOG(INFO) << "Usage: path\\mobilenet --modeldir=path/to/your/model";
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exit(1);
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}
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// CreateConfig(&config);
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if (FLAGS_use_gpu) {
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config.EnableUseGpu(100, 0);
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}
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config.SetModel(FLAGS_modeldir + "/__model__",
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FLAGS_modeldir + "/__params__");
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// use ZeroCopyTensor, Must be set to false
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config.SwitchUseFeedFetchOps(false);
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// 2. create predictor, prepare input data
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std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
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int batch_size = 1;
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int channels = 3;
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int height = 300;
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int width = 300;
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int nums = batch_size * channels * height * width;
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float* input = new float[nums];
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for (int i = 0; i < nums; ++i) input[i] = 0;
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// 3. create input tensor, use ZeroCopyTensor
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auto input_names = predictor->GetInputNames();
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auto input_t = predictor->GetInputTensor(input_names[0]);
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input_t->Reshape({batch_size, channels, height, width});
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input_t->copy_from_cpu(input);
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// 4. run predictor
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predictor->ZeroCopyRun();
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// 5. get out put
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std::vector<float> out_data;
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auto output_names = predictor->GetOutputNames();
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auto output_t = predictor->GetOutputTensor(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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out_data.resize(out_num);
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output_t->copy_to_cpu(out_data.data());
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delete[] input;
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}
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} // namespace demo
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} // namespace paddle
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int main(int argc, char** argv) {
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::GFLAGS_NAMESPACE::ParseCommandLineFlags(&argc, &argv, true);
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paddle::demo::RunAnalysis();
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std::cout << "=========================Runs successfully===================="
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<< std::endl;
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return 0;
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}
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