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91 lines
2.6 KiB
91 lines
2.6 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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/*
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* This file contains demo for mobilenet, se-resnext50 and ocr.
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*/
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#include <gflags/gflags.h>
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#include <glog/logging.h>
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#include "utils.h" // NOLINT
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#ifdef PADDLE_WITH_CUDA
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DECLARE_double(fraction_of_gpu_memory_to_use);
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#endif
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DEFINE_string(modeldir, "", "Directory of the inference model.");
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DEFINE_string(refer, "", "path to reference result for comparison.");
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DEFINE_string(
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data, "",
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"path of data; each line is a record, format is "
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"'<space splitted floats as data>\t<space splitted ints as shape'");
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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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/*
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* Use the native and analysis fluid engine to inference the demo.
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*/
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void Main(bool use_gpu) {
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std::unique_ptr<PaddlePredictor> predictor, analysis_predictor;
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AnalysisConfig config;
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if (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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predictor = CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
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analysis_predictor = CreatePaddlePredictor(config);
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// Just a single batch of data.
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std::string line;
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std::ifstream file(FLAGS_data);
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std::getline(file, line);
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auto record = ProcessALine(line);
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file.close();
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// Inference.
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PaddleTensor input;
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input.shape = record.shape;
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input.data =
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PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
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input.dtype = PaddleDType::FLOAT32;
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std::vector<PaddleTensor> output, analysis_output;
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predictor->Run({input}, &output, 1);
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auto& tensor = output.front();
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// compare with reference result
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CheckOutput(FLAGS_refer, tensor);
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// the analysis_output has some diff with native_output,
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// TODO(luotao): add CheckOutput for analysis_output later.
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analysis_predictor->Run({input}, &analysis_output, 1);
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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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google::ParseCommandLineFlags(&argc, &argv, true);
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if (FLAGS_use_gpu) {
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paddle::demo::Main(true /*use_gpu*/);
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} else {
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paddle::demo::Main(false /*use_gpu*/);
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}
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return 0;
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}
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