Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-pyreader
Qiao Longfei
7 years ago
commit
ea97e83e41
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/* 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 of mobilenet for tensorrt.
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*/
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#include <gflags/gflags.h>
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#include <glog/logging.h> // use glog instead of CHECK to avoid importing other paddle header files.
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#include "paddle/fluid/inference/demo_ci/utils.h"
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DECLARE_double(fraction_of_gpu_memory_to_use);
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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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namespace paddle {
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namespace demo {
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/*
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* Use the tensorrt fluid engine to inference the demo.
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*/
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void Main() {
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std::unique_ptr<PaddlePredictor> predictor;
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paddle::contrib::MixedRTConfig config;
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config.param_file = FLAGS_modeldir + "/__params__";
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config.prog_file = FLAGS_modeldir + "/__model__";
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config.use_gpu = true;
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config.device = 0;
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config.max_batch_size = 1;
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config.fraction_of_gpu_memory = 0.1; // set by yourself
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predictor = CreatePaddlePredictor<paddle::contrib::MixedRTConfig>(config);
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VLOG(3) << "begin to process data";
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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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VLOG(3) << "run executor";
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std::vector<PaddleTensor> output;
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predictor->Run({input}, &output, 1);
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VLOG(3) << "output.size " << output.size();
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auto& tensor = output.front();
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VLOG(3) << "output: " << SummaryTensor(tensor);
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// compare with reference result
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CheckOutput(FLAGS_refer, tensor);
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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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paddle::demo::Main();
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return 0;
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}
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