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91 lines
2.7 KiB
91 lines
2.7 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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#include <gflags/gflags.h>
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include <cmath>
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#include <mutex> // NOLINT
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#include <thread> // NOLINT
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#include "paddle/fluid/inference/tests/api/tester_helper.h"
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namespace paddle {
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namespace inference {
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int test_main(const AnalysisConfig& config, Barrier* barrier = nullptr) {
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static std::mutex mutex;
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std::unique_ptr<PaddlePredictor> predictor;
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{
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std::unique_lock<std::mutex> lock(mutex);
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predictor = std::move(CreatePaddlePredictor(config));
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}
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if (barrier) {
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barrier->Wait();
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}
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std::vector<PaddleTensor> inputs;
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std::vector<float> input({1});
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PaddleTensor in;
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in.shape = {1, 1};
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in.data = PaddleBuf(static_cast<void*>(input.data()), 1 * sizeof(float));
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in.dtype = PaddleDType::FLOAT32;
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inputs.emplace_back(in);
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std::vector<PaddleTensor> outputs;
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predictor->Run(inputs, &outputs);
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const std::vector<float> truth_values = {
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-0.00621776f, -0.00620937f, 0.00990623f, -0.0039817f, -0.00074315f,
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0.61229795f, -0.00491806f, -0.00068755f, 0.18409646f, 0.30090684f};
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const size_t expected_size = 1;
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EXPECT_EQ(outputs.size(), expected_size);
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float* data_o = static_cast<float*>(outputs[0].data.data());
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for (size_t j = 0; j < outputs[0].data.length() / sizeof(float); ++j) {
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EXPECT_LT(std::abs(data_o[j] - truth_values[j]), 10e-6);
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}
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return 0;
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}
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#ifdef PADDLE_WITH_CUDA
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TEST(AnalysisPredictor, thread_local_stream) {
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const size_t thread_num = 5;
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std::vector<std::thread> threads(thread_num);
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Barrier barrier(thread_num);
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for (size_t i = 0; i < threads.size(); ++i) {
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threads[i] = std::thread([&barrier, i]() {
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AnalysisConfig config;
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config.EnableUseGpu(100, 0);
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config.SetModel(FLAGS_infer_model + "/" + "mul_model");
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config.EnableGpuMultiStream();
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test_main(config, &barrier);
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});
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}
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for (auto& th : threads) {
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th.join();
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}
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}
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TEST(AnalysisPredictor, lite_engine) {
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AnalysisConfig config;
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config.EnableUseGpu(100, 0);
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config.SetModel(FLAGS_infer_model + "/" + "mul_model");
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config.EnableLiteEngine(paddle::AnalysisConfig::Precision::kFloat32);
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test_main(config);
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}
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#endif
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} // namespace inference
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} // namespace paddle
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