wehub-resource-sync
127 lines
4.1 KiB
C++
127 lines
4.1 KiB
C++
/* 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 <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/common/flags.h"
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#include "test/cpp/inference/api/tester_helper.h"
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namespace paddle {
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namespace inference {
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int test_predictor(const AnalysisConfig& config_in,
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Barrier* barrier = nullptr) {
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static std::mutex mutex;
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AnalysisConfig config{config_in};
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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 = 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 = {-0.00621776f,
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-0.00620937f,
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0.00990623f,
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-0.0039817f,
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-0.00074315f,
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0.61229795f,
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-0.00491806f,
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-0.00068755f,
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0.18409646f,
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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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int test_predictor_zero_copy(const AnalysisConfig& config_in,
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Barrier* barrier = nullptr) {
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static std::mutex mutex;
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AnalysisConfig config{config_in};
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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 = 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<float> input({1});
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auto in_tensor =
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predictor->GetInputTensor(predictor->GetInputNames().front());
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in_tensor->Reshape({1, 1});
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in_tensor->copy_from_cpu(input.data());
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predictor->ZeroCopyRun();
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auto out_tensor =
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predictor->GetOutputTensor(predictor->GetOutputNames().front());
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std::vector<float> data_o(10);
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out_tensor->copy_to_cpu(data_o.data());
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const std::vector<float> truth_values = {-0.00621776f,
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-0.00620937f,
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0.00990623f,
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-0.0039817f,
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-0.00074315f,
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0.61229795f,
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-0.00491806f,
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-0.00068755f,
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0.18409646f,
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0.30090684f};
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const size_t expected_size = 1;
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EXPECT_EQ(predictor->GetOutputNames().size(), expected_size);
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for (size_t j = 0; j < truth_values.size(); ++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_XPU
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TEST(AnalysisPredictor, native_xpu) {
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AnalysisConfig config;
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config.EnableXpu();
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config.SetModel(FLAGS_infer_model + "/" + "mul_model");
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test_predictor(config);
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test_predictor_zero_copy(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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