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75 lines
2.7 KiB
75 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 "paddle/fluid/inference/tests/api/tester_helper.h"
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namespace paddle {
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namespace inference {
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TEST(AnalysisPredictor, use_gpu) {
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std::string model_dir = FLAGS_infer_model + "/" + "model";
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AnalysisConfig config;
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config.EnableUseGpu(100, 0);
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config.SetModel(model_dir + "/model", model_dir + "/params");
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config.EnableLiteEngine(paddle::AnalysisConfig::Precision::kFloat32);
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std::vector<PaddleTensor> inputs;
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auto predictor = CreatePaddlePredictor(config);
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const int batch = 1;
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const int channel = 3;
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const int height = 318;
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const int width = 318;
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const int input_num = batch * channel * height * width;
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std::vector<float> input(input_num, 1);
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PaddleTensor in;
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in.shape = {1, 3, 318, 318};
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in.data =
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PaddleBuf(static_cast<void*>(input.data()), input_num * 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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ASSERT_TRUE(predictor->Run(inputs, &outputs));
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const std::vector<float> truth_values = {
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127.780396f, 738.16656f, 1013.2264f, -438.17206f, 366.4022f,
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927.66187f, 736.2241f, -633.68567f, -329.92737f, -430.15637f,
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-633.0639f, -146.54858f, -1324.2804f, -1349.3661f, -242.67671f,
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117.44864f, -801.7251f, -391.51495f, -404.8202f, 454.16132f,
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515.48206f, -133.03114f, 69.293076f, 590.09753f, -1434.6917f,
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-1070.8903f, 307.0744f, 400.52573f, -316.12177f, -587.1265f,
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-161.05742f, 800.3663f, -96.47157f, 748.708f, 868.17645f,
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-447.9403f, 112.73656f, 1127.1992f, 47.43518f, 677.7219f,
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593.1881f, -336.4011f, 551.3634f, 397.82474f, 78.39835f,
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-715.4006f, 405.96988f, 404.25684f, 246.01978f, -8.430191f,
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131.36617f, -648.0528f};
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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 += 10) {
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EXPECT_NEAR((data_o[j] - truth_values[j / 10]) / truth_values[j / 10], 0.,
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10e-5);
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}
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}
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} // namespace inference
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} // namespace paddle
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