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| 1 | +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. |
| 2 | +
|
| 3 | +Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +you may not use this file except in compliance with the License. |
| 5 | +You may obtain a copy of the License at |
| 6 | +
|
| 7 | + http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +
|
| 9 | +Unless required by applicable law or agreed to in writing, software |
| 10 | +distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +See the License for the specific language governing permissions and |
| 13 | +limitations under the License. */ |
| 14 | + |
| 15 | +#include <gflags/gflags.h> |
| 16 | +#include <glog/logging.h> |
| 17 | +#include <gtest/gtest.h> |
| 18 | +#include <cmath> |
| 19 | + |
| 20 | +#include "paddle/fluid/inference/tests/api/tester_helper.h" |
| 21 | + |
| 22 | +namespace paddle { |
| 23 | +namespace inference { |
| 24 | + |
| 25 | +TEST(AnalysisPredictor, use_gpu) { |
| 26 | + std::string model_dir = FLAGS_infer_model + "/" + "model"; |
| 27 | + AnalysisConfig config; |
| 28 | + config.EnableUseGpu(100, 0); |
| 29 | + config.SetModel(model_dir + "/model", model_dir + "/params"); |
| 30 | + config.EnableLiteEngine(paddle::AnalysisConfig::Precision::kFloat32); |
| 31 | + |
| 32 | + std::vector<PaddleTensor> inputs; |
| 33 | + auto predictor = CreatePaddlePredictor(config); |
| 34 | + const int batch = 1; |
| 35 | + const int channel = 3; |
| 36 | + const int height = 318; |
| 37 | + const int width = 318; |
| 38 | + const int input_num = batch * channel * height * width; |
| 39 | + std::vector<float> input(input_num, 1); |
| 40 | + |
| 41 | + PaddleTensor in; |
| 42 | + in.shape = {1, 3, 318, 318}; |
| 43 | + in.data = |
| 44 | + PaddleBuf(static_cast<void*>(input.data()), input_num * sizeof(float)); |
| 45 | + in.dtype = PaddleDType::FLOAT32; |
| 46 | + inputs.emplace_back(in); |
| 47 | + |
| 48 | + std::vector<PaddleTensor> outputs; |
| 49 | + ASSERT_TRUE(predictor->Run(inputs, &outputs)); |
| 50 | + |
| 51 | + const std::vector<float> truth_values = { |
| 52 | + 127.780396, 738.16656, 1013.2264, -438.17206, 366.4022, 927.66187, |
| 53 | + 736.2241, -633.68567, -329.92737, -430.15637, -633.0639, -146.54858, |
| 54 | + -1324.2804, -1349.3661, -242.67671, 117.44864, -801.7251, -391.51495, |
| 55 | + -404.8202, 454.16132, 515.48206, -133.03114, 69.293076, 590.09753, |
| 56 | + -1434.6917, -1070.8903, 307.0744, 400.52573, -316.12177, -587.1265, |
| 57 | + -161.05742, 800.3663, -96.47157, 748.708, 868.17645, -447.9403, |
| 58 | + 112.73656, 1127.1992, 47.43518, 677.7219, 593.1881, -336.4011, |
| 59 | + 551.3634, 397.82474, 78.39835, -715.4006, 405.96988, 404.25684, |
| 60 | + 246.01978, -8.430191, 131.36617, -648.0528}; |
| 61 | + |
| 62 | + const size_t expected_size = 1; |
| 63 | + EXPECT_EQ(outputs.size(), expected_size); |
| 64 | + float* data_o = static_cast<float*>(outputs[0].data.data()); |
| 65 | + for (size_t j = 0; j < outputs[0].data.length() / sizeof(float); j += 10) { |
| 66 | + EXPECT_NEAR((data_o[j] - truth_values[j / 10]) / truth_values[j / 10], 0., |
| 67 | + 10e-5); |
| 68 | + } |
| 69 | +} |
| 70 | + |
| 71 | +} // namespace inference |
| 72 | +} // namespace paddle |
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