wehub-resource-sync
87 lines
3.2 KiB
C++
87 lines
3.2 KiB
C++
// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 "test_helper.h" // NOLINT
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#include "test_suite.h" // NOLINT
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DEFINE_string(modeldir, "", "Directory of the inference model.");
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namespace paddle_infer {
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paddle::test::Record PrepareInput(int batch_size, int shape_size = 224) {
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// init input data
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int channel = 3;
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int width = shape_size; // w = 224
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int height = shape_size; // h = 224
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paddle::test::Record image_Record;
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int input_num = batch_size * channel * width * height;
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std::vector<float> input_data(input_num, 1);
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image_Record.data = input_data;
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image_Record.shape = std::vector<int>{batch_size, channel, width, height};
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image_Record.type = paddle::PaddleDType::FLOAT32;
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return image_Record;
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}
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TEST(tensorrt_tester_mobilenetv1, tuned_dynamic_trt_fp32_bz2) {
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bool tuned_shape = true;
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std::string shape_range_info = FLAGS_modeldir + "/shape_range_info.pbtxt";
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LOG(INFO) << "tensorrt tuned info saved to " << shape_range_info;
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// init input data
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std::map<std::string, paddle::test::Record> my_input_data_map;
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my_input_data_map["x"] = PrepareInput(2, 448);
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// init output data
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std::map<std::string, paddle::test::Record> infer_output_data,
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truth_output_data;
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if (tuned_shape) {
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// NOTE: shape_range_info will be saved after destructor of predictor
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// function
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// prepare ground truth config
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paddle_infer::Config tune_config;
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tune_config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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tune_config.SwitchIrOptim(false);
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tune_config.EnableUseGpu(1000, 0);
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tune_config.CollectShapeRangeInfo(shape_range_info);
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auto predictor_tune = paddle_infer::CreatePredictor(tune_config);
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SingleThreadPrediction(
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predictor_tune.get(), &my_input_data_map, &truth_output_data, 1);
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}
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// prepare inference config
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paddle_infer::Config config;
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config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config.EnableUseGpu(1000, 0);
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config.EnableTensorRtEngine(
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1 << 20, 2, 5, paddle_infer::PrecisionType::kFloat32, false, false);
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config.EnableTunedTensorRtDynamicShape(shape_range_info, true);
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LOG(INFO) << config.Summary();
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paddle_infer::services::PredictorPool pred_pool(config, 1);
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SingleThreadPrediction(
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pred_pool.Retrieve(0), &my_input_data_map, &infer_output_data);
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// check outputs
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CompareRecord(&truth_output_data, &infer_output_data);
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VLOG(1) << "finish test";
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}
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} // namespace paddle_infer
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int main(int argc, char** argv) {
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::testing::InitGoogleTest(&argc, argv);
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gflags::ParseCommandLineFlags(&argc, &argv, true);
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return RUN_ALL_TESTS();
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}
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