wehub-resource-sync
189 lines
7.4 KiB
C++
189 lines
7.4 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_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 image_shape = 640) {
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// init input data
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int channel = 3;
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int width = image_shape;
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int height = image_shape;
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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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void PrepareDynamicShape(paddle_infer::Config* config, int max_batch_size = 4) {
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// set dynamic shape range
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std::map<std::string, std::vector<int>> min_input_shape = {
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{"x", {1, 3, 224, 224}},
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{"conv2d_124.tmp_0", {1, 256, 56, 56}},
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{"nearest_interp_v2_2.tmp_0", {1, 256, 56, 56}},
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{"nearest_interp_v2_3.tmp_0", {1, 64, 56, 56}},
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{"nearest_interp_v2_4.tmp_0", {1, 64, 56, 56}},
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{"nearest_interp_v2_5.tmp_0", {1, 64, 56, 56}}};
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std::map<std::string, std::vector<int>> max_input_shape = {
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{"x", {max_batch_size, 3, 448, 448}},
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{"conv2d_124.tmp_0", {max_batch_size, 256, 112, 112}},
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{"nearest_interp_v2_2.tmp_0", {max_batch_size, 256, 112, 112}},
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{"nearest_interp_v2_3.tmp_0", {max_batch_size, 64, 112, 112}},
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{"nearest_interp_v2_4.tmp_0", {max_batch_size, 64, 112, 112}},
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{"nearest_interp_v2_5.tmp_0", {max_batch_size, 64, 112, 112}}};
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std::map<std::string, std::vector<int>> opt_input_shape = {
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{"x", {1, 3, 256, 256}},
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{"conv2d_124.tmp_0", {1, 256, 64, 64}},
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{"nearest_interp_v2_2.tmp_0", {1, 256, 64, 64}},
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{"nearest_interp_v2_3.tmp_0", {1, 64, 64, 64}},
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{"nearest_interp_v2_4.tmp_0", {1, 64, 64, 64}},
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{"nearest_interp_v2_5.tmp_0", {1, 64, 64, 64}}};
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config->SetTRTDynamicShapeInfo(
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min_input_shape, max_input_shape, opt_input_shape);
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}
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TEST(gpu_tester_det_mv3_db, analysis_gpu_bz4) {
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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(4, 640);
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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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// prepare ground truth config
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paddle_infer::Config config, config_no_ir;
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config_no_ir.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config_no_ir.SwitchIrOptim(false);
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// prepare inference config
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config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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// get ground truth by disable ir
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paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
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SingleThreadPrediction(
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pred_pool_no_ir.Retrieve(0), &my_input_data_map, &truth_output_data, 1);
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// get infer results
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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, 1e-4);
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std::cout << "finish test" << std::endl;
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}
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TEST(tensorrt_tester_det_mv3_db, multi_thread2_trt_fp32_dynamic_shape_bz2) {
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int thread_num = 2; // thread > 2 may OOM
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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, 256);
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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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// prepare ground truth config
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paddle_infer::Config config, config_no_ir;
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config_no_ir.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config_no_ir.SwitchIrOptim(false);
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// prepare inference 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(100, 0);
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config.EnableTensorRtEngine(
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1 << 20, 4, 3, paddle_infer::PrecisionType::kFloat32, false, false);
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PrepareDynamicShape(&config, 4);
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// get ground truth by disable ir
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paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
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SingleThreadPrediction(
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pred_pool_no_ir.Retrieve(0), &my_input_data_map, &truth_output_data, 1);
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// get infer results from multi threads
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std::vector<std::thread> threads;
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services::PredictorPool pred_pool(config, thread_num);
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for (int i = 0; i < thread_num; ++i) {
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threads.emplace_back(paddle::test::SingleThreadPrediction,
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pred_pool.Retrieve(i),
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&my_input_data_map,
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&infer_output_data,
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2);
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}
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// thread join & check outputs
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for (int i = 0; i < thread_num; ++i) {
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LOG(INFO) << "join tid : " << i;
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threads[i].join();
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CompareRecord(&truth_output_data, &infer_output_data, 1e-4);
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}
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std::cout << "finish multi-thread test" << std::endl;
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}
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TEST(onednn_tester_det_mv3_db, multi_thread2_mkl_fp32_bz2) {
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int thread_num = 2; // thread > 2 may OOM
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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, 640);
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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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// prepare ground truth config
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paddle_infer::Config config, config_no_ir;
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config_no_ir.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config_no_ir.SwitchIrOptim(false);
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// prepare inference config
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config.SetModel(FLAGS_modeldir + "/inference.pdmodel",
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FLAGS_modeldir + "/inference.pdiparams");
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config.DisableGpu();
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config.EnableONEDNN();
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config.SetOnednnCacheCapacity(10);
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config.SetCpuMathLibraryNumThreads(10);
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// get ground truth by disable ir
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paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
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SingleThreadPrediction(
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pred_pool_no_ir.Retrieve(0), &my_input_data_map, &truth_output_data, 1);
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// get infer results from multi threads
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std::vector<std::thread> threads;
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services::PredictorPool pred_pool(config, thread_num);
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for (int i = 0; i < thread_num; ++i) {
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threads.emplace_back(paddle::test::SingleThreadPrediction,
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pred_pool.Retrieve(i),
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&my_input_data_map,
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&infer_output_data,
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2);
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}
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// thread join & check outputs
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for (int i = 0; i < thread_num; ++i) {
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LOG(INFO) << "join tid : " << i;
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threads[i].join();
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CompareRecord(&truth_output_data, &infer_output_data, 1e-4);
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}
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std::cout << "finish multi-thread test" << std::endl;
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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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