chore: import upstream snapshot with attribution

test/cpp/inference/infer_ut/test_yolov3.cc

@@ -0,0 +1,159 @@
+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "test_suite.h"  // NOLINT
+
+DEFINE_string(modeldir, "", "Directory of the inference model.");
+
+namespace paddle_infer {
+
+std::map<std::string, paddle::test::Record> PrepareInput(int batch_size) {
+  // init input data
+  int channel = 3;
+  int width = 608;
+  int height = 608;
+  paddle::test::Record image, im_shape, scale_factor;
+  int input_num = batch_size * channel * width * height;
+  int shape_num = batch_size * 2;
+  std::vector<float> image_data(input_num, 1);
+  for (int i = 1; i < input_num + 1; ++i) {
+    image_data[i] = i % 10 * 0.5;
+  }
+  std::vector<float> im_shape_data(shape_num, 1);
+  std::vector<float> scale_factor_data(shape_num, 1);
+
+  image.data = std::vector<float>(image_data.begin(), image_data.end());
+  image.shape = std::vector<int>{batch_size, channel, width, height};
+  image.type = paddle::PaddleDType::FLOAT32;
+
+  im_shape.data =
+      std::vector<float>(im_shape_data.begin(), im_shape_data.end());
+  im_shape.shape = std::vector<int>{batch_size, 2};
+  im_shape.type = paddle::PaddleDType::FLOAT32;
+
+  scale_factor.data =
+      std::vector<float>(scale_factor_data.begin(), scale_factor_data.end());
+  scale_factor.shape = std::vector<int>{batch_size, 2};
+  scale_factor.type = paddle::PaddleDType::FLOAT32;
+
+  std::map<std::string, paddle::test::Record> input_data_map;
+  input_data_map.insert({"image", image});
+  input_data_map.insert({"im_shape", im_shape});
+  input_data_map.insert({"scale_factor", scale_factor});
+
+  return input_data_map;
+}
+
+TEST(test_yolov3, multi_thread3_trt_fp32_bz2) {
+  int thread_num = 3;
+  // init input data
+  auto input_data_map = PrepareInput(2);
+  // init output data
+  std::map<std::string, paddle::test::Record> infer_output_data,
+      truth_output_data;
+  // prepare ground truth config
+  paddle_infer::Config config, config_no_ir;
+  config_no_ir.SetModel(FLAGS_modeldir + "/model.pdmodel",
+                        FLAGS_modeldir + "/model.pdiparams");
+  config_no_ir.EnableUseGpu(100, 0);
+  config_no_ir.SwitchIrOptim(false);
+  // prepare inference config
+  config.SetModel(FLAGS_modeldir + "/model.pdmodel",
+                  FLAGS_modeldir + "/model.pdiparams");
+  config.EnableUseGpu(100, 0);
+  config.EnableTensorRtEngine(
+      1 << 20, 2, 3, paddle_infer::PrecisionType::kFloat32, false, false);
+  LOG(INFO) << config.Summary();
+  // get ground truth by disable ir
+  paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
+  SingleThreadPrediction(
+      pred_pool_no_ir.Retrieve(0), &input_data_map, &truth_output_data, 1);
+
+  // get infer results from multi threads
+  std::vector<std::thread> threads;
+  services::PredictorPool pred_pool(config, thread_num);
+  for (int i = 0; i < thread_num; ++i) {
+    threads.emplace_back(paddle::test::SingleThreadPrediction,
+                         pred_pool.Retrieve(i),
+                         &input_data_map,
+                         &infer_output_data,
+                         2);
+  }
+
+  // thread join & check outputs
+  for (int i = 0; i < thread_num; ++i) {
+    LOG(INFO) << "join tid : " << i;
+    threads[i].join();
+    CompareRecord(&truth_output_data, &infer_output_data, 1e-2);
+    // TODO(OliverLPH): precision set to 1e-2 since input is fake, change to
+    // real input later
+  }
+
+  std::cout << "finish multi-thread test" << std::endl;
+}
+
+TEST(test_yolov3, multi_thread4_mkl_bz2) {
+  int thread_num = 4;
+  // init input data
+  auto input_data_map = PrepareInput(2);
+  // init output data
+  std::map<std::string, paddle::test::Record> infer_output_data,
+      truth_output_data;
+  // prepare ground truth config
+  paddle_infer::Config config, config_no_ir;
+  config_no_ir.SetModel(FLAGS_modeldir + "/model.pdmodel",
+                        FLAGS_modeldir + "/model.pdiparams");
+  config_no_ir.DisableGpu();
+  config_no_ir.SwitchIrOptim(false);
+  // prepare inference config
+  config.SetModel(FLAGS_modeldir + "/model.pdmodel",
+                  FLAGS_modeldir + "/model.pdiparams");
+  config.DisableGpu();
+  config.EnableONEDNN();
+  config.SetOnednnCacheCapacity(10);
+  config.SetCpuMathLibraryNumThreads(10);
+  LOG(INFO) << config.Summary();
+  // get ground truth by disable ir
+  paddle_infer::services::PredictorPool pred_pool_no_ir(config_no_ir, 1);
+  SingleThreadPrediction(
+      pred_pool_no_ir.Retrieve(0), &input_data_map, &truth_output_data, 1);
+
+  // get infer results from multi threads
+  std::vector<std::thread> threads;
+  services::PredictorPool pred_pool(config, thread_num);
+  for (int i = 0; i < thread_num; ++i) {
+    threads.emplace_back(paddle::test::SingleThreadPrediction,
+                         pred_pool.Retrieve(i),
+                         &input_data_map,
+                         &infer_output_data,
+                         2);
+  }
+
+  // thread join & check outputs
+  for (int i = 0; i < thread_num; ++i) {
+    LOG(INFO) << "join tid : " << i;
+    threads[i].join();
+    CompareRecord(&truth_output_data, &infer_output_data, 1e-4);
+  }
+
+  std::cout << "finish multi-thread test" << std::endl;
+}
+
+}  // namespace paddle_infer
+
+int main(int argc, char** argv) {
+  ::testing::InitGoogleTest(&argc, argv);
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+  return RUN_ALL_TESTS();
+}