[DO NOT MERGE] detect model test2 for dynamic shape

paddle/fluid/inference/tests/api/CMakeLists.txt

@@ -161,6 +161,10 @@ if (NOT EXISTS ${MOBILENET_INSTALL_DIR})
 endif()
 inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose ${MOBILENET_INSTALL_DIR} analyzer_vis_tester.cc)
 
+# detect
+inference_analysis_api_test_with_refer_result(test_analyzer_detect ${OCR_INSTALL_DIR} analyzer_detect_tester.cc)
+#target_link_libraries(test_analyzer_detect tcmalloc)
+
 ### Image classification tests with fake data
 set(IMG_CLASS_TEST_APP "test_analyzer_image_classification")
 set(IMG_CLASS_TEST_APP_SRC "analyzer_image_classification_tester.cc")

paddle/fluid/inference/tests/api/analyzer_detect_tester.cc

@@ -0,0 +1,154 @@
+/* Copyright (c) 2018 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 <gperftools/heap-checker.h>
+#include <gtest/gtest.h>
+#include <fstream>
+#include <iostream>
+#include "paddle/fluid/inference/tests/api/tester_helper.h"
+DEFINE_string(infer_shape, "", "data shape file");
+DEFINE_int32(sample, 1, "number of sample");
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+struct Record {
+  std::vector<float> data;
+  std::vector<int32_t> shape;
+};
+
+Record ProcessALine(const std::string &line, const std::string &shape_line) {
+  VLOG(3) << "process a line";
+  std::vector<std::string> columns;
+
+  Record record;
+  std::vector<std::string> data_strs;
+  split(line, ' ', &data_strs);
+  for (auto &d : data_strs) {
+    record.data.push_back(std::stof(d));
+  }
+
+  std::vector<std::string> shape_strs;
+  split(shape_line, ' ', &shape_strs);
+  for (auto &s : shape_strs) {
+    record.shape.push_back(std::stoi(s));
+  }
+  // VLOG(3) << "data size " << record.data.size();
+  // VLOG(3) << "data shape size " << record.shape.size();
+  // LOG(INFO) << "data shape size " << record.shape[3];
+  return record;
+}
+
+void SetConfig(AnalysisConfig *cfg) {
+  cfg->SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params");
+  cfg->DisableGpu();
+  cfg->SwitchIrDebug();
+  cfg->SwitchSpecifyInputNames(false);
+  cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
+}
+
+void SetInput(std::vector<std::vector<PaddleTensor>> *inputs,
+              const std::string &line, const std::string &shape_line) {
+  auto record = ProcessALine(line, shape_line);
+
+  PaddleTensor input;
+  input.shape = record.shape;
+  input.dtype = PaddleDType::FLOAT32;
+  size_t input_size = record.data.size() * sizeof(float);
+  input.data.Resize(input_size);
+  memcpy(input.data.data(), record.data.data(), input_size);
+  std::vector<PaddleTensor> input_slots;
+  input_slots.assign({input});
+  (*inputs).emplace_back(input_slots);
+}
+
+// Easy for profiling independently.
+//  ocr, mobilenet and se_resnext50
+void profile(bool use_mkldnn = false) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+    cfg.SetMkldnnCacheCapacity(10);
+  }
+  // cfg.pass_builder()->TurnOnDebug();
+  std::vector<std::vector<PaddleTensor>> outputs;
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+
+  Timer run_timer;
+  double elapsed_time = 0;
+
+  int iterations = FLAGS_sample;
+  int num_times = FLAGS_repeat;
+  auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
+  outputs.resize(iterations);
+
+  std::vector<std::thread> threads;
+
+  for (int j = 0; j < num_times; j++) {
+    std::ifstream file(FLAGS_infer_data);
+    std::ifstream infer_file(FLAGS_infer_shape);
+    std::string line;
+    std::string shape_line;
+
+    for (int i = 0; i < iterations; i++) {
+      threads.emplace_back([&, i]() {
+        std::getline(file, line);
+        std::getline(infer_file, shape_line);
+        SetInput(&input_slots_all, line, shape_line);
+
+        run_timer.tic();
+        predictor->Run(input_slots_all[0], &outputs[0], FLAGS_batch_size);
+        elapsed_time += run_timer.toc();
+      });
+      threads[0].join();
+      threads.clear();
+      if (i % 100 == 0) LOG(INFO) << i << " samples";
+      std::vector<std::vector<PaddleTensor>>().swap(input_slots_all);
+    }
+
+    file.close();
+    infer_file.close();
+  }
+
+  auto batch_latency = elapsed_time / (iterations * num_times);
+  PrintTime(FLAGS_batch_size, num_times, FLAGS_num_threads, 0, batch_latency,
+            iterations, VarType::FP32);
+}
+
+TEST(Analyzer_vis, profile) { profile(); }
+
+#ifdef PADDLE_WITH_MKLDNN
+TEST(Analyzer_vis, profile_mkldnn) { profile(true /* use_mkldnn */); }
+#endif
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle
+
+// following lines are used for pprof
+// int main(int argc, char **argv) {
+//   HeapLeakChecker heap_checker("test_foo");
+//   FLAGS_infer_model = "third_party/inference_demo/face_model/densebox";
+//   FLAGS_infer_data =
+//   "third_party/inference_demo/face_model/detect_input.txt";
+//   FLAGS_infer_shape = "third_party/inference_demo/face_model/shape.txt";
+//   FLAGS_paddle_num_threads = 4;
+//   FLAGS_repeat = 1;
+//   FLAGS_batch_size = 1;
+//   FLAGS_sample = 10;
+//   paddle::inference::analysis::profile(true);
+//   std::cout << heap_checker.NoLeaks() << std::endl;
+// }