add pyramid_dnn c++ inference test

test=develop

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

@@ -86,6 +86,11 @@ set(MM_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mm_dnn")
 download_model_and_data(${MM_DNN_INSTALL_DIR} "MM_DNN_model.tar.gz" "MM_DNN_data.txt.tar.gz")
 inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc)
 
+# Pyramid DNN
+set(PYRAMID_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/pyramid_dnn")
+download_model_and_data(${PYRAMID_DNN_INSTALL_DIR} "PyramidDNN_model.tar.gz" "PyramidDNN_data.txt.tar.gz")
+inference_analysis_api_test(test_analyzer_pyramid_dnn ${PYRAMID_DNN_INSTALL_DIR} analyzer_pyramid_dnn_tester.cc)
+
 # text_classification
 set(TEXT_CLASSIFICATION_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/text_classification")
 download_model_and_data(${TEXT_CLASSIFICATION_INSTALL_DIR} "text-classification-Senta.tar.gz" "text_classification_data.txt.tar.gz")

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

+// 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 "paddle/fluid/inference/tests/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+using contrib::AnalysisConfig;
+
+struct DataRecord {
+  std::vector<std::vector<int64_t>> query_basic, query_phrase, title_basic,
+      title_phrase;
+  std::vector<size_t> lod1, lod2, lod3, lod4;
+  size_t batch_iter{0}, batch_size{1}, num_samples;  // total number of samples
+  DataRecord() = default;
+  explicit DataRecord(const std::string &path, int batch_size = 1)
+      : batch_size(batch_size) {
+    Load(path);
+  }
+  DataRecord NextBatch() {
+    DataRecord data;
+    size_t batch_end = batch_iter + batch_size;
+    // NOTE skip the final batch, if no enough data is provided.
+    if (batch_end <= query_basic.size()) {
+      GetInputPerBatch(query_basic, &data.query_basic, &data.lod1, batch_iter,
+                       batch_end);
+      GetInputPerBatch(query_phrase, &data.query_phrase, &data.lod2, batch_iter,
+                       batch_end);
+      GetInputPerBatch(title_basic, &data.title_basic, &data.lod3, batch_iter,
+                       batch_end);
+      GetInputPerBatch(title_phrase, &data.title_phrase, &data.lod4, batch_iter,
+                       batch_end);
+    }
+    batch_iter += batch_size;
+    return data;
+  }
+  void Load(const std::string &path) {
+    std::ifstream file(path);
+    std::string line;
+    int num_lines = 0;
+    while (std::getline(file, line)) {
+      std::vector<std::string> data;
+      split(line, ';', &data);
+      // load query data
+      std::vector<int64_t> query_basic_data;
+      split_to_int64(data[1], ' ', &query_basic_data);
+      std::vector<int64_t> query_phrase_data;
+      split_to_int64(data[2], ' ', &query_phrase_data);
+      // load title data
+      std::vector<int64_t> title_basic_data;
+      split_to_int64(data[3], ' ', &title_basic_data);
+      std::vector<int64_t> title_phrase_data;
+      split_to_int64(data[4], ' ', &title_phrase_data);
+      // filter the empty data
+      bool flag =
+          data[1].size() && data[2].size() && data[3].size() && data[4].size();
+      if (flag) {
+        query_basic.push_back(std::move(query_basic_data));
+        query_phrase.push_back(std::move(query_phrase_data));
+        title_basic.push_back(std::move(title_basic_data));
+        title_phrase.push_back(std::move(title_phrase_data));
+        num_lines++;
+      }
+    }
+    num_samples = num_lines;
+  }
+};
+
+void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
+                   int batch_size) {
+  PaddleTensor query_basic_tensor, query_phrase_tensor, title_basic_tensor,
+      title_phrase_tensor;
+  query_basic_tensor.name = "query_basic";
+  query_phrase_tensor.name = "query_phrase";
+  title_basic_tensor.name = "pos_title_basic";
+  title_phrase_tensor.name = "pos_title_phrase";
+  auto one_batch = data->NextBatch();
+  // assign data
+  TensorAssignData<int64_t>(&query_basic_tensor, one_batch.query_basic,
+                            one_batch.lod1);
+  TensorAssignData<int64_t>(&query_phrase_tensor, one_batch.query_phrase,
+                            one_batch.lod2);
+  TensorAssignData<int64_t>(&title_basic_tensor, one_batch.title_basic,
+                            one_batch.lod3);
+  TensorAssignData<int64_t>(&title_phrase_tensor, one_batch.title_phrase,
+                            one_batch.lod4);
+  // Set inputs.
+  input_slots->assign({query_basic_tensor, query_phrase_tensor,
+                       title_basic_tensor, title_phrase_tensor});
+  for (auto &tensor : *input_slots) {
+    tensor.dtype = PaddleDType::INT64;
+  }
+}
+
+void SetConfig(contrib::AnalysisConfig *cfg) {
+  cfg->SetModel(FLAGS_infer_model);
+  cfg->DisableGpu();
+  cfg->SwitchSpecifyInputNames();
+  cfg->SwitchIrOptim();
+}
+
+void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
+  DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
+  std::vector<PaddleTensor> input_slots;
+  int epoch = FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
+  LOG(INFO) << "number of samples: " << epoch * FLAGS_batch_size;
+  for (int bid = 0; bid < epoch; ++bid) {
+    PrepareInputs(&input_slots, &data, FLAGS_batch_size);
+    (*inputs).emplace_back(input_slots);
+  }
+}
+
+// Easy for profiling independently.
+TEST(Analyzer_Pyramid_DNN, profile) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+  std::vector<PaddleTensor> outputs;
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                 input_slots_all, &outputs, FLAGS_num_threads);
+
+  if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
+    PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
+    size_t size = GetSize(outputs[0]);
+    PADDLE_ENFORCE_GT(size, 0);
+    float *result = static_cast<float *>(outputs[0].data.data());
+    // output is probability, which is in (0, 1).
+    for (size_t i = 0; i < size; i++) {
+      EXPECT_GT(result[i], 0);
+      EXPECT_LT(result[i], 1);
+    }
+  }
+}
+
+// Check the fuse status
+TEST(Analyzer_Pyramid_DNN, fuse_statis) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  int num_ops;
+  auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
+  auto fuse_statis = GetFuseStatis(
+      static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
+}
+
+// Compare result of NativeConfig and AnalysisConfig
+TEST(Analyzer_Pyramid_DNN, compare) {
+  contrib::AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareNativeAndAnalysis(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
+}
+
+// Compare Deterministic result
+TEST(Analyzer_Pyramid_DNN, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
+}  // namespace inference
+}  // namespace paddle