unify the normal and small dam model.

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

@@ -48,10 +48,13 @@ inference_analysis_api_test(test_analyzer_rnn2 ${RNN2_INSTALL_DIR} analyzer_rnn2
 
 # DAM
 set(DAM_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/dam")
-# For the normal DAM model
+# For the normal DAM model: download DAM_model.tar.gz and DAM_data.txt.tar.gz.
 # download_model_and_data(${DAM_INSTALL_DIR} "DAM_model.tar.gz" "DAM_data.txt.tar.gz")
-download_model_and_data(${DAM_INSTALL_DIR} "small_dam_model.tar.gz" "small_dam_data.txt.tar.gz")
-inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc)
+download_model_and_data(${DAM_INSTALL_DIR} "dam_small_model.tar.gz" "dam_small_data.txt.tar.gz")
+# For the normal DAM model: --max_turn_num=9.
+inference_analysis_test(test_analyzer_dam SRCS analyzer_dam_tester.cc
+        EXTRA_DEPS ${INFERENCE_EXTRA_DEPS}
+        ARGS --infer_model=${DAM_INSTALL_DIR}/model --infer_data=${DAM_INSTALL_DIR}/data.txt --max_turn_num=1)
 
 # chinese_ner
 set(CHINESE_NER_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/chinese_ner")

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

@@ -14,38 +14,54 @@
 
 #include "paddle/fluid/inference/tests/api/tester_helper.h"
 
+DEFINE_int32(max_turn_num, 1,
+             "The max turn number: 1 for the small and 9 for the normal.");
+
 namespace paddle {
 namespace inference {
 using contrib::AnalysisConfig;
-#define MAX_TURN_NUM 1
-#define MAX_TURN_LEN 50
+
+constexpr int32_t kMaxTurnLen = 50;
+
 static std::vector<float> result_data;
 
 struct DataRecord {
-  std::vector<std::vector<int64_t>>
-      turns[MAX_TURN_NUM];  // turns data : MAX_TURN_NUM
-  std::vector<std::vector<float>>
-      turns_mask[MAX_TURN_NUM];                // turns mask data : MAX_TURN_NUM
-  std::vector<std::vector<int64_t>> response;  // response data : 1
+  std::vector<std::vector<int64_t>> *turns;
+  std::vector<std::vector<float>> *turns_mask;
+  std::vector<std::vector<int64_t>> response;     // response data : 1
   std::vector<std::vector<float>> response_mask;  // response mask data : 1
   size_t batch_iter{0};
   size_t batch_size{1};
   size_t num_samples;  // total number of samples
-  DataRecord() = default;
+
+  DataRecord() {
+    turns = new std::vector<std::vector<
+        int64_t>>[FLAGS_max_turn_num];  // turns data : FLAGS_max_turn_num
+    turns_mask = new std::vector<std::vector<
+        float>>[FLAGS_max_turn_num];  // turns mask data : FLAGS_max_turn_num
+  }
+
   explicit DataRecord(const std::string &path, int batch_size = 1)
-      : batch_size(batch_size) {
+      : DataRecord() {
+    this->batch_size = batch_size;
     Load(path);
   }
+
+  ~DataRecord() {
+    delete[] turns;
+    delete[] turns_mask;
+  }
+
   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 <= response.size()) {
-      for (int i = 0; i < MAX_TURN_NUM; ++i) {
+      for (int i = 0; i < FLAGS_max_turn_num; ++i) {
         data.turns[i].assign(turns[i].begin() + batch_iter,
                              turns[i].begin() + batch_end);
       }
-      for (int i = 0; i < MAX_TURN_NUM; ++i) {
+      for (int i = 0; i < FLAGS_max_turn_num; ++i) {
         data.turns_mask[i].assign(turns_mask[i].begin() + batch_iter,
                                   turns_mask[i].begin() + batch_end);
       }
@@ -60,6 +76,7 @@ struct DataRecord {
     batch_iter += batch_size;
     return data;
   }
+
   void Load(const std::string &path) {
     std::ifstream file(path);
     std::string line;
@@ -69,30 +86,30 @@ struct DataRecord {
       num_lines++;
       std::vector<std::string> data;
       split(line, ',', &data);
-      CHECK_EQ(data.size(), (size_t)(2 * MAX_TURN_NUM + 3));
+      CHECK_EQ(data.size(), (size_t)(2 * FLAGS_max_turn_num + 3));
       // load turn data
-      std::vector<int64_t> turns_tmp[MAX_TURN_NUM];
-      for (int i = 0; i < MAX_TURN_NUM; ++i) {
+      std::vector<int64_t> turns_tmp[FLAGS_max_turn_num];
+      for (int i = 0; i < FLAGS_max_turn_num; ++i) {
         split_to_int64(data[i], ' ', &turns_tmp[i]);
         turns[i].push_back(std::move(turns_tmp[i]));
       }
       // load turn_mask data
-      std::vector<float> turns_mask_tmp[MAX_TURN_NUM];
-      for (int i = 0; i < MAX_TURN_NUM; ++i) {
-        split_to_float(data[MAX_TURN_NUM + i], ' ', &turns_mask_tmp[i]);
+      std::vector<float> turns_mask_tmp[FLAGS_max_turn_num];
+      for (int i = 0; i < FLAGS_max_turn_num; ++i) {
+        split_to_float(data[FLAGS_max_turn_num + i], ' ', &turns_mask_tmp[i]);
         turns_mask[i].push_back(std::move(turns_mask_tmp[i]));
       }
       // load response data
       std::vector<int64_t> response_tmp;
-      split_to_int64(data[2 * MAX_TURN_NUM], ' ', &response_tmp);
+      split_to_int64(data[2 * FLAGS_max_turn_num], ' ', &response_tmp);
       response.push_back(std::move(response_tmp));
       // load response_mask data
       std::vector<float> response_mask_tmp;
-      split_to_float(data[2 * MAX_TURN_NUM + 1], ' ', &response_mask_tmp);
+      split_to_float(data[2 * FLAGS_max_turn_num + 1], ' ', &response_mask_tmp);
       response_mask.push_back(std::move(response_mask_tmp));
       // load result data
       float result_tmp;
-      result_tmp = std::stof(data[2 * MAX_TURN_NUM + 2]);
+      result_tmp = std::stof(data[2 * FLAGS_max_turn_num + 2]);
       result_data.push_back(result_tmp);
     }
     num_samples = num_lines;
@@ -101,8 +118,8 @@ struct DataRecord {
 
 void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
                    int batch_size) {
-  PaddleTensor turns_tensor[MAX_TURN_NUM];
-  PaddleTensor turns_mask_tensor[MAX_TURN_NUM];
+  PaddleTensor turns_tensor[FLAGS_max_turn_num];
+  PaddleTensor turns_mask_tensor[FLAGS_max_turn_num];
   PaddleTensor response_tensor;
   PaddleTensor response_mask_tensor;
   std::string turn_pre = "turn_";
@@ -110,16 +127,16 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
 
   auto one_batch = data->NextBatch();
   int size = one_batch.response[0].size();
-  CHECK_EQ(size, MAX_TURN_LEN);
+  CHECK_EQ(size, kMaxTurnLen);
   // turn tensor assignment
-  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+  for (int i = 0; i < FLAGS_max_turn_num; ++i) {
     turns_tensor[i].name = turn_pre + std::to_string(i);
     turns_tensor[i].shape.assign({batch_size, size, 1});
     turns_tensor[i].dtype = PaddleDType::INT64;
     TensorAssignData<int64_t>(&turns_tensor[i], one_batch.turns[i]);
   }
   // turn mask tensor assignment
-  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+  for (int i = 0; i < FLAGS_max_turn_num; ++i) {
     turns_mask_tensor[i].name = turn_mask_pre + std::to_string(i);
     turns_mask_tensor[i].shape.assign({batch_size, size, 1});
     turns_mask_tensor[i].dtype = PaddleDType::FLOAT32;
@@ -137,10 +154,10 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
   TensorAssignData<float>(&response_mask_tensor, one_batch.response_mask);
 
   // Set inputs.
-  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+  for (int i = 0; i < FLAGS_max_turn_num; ++i) {
     input_slots->push_back(std::move(turns_tensor[i]));
   }
-  for (int i = 0; i < MAX_TURN_NUM; ++i) {
+  for (int i = 0; i < FLAGS_max_turn_num; ++i) {
     input_slots->push_back(std::move(turns_mask_tensor[i]));
   }
   input_slots->push_back(std::move(response_tensor));
@@ -148,7 +165,8 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
 }
 
 void SetConfig(contrib::AnalysisConfig *cfg) {
-  cfg->model_dir = FLAGS_infer_model;
+  cfg->prog_file = FLAGS_infer_model + "/__model__";
+  cfg->param_file = FLAGS_infer_model + "/param";
   cfg->use_gpu = false;
   cfg->device = 0;
   cfg->specify_input_name = true;
@@ -201,8 +219,6 @@ TEST(Analyzer_dam, fuse_statis) {
   auto fuse_statis = GetFuseStatis(
       static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
   ASSERT_TRUE(fuse_statis.count("fc_fuse"));
-  EXPECT_EQ(fuse_statis.at("fc_fuse"), 45);
-  EXPECT_EQ(num_ops, 292);
 }
 
 // Compare result of NativeConfig and AnalysisConfig