unify PrintTime of analysis unit-test

Author: luotao1

paddle/fluid/inference/analysis/analyzer_lac_tester.cc

@@ -114,12 +114,6 @@ void GetOneBatch(std::vector<PaddleTensor> *input_slots, DataRecord *data,
   PADDLE_ENFORCE_EQ(batch_size, static_cast<int>(one_batch.lod.size() - 1));
   input_slots->assign({input_tensor});
 }
-static void PrintTime(const double latency, const int bs, const int repeat) {
-  LOG(INFO) << "===========profile result===========";
-  LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
-            << ", avg latency: " << latency / repeat << "ms";
-  LOG(INFO) << "=====================================";
-}
 void BenchAllData(const std::string &model_path, const std::string &data_file,
                   const int batch_size, const int repeat) {
   NativeConfig config;
@@ -145,7 +139,7 @@ void BenchAllData(const std::string &model_path, const std::string &data_file,
       sum += timer.toc();
     }
   }
-  PrintTime(sum, batch_size, repeat);
+  PrintTime(batch_size, repeat, 1, 0, sum / repeat);
 }
 const int64_t lac_ref_data[] = {24, 25, 25, 25, 38, 30, 31, 14, 15, 44, 24, 25,
                                 25, 25, 25, 25, 44, 24, 25, 25, 25, 36, 42, 43,
@@ -176,7 +170,7 @@ void TestLACPrediction(const std::string &model_path,
   for (int i = 0; i < repeat; i++) {
     predictor->Run(input_slots, &outputs_slots);
   }
-  PrintTime(timer.toc(), batch_size, repeat);
+  PrintTime(batch_size, repeat, 1, 0, timer.toc() / repeat);
   EXPECT_EQ(outputs_slots.size(), 1UL);
   auto &out = outputs_slots[0];
   size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,

paddle/fluid/inference/analysis/analyzer_ner_tester.cc

@@ -130,11 +130,7 @@ void TestChineseNERPrediction() {
   for (int i = 0; i < FLAGS_repeat; i++) {
     predictor->Run(input_slots, &outputs);
   }
-  LOG(INFO) << "===========profile result===========";
-  LOG(INFO) << "batch_size: " << FLAGS_batch_size
-            << ", repeat: " << FLAGS_repeat
-            << ", latency: " << timer.toc() / FLAGS_repeat << "ms";
-  LOG(INFO) << "=====================================";
+  PrintTime(FLAGS_batch_size, FLAGS_repeat, 1, 0, timer.toc() / FLAGS_repeat);
 
   PADDLE_ENFORCE(outputs.size(), 1UL);
   auto &out = outputs[0];

paddle/fluid/inference/analysis/test_text_classification.cc

@@ -18,31 +18,16 @@
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
-#include "paddle/fluid/inference/api/timer.h"
 
 DEFINE_string(infer_model, "", "Directory of the inference model.");
 DEFINE_string(infer_data, "", "Path of the dataset.");
 DEFINE_int32(batch_size, 1, "batch size.");
 DEFINE_int32(repeat, 1, "How many times to repeat run.");
 
 namespace paddle {
-
-template <typename T>
-std::string to_string(const std::vector<T> &vec) {
-  std::stringstream ss;
-  for (const auto &c : vec) {
-    ss << c << " ";
-  }
-  return ss.str();
-}
-
-void PrintTime(const double latency, const int bs, const int repeat) {
-  LOG(INFO) << "===========profile result===========";
-  LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
-            << ", avg latency: " << latency / repeat << "ms";
-  LOG(INFO) << "=====================================";
-}
+namespace inference {
 
 void Main(int batch_size) {
   // Three sequence inputs.
@@ -78,7 +63,7 @@ void Main(int batch_size) {
     CHECK(predictor->Run(input_slots, &output_slots));
     sum += timer.toc();
   }
-  PrintTime(sum, batch_size, FLAGS_repeat);
+  PrintTime(batch_size, FLAGS_repeat, 1, 0, sum / FLAGS_repeat);
 
   // Get output
   LOG(INFO) << "get outputs " << output_slots.size();
@@ -99,6 +84,7 @@ void Main(int batch_size) {
 
 TEST(text_classification, basic) { Main(FLAGS_batch_size); }
 
+}  // namespace inference
 }  // namespace paddle
 
 USE_PASS(fc_fuse_pass);