Refine/text classification support data (#13256)

Author: Superjomn

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -100,12 +100,17 @@ inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
 
 
 set(TEXT_CLASSIFICATION_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/text-classification-Senta.tar.gz")
+set(TEXT_CLASSIFICATION_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/text_classification_data.txt.tar.gz")
 set(TEXT_CLASSIFICATION_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/text_classification" CACHE PATH "Text Classification model and data root." FORCE)
 
 if (NOT EXISTS ${TEXT_CLASSIFICATION_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
   inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
+  inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_DATA_URL} "text_classification_data.txt.tar.gz")
 endif()
 
 inference_analysis_test(test_text_classification SRCS analyzer_text_classification_tester.cc
     EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
-    ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)
+    ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta
+         --infer_data=${TEXT_CLASSIFICATION_INSTALL_DIR}/data.txt
+         --topn=1 # Just run top 1 batch.
+    )

paddle/fluid/inference/analysis/analyzer_text_classification_tester.cc

@@ -16,8 +16,10 @@
 #include <gflags/gflags.h>
 #include <glog/logging.h>  // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
 #include <gtest/gtest.h>
+#include <fstream>
 #include "paddle/fluid/framework/ir/pass.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/paddle_inference_pass.h"
 #include "paddle/fluid/inference/api/timer.h"
@@ -26,6 +28,7 @@ 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.");
+DEFINE_int32(topn, -1, "Run top n batches of data to save time");
 
 namespace paddle {
 
@@ -45,41 +48,67 @@ void PrintTime(const double latency, const int bs, const int repeat) {
   LOG(INFO) << "=====================================";
 }
 
-void Main(int batch_size) {
-  // Three sequence inputs.
-  std::vector<PaddleTensor> input_slots(1);
-  // one batch starts
-  // data --
-  int64_t data0[] = {0, 1, 2};
-  for (auto &input : input_slots) {
-    input.data.Reset(data0, sizeof(data0));
-    input.shape = std::vector<int>({3, 1});
-    // dtype --
-    input.dtype = PaddleDType::INT64;
-    // LoD --
-    input.lod = std::vector<std::vector<size_t>>({{0, 3}});
+struct DataReader {
+  DataReader(const std::string &path) : file(new std::ifstream(path)) {}
+
+  bool NextBatch(PaddleTensor *tensor, int batch_size) {
+    PADDLE_ENFORCE_EQ(batch_size, 1);
+    std::string line;
+    tensor->lod.clear();
+    tensor->lod.emplace_back(std::vector<size_t>({0}));
+    std::vector<int64_t> data;
+
+    for (int i = 0; i < batch_size; i++) {
+      if (!std::getline(*file, line)) return false;
+      inference::split_to_int64(line, ' ', &data);
+    }
+    tensor->lod.front().push_back(data.size());
+
+    tensor->data.Resize(data.size() * sizeof(int64_t));
+    memcpy(tensor->data.data(), data.data(), data.size() * sizeof(int64_t));
+    tensor->shape.clear();
+    tensor->shape.push_back(data.size());
+    tensor->shape.push_back(1);
+    return true;
   }
 
+  std::unique_ptr<std::ifstream> file;
+};
+
+void Main(int batch_size) {
   // shape --
   // Create Predictor --
   AnalysisConfig config;
   config.model_dir = FLAGS_infer_model;
   config.use_gpu = false;
   config.enable_ir_optim = true;
-  config.ir_passes.push_back("fc_lstm_fuse_pass");
   auto predictor =
       CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
           config);
 
+  std::vector<PaddleTensor> input_slots(1);
+  // one batch starts
+  // data --
+  auto &input = input_slots[0];
+  input.dtype = PaddleDType::INT64;
+
   inference::Timer timer;
   double sum = 0;
   std::vector<PaddleTensor> output_slots;
-  for (int i = 0; i < FLAGS_repeat; i++) {
-    timer.tic();
-    CHECK(predictor->Run(input_slots, &output_slots));
-    sum += timer.toc();
+
+  int num_batches = 0;
+  for (int t = 0; t < FLAGS_repeat; t++) {
+    DataReader reader(FLAGS_infer_data);
+    while (reader.NextBatch(&input, FLAGS_batch_size)) {
+      if (FLAGS_topn > 0 && num_batches > FLAGS_topn) break;
+      timer.tic();
+      CHECK(predictor->Run(input_slots, &output_slots));
+      sum += timer.toc();
+      ++num_batches;
+    }
   }
-  PrintTime(sum, batch_size, FLAGS_repeat);
+
+  PrintTime(sum, batch_size, num_batches);
 
   // Get output
   LOG(INFO) << "get outputs " << output_slots.size();