Refactor/remove sensitive (#13314)

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -48,18 +48,18 @@ function (inference_download_and_uncompress install_dir url gz_filename)
     message(STATUS "finish downloading ${gz_filename}")
 endfunction(inference_download_and_uncompress)
 
-set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz")
-set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz")
-set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE)
-if (NOT EXISTS ${DITU_INSTALL_DIR} AND WITH_TESTING)
-  inference_download_and_uncompress(${DITU_INSTALL_DIR} ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz")
-  inference_download_and_uncompress(${DITU_INSTALL_DIR} ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz")
+set(RNN1_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/rnn1%2Fmodel.tar.gz")
+set(RNN1_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/rnn1%2Fdata.txt.tar.gz")
+set(RNN1_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/rnn1" CACHE PATH "RNN1 model and data root." FORCE)
+if (NOT EXISTS ${RNN1_INSTALL_DIR} AND WITH_TESTING)
+  inference_download_and_uncompress(${RNN1_INSTALL_DIR} ${RNN1_MODEL_URL} "rnn1%2Fmodel.tar.gz")
+  inference_download_and_uncompress(${RNN1_INSTALL_DIR} ${RNN1_DATA_URL} "rnn1%2Fdata.txt.tar.gz")
 endif()
 
 inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
     EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
-    ARGS --infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
-         --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
+    ARGS --infer_model=${RNN1_INSTALL_DIR}/model
+         --infer_data=${RNN1_INSTALL_DIR}/data.txt)
 
 inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
 inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -26,8 +26,8 @@
 #include "paddle/fluid/inference/api/paddle_inference_pass.h"
 #include "paddle/fluid/inference/utils/singleton.h"
 
-DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
-DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN");
+DEFINE_string(infer_model, "", "model path");
+DEFINE_string(infer_data, "", "data path");
 DEFINE_int32(batch_size, 10, "batch size.");
 DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
 DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
@@ -223,17 +223,6 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
 
 }  // namespace
 
-const float ditu_rnn_target_data[] = {
-    104.711, 11.2431, 1.35422, 0,       0,       0,       0,       0,
-    27.7039, 1.41486, 7.09526, 0,       0,       0,       0,       0,
-    7.6481,  6.5324,  56.383,  2.88018, 8.92918, 132.007, 4.27429, 2.02934,
-    14.1727, 10.7461, 25.0616, 16.0197, 14.4163, 16.9199, 6.75517, 0,
-    80.0249, 4.77739, 0,       0,       0,       0,       0,       0,
-    47.5643, 2.67029, 8.76252, 0,       0,       0,       0,       0,
-    51.8822, 4.4411,  0,       0,       0,       0,       0,       0,
-    10.7286, 12.0595, 10.6672, 0,       0,       0,       0,       0,
-    93.5771, 3.84641, 0,       0,       0,       0,       0,       0,
-    169.426, 0,       0,       0,       0,       0,       0,       0};
 void CompareResult(const std::vector<PaddleTensor> &outputs,
                    const std::vector<PaddleTensor> &base_outputs) {
   PADDLE_ENFORCE_GT(outputs.size(), 0);
@@ -255,11 +244,10 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
   }
 }
 // Test with a really complicate model.
-void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
-                           int num_threads) {
+void TestRNN1Prediction(bool use_analysis, bool activate_ir, int num_threads) {
   AnalysisConfig config;
-  config.prog_file = FLAGS_infer_ditu_rnn_model + "/__model__";
-  config.param_file = FLAGS_infer_ditu_rnn_model + "/param";
+  config.prog_file = FLAGS_infer_model + "/__model__";
+  config.param_file = FLAGS_infer_model + "/param";
   config.use_gpu = false;
   config.device = 0;
   config.specify_input_name = true;
@@ -277,7 +265,7 @@ void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
       CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
           config);
   std::vector<PaddleTensor> input_slots;
-  DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
+  DataRecord data(FLAGS_infer_data, batch_size);
   // Prepare inputs.
   PrepareInputs(&input_slots, &data, batch_size);
   std::vector<PaddleTensor> outputs, base_outputs;
@@ -307,7 +295,7 @@ void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
       threads.emplace_back([&, tid]() {
         // Each thread should have local input_slots and outputs.
         std::vector<PaddleTensor> input_slots;
-        DataRecord data(FLAGS_infer_ditu_rnn_data, batch_size);
+        DataRecord data(FLAGS_infer_data, batch_size);
         PrepareInputs(&input_slots, &data, batch_size);
         std::vector<PaddleTensor> outputs;
         Timer timer;
@@ -354,24 +342,22 @@ void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
 }
 
 // Inference with analysis and IR, easy for profiling independently.
-TEST(Analyzer, DituRNN) {
-  TestDituRNNPrediction(true, true, FLAGS_num_threads);
-}
+TEST(Analyzer, rnn1) { TestRNN1Prediction(true, true, FLAGS_num_threads); }
 
-// Other unit-tests of DituRNN, test different options of use_analysis,
+// Other unit-tests of RNN1, test different options of use_analysis,
 // activate_ir and multi-threads.
-TEST(Analyzer, DituRNN_tests) {
+TEST(Analyzer, RNN_tests) {
   int num_threads[2] = {1, 4};
   for (auto i : num_threads) {
     // Directly infer with the original model.
-    TestDituRNNPrediction(false, false, i);
+    TestRNN1Prediction(false, false, i);
     // Inference with the original model with the analysis turned on, the
     // analysis
     // module will transform the program to a data flow graph.
-    TestDituRNNPrediction(true, false, i);
+    TestRNN1Prediction(true, false, i);
     // Inference with analysis and IR. The IR module will fuse some large
     // kernels.
-    TestDituRNNPrediction(true, true, i);
+    TestRNN1Prediction(true, true, i);
   }
 }