fix dataset reading and add support for full dataset (#16559)

paddle/fluid/inference/api/helper.h

@@ -27,6 +27,7 @@
 #include <string>
 #include <vector>
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/port.h"
 #include "paddle/fluid/string/printf.h"
 
@@ -266,17 +267,17 @@ static std::string DescribeZeroCopyTensor(const ZeroCopyTensor &tensor) {
 }
 
 static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
-                      double latency, int epoch = 1) {
-  LOG(INFO) << "====== batch_size: " << batch_size << ", repeat: " << repeat
-            << ", threads: " << num_threads << ", thread id: " << tid
-            << ", latency: " << latency << "ms, fps: " << 1 / (latency / 1000.f)
+                      double batch_latency, int epoch = 1) {
+  PADDLE_ENFORCE(batch_size > 0, "Non-positive batch size.");
+  double sample_latency = batch_latency / batch_size;
+  LOG(INFO) << "====== threads: " << num_threads << ", thread id: " << tid
             << " ======";
-  if (epoch > 1) {
-    int samples = batch_size * epoch;
-    LOG(INFO) << "====== sample number: " << samples
-              << ", average latency of each sample: " << latency / samples
-              << "ms ======";
-  }
+  LOG(INFO) << "====== batch_size: " << batch_size << ", iterations: " << epoch
+            << ", repetitions: " << repeat << " ======";
+  LOG(INFO) << "====== batch latency: " << batch_latency
+            << "ms, number of samples: " << batch_size * epoch
+            << ", sample latency: " << sample_latency
+            << "ms, fps: " << 1000.f / sample_latency << " ======";
 }
 
 static bool IsFileExists(const std::string &path) {

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

@@ -26,7 +26,11 @@ endfunction()
 function(inference_analysis_api_int8_test target model_dir data_dir filename)
     inference_analysis_test(${target} SRCS ${filename}
         EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} benchmark
-        ARGS --infer_model=${model_dir}/model --infer_data=${data_dir}/data.bin --batch_size=100)
+        ARGS --infer_model=${model_dir}/model
+             --infer_data=${data_dir}/data.bin
+             --warmup_batch_size=100
+             --batch_size=50
+	     --iterations=2)
 endfunction()
 
 function(inference_analysis_api_test_with_fake_data target install_dir filename model_name)
@@ -146,22 +150,22 @@ inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_con
 
 # int8 image classification tests
 if(WITH_MKLDNN)
-  set(INT8_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8")
+  set(INT8_DATA_DIR "${INFERENCE_DEMO_INSTALL_DIR}/int8v2")
   if (NOT EXISTS ${INT8_DATA_DIR})
-    inference_download_and_uncompress(${INT8_DATA_DIR} ${INFERENCE_URL}"/int8" "imagenet_val_100.tar.gz")
+    inference_download_and_uncompress(${INT8_DATA_DIR} "${INFERENCE_URL}/int8" "imagenet_val_100_tail.tar.gz")
   endif()
 
   #resnet50 int8
   set(INT8_RESNET50_MODEL_DIR "${INT8_DATA_DIR}/resnet50")
   if (NOT EXISTS ${INT8_RESNET50_MODEL_DIR})
-    inference_download_and_uncompress(${INT8_RESNET50_MODEL_DIR} ${INFERENCE_URL}"/int8" "resnet50_int8_model.tar.gz" )
+    inference_download_and_uncompress(${INT8_RESNET50_MODEL_DIR} "${INFERENCE_URL}/int8" "resnet50_int8_model.tar.gz" )
   endif()
   inference_analysis_api_int8_test(test_analyzer_int8_resnet50 ${INT8_RESNET50_MODEL_DIR} ${INT8_DATA_DIR} analyzer_int8_image_classification_tester.cc SERIAL)
 
   #mobilenet int8
   set(INT8_MOBILENET_MODEL_DIR "${INT8_DATA_DIR}/mobilenet")
   if (NOT EXISTS ${INT8_MOBILENET_MODEL_DIR})
-    inference_download_and_uncompress(${INT8_MOBILENET_MODEL_DIR} ${INFERENCE_URL}"/int8" "mobilenetv1_int8_model.tar.gz" )
+    inference_download_and_uncompress(${INT8_MOBILENET_MODEL_DIR} "${INFERENCE_URL}/int8" "mobilenetv1_int8_model.tar.gz" )
   endif()
   inference_analysis_api_int8_test(test_analyzer_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} analyzer_int8_image_classification_tester.cc SERIAL)
 endif()

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

@@ -154,7 +154,7 @@ void profile(bool use_mkldnn = false) {
     config.EnableMKLDNN();
   }
 
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
   std::vector<std::vector<PaddleTensor>> inputs;
   LoadInputData(&inputs);
   TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&config),

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

@@ -197,7 +197,7 @@ void profile(bool use_mkldnn = false) {
     cfg.SetMKLDNNOp(op_list);
   }
 
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
 
@@ -206,9 +206,11 @@ void profile(bool use_mkldnn = false) {
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
     PADDLE_ENFORCE_GT(outputs.size(), 0);
-    size_t size = GetSize(outputs[0]);
+    auto output = outputs.back();
+    PADDLE_ENFORCE_GT(output.size(), 0);
+    size_t size = GetSize(output[0]);
     PADDLE_ENFORCE_GT(size, 0);
-    float *result = static_cast<float *>(outputs[0].data.data());
+    float *result = static_cast<float *>(output[0].data.data());
     for (size_t i = 0; i < size; i++) {
       EXPECT_NEAR(result[i], result_data[i], 1e-3);
     }

paddle/fluid/inference/tests/api/analyzer_int8_image_classification_tester.cc

@@ -17,8 +17,6 @@ limitations under the License. */
 #include "paddle/fluid/inference/api/paddle_analysis_config.h"
 #include "paddle/fluid/inference/tests/api/tester_helper.h"
 
-DEFINE_int32(iterations, 0, "Number of iterations");
-
 namespace paddle {
 namespace inference {
 namespace analysis {
@@ -30,8 +28,13 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->SwitchIrOptim();
   cfg->SwitchSpecifyInputNames(false);
   cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
-
   cfg->EnableMKLDNN();
+  cfg->pass_builder()->SetPasses(
+      {"infer_clean_graph_pass", "mkldnn_placement_pass",
+       "depthwise_conv_mkldnn_pass", "conv_bn_fuse_pass",
+       "conv_eltwiseadd_bn_fuse_pass", "conv_bias_mkldnn_fuse_pass",
+       "conv_elementwise_add_mkldnn_fuse_pass", "conv_relu_mkldnn_fuse_pass",
+       "fc_fuse_pass", "is_test_pass"});
 }
 
 template <typename T>
@@ -40,8 +43,8 @@ class TensorReader {
   TensorReader(std::ifstream &file, size_t beginning_offset,
                std::vector<int> shape, std::string name)
       : file_(file), position(beginning_offset), shape_(shape), name_(name) {
-    numel =
-        std::accumulate(shape_.begin(), shape_.end(), 1, std::multiplies<T>());
+    numel = std::accumulate(shape_.begin(), shape_.end(), size_t{1},
+                            std::multiplies<size_t>());
   }
 
   PaddleTensor NextBatch() {
@@ -71,10 +74,14 @@ class TensorReader {
 };
 
 std::shared_ptr<std::vector<PaddleTensor>> GetWarmupData(
-    const std::vector<std::vector<PaddleTensor>> &test_data, int num_images) {
+    const std::vector<std::vector<PaddleTensor>> &test_data,
+    int num_images = FLAGS_warmup_batch_size) {
   int test_data_batch_size = test_data[0][0].shape[0];
-  CHECK_LE(static_cast<size_t>(num_images),
-           test_data.size() * test_data_batch_size);
+  auto iterations_max = test_data.size();
+  PADDLE_ENFORCE(
+      static_cast<size_t>(num_images) <= iterations_max * test_data_batch_size,
+      "The requested quantization warmup data size " +
+          std::to_string(num_images) + " is bigger than all test data size.");
 
   PaddleTensor images;
   images.name = "input";
@@ -120,20 +127,17 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs,
 
   std::vector<int> image_batch_shape{batch_size, 3, 224, 224};
   std::vector<int> label_batch_shape{batch_size, 1};
+  auto images_offset_in_file = static_cast<size_t>(file.tellg());
   auto labels_offset_in_file =
-      static_cast<size_t>(file.tellg()) +
-      sizeof(float) * total_images *
-          std::accumulate(image_batch_shape.begin() + 1,
-                          image_batch_shape.end(), 1, std::multiplies<int>());
+      images_offset_in_file + sizeof(float) * total_images * 3 * 224 * 224;
 
-  TensorReader<float> image_reader(file, 0, image_batch_shape, "input");
+  TensorReader<float> image_reader(file, images_offset_in_file,
+                                   image_batch_shape, "input");
   TensorReader<int64_t> label_reader(file, labels_offset_in_file,
                                      label_batch_shape, "label");
 
-  auto iterations = total_images / batch_size;
-  if (FLAGS_iterations > 0 && FLAGS_iterations < iterations)
-    iterations = FLAGS_iterations;
-  for (auto i = 0; i < iterations; i++) {
+  auto iterations_max = total_images / batch_size;
+  for (auto i = 0; i < iterations_max; i++) {
     auto images = image_reader.NextBatch();
     auto labels = label_reader.NextBatch();
     inputs->emplace_back(
@@ -148,20 +152,21 @@ TEST(Analyzer_int8_resnet50, quantization) {
   AnalysisConfig q_cfg;
   SetConfig(&q_cfg);
 
+  // read data from file and prepare batches with test data
   std::vector<std::vector<PaddleTensor>> input_slots_all;
-  SetInput(&input_slots_all, 100);
+  SetInput(&input_slots_all);
 
+  // prepare warmup batch from input data read earlier
+  // warmup batch size can be different than batch size
   std::shared_ptr<std::vector<PaddleTensor>> warmup_data =
-      GetWarmupData(input_slots_all, 100);
+      GetWarmupData(input_slots_all);
 
+  // configure quantizer
   q_cfg.EnableMkldnnQuantizer();
   q_cfg.mkldnn_quantizer_config()->SetWarmupData(warmup_data);
-  q_cfg.mkldnn_quantizer_config()->SetWarmupBatchSize(100);
+  q_cfg.mkldnn_quantizer_config()->SetWarmupBatchSize(FLAGS_warmup_batch_size);
 
-  CompareQuantizedAndAnalysis(
-      reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
-      reinterpret_cast<const PaddlePredictor::Config *>(&q_cfg),
-      input_slots_all);
+  CompareQuantizedAndAnalysis(&cfg, &q_cfg, input_slots_all);
 }
 
 }  // namespace analysis

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

@@ -124,7 +124,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 TEST(Analyzer_LAC, profile) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -137,11 +137,13 @@ TEST(Analyzer_LAC, profile) {
         24, 25, 25, 25, 38, 30, 31, 14, 15, 44, 24, 25, 25, 25, 25, 25,
         44, 24, 25, 25, 25, 36, 42, 43, 44, 14, 15, 44, 14, 15, 44, 14,
         15, 44, 38, 39, 14, 15, 44, 22, 23, 23, 23, 23, 23, 23, 23};
-    PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
-    size_t size = GetSize(outputs[0]);
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    auto output = outputs.back();
+    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    size_t size = GetSize(output[0]);
     size_t batch1_size = sizeof(lac_ref_data) / sizeof(int64_t);
     PADDLE_ENFORCE_GE(size, batch1_size);
-    int64_t *pdata = static_cast<int64_t *>(outputs[0].data.data());
+    int64_t *pdata = static_cast<int64_t *>(output[0].data.data());
     for (size_t i = 0; i < batch1_size; ++i) {
       EXPECT_EQ(pdata[i], lac_ref_data[i]);
     }

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

@@ -96,7 +96,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 void profile(bool use_mkldnn = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   if (use_mkldnn) {
     cfg.EnableMKLDNN();
@@ -108,8 +108,9 @@ void profile(bool use_mkldnn = false) {
                  input_slots_all, &outputs, FLAGS_num_threads);
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
-    PADDLE_ENFORCE_EQ(outputs.size(), 2UL);
-    for (auto &output : outputs) {
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    PADDLE_ENFORCE_EQ(outputs.back().size(), 2UL);
+    for (auto &output : outputs.back()) {
       size_t size = GetSize(output);
       PADDLE_ENFORCE_GT(size, 0);
       float *result = static_cast<float *>(output.data.data());

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

@@ -106,7 +106,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 void profile(bool memory_load = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg, memory_load);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -117,10 +117,12 @@ void profile(bool memory_load = false) {
     // the first inference result
     const int chinese_ner_result_data[] = {30, 45, 41, 48, 17, 26,
                                            48, 39, 38, 16, 25};
-    PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
-    size_t size = GetSize(outputs[0]);
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    auto output = outputs.back();
+    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    size_t size = GetSize(output[0]);
     PADDLE_ENFORCE_GT(size, 0);
-    int64_t *result = static_cast<int64_t *>(outputs[0].data.data());
+    int64_t *result = static_cast<int64_t *>(output[0].data.data());
     for (size_t i = 0; i < std::min(11UL, size); i++) {
       EXPECT_EQ(result[i], chinese_ner_result_data[i]);
     }

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

@@ -127,7 +127,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 TEST(Analyzer_Pyramid_DNN, profile) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -135,10 +135,12 @@ TEST(Analyzer_Pyramid_DNN, profile) {
                  input_slots_all, &outputs, FLAGS_num_threads);
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data && !FLAGS_zero_copy) {
-    PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
-    size_t size = GetSize(outputs[0]);
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    auto output = outputs.back();
+    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    size_t size = GetSize(output[0]);
     PADDLE_ENFORCE_GT(size, 0);
-    float *result = static_cast<float *>(outputs[0].data.data());
+    float *result = static_cast<float *>(output[0].data.data());
     // output is probability, which is in (0, 1).
     for (size_t i = 0; i < size; i++) {
       EXPECT_GT(result[i], 0);

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -40,7 +40,7 @@ void profile(bool use_mkldnn = false) {
   if (use_mkldnn) {
     cfg.EnableMKLDNN();
   }
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);

paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc

@@ -229,7 +229,7 @@ TEST(Analyzer_rnn1, profile) {
   SetConfig(&cfg);
   cfg.DisableGpu();
   cfg.SwitchIrDebug();
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -280,7 +280,7 @@ TEST(Analyzer_rnn1, compare_determine) {
 TEST(Analyzer_rnn1, multi_thread) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);

paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc

@@ -126,7 +126,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 TEST(Analyzer_rnn2, profile) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -136,9 +136,11 @@ TEST(Analyzer_rnn2, profile) {
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
     // the first inference result
     PADDLE_ENFORCE_GT(outputs.size(), 0);
-    size_t size = GetSize(outputs[0]);
+    auto output = outputs.back();
+    PADDLE_ENFORCE_GT(output.size(), 0);
+    size_t size = GetSize(output[0]);
     PADDLE_ENFORCE_GT(size, 0);
-    float *result = static_cast<float *>(outputs[0].data.data());
+    float *result = static_cast<float *>(output[0].data.data());
     for (size_t i = 0; i < size; i++) {
       EXPECT_NEAR(result[i], result_data[i], 1e-3);
     }

paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc

@@ -110,7 +110,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 TEST(Analyzer_seq_conv1, profile) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -119,10 +119,12 @@ TEST(Analyzer_seq_conv1, profile) {
 
   if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
     // the first inference result
-    PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
-    size_t size = GetSize(outputs[0]);
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    auto output = outputs.back();
+    PADDLE_ENFORCE_EQ(output.size(), 1UL);
+    size_t size = GetSize(output[0]);
     PADDLE_ENFORCE_GT(size, 0);
-    float *result = static_cast<float *>(outputs[0].data.data());
+    float *result = static_cast<float *>(output[0].data.data());
     // output is probability, which is in (0, 1).
     for (size_t i = 0; i < size; i++) {
       EXPECT_GT(result[i], 0);

paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc

@@ -156,7 +156,7 @@ void profile(bool use_mkldnn = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg, use_mkldnn);
 
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
   TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),

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

@@ -70,7 +70,7 @@ TEST(Analyzer_Text_Classification, profile) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
   cfg.SwitchIrDebug();
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -79,8 +79,9 @@ TEST(Analyzer_Text_Classification, profile) {
 
   if (FLAGS_num_threads == 1) {
     // Get output
-    LOG(INFO) << "get outputs " << outputs.size();
-    for (auto &output : outputs) {
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    LOG(INFO) << "get outputs " << outputs.back().size();
+    for (auto &output : outputs.back()) {
       LOG(INFO) << "output.shape: " << to_string(output.shape);
       // no lod ?
       CHECK_EQ(output.lod.size(), 0UL);

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

@@ -186,7 +186,7 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 void profile(bool use_mkldnn = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
   if (use_mkldnn) {
     cfg.EnableMKLDNN();
   }

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

@@ -87,7 +87,7 @@ void profile(bool use_mkldnn = false) {
     cfg.EnableMKLDNN();
   }
   // cfg.pass_builder()->TurnOnDebug();
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -100,7 +100,8 @@ void profile(bool use_mkldnn = false) {
     auto refer = ProcessALine(line);
     file.close();
 
-    auto &output = outputs.front();
+    PADDLE_ENFORCE_GT(outputs.size(), 0);
+    auto &output = outputs.back().front();
     size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
     CHECK_EQ(numel, refer.data.size());
     for (size_t i = 0; i < numel; ++i) {

paddle/fluid/inference/tests/api/tester_helper.h

@@ -41,7 +41,10 @@ DEFINE_string(model_name, "", "model name");
 DEFINE_string(infer_model, "", "model path");
 DEFINE_string(infer_data, "", "data file");
 DEFINE_string(refer_result, "", "reference result for comparison");
-DEFINE_int32(batch_size, 1, "batch size.");
+DEFINE_int32(batch_size, 1, "batch size");
+DEFINE_int32(warmup_batch_size, 100, "batch size for quantization warmup");
+// setting iterations to 0 means processing the whole dataset
+DEFINE_int32(iterations, 0, "number of batches to process");
 DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
 DEFINE_bool(test_all_data, false, "Test the all dataset in data file.");
 DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
@@ -239,7 +242,7 @@ void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
     }
     input.shape = shape;
     input.dtype = PaddleDType::FLOAT32;
-    size_t len = std::accumulate(shape.begin(), shape.end(), 1,
+    size_t len = std::accumulate(shape.begin(), shape.end(), size_t{1},
                                  [](int a, int b) { return a * b; });
     input.data.Resize(len * sizeof(float));
     input.lod.assign({{0, static_cast<size_t>(FLAGS_batch_size)}});
@@ -286,17 +289,18 @@ void ConvertPaddleTensorToZeroCopyTensor(
 
 void PredictionWarmUp(PaddlePredictor *predictor,
                       const std::vector<std::vector<PaddleTensor>> &inputs,
-                      std::vector<PaddleTensor> *outputs, int num_threads,
-                      int tid) {
+                      std::vector<std::vector<PaddleTensor>> *outputs,
+                      int num_threads, int tid) {
   int batch_size = FLAGS_batch_size;
   LOG(INFO) << "Running thread " << tid << ", warm up run...";
   if (FLAGS_zero_copy) {
     ConvertPaddleTensorToZeroCopyTensor(predictor, inputs[0]);
   }
+  outputs->resize(1);
   Timer warmup_timer;
   warmup_timer.tic();
   if (!FLAGS_zero_copy) {
-    predictor->Run(inputs[0], outputs, batch_size);
+    predictor->Run(inputs[0], &(*outputs)[0], batch_size);
   } else {
     predictor->ZeroCopyRun();
   }
@@ -308,11 +312,16 @@ void PredictionWarmUp(PaddlePredictor *predictor,
 
 void PredictionRun(PaddlePredictor *predictor,
                    const std::vector<std::vector<PaddleTensor>> &inputs,
-                   std::vector<PaddleTensor> *outputs, int num_threads,
-                   int tid) {
-  int batch_size = FLAGS_batch_size;
+                   std::vector<std::vector<PaddleTensor>> *outputs,
+                   int num_threads, int tid) {
   int num_times = FLAGS_repeat;
-  LOG(INFO) << "Thread " << tid << " run " << num_times << " times...";
+  int iterations = inputs.size();  // process the whole dataset ...
+  if (FLAGS_iterations > 0 && FLAGS_iterations < inputs.size())
+    iterations =
+        FLAGS_iterations;  // ... unless the number of iterations is set
+  outputs->resize(iterations);
+  LOG(INFO) << "Thread " << tid << ", number of threads " << num_threads
+            << ", run " << num_times << " times...";
   Timer run_timer;
   double elapsed_time = 0;
 #ifdef WITH_GPERFTOOLS
@@ -320,14 +329,14 @@ void PredictionRun(PaddlePredictor *predictor,
 #endif
   if (!FLAGS_zero_copy) {
     run_timer.tic();
-    for (size_t i = 0; i < inputs.size(); i++) {
+    for (size_t i = 0; i < iterations; i++) {
       for (int j = 0; j < num_times; j++) {
-        predictor->Run(inputs[i], outputs, batch_size);
+        predictor->Run(inputs[i], &(*outputs)[i], FLAGS_batch_size);
       }
     }
     elapsed_time = run_timer.toc();
   } else {
-    for (size_t i = 0; i < inputs.size(); i++) {
+    for (size_t i = 0; i < iterations; i++) {
       ConvertPaddleTensorToZeroCopyTensor(predictor, inputs[i]);
       run_timer.tic();
       for (int j = 0; j < num_times; j++) {
@@ -340,13 +349,14 @@ void PredictionRun(PaddlePredictor *predictor,
   ProfilerStop();
 #endif
 
-  PrintTime(batch_size, num_times, num_threads, tid, elapsed_time / num_times,
-            inputs.size());
+  auto batch_latency = elapsed_time / (iterations * num_times);
+  PrintTime(FLAGS_batch_size, num_times, num_threads, tid, batch_latency,
+            iterations);
   if (FLAGS_record_benchmark) {
     Benchmark benchmark;
     benchmark.SetName(FLAGS_model_name);
-    benchmark.SetBatchSize(batch_size);
-    benchmark.SetLatency(elapsed_time / num_times);
+    benchmark.SetBatchSize(FLAGS_batch_size);
+    benchmark.SetLatency(batch_latency);
     benchmark.PersistToFile("benchmark_record.txt");
   }
 }
@@ -354,16 +364,17 @@ void PredictionRun(PaddlePredictor *predictor,
 void TestOneThreadPrediction(
     const PaddlePredictor::Config *config,
     const std::vector<std::vector<PaddleTensor>> &inputs,
-    std::vector<PaddleTensor> *outputs, bool use_analysis = true) {
+    std::vector<std::vector<PaddleTensor>> *outputs, bool use_analysis = true) {
   auto predictor = CreateTestPredictor(config, use_analysis);
-  PredictionWarmUp(predictor.get(), inputs, outputs, 1, 0);
-  PredictionRun(predictor.get(), inputs, outputs, 1, 0);
+  PredictionWarmUp(predictor.get(), inputs, outputs, FLAGS_paddle_num_threads,
+                   0);
+  PredictionRun(predictor.get(), inputs, outputs, FLAGS_paddle_num_threads, 0);
 }
 
 void TestMultiThreadPrediction(
     const PaddlePredictor::Config *config,
     const std::vector<std::vector<PaddleTensor>> &inputs,
-    std::vector<PaddleTensor> *outputs, int num_threads,
+    std::vector<std::vector<PaddleTensor>> *outputs, int num_threads,
     bool use_analysis = true) {
   std::vector<std::thread> threads;
   std::vector<std::unique_ptr<PaddlePredictor>> predictors;
@@ -376,7 +387,7 @@ void TestMultiThreadPrediction(
     threads.emplace_back([&, tid]() {
       // Each thread should have local inputs and outputs.
       // The inputs of each thread are all the same.
-      std::vector<PaddleTensor> outputs_tid;
+      std::vector<std::vector<PaddleTensor>> outputs_tid;
       auto &predictor = predictors[tid];
 #ifdef PADDLE_WITH_MKLDNN
       if (use_analysis) {
@@ -384,8 +395,8 @@ void TestMultiThreadPrediction(
             ->SetMkldnnThreadID(static_cast<int>(tid) + 1);
       }
 #endif
-      PredictionWarmUp(predictor.get(), inputs, outputs, num_threads, tid);
-      PredictionRun(predictor.get(), inputs, outputs, num_threads, tid);
+      PredictionWarmUp(predictor.get(), inputs, &outputs_tid, num_threads, tid);
+      PredictionRun(predictor.get(), inputs, &outputs_tid, num_threads, tid);
     });
   }
   for (int i = 0; i < num_threads; ++i) {
@@ -395,8 +406,8 @@ void TestMultiThreadPrediction(
 
 void TestPrediction(const PaddlePredictor::Config *config,
                     const std::vector<std::vector<PaddleTensor>> &inputs,
-                    std::vector<PaddleTensor> *outputs, int num_threads,
-                    bool use_analysis = FLAGS_use_analysis) {
+                    std::vector<std::vector<PaddleTensor>> *outputs,
+                    int num_threads, bool use_analysis = FLAGS_use_analysis) {
   PrintConfig(config, use_analysis);
   if (num_threads == 1) {
     TestOneThreadPrediction(config, inputs, outputs, use_analysis);
@@ -406,30 +417,41 @@ void TestPrediction(const PaddlePredictor::Config *config,
   }
 }
 
-void CompareTopAccuracy(const std::vector<PaddleTensor> &output_slots1,
-                        const std::vector<PaddleTensor> &output_slots2) {
-  // first output: avg_cost
-  if (output_slots1.size() == 0 || output_slots2.size() == 0)
+void CompareTopAccuracy(
+    const std::vector<std::vector<PaddleTensor>> &output_slots_quant,
+    const std::vector<std::vector<PaddleTensor>> &output_slots_ref) {
+  if (output_slots_quant.size() == 0 || output_slots_ref.size() == 0)
     throw std::invalid_argument(
         "CompareTopAccuracy: output_slots vector is empty.");
-  PADDLE_ENFORCE(output_slots1.size() >= 2UL);
-  PADDLE_ENFORCE(output_slots2.size() >= 2UL);
 
-  // second output: acc_top1
-  if (output_slots1[1].lod.size() > 0 || output_slots2[1].lod.size() > 0)
-    throw std::invalid_argument(
-        "CompareTopAccuracy: top1 accuracy output has nonempty LoD.");
-  if (output_slots1[1].dtype != paddle::PaddleDType::FLOAT32 ||
-      output_slots2[1].dtype != paddle::PaddleDType::FLOAT32)
-    throw std::invalid_argument(
-        "CompareTopAccuracy: top1 accuracy output is of a wrong type.");
-  float *top1_quantized = static_cast<float *>(output_slots1[1].data.data());
-  float *top1_reference = static_cast<float *>(output_slots2[1].data.data());
-  LOG(INFO) << "top1 INT8 accuracy: " << *top1_quantized;
-  LOG(INFO) << "top1 FP32 accuracy: " << *top1_reference;
+  float total_accs1_quant{0};
+  float total_accs1_ref{0};
+  for (size_t i = 0; i < output_slots_quant.size(); ++i) {
+    PADDLE_ENFORCE(output_slots_quant[i].size() >= 2UL);
+    PADDLE_ENFORCE(output_slots_ref[i].size() >= 2UL);
+    // second output: acc_top1
+    if (output_slots_quant[i][1].lod.size() > 0 ||
+        output_slots_ref[i][1].lod.size() > 0)
+      throw std::invalid_argument(
+          "CompareTopAccuracy: top1 accuracy output has nonempty LoD.");
+    if (output_slots_quant[i][1].dtype != paddle::PaddleDType::FLOAT32 ||
+        output_slots_ref[i][1].dtype != paddle::PaddleDType::FLOAT32)
+      throw std::invalid_argument(
+          "CompareTopAccuracy: top1 accuracy output is of a wrong type.");
+    total_accs1_quant +=
+        *static_cast<float *>(output_slots_quant[i][1].data.data());
+    total_accs1_ref +=
+        *static_cast<float *>(output_slots_ref[i][1].data.data());
+  }
+  float avg_acc1_quant = total_accs1_quant / output_slots_quant.size();
+  float avg_acc1_ref = total_accs1_ref / output_slots_ref.size();
+
+  LOG(INFO) << "Avg top1 INT8 accuracy: " << std::fixed << std::setw(6)
+            << std::setprecision(4) << avg_acc1_quant;
+  LOG(INFO) << "Avg top1 FP32 accuracy: " << std::fixed << std::setw(6)
+            << std::setprecision(4) << avg_acc1_ref;
   LOG(INFO) << "Accepted accuracy drop threshold: " << FLAGS_quantized_accuracy;
-  CHECK_LE(std::abs(*top1_quantized - *top1_reference),
-           FLAGS_quantized_accuracy);
+  CHECK_LE(std::abs(avg_acc1_quant - avg_acc1_ref), FLAGS_quantized_accuracy);
 }
 
 void CompareDeterministic(
@@ -455,20 +477,35 @@ void CompareNativeAndAnalysis(
     const PaddlePredictor::Config *config,
     const std::vector<std::vector<PaddleTensor>> &inputs) {
   PrintConfig(config, true);
-  std::vector<PaddleTensor> native_outputs, analysis_outputs;
+  std::vector<std::vector<PaddleTensor>> native_outputs, analysis_outputs;
   TestOneThreadPrediction(config, inputs, &native_outputs, false);
   TestOneThreadPrediction(config, inputs, &analysis_outputs, true);
-  CompareResult(analysis_outputs, native_outputs);
+  PADDLE_ENFORCE(native_outputs.size() > 0, "Native output is empty.");
+  PADDLE_ENFORCE(analysis_outputs.size() > 0, "Analysis output is empty.");
+  CompareResult(analysis_outputs.back(), native_outputs.back());
 }
 
 void CompareQuantizedAndAnalysis(
-    const PaddlePredictor::Config *config,
-    const PaddlePredictor::Config *qconfig,
+    const AnalysisConfig *config, const AnalysisConfig *qconfig,
     const std::vector<std::vector<PaddleTensor>> &inputs) {
-  PrintConfig(config, true);
-  std::vector<PaddleTensor> analysis_outputs, quantized_outputs;
-  TestOneThreadPrediction(config, inputs, &analysis_outputs, true);
-  TestOneThreadPrediction(qconfig, inputs, &quantized_outputs, true);
+  PADDLE_ENFORCE_EQ(inputs[0][0].shape[0], FLAGS_batch_size,
+                    "Input data has to be packed batch by batch.");
+  LOG(INFO) << "FP32 & INT8 prediction run: batch_size " << FLAGS_batch_size
+            << ", warmup batch size " << FLAGS_warmup_batch_size << ".";
+
+  LOG(INFO) << "--- FP32 prediction start ---";
+  auto *cfg = reinterpret_cast<const PaddlePredictor::Config *>(config);
+  PrintConfig(cfg, true);
+  std::vector<std::vector<PaddleTensor>> analysis_outputs;
+  TestOneThreadPrediction(cfg, inputs, &analysis_outputs, true);
+
+  LOG(INFO) << "--- INT8 prediction start ---";
+  auto *qcfg = reinterpret_cast<const PaddlePredictor::Config *>(qconfig);
+  PrintConfig(qcfg, true);
+  std::vector<std::vector<PaddleTensor>> quantized_outputs;
+  TestOneThreadPrediction(qcfg, inputs, &quantized_outputs, true);
+
+  LOG(INFO) << "--- comparing outputs --- ";
   CompareTopAccuracy(quantized_outputs, analysis_outputs);
 }
 
@@ -578,9 +615,9 @@ static bool CompareTensorData(const framework::LoDTensor &a,
                               const framework::LoDTensor &b) {
   auto a_shape = framework::vectorize(a.dims());
   auto b_shape = framework::vectorize(b.dims());
-  size_t a_size = std::accumulate(a_shape.begin(), a_shape.end(), 1,
+  size_t a_size = std::accumulate(a_shape.begin(), a_shape.end(), size_t{1},
                                   [](int a, int b) { return a * b; });
-  size_t b_size = std::accumulate(b_shape.begin(), b_shape.end(), 1,
+  size_t b_size = std::accumulate(b_shape.begin(), b_shape.end(), size_t{1},
                                   [](int a, int b) { return a * b; });
   if (a_size != b_size) {
     LOG(ERROR) << string::Sprintf("tensor data size not match, %d != %d",

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

@@ -74,7 +74,7 @@ void profile(std::string model_dir, bool use_analysis, bool use_tensorrt) {
     SetFakeImageInput(&inputs_all, model_dir, false, "__model__", "");
   }
 
-  std::vector<PaddleTensor> outputs;
+  std::vector<std::vector<PaddleTensor>> outputs;
   if (use_analysis || use_tensorrt) {
     AnalysisConfig config;
     config.EnableUseGpu(100, 0);