chore: import upstream snapshot with attribution

test/cpp/inference/api/analyzer_vis_tester.cc

@@ -0,0 +1,168 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gtest/gtest.h>
+
+#include <fstream>
+#include <iostream>
+
+#include "test/cpp/inference/api/tester_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+struct Record {
+  std::vector<float> data;
+  std::vector<int32_t> shape;
+  Record() : data(), shape() {}
+};
+
+Record ProcessALine(const std::string &line) {
+  VLOG(3) << "process a line";
+  std::vector<std::string> columns;
+  split(line, '\t', &columns);
+  PADDLE_ENFORCE_EQ(columns.size(),
+                    2UL,
+                    common::errors::InvalidArgument(
+                        "data format error, should be <data>\t<shape>"));
+
+  Record record;
+  std::vector<std::string> data_strs;
+  split(columns[0], ' ', &data_strs);
+  for (auto &d : data_strs) {
+    record.data.push_back(std::stof(d));
+  }
+
+  std::vector<std::string> shape_strs;
+  split(columns[1], ' ', &shape_strs);
+  for (auto &s : shape_strs) {
+    record.shape.push_back(std::stoi(s));
+  }
+  VLOG(3) << "data size " << record.data.size();
+  VLOG(3) << "data shape size " << record.shape.size();
+  return record;
+}
+
+void SetConfig(AnalysisConfig *cfg) {
+  cfg->SetModel(FLAGS_infer_model + "/__model__",
+                FLAGS_infer_model + "/__params__");
+  cfg->DisableGpu();
+  cfg->SwitchIrDebug();
+  cfg->SwitchSpecifyInputNames(false);
+}
+
+void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
+  PADDLE_ENFORCE_EQ(FLAGS_test_all_data,
+                    0,
+                    ::common::errors::Fatal("Only have single batch of data."));
+  std::string line;
+  std::ifstream file(FLAGS_infer_data);
+  std::getline(file, line);
+  auto record = ProcessALine(line);
+
+  PaddleTensor input;
+  input.shape = record.shape;
+  input.dtype = PaddleDType::FLOAT32;
+  size_t input_size = record.data.size() * sizeof(float);
+  input.data.Resize(input_size);
+  memcpy(input.data.data(), record.data.data(), input_size);
+  std::vector<PaddleTensor> input_slots;
+  input_slots.assign({input});
+  (*inputs).emplace_back(input_slots);
+}
+
+// Easy for profiling independently.
+//  ocr, mobilenet and se_resnext50
+void profile(bool use_onednn = false) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  if (use_onednn) {
+    cfg.EnableONEDNN();
+  }
+  // cfg.pass_builder()->TurnOnDebug();
+  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),
+                 input_slots_all,
+                 &outputs,
+                 FLAGS_num_threads);
+  if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
+    std::string line;
+    std::ifstream file(FLAGS_refer_result);
+    std::getline(file, line);
+    auto refer = ProcessALine(line);
+    file.close();
+
+    PADDLE_ENFORCE_GT(outputs.size(),
+                      0,
+                      ::common::errors::Fatal(
+                          "The size of output should be greater than 0."));
+    auto &output = outputs.back().front();
+    size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
+    PADDLE_ENFORCE_EQ(
+        numel,
+        refer.data.size(),
+        common::errors::InvalidArgument(
+            "value of numel is wrong, expected %d but received %d",
+            refer.data.size(),
+            numel));
+    for (size_t i = 0; i < numel; ++i) {
+      EXPECT_NEAR(
+          static_cast<float *>(output.data.data())[i], refer.data[i], 1e-5);
+    }
+  }
+}
+
+TEST(Analyzer_vis, profile) { profile(); }
+
+#ifdef PADDLE_WITH_DNNL
+TEST(Analyzer_vis, profile_onednn) { profile(true /* use_onednn */); }
+#endif
+
+// Compare result of NativeConfig and AnalysisConfig
+void compare(bool use_onednn = false) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+  if (use_onednn) {
+    cfg.EnableONEDNN();
+  }
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareNativeAndAnalysis(
+      reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
+}
+
+TEST(Analyzer_vis, compare) { compare(); }
+#ifdef PADDLE_WITH_DNNL
+TEST(Analyzer_vis, compare_onednn) { compare(true /* use_onednn */); }
+#endif
+
+// Compare Deterministic result
+TEST(Analyzer_vis, compare_determine) {
+  AnalysisConfig cfg;
+  SetConfig(&cfg);
+
+  std::vector<std::vector<PaddleTensor>> input_slots_all;
+  SetInput(&input_slots_all);
+  CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
+                       input_slots_all);
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle