add ocr analysis ut

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

@@ -55,3 +55,14 @@ inference_analysis_test(test_text_classification SRCS analyzer_text_classificati
          --infer_data=${TEXT_CLASSIFICATION_INSTALL_DIR}/data.txt
          --topn=1 # Just run top 1 batch.
          )
+
+# ocr
+set(OCR_MODEL_URL "http://paddlemodels.cdn.bcebos.com/inference-vis-demos%2Focr.tar.gz")
+set(OCR_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/ocr")
+if (NOT EXISTS ${OCR_INSTALL_DIR} AND WITH_INFERENCE)
+    inference_download_and_uncompress(${OCR_INSTALL_DIR} ${OCR_MODEL_URL})
+endif()
+inference_analysis_test(test_analyzer_ocr SRCS analyzer_vis_tester.cc
+    EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
+    ARGS --infer_model=${OCR_INSTALL_DIR}/model
+        --infer_data=${OCR_INSTALL_DIR}/data.txt)

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

@@ -0,0 +1,170 @@
+/* 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 "paddle/fluid/inference/analysis/analyzer.h"
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+#include <fstream>
+#include <iostream>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/analysis_predictor.h"
+#include "paddle/fluid/inference/api/helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
+
+DEFINE_string(infer_model, "", "model path for LAC");
+DEFINE_string(infer_data, "", "data file for LAC");
+DEFINE_int32(batch_size, 1, "batch size.");
+DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+struct Record {
+  std::vector<float> data;
+  std::vector<int32_t> shape;
+};
+
+Record ProcessALine(const std::string &line) {
+  VLOG(3) << "process a line";
+  std::vector<std::string> columns;
+  split(line, '\t', &columns);
+  CHECK_EQ(columns.size(), 2UL)
+      << "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;
+}
+
+/*
+ * Use the native and analysis fluid engine to inference the demo.
+ * ocr, mobilenet and se_resnext50
+ */
+void TestVisualPrediction() {
+  std::unique_ptr<PaddlePredictor> predictor;
+  AnalysisConfig cfg;
+  cfg.param_file = FLAGS_infer_model + "/__params__";
+  cfg.prog_file = FLAGS_infer_model + "/__model__";
+  cfg.use_gpu = false;
+  cfg.device = 0;
+  // cfg.specify_input_name = true;
+  cfg.enable_ir_optim = true;
+  predictor =
+      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(cfg);
+
+  // Only have single batch of data.
+  std::string line;
+  std::ifstream file(FLAGS_infer_data);
+  std::getline(file, line);
+  auto record = ProcessALine(line);
+  file.close();
+
+  // Inference.
+  PaddleTensor input;
+  input.shape = record.shape;
+  input.data =
+      PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
+  input.dtype = PaddleDType::FLOAT32;
+
+  std::vector<PaddleTensor> outputs_slots;
+  Timer timer;
+  timer.tic();
+  for (int i = 0; i < FLAGS_repeat; i++) {
+    predictor->Run({input}, &outputs_slots);
+  }
+  PrintTime(/*batch size*/ 1, FLAGS_repeat, /*num threads*/ 1, /*thread id*/ 0,
+            timer.toc() / FLAGS_repeat);
+
+  VLOG(3) << "output.size " << outputs_slots.size();
+
+  // run native as reference
+  NativeConfig config;
+  config.param_file = FLAGS_infer_model + "/__params__";
+  config.prog_file = FLAGS_infer_model + "/__model__";
+  config.use_gpu = false;
+  config.device = 0;
+  // config.specify_input_name = true;
+  auto ref_predictor =
+      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
+  std::vector<PaddleTensor> ref_outputs_slots;
+  ref_predictor->Run({input}, &ref_outputs_slots);
+  EXPECT_EQ(ref_outputs_slots.size(), outputs_slots.size());
+  for (size_t i = 0; i < outputs_slots.size(); ++i) {
+    auto &ref_out = ref_outputs_slots[i];
+    auto &out = outputs_slots[i];
+    size_t ref_size =
+        std::accumulate(ref_out.shape.begin(), ref_out.shape.end(), 1,
+                        [](int a, int b) { return a * b; });
+    size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
+                                  [](int a, int b) { return a * b; });
+    EXPECT_EQ(size, ref_size);
+    EXPECT_EQ(out.dtype, ref_out.dtype);
+    switch (out.dtype) {
+      case PaddleDType::INT64: {
+        int64_t *pdata = static_cast<int64_t *>(out.data.data());
+        int64_t *pdata_ref = static_cast<int64_t *>(ref_out.data.data());
+        for (size_t j = 0; j < size; ++j) {
+          EXPECT_EQ(pdata_ref[j], pdata[j]);
+        }
+        break;
+      }
+      case PaddleDType::FLOAT32: {
+        float *pdata = static_cast<float *>(out.data.data());
+        float *pdata_ref = static_cast<float *>(ref_out.data.data());
+        for (size_t j = 0; j < size; ++j) {
+          EXPECT_NEAR(pdata_ref[j], pdata[j], 1e-3);
+        }
+        break;
+      }
+    }
+    // print what are fused
+    AnalysisPredictor *analysis_predictor =
+        dynamic_cast<AnalysisPredictor *>(predictor.get());
+    auto &fuse_statis = analysis_predictor->analysis_argument()
+                            .Get<std::unordered_map<std::string, int>>(
+                                framework::ir::kFuseStatisAttr);
+    for (auto &item : fuse_statis) {
+      LOG(INFO) << "fused " << item.first << " " << item.second;
+    }
+    int num_ops = 0;
+    for (auto &node :
+         analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
+      if (node->IsFunction()) {
+        ++num_ops;
+      }
+    }
+    LOG(INFO) << "has num ops: " << num_ops;
+  }
+}
+
+TEST(Analyzer_vis, analysis) { TestVisualPrediction(); }
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle